esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calculation Model and Parameter Selection
### Validation of the Computational Model
Among the calculation parameters, the values of compression index \(C_{C}\) and permeability index \(C_{k}\) greatly affect the deformation, and the parameter values derived from the previous research results and relevant empirical formulas may differ from the actual situation, which ultimately leads to the difference between the calculated values and the measured values. Figure 6: Variation in the mean excess pore water pressure inside the mud cake over time. In addition, the influence of the change in the dewatering time on the filtration effect (characterized by changes in mud cake thickness) is explored by varying only the dewatering time under the Group A1 test condition, and the comparison between the calculated and measured mud cake thicknesses is shown in Figure 8.  
Obviously, with an increase in the dewatering time, the thickness of the mud cake becomes smaller, from the theoretical calculation results, the mud cake permeability coefficient is larger in the early stage of pressure filtration, and the pore water is discharged more quickly, reflecting the rapid reduction in the mud cake thickness, but the trend of the change slows down with the growth of time, and the thickness of the mud cake is basically stable after a certain dewatering time. Figure 8 also indicates that the calculated and measured cake thicknesses after pressure filtration are close to each other, with the measured values slightly larger than the calculated values in the early stage of filtration and slightly smaller than the calculated values in the late stage of filtration. By comparing the mud cake thickness shown in Figure 8, it is found that there is a slight difference between the calculated and measured mud cake thickness, but the variation is basically the same. On the one hand, the values of the parameters in a theoretical calculation are not exactly the same as those in practice, and on the other hand, there are some errors in actual measurements. In general, according to the parameter values mentioned above, it is  
Figure 8: Variation in mud cake thickness with dewatering time. Figure 7: Variation in mud cake thickness and comparison of predicted and measured values for Group A conditions: (**a**) variation of calculated mud cake thickness over time and (**b**) comparison of final mud cake thickness. feasible to use the one-dimensional large-strain nonlinear consolidation theory to calculate and analyze the mud cake deformation against the time, and the results obtained from the calculations are of reference value.
## 3 Numerical Results
### Vibration Response  
The linearization method is used to calculate the vibrations field. The vibrations problem is solved using a time integration technique via an implicit Euler scheme. Since the prototype has a relatively simple geometry and constitutive material law, the dynamic problem could also be solved using the same integration scheme. The validity of the linearization process is then verified. Afterward, the coefficient of friction influence on the vibratory response is investigated.
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 3 Dynamic and Automated Resource Allocation Based on VDN and VDNO
### Dynamic Virtual Dedicated Networking (VDN) Framework and Procedure
It is also interoperable with several external systems, such as the ID federation system for authentication and authorization management and Grafana/CACTI systems for network status monitoring and traffic measurement.
## 4 Technologies for Nanocellulose Production
### Ionic Liquid Pretreatment
#### 4.1.4 Biological Pretreatment of Lignocellulosic Biomass  
The biological pretreatment of the LB involves the use of microorganisms such as fungi and bacteria, which are allowed to grow on the biomass to be pretreated or the enzymes produced by such microbes for the degradation of lignocellulosic. Enzymes play a crucial role in the bioprocessing of organic feedstocks into simpler products [91]. The biological pretreatment is a green approach which does not involve the use of any chemical, requires less energy and does not produce inhibitory or toxic by-products [92]. Various fungi (white rot, brown rot and soft rot), as well as bacterial genera (_Acetobacter_, _Alcaligenes_, _Sarcina_ and _Pseudomonas_), are used for biological pretreatment. These microbes produce cellulose-degrading (cellulose, lytic polysaccharide monooxygenases and cellobiose dehydrogenase) and hemicellulose-degrading (endoxylanase, arabinofuranosidase, \(\beta\)-p-xylosidase and esterase) and lignin degrading (lactase, manganese peroxidase, lignin peroxidase, \(\beta\)-etherase and dye-decolorizing peroxidase) enzymes for the breakdown of different components of LB [4; 91]. In a study, six white-rot fungi (_Trametes versicolor_, _Pleurotus ostreatus_, _Phanerochate chrysosporium_, _Coriologsis gallica_, _Pleurotus sajor-caju_ and _Lentitula eddes_) were used for the pretreatment of corn stover. _Pleurotus sajor-caju_ resulted in the highest delignification of 38.29% after 30 days of pretreatment [93].  
A lignin degradation of 28.55% was achieved from tobacco straw pretreated by using _Bacillus amyloidatefaciens_ SL-7 after degradation of 15 days [94]. Even though biological pretreatment offers the advantages of being environment friendly, it is very time consuming and laborious, and also involves a lot of crucial steps prior to the pretreatment such as sterilization, growth of desirable microbes without contamination, maintaining optimal cultural and environmental conditions, etc. All these factors limit the application of biological treatment on a larger scale. Therefore, to address such issues, more research and development involving cutting-edge molecular approaches are required.
## 3 Methods
### Generalized Separated and Enhancement Aggregation Network (GSEAM)
For the input feature map \(\mathbf{y}\), a global average pooling operation is performed to obtain a feature vector \(\mathbf{z}\), where each channel of \(\mathbf{z}\) corresponds to the average value of the corresponding channel of the input feature map. This can be represented by Equation (12), as follows:  
\[z_{i}=\frac{1}{h\times w}\sum_{x=1}^{h}\sum_{y=1}^{w}y_{i}(x,y) \tag{12}\]  
In this context, \(\mathrm{z_{i}}\) represents the i-th element of the feature vector \(\mathbf{z}\), while \(\mathrm{y_{i}(x,y)}\) signifies the pixel value of the i-th channel of the input feature map \(\mathbf{y}\) at position (\(\mathrm{x}\), \(\mathrm{y}\)). Here, h and w denote the height and width of the input feature map, respectively. The fully connected layer maps the feature vector \(\mathbf{z}\) to the attention weight vector \(\mathbf{a}\). It then leverages this attention weight to weight the input feature map \(\mathbf{y}\), resulting in a weighted feature map, as depicted in Equations (13) and (14) as follows:  
\[a_{i}=Sigmoid(Wz_{i}+b)=\frac{1}{1+e^{-(\frac{W}{h\times w}\sum_{x=1}^{h} \sum_{y=1}^{w}y_{i}(x,y)+b)}} \tag{13}\]  
\[Conv_{(i,j)}^{p}=\sum_{m}^{M}W_{P}\cdot y_{(i,j,m)} \tag{7}\]0  
Here, \(\mathbf{W}\) represents the weight matrix of the fully connected layer, \(\mathbf{b}\) is the bias term, and \(\mathrm{y^{\prime}_{i}(x,y)}\) denotes the pixel value of the i-th channel of the output feature map \(\mathbf{y^{\prime}}\) at position (\(\mathrm{x}\), \(\mathrm{y}\)). The GSEAM module splits feature maps into two segments, recombining one after processing through the RepNCSP module to enhance sensitivity to subtle features in low-light conditions and prevent the loss of dark details. Depth-wise separable and pointwise convolutions enhance feature representations by dissecting feature maps spatially and in depth. A two-layer fully connected network then consolidates information from each channel, addressing information loss from uneven illumination by learning relationships between adjacent human target areas. Finally, the resulting output is multiplied with the original features to amplify feature representation, thereby effectively resolving illumination discrepancies.
## 3 Research Objectives  
The primary objectives of this research are as follows:  
* Develop regression models to predict the Direct Current (DC) power and current output of SPVSs based on environmental and meteorological factors. Various regression techniques such as Linear, Lasso, Ridge, and Elastic Net are employed to examine their performance in terms of predictive accuracy.
* Analyze the influence of specific environmental predictors--including average irradiance, temperature, air pressure, humidity, Wind Speed, and Wind Direction--on the performance of SPVSs. These predictors aim to explain the variance in the DC current and power output.
* Compare the performance of different regression models using metrics such as Root Mean Square Error (RMSE) and R-squared values to determine the most effective model for predicting DC power and current. The models will be assessed based on their ability to reduce prediction errors and simplify model complexity via variable selection techniques.
* Use the insights gained from the analysis to provide a basis for optimizing SPVS performance by identifying key environmental factors that significantly impact the output, which could guide system management and operational decisions.
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
###### Abstract  
Differential code bias (DCB) of satellite is an error that cannot be ignored in precise positioning, timing, ionospheric modeling, satellite clock correction, and ambiguity resolution. The completion of the third generation of BeiDou Navigation Satellite System (BDS-3) has extended DCB to multichannel code bias observations and observable-specific signal bias (OSB). In this paper, the DCB and OSB products provided by the Chinese Academy of Sciences (CAS) are analyzed and compared. The DCB parameters for the BDS satellites are applied in both single- and dual-frequency single point positioning (SPP), and the results are intensively investigated. Based on the satellite DCB parameters of the BDS, the performance of precise point positioning (PPP) with different frequency combinations is also analyzed in terms of positioning accuracy and convergence time. The standard deviations (STDs) of DCBs at each signal pair fluctuate from 0.2 ns to 1.5 ns. The DCBs of BDS-2 are slightly more stable than those of BDS-3. The mean values and STDs of C2I and C7I OSBs for BDS-2 are at the same level and are numerically smaller than their counterparts for the C6I OSBs. The mean OSBs for each signal of the BDS-3, excluding C2I, fluctuate between 12.35 ns and 12.94 ns, and the STD fluctuates between 2.11 ns and 3.10 ns. The DCBs and OSBs of the BDS-3 of the IGSO satellites are more stable than those of the MEO satellites. The corrections for TGD and DCB are able to improve the accuracy of single-frequency SPP by 44.09% and 44.07%, respectively, and improve the accuracy of dual-frequency SPP by 6.44% and 12.85%, respectively. The most significant improvements from DCB correction are seen in single-frequency positioning with B1I and dual-frequency positioning with B2A+B3I. DCB correction can improve the horizontal and three-dimensional positioning accuracy of the dual-frequency PPP of different ionosphere-free combinations by 13.53% and 13.84% on average, respectively, although the convergence is slowed.  
BDS; multichannel; differential code bias; observable specific signal bias; single point positioning; precise point positioning ()
## 3 Results
### Model Establishment and Simulation
corresponding to the theoretical value does not change, thus it is still effective for OAM detection in this range. To verify the accuracy of the OSM simulation, the corresponding relationship between simulation results and theoretical calculated values is given here, as shown in Figure 3b. Then, we can obtain that the azimuth is linearly related to the topological charge, and  
Figure 3: Test results of different topological OAM in simulation at 633 nm. (**a**). Detection results of the OAM topological charges from \(-600\) to \(550\) in simulation (\(\Delta l=50\)). (**b**). Comparison of azimuth coordinates of the OAM focusing peak of different topological charges between theoretical and FDTD simulation. (**c**). Topological charge of the OAM predicted by azimuth coordinate theory. the difference between the azimuth coordinates of FDTD simulation and the theoretical calculated values is not more than 0.001 rad. For the coefficient \(l_{0}\) = 200 of the OSM, the azimuth interval between the intensity peaks of the two superimposed adjacent modes is \(1/l_{0}\) = 0.005 rad. Therefore, the topological charge can be unambiguously predicted as follows:  
\[l_{\text{predict}}=-l_{0}\phi_{l} \tag{13}\]  
Theoretically, the designed OSM can detect the intensity modes of 1256 OAMs' modes with topological charges ranging from \(-\)628 to 628, indicating an excellent OAM mode detection ability. In summary, the OSM has a wide mode resolution range for single isolated OAMs. In addition, the position of the focusing spot formed by the OAM through the OSM has a very high coincidence degree with the theory, which is conducive to reverse inferring the topological charge of incident vortex light from the position of the focal spot in a practical application and dramatically improves the detection accuracy.
## 2 Literature Review
### Scheduling of Bottling Operations
#### 2.1.2 Impact of Particle Content on Membrane Fouling  
The accumulation of suspended particles, including phenolic compounds, proteins, and sediments, plays a central role in membrane fouling during wine filtration. These particles tend to form deposits on the membrane surface, reducing permeability and increasing hydraulic resistance over time. [PERSON] and colleagues demonstrated that such particle accumulation decreases the effective filtration area and leads to a rise in the pressure required to maintain a constant flow rate, which in turn raises operational costs and prolongs filtration cycles [36]. This phenomenon is further intensified in fluids with high organic load or fine particulate matter, where pore blockage and surface layeringbecome more frequent [7; 9]. Mathematical models developed by researchers such as [PERSON] and [PERSON] have provided tools to simulate these effects by incorporating particle transport, adsorption dynamics, and resistance accumulation into predictive frameworks [37; 38]. These models highlight how particle content not only influences membrane performance but also directly impacts the frequency of maintenance, cleaning protocols, and overall process stability. Understanding the interaction between particle properties and membrane dynamics is therefore essential for improving filtration efficiency and designing more resilient wine-processing systems.  
In Figure 1, the evolution of the hydraulic performance of a bottling line is presented. The figure shows how the progressive reduction in flow rate, due to the obstruction of pores on the filtration surface, affects the processing time of a batch.
## 4 Simulation Result Analysis and Compensation Mechanism
Perhaps using the FFT interpolation method can achieve excellent effectiveness in wireless channels, but the same effectiveness might not be attainable in fiber channels; therefore, the appropriate interpolation method should be selected according to various channel effects. In addition, because of the assumption that the DCF could not completely suppress the dispersion, the received signal will be influenced by small amounts of dispersion. That is why the pilot symbol insertion spacing was increased to 64, and a cyclic prefix with the length 32 (generally 1/4 of the IFFT size) was used to compensate for the dispersion-induced ISI. Regarding the interpolation methods, because the system effectiveness obtained by using linear, piecewise cubic spline, and piecewise cubic Hermite interpolation was identical, the linear interpolation method was adopted as an equalizer technology in the OFDM-OCDMA compensated scheme. The main reason for selecting the linear interpolation method for channel estimation in this study was because the associated calculation amount is relatively small. In Figure 12, we can find that proposed dispersion equalizer scheme of electrical domain plus with DCF of optical domain is effectively suppressing CD effect and hence improving system performance.
## 2 Related Works  
### Pedestrian Tracking for Thermal Infrared Image  
Thermal infrared (TIR) pedestrian tracking has a major advantage. The main advantages of thermal sensors are their ability to see in complete darkness, their robustness to illumination changes and shadow effects. It has wide applications such as surveillance, driving assistance, and rescue at night [14]. In particular, multiple-target tracking is generally researched using tracking-by-detection method [15; 16; 17]. These were introduced for a real-time pedestrian detection and tracking from a moving vehicle in night time. Also, many single TIR trackers, such as Adaptive Structural Local Appearance Model tracker (ASLA) [18], Enhanced Distribution Field Tracking (EDFT) [19], Sparsity-based Collaborative Model tracker (SCM) [20], Discriminative Scale Space Tracking (DSST) [21] introduced in the Visual Object Tracking (VOT) challenge. Most trackers based on spatial structure and/or sparse representations are ranked better on visual imagery than they are on thermal imagery [22]. Additionally, 24 trackers were introduced in the VOT-TIR2015 challenge. The following trackers were based on various features such as color, or histogram of oriented gradient (HOG) [23]. The tracking-by-detection approach commonly uses HOG features, especially in the advanced driver assistance systems (ADASs) [24]. From the result of the VOT-TIR2015 challenge, it was demonstrated is that tuning of input features is more important to maintain good performance. However, there is not yet introduced temperature feature-based trackers.
# Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model
## 3 Image Segmentation Evaluation Metrics  
In this experiment, the Dice similarity coefficient (DSC), recall, and precision were selected as the evaluation metrics to assess the performance of the network. The formulas for calculating these evaluation metrics are as follows:  
\[\text{Dice}=\frac{\text{2 TP}}{\text{2 TP}+\text{FP}+\text{FN}} \tag{1}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
where TP represents the true positives, which denotes the locations where the predicted results overlap with the manually annotated ground truth. FP represents false positives, which refer to the locations where the predicted results are not present in the ground truth. FN represents false negatives, which indicate the locations that are present in the ground truth but not in the predicted results. The Dice coefficient is used to measure the similarity between the predicted results and the ground truth, with values ranging from 0 to 1. A higher value indicates a higher similarity between the predicted results and the ground truth. Recall is the ratio of true positives to the sum of true positives and false negatives, while precision is the ratio of true positives to the sum of true positives and false positives. Both the recall and precision range from 0 to 1, with a higher value indicating a lower under-segmentation rate and over-segmentation rate for the predicted results.  
Due to the imbalanced nature of positive and negative samples in the data images, this study employs a combination of Dice Loss (DL) and weighted binary cross entropy (BCE) loss. This is because the Dice Loss (DL) can potentially lead to abrupt gradient changes, thereby affecting the backpropagation process and making training difficult. Hence, the weight of the DL is appropriately reduced. The calculation formula for the loss function is as follows:  
\[\text{L}(y,\hat{y})=\omega\text{DL}(y,\hat{y})+(1-\omega)\text{BCE}(y,\hat{y})\]  
\[\text{BCE}(y,\hat{y})=-(y\log\hat{y}+(1-y)\log(1-\hat{y})) \tag{4}\]  
\[\text{DL}(y,\hat{y})=1-\frac{2|y\cap\hat{y}|+\epsilon}{|y|+|\hat{y}|+\epsilon}.\]  
In the equation, \(y\) represents the true label values, and \(\hat{y}\) represents the corresponding predicted values by the model. \(\omega\) is the weight assigned to the two losses, set to 0.3. \(\varepsilon\) is a smoothing parameter introduced to avoid division by zero, and it is set to 1.0.  
Figure 1: U-Net model.
## 3 Data Collection and Methods
### Watershed Modeling System (WMS)  
The goal of this section is to establish a hydraulic and hydrologic model, delineate catchments, and extract hydrographical curves by using advanced software such as WMS and HEC-HMS. For this purpose, different datasets were collected and categorized, including digital elevation models (DEMs), storm data, and soil type. WMS is an effective watershed computer simulation and modeling software package that is used to develop the watershed hydrologic and hydraulic modeling. WMS software (version 11.1) develops different hydrological models and provides tools to automate digital terrain models such as catchments delineations and hydrographic curves. The hydrology tools inside the WMS program can easily run many models, including the interaction between the surface-water and groundwater, wetland areas, different kinds of sediments, pollution simulation, leakage and storms analysis, and submerged surface [41]. This software also provides a control mechanism that is able to evaluate the input data and check if there are any modeling errors [20; 36]. In addition, HEC-HMS version 4.8 is an another program that automates and simulates the different hydrological properties of hydrographic catchments and sub-catchments. The HEC-HMS software processes the collected data in order to automate catchments and sub-catchments boundaries, extract catchments drainage systems, and compute the hydrographic curves for the different watersheds.
## 5 System Model
### Time and Causal Order  
Time is an important theoretical construct to understand how the transactions are executed in distributed systems. Indeed, it is difficult to determine if an event takes place before another one in the absence of a global physical time. However, the logical time introduces an agreed time between all processes based on which one can establish the execution order relation between any two events in a distributed system. This order relation was defined by [PERSON] (1986) by means of the Happened-Before Relation (HBR). The HBR is a strict partial order, and it establishes precedence dependencies between events. The HBR is also known as the relation of causal order or causality.  
**Definition 1**.: _The Happened-Before Relation (HBR) [PERSON] (1986)\"\(\rightarrow\)\"is the smallest relation on a set of events \(E\) satisfying the following conditions:_  
* _If_ \(a\) _and_ \(b\) _are events that belong to the same process and_ \(a\) _occurred before_ \(b\)_, then_ \(a\to b\)_._
* _If_ \(a\) _is the sending of a message by one process and_ \(b\) _is the receipt of the same message by another process, then_ \(a\to b\)_._
* _If_ \(a\to b\) _and_ \(b\to c\)_, then_ \(a\to c\)_._  
In practice, the use of HBR to maintain the causality is expensive since the relation between each pair of events should be considered, including the transitively Happened-before relationships between events (defined in the third condition of the HBR definition). To obliterate the notion that causality is expensive to set up in distributed systems, the author of HBR (2018) proposed the Immediate Dependency Relation (IDR). The IDR is the minimal binary relation of the HBR that has the same transitive closure. We use the IDR to send the necessary and sufficient amount of control information sent per message to ensure causal order.  
**Definition 2**.: _The Immediate Dependency Relation (IDR) HBR (2018)\"\(\downarrow\)\"is the transitive reduction of the HBR, and it is defined as follows:_  
\[a\downarrow b\;\;\text{if}\;\;a\to b\wedge\forall c\in E,\
eg(a \to c\to b)\]