esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adjustments  
The patient-generator algorithm includes a post-processing step to correct for small deviations between simulated and target means. These discrepancies can arise due to transformations, correlation adjustments, or rounding effects. To ensure that the simulated dataset remains aligned with user-defined specifications, the algorithm applies an iterative mean correction procedure.  
This process applies only to non-binary and non-linked parameters. At each iteration, the model calculates an adjustment factor as the difference between the target mean \(\mu_{\text{target}}\) and the current mean of the simulated values \(\mu_{\text{actual}}\):  
\[\Delta=\mu_{\text{target}}-\mu_{\text{actual}}\]  
This difference \(\Delta\) is then uniformly added to all values of the parameter across the patient cohort:  
\[X_{i}^{\text{new}}=X_{i}^{\text{old}}+\Delta\text{ for all }i\]  
After each adjustment, the algorithm enforces user-specified minimum and maximum bounds. Any value that falls outside the valid range is clamped back to the nearest allowable limit. This constraint ensures biological plausibility and prevents outcomes such as negative ages or implausible clinical measurements.  
The process is repeated iteratively, recalculating \(\Delta\) after each adjustment, until the mean error is sufficiently small:  
\[|\Delta|<e\cdot\mu_{\text{target}}\text{ where }e=0.0001\]  
To avoid infinite loops, a maximum of 100 iterations is permitted. This mean correction method ensures that simulated values closely reflect user-defined distributions while maintaining all logical and clinical constraints. It enhances the statistical robustness of the patient-generator algorithm without compromising data integrity.  
To evaluate the convergence behavior of the iterative mean correction step, we ran 20 independent simulations using the Framingham dataset with different random seeds, yielding a total of 398 convergence events across continuous parameters. The results demonstrated rapid and consistent convergence: 70% of parameters reached the target mean within a single iteration, 28% within two iterations, and 2% within three iterations. No parameter required more than three iterations, with the mean number of 1.33 iterations. These findings indicate that the initially generated values closely approximate the target means in the majority of cases and that the adjustment algorithm is both computationally efficient and highly effective in fine-tuning the output to meet user-defined specifications.
## 1 Introduction
To further increase the number of output voltage levels with a reduced number of devices, the authors of [29] presented an SCMLI topology. However, the topologies presented in [28; 29] have high voltage stress across H- bridge switches due to which it cannot be used in high voltage applications except medium voltage applications. Moreover, the per-unit total standing voltage (TSV) in both topologies is high. Self-balanced step-up SCMLI topologies were presented in [30; 31] to overcome these problems. Thus, there is a requirement for a generalized structure of SCMLI, which can be used to obtain the desired number of voltage levels. In this paper, a generalized SCMLI topology structure was proposed with the target being reduced power electronic switches, driver circuits, capacitors and reduced TSV per unit for a higher number of voltage levels. This paper is organized as follows: Section 2 discusses the detailed analysis of the proposed generalized structure. In Section 3, the generalized SCMLI topology structures are compared with recently proposed topologies. In Section 4, an analysis of the basic unit is presented. The simulation results are discussed and its experimental validation are presented in Section 5. The paper is concluded in Section 6.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 3 Methodology
### Training the CEM Model  
We employed cross entropy [48; 49; 50; 51; 57] to define the loss function of the CEM model. Equation (2) illustrates the definition of the cross entropy of a sample:  
\[H(p,q)=-\sum_{i=1}^{8}q_{i}\log(p_{i}) \tag{2}\]  
where \(p\) denotes the category probability distribution predicted by the CEM model, \(q\) denotes the actual distribution, and \(i\) presents the index of an element in a category probability distribution.  
On this basis, we defined the loss function of the CEM model as Equation (3):  
\[loss=-\frac{1}{ts}\sum_{ts}\sum_{i=1}^{8}q_{i}\log(p_{i}) \tag{3}\]  
where \(ts\) is the number of samples used to train the CEM model.  
The CEM model was trained in an end-to-end manner, using six steps, as follows:  
1. The parameters of the CEM model were initialized.
2. The selected image-label pairs, which act as the training dataset, were input into the CEM model.
3. Forward propagation of sample images was performed.
4. Loss was calculated and then back-propagation to the CEM model was performed.
5. The stochastic gradient descent was employed to update the network parameters.
6. Re-iteration of steps 3-5 was done until the loss was below the predetermined threshold values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Decoder Unit** & **Depth of Feature Map** \\ \hline D5 & 512 \\ D4 & 512 \\ D3 & 256 \\ D2 & 128 \\ D1 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Depth of feature map generated by each decoder unit.
## 4 Results and Discussion
### Potential Health Hazards Assessment
#### 4.3.4 Yearly Effective Gamma Dose (YEGD)  
Based on the excess AGDR computed from Equation (7), the indoor yearly effective gamma dose increment (YEGD) in mSv y\({}^{-1}\) to individuals in general society (the normal individuals invest 80% of their days indoors) has been estimated by Equation (9) as follows [20; 21]:  
\[\mathrm{YEGD}\left[\mathrm{mSvy^{-1}}\right]=\mathrm{IAGDR}\left[\mathrm{nGy~{ }h^{-1}}\right]\times 8766\times 0.8\times 0.7~{}\mathrm{SvGy^{-1}}\times 10^{-6} \tag{7}\]  
where 0.8, 0.7 Sv Gy\({}^{-1}\), 8766 h, and \(10^{-6}\) are the indoor occupancy factor, conversion coefficient from the absorbed dose in the air to the effective dose, yearly hours number, transformation number from nano to milli, respectively. The computed increments in the YEGD rates owing to the usage of ElSibae-Abu EITiyur granitic samples as superficial materials indoors are illustrated in Table 4 and Figure 10. European Commission European Commission [20] reported that \"building materials should be exempted from all restrictions concerning their radioactivity if the excess gamma radiation originating from them excel the annual effective dose of a member of the public by 0.3 mSv, at most. It is, therefore,  
Figure 9: AGDR values variation in the investigated samples.  
recommended that controls should be based on a dose range from 0.3-1 mSv y\({}^{-1\prime}\). In light of this approach, all current increments of the YEGD originating from the granites under investigation fluctuated in the exemption level (Figure 10), denoting that the granites under consideration can be safely used as superficial building materials.
# Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
## Data
### MeaSUREs Ice Velocity  
MEaSUREs InSAR-Based Antarctica Ice Velocity Map, assembling 1384 tracks optical and SAR images from multiple sensors, takes full advantage of offset tracking and interferometric techniques (InSAR) [18]. The well-corrected error of 17 m\(\cdot\)a\({}^{-1}\), together with high spatial resolution and accurate geolocation, wholly make it a nearly perfect benchmark for future investigation. Source data are largely acquired during the International Polar Year (IPY) 2007 to 2009, as well as between 2013 and 2016. Additional data acquired between 1996 and 2016 are used as needed to maximize spatial coverage. It is set as the validation reference in this study, although it is worth pointing out that the data collection time period spans 20 years from 1996 for ERS-1 to 2016 for Radarsat-2, which is quite different from the acquisition time of MOA.
###### Abstract  
Underwater visible light communication (UVLC) has recently come to light as a viable wireless carrier for signal transmission in risky, uncharted, and delicate aquatic environments like seas. Despite the potential of UVLC as a green, clean, and safe alternative to conventional communication methods, it is challenged by significant signal attenuation and turbulent channel conditions compared to long-distance terrestrial communication. To address linear and nonlinear impairments in UVLC systems, this paper presents an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 Quadrature Amplitude Modulation-Component minimal Amplitude Phase shift (QAM-CAP)-modulated UVLC systems. The proposed AFL-DLE is dependent on complex-valued neural networks and constellation partitioning schemes and utilizes the Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA) to improve overall system performance. Experimental outcomes demonstrate that the suggested equalizer achieves significant reductions in bit error rate (55%), distortion rate (45%), computational complexity (48%), and computation cost (75%) while maintaining a high transmission rate (99%). This approach enables the development of high-speed UVLC systems capable of processing data online, thereby advancing state-of-the-art underwater communication.  
underwater visible light communication; deep learning; equalization; adaptive fuzzy logic; deep-learning equalizer; sparrow search optimization
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 3 Proposed Framework for Enhancing Arabic Spam Detection
### Applying Machine Learning for Spam Detection
#### 3.3.1 Data Pre-Processing  
Several techniques were applied to the dataset to ensure data cleansing and the removal of noise that could affect the system's accuracy. Tokenization, normalization, removing diacritics, removing repeated chars, removing punctuations, removing stop words, removing non-Arabic alphabets, and light stemming are some of the techniques used.
## 5 Conclusions  
This study proposes a routing-delivery scheme (Predict and Forward) based on node profile and the optimal stopping theory in opportunistic networks. This strategy is based on a node that will have a high probability of meeting another if it encountered another node frequently in the past. This algorithm determines the probability of the future meeting between nodes according to the historical encounter information. To avoid missing the optimal moment to make message delivery decisions, this method adopts the optimal stopping theory to select an optimal transition phase between studying the state of the network and forwarding the Prelearn messages. Meanwhile, in order to overcome the limitation that destination addresses are not always available by the message carriers, the nodes in opportunistic networks implement end-to-end communication based on their attributes instead of specific network addresses. Compared with the other three algorithms, the Predict and Forward scheme obtains improved performances in increasing delivery ratio and reducing the  
Figure 11: Overhead on average.  
Figure 12: Average end-to-end delay.  
delay of end-to-end data transmission. In the future work, this algorithm will be used for the mobile communication in the application environment of big data and 5G networks.  
Conceptualization, K.L., Z.C. and J.W.; Methodology, K.L.; Software, Z.C.; Validation, K.L., Z.C. and J.W.; Formal Analysis, K.L.; Investigation, Z.C.; Resources, Z.C.; Data Curation, J.W.; Writing-Original Draft Preparation, K.L.; Writing-Review & Editing, Y.X. and H.Z.; Visualization, Z.C.; Supervision, Z.C. and J.W.; Project Administration, Z.C.; Funding Acquisition, Z.C.  
This work was supported in part by the Major Program of National Natural Science Foundation of China (NO. 71633006); The National Natural Science Foundation of China (NO. 616725407); China Postdoctoral Science Foundation funded project (2017M612586); The Postdoctoral Science Foundation of Central South University (185684); the Fundamental Research Funds for the Central Universities of Central South University (No. 2018 zzts615).  
This work was supported partially by \"Mobile Health\" Ministry of Education--China Mobile Joint Laboratory.  
The authors declare that they have no competing interests.
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 4 Performance Evaluation
### Simulation Results and Analysis
Figure 17 shows that the network connectivity ratio is hardly affected by the value for \(w_{e}\) or \(w_{r}\). As shown in Figure 11, the network connectivity ratio is more than 99% but less than 100% under \(\mu\)'s taking 0.6. Therefore, it is sufficient for \(\mu\)'s taking 0.33 to ensure network connectivity. Figure 18 reveals the similar change trend of curves to that in Figure 15, which has the same reasons as those explained in the related part of the second group of simulations. A path with a smaller hop-count usually has a higher packet delivery ratio, since the frame success rate for each link of path is at most 100% and usually less than 100%. As shown in Figure 15, the ICV-MIMO scheme with \(w_{e}=0.2\) and \(w_{r}=0.8\) has the shortest average path hop-count, while the ICV-MIMO scheme with \(w_{e}=0.8\) and \(w_{r}=0.2\) has the longest one. Therefore, from Figure 19, one can see that the former has the highest packet delivery ratio while the latter has the lowest one. Figure 3:5 Network lifetime for the ICV-MIMO scheme under the different typical values for \(w_{e}\) and \(w_{r}\). Figure 3:6 Network connectivity ratio for the ICV-MIMO scheme under the different typical values for \(w_{e}\) and \(w_{r}\). In Figures 2-19, each data point in each plot represents an average of 10 simulation runs, and the simulation process continues until the first death of MCHs occurs. In addition, although there is not randomness (e.g., node movement and channel fading) in our simulation, there is still randomness in terms of the node density distribution and the maximum transmission power of nodes. Therefore, the plots of part simulation results go up and go down in a certain number of nodes. In addition, although there is only a factor (i.e., the number of nodes) on the \(X\) axis in our simulation diagrams, we implicitly consider the other factors (e.g., node density, inter-cluster range, traffic intensity), as mentioned in [3]. The simulation field is fixed in our simulation settings, so the change of the number of nodes reflects the change of node density. The simulation results of the ICV-MIMO scheme under the different values of the parameter \(\mu\) also reflect the simulation performance of the different inter-cluster ranges. When the parameter \(\mu\) gets large, the required distance for inter-cluster connectivity is also large, and the six metrics also have some changes accordingly. Since each cluster member must send a packet to the sink, traffic intensity increases with the number of nodes. Therefore, the factor \"traffic intensity\" is considered implicitly in our simulations. Figure 3:7 Packet delivery ratio for the ICV-MIMO scheme under the different typical values for \(w_{e}\) and \(w_{r}\). Figure 3:8 Path delay for the ICV-MIMO scheme under the different typical values for \(w_{e}\) and \(w_{r}\).
### Our Method  
In order to construct paths for reinforcing additively manufactured parts, evaluation criteria motivated by the process and material properties of carbon fiber prepreg filaments are first established. These serve as guidelines in the development of the algorithms and are employed as a way of measuring their performance.  
A framework has been developed to embed the algorithms and enable further work:  
* The proposed procedure starts by analyzing the part to be manufactured in an external finite element method (FEM) software to extract the major principle stress vectors. An exemplary result can be found in Figure 1a.
* The resulting information enables the partitioning of the surface to be reinforced into subsurfaces with the goal of achieving a high compliance of local fiber direction and directions of major principle stress. Figure 1b illustrates such a partitioning. This is similar to the zoning approach, used in the layup process.
* The subsurfaces are triangulated with high precision to ensure minimal computation error.
* For every subsurface a suitable algorithm is chosen. This is performed with the intent of maximizing the measured values of the evaluation criteria and relies on knowledge of the procedures.
* The chosen algorithm is executed for every surface resulting in paths that include possible travel motions, an example of which can be seen in Figure 1c.
* Post-processing of the paths yields a Goode to be executed on the multi-axis printer.  
The focus of this paper will be the developed evaluation criteria and algorithms. The partitioning and the load cases for the finite element analysis (FEA) are subject to further optimization and will only be considered in an exemplary form. Some of the algorithms  
Figure 1: (**a**) Maximum principle stress vectors resulting from finite element analysis (FEA). (**b**) Decomposition of part into subsurfaces. (**c**) Exemplary planned paths for all subsurfaces with \(d=1\) mm. Filament thickness displayed does not correspond to thickness when printed.  
involve user input for choosing the most fitting fiber direction. This is motivated by the inability of current algorithms to deal with the high amount of divergence and turbulence observed in FEA results. This leads to the approaches discussed above that either only consider small areas of the entire dataset or the need to apply topology optimization, which can negatively effect the spectrum of possible applications. With user involvement at this step higher quality and consideration of the available data can be ensured, although automating this process may be considered for further research.
## 1 Introduction
### Scientific Objective of the Current Analysis  
The main objective of the current study is to select the type of glazing for hybrid buildings located in a cold climate zone that will be optimal in terms of energy consumption and at the same time economically justified. Three types of glazing (double, triple and quadruple) were analysed in terms of their impact on the building's energy consumption and thermal comfort, as well as capital expenditure and operating costs. The novelty includes the selection of a modular reference house, the construction of which is hybrid, i.e., a combination of a wooden structure and steel beams. The technology of these modular houses is based on a spatial structure made of steel. An example of this type of steel frame is shown in Figure 1. The distance between load-bearing pillars should not exceed 4 m. It is required that the load-bearing columns align axially on both floors. Wooden panels fill the interiors of the steel frames. The wall panels with optional window openings are made of two oriented strand boards with thermal insulation at the centre of this sandwich structure. The main advantage of this type of construction is the lack of load-bearing walls and beams. This allows for great freedom in creating the interior architecture and integrating large glazing areas. These types of houses can be made in single- or two-storey designs. Of course, it is also possible to create a building with a variable shape, i.e., 1.5 storeys.  
The main issue in this type of house construction with a large area of window openings is the selection of an appropriate glazing system. An extensive review of the literature allows the author to conclude that no similar analysis has been performed so far for the discussed hybrid structure of a single-family house in cold climatic conditions. It was decided to implement a complete solution to this problem. First, an energy analysis of the reference building combined with the HVAC system was performed. The energy simulation results were then used to evaluate the three glazing systems using basic economic indicators. The influence of large-area multi-glazed windows on the thermal environment inside the case study house was also determined. The current analysis focused on cold climate conditions. For this purpose, the reference house was located in Bialystok (Northeastern Poland) and Kiruna (Northern Sweden).
## 3 Results and Discussions
### Judgment of Hazardous Solid Waste Properties of Petrochemical Excess Sludge
#### 3.1.3 Analysis of Dioxins in Petrochemical Excess Sludge  
The contents of dioxins in Ah, Bl, and Cq wet sludge with a water content of 85% are shown in the Table 4. It can be seen from Table 4 that the dioxin substances in Ah, Bl, and Cq wet sludge were not detected except for octachlorated dibenzofurans (OCDF) and octachlorated dibenzo-p-dioxins (OCDD). The international toxic equivalence quantity (I-TEQ) values of Ah, Bl, and Cq wet sludge dioxin were 0.20, 0.36 and 0.27 ng-TEQ/kg, respectively. The I-TEQ values of PCDD/Fs in Ah, Bl, and Cq wet sludge is lower than the in Ah, Bl, and Cq wet sludge is lower than the dioxin content in the identification of hazardous wastes in GB 5085.6-2007 (15 \(\upmu\)g-TEQ/kg) [36]. Similar results are found in the studies of sewage sludge from [PERSON] et al. [20] that OCDD and OCDF also dominate the total PCDD/Fs amount of wet sewage sludge, and the I-TEQ value of PCDD/Fs is below the limit value specified in Chinese national Standard GB 5085.6-2007 [36]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Leaching Concentration (mg/L)**} & \multirow{2}{*}{**Limit Value [34] (mg/L)**} \\ \cline{2-2} \cline{4-4}  & **Ah** & & **Bl** & **Cq** \\ \hline Nitrobenzene & N.D. & N.D. & N.D. & 20 \\ Dinitrobenzene & N.D. & N.D. & N.D. & 5 \\ p-Nitrchlorobenzene & N.D. & N.D. & N.D. & 5 \\
2,4-Dinitochlorobenzene & N.D. & N.D. & N.D. & 5 \\ Pentachlorophenol & N.D. & N.D. & N.D. & 50 \\ Phenol & N.D. & N.D. & N.D. & 3 \\
2,4-Dichlorophenol & N.D. & N.D. & N.D. & 6 \\
2,4,6-Trichlorophenol & N.D. & N.D. & N.D. & 6 \\ Benzopyrene & N.D. & N.D. & N.D. & \textless{}0.0003 \\ Dibutyl phthalate & N.D. & 0.063 & N.D. & 2 \\ Dioctyl phthalate & N.D. & N.D. & N.D. & 3 \\ Polychlorinated biphenyl & N.D. & N.D. & N.D. & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Determination of non-volatile organic matter in excess sludge leachate. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Leaching Concentration (mg/L)**} & \multirow{2}{*}{**Limit Value [34] (mg/L)**} \\ \cline{2-2} \cline{4-4}  & **Ah** & & **Bl** & **Cq** \\ \hline Benzene & N.D. & N.D. & N.D. & 1 \\ Toluene & N.D. & N.D. & N.D. & 1 \\ Ethlybenzene & N.D. & N.D. & N.D. & 4 \\ Xylene & N.D. & N.D. & N.D. & 4 \\ Chlorobenzene & N.D. & N.D. & N.D. & 2 \\
1,2-Dichlorobenzene & N.D. & N.D. & N.D. & 4 \\
1,4-Dichlorobenzene & N.D. & N.D. & N.D. & 4 \\ Acrylonitrile & N.D. & N.D. & N.D. & 20 \\ Trichloromethane & N.D. & N.D. & N.D. & 3 \\ Carbon tetrachloride & N.D. & 0.067 & N.D. & 0.3 \\ Trichloroethylene & N.D. & N.D. & N.D. & 3 \\ Carbon tetrachloride & N.D. & N.D. & N.D. & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Determination of volatile organic compounds in excess sludge leachate.