esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Molecular Weight Distribution of CPH and CPHD  
After controlled proteolysis with the commercial enzyme alcalase, the cricket protein hydrolysate (CPH) was subjected to in vitro simulated gastrointestinal digestion (SGID). Alcalase proteolysis, followed by SGID, generated a complex mixture of peptide-rich digests (CPHD). Before enzyme treatment, more than 50% of the native cricket proteins were above 10 kDa (Figure 0(b)) The alcalase proteolysis (3.0% E:S; 80 min) resulted in CPH consisting mostly of peptides <1 kDa (Figure 0(a)). After SGID, the composition of <1 kDa was more than 90%. The size exclusion chromatograms (Figure 0(b)) visually illustrate the changes in molecular weight distribution of the untreated cricket protein, cricket protein hydrolysates (CPH), and its digests (CPHD), after SGID. The untreated cricket protein chromatogram exhibited more peaks at earlier retention times (10-25 min), as compared to the hydrolysate mixtures. Both CPH and CPHD chromatograms showed an absence of the peaks seen in cricket protein, and instead showed the presence of a larger cluster of peaks at a later retention time (25-55 min), indicating substantial protein hydrolysis and generation of smaller peptides. The peptide molecular weight distribution of CPH and CPHD was mostly similar, indicating some resistance to digestion by the gastrointestinal proteases used in the SGID.  
Figure 1: Molecular weight distribution of cricket protein after enzymatic hydrolysis and in vitro digestion. (**a**) Gel filtration peptide profile of non-hydrolyzed cricket protein, cricket protein hydrolyzed with alcalase, and cricket protein hydrolyses (CPH), after digestion with gastrointestinal enzymes. (**b**) Gel filtration peptide profile of non-hydrolyzed cricket protein, cricket protein hydrolyzed with alcalase, and CPH, after digestion with gastrointestinal enzymes.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 3 System Model
### Network Architecture  
The suggested protocol utilizes a single-sink design, which includes anchored nodes, relay nodes, and one central sink node. The sink node, labeled as A, is located in the middle of the water surface. Meanwhile, N sensor nodes are evenly spread throughout a three-dimensional underwater area in the form of a cubic shape, as shown in Figure 1. Anchored nodes are securely positioned at the bottom of the ocean and are responsible for sensing and collecting data. Relay nodes are deployed at various depths and have the role of finding paths toward the destination, forwarding received packets to the next hop and sometimes collecting environmental data. The sink node functions as the endpoint for routing and is equipped with both underwater acoustic and above-water radio modems. Underwater communication is achieved through the acoustic link, while communication above the water surface is enabled through the radio link. Anchored nodes detect data and send them to the sink node on the water surface through intermediary nodes. Then, the sink node employs the radio link to transmit the received data to a satellite, which in turn relays the data to the control center located on the shore.  
In this study, the following assumptions are made:  
1. The surface sink node has an unlimited energy supply;
2. Each underwater sensor node can determine its location as well as the sink node's location on the water surface using location services.
3. Sensor nodes consistently have data to transmit, and data collection follows a periodic mechanism;  
Figure 1: Network architecture.  
The sink node acts as a superior node capable of receiving multiple packets simultaneously without any collisions. These assumptions lay the foundation for the proposed protocol and the subsequent analysis.
# On Analyzing Beamforming Implementation in O-RAN 5G
## 2 Related Work
### O-RAN Functional Units and Nodes
** Near-RT RIC is a logical function that enables near-real-time control and optimization of E2 node functions and resources via fine-grained data collection and actions over the E2 interface with control loops in the order of 10 ms-1s. The specification can be found in O-RAN.WG3.E2 GAP-v01.01. The near-RT RIC hosts one or more xAPPs that use the E2 interface to collect near real-time information (e.g., on a UE basis or a cell basis) and provides value-added services. The near-RT RIC control over the E2 nodes is steered via the policies and the enrichment data provided via A1 from the non-RT RIC [13; 14; 16]. In this scheme, xAPPs are responsible for advanced functions such as handover and traffic steering [15; 17]. **SMO (Service Management and Orchestration). ** SMO is a consolidation of a wide range of management services. In a service provider's network, SMO includes core management, transport management, end-to-end slice management, etc. [11]. The fundamental capabilities of the SMO that provide RAN support in O-RAN are (1) fault, configuration, accounting, performance, security (FCAPS) interface to O-RAN network functions; (2) non-RT RIC for RAN optimization; and (3) O-Cloud management, orchestration, and workflow management. As shown in many publications, security and accountability are crucial issues of the Open RAN initiative [18; 19].
## 2 Materials and Methods
Each specimen was measured in 16 axial sections distributed along the machined length, spaced 10 mm apart. In each section, four diameter measurements were taken at different orientations. The aim of this measurement was to quantify the local deviation by analyzing how the measured diameter varies along the length of the part. The data collected provided insight into how turning affects the geometric accuracy of PLA-CF parts printed with FFF, especially in slender geometries. The evaluation of form deviations was carried out using a Rondcom Nex form measuring system (ACCREETECH) (Tokyo Seimitsu Co., Ltd. Tokyo, Japan). The software ACCTee (Version 2.4.) as used for data acquisition, and analysis, following ISO 1101:2017 [45]. The analysis was focused on three form parameters: roundness (_RON_), straightness (_STR_), and cylindricity (_CYL_). _RON_ was measured at 15 axial sections, spaced 10 mm apart, along the machined specimen length. _RON_ value was obtained by the least-squares circle fitting method, in accordance with ISO 12181-2:2011 [46]. To evaluate _STR_, 12 generatrix profiles were recorded along the cylindrical surface, distributed at angular positions of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) and taken at three heights. The least-squares line fitting method was applied to determine the deviation for each profile. Based on the _RON_ and _STR_ data, the cylindricity (_CYL_) was calculated as the total volumetric deviation combining radial and axial form errors. Finally, the surface roughness of the machined PLA-CF specimens was assessed using the confocal optical microscope Alicona Infinite Focus([PERSON], Raaba, Austria), which enables high-resolution, non-contact 3D measurement of surface topography. The system was calibrated prior to each measurement session, and image acquisition and processing were carried out using the Alicona MeasureSuite software (Version 3.7). To ensure accurate evaluation of the roughness parameters, the cylindrical shape of the specimen was digitally removed using the form elimination tool, and a new reference plane was established. The surface roughness parameters were computed in accordance with ISO 25178-3:2012 [47], focusing on the surface arithmetical mean height (_Sa_) and the surface maximum height (_Sz_).
## 4 Proposed Work
### Mobile Anchor--Unknown Node Communication and Localization Estimation Phase
#### Obstacle-Presence Scenario  
There may be obstacles in the network area in a realistic environment, blocking the MA's path. In the obstacle-resistant trajectory developed in [34] and used in [9], MA detours the obstacle and issues a detour flag at the obstacle's corner points. A UNN uses these flag signals to determine its location. When the MA moves away from the obstacle, it returns to the original trajectory and starts broadcasting standard beacon messages. Since the path planning models discussed in this study are deterministic, the movement pattern and beacon positions for message broadcast are already known. Therefore, the MA can cross the edge of the obstacle and continue the path planning pattern where it left off.  
In this study, \"obstacle-handling trajectory\" detailed in [9], which is briefly mentioned above, is used for all path planning models in case of obstacle-presence. The obstacle-handling trajectory determined by applying this logic to the proposed NHexCurves path planning model can be seen in Figure 9.  
Figure 9: NHexCurves in obstacle-handling trajectory.
## 5 The Simulation Validation and Results Analysis  
### Comparative Simulations for Communication Optimization  
In Scenario 1, a single moving target \(T\) is continuously tracked by two UAVs, \(A_{1}\) and \(A_{2}\). The detailed initial settings of the UAVs are listed in Table 2. The ground speed of the UAVs is  
Figure 5: Processing diagram for multi-UAVs cooperative target tracking algorithm.  
\(v_{i}=50\) m/s, and their maximum turning rate is \(\omega_{\text{max}}=30\) deg/s. The target motion is modeled by Equation (4), its initial position is (\(-800\), 0) m, its initial movement direction is \(-20\) deg, and its noise is characterized by \(\delta_{x}=\delta_{y}=0.1\) m/s\({}^{2}\). The standard deviations in the sensor model are \(\delta_{r}=2\) m and \(\delta_{\theta}=0.05\) rad. The position of the base station \(B\) is (\(-500\), \(-800\)) m. The communication model is shown in Figure 2 and the relevant parameters in Table 1. The simulating time of each simulation is 100 s, with a time step of \(T_{\text{s}}=1\) s. Finally, the planning time horizon is set at \(T_{\text{P}}=3\) s and the safety distance for collision avoidance is \(d_{\text{safe}}=100\) m.  
To verify the effectiveness of cooperative target tracking algorithm based on co-optimization of communication and sensing, the two groups of experiments are carried out.  
* Group A: with communication optimization; and
* Group B: without communication optimization.
# Designing Audio Equalization Filters by Deep Neural Networks
## 4 Baseline Methods
### Frequency Deconvolution Method  
The fast deconvolution method is described in Reference ([PERSON] et al., 2018), where deconvolution is performed in the frequency domain and made fast by taking advantage of the Fast Fourier Transform (FFT) algorithm. A matrix of optimal filters is computed in the frequency domain according to the following  
\[\mathbf{G}(k)=[\mathbf{H}^{H}(k)\mathbf{H}(k)+\beta\mathbf{I}]^{-1}\mathbf{H} ^{H}(k)\mathbf{A}(k), \tag{5}\]  
where \(k\) is the FFT bin, \(\mathbf{I}\) is the identity matrix, \({}^{H}\) is the Hermitian operator, \(\mathbf{H}(k)\) contains the FFT of the impulse responses, \(\mathbf{A}(k)\) contains the target frequency responses. The term \(\beta\) is an empirical regularization term that is necessary to avoid extreme peaks in the inverse filters, that would result in an excessive length of the filters in the frequency domain. Once \(\mathbf{G}(k)\) is computed, its inverse FFT is computed and a circular shift of \(K/2\) samples is performed, where \(K\) is the FFT size. This method is used to design \(\mathcal{S}\) different filters, one per loudspeaker, for a fair comparison with our approach.  
The FD method is expressed as a least-squares optimization problem in the frequency domain. The loss is:  
\[J=\mathbf{e}^{H}\mathbf{e}+\beta\mathbf{v}^{H}\mathbf{v}, \tag{6}\]  
where \(\mathbf{e}\) is the error and \(\beta\mathbf{v}^{H}\mathbf{v}\) is a regularization term meant as an effort penalty proportional to the total energy of the filtered input signals to the sources \(\mathbf{v}\). The problem is, thus expressed as a convex optimization problem, where the squared error is minimized by a unique solution that is analytically found in Reference ([PERSON] et al., 2018) by imposing the gradient of the loss function to zero.  
There are important differences between the proposed method and FD. In our framework there are no assumptions on the convexity of the error surface. The gradient of the loss \(J\) in the proposed method is a nonlinear function. Specifically, our loss can be expressed as a function \(f\) of the magnitude frequency response of the impulse responses \(\mathbf{h}\) and of the network output \(\mathbf{g}\). The network output, that is, the filter coefficients \(\mathbf{g}\), in turn, is a nonlinear function of the network weights \(\theta\) and the network input \(\mathbf{u}\) (i.e., the impulse responses, when not specified differently). In more rigorous terms:  
Figure 4: Scheme of the proposed method using Autoencoder.  
\[J=f(|\mathcal{F}(\mathbf{g})|,|\mathcal{F}(\mathbf{h})|). \tag{7}\]  
\[\mathbf{g}=\phi(\theta,\mathbf{u}). \tag{8}\]
## 2 Literature Review
### The General Lot Sizing and Scheduling Problem  
The GLSP was originally introduced by [12]. The authors presented two versions of the model, namely the GLSP-CS (Conservation of Setup state) where the setup state is preserved over idle periods and the GLSP-LS (Loss of Setup state). In this section, with the aim of making the paper self-contained, we present an extended mixed-integer programming (MIP) formulation for the GLSP-CS that includes sequence-dependent setup times as presented by [13] and a revised version of the minimum lot size constraint introduced by [14] and use the name GLSP for this formulation in the further course of this paper. The GLSP features integrated production planning and scheduling for capacitated single facility environments with multiple products. The setups for these products are sequence-dependent in times and costs, implying that setting up the production of the products in a certain sequence can be more favorable than another in terms of time and costs; see also [15; 16]. To simultaneously determine the production sequence and the quantity of production lots, the GLSP includes a two-level time structure. Products \(j=1,\ldots,J\) are scheduled over a finite planning horizon consisting of macro-periods \(t=1,\ldots,T\) with given length. Micro-periods \(m=1,\ldots,L_{T}\) (with \(L_{T}\) denoting the last micro-period of the last macro-period \(T\)) are used to model the production sequence of products within the macro-periods. Each macro-period \(t\) consists of a predefined non-overlapping sequence of micro-periods with \(|M_{t}|\) being an advance fixed total number of micro-periods within a macro-period \(t\). The length of each macro-period is given by the input parameter for the resource capacity \(b_{t}\), the lengths of micro-periods are decision variables expressed by the number of productions in the micro-period. A production lot consists of a sequence of consecutive micro-periods where the same product is produced and may continue over a series of micro-periods and macro-periods. As described, this model presents an extended version of the GLSP-CS where the setup state is preserved over idle periods, i.e., if after an idle micro-period the same product is produced as before, no additional setup is necessary. The objective is to find an optimal production quantity and a sequence that minimizes overall production costs, consisting of setup changeover and inventory-holding costs. Table 1 presents the variables, parameters and indices of the GLSP model. The problem is formulated as follows:  
\[min \sum_{j=1}^{J}\sum_{t=1}^{T}h_{j}\cdot s_{j,t}+\sum_{i=1}^{J}\sum_{j=1}^{L _{T}}\sum_{m=1}^{L_{T}}f_{i,j}\cdot z_{i,j,m} \tag{1}\] \[s_{j,t-1}+\sum_{m\in M_{t}}y_{j,m}-d_{j,t}=s_{j,t}\qquad\forall j,t\] (2) \[\sum_{j=1}^{J}\sum_{m\in M_{t}}tp_{j}\cdot y_{j,m}\leq b_{t}-\sum _{i,j=1}^{J}\sum_{m\in M_{t}}st_{i,j}\cdot z_{i,j,m}\qquad\forall t,i\
eq j\] (3) \[y_{j,m}\leq M\cdot x_{j,m}\qquad\forall j,m \tag{4}\]
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 3 Experiments and Evaluation
### The Evaluation of the Improved Optical Flow Algorithm for Velocity Calculation
#### 3.2.1 Experiment One in the Normal Application Circumstance  
Experiment 1 is executed in normal application circumstance with many cars and few persons. Mask-R-CNN helps to recognize the objects and the RGB of the masks, as it made it the same as the background. The progress of dealing with the images is shown in Figure 8.  
The comparison of the four different algorithms in velocity calculation is illustrated in Figure 9. Meanwhile, in order to prove the superiority of the proposed algorithm, the quantitative comparisons with other algorithms are listed in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **LK** & **LK + Mask-R-CNN** & **LK + KM** & **LK + Mask-R-CNN + KM** \\ \hline RMSE & 2.1632 & 2.1121 & 1.1781 & 0.6845 \\ \hline STD & 1.5276 & 1.5522 & 0.8628 & 0.5011 \\ \hline \end{tabular}
\end{table}
Table 3: Velocity errors of four algorithms in the X direction.  
Figure 8: (**a**) Image in experiment 1; (**b**) the result of instance segmentation; (**c**) covering the objects with similar color masks.  
Figure 9 shows the velocity error comparison after the joint of clustering algorithm and Mask-R-CNN. Also, it indicates the improvement of velocity accuracy in X and Y direction in four algorithms. The addition of the Mask-R-CNN algorithm eliminates the large deviation caused by relative motion because it covers the objects, while the K-Means clustering algorithm makes the velocity curve closer to the basic value and reduces the impact of the wrong optical flow. It means that the proposed algorithm performed better than the other three algorithms and can be applied to the enhancement of velocity calculation precision.  
It can be easily seen from Tables 3 and 4, that compared to the normal pyramid LK algorithm and pyramid LK based on K-Means algorithm, the performance of the proposed method was largely improved, which is illustrated by the increase in the accuracy the decrease in the dispersion degree of data. Specifically, decrease of the standard deviation means that the results are more average and concentrated, which indicates that the velocity result are more stable. The least RMES obtained by the proposed method implies that its velocity calculation accuracy is superior to the other three algorithms.
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 2 Project Overview
### Numerical Models and Simulation Scheme
#### 3.2.4 Method of Determining the Loose Zone in the Surrounding Rock Mass  
The pressure arch in the surrounding rock mass was characterized by an arch structure. The pressure arch body bears its own load and the load of the rock mass above the arch so that the surrounding rock maintains a certain degree of self-stability after excavation. However, the rock mass below the pressure arch has poor stability because it is close to the tunnel excavation outline, which leads to the stress exceeding the rock mass strength and causing damage. Therefore, the loose zone in the surrounding rock mass is defined as the rock mass from the inner boundary of the pressure arch to the tunnel contour. The principal stress vector diagram after the excavation of the No. 16 model is in Figure 8, showing the pressure arch shape and the ring-shaped distribution of the maximum principal stress.  
Based on existing research results [34], this paper took the connection of the peak point of the surrounding rock's tangential stress after the excavation of the tunnel as the judgment basis for the pressure arch inner boundary. The inner boundary criterion was embedded into FLAC3D with the Fish programming language to conduct numerical simulations. The correlation of the tangential stress can be calculated as follows:  
\[\sigma_{\theta}=\sigma_{x}\text{sin}^{2}\theta+\sigma_{z}\text{cos}^{2}\theta -2\tau_{xz}\text{sin}\theta\text{cos}\theta \tag{5}\]  
Figure 8: Maximum principal stress vector cloud diagram (No. 16).  
Figure 7: Numerical model.  
The simulation was processed as follows: (1) The model was established according to the orthogonal test scheme and the initial stress field was calculated. (2) The element stress component was extracted after the calculation was balanced and the tangential stress of each component was calculated using the correlation. (3) Values of the tangential stress of each element on the search path and were compared, and the element with the peak tangential stress point was marked. Owing to the model's symmetry, half of the model was selected for the search. The specific shape of the loose zone was obtained by connecting the inner boundary position of the pressure arch on each search path. (4) The loose zone was smoothed by the least square method to obtain the final scope of the loose zone in the surrounding rock mass, as shown in Figure 9. It should be noted that the influence of floor heave was not considered in this study.
# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 3 DM Evaluation
### Analysis of the Selection of Regression Data Mining Technique
#### 3.2.4 Support Vector Machines (SVMs)  
At the core of the support vector machines (SVMs) method is the concept of a decision space, which is divided by building boundaries separating objects with different classifications. The support vector machines method performs classification tasks by constructing hyperplanes in a multidimensional space that separate cases belonging to different classes. However, regression can also be performed, as well as both tasks, for multiple variables, both continuous and categorized. For each categorized variable, a set of variables is created with codes indicating the classification of each case (0 or 1). For example, a variable taking three values; A, B, and C; will be represented by three variables. A key element of the SVM method is the selection of the form of the kernel function and the model parameters coupled to it. There are several types of kernel functions to use in the SVM model: linear, polynomial, radial basis functions (RBF), and sigmoid function. The support vector machines (SVMs) method is a comprehensive machine learning model that enables both linear and non-linear classification, regression, and identification of outliers. SVR differs from simple regression, which aims to minimize the error between the predicted and actual values, because SVR aims to fit the error (usually the square of the residuals) into an interval with a certain threshold value. In this way, the SVR method attempts to find the closest fit between the actual data points and the function representing them [52].  
In the experiment for analysis, the preliminary evaluation of some important parameters needed to be determined. To achieve this, a special script was elaborated in the R programming language and then was exported in the Statistic(r) program. The main parameters, the best for this dataset, were given below (in the Statistic(r) program):  
* Regression type 1: (coefficients: C = 1, epsilon = 0.5);
* Kernel type: radial basis function (RBF, gamma = 3);
* WN number 41.