esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Studies of Scale Effect on the Shear Strength of Coarse-Grained Soil
## 4 Conclusions  
Based on the continuous grading equation of soil and the idea of scale reduction via the similar grading method, 24 groups of test gradations with a maximum particle size \(d_{\max}\) of 40, 20, and 10 mm, respectively, were utilized to quantitatively investigate the shear strength of CGS with different gradation structures and maximum particle sizes under the same relative density by a large-scale direct shear instrument. The main conclusions obtained in the present research are as follows:  
1. Based on the continuous grading equation of soil, combined with the similar grading method, the purpose of quantitatively and comprehensively studying the scale-induced effect on the shear strength of CGS under consideration of the maximum particle size and gradation structure can be achieved;
2. When the grading area is fixed such that the gradation structure is unchanged, the cohesion \(c\) and internal friction angle \(\varphi\) gradually increase with increases in the maximum particle size \(d_{\max}\); furthermore, \(c\) and \(\varphi\) are logarithmic functions of \(d_{\max}\);
3. If the maximum particle size \(d_{\max}\) is constant, the cohesion and internal friction angle increase rapidly with the reduction of grading area \(S\) and decrease slightly after reaching a certain level. The relationships between \(c\), \(\varphi\), and grading area \(S\) are established;
4. A new prediction model of the shear strength of CGS considering the influence of the maximum particle size \(d_{\max}\) and the gradation structure is established. The reliability of the model to predict the shear strength of arbitrarily graded CGS is verified through test data in the relevant literature, and the applicability of the model to different types of CGS is also explored.  
Conceptualization, S.L., T.W. and M.J.; methodology, M.J.; validation, J.Z.; formal analysis, S.L. and T.W.; investigation, M.J.; resources, J.Z.; data curation, S.L., T.W. and H.W.; writing--original draft, S.L., writing--review and editing, T.W., H.W. and M.J.; supervision, J.Z.; project administration, M.J.; funding acquisition, M.J. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Yalong River Joint Fund of Natural Science Foundation of China (U1865104), key project of the Yangtze River Water Science Research Joint Fund of National Natural Science Foundation of China (U2040221) and the Foundation of Guangdong Key Laboratory of Oceanic Civil Engineering (LMCE202103).  
Not applicable.  
Figure 16: Measured and predicted values of the internal friction angle of rockfill materials.
# Performance Modeling and Analysis of LTE/Wi-Fi Coexistence
## 3 Modeling
### Mathematical Formulation of the Model  
Based on the MOSEL-2 solution, the following mathematical formulation of the model is provided. Table 1 contains the list and descriptions of the symbols used in the model formulation. Assume the following parameters:  
\(NWB\_Zi\) (\(t\)): the number of web-browsing sessions in zone \(i\) at time \(t\). \(i\) = 1,..., \(k\), \(k\) is the number of zones. \(NSTR\_Zi\) (\(t\)): the number of video streaming sessions in zone \(i\) at time \(t\). \(i\) = 1,..., \(k\)  
\(NVOLTE\_Zi\) (\(t\)): the number of voice calls sessions in zone \(i\) at time \(t\). \(i\) = 1,..., \(k\)  
Therefore, we define a system state at a given point of time as a vector \(S(t)\):  
\[S(t)=(NWB\_Zi(t),\ NSTR\_Zi(t),\ NUOLTE\_Zi(t)) \tag{1}\]  
Represents the stochastic process, which is based on the time, \(t\), where \(i\) = 1,..., z, representing a specific zone. Moreover, the total number of users in all zones is given by:  
\[N_{TOTAL}=\sum_{i=1}^{k}(NWB\_Zi+NSTR\_Zi+NVOLTE\_Zi) \tag{2}\]  
Given the above system state, the stochastic process of the system represents a multi-dimensional Continues Time Markov Chain Process (CTMCP) to solve a multi-dimensional transition matrix \(Q\). \[Q=||Q_{lm}||\ =\left| \begin{array}{cccc}Q_{00}&Q_{01}&Q_{02}\\ Q_{10}&Q_{11}&Q_{12}\\ &&\vdots\\ &&\vdots\\ Q_{l0}&Q_{l1}&\dots\dots\end{array} \right| \tag{3}\]  
where \(l\) and \(m\) are the two states of the process and the transition matrix of the system \(Q\) can then be expressed as \(Q=\{q_{lm}\}\), where \(q_{lm}\) is the transition rate from state \(l\) to state \(m\). \(l\)  
Figure 2: LTE cell divided into two zones. \(m=0,\ldots,R\), where \(R\) is the total number of possible states in the system. Assuming that the number of gained states is predetermined, then the result is a predetermined square matrix, for which the rows and the columns are equal. The provided solver attached to the MOSEL-2 language solves this system state.
## 5 Conclusions
* Applicability on constrained platforms: Dilithium and Falcon are more suitable for devices like IoT sensors, whereas Sphincs+'s larger signature size may hinder its use in the most resource-limited environments. In summary, each algorithm has distinct advantages, and the choice depends on the specific requirements of the deployment context. In the future, there will be a significant push to integrate post-quantum cryptographic algorithms into existing communication protocols and security infrastructures. Consequently, resource-constrained devices, such as those analyzed in this paper, will require lightweight cryptographic solutions due to their limited resources. Research into efficient algorithms that are both quantum-resistant and suitable for low-power, low-compute environments is likely to increase. Thereby, considering smart cards, the weakest device type analyzed, the emerging trend of substituting them with more resourceful mobile devices aligns with the need for stronger, quantum-resistant cryptographic solutions. From the authors' personal viewpoint, a paradigm shift in research and development (for example, changes in hardware and/or materials as an approach to optimizing algorithms) is bound to occur when the development of quantum computers will pose a tangible threat to the usual algorithms. Predicting this shift is a challenging task and falls outside the scope of this paper. **Author Contributions:** Conceptualization, M.V. and K.M. ; methodology, K.M. ; validation, M.V. ; formal analysis, K.M. ; investigation, K.M. ; resources, M.V. ; data curation, M.V. ; writing--original draft preparation, K.M. ; writing--review and editing, M.V. ; visualization, K.M. ; supervision, M.V. ; project administration, M.V. ; funding acquisition, M.V. All authors have read and agreed to the published version of the manuscript. **Funding:** This paper was supported in part by the European Union, through the European Regional Development Fund, under Grant KK.01.2.1.02.0294. The findings presented in the paper are the sole responsibility of the authors and do not necessarily reflect the official position of the European Union or the European Commission. **Conflicts of Interest**: [PERSON] is working at FERIT as a full professor, but [PERSON] also has a private company (Random Red Ltd.). [PERSON]. (as CEO of Random Red Ltd.) wrote this paper together with [PERSON]. (from company Sedmi Odjel Ltd.). Sedmi Odjel Ltd. has a grant from the European Union. However, the findings presented in the paper are the sole responsibility of the authors and do not necessarily reflect the official position of the European Union or the European Commission. [PERSON] personally doesn't have any connection to the grant, neither his faculty (FERIT) nor his company (Random Red Ltd.). Sedmi Odjel Ltd. is funded as a legal entity (as reported in \"Funding\" section). [PERSON] is not funded personally (\"as an author\").
## 3 Global Positioning System (GPS) L1 Receiver: 2D Pattern vs. 3D Pattern
### Navigation Solution
#### 3.4.2 Range-Based Orbital Filter  
Figure 18 shows the navigation solution obtained when processing GPS L1 pseudoranges and pseudorange rates by means of the implemented orbital filter. We can see that, unlike when we compute the position and velocities with a least squares estimator, when using the filter, the results obtained assuming a 2D antenna pattern are very similar to the ones obtained assuming a 3D antenna pattern. The exact statistics are shown in Table 5. In particular, the difference in velocity estimation errors never exceeds 0.011 m/s. For the position estimation error, the mean and the standard deviation have a maximum difference of about 0.4 m in the first portion, 6 m in the second portion, 1 m in the third portion, and 9 m for the entire MTO, while the difference in maximum is more important (more than 50 m for the entire MTO).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Portion**} & \multicolumn{4}{c}{**L1 Antenna Pattern 3D**} & \multicolumn{4}{c}{**L1 Antenna Pattern 2D**} \\ \cline{2-10}  & & **1** & **2** & **3** & **All MTO** & **1** & **2** & **3** & **All MTO** \\ \hline Position & **Mean** & 22.6 & 602.0 & 4976.2 & 135,948.0 & 23.5 & 978.2 & 10,973.4 & 4290.7 \\ error & **Std** & 27.9 & 594.6 & 5266.6 & 6,474,173.1 & 31.1 & 1490.1 & 162,226.0 & 41,018.5 \\ (m) & **Max** & 314.4 & 9080.8 & 124,313.9 & 381,775,899.5 & 288.6 & 39,294.8 & 19,249,062.5 & 19,249,062.5 \\ \hline Velocity & **Mean** & 0.9 & 24.5 & 197 & 2620 & 0.9 & 38.7 & 1066 & 206 \\ error & **Std** & 1.1 & 24.1 & 207 & 206,914 & 1.2 & 60.2 & 56,428 & 13,801 \\ (m/s) & **Max** & 11.0 & 300.0 & 4027 & 69,692,351 & 11.7 & 1445.4 & 7,249,068 & 7,249,068 \\ \hline \end{tabular}
\end{table}
Table 4: Statistics of the single-point least squares estimation error.  
Figure 17: Single-point least squares estimation error for the assumed MTO.  
Figure 18: Range-based orbital filter estimation error for the assumed MTO.
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Data Acquisition  
Aiming at using a small number of electrodes for AAD, EEG responses were acquired using \(C=16\) electrodes. The electrodes were placed on the scalp area at F1, F2, FC3, FC4, FT7, FT8, Cz, C5, C6, P3, P4, P7, P8, Oz, PO3, and PO4 (see Figure 3). This electrode placement was inspired by the results in Reference [6; 28], where it was shown that an electrode configuration covering the temporal, central, frontal, and parietal scalp areas yields a reliable decoding performance. The EEG responses were referenced to the P9 electrode (Since we did not observe a significant difference in AAD performance between referencing to the P9 electrode or to the nose electrode, we decided to use P9 as the fixed reference electrode for all phases.). The EEG responses were acquired using active (g.LADYbird) electrodes and a g.USBamp bio-signal amplifier (provided by g.tec, Austria). The acquired EEG responses and EEG trigger markers were streamed at a sampling frequency of 500 Hz using the gUSBamp application from the Lab Streaming Layer (LSL) software package (provided by Swartz Center for Computational Neuroscience, UCSD). Using the gUSBamp application, the streamed EEG responses were also forwarded to RAAD for online decoding, to the Lab Recorder application (provided by Swartz Center for Computational Neuroscience and Kothe) for recording, and to the OpenViBE software for online EEG visualization (see Table 1). The gUSBamp application, the OpenViBE software and the Lab Recorder application were run on the same computer employed for the cognitive-driven gain controller system (see Figure 2).
## 4 Node Insertion Algorithm for Determining Viewpoints
### Node Propagation Stoppage Criteria  
In this section, we will discuss the criteria for stopping the node propagation process. In our previous work [10], we observed that since the geodesic lines could draw close together or move apart on a highly curved surface like a hemisphere, too much overlap or separation of adjacent projections (bad behavior) could occur. The only stopping criteria at that point was to check if the newly placed node was outside the boundary of the mesh. In this paper, we introduce criteria for node stoppage to limit the view propagation so that the neighboring nodes do not converge or diverge from each other too much. We propose and explore two approaches here--the 'indirect' approach which relies on normal vectors not diverging too much from the starting point, and the 'direct' approach (which relies on managing the distance between neighboring viewpoints). Both the approaches are explained in the next section and the Results section shows the output from both methods so that we can compare them for many different use cases.  
As we are not partitioning the surface, when we use a stopping criterion, only a portion of the surface may be covered before stopping. So we need to repeat the process from a yet-to-be-covered portion. Hence, in addition to the primary stopping criteria that we discussed in the previous paragraph, we need to also ensure that the current node propagation process does not move into an already existing set of nodes.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 5 Use Cases
### Follow-Up Telemedicine  
Telemedicine is often employed in the progress monitoring and treatment of patients who are not in emergency or critical life-threatening condition, as in [11]. Patients who have initially received orthopedic or trauma surgery treatment in Germany received remote follow-up, and no substantial drawbacks were observed compared with face-to-face consultation. The communication technology used for this was telemonitoring with the aid of sensors to collect vital WBAN data for proper assessment and follow-up.
# Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks
## 2 System Model
* _Phase 2.1_\(R\) is visible, and \(S_{1}\) and \(S_{2}\) generate distributed UEP rateless coded symbols from information symbols using LT-coding degree distribution \(\Psi_{m}(x)\), and transmit \(N_{2}\) coded symbols to \(R\) by primary links. In an encoding process at \(S_{m}\), if a degree \(d_{m}\) is randomly selected with probability \(\Psi_{m,d_{m}}\) using the degree distribution \(\Psi_{m}(x)=\sum_{d_{m}=\,1}^{D_{m}}\Psi_{m,d_{m}}x^{d_{m}}\), then \(d_{m}\) information symbols are selected with probability \(\frac{w_{mi}}{\pi_{mi}k_{m}}\) in \(I_{mi}\) and are bitwise XORed to form the coded symbol. * _Phase 2.2_ The coded symbols are transmitted to relay \(R\) from \(S_{1}\) and \(S_{2}\), and based on the network coding rule \(P=\{p_{1},p_{2},p_{3}\}\) and \(\sum_{i=\,1}^{3}p_{i}=1\), \(R\) generates three types of network coded symbols and forwards them to the destination \(D\). \(R\) forwards \(S_{1}\)'s output symbol directly with the probability \(p_{1}\), while forwarding \(S_{2}\)'s output symbol directly with the probability \(p_{2}\), and XORs the two incoming symbols with the probability \(p_{3}\) and then forwards to \(D\). * _Decoding_ After receiving enough coded symbols from \(S_{1}\), \(S_{2}\) and \(R\), a joint decoding is performed on \(D\) to recover the information symbols of \(S_{1}\) and \(S_{2}\). Considering the erasure probabilities of the links, in _Phase 1_ and _Phase 2_, the expected number of the coded symbols successfully received at \(D\) can be expressed as \(n_{1}=N_{1}(1-\epsilon_{1D})\), \(n_{2}=N_{1}(1-\epsilon_{2D})\) and \(n_{NC}=N_{2}(1-\epsilon_{RD})\), where \(n_{1}\) and \(n_{2}\) are the number of the received coded symbols on the \(S_{1}-D\) and \(S_{2}-D\) links in _Phase 1_, respectively, and \(n_{NC}\) is the number of the received output coded symbols on the link \(R-D\) in _Phase 2_.
## 3 Results
### Performance Measure
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Parameters** & **Structure** \\ \hline Logistics Regression (LR) & Batch Size = 100 & Sigmoid function \\ \hline Bayesian Network (BN) & Batch Size = 100; & Search algorithm = Hill Climber \\ \hline Support Vector Machine (SVM) & Batch Size = 100; degree = 3; eps = 0.001; & \\  & gamma = 0.0; loss = 0.1; & Kernel = Radial basis function \\ \hline Random Forest (RF) & Batch size = 100; Literation = 100 & Bootstrap = 10 \\ \hline Neural Network (NN) & Batch Size = 100; Learning Rate = 0.3; & Input node = input variable count; \\  & Momentum =0.2; epoch = 500; & Hidden layer = 1 layer (node: 2); \\  & Activation function = sigmoid & Output Layer = 1 node \\ \hline Recurrent Neural Network (RNN) & Learning\_rate = 0.1; epoch = 10; & Input Layer = input variable count \\  & Batch\_size = 100; & Hidden Layer = 1 layer (node: input \\  & Activation = Softmax Function & variable count); Output Layer = 1 node \\ \hline Long Short-Term Memory (LSTM) & learning\_rate = 0.1; epoch = 10; & Input Layer = input variable count \\  & clipping = 5; Activation = Softmax Function; & Hidden Layer = 1 layer, (node: input \\  & Gate Activation Function = Sigmoid & variable count); Output Layer = 1 node \\ \hline \end{tabular}
\end{table} 
Table 3: Parameters of the ML models. Finally, the performance of the proposed model was evaluated by measuring whether the predictions of pathological T stage and BCR were significant using all of the algorithms. The experimental method involved adjusting the weights of the training and testing sets and the results are shown in Figure 9. The pathological T stage had a very high accuracy of 98.5% when all data were used, and BCR was significantly high as well (96.25%). The more data, the more accurate the proposed model became, because the performance gradually increased according to the amount of learned data. Using the proposed model, as cancer progression can be predicted, physician's can make optimal decisions, minimizing costs and providing precise medical services.
# Simulation of Hybrid Photovoltaic Solar Assisted Loop Heat Pipe/Heat Pump System
###### Abstract  
A hybrid photovoltaic solar assisted loop heat pipe/heat pump (PV-SAHP/HP) water heater system has been developed and numerically studied. The system is the combination of loop heat pipe (LHP) mode and heat pump (HP) mode, and the two modes can be run separately or compositely according to the weather conditions. The performances of independent heat pump (HP) mode and hybrid loop heat pipe/heat pump (LHP/HP) mode were simulated and compared. Simulation results showed that on typical sunny days in spring or autumn, using LHP/HP mode could save 40.6% power consumption than HP mode. In addition, the optimal switchover from LHP mode to HP mode was analyzed in different weather conditions for energy saving and the all-year round operating performances of the system were also simulated. The simulation results showed that hybrid LHP/HP mode should be utilized to save electricity on sunny days from March to November and the system can rely on LHP mode alone without any power consumption in July and August. When solar radiation and ambient temperature are low in winter, HP mode should be used.  
solar energy; photovoltaic; heat pump; loop heat pipe; hybrid mode; simulation +
Footnote †: journal: Applied Sciences
# Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction
## 1 Introduction
### Challenges in Arabic Event Coreference
As opposed to within-document resolution, which emphasizes coherence within a single text by connecting mentions of events, cross-document resolution focuses on connecting and consolidating information about events across multiple texts to improve understanding of events that occur in various contexts. Among the NLP tasks, both event-centered analysis and information retrieval have their own challenges and applications. It is more challenging to resolve cross-document event coreference than to resolve within-document events because it often involves reasoning about the same event in various contexts, dealing with variations in event descriptions, and resolving ambiguity caused by various descriptions of the same event.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 6 Analysis of in-Orbit Performance and Modeling Modification
### On-Orbit Data Analysis
The power consumption for active control and comparison to the ground thermal balance test and thermal analysis are shown in Table 16. The average power consumption is 1.68 W, which falls in between the high and low temperature conditions of the ground tests while not exceeding 2 W.
## Appendix A Polynomial Chaos Expansion
The posterior distribution, conditioned on the model responses, is given by combining the likelihood in Equation (11) and the prior Equation in (12) according to [PERSON] rule. This posterior, given \(\mathbf{\alpha}\), can take the form:  
\[p(\mathbf{c}|\mathbf{Y},\mathbf{\alpha},\beta)=\frac{p(\mathbf{Y}|\mathbf{X}, \mathbf{c},\beta)p(\mathbf{c}|\mathbf{\alpha})}{p(\mathbf{Y}|\mathbf{X},\mathbf{ \alpha},\beta)}, \tag{13}\]  
which is Gaussian \(\mathcal{N}(\mathbf{c}|\mathbf{\mu},\mathbf{\Sigma})\) with:  
\[ \begin{split}\mathbf{\mu}&=\beta\mathbf{\Sigma}\mathbf{\Psi}^{ \top}\mathbf{Y}\\ \mathbf{\Sigma}&=\Big{(}\mathbf{A}+\mathbf{\Psi}^{\top}\beta \mathbf{\Psi}\Big{)}^{-1},\end{split}  \tag{14}\]  
where \(\mathbf{\
abla}\) is the design matrix of the size \(N\times P\) with elements \(\Psi_{ni}=\psi_{i}(x_{n})\), and \(\mathbf{A}=\text{diag}(\alpha_{i})\). The values of \(\mathbf{\alpha}\) and \(\beta\) can be determined via type-II maximum likelihood [63], also known _evidence approximation_ in the machine learning literature [64; 65]. Having found values \(\mathbf{\alpha}^{*}\) and \(\beta^{*}\) for the hyperparameters that maximize the marginal likelihood, one can evaluate the predictive distribution over \(\mathrm{Y}\) for a new input \(\mathbf{x}\) by:  
\[ \begin{split} p(\mathrm{Y}|\mathbf{x},\mathbf{X},\mathbf{Y}, \mathbf{\alpha}^{*},\beta^{*})&=\int p(\mathrm{Y}|\mathbf{x}, \mathbf{c},\beta^{*})p(\mathbf{c}|\mathbf{X},\mathbf{Y},\mathbf{\alpha}^{*},\beta ^{*})d\mathbf{c}\\ &=\mathcal{N}(\mathrm{Y}|\mathbf{\mu}^{\top}\Psi(\mathbf{x}),\sigma^{2 }(\mathbf{x})).\end{split}  \tag{15}\]The predictive mean is given by Equation (A11) with \(\mathbf{c}\) set to the posterior mean \(\mathbf{\mu}\), and the variance of the predictive distribution is computed by:  
\[\sigma^{2}(\mathbf{x})=(\beta^{*})^{-1}+\Psi(\mathbf{x})^{\top}\mathbf{\Sigma}\Psi( \mathbf{x})\] (A13)  
where \(\mathbf{\Sigma}\) is calculated by Equation (A11) in which \(\mathbf{\alpha}\) and \(\beta\) set to their optimized values \(\mathbf{\alpha}^{*}\) and \(\beta^{*}\).