esa-sceva/satcom-chunk-collection huggingface dataset:
# Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning +
## 4 Description of the Model to Identify Local Persistence
When the answer is correct, as \(grade=1\), the penalty is avoided. * _stop:_ Represents the maximum number of attempts that can be summed for each exercise. This is used to avoid single exercises with many attempts have a huge weight that may make local persistence to be high even when students never attempt incorrect exercises. In the equation, there are two values that need to be fixed beforehand, and their values may depend on the specific context. These variables are _penalty_ and _stop_. The idea of the _penalty_ is that it represents the number of attempts needed to compensate an event of non-persistence so as to make the overall local persistence 0.5. For example, if the _penalty_ is 1, if one student gets 0 in one exercise and 1 in another one in the second attempt (sequence 0-1), local persistence would be 0.5. When choosing this value, one should think that low penalties allow overcoming non-persistent events easily while very high values would require showing very high local persistence to overcome those events. In this case, as most exercises with more than two options are answered with 3 (71%) or 4 (82%) attempts at most, a penalty of 4 was chosen so that non-persistent events are compensated with two exercises with three options at most where the students get the answer in the last attempt. Nevertheless, this value can be context and methodology dependent and can be adapted in each scenario. With regard to _stop_, it is used to avoid single exercises having excessive weight in the equation, and it would represent the maximum local persistence that you can show in an exercise (e.g., if _stop_ is 10, an exercise with 10 attempts will have the same contribution into the local persistence as an exercise with 11 attempts, same as with 12 attempts, etc.). In this scenario, _stop_ is set as 10 although results barely affected since only 860 out of 58,217 (1.5%) of exercises have four or more attempts. Nevertheless, it is included as it may be relevant in other courses where there are more open exercises and students may need more attempts to solve the exercises. Using Equation (1), Table 3 shows the values of accumulated local persistence for the sequences presented in Table 2, together with the attempts and grade for each exercise (_attempts\({}_{i}\)_ and _grade\({}_{i}\)_). In this table, exercises are supposed to be taken sequentially (ID1, ID2, ID3 and ID4) and local persistence is updated with each exercise, as the set of exercises done is updated (i.e., local persistence in row 1 is computed when the student has only done exercise 1, in row 2 when s/he has done exercises 1 and 2, and so forth). Values show how local persistence is reasonably increased when students attempt incorrect exercises until getting the right answer and decreases when they give in.
# Pricing Strategies of Logistics Distribution Services for Perishable Commodities
## 2 Problem Description and Symbol Representation  
The research object of this paper is the logistics service providers of perishable goods. The logistics service providers can independently determine the logistics distribution pricing \(P\). They also have to bear the main costs, including the unit cost \(C\) of commodity distribution, the unit cost \(h\) of commodity storage during the process of distribution, and the variable cost \(W\) during the process of distribution. The distribution time is \(T\), and the customer satisfaction of service is \(l\).  
The shorter the time spent by the logistics service provider during the distribution process, the lower the costs and the higher the consumer satisfaction, which will face more risk of deterioration [9]. Therefore, the delivery time is a variable. This paper studies the impact of different delivery times on different pricing models, and gives some advice to logistics service providers for pricing logistics distribution services. There are two notable characteristics during the distribution process of perishable goods:  
(1) Periodicity, which means that the freshness of goods decreases with the increase of delivery time. Therefore, we define \(V_{t}=V_{0}e^{-\lambda t}\) as the value of perishable goods at time \(t\)[21], here \(V_{0}\) is the initial value of the perishable goods at \(t=0\), and \(\lambda\) is the parameter of attenuation value.  
(2) Distribution pricing and service satisfaction, which are related to the delivery quantity [22]. Consumers are more inclined to choose logistics service providers with higher cost performance. When the price is identical, consumers are more tend to choose providers with higher service satisfaction. The model of distribution pricing is defined as follows:  
\[Q(t,P)=\frac{kl+V_{t}-P}{s} \tag{1}\]  
Here, \(s\) indicates the sensitivity of consumers to the cost performance of perishable goods, \(k\) is the influence coefficient of service satisfaction on the quantity of goods. If and only if \(P\leq kl+V_{t}\), the quantity of distribution is meaningful.  
The main parameters (variables) used in the model are as follows: \(P_{i}\) is the pricing decision variable under the fixed price and dynamic pricing strategies, \(i=F\), \(TF\), \(D\), \(TD\) represent the fixed pricing model, fixed pricing model with time constraints, dynamic pricing model and dynamic pricing model with time constraints, respectively. \(\pi_{i}\) (\(i=F\), \(TF\), \(D\), \(TD\)) indicates the average profit of the logistics service providers in the different models.
## 1 Introduction
The majority of prior studies have primarily employed conventional network-based scheduling techniques for time-space planning. However, these methods, including the CPM, have some intrinsic limitations when applied to time-space planning. CPM-based scheduling strategies do not consider space as a restrictive factor in coordinating activities. They focus mainly on time management, often overlooking the spatial aspect. This omission can result in conflicts and inefficiencies, as space is a vital resource in project execution. Hence, this points to a significant limitation of these conventional scheduling methods, especially in scenarios requiring simultaneous management of both time and space. Furthermore, the majority of prior research has primarily focused on identifying and reporting time-space conflicts throughout a project's duration. However, one of the significant challenges in time-space planning involves finding solutions for these identified conflicts. In most earlier studies, it was left up to the practitioners to address the reported conflicts by adjusting the project timeline or delaying nonessential tasks. To the authors' understanding, no previous study has suggested an automated method for managing these identified conflicts. Therefore, we propose this approach as a method of time-space conflict management to automatically detect and resolve time-space conflicts in construction projects.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
###### Abstract  
In vehicular ad hoc networks (VANETs), the presence of untrustworthy nodes poses a significant threat, impacting the network's reliability. This has led to the emergence of node-centric misbehavior detection as a crucial aspect of VANET security, focusing on the behavior of vehicles rather than the content of their interactions. While the trust model is a popular approach, the computational complexity of trust computations and management in VANETs is attributed to the intricate relationships among vehicles and the dynamic autonomous movement of nodes. To tackle these challenges, we developed a hybrid trust model scheme for node-centric misbehavior detection. Our method represents complex vehicular relationships using a hyper-graph within a dynamic behavior-homogeneous cluster. The model incorporates direct and indirect trust in a multi-layered hybrid trust framework, enabling accurate computation of the aggregate trust level for each cluster member vehicle. Experimental results demonstrate the effectiveness of our scheme, particularly in high-density vehicle cooperation scenarios, highlighting its promising ability to detect misbehaving nodes.  
v 2020
## 4 Experimental Results and Analysis
### Load-Displacement Relationships and Strains
The compressive force of the steel plate at the beginning of the experiment was negligible. Figure 15: Comparison of strain gauges: (**a**) concrete strain gauge; (**b**) rebar strain gauge; (**c**) steel plate strain gauge. The test results indicated that it is possible to ensure structural performance as well as splicing performance between the PC modules through the steel plate resistive tensile force. In PCM-A and PCM-B, similar to monolithic beams, the strain in the compression zone reached the ultimate compressive strain of concrete, and the strain in the tensile zone exceeded the yield strain of steel plate. According to this result, it was confirmed that the stress between each module was transferred well through the joint.
## 2 Background and Related Work
### Overview of Related Studies
#### 2.2.4 Spectral Efficiency  
Improving spectral efficient is a generic task that requires mechanisms from different layers. In [42], the authors considered the problem of IAB network deployment. The work focused on minimizing the blockage of the propagation paths by trees (foliage) using the multi-connectivity feature specified by 3 GPP [62], namely by switching the user to another IAB-node or the IAB-donor. It was concluded that a planned node deployment provides benefits compared to a random deployment, especially in specific cases where environmental factors must be taken into account.  
The authors in [63] investigated the impact of scheduling strategies, multi-beam antennas, and multi-connectivity strategies on the throughput of IAB systems operating in half-duplex mode. They showed that the use of multi-beam antennas for throughput improvement is only warranted at IAB-nodes, whereas multi-connectivity may not only improve system performance, but also induce self-load balancing in IAB systems.
## 3 Results and Discussion
### Effect of Silane/Methane Ratio on Silicon-Carbon Nanocomposite  
The Si nanoparticles act as a high energy store, and the carbon materials are expected to prevent the pulverization of the Si nanoparticles. Low silicon content will reduce the theoretical capacity of the composite, and low carbon content cannot restrain the volume changes of the silicon well. Therefore, the appropriate silicon-carbon ratio directly affects the electrochemical properties of the composite. Under the condition with a voltage of 8000 V, carrier gas flow of 6000 mL/min, and methane flow of 360 mL/min, the silicon-carbon nanocomposite was prepared with different silane and methane ratios. The ratio of silane to methane was 0:1, 1:10, 1:5, 1:3, 1:2, and 1:0, respectively, and the optimal ratio of silane to methane was determined to be 1:3.  
Figure 3 shows the cycling stability of these six composites at a current of 150 mA g\({}^{-1}\). It can be seen that the discharge capacity generally improves rapidly as the silane ratio increases, and then tends to decline. In terms of pure tremelliform carbon nanosheets, the specific capacity of the electrode is stabilized at 343 mAh g\({}^{-1}\) after 50 cycles. With the addition of the silane, the specific capacity of the silicon-carbon nanocomposite increases significantly. As the ratio of silane to methane increases from 1:10 to 1:3, the specific capacity of the composite changes from 539 to 948 mAh g\({}^{-1}\) after 50 cycles. However, when the ratio of silane to methane increases to 1:2, the discharge-specific capacity tends to decline. This may be because the amount of carbon materials is not sufficient to alleviate the volume change of the Si nanoparticles during the cycles, and the electrolyte decomposition repeatedly forms the SEI film on the Si nanoparticles surface during the cycles, which consumes a large amount of Li\({}^{+}\) and leads to a decrease in the specific capacity (831 mAh g\({}^{-1}\) after 50 cycles). A quick capacity fading is observed in the pure Si nanoparticles, which further suggests that the Si nanoparticles rapidly  
Figure 2: Yield of solid products under different carrier gas flows.  
pulverize without the buffering effect of carbon materials. Above all, the optimal ratio of silane to methane is 1:3, and under this condition, the composite exhibits a higher stable specific capacity.
## 7 Research Challenges, Open Issues, and Future Directions
### Handling Big Data  
Traditional centralized VANET technology is ineffective in dealing with vast volumes of traffic (data) provided by smart cars, such as video and sensor data. More servers are necessary for traffic information in scattered locations to gather and analyze such enormous data in real-time. Cloud-based VANETs might be a viable option. Generally speaking, uploading to vehicles utilizes data downloaded from the cloud. With time and effort, as the number of cars on the roads grows, cities' current cloud computing paradigm can barely meet the location awareness standards, latency, and mobility assistance.
## 2 Materials and Methods
### Classification Model  
Convolutional Neural Network (CNN) is a class of deep learning models particularly effective for analyzing structured data such as images or time-series signals. They automatically extract hierarchical features through layers of convolution and pooling operations, enabling the model to capture both local and global patterns in the data. In recent years, CNN has shown strong performance in biomedical signal analysis, including ECG interpretation. In this study, we used CNN as the classification model to detect LVSD by learning residual ECG patterns characteristic of the non-LVSD group. Specifically, we used the residual signal, obtained by subtracting the reconstructed data from the original data of the pre-trained anomaly detection model, as input for CNN training. Figure 7 shows the CNN classification model structure.  
The CNN architecture consisted of three 1D convolutional layers with 8, 16, and 16 filters, respectively, each followed by batch normalization, ReLU activation, and max pooling. The feature maps were then flattened and passed through six fully connected layers with 128, 64, 32, 16, 8, and 2 units, respectively, with dropout layers applied after each dense layer to reduce overfitting.  
The model was trained using the Adam optimizer with default parameters, a learning rate of 0.0001, and a batch size of 16. Binary cross-entropy was used as the loss function. To prevent overfitting, early stopping with a patience of 10 epochs based on validation loss was employed. Model performance was monitored using accuracy and loss metrics on the validation dataset.  
Given the limited dataset size, this relatively simple architecture yielded better generalization performance than deeper or more complex models.  
We conducted Grad-CAM analysis to better understand how the CNN classifier distinguishes LVSD from non-LVSD predictions. Figure 8 presents representative examples of Grad-CAM overlays for one non-LVSD and one LVSD case. In the non-LVSD example, attention was sparse and localized, while in the LVSD example, attention was broadly distributed across the entire signal.  
Figure 6: The comparison between the original and restored graphs: (**a**) non-LVSD; (**b**) LVSD.  
To confirm this pattern at the dataset level, we computed the mean and standard deviation Grad-CAM profiles for all test predictions (Figure 9). The non-LVSD group showed consistently low attention with minimal variability, whereas the LVSD group exhibited elevated global attention with greater variability, suggesting that the model generally relies on global residual abnormalities when predicting LVSD.  
Figure 8: Representative Grad-CAM overlays for individual test cases: (**a**) non-LVSD prediction; (**b**) LVSD prediction.  
Figure 7: Proposed 1D CNN classification model architecture.  
These results indicate that the model adopts distinct attention strategies: minimal, localized attention for non-LVSD predictions, and diffuse, global attention for LVSD predictions, providing explainable insight into the model's decision process.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 5 Performance Evaluation
### Performance Evaluation
#### 5.3.2 Impact of Smooth Penalty Parameter \(\lambda\)  
Figure 7 shows how the the estimation's Mean Relative Error varies when \(\lambda\) increases from \(10^{-4}\) to \(10^{3}\). We make two interesting observations. First, \(\lambda\) around 1\(\sim\)10 usually gives the best performance. Too big or too small \(\lambda\) will decrease the estimation accuracy. This is reasonable, as when \(\lambda\) is too small, we put little emphasis on the smoothness of estimated surface, thus the performance will suffer. If \(\lambda\) is too big, it enforces a smooth surface, which also doesn't match the reality. Second, when we have less base stations, \(\lambda\) that gives the best performance will increase (from 1 to 10). In addition, we can see that the performance of the model with \(\lambda\) between 1\(\sim\)100 does not significantly change when \(n_{\text{BS}}=100\). That indicates the following: when the base station distribution is more sparse,  
Figure 6: Comparison of the activity distributions. (**a**) real cell phone activity distribution; (**b**) estimated distribution by Patched Estimation method; (**c**) estimated distribution by our method.  
the estimation performance is less sensitive to \(\lambda\) when it is around the best value (1 for \(n_{\text{BS}}=200\) and 10 for \(n_{\text{BS}}=100\)).
# Efficient LDPC Encoder Design for IoT-Type Devices
## 3 LDPC Codes
### QC-LDPC Codes  
The quasi-cyclic subclass of low-density parity check codes (QC-LDPC) [44] is known to possess a desirable feature of organized message routing in hardware implementations of a partially parallel decoder. QC-LDPC codes are used for most LDPC coding system designs, industrial applications [11; 22; 37; 39; 44], etc. Their characteristic feature is the block-circulant structure of \(\mathbf{H}\). The parity-check matrix \(\mathbf{H}\) of a QC-LDPC code consists of a grid of square \(P\times P\) submatrices, with each submatrix being the following:  
* An all-zero zero matrix;
* An identity matrix;
* An identity matrix with circularly shifted columns.  
Additionally, the implementation of a low-complexity encoder using the classic Richardson-Urbanke method [43] requires an ALT (almost lower triangular) form, preferably a dual-diagonal structure of \(\mathbf{H}\)[45]. Let us denote a \(P\times P\) circulant permutation matrix (CPM) as \(\mathbf{P}^{s}\), which is obtained by cyclically shifting the identity matrix \(\mathbf{I}_{P\times P}\) by \(s\) positions to the right. In this article, a class of codes with the following structure of the parity-check matrix \(\mathbf{H}\) is considered:  
\[\mathbf{H}=\begin{bmatrix}\mathbf{P}^{s_{1},1}&\mathbf{P}^{s_{1},2}&\dots& \mathbf{P}^{s_{1},J}\\ \mathbf{P}^{s_{2},1}&\mathbf{P}^{s_{2},2}&\dots&\mathbf{P}^{s_{2},J}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{P}^{s_{1},1}&\mathbf{P}^{s_{1},2}&\dots&\mathbf{P}^{s_{1},J}\\ \end{bmatrix}, \tag{1}\]  
where \(s_{i,j}\in\{0,1,\dots,P-1,\infty\}\), where \(\mathbf{P}^{\infty}=\mathbf{0}\) is used to denote the all-zero matrix [45]. Codes associated with the parity-check matrix as in (1) are \((N,K)\) QC-LDPC codes with code vector length \(N=JP\), information vector length \(K=(J-I)P\), and rate \(R=(J-I)/J\).  
Alternatively to the matrix representation, the LDPC codes can also be represented by the associated bipartite Tanner graph [7; 46]. In the Tanner graph, variable nodes representing data bits are associated with columns of \(\mathbf{H}\), and check nodes representing parity checks are associated with rows of \(\mathbf{H}\). The nonzero elements in \(\mathbf{H}\) are represented by the edges in the code graph. The Tanner graph defines the passage of the message through the iterative decoding algorithm [7], but it is also involved in numerous methods of code construction with optimized performance [20; 47; 48].
## 4 Our Proposed Algorithm
### Data Gathering with Sparse Random Projections  
The aforementioned methods (plain CS and hybrid CS) still experience great challenges for all nodes involved in each measurement; in other words, the two mechanisms follow the dense matrix. Therefore, [PERSON] et al. (2019) propose a distributed data-gathering algorithm according to sparse random projections. In this algorithm, \(M\) nodes are randomly chosen to collect \(M\) weight sums in WSNs. Each projection sensor node collects one weight sum. Now, we explain how to accomplish data gathering in detail by means of Figure 9. Suppose that node 5 is a projection node, and \(\phi_{ij}\
eq 0\) of nodes 10, 15, 20, and 26. Then, node 5 initializes the projection by querying nodes 10, 15, 20, and 26. These nodes reply to node 5's query with their readings \(x_{j}\) in one packet. Finally, node 5 gathers all the data with its own data: \(\sum\limits_{j=1}^{N}\phi_{ij}x_{j}\) and transmits them to the sink node via the shortest path. Meanwhile, the transmission costs for each measurement reduce dramatically from \(O(N)\) in a dense matrix to \(O(\log N)\) in a sparse random matrix.  
Figure 8: Compressive data gathering (CDG).  
Figure 7: **(a–c)** Comparison of the three different data-gathering schemes.  
Figure 9: An example of data gathering per random projection node using \(\Phi\).
###### Abstract  
Smoothing is one of the fundamental procedures in functional data analysis (FDA). The smoothing parameter \(\lambda\) influences data smoothness and fitting, which is governed by selecting automatic methods, namely, cross-validation (CV) and generalized cross-validation (GCV) or subjective assessment. However, previous biomechanics research has only applied subjective assessment in choosing optimal \(\lambda\) without using any automatic methods beforehand. None of that research demonstrated how the subjective assessment was made. Thus, the goal of this research was to apply the FDA method to smoothing and differentiating kinematic data, specifically right hip flexion/extension (F/E) angle during the American kettlebell swing (AKS) and determine the optimal \(\lambda\). CV and GCV were applied prior to the subjective assessment with various values of \(\lambda\) together with cubic and quintic spline (B-spline) bases using the FDA approach. The selection of optimal \(\lambda\) was based on smoothed and well-fitted first and second derivatives. The chosen optimal \(\lambda\) was \(1\times 10^{-12}\) with a quintic spline (B-spline) basis and penalized fourth-order derivative. Quintic spline is a better smoothing and differentiation method compared to cubic spline, as it does not produce zero acceleration at endpoints. CV and GCV did not give optimal \(\lambda\), forcing subjective assessment to be employed instead.  
functional data analysis; cross-validation; generalized cross-validation; roughness penalty; B-spline; smoothing +
Footnote †: journal: Applied  
Article Smoothing and Differentiation of Kinematic Data Using Functional Data Analysis Approach  
An Application of Automatic and Subjective Methods