esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Proposed Interaction Diagram for Plugging  
### Development of Interaction Diagram  
The authors explored if plugging could be better forecasted using an interaction diagram that separates piles into two zones (plugged and unplugged) based on both the values of the pile diameter and the L/D ratio. Multiple interaction diagrams with D = 0.5-1.78 m (20-70 in) and L/D = 75-125 were explored (121 interaction diagrams in total), and their performance was evaluated by computing the number of cases correctly forecasted based on the voted overall plugging condition. The authors also explored the use of two lines to separate the cases into three zones representing plugged, internally plugged, and unplugged; however, the accuracy was lower than using a single line separating the plugged from unplugged conditions. This could be attributed to the small number of internally plugged cases.  
The highest achieved forecasting accuracy using an interaction diagram was 74.3% (55 out of 74 cases forecasted correctly) and was achieved by five possible lines. These five lines are plotted in Figure 6a where the shaded zone is where the piles are likely to be plugged. The initial proposal was to find the line with the highest accuracy and use it; however, upon finding 5 lines achieving identical accuracies, it was theorized that the area created by the union of these 5 lines would give more confidence in the likelihood of the plugging occurrence. This union zone is presented in Figure 6b along with the associated accuracy.  
Figure 6: Performance of various interaction diagrams for forecasting pile plugging based on pile dimensions. (**a**) The top performing 5 interaction diagrams; (**b**) The union zone created by the top 5 interactive diagrams and their respective accuracy; (**c**) The top achieving 25 interaction diagrams; (**d**) The union zone created by the top 25 interactive diagrams and their respective accuracy.  
The second highest accuracy was 73% (54 out of 74) and was achieved by 20 possible lines. All 25 lines are plotted in Figure 6c, where the shaded zone is where the piles are likely to be plugged. Again, upon finding 25 lines achieving nearly identical accuracies, it was theorized that the area created by the union of these 25 lines would give more confidence in the likelihood of the plugging occurrence. This union zone is presented in Figure 6d along with the associated accuracy.
# Advanced Bayesian Network for Task Effort Estimation in Agile Software Development
## 3 The BN Model  
The BN is a graphical model that describes probabilistic relationships between causally related variables.  
The BN is formally determined by the pair BN = (G, P), where G is a directed acyclic graph (DAG), and P is a set of local probability distributions for all the variables in the network. A directed acyclic graph G = (V (G), E (G)) consists of a finite, nonempty set of tuples V (G) = {(s\({}_{1}\), V\({}_{1}\)), (s\({}_{2}\), V\({}_{2}\)), \(\ldots\), (s\({}_{n}\), V\({}_{n}\))} and a finite set of directed edges E (G) \(\subseteq\) V (G) \(\times\) V (G). Nodes V\({}_{1}\), V\({}_{2}\), \(\ldots\), V\({}_{n}\) correspond to random variables X = (X\({}_{1}\), \(\ldots\), X\({}_{n}\)), which can take on a certain set of values s\({}_{i}\) (depending on the problem being modelled). The terms variable and node will be used interchangeably in this paper. The edges E (G) = {e\({}_{i,j}\)} represent dependencies among variables. A directed edge e\({}_{i,j}\) from V\({}_{i}\) to V\({}_{j}\) for V\({}_{i}\), V\({}_{j}\)\(\in\) V(G) shows that V\({}_{i}\) (parent node) is a direct cause of V\({}_{j}\) (child node).  
Each variable X\({}_{i}\) has a joint probability distribution P (X\({}_{i}\)! parent (X\({}_{i}\))), which shows the impact of a parent on a child. If X\({}_{i}\) has no parents, its probability distribution is unconditional, otherwise, it is conditional. The probability distribution of variables in a BN must satisfy the Markov condition, which states that each variable X\({}_{i}\) is independent of its nondescendents, given its parents in G [32]. The BN decomposes the joint probability distribution P (X\({}_{1}\), \(\ldots\), X\({}_{n}\)) into a product of conditional probability distributions for each variable, given its parents in the following:  
\[\text{P}(\text{x}_{1},\ldots,\text{x}_{n})=\prod_{i=1}^{n}\text{P}(\text{x}_ {i}\mid\pi(\text{x}_{i})) \tag{1}\]  
where \(\pi(\text{x}_{i})\) stands for the set of parents of x\({}_{i}\), or, in other words, the set of nodes that are directly connected to x\({}_{i}\) via a single edge.
# Detection of Helmet Use in Motorcycle Drivers Using Convolutional Neural Network
## 3 Results
### Results of Model Generation and Validation  
Two models were trained with the set of 10,602 images, varying the number of epochs, specifically 500 and 2000 respectively. In this phase, the accuracy of the model and the cross-entropy were monitored every 10 epochs. As can be seen in Figure 8, the performance of both models is satisfactory, since the loss of the validation is higher than the loss in the training process [48].  
The Figure 9A, present the confusion matrix obtained through the classification of helmeted and unhelmeted motorcyclist using the model one trained by 500 epochs in the test set, and the Figure 9B, shows the confusion matrix corresponding to the results obtained from the classification using the model two trained by 2000 epochs. It is easy to see which in the first confusion matrix, a total of 4209 images, equivalent to 92.64% of the set of test images, were correctly categorized; 1987 of study subjects without helmets and 2222 with helmets. In the case of the second confusion matrix, a total of 4418 images were correctly classified, equivalent to 97.24% of the test set; 2187 from study subjects without helmets and the remaining 2231 with helmets.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class** & **Training Set (70\%)** & **Test Set (30\%)** \\ \hline C0: Without Helmet & 5376 & 2304 \\ C1: With Helmet & 5226 & 2239 \\ \hline Total & 10,602 & 4543 \\ \hline \end{tabular}
\end{table}
Table 3: Training and testing sets.  
Figure 7: Example of collected videos.  
In Figure 10 shows the two ROC curves resulting from testing the aforementioned models on the test set, the line blue shows the relation between the sensitivity and the specificity of each model. The red line shows what the result would be like if the model were very poor in classification. The respective AUC values are: 0.9274 for the first model and 0.9728 for the second.  
Figure 8: Accuracy and cross-entropy monitoring in the model training stage.  
Figure 9: (**A**) Confusion matrix corresponding to Model 1 test (trained by 500 epochs). (**B**) Confusion matrix corresponding to Model 2 test (trained by 2000 epochs).  
Table 4 presents the Accuracy, Sensitivity and Specificity metrics resulting from the evaluations of the models using the test set.  
These results show that thanks to the proposed methodology it is possible to detect in an objective and satisfactory manner the adequate use of the helmet in real time, since the validation rates obtained from different metrics prove it, and having the best performance model 2 trained for 2000 epochs.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 1 Introduction
Vehicles only can request their own historical status and the real-time status values of other vehicles, which naturally prevents MITM (Man-in-the-Middle) attacks. Even if the attacker intercepts the transmission data, the ciphertext cannot be successfully decrypted, since the timestamps cannot be tampered with. If you do not change the timestamp, there is no permission to obtain information about the state of the history of others. Otherwise, you cannot get the correct time state value, and thus the decryption fails. We mainly discuss how the data of end-to-end vehicle transmissions in the VANET environment can be transmitted more efficiently and safely, and then we propose a more suitable encryption method for VANET. We use the state value of the vehicle to encrypt the plaintext to reduce transmission delay. With respect to the bandwidth overhead, compared with the traditional large number of loads, requirement of the bandwidth is less in our method. In terms of security, we use the cloud mechanism and the unchangeable timestamps to ensure the data security and privacy. The rest of this paper is organized as follows. Section 2 provides a summary of the relevant work, and Section 3 introduces the system model and thread model used in this paper. Section 4 describes the details of the NFEM. Section 5 analyzes the feasibility and safety of the NFEM. Section 6 shows the encryption method performance analysis and simulation. Section 7 draws a conclusion and offers ideas for future work.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Signal Model
#### 3.1.1 Signal Model of Uplink IRS-Aided Systems  
Figure 3 shows an exemplary scenario corresponding to the uplink of a single-stream MIMO multiuser multi-IRS-assisted system with \(K\) users with \(N_{\text{t}}\) antennas each. \(N_{\text{r}}\) antennas are deployed at the BS and \(L\) IRSs with \(N\) elements each are used to assist the communication. The symbol sent by the \(k\)-th user is represented by \(s_{k}\) with \(k=1,\ldots,K\). As observed, \(\mathbf{H}_{\text{UI}_{k}}^{(\ell)}\in\mathbb{C}^{N\times N_{\text{t}}}\) and \(\mathbf{H}_{\text{UB}_{k}}\in\mathbb{C}^{N_{\text{r}}\times N_{\text{t}}}\) represent the channel matrix response from the \(k\)-th user to the \(\ell\)-th IRS and the channel matrix response from the \(k\)-th user to the BS, respectively. Each IRS is mathematically modelled as a diagonal matrix \(\mathbf{\Theta}\in\mathbb{C}^{N\times N}\), which contains complex exponential functions in its diagonal \(\varrho^{\theta_{\mathbf{\theta}}},\ \forall n\in\{1,\ldots,N\}\), \(N\) being the number of passive elements at the IRS. Accordingly, \(\mathbf{\Theta}^{(\ell)}\), \(\ell=1,\ldots,L\), are the matrices containing the phase shifts introduced by the \(\ell\)-th IRS. \(\mathbf{H}_{\text{IB}}^{(\ell)}\in\mathbb{C}^{N_{\text{r}}\times N}\) is the channel matrix response from the \(\ell\)-th IRS to the BS. The channels from the users to the BS are assumed to be available, i.e., there is line-of-sight (LOS) between each user and the BS. The path from the \(k\)-th user to the BS through the IRS, i.e., \(\mathbf{H}_{\text{UI}_{k}}^{(\ell)}\) and \(\mathbf{H}_{\text{IB}}^{(\ell)}\) composes the usually called cascaded channel or reflected path. According to this system model, the received signal at the BS is  
\[\mathbf{y}=\sum_{k=1}^{K}\left(\underbrace{\mathbf{H}_{\text{UB}_{k}}}_{\text {LOS term}}+\sum_{\ell=1}^{L}\mathbf{H}_{\text{IB}}^{(\ell)}\mathbf{\Theta}^{ (\ell)}\mathbf{H}_{\text{UI}_{k}}^{(\ell)}\right)\mathbf{p}_{k}s_{k}+\mathbf{ n}, \tag{1}\]  
where \(\mathbf{n}=[n_{1},n_{2},\ldots,n_{N_{\text{r}}}]^{T}\) represents the complex-valued additive white Gaussian noise (AWGN) which is modeled as \(\mathbf{n}\sim\mathcal{N}_{\text{C}}(0,\sigma_{\text{n}}^{2}\mathbf{I}_{N_{ \text{r}}})\), whereas \(\mathbf{p}_{k}\in\mathbb{C}^{N_{\text{r}}\times 1}\) represents the \(k\)-th user precoders in the MIMO system.  
The estimated user symbols \(\mathbf{\hat{s}}=[s_{1},\ldots,s_{K}]^{T}\) are obtained by linear filtering the signal received at the BS, i.e., \(\mathbf{\hat{s}}=\mathbf{W}^{H}\mathbf{y}\), where \(\mathbf{W}\in\mathbb{C}^{N_{\text{r}}\times K}\) represents the BS filter. Note that this model simplifies when the direct paths between the users and the BS are unavailable, i.e., \(\mathbf{H}_{\text{UB}_{k}},\forall k\) are blocked and then the LOS term is omitted in (1). Note that the index \(\ell\) is used to describe each IRS and it reduces to a single term when considering a system aided with a single IRS, i.e., \(L=1\).
## 2 Materials and Methods  
### Establishment of Soil CT Image Datasets  
Soil samples were collected from Keshan Farm in Heilongjiang Province, China, with approximate geographical coordinates of 125\({}^{\circ}\)08\({}^{\prime}\)-125\({}^{\circ}\)37\({}^{\prime}\) E and 48\({}^{\circ}\)12\({}^{\prime}\)-48\({}^{\circ}\)23\({}^{\prime}\) N, where seasonal freeze-thaw occurs annually. The dominant soil type in this area is black soil, which is classified as a Mollisol (USDA taxonomy). Soil samples were collected at depths of 40 to 50 cm, where the soil is sensitive to freeze-thaw cycles [39]. In this layer, three soil samples were collected using custom Plexiglas tubes (10 cm inner diameter, 10 cm length). To simulate freeze-thaw cycles, the soil samples were frozen at \(-\)20 \({}^{\circ}\)C for 15 h and then thawed at 5 \({}^{\circ}\)C for 5 h [40].  
Soil CT images were obtained by scanning the soil samples using a Philips Brilliance 64-row, 128-slice spiral CT machine. The parameters for scanning were a voltage of 120 v, current of 196 mA, window width and window level of 2000 and 800, respectively, rotation time of 0.5 s, scanning layer thickness of 0.9 mm, field of view of 120 mm, actual length corresponding to a single pixel of 0.23 mm, and image size of 289 \(\times\) 289 pixels. A total of 3570 soil CT images were used in our experiments, with 80% being used for the training set and the remaining 20% being used for the testing set.
# How Human Mobility Models Can Help to Deal with COVID-19
## 7 Evaluation of the Infection Risk
### ERV Evaluation  
We first analyse the results obtained using the Exposure Risk Value (ERV) measured in Meaningful Exposure Minutes (MEMs) units, as described in Section 6.2. In this evaluation, as in Section 5.2, the evaluated area is divided into cells of 1 m\({}^{2}\), and for each cell, we obtain the accumulated ERV of all people contacts initiated in this cell.  
In detail, the method for obtaining the contacts and the cell's ERV is the following: using the position traces generated from the different scenarios, we determine every second which pairs of nodes are within the 2 m range. If the duration of the contact is greater than 1 min, this duration is added to the cells where the contact started. This means that, since every contact involves two individuals, we add the associate ERV of these contact to two cells, which corresponds to the cell where each individual was when the contact started. This way, we can obtain a measure of the risk of each cell measured in MEMs.  
Figure 7 shows the ERV heat maps for the five scenarios. Starting with the RWP and SWIM scenarios, we can see that there are small risky spots randomly distributed throughout the area, although in the SWIM scenario there are more risks at the edges. In the RWAA, as expected, the risky areas are around the attraction points, due to the high number of contact generated near to these points. For the Plaza scenario, we can see that the risky areas are also around the attraction points and along the entry and exit paths. Finally, for the Station scenario, the risky areas are in the centre of the platforms and near the turnstiles, where the contact have longer durations.  
Now, we study the temporal evolution of the exposure risk. This risk is also measured using MEMs and represents the current ERV depending on time, that is, the aggregated minutes of all risky contacts which are active at the given time. For the synthetic scenarios, the evolution is quite constant, so we do not show here the results. More interesting are the results for the PedSim generated scenarios shown in Figure 8. For the Plaza scenario, we can see the variability of the risk depending on the periodical renewal of the people. In addition, in the Station scenario, we can see the impact of the trains arrivals. The ERV peaks coincide with the arrivals of the trains.
## 3 Results and Discussion
### Effects of Cutting Parameters on Cutting Forces in Micro-Milling
At lower feed rates, since the cutting tool removes less material per spindle revolution, the chip load is relatively low. This can be expounded with the situation where although the cutting tool remains in contact with  
Figure 5: Feed of 0.25–0.5–1–2 and depth of 100 \(\upmu\)m in cutting forces: (**a**) Fx, (**b**) Fy, and (**c**) Fz. the machined material per unit time, the total of material removed in contact instance is smaller. Consequently, no notable grow in cutting forces can be seen. The mechanics of chip formation in conventional machining and micro-milling differ significantly [46]. These differences arise due to factors such as tool runout, dimensional variations, and the elastic recovery of the workpiece [47]. Such phenomena are more likely to take place when micro-milling mushy metallic or plastic specimens that need relatively low cutting forces [48]. As observed in Figures 5 and 6, an increase in cutting depth from 50 \(\upmu\)m to 100 \(\upmu\)m naturally resulted in a rise in all cutting forces. This increase in cutting depth led to greater surface contact between the cutter and the specimen through each material removal, causing a higher mass of specimen to undergo deformation and chip removal. Consequently, this indicates a greater requirement for cutting forces [49]. It was observed that in tests where the feed rate was high and the spindle speed was low, the cutting forces increased by 2 to 3 times (Figure 4a-c). A spindle speed of 20,000 rpm was found to be more suitable for reducing cutting forces. However, when considering machining time, a cutting depth ap = 100 \(\upmu\)m was more appropriate for minimizing the total machining duration. These findings highlight a trade-off between minimizing cutting forces and optimizing machining time, a balance that should be carefully managed in industrial production to enhance efficiency and tool performance.
## 2 Experimental Test and Machine Learning Technique
### Machine Learning Technique
#### 2.3.2 Ensemble Model: Random Forest  
Random forest (RF), suggested by [PERSON] [35], is one of the ensemble techniques composed of a bunch of decision trees (DT) [36]. The DT is a decision support algorithm that uses a tree-like model of decisions and their possible output. The DT algorithm is conducted by partitioning a data space into subspace. During the building of a DT, three components are created, namely internal node, terminal node, and branch of DT. The internal node is linked with decision functions to determine which next node to face. The terminal node is the node that is no longer to be separated in a DT. This node is the space to classify samples that fall into the node. Branch of DT plays a role of connecting internal and external nodes in a DT.  
RF is more powerful algorithm and has been validated to be more robust in many more data sciences than a DT [25; 37]. The principle behind the RF is to create a variety of overfitted DTs in different directions and is to average the results of each DT. It is an effective way to reduce the overfitting problem. DTs can be implemented by randomly sampling the dataset for each DT (bagging) and by selecting an input parameter randomly (Figure 8). RF is the convenient method in that it does not need to consider the data scales and works properly without the delicate hyper-parameter tuning. A detailed explanation and theory of RF can be found in [35].  
Figure 8: Principle of a random forest (RF). Bagging is the process of data sampling from a training set, randomly. Sampled datasets are used to make different kinds of decision trees (DTs). In the prediction phase, all the values predicted from various DTs are averaged and it is called the prediction value of RF.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Kernel Function** & **Formula** \\ \hline Linear function & \(K\Big{(}\mathbf{x}_{i},\mathbf{x}_{j}\Big{)}\) = \(\left\langle\mathbf{x}_{i},\mathbf{x}_{j}\right\rangle\) \\ \hline Radial basis function & \(K\Big{(}\mathbf{x}_{i},\mathbf{x}_{j}\Big{)}\) = \(exp\Big{(}-\gamma\|\mathbf{x}_{i}-\mathbf{x}_{j}\|^{2}\Big{)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Representative kernel functions of SVR used in this study.
## 2 Materials and Methods
### Analytical Approach
#### 2.1.3 X-ray Fluorescence (XRF)  
XRF analyses (a total of 310 measurements) were carried out with the Niton XL3t 900 X-ray spectrometer produced by Thermo Scientific. The instrument is equipped with an X-ray tube (maximum voltage 50 kV), an Ag anode and a semiconductor detector Si-PIN which allows a resolution of 195 eV. Acquisition time was 30 s. This instrumental set-up, with a sampling area of ca. 3 mm \(\O\), is suitable for the detection of chemical elements with atomic number Z \(\geq\) 16 (from sulfur; S).
# Power Control Techniques for Interference Management-
## 4 Power Control Technique Evaluation
### Power Control Techniques: Case Studies
#### 4.2.1 Case Study 1: METIS-II Perspective  
Ultra-dense networks in 5G NR are deployed to address the growing wireless data traffic challenge. However, when data traffic increases, interference problems arise, and the user's network performance is affected.  
The study discusses various interference management drivers to improve network performance. The survey focuses on enhancing cell-edge throughput, reducing energy consumption, and minimizing signalling overhead.  
The study explores techniques such as beamforming, dynamic time-division duplex (TDD), and self-backhauling.  
Details of the case study are found in [150].
# Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs
## 3 Proposed Protocol
### Preliminaries and Motivation
Also, usually the MAC header is encoded using the same MCS with the payload, so distant neighbors may not be able to decode the MAC header and obtain the information. Thus, it is better to include the information in the preamble. The next design choice is on what information should be included in the preamble. Since preamble is transmitted at the lowest link rate, minimum number of bits should be used to carry the information. It is not feasible to include a 48-bit MAC address in the preamble. Thus, it is better to include an encoded CST level which should be used by the neighboring node to decide whether it should regard the channel as idle or busy. Consider the scenario in Figure 1 once again. When AP1 transmits a packet to node A, it includes in the preamble the maximum CST that must be used by the neighboring nodes, to protect its transmission. We call this CST value as advertised CST. For example, suppose AP1 knows that when it transmits a packet to node A, the received signal strength at node A will be \(-60\) dB. Also, suppose AP1 knows that to decode the packet reliably, the SNR at node A must be at least 23 dB. Then, it means the maximum interference that can be tolerated at node A is approximately \(-83\) dBm (neglecting the noise floor.) Thus, AP1 should block any neighboring nodes that can cause interference at node A at a strength higher than \(-83\) dBm. AP1 should select the advertised CST so that these candidate interfering nodes find the channel busy after receiving the preamble. By including CST instead of MAC address, we can use smaller number of bits in the preamble. For example, 6 bits can be allocated to the advertised CST, which can represent 64 different values. It will be sufficient to indicate the advertised CST, ranging from \(-99\) dBm to \(-36\) dBm. If fewer bits are available, quantization can be used to further reduce the size of information at the cost of degraded accuracy. We propose two methods for selecting the CST; model-based scheme and measurement-based scheme. We describe the two schemes in the next sections.