esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis and Evaluation  
The evaluation of performance is implemented in MATLAB, and the comparison is made with existing algorithms such as GA [9], ALO [11] and PSO [8] in terms of energy efficiency, bandwidth, packet delivery ratio and network longevity [35; 36; 37]. With GA, the structure of the network differs with every round based on the features of the nodes; ALO maximizes the energy utilization and distance, having a minimized response time; PSO calculates the optimal set of cluster heads which maximizes the efficiency of energy and coverage of the network. The simulations carried out during the implementation are shown in Table 1:  
**Network Longevity:** It indicates how long or for how many rounds the network can operate. It refers to the number of rounds after which the nodes in the field will expire whilst completing their jobs. In Figure 5, the number of nodes is 100 to 500. It shows the proposed framework achieves more longevity compared to the other existing approaches. The proposed framework delivered the packets successfully with the 3500 rounds for 500 nodes based on the number of rounds; for every round, longevity of the network increases.  
**Energy Efficiency:** This is the amount of energy utilized by the sensor nodes in the network. As shown in Figure 6, the proposed framework has utilized the energy with a maximum of 0.5 Joules with 500 nodes, whereas the other existing approaches utilize more energy than the proposed approach.  
\begin{table}
\begin{tabular}{l l} \hline
**QoS Metrics** & **Values Deployed** \\ \hline Area & 100 m \(\times\) 100 m \\ Number of nodes & 100 to 500 \\ Number of Clusters & Varies \\ Energy of nodes & 1 Joule \\ Network energy & Based on the number of nodes \\ Size of packet & 1250 bytes \\ Bandwidth & 1 Mbps \\ \hline \end{tabular}
\end{table}
Table 1: Simulation environment.  
Figure 5: Network lifetime with the number of nodes [10; 11; 15].  
**Bandwidth:** It represents the amount of data transmitted to the base station with several nodes. It is denoted as the number of packets transmitted *size of the packet which is divided by the time taken by the number of nodes. As shown in Figure 7, the comparison amongst the existing approaches is depicted in the figure bandwidth w.r.t number of nodes [31]. It shows that the proposed framework with 500 nodes transmits data at the rate of 0.52 MBPS compared to the other approaches.  
**Packet Delivery Ratio:** It expresses the ratio of data packets effectively transmitted to the base station with various numbers of nodes. In Figure 8, the proposed framework shows that it has successfully transmitted the packets with 500 nodes at the rate of 96% compared to the other approaches. The figure shows that as the number of nodes are increasing, the ratio of PDR decreases.  
Figure 6: Energy utilization with regard to number of nodes [10; 11; 15].  
Figure 7: Bandwidth with regard to number of nodes [10; 11; 15].
# A Secure Median Implementation for the Federated Secure Computing Architecture
###### Abstract  
In Secure Multiparty Computation (MPC or SMPC), functions are evaluated in encrypted peer-to-peer networks without revealing the private inputs of the participating parties. The median is a non-trivial computation in MPC and is particularly relevant in fields like medicine and economics. Here, we provide an MPC implementation of the median for the Federated Secure Computing (FSC) framework. It is tested on synthetic datasets with varying sizes (\(N=10^{2}\) to \(N=10^{7}\)) and number of participants (\(M=2\) to \(M=10\)) across different network environments and hardware configurations. Using minimal networking and computational resources on a commercial hyperscaler, we evaluated real-world performance with breast cancer (\(N=569\)) and heart disease (\(N=920\)) datasets. Our results showed effective scaling up to \(N=10^{6}\) entries with runtime between 1 and 4 s, but runtime exceeded 15 s for \(10^{7}\) entries. The runtime increased linearly with the number of parties, remaining below one minute for up to \(M=10\) parties. Tests with real-world medical data highlight significant network overhead, with runtime increasing from 16 to 17 s locally to over 800 s across hyperscaler regions, emphasizing the need to minimize latency for practical deployment.  
privacy; secure multiparty computation; federated secure computing; _k_th-ranked element 4.0.00000
## 5 Case Studies
### Comparison with Traditional Machine Learning Methods  
In this section, a comparison is made between the DSEC model and the traditional machine learning methods including SVM, ELM, CART, and AdaBoost, which do not consider the imbalanced data problem. The Gaussian function is chosen as the kernel function of SVM, and its parameters includes the penalty coefficient, c, and the kernel function parameter, \(\gamma\). The grid search method is utilized for selecting the optimal parameters of SVM and the value range of both parameters is [2; 3; 4; 5; 6; 7; 8], \(2^{-7},\ldots,2^{8}\)]. The main parameter of ELM is the number of hidden layer nodes, L, and its range is set as [50, 100,..., 1500]. The default parameters are adopted in the CART algorithm and the iteration round of the AdaBoost classifier is 40.  
The results of these methods for TSSP are compared and tabulated in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Subset** & **Stable Samples** & **Unstable Samples** & **Total** \\ \hline
1 & 1583 & 1689 & 3272 \\
2 & 1582 & 1689 & 3271 \\
3 & 1582 & 1689 & 3271 \\
4 & 1582 & 1689 & 3271 \\
5 & 1582 & 1689 & 3271 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sample distribution in each training subset.  
Figure 5: GM performance with different values of R.  
From Table 5, when dealing with the TSSP problem with imbalanced data, the traditional machine learning methods lead to a high TSR but quite low TUR and GM. While the proposed DSEC model can significantly improve the TUR and GM, which become as high as 97.03% and 94.62% respectively, with the TSR still being maintained at 92.28%.
## 2 Results and Discussion  
The structures of the final products, Bz-DVB-AN and Et-DVB-AN, are presented in Scheme 1. The proposed structure resulted from the functionalization of the copolymers SV3 and SV4 with 3-pyridinecarboxaldehyde, and either dibenzylphosphite (Bz-DVB-AN) or diethylphosphite (Et-DVB-AN).
# The Boundary Element Method in Acoustics: A Survey
## 3 The Boundary Element Method and the Laplace Equation Stem
### Derivation of the Boundary Element Method
#### 3.3.3 The Galerkin Method  
Although most of the implementations of the boundary element method are derived through collocation, other techniques can be used, most typically the Galerkin method. The Galerkin method and collocation can both be derived from a more generalised approach that are termed weighted residual methods. For example, for the approximation (40), the residual is the difference between the approximation and the exact solution;  
\[R\big{(}\underline{\sigma}_{S};\mathbf{p}\big{)}=\sum_{j=1}^{n}\widehat{\sigma}_{ j}\{L\mathbf{j}\}_{\Delta\widehat{S}_{j}}(\mathbf{p})-\varphi(\mathbf{p})\ \ (\mathbf{p}\in S).\]  
In weighted residual methods, \(R\big{(}\underline{\sigma}_{S};\mathbf{p}\big{)}\) is integrated with _test_ basis functions \(\chi_{i}(\mathbf{p})\ \ (\mathbf{p}\cdot S)\) and the methods arise by setting the result to zero;  
\[\int_{S}R\big{(}\underline{\sigma}_{S};\mathbf{p}_{i}\big{)}\chi_{i}\big{(}\mathbf{p} _{i}\big{)}dS=0\]  
at points \(\mathbf{p}_{i}\) on the boundary, for \(i=1,2,\ldots n_{S}\).  
If \(\chi_{i}(\mathbf{p})=\delta\big{(}\mathbf{p}-\mathbf{p}_{i}\big{)}\), the Dirac delta function, the collocation method is derived;  
\[\int_{S}R\big{(}\underline{\sigma}_{S};\mathbf{p}\big{)}\delta\big{(}\mathbf{p}-\mathbf{p }_{i}\big{)}\ dS=\ R\big{(}\underline{\sigma}_{S};\mathbf{p}_{i}\big{)}=0,\]  
which leads to the methods outlined above. In the Galerkin method, the test functions are the same as the original basis functions. For example, for constant elements, the basis and the test functions are defined as  
\[\chi_{i}(\mathbf{p})=\left\{\begin{array}{ll}1\ \text{or}\ \tilde{e},\ \text{if}\ \mathbf{p}\in\Delta\tilde{S}_{i}\\ 0,\ \text{otherwise}\end{array}\right..\]  
Substituting this and the definition of the residual equation  
\[\int_{S}R\big{(}\underline{\sigma}_{S};\mathbf{p}_{i}\big{)}\chi_{i}(\mathbf{p})dS= \int_{\Delta\tilde{S}_{i}}\sum_{j=1}^{n}\widehat{\sigma}_{j}\{L\mathbf{j}\}_{ \Delta\widehat{S}_{j}}\big{(}\mathbf{p}_{i}\big{)}-\varphi\big{(}\mathbf{p}_{i}\big{)} \ dS=\ \ 0\.\]The reason for the relative unpopularity of the Galerkin approach in boundary elements is that the matrix is now the result of a double integration, rather than the single integration in the collocation method. However, the matrix is understood to be symmetric.
# Utilising Acknowledge for the Trust in Wireless Sensor Networks
## 3 Problem Definition  
The network could be modeled as a set of static sensor nodes S are randomly deployed in a 2-D field F and a sink node sink(s). The field F is assumed to have Sm as a number of misbehaving sensors in WSN, and Pm P\({}_{\text{m}}\)(equal to SmS\({}_{\text{m}}\)/S\({}_{\text{S}}^{\text{S}}\))\({}_{\text{S}}^{\text{S}}\) is the probability that the node is malicious. The deployed sensors are assumed to be homogeneous, considering their initial energy, sensing range, and communication range are the same. Therefore, the communication range (cs) is the same for all nodes. Some sensors are marked as the transmission source, while others act as intermediate nodes. Malicious sensors attempt to obstruct network performance, participating in route setup operations. All sensors are assumed to participate in the routing process, including packets forwarding in a bi-directional communication symmetry on every link between the sensors. In addition, the destination sensor is assumed reliable; moreover, any route in the network can have only one misbehaving sensor. Each node can detect its neighbors in its sensing range; moreover, the node can mark some control packets as high priority by setting the priority flag; besides, each node has a unique identifier (ID). Every sensor has a prevention list, including the misbehavior nodes, the trustworthy table holding the trustworthy value of nodes, which broadcast to neighbors, and the Information of Packet (IOP) table to save the information about the received and processed data of TCP acknowledgment packets. The routing table of each node will create a new entry to store the value of trustworthiness of other nodes. The following questions can summarize the problem:  
1. What are the nodes that can be trusted?
2. How can the cross-layer concept be sued to specify an accurate trust threshold to differentiate between legitimate and malicious nodes?
3. Is it possible to detect and isolate the misbehaving sensors in the network?
4. Can we guarantee that nodes are transferring the data packets correctly?
5. To prolong the network lifetime, which nodes should be chosen as the next hop minimizing the overall network's energy consumption?
6. What arrangements can be made to evade link break so the malicious-free route can appropriately be selected?
## 5 Discussion  
### Potential Impact of IoES Deployment on Public Safety and Crisis Management  
The deployment of the Internet of Emergency Services (IoES) has the potential to greatly improve public safety and crisis management by providing a more integrated and coordinated approach to the emergency response. Some of the potential impacts of IoES deployment are:  
* Improved Response Times: IoES can provide real-time data from various sources, enabling emergency services to respond more quickly and efficiently to emergencies. This can help reduce the impact of disasters and save lives.
* Better Coordination: IoES can facilitate better coordination between different emergency services, such as police, fire, and medical services. It can enable seamless communication, data sharing, and collaboration among different agencies, resulting in a more effective response.
* Increased Situational Awareness: IoES can provide real-time information on the location, severity, and nature of emergencies, enabling emergency services to make informed decisions about resource allocation and response strategies. This can help reduce the impact of disasters and save lives.
* Enhanced Predictive Capabilities: IoES can also help emergency services predict potential disasters and emergencies before they occur, through the analysis of real-time data from various sources. This can help improve preparedness and enable more effective response and recovery efforts.
* Improved Recovery Efforts: IoES can also help in post-disaster recovery efforts, by providing data and insights to aid in the assessment of damages, identification of areas of need, and allocation of resources for recovery and reconstruction.  
Figure 9: Industrial Accident scenario.  
However, the deployment of IoES also raises some concerns around privacy, data security, and data ownership. The collection and sharing of sensitive data from various sources could potentially lead to misuse or abuse of data, and it is important to establish clear guidelines and protocols to ensure the responsible use of data.  
In conclusion, the deployment of IoES has the potential to greatly enhance public safety and crisis management by enabling a more coordinated, integrated, and data-driven approach to an emergency response. However, it is important to address the potential challenges and concerns associated with the deployment of IoES, to ensure that it is implemented in a responsible and ethical manner.
## 2 Materials and Methods
### Parameters to Measure the Strength of AICLDH against the Differential Attack  
The strength of AICLDH against differential attack is tested based on Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and Avalanche Criterion (AC).  
The NPCR parameter requires two images ciphered to compare their values in the same position \((i,j)\). A function \(D(i,j)\) measures the differences between the bytes of the encrypted image number 1 and belongs to encrypted image 2. That is, given the position \((i,j)\), it is observed that bytes are different or equal. If they are identical, the function \(D(i,j)\) takes the value 0. Otherwise, it takes the value 1. The NPCR parameter is defined in Equation (29). The subscript indicates the analyzed color, and variables \(W,H\) are the width and height of the encrypted images. Convenient values to avoid a differential attack are around 99.6% [40].  
The UACI measure also evaluates byte differences between the encrypted images 1 and 2 by means of Equation (30). Although, it considers the intensity levels of each color, from 0 to 255. The desired value of UACI to resist the differential attack is 33.4% [41].  
\[\text{NPCR}_{c}=\frac{\Sigma_{i,j}D(i,j)_{c}}{W\times H}\times 100\% \tag{29}\]  
\[\text{UACI}_{c}=\frac{1}{W\times H}[\Sigma_{i,j}\frac{\mid\text{C}_{1,c}-\text{ C}_{2,c}\mid}{255}]\times 100\% \tag{30}\]  
Finally, the AC parameter is calculated according to Equation (31). \(T\) indicates the number of bits from images 1, 2, and the subscript \(c\) is the color analyzed. This measure evaluates the bitwise differences between encrypted images 1 and 2. In this sense, the function \(b(i,j)\), which appears in Equation (31), is described in Equation (32). The appropriate value for this parameter is 50%.  
\[\text{AC}_{c}=\frac{\Sigma_{i,j}b(i,j)_{c}}{T}\times 100\% \tag{31}\]  
\[b(i,j)_{c}=\left\{\begin{array}{c}0\text{ otherwise}\\ 1\text{ if bits are diffent}\end{array}\right. \tag{32}\]
## 3 Modified Smith Predictor Control Scheme  
A modified \(SP\) for \(TRMS\) is depicted in Figure 2. In this figure, the original \(SP\) scheme is illustrated by the black lines and boxes. In this scheme, \(G(s)\) is the actual dynamic model of the \(TRMS\), as defined in the Equation (6), while \(\mathbf{G}_{0}(s)\) represents a nominal model of \(\mathbf{G}(s)\), and \(\mathbf{L}_{0}(s)\) represents a nominal time delay model of \(\mathbf{L}(s)\). \(\mathbf{Y}^{*}(s)=[\psi^{*},\phi^{*}]^{T}\) is the reference vector of the system, where \(\psi^{*}(s)\) is the reference for the vertical plane (pitch angle), and \(\phi^{*}(s)\) for the horizontal plane (yaw angle). \(\mathbf{R}(s)=diag(R_{1}(s),R_{2}(s))\) performs the control function and \(\mathbf{D}(s)=[D_{11}(s),D_{12}(s);D_{21}(s),D_{22}(s)]\) is a decoupler. \(\mathbf{U}(s)=[u_{1}(s),u_{2}(s)]^{T}\) is the control signal vector, where \(u_{1}(s)\) is the control signal for the vertical plane, and \(u_{2}(s)\) for the horizontal plane. \(\hat{\mathbf{U}}(s)=[\hat{u}_{1}(s),\hat{u}_{2}(s)]^{T}\) is the fictitious control signal vector applied to the decoupled system. \(\mathbf{H}(s)=diag([H_{1}(s),H_{2}(s))\) is the disturbance controller. \(\mathbf{Z}(s)=[z_{1}(s),z_{2}(s)]^{T}\) is the external disturbance applied to the \(TRMS\). \(\mathbf{Y}(s)=[\psi(s),\phi(s)]^{T}\) is the response of the system vector, where \(\psi(s)\) is the yaw angle in the vertical plane, and \(\phi(s)\) in the horizontal plane.  
We denote the decoupled system (removing the delay) as \(\mathbf{Q}(s)=\mathbf{G}(s)\cdot\mathbf{D}(s)\) and its nominal value as \(\mathbf{Q}_{0}(s)=\mathbf{G}_{0}(s)\cdot\mathbf{D}(s)\).
## 4 Experimental and Results  
### Experimental Datasets  
Detecting PPE in substations quickly and accurately is vital. However, there is a shortage of open-source datasets, including a range of this equipment. Therefore, this study created a dataset featuring six categories: individuals, insulating gloves, safety helmets, armbands, insulating rods, and regular gloves. This dataset combines online resources and on-site photography to ensure it represents the diversity of real application scenarios. The photography captures various times and backgrounds, simulating different working environments at substations. This study prepared for training by using LabelImg software (v.1.8.1) to create text files with image paths, annotations, and labels. Moreover, to enhance the model's generalization capabilities, this study employed techniques such as shifting, modifying brightness and saturation, and introducing noise. These steps increased the dataset to 2800 images. These techniques increase the dataset's coverage of different scenes and realistically mimic changes in lighting and background, enhancing the model's robustness. Each category of samples is extensively represented, both in quantity and scene complexity. The dataset promotes diversity by including various types of PPE and different wearers, capturing the range of conditions in substation work environments. Furthermore, it is noted that many workers use insulated tools but do not wear insulated gloves. This diversity is secured through a meticulously planned collection strategy, designed to capture a wide range of anomalies to train a more resilient model. Examples of the enhanced data are shown in Figure 9.  
Figure 8: GhostConv module structure diagram.  
The dataset is split into training and testing sets using an 8:2 ratio to ensure that the model performs well on new data. Figure 10a displays a heat map depicting the density distribution of data points across a two-dimensional plane. Darker colors indicate a higher probability of the target's presence. The graph's x and y axes extend from 0 to 1, indicating the normalized coordinates of object centers within bounding boxes. A notable clustering of data points towards the center implies a frequent central placement of target objects in images. In contrast, Figure 10b shows a bar chart detailing the occurrence rates of various categories in the dataset.
## 3 Results and Discussion
### Toughening Mechanism of the Composites  
Studies have shown that the micro and nanoparticles improve the compatibility between the PLA and PBAT phases [63,64]. The carbon nanotubes introduced into the PLA and PBAT blend shows a greater affinity towards the PBAT phase; however, they make protrusions into the PLA phase, which acts as a bridge between the PLA and PBAT phases. The strong affinity of the carbon nanotubes to PBAT is due to the presence of aromatic molecules in PBAT, and the fact that carbon nanotubes prefer aromatic molecules [38,65]. Similarly, in PLA/PBAT/biochar composites, the interfacial interaction of biochar and the polymer matrix is strong, and the biochar particles are wrapped with polymer material, indicating a greater affinity of the biochar with the polymer matrix and an improvement in the compatibility of the polymer phases. The biochar particles in the interface between the PLA and PBAT matrix improve the compatibility between the polymer phases and the mechanical properties of the composite, as illustrated in Figure 9.  
Mechanical Properties of the 3D-Printed Composites  
Combined mechanical and antistatic properties are essential for an antistatic personal-protective-equipment (PPE) and packing application. Figure 10 depicts the tensile properties of the PLA/PBAT blend and PLA/PBAT/BC composites. The results show that with a small concentration of biochar (1 wt%), the PLA/PBAT/BC (79/20/1) composite shows the highest tensile strength, with the maximum mean value of 42.3 MPa, while the (70/20/10) composite shows the lowest mean value of 24.62 MPa, with 10 wt% of biochar, Figure 10.  
The improvement of the tensile strength of the composite with biochar may be attributed to the increased interfacial-interaction between the biochar and polymer matrix, which is evident in the SEM analysis. The biochar particles can divert part of the stress from the polymer matrix, and obstruct crack formation [66]. Furthermore, biochar improves the compatibility of the PLA and PBAT phases and the interfacial interaction between these polymer phases. Moreover, biochar increases the composites' total crystallinity by improving the crystallinity of the PLA [67]. As discussed earlier, the crystallinity of the composites improves, due to the nucleation effect of the biochar particles. However, the agglomeration behaviour becomes dominant at high concentrations of biochar, leading to decreased tensile-strengths [38,68]. Comparing the tensile moduli of the blend and the  
Figure 9: Schematic diagram of **(a)** phase-separated PLA/PBAT blend and **(b)** biochar polymer matrix interaction in PLA/PBAT/biochar composites.  
composites, there is only a slight change. In addition, elongations at yield of the composites and blend remain almost the same with different biochar and polymer-blend compositions.