esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Prediction Models and Accuracy Assessment
#### 2.6.3 Deep Neural Networks  
Neural networks consist of layers of artificial neurons (also called \"perceptrons\" or \"nodes\") that include an input layer, one or more hidden layers, and an output layer. Each node is connected to another and has an associated weight and threshold. It, thus, combines inputs with a set of coefficients that either amplify or dampen that input. An activation function then determines whether and to what extent that signal should progress further through the network to influence the result. Depending on the nature of the problem studied, neural network architectures can be classified by the number and type of layers used, and the number of nodes, or simply as supervised vs. unsupervised learning networks [103; 104]. Among the architectures used to predict soil properties [59; 105; 106], we used a DNN, which has at least two fully connected hidden layers between the input and output layers and provides linear and non-linear relationships between the response variable and a set of predictor variables (Figure 4b) [103]. It was better suited to our case study than a CNN, which is more adapted to data with spatial structure and most commonly used in remote sensing [107]. Thus, the abundant cloud over over our study area led to a loss of spatial and temporal information. The rectified linear unit (ReLU) activation function was used to establish relationships between input and output neurons in each hidden layer. The optimal number of hidden layers and neurons in each layer was selected based on the RSME using the \"tuner.search\" framework implemented in the _keras_tuner 1.1.3_ package (Python 3.8.16) (Tables A2 and A5 in Appendix B), which is a scalable hyperparameter optimization framework. It is based on Bayesian optimization, Hyperband, and random search algorithms [108]. The random search strategy was used in our case. It consists of a random selection of hyperparameter combinations according to a defined number of iterations, during which the model is trained for a certain number of epochs. As a result, the algorithm returns the best hyperparameters, corresponding to the combination that gave the best performance [97; 108]. In our case, the maximum number of iterations was set to 50 (max_trials = 50), the learning rate to 0.01, and the number of epochs to 50.
###### Abstract  
In an inductive wireless power transfer (WPT) system, the transferred power depends on the coupling between the transmitter coil and the receiver coil and, consequently, on their spatial positioning with respect to each other. Since this positioning needs to be as flexible as possible, one can design various construction shapes for the coils, in terms of both winding geometry and core geometry. The system efficiency for different positionings and configurations can be assessed if the structure and distribution of the electromagnetic field (EMF) in the space close to the system are determined. In this context, this paper proposes an investigation method based on measuring the intensity of the electromagnetic field generated by the emitting coil in the WPT space in terms of both modulus and direction. The measurements needed to be performed at a sufficient number of points, so that the entire field was determined. The EMF value was estimated by linear spatial interpolation between the measured points. Moreover, this paper presents the method and the results of investigations carried out only in the presence of the emitting coil. Calibration or correction solutions were also proposed to obtain sufficient accuracy, and the viability of the method was therefore demonstrated.  
wireless power transfer system; emitting coil; electromagnetic field intensity; EMF distribution; investigation method; calibration solutions Article
## 4 Discussion  
Overall, the prediction accuracy (Table 4) was between 57% and 79% for the reduced feature set, corresponding to 66%-91% of the prediction accuracy achieved when the full feature set was used. It demonstrates that higher prediction accuracy was attained when the full feature set was used. The prediction accuracy for all tests decreased by 2%-20% when the reduced feature set was used.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 3 Preliminaries and Background
### Encryption for Secure Deduplication
#### 3.1.2 Message-Locked Encryption  
[PERSON] et al. introduced message-locked encryption (MLE) as a technology for ensuring data integrity in the context of secure data deduplication [9]. MLE is a generic term for encryption techniques that generate encryption keys based on the data themselves. Prior to the introduction of MLE, various encryption methods, including those based on CE, were proposed for data deduplication. However, since the publication of their paper, there has been active research in the field of secure data deduplication, with a particular focus on using cryptographic hash functions to verify data integrity. Currently, there are prominent encryption methods used for secure data deduplication under the MLE umbrella, including CE, hash and CE without tag check (HCE1), hash and CE with tag check (HCE2), and randomized convergent encryption (RCE). In this paper, the MLE technique employed is HCE2.  
HCE2 is a technique that builds upon CE and uses keys derived from the data to encrypt and verify integrity through tag consistency. When plaintext data \(m\) is hashed, resulting in the string \(h(m)\), HCE2 uses \(h(m)\) as the encryption key. Using \(h(m)\) to encrypt \(m\) produces ciphertext \(C\), and decrypting \(C\) allows for the recovery of \(m\). In contrast to CE, MLE employs \(C\) as the input to a hash function to generate \(T\), which represents the tag. \(T\) serves to ensure the integrity of the ciphertext. In summary, HCE2, as a representative MLE encryption method, is used to achieve data deduplication with integrity verification by deriving encryption keys from data and utilizing tag consistency. This approach is a crucial component of the broader field of secure data deduplication.  
* KeyGen\((h,m)\). Given a cryptographic hash function \(h\) and plaintext \(m\) as input, an encryption key for MLE \(K=h(m)\) is output.
* Encrypt\((K,m)\). Given an encryption key \(K\) and plaintext \(m\) as input, it produces encrypted data \(C\).
* Decrypt\((K,C)\). Given an encryption key \(K\) and ciphertext \(C\) as input, it produces decrypted data \(m\).
* TagGen\((h,C)\). Given a cryptographic hash function \(H\) and ciphertext \(C\) as input, it produces an integrity verification tag \(T\), which corresponds to ciphertext \(C\).
# Helmet Wearing Detection Algorithm Based on YOLOv5s-FCW
## 4 Experimental Results and Analysis
### Analysis of Test Results  
The partial visualization of the detection results is presented in Figure 12. The left-hand side of the figure displays the original YOLOv5s detection graph, while the right-hand side displays the improved YOLOv5s detection effect. The images in the figure include (a) dense helmet targets, (b) small helmet targets, and (c) obscured helmet targets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **mAP** & \multirow{2}{*}{**AP**} & **R** & **Parameters (10\({}^{6}\))** & **GFLOPs** \\  & **(IoU = 0.5)** & & & & \\ \hline YOLOv5s & 0.812 & 0.853 & 0.752 & 7.07 & 16.5 \\ YOLOv5s + CBAM & 0.824 & 0.885 & 0.758 & 7.09 & 16.5 \\ YOLOv5s + FasterNet & 0.81 & 0.837 & 0.574 & 5.62 & 13.1 \\ YOLOv5s + WIOU & 0.819 & 0.862 & 0.77 & 7.07 & 16.5 \\ YOLOv5s + CBAM + WIOU & 0.815 & 0.878 & 0.732 & 7.09 & 16.5 \\ YOLOv5s + CBAM+ FasterNet & 0.772 & 0.854 & 0.713 & 5.62 & 13.1 \\ YOLOv5s + FasterNet + WIOU & 0.802 & 0.888 & 0.721 & 5.62 & 13.1 \\ YOLOv5s + FasterNet + WIOU + & 0.837 & 0.889 & 0.813 & 5.62 & 13.1 \\ CBAM(YOLOv5-FCW) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results for YOLOv5s-based improvement and its ablation.  
From the visualization of the results in Figure 12, it is evident that the initial YOLOv5s model failed to detect the target without a helmet in the center of Figure 12a, as well as the small helmet target on the left side of Figure 12b and the obscured helmet target in Figure 12c. However, the improved model accurately detected all of these targets. At long ranges and for tiny targets, the original model is even more serious in missing detection, even if the enhanced model can still recognize extremely tiny objects at a great distance with accuracy, and the confidence score has been improved. It is shown that the improved model has better generalization ability in crowded targets, small targets, and occluded target scenarios.  
Figure 12: Comparison of model detection results in different scenarios. (**a**) Comparison of dense helmet detection, (**b**) Comparison of long-range small helmet detection, (**c**) Comparison of obscured helmet detection.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 4 Error Factors of TCR RCS  
In Section 3, the bistatic RCS characteristics of the TCR were analyzed, which indicated that the TCR could meet the requirements listed in Section 2. For practical calibration of a bistatic SAR, it is very important to ensure the accurate RCS of the calibration targets. There are several error factors that can introduce a change of RCS for the TCR, including that limited machining accuracy will mainly cause the interplate to be nonorthogonal and the interleg length to not reach the standard during the manufacturing process. Moreover, the TCR deviates from the design route during the deployment, or the attitude error (yaw, pitch, and roll) caused the deviation of the platform, which will result in the pointing deviation. In addition, thermal deformation caused by the environment will also change the interleg length. Proper control of the above errors will guarantee or improve the accuracy of the bistatic SAR radiometric calibration. In order to meet the RCS accuracy requirement, we analyzed the error factors such as interplate orthogonality, machining error of length, pointing deviation, and thermal deformation quantitatively with electromagnetic simulations. Furthermore, the machining tolerance, deployment requirements for pointing, and deformation tolerance are recommended in this section.
# Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion
## 1 Introduction
In [15], the authors presented an MLSE and a MAP equalizer using the discrete-Fourier basis expansion model (BEM) of the channel. Despite that reduction of the effective channel impulse response decreases the number of states in the equalizer, the accuracy of estimation is closely affected. Hence, these solutions create an error floor because of the modeling error. In this tenor, this paper presents a modification of MLSE and MAP algorithms using a basis expansion model, which allows translating part of the computational complexity into memory storage. In this way, the computational complexity is reduced without error floor, notably when the channel is varying during the transmission of a block of N symbols using read-only memories. There are two main contributions of this paper. Firstly, the modified versions of the prolate functions (MPFs) have never been used to reduce the computational complexity of MLSE and MAP algorithms, to the best of the authors' knowledge. Thus, for a given approximation error, it can be observed that the number of basis functions is fewer when MPFs are used compared to the suggested basis in the state of the art implementations. Secondly, contrary to intuition, the use of MPFs in the time-delay domain gather better performance than the Fourier base using the same number of functions. The paper is organized as follows: The mathematical model of the communication system and the representation of the channel using a BEM are presented in Section 2.1. The MAP and MLSE algorithms are outlined in Section 2.2. The modification of the MAP and MLSE algorithms using BEM and their computational complexity are established in Section 2.3 and Section 2.4, respectively. In Section 3, the corroboration of the proposed methods through simulation is presented. Finally, the conclusions are presented in Section 4.
## 4 MATANA Detailed Architecture
### Software Support  
The hardware Detection Module may be used alone in order to collect and analyze the probed signals and provide results to the outside, using the debug port, for further offline analysis. This would be very similar to hardware signal probing techniques but slightly better because the analysis of the different signals enables the output of more meaningful information. However, hardware-only analysis is too restrictive to our viewpoint because (1) some detection logics are quite difficult to implement in hardware; (2) it cannot benefit from important information that can be retrieved from the global state of the runtime environment; and (3) when an attack is detected, the hardware Detection Module can only perform limited actions defined by the physical logic implemented, which may be insufficient for a complex system. For that purpose, we argue that the software part in the Detection Module of MATANA framework is important.  
The software Detection Module is located in the system kernel, and, as mentioned in the threat model, is considered trusted. Its main functionalities consist of (but are not limited to) the following; (1) providing additional high-level information to the hardware Detection Module, such as the current Process Identifier (PID); (2) performing runtime data analysis that is not easily feasible or too costly at the hardware level; (3) dynamically adjusting the configuration of the hardware Detection Module by setting values to MMIO registers; (4) handling interruptions sent by hardware Detection Module; (5) performing postrun data collection and analysis.  
Let us note that this framework does not require any modification on userland programs executed (either benign or malicious), e.g., to include tags or special instructions. It guarantees that MATANA is compatible with existing software environments and, as only few modifications are required at kernel level, it can be widely used even for nonembedded applications.  
A typical use case scenario of MATANA is as follows: the hardware Detection Module processes probed signals with information and the configuration sent by the software Detection Module and places the analysis result into the MMIO registers. Then, by means of an interruption or a polling mechanism, the software Detection module can fetch the MMIO register's content and perform a more powerful analysis.
## 2 Our Proposed Methods
### Image Encryption
Referring to Equation (11) with the index \(i=0\), we can derive \(C_{1,\ R^{\prime}}=38\oplus 164\oplus 196\oplus 49=119\). We can further adopt the resultant \(C_{1,\ R^{\prime}}\) to encrypt \(C_{1,G^{\prime}}\), thus producing \(C_{1,G^{\prime}}=39\oplus 87\oplus 118\oplus 119=113\). Similarly, we can encrypt \(C_{1,B^{\prime}}=143\) using the previous result, \(C_{1,G^{\prime}}\); we can encrypt \(C_{1,E^{\prime}}=77\) using the previous result, \(C_{1,B^{\prime}}\), accordingly. Consequently, the stego pixel, \(\mathbf{P}^{\prime}_{1}=\left(164,\ 87,\ 17,\ 126\right)\), has been ciphered to become \(\mathbf{C}^{\prime}_{1}=\left(119,\ 113,\ 143,\ 77\right)\). Our scheme provides the benefit of the avalanche effect [23; 24], which means that an error encountered in a previous component will produce an error in the current component being processed. This chain-reaction feature will be propagated across the entire components to be encrypted, causing a drastic change in the ciphertext stego HDR RGBE image. We now analyze the time complexity of the image encryption process. In the image encryption, we first obtain an image feature \(h\), which requires the time complexity of \(O(H\times V\times 4)\). Next, referring to a 2D-SLMM map, we generate a pseudo-random sequence, which needs the time complexity of \(O(q+H\times V\times 4)\), where \(q\) is used to avoid the transient effects. The third step performs the bit-level permutation in all four channels, thus requiring the time complexity of \(O(4\times H\times V)\). In the final step, the pixel diffusion needs the time complexity of \(O(H\times V\times 4)\). In summary, the time complexity of four steps determines the computational complexity of our image encryption algorithm Consequently, our stego HDR encryption scheme has the computational complexity of \(O(q+H\times V\times 16)\). We consider this complexity has satisfactory performance because there is an extra exponent channel in an HDR RGBE image.
## 4 Results and Discussion
### Subject Wise Glucose Level Prediction
#### 4.2.2 Machine Learning Approach  
Subject wise tests were also carried out for SVR- and RFR-based models to compare their SEPs with the previous results. It is notable to mention that the performance has degraded here as well. The summary of the predicted error for both of these machine learning techniques have been provided in Table 7. The SEPs for different SVR models were too close to each other and they differ only by +/\(-\)0.1 mg/dL. The same is true for RFR models too. However, SVR models have overall produced slightly better results than RFR. The lowest SEP was recorded to be 26.56 mg/dL for SVR model built with second derivative characteristics points, whereas RFR had the lowest SEP of 29.6 mg/dL for the first derivative model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & & **PCR** & **PLS** \\ \hline \multirow{3}{*}{DelT} & SEP (mg/dL) & 27.78 & 28.57 \\  & PC & 2 & 2 \\ \multirow{3}{*}{1 st Derivative} & SEP (mg/dL) & 25.50 & 27.42 \\  & PC & 5 & 2 \\ \multirow{3}{*}{2 nd Derivative} & SEP (mg/dL) & 25.9 & 27.37 \\  & PC & 11 & 2 \\ \multirow{3}{*}{1 st Derivative Characteristics points} & SEP (mg/dL) & 25.6 & 26.44 \\  & PC & 5 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Subject wise glucose level prediction error using PCR and PLS models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **SEP (mg/dL)** & **n** \\ \hline
1 st Derivative & 21.88 & 25 \\
2 nd Derivative & 24.19 & 25 \\
2 nd Derivative Characteristics Points & 22.11 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Subject wise glucose level prediction error using SVR and RFR models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **SEP (mg/dL)** & **n** \\ \hline
1 st Derivative & 21.88 & 25 \\
2 nd Derivative & 24.19 & 25 \\
2 nd Derivative Characteristics Points & 22.11 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of performances of different RFR models.  
We have also performed k-fold cross-validation on the proposed models. The summary of the experiments and the results are provided in the Supplementary Material Tables S2 and S3.
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 4 Performance Metrics for the SCAR-Phase of Spectrum Sensing
### Analysis of a Secondary User's Throughput  
In periodic spectrum sensing, the frame structure for the SU's network consists of one spectrum sensing phase followed by one transmission phase in which the SU transmits data if the PU is inactive. Considering that the total number of samples of a frame is \(N_{f}\), the average throughput for the SU's network is given by [27]  
\[R(N)=\left(\frac{N_{f}-N}{N_{f}}\right)C_{0}(1-P_{\text{FA}})\text{Pr}\left\{ \mathcal{H}_{0}\right\}+\left(\frac{N_{f}-N}{N_{f}}\right)C_{1}(1-P_{\text{D} })\text{Pr}\left\{\mathcal{H}_{1}\right\}, \tag{45}\]  
where \(N_{f}-N\) is the length of the data transmission phase. Moreover, \(C_{0}=\log_{2}(1+\gamma_{s})\) is the throughput of the SU's network when the SU transmits under \(\mathcal{H}_{0}\) with \(\gamma_{s}\) denoting the SNR of the secondary link, whereas \(C_{1}=\log_{2}(1+\frac{\gamma_{s}}{1+\gamma_{p}})\) is the throughput of the SU's network when the SU transmits under \(\mathcal{H}_{1}\) with \(\gamma_{p}\) denoting the SNR of the link between the PU and the SU. Note that \(R\) depends on the value of \(N\), hence, the optimal value of \(N\) that maximizes the achievable SU's throughput for a given target probability of detection, \(\beta_{d}\), must be found. In this paper, it is used (45) to obtain the SU's throughput for a determined value of \(\beta_{d}\).
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 6 Implementation and Results
### Global Navigation  
The real world environment usually has static obstacles in addition to passive agents. These static obstacles are usually concave, and the VO based approaches are subjected to a local minimum in such environments. The presented concept of safe control space allows us to integrate the control space in a graph search fashion. Figure 12 shows the framework where multiple snapshots at different time instances are shown. In this simulation, the interaction time horizon \(\tau\) is set to 4 s and 256 control inputs are sampled in an admissible control space. All samples that lie in the control obstacle or collide with static obstacles are discarded to obtain a tree depth of one. In this experiment, the graph search terminates at the depth of one, the point at which \(f(h(x_{a},u,\tau))\) connects to Dijkstra heuristic function. The trajectory that is executed for one control cycle is the one that minimize the Dijkstra heuristic function and has a margin greater than or equal to 1.2. As a result, we obtain a global optimization criteria for navigating the agent. However, in case if all tested samples has margin less then \(\beta\), than the sample with the biggest margin is selected.
# Utilizing Energy Storage for Reliability Solutions in Active Distribution Systems
## Nomenclature  
\(l/L\)Index of load point and set of network load points.  
\(N_{l}\)Number of customers at load point \(l\).  
\(NC\)Total number of customers in a network under consideration.  
\(c^{x}\)Index of contingency; where \(x\) denotes temporary and permanent failure.  
\(DSE\), \(MI\), \(SI\)Disruptive voltage sags, momentary, and sustained interruptions, respectively.  
\(RE\)Reliability event; \(RE\)\(\in\)\(\{DSE,MI,SI\}\).  
\(M_{Sag}\)Magnitude of voltage sag.  
\(D_{Sag}\)Duration of voltage sag.  
\(f^{c,x}\)Rate of occurrence of \(c^{x}\) (occ/yr).  
\(L^{c,x}_{RE}\)Set of load points experiencing \(RE\) due to \(c^{x}\).  
\(T^{x,l}_{ISI}\)Interruption time for load point \(l\) experiencing \(SI\) due to \(c^{x}\) (hr).  
\(F^{c,x}_{l,RE}\)Frequency of occurrence of \(RE\) for \(l\) due to \(c^{x}\) considering DERs/microgrids (occ/yr).  
\(F^{c,x}_{l,RE}\)Value of \(F^{c,x}_{l,RE}\)without considering DERs/microgrids (occ/yr).  
\(U^{c,x}_{ISI}\)Unavailability due to \(SI\) for \(l\) due to \(c^{x}\) considering DERs/microgrids (hr/yr).  
\(U^{c,x}_{ISI}\)Value of \(U^{c,x}_{ISI}\)without considering DERs/microgrids (hr/yr).  
\(D^{c,x}_{l,RE}\)Damage cost of RE for \(l\) due to \(c^{x}\) (k$/yr).  
\(P^{c,x}_{l,RE}\)Probability of reliability event (\(RE\)) caused by \(c^{x}\) not being mitigated for \(l\).  
\(F_{l,RE}\)Frequency of occurrence of \(RE\) for load point \(l\) (occ/yr).  
\(F_{RE}\)System frequency index for of \(RE\) (occ/cust-yr).  
\(U_{SI}\)System index of unavailability of \(SI\) (hr/cust-yr).  
\(D_{l,RE}\)Damage cost due to \(RE\) for \(l\) (k$/yr).  
\(D_{l,tot}\)Total damage cost due to all the reliability events for \(l\) (k$/yr).  
\(D_{RE}\)System index of customer damage cost due to \(RE\) (k$/yr).  
\(D_{tot}\)Total system damage cost due to all the reliability events (k$/yr).