esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 2 Background and Motivation
### Motivation and Challenges  
In this work, our main goal is to design a efficient decentralized server-aided MLE scheme that can be applied to small-size chunks (e.g., 8 KB) in encrypted deduplication storage systems.  
Note that the secret-sharing algorithm can be leveraged to share data among multiple nodes (Section 2.2). Thus, it is natural to ask whether we can apply secret sharing with decentralized server-aided MLE to share a single MLE key among multiple key servers to allow multiple key servers with different secret keys to encrypt one MLE key collaboratively without any single server knowing the entire MLE key.  
However, sharing the MLE key of the plaintext chunk incurs independently of the encryption and encoding processes in addition to the original encryption and encoding on the plaintext chunks, which leads to high computation overhead. Fortunately, non-systematic codes [18] can be leveraged to couple the encryption and encoding processestogether. Unlike systematic codes, non-systematic codes only store the parity chunks encoded from plaintext data chunks, where the parity chunks cannot be inferred without the entire coding matrix [18], meaning that the parity chunks naturally maintain data confidentiality. This motivates us to encode plaintext chunks into parity chunks using non-systematic codes, such that we only need to perform one encoding process to enable both the encryption and secret-sharing processes simultaneously, while the coding matrix can naturally be decomposed and shared.
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 5 Fundamental Issues
### Design Technology of Tanks
#### 5.2.3 Thermal Performance
[132] investigated hydrogen storage systems and the development of rapid refueling technology for fuel cell vehicles. High-pressure storage (350 bar or 700 bar) is the preferred option for transportation due to its technical simplicity, reliability, efficiency, and affordability. However, the rapid increase in temperature that occurs during the refueling of high-pressure hydrogen storage cylinders has the potential to reduce the State of Charge (SOC) and cause damage to the tank walls, thereby posing a safety risk. Thermal stress resulting from temperature fluctuations can precipitate the formation of microcracks in resin and composite failure in cylinders. The temperature rise and SOC are influenced by several parameters, including the initial temperature, the type of cylinder, and the initial pressure [181]. Optimal control of initial inlet gas temperature and filling rate is crucial, with precooledliping to reduce initial inlet gas temperature. Coupling cooling temperature and filling rate control can achieve temperature and filling time targets. Numerical simulations based on experimental data verify computational fluid dynamics (CFD). Fast-filling strategies include rate control, refueling with multi-stage pressures, and determining precooling from refueling parameters. Coupling these strategies can create an optimal multi-stage filling strategy to decrease temperature rise and energy consumption [181]. On-board Cold Thermal Energy Storage (CTES) systems, cooled by expanded hydrogen, have been studied to improve energy efficiency and address safety concerns, avoiding significant temperature rise during fast filling, which poses safety risks [164; 182]. During the last decade, lots of analysis have been conducted on tanks filling and the related thermal problem, such as fast filling [134; 183], rupture during fire tests [136; 175], thermal behavior during cycling [40; 133], increasing temperature and thermal boundaries [152; 184], the correlation with the filling pressure [185; 186] and with ultimate pressure-bearing capacity [60].
## 2 Methods
### sEMG Signals Acquisition of Wireless 8-Channel Armband  
sEMG signals are collected using an 8-channel wireless sEMG armband developed by researchers at Shanghai Jiao Tong University. This armband, capable of recognizing 8 hand gestures, is illustrated in Figure 2. The armband consists of 8 dry electrodes, a DC power supply, and a microcontroller. Circuit-wise, the 8 dry electrodes are interconnected via flexible flat cables (FPC), which individually transmit the collected signals to the microcontroller for further processing. Structurally, the electrodes are paired and connected by elastic cords, allowing the armband to accommodate arms of various thicknesses,  
Figure 1: Basic flow chart of grasping mode recognition of manipulator.  
as shown in Figure 2a,b. Each dry electrode incorporates a differential amplification circuit for filtering and amplifying sEMG signals. The signal processing unit, centered around the microcontroller, refilters, amplifies, and converts analog signals to digital before transmitting them to a computer or other devices via Bluetooth. The receiving device reads the 8-channel EMG signals in real-time following the serial communication protocol. The sEMG armband has a sampling frequency of 1000 Hz, an amplification factor of 500, and a baud rate of 57,600 bits/s.  
Subjects participating in the data acquisition using an 8-channel wireless sEMG armband performed eight hand gestures in each cycle, with each gesture lasting 5 s. Upon hearing an announcement through headphones, subjects changed their grasp gestures. After each data acquisition cycle, subjects rested for 1 min before proceeding to the next cycle. From each subject, sEMG data totaling 100 s were collected, accumulating to 1000 s of sEMG data. Figure 2c shows the first round of sEMG signals collected from a subject. The eight hand gestures are natural grasp, spherical grasp, cylindrical grasp, tripod pinch, tip pinch, lateral pinch, hook grasp, and thumb extension grasp, as depicted in Figure 3.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 3 Intelligent MTD Techniques  
### Background  
As we have mentioned above, MTD gradually reveals its shortcomings and deficiencies in the continuous development of both cyber-attack and defense, and existing MTD cannot undertake a plethora of defense tasks satisfactorily.  
With an improvement in computer hardware and the advent of the big data era, machine learning theories and techniques [58; 59] are becoming increasingly heated, and their application areas are expanding. Machine learning-related techniques have demonstrated excellent processing capabilities for complex, massive, and high-dimension data in many tasks and have shown more accurate and collegiate performance in many complex problems compared to traditional decision-making methods, which makes machine learning considered a strategic technology, leading a new round of technological and industrial revolutions.  
Thereby, MTD, based on machine learning, enables systems to capture evolving attack patterns with high scalability and applicability. However, since machine learning requires a large amount of data for training to ensure a certain level of prediction accuracy, insufficient data can result in reduced performance despite high levels of overhead and complexity. Moreover, it is of great importance to ensure that sufficient computational power is available in the environment where MTD is deployed, as resource-constrained environments may be unable to afford it.
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 2 Application Scenarios
### Sensor Nodes in the Industrial Internet of Things (IloT)  
An illustrative scenario is the IIoT. Cyber-attacks in the industrial domain have been mostly focused on the loss of data. However, since sensors allow IIoT devices to interact with the physical world, cyber-attacks can enter now into the physical world and, hence, can cause physical harm. A chemical or a nuclear plant or a multimillion dollar industrial process can be destroyed or lead to threaten the lives of their employees and citizens in the neighborhood.  
Ciphering data (which reduces the possibility of transmitting false data) may not be sufficient in this scenario. Avoiding counterfeiting is very important because fake sensors do not meet usually the required quality provided by legitimate sensors. In addition, fake sensors can be the trapdoor for industrial espionage. If the sensor nodes proposed herein are copied, the resulting fake sensors cannot impersonate them because it is not possible to clone the manufacturing properties of the device to generate the same PUF. In this case, they are unable to recover the secret key from the Helper Data stolen.  
Since many sensors involved with critical tasks should provide high precision and resolution, they should be calibrated and tested regularly on-site by accredited professionals. During calibration and testing, it is very important to avoid impersonation attacks of adversaries assuming the identity of accredited professionals. Otherwise, adversaries or competitors would be able to modify maliciously the sensor behavior.  
Avoiding repudiation attacks is also very important, because an accredited professional may become malicious. If the proposed sensors are used by this attacker, the base station can prove that the transmitted data came from that sensor used by that individual.
## 3 Results
### Dynamic Simulations
#### 3.2.1 Indoor Temperatures  
Table 6 shows the annual average, maximum, and minimum indoor temperatures that take place inside the house when no HVAC system is operated. The results were obtained from the cases analyzed, representing a traditional concrete block wall (Case 0) and the other three cases (1-3) corresponding to the three different densities of the cellular concrete studied. Likewise, the temperatures that would occur in the scenarios (A1B) of climate change for the years 2030 and 2050 are presented, as well as for the heterogeneous scenario known as A2 for 2050. In Table 6, values in bold black letters were marked to represent average temperatures that are considered as too high and completely out of the thermal comfort range. Additionally, in the maximum and minimum temperatures, the numbers in red and blue bold letters were added to point out very high (in red) and very low (in blue) indoor temperatures.  
It is observed that when using the TMY with historical information, the average temperatures occurring inside are adequate. However, for all future scenarios, the average annual temperatures that are registered are greater than 28 \({}^{\circ}\)C and considered out of the comfort range. On the other hand, the maximum temperatures reached inside the house range between 38.41 \({}^{\circ}\)C (Case 3, TMY) and 42.48 \({}^{\circ}\)C (Case 0, alb-2050). It is also noted that cellular concrete's application manages to reduce, on average, 0.75 \({}^{\circ}\)C. This maximum temperature occurs inside the house when the temperatures are compared between the case with a conventional block (Case 0) and the lighter cellular concrete (Case 3).  
Similarly, when observing the house's minimum temperatures, the lower would be 11.07 \({}^{\circ}\)C (Case 0, TMY) and 15.10 \({}^{\circ}\)C (Case 3, A1B-2050). The average difference between the conventional block (Case 0) and the cellular concrete of Case 3 would be 1.57 \({}^{\circ}\)C.
# Sustainable Removal of Cr(VI) by Line Peel and Pineapple Core Wastes
###### Abstract  
The search for efficient and environmentally friendly adsorbents has positioned lignocellulosic materials as attractive and low-cost alternatives instead of synthetic materials. Consequently, the present work investigates the efficacy of untreated lime peel (LM) and pineapple core (PP) as biosorbents for Cr(VI) removal. The maximum adsorption capacities (acquired at 24 h) of these sorbents were 9.20 and 4.99 mg/g, respectively. The use of these sorbents is expected to offer a rapid and efficient solution to treat effluents containing Cr(VI). Pineapple core showed the best biosorption properties and good distribution coefficients (distribution coefficient K\({}_{\text{D}}\) 8.35-99.20 mL/g) and the optimization of the adsorption was carried out by a response surface methodology using the Box-Behnken design. Thus, the effect of pH, biosorbent dosage, and temperature were assessed during the whole procedure. Three different responses were studied--Cr(VI) removal, Cr biosorption, and distribution coefficient--and the optimal conditions for maximizing the responses were identified by numerical optimization applying the desirability function. The resulting optimal conditions were: initial solution pH 2.01, biosorbent dosage 30 g/L, and temperature 30.05 \({}^{\circ}\)C. Finally, the process scale-up was evaluated by the simulation of the process working with a column of 100 L using the Fixed-bed Adsorption Simulation Tool (FASTv2.1). This research presents the obtained environmental benefits: i) reduction of pineapple waste, ii) Cr(VI) reduction and biosorption, iii) shortest sorption time for Cr, iv) properties that allow the biosorption process on the flow system, and v) low-cost process.  
Keywords:agronidustrial wastes; biosorption; breakthrough curves; Cr(VI); lime peel; pineapple core +
Footnote †: journal: Applied Sciences
## 3 Mathematical Modeling Formulation
### Mathematical Formulation of Technical Objectives  
#### 3.4.1 Power Losses and Loss Sensitivity Index  
Minimizing power network losses is a crucial goal for microgrid planners and operators of smart distribution networks because these losses can restrict transmission capacity and lead to increased operational expenses. Therefore, the total losses within distribution networks play a significant role in the optimization process, as reflected in the computation outlined in Equation (22). By prioritizing the reduction of these losses, stakeholders can enhance the efficiency and reliability of their systems while also potentially reducing operational costs and environmental impacts [26].  
\[\text{Obj}_{2}=\text{Losses}=\underbrace{\left(|\mathbf{V}|^{2}/R_{Z_{j}} \right)_{\text{end}}-\left(|\mathbf{V}|^{2}/R_{Z_{j}}\right)_{\text{start}}}_ {\text{Active Losses}}+\underbrace{\left(|\mathbf{V}|^{2}/X_{Z_{j}}\right)_{ \text{end}}-\left(|\mathbf{V}|^{2}/X_{Z_{j}}\right)_{\text{start}}}_{\text{ Reactive Losses}} \tag{22}\]  
On the other hand, the power loss sensitivity factor (PLSF) serves as a valuable tool for assessing the susceptibility of individual buses within the network to changes in active power injection and their impact on overall power losses, as detailed in Equation (23). This metric is utilized to pinpoint the most favorable bus locations for integrating distributed generation (DG) within a radial distribution system (RDS). By analyzing the PLSF values of each bus in the network, planners can identify strategic sites with high sensitivity, indicatingtheir suitability for DG deployment. This approach allows for targeted placement of DG resources to optimize system performance and enhance overall network efficiency.  
\[\text{Obj}_{3}=\text{PLSF}=\sum_{i=1}^{N}\left(\frac{\partial P_{\text{loss}(i, i+1)}}{\partial P_{(i+1)}}\right)=\sum_{i=1}^{N}R_{i}\left(\frac{2P_{(i+1)}}{ \left|\text{V}_{(i+1)}\right|^{2}}\right) \tag{23}\]
## Materials and Methods
### Formulation of the Problem
\begin{array}{l}\gamma_{i}(t)\in L=\{0,1\},\;\sum\limits_{i=1}^{I}\gamma_{i }(t)\in L,\\ \int\limits_{0}^{t_{0}}\sum\limits_{i=1}^{I}\gamma_{i}(t)dt=t_{s}\leq t_{f},\; C_{i}(t)\in\overline{C}_{i}(t).\end{array}  \tag{3}\]  
Constraints (3) determine the range of tasks to be solved, i.e., the following calculations correspond to the conditions for the operation of AIDS with a time division of computing resources: at the same time, it can control only one object. The observation plan is described by the following expression:  
\[\left\{\mathrm{P}_{i},i=\overline{1,I}\right\}=P, \tag{4}\]  
\[P_{i}=\left\{\gamma_{i}\in L,C_{i}\in\overline{C_{i}}\right\}.\]  
Models (1) and (2) correspond to the [PERSON] filtering algorithm, which is described by the following analytical relations:  
\[ \begin{array}{l}{x^{*}}_{i}{}^{\prime}=S_{i}x^{*}{}_{i}+\gamma_{i}D_{i}C_{i} ^{T}N_{ig}{}^{-1}[u-C_{i}x^{*}{}_{i}],\\ {x^{*}}(t_{0i})=x^{*}{}_{i0},\end{array}  \tag{5}\]  
\[ \begin{array}{l}{D_{i}}^{\prime}=S_{i}D_{i}+D_{i}S_{i}{}^{T}+Q_{i}-\gamma_{i} D_{i}K_{i}D_{i},\\ i=\overline{1,I},\ t\in[t_{0i},t_{f}],\ t_{0i}\in[0,t_{f}],\\ D_{i}(t_{0i})=D_{i0},\end{array}  \tag{6}\]  
\[Q_{i}=G_{i}N_{ig}G^{T};\ K_{i}=C_{i}{}^{T}N_{ig}{}^{-1}C_{i}.\]  
The statement of the problem of optimization of observation processes in the framework of these studies is based on the use of the \(l\)-optimality criterion [23]:  
\[l=\sum_{i=1}^{I}m_{i}{}^{T}D_{i}(t_{f})m_{i}\rightarrow\min_{P},\ m_{i}\in R ^{n_{i}}. \tag{7}\]  
Criterion (7) minimizes the sum of errors in estimating the state of controlled objects at the final moment of time. The optimization problem is to determine the optimal observation plan in terms of the \(l\)-optimality criterion (7). The use of the terminal criterion (7) within the framework of this problem is quite justified, given that the decision will be made on local time intervals. Thus, the conditions of the optimization problem are formalized as follows: a generalized model of the dynamics of a set of controlled objects is described by Equation (1); the observation model for a set of controlled objects is described by the Formula (2); the observation plan (4) is determined by control functions that obey the constraints (3); and the quality criterion (7) minimizes the error in estimating the state vector at the final moment of the observation interval.
## 5 Conclusions  
In recent years, the Internet of Things (IoT) and wireless sensor networks (WSNs) have stimulated academics and gained great attention due to their ability to serve a wide range of applications. Security and privacy, in addition to routing tasks, are the most challenging areas of the WSNs due to the constraints of the sensor devices. We present in this paper an efficient key management method to generate, distribute, and re-key processes using elliptic curve cryptography for enabling security services in challenging WSN scenarios. The results show secure and reliable connections with fewer overheads and time consumption. Details about the key generation, distribution, and rekeying processes, in addition to the proposed enhanced LEACH routing protocol, have been presented. Finally, a performance evaluation has been conducted based on metrics such as the number of rounds, energy consumption, time consumption, dead nodes, and hop counts to demonstrate the method's effectiveness compared to other routing protocols in WSN networks.  
Despite the impressive and promising results obtained from the proposed method, the model considers only a two-dimensional topology for the sensors' placement, where this assumption may not be properly applicable in places where sensors are placed on an uneven surface. Another limitation is that a homogenous setting was considered where all sensors have the same properties, whereas in reality there may be cases of heterogeneity where sensors of different characteristics are used to monitor different physical events. In future work, an experimental test of the proposed solution in more complex WSN systems to analyse the robustness of the method against some types of attacks, as well a 3D implementation, are planned.  
Conceptualization, S.M.H., J.A.L.R. and A.M.A.; methodology, S.M.H. and J.A.L.R.; software, S.M.H. and A.M.A.; writing--original draft preparation, S.M.H., J.A.L.R. and A.M.A.; writing--review and editing, S.M.H. and J.A.L.R.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Validation
A brief precision-recall examination of the FSSML-EDM model on the test dataset is portrayed in Figure 9. By observing the figure, it is noticed that the DLBCL-MRI model accomplished maximum precision-recall performance under all classes. Figure 6: Result analysis of FSSML-EDM technique on 30% of testing dataset. Figure 7: Accuracy graph analysis of FSSML-EDM technique. Figure 8: Loss graph analysis of FSSML-EDM technique. Figure 9: Precision-recall curve analysis of FSSML-EDM technique. A detailed ROC investigation of the FSSML-EDM approach on the distinct datasets is portrayed in Figure 10. The results indicate that the FSSML-EDM technique exhibited its ability in categorizing two different classes such as pass and fail on the test datasets. Table 3 reveals an extensive comparative study of the FSSML-EDM model with existing models such as improved evolutionary algorithm-based feature subsets election with neuro-fuzzy classification (IEAFSSNFC) [22], neuro-fuzzy classification (NFC) [21], neural network (NN), support vector machines (SVM), decision tree (DT), and random forest (RF). Figure 11 inspects the comparative \(prec_{n}\), \(rec_{l}\), and \(acc_{y}\) investigation of the FSSML-EDM model with recent methods. The figure reveals that the NN and SVM models showed poor performance with lower values of \(prec_{n}\), \(rec_{l}\), and \(acc_{y}\). The NFC, DT, and RF models have showed slightly improved values of \(prec_{n}\), \(rec_{l}\), and \(acc_{y}\). Moreover, the IEAFSS-NFC model resulted in reasonable \(prec_{n}\), \(rec_{l}\), and \(acc_{y}\) of 93.81%, 92.39%, and 90.33%, respectively. Furthermore, the FSSML-EDM model accomplished effectual outcomes with maximum \(prec_{n}\), \(rec_{a}\), and \(acc_{y}\) of 94.58%, 93.65%, and 96.41%, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **Accuracy** & **F-Score** & **MCC** & **Kappa** \\ \hline FSSML-EDM & 94.58 & 93.65 & 96.41 & 94.10 & 88.22 & 88.20 \\ \hline IEAFSSNFC & 93.81 & 92.39 & 90.33 & 93.01 & 73.78 & 73.37 \\ \hline NFC Algorithm & 81.76 & 91.41 & 81.66  
Moreover, the IEAFSSNFC model resulted a in reasonable \(F_{score}\), \(MCC\), and \(kappa\) of 93.01%, 73.78%, and 73.37%, respectively. Additionally, the FSSML-EDM methodology accomplished effectual outcomes with maximum \(F_{score}\), \(MCC\), and \(kappa\) of 94.10%, 82.22%, and 88.20%, respectively. Therefore, the FSSML-EDM model has the capability of assessing student performance in real time.