esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Dataset Building
### Attack Sample Data Collection  
The attack data primarily comprised three common types of network attack behaviors: denial of service, tampering attacks, and replay attacks. Analyzing the control process of traffic signal systems revealed that the main targets for attackers, who aim to influence traffic control for their own purposes, are timing \(g(k+1)\), phase differences \(offset(k+1)\), and detection of queue lengths \(l(k+1)\), where \(k\) is the current moment. By altering these factors, attackers can quickly and directly impact intersection control effectiveness. The data types are shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attack Behaviors** & **Data Set** \\ \hline \multirow{4}{*}{Denial of Service} & \(g(k+1)=null\) \\  & \(l(k+1)=null\) \\  & \(offset(k+1)=null\) \\ \cline{2-3}  & \(g(k+1)=false(g(k+1))\) \\ \cline{2-3}  & \(l(k+1)=false(l(k+1))\) \\ \cline{2-3}  & \(offset(k+1)=false(offset(k+1))\) \\ \cline{2-3}  & \(g(k+1)=g(k-t),t\in[0,k]\) \\ \cline{2-3}  & Replay attacks & \(l(k+1)=l(k-t),t\in[0,k]\) \\ \cline{2-3}  & \(offset(k+1)=offset(k-t),t\in[0,k]\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Illustration of Attack Data.  
Figure 4: Schematic diagram of sample data. (**a**) Simulation Road Network Topology Diagram, the number represents the simulation intersection number; (**b**) Detection Data Matrix and Control Data Matrix Graph, the bar in blue in the front is the input detection data, and the bar in yellow is the output control data.  
Denial of service (DoS) attacks disrupt and block normal communication between system components by flooding the communication channel with a large volume of meaningless access requests [18,19].  
Tampering (or spoofing) attacks involve an attacker eavesdropping on and recording communication data over a period to avoid detection when injecting external inputs. Later, they resend the recorded data to deceive the system to achieve their goals, as seen with the Stuxnet virus [20,21].  
Replay attacks involve malicious actions that disrupt or damage the system's normal operation by fully injecting or propagating data from a prior time period.  
The simulation environment randomly utilized two types of attack models, centralized and distributed, to analyze the relationship between data changes and to collect relevant data, thereby validating the effectiveness of the proposed algorithm in attack detection and data restoration. We focused on the detection of anomalous data, so we chose the three most common types of attacks: denial of service, tampering, and replay attacks. The system we built randomly generates attack types and frequencies to validate the detection capability of the trained system.
## 5 Simulation and Results
### Scheduling Time for Convergecast in General Topology  
Each simulation experiment for getting the scheduling time for convergecast in a general topology was repeated 30 times based on the number of nodes, and the average values of all trials for each are reported. The simulation displays randomly distributed nodes. The FMSTC algorithm chooses nodes that are closer to the sink node. It shows a fully connected graph and constructs the minimum spanning trees, which are independent sets to get the line topologies. Additionally, it starts sending messages to the sink node. Figure 6 is an example of the simulation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Power**} & \multicolumn{8}{c}{**Number of Nodes**} \\ \cline{2-11}  & 10 & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline
3 & 17.00 & 39.67 & 63.00 & 86.33 & 109.67 & 133.00 & 156.33 & 179.67 & 203.00 & 226.33 \\
6 & 14.00 & 34.00 & 54.00 & 74.00 & 95.33 & 117.00 & 138.67 & 160.33 & 182.00 & 203.67 \\
9 & 11.00 & 31.00 & 51.00 & 71.00 & 91.00 & 111.00 & 131.00 & 151.00 & 171.00 & 192.11 \\
12 & 10.00 & 28.00 & 48.00 & 68.00 & 88.00 & 108.00 & 128.00 & 148.00 & 168.00 & 188.00 \\
15 & 10.00 & 25.00 & 45.00 & 65.00 & 85.00 & 105.00 & 125.00 & 145.00 & 165.00 & 185.00 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal time schedule in a linear topology with fixed power.  
Figure 5: Optimal scheduling time in a linear topology with fixed power.  
As shown in Figure 6a (1), 40 nodes are distributed in a \(500\times 500\) square area. We set up the transmission range to three units. After deciding on the sink node, the nodes construct the fully connected graph based on the chosen nodes near the sink node in Figure 6a (2). Finally, the nodes in the square area can construct the minimum spanning tree using our proposed algorithm (FMLSP), as shown in Figure 6a (3). We present the results of the overall simulation in Table 9 and Figure 7.  
As shown in Figure 7, the more nodes, the more time slots in each power level. When the power is increased, the time slots decrease. Some of the time slots are increased by a greater number. For example, time slots increased more than 100, from 60 nodes to 70 nodes, for Power Levels 6, 9 and 12. This increase is due to the fact that more sub-spanning trees are made during the simulation. That means that the proposed algorithm may generate more branches when the node number is increased.  
Figure 6: **(a)** 40 sensors distribution with p = 3; **(b)** 50 sensors distribution with p = 3. Example of the simulation: **1**, node distribution; **2**, fully connected graph based on chosen nodes near the sink node; **3**, constructing minimum spanning trees.  
Figure 7: Scheduling time for convergecast in the general topology.
## 3 Results and Discussion
### Nonlinear Fitting
Additionally, the calculated Toth constant, \(k_{T}\), for all the error functions is close to the experimental data and meets the fitting criteria. Interestingly, the curve fitting to both error functions shows that the R-P and Toth models overlap each other at almost every test temperature (Figures 10 and 11). This shows that the model theories apparently show contrasting natures in selecting the most appropriate isotherm model based on the analysis of the SNE calculations. Therefore, to address this issue, combining more than one measuring technique provides information about the difference between the observed data and predicted data with different interpretations in order to compare the accuracy of error analysis. The use of multiple statistical processes provides a more complete picture of the model's fit to the data and allows one to makes more informed decisions about model choice. The following section discusses the results of the CND analysis of the error functions. Unlike the SNE, the CND is a measure of the variability in the outcome of a regression model that remains unexplained. Since the coefficient of determination (\(R^{2}\)) is a real number between 0 and 1, 1-\(R^{2}\) defines \(K^{2}\) as having a value within 0 and 1. A value of \(K^{2}\) close to 0 means a better alienation of the parameters that are well-explained by an isotherm model and, consequently, better fitted with the particular error function. Figure 10 presents the calculated \(K^{2}\) values for the studied error functions for all the isotherm models at four different temperatures. Figure 12 shows that the R-P isotherm has the lowest \(K^{2}\) (0.0025-0.0048) values for the SSE and HYBRID functions at all four of the temperatures, suggesting that this isotherm best fits the equilibrium data. Figure 12: CND outcomes of MB sorption for the studied isotherms on AG at: (**a**) 303, (**b**) 313, (**c**) 323, and (**d**) 333 K.  
The Freundlich isotherm shows an exponential-type curve that diverges from the experimental data and thereby fails to meet the equilibrium and saturation point of the present study. This suggests that the sorption of MB progressed by covering the monolayer surface of the adsorbent AG. In the CND analysis, a much lower \(K^{2}\), excepting R-P (0.00776-0.01362), was found for the Toth isotherm, but according to a similar trend in some cases, this overlapped the R-P isotherm, indicating that this isotherm can also be considered for the ideal fit in the present study. Hence, the goodness-of-fit order of the adsorption isotherm models in the present study is as follows: R-P > Toth > Langmuir > Sips > Freundlich. According to the present study, an important step to note before deciding on a best-fit isotherm model is to analyze whether the experimental data are consistent with the chosen isotherm theory. Additionally, both the theoretical basis of the model parameters and the residuals obtained from the error functions should be appropriately compared to investigate the accuracy of a good isotherm model.
## 4 Conclusions  
IDE-loaded NLC containing tocopheryl acetate as the liquid lipid showed good technological properties (small particle sizes, low polydispersity indices, and good stability at room temperature). In vitro antioxidant assays (DPPH and reduction power) showed that, at the highest non-toxic concentration determined by the MTT test, the antioxidant activity of NLC containing different percentages of IDE (1.0 and 1.5 % w/w) was similar to that of SLN loaded with the same percentages of IDE. Analogously, similar photo-protective effects of IDE-loaded NLC and SLN were observed on HS68 fibroblast cells after exposure to UV light. The topical efficacy of these lipid nanocarriers loading IDE was evaluated on human volunteers from gel vehicles. The results of in vivo studies highlighted a greater photo-protective activity of IDE-loaded NLC compared to IDE-loaded SLN, thus suggesting a synergic effect between IDE and tocopheryl acetate. Therefore, co-loading IDE and tocopheryl acetate into NLC could be regarded as a promising approach to develop topical formulations with improved photo-protective efficacy.  
Figure 8: Decrease of skin pigmentation values (\(\Delta\) pigmentation) obtained after a two-week in vivo topical treatment with the gels under investigation. Statistical analysis for the comparison: * \(p\) < 0.05 vs. Gel C, Gel SLN, Gel NLC, Gel SLN IDE1, and Gel SLN IDE1.5. Gel C = control gel (without lipid nanocarriers), Gel SLN = gel containing unloaded solid lipid nanoparticles (SLN), Gel NLC = gel containing unloaded nanostructured lipid carriers, Gel SLN IDE1 = gel containing idebenone (IDE) 1% w/w loaded SLN, Gel IDE1.5 = gel containing IDE 1.5 % w/w loaded SLN, Gel NLC IDE1 = gel containing NLC loaded with IDE 1% w/w, Gel NLC IDE1.5 = gel containing NLC loaded with IDE 1.5 % w/w.  
**Author Contributions:** Conceptualization, L.M.; methodology, L.M., C.M.M., and A.S.; validation, R.T., L.P., and C.P.; formal analysis, C.M.M., S.M., R.A., R.T., L.P., and C.P.; investigation, L.S. and R.A.; resources, L.M., C.M.M., and A.S.; writing--original draft preparation, L.M., C.M.M., S.M., and R.A.; writing--review and editing, L.M., C.M.M., and A.S.; visualization, L.M.; supervision, L.M.; project administration, L.M.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results  
Figure 2 shows the 3D scanning of the patient's feet and legs in weight-bearing [18]. The 3D model and the laser-sintered custom AFO developed for the present patient are shown in Figure 3. Figure 4 shows the scheme of the experimental setup for the mechanical characterization of the custom AFO (Figure 4, left) and a 0-15-0 deg flexion/extension cycle (Figure 4 Right). The average stiffness of the custom AFO was 0.25 N*m/deg.  
Figure 5 shows the outcome of the gait analysis. Sagittal-plane rotation (mean \(\pm\) SD) of the hip (top), knee (middle), and ankle joint during normalized gait cycle rotations across five walking trials are shown for the custom AFO, Codivilla AFO, and shod conditions. Increased ankle dorsiflexion is significant in both AFO conditions. In terms of spatiotemporal parameters, the patient walked faster with the custom AFO (0.91 \(\pm\) 0.04 m/s) with respect to the Codivilla (0.85 \(\pm\) 0.03 m/s) and the shod (0.67 \(\pm\) 0.01 m/s) conditions. The  
Figure 4: **(Left)**, 3D model of the experimental setup used to measure the mechanical properties of the custom AFO. Controlled-rotations are applied to the AFO via the actuator of a servohydraulic testing machine. A torque cell measures the AFO resistance to flexion/extension rotations. (Right), angle/torque data for a 0-15-0 deg flexion/extension controlled displacement.  
patient scored the custom AFO as more comfortable than the Codivilla in the leg support (9.6 vs. 7.5), in the plantar aspect (9.8 vs. 3.5), and overall (9.7 vs. 7.3). The perceived push was also higher for the custom AFO (9.7 vs. 6.9).
## Acronyms  
CFI coiled flow inverter DLSdynamic light scattering EG ethylene glycol GNPgraphene nanoplate HVAC-R heating, ventilation, air conditioning, and refrigeration ICSD inorganic crystal structure database LPM liters per minute MWCNT multi-walled carbon nanotube NF(s) nanofluid(s) NP(s) nanoparticle(s) PALS phase analysis light scattering PHE plate and frame heat exchanger PTFE polytetrafluoroethylene PVC polyvinyl chloride SEM scanning electronic microscopy STHE shell and tube heat exchanger XRD X-ray diffraction W water
## 1 Introduction
Using SVD and Huffman to compress encrypted detail sub-bands. Choosing important information from detail subbands results in effective compression while maintaining image quality. To reconstitute the image, the receiver decompresses, decrypts, and inverts DWT-encrypted and compressed bit streams. Memory use and the suitable architecture of the intended result from the compression method impact the entire usefulness of the generated work. Therefore, effectiveness is crucial in the development of the compression strategy. SVD is a common factorization process for collecting precise information on a matrix since it is a technologically costly method. Data compression is one of its many applications since it may minimize the volume of data needed to encode an image while keeping the image quality intact; therefore, it is a suitable choice among various compression techniques. Among all of the discussed methods above, high-dimensional and multidimensional systems [19; 20; 21] are proven to be more secure as compared to their lower-order counterparts, but high security with these complex sets of equations and parameters decreases the processing speed of the transmission system adversely; therefore, there is a requirement of an algorithm, which not only secures the system with higher dimensional orders but also makes the system lightweight so the processing speed cannot be compromised. Keep in mind that the quad key-secured 3D Gauss chaotic map is proposed here in this work. The main highlights of the proposed scheme are as follows:  
1. The suggested quad key-secured system is based on the novel 3D Gauss map. 2. To assure dependability and unpredictability, the pixels of the quad key-secured encryption system are permuted with four recommended chaotic sequences. 3. The integrated image's pixels are changed with the proposed four types of chaos after being randomly shuffled in rows and columns in order to withstand the attack and obtain better results. 4. The proposed 3D Gauss map boosts the key's sensitivity while also creating significant variances in the image pixels. 5. An increased set of parameters might boost resistance to an attack while boosting unpredictability and reducing security breaches. 6. The SVD compression strategy provides the user with a degree of compression flexibility, allowing the user to compress the image proportional to the demand for space and storage while still providing good privacy. 7. The simulation and comparison results are used to examine the superiority and efficiency of the proposed strategy with several attacks and tests. The rest of the contents are split into the following sections: The essential concepts are discussed in Section 2. Then, the recommended algorithm is illustrated in Section 3, and the simulation results are presented in Section 4 by the simulation experiments. Further, Section 5 is dedicated to the security analysis and comparison. Finally, Section 6 presents our conclusion.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## 2 Related Work  
The settlement of where and when to perform handover is a driving issue in the realm of mobile communication. It is a pivotal measure for the handover process in order to grant universal handover, accomplish the best QoS in a heterogeneous environment, and provide the best Quality of Experience (QoE) for mobile UE. This decision requires us to investigate UE velocity and assemble data regarding the available services of the competitor networks in order examine this information with a specific end goal to settle on a decision at a suitable time.
# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
###### Abstract  
The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm's advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.  
short-term electrical load forecasting; machine learning; deep learning; statistical analysis; parameters tuning; CNN; LSTM +
Footnote †: journal:  
_Applied_  
_sciences_
# Enhanced Spectral Amplitude Coding OCDMA System Utilizing a Single Photodiode Detection
## 4 Results and Discussion
### Presence of DCF Only  
Nowadays, tens of millions of kilometers of conventional SMFs already exist in the underground ducts and are operating at 1300 nm. Transmission capacity can be increased by operating these fibers at 1550 nm and using optical amplifiers such as EDFA. However, there will be significant residual (positive) dispersion. On the other hand, replacing these fibers with dispersion shifted fibers (DSFs) would incur huge costs. In recent years, considerable work has been done to upgrade the installed 1310-nm optimized optical fiber links to operate at 1550 nm. This is achieved by developing fibers with very large negative dispersion coefficients, a few hundred meters to a kilometer, which can be used to compensate for dispersion over tens of kilometers of the fiber in the link. Compensation of dispersion at a wavelength around 1550 nm in a 1310 nm optimized single-mode fiber can be achieved by using specially designed fibers whose dispersion coefficient (D) is negative and large at 1550 nm to improve performance. These types of fibers are known as dispersion compensating fibers (DCFs). The length of the DCF required for compensation can be reduced by having fibers with very large negative dispersion coefficients. Thus, there has been a considerable research effort to achieve DCFs with very large (negative) dispersion coefficients in order to reduce costs [23].  
The DCF could be designed to have a negative GVD value at 1550 nm, and is usually used to compensate the accumulated dispersion of a conventional SMF-based transmission link. The length, \(L_{DCF}\), of the DCF used in the simulation was calculated using [5]  
\[L_{DCF}=-\frac{L_{SMF}D_{SMF}}{D_{DCF}}, \tag{8}\]  
where \(L_{SMF}\) is the original SMF length, and \(D_{SMF}\) and \(D_{DCF}\) are, respectively, the total dispersion parameters of SMF and DCF.  
The total link length, L, is  
\[L=L_{SMF}+L_{DCF}, \tag{9}\]  
Figure 6 displays the average variation of BER of three users with respect to the fiber length at 622 Mbps when using APD and PIN photodiodes. When compared with Figure 3a, an improvement is achieved in the system BER. As an example, at a distance of 72 km, the SAC-OCDMA system achieves a BER of 2.14 \(\times\) 10\({}^{-8}\) and 5.01 \(\times\) 10\({}^{-19}\) for PIN and APD, respectively, while the corresponding values without DCF are 7.90 \(\times\) 10\({}^{-8}\) and 2.26 \(\times\) 10\({}^{-16}\) at 622 Mbps.  
Figure 6: BER with transmission distance of SAC-OCDMA system using EDW codes using two different detectors in presence of DCF.
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
## 2 The Proposed Approach
### Feature Matching  
After all training images are mapped into Hamming space, the Hamming distance is employed to match two images. In general, the capturing images are subject to translation and rotation normalization, but there are still some variations due to inaccurate localization and normalization. However, Hamming distance is not robust enough to reduce these variations. So, an enhanced Hamming distance is employed to compute the non-overlapping region between two images with possible spatial shifts for finger-vein matching. Assuming \(Q\) and \(B\) are enrolment and test binarized feature codes with size of \(I\times J\), respectively (as shown in Figure 6), the height and width of \(Q\) are extended to \(2E+I\) and \(2H+J\), and then its expanded image \(\bar{Q}\) is obtained and expressed as:  
\[\bar{Q}(i,j)=\left\{\begin{aligned} & Q(i-E,j-H)\quad\text{if}\quad 1+E \leq i\leq I+E,\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad 1+H \leq j\leq J+H\\ -1\quad\text{otherwise}\end{aligned}\right. \tag{16}\]  
Figure 6b illustrates the extended image \(\bar{Q}\) of a template \(Q\) and the extend region with values of \(-1\) is marked in color. The matching distance between \(Q\) and \(B\) is obtained by  
\[d(Q,B)=\] \[\min_{0\leq e\leq 2E,0\leq h\leq 2H}\frac{Hamdistance(\bar{Q}^{e,h},B) -\Phi(\bar{Q}^{e,h})}{Hamdistance(\bar{Q}^{e,h},U)} \tag{17}\]  
In Equation (17), \(\Phi(V)\) is the amount of \(-1\) values in matrix \(V\). \(U\) is a matrix with size of \(I\times J\) and the values of its elements are \(-1\). \(Hamdistance\) represents the hamming distance between two encoding images, i.e., summation of the number of positions that are different. \(\bar{Q}^{(e,h)}\) is a matrix (the red rectangle box in Figure 6b) when the translation distances are \(e\) and \(h\) over horizontal and vertical directions. \(d(Q,B)\) basically computes the minimal amount of non-overlap between \(Q\) and \(B\) at different spatial shifts, excluding the pixels located in the expanded region (e.g., the green region in Figure 6b). The parameters \(E\) and \(H\) control the translation distance in horizontal and vertical directions and are heuristically set to 20 and 60.