esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Stage 2--Conducting the Review
#### 2.2.3 Step 7 Analyzing and Synthesizing Data  
The analysis of the graph in Figure 3 reveals a significant increase in scientific output on the application of IoT and deep learning in public security, particularly in the context of violence and crime, between 2019 and 2024. Research in this field has been primarily concentrated between 2021 and 2024, with a peak in 2023, reaching 16 publications.  
During the early years (2019-2020), scientific production was limited, reflecting an initial exploratory phase. The subsequent sustained growth suggests that these technologies have become key tools to address security challenges. Although 2024 shows a slight decline, the publication volume remains significant, reinforcing the consolidation of this research area as a priority at the intersection of technology and security.  
On the other hand, as illustrated in Figure 4, research on this subject has been predominantly concentrated in India, which accounts for 18 studies (40%), followed by China and Saudi Arabia, each contributing 4 studies (9%).  
India has emerged as a global leader in the research and application of IoT and deep learning for urban security, driven by a robust technological ecosystem, collaborative synergy between academia, government, and industry, and government-backed initiatives such as the Smart Cities Mission [65]. In contrast, China's significant role in this field is rooted in its state-driven adoption of advanced video surveillance, facial recognition, and behavioral analysis technologies, which are integral to its national security and social control strategy. Large-scale programs such as the Skynet Project [66] have positioned China as the country with the world's most extensive video surveillance system, leveraging millions of interconnected cameras, AI algorithms, and IoT networks to enhance monitoring capabilities.  
Additionally, Morocco and Pakistan each contributed 3 studies (7%), while Egypt and Australia contributed 2 studies (4%) each. These contributions highlight a growing global interest in the integration of IoT and deep learning for urban security, extending beyond the dominant research hubs of India and China.  
Beyond these countries, other nations actively involved in advancing this field include Brazil, France, Nigeria, Portugal, Qatar, Russia, South Korea, Taiwan, Turkey, and the United States. These contributions reflect the increasing adoption of smart city technolo  
Figure 3: Number of studies published by year.  
gies worldwide, driven by regional security challenges, technological advancements, and government-backed initiatives.
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
## 1 Introduction
In their method, the binocular just noticeable difference (BJND) [14] mode is utilized to complete watermark embedding, and a two-stage detection method is performed to increase the detection accuracy. In this paper, we propose a pixel-based fragile watermarking algorithm for image tamper identification and localization. By analyzing singular value decomposition (SVD), it is found that the matrix product between the first column of the left singular matrix and the transposition of the first column in the right singular matrix has a strong relationship with the image texture information. By setting an appropriate threshold, a binary authentication watermark is generated. After the watermarking message is encrypted via Arnold permutation, it is inserted into the LSB plane of the original image. In tamper detection, the falsified region is revealed by the difference map between the extracted and regenerated watermarks. The rest of this paper is organized as follows. In Section 2, we describe the SVD in brief and analyze its intrinsic characteristic utilized in this paper. Section 3 introduces the presented fragile watermarking algorithm. Experiments and performance analysis are expatiated in Section 4. The conclusions are given at the end of this paper.
## 2 Results and Discussion
### Antioxidant Activity of Scepe-A, Scepe-B and Scepe-C
#### 2.4.5 Lipid Peroxidation Inhibition Assay  
Scavenging activities on DPPH\(\bullet\), ABTS\({}^{+}\bullet\), HO\(\bullet\) and O\({}_{2}^{-}\bullet\) have been widely used to assess the antioxidant capacities of protein hydrolysates and peptides. However, each of these assays only measures an antioxidant property representing a different mechanism, which does not reflect the multiple mechanisms by which samples may act as antioxidants to retard or inhibit lipid oxidation in a food system [42]. Therefore, in this section, the ability of the soluble samples to suppress lipid peroxidation in a linoleic acid model system was investigated. Lipid peroxidation is a complex process that involves the formation and propagation of lipid radicals and lipid hydroperoxides, which are formed as the primary oxidation products in the presence of oxygen [43]. The inhibitory activities of SCPE-A, SCPE-B and SCPE-C were measured by lipid peroxidation in an in vitro model and werecompared with the commercially available antioxidant BHT for 7 days. Figure 7 shows that SCPE-A had a similar inhibitory effect on lipid peroxidation as BHT and significantly retarded the lipid peroxidation compared with the control (without sample), SCPE-B and SCPE-C. In previous research, SCPE-A showed excellent scavenging activity on DPPH\(\bullet\), HO\(\bullet\), ABTS\({}^{+}\bullet\) and O\({}_{2}^{-}\bullet\), with EC\({}_{50}\) values of 2.43, 0.28, 0.24, and 0.08 mg/mL, respectively. Therefore, the inhibition effect of lipid peroxidation caused by SCPE-A could be attributed to its radical-scavenging activity. In addition, SCPE-A may have potential applications in the food industry for retarding the production of unwanted off-flavors and toxic products.
# Health Symptom Checking System for Elderly People Using Fuzzy Analytic Hierarchy Process
## 2 MCDA Fuzzy AHP  
Healthcare problems and medical decision-making are unavoidable, and present challenges confronted in everyday life. In the healthcare system, using a structured assessment approach for decision-making and determining the complex problems with a feasible solution can have a positive impact on risk analysis. With these prevailing problems, MCDA strategies can help decision makers to proficiently deal with conflict states. The MCDA is a prominent method for decision-making, which analyzes and resolves multifaceted complications by evaluating the best sensible elucidation from assorted conflicting goals [2; 3; 4] to yield an optimized hassle-free healthcare system. This methodology is widely used in diverse application disciplines viz. information systems, telemedicine, internet search engines, hotels, food service, engineering education, etc. by consolidating the multidimensional difficulties [5; 6]. In this study, we used the fuzzy AHP technique, which was embedded with a fuzzy theory for AHP. AHP is a hierarchical technique developed by [PERSON] [7], which is widely used for finding the most appropriate alternative that satisfies the complete set of objectives in multi-attribute decision-making problems.  
In order to systematize complex problems in an unknown situation, AHP was carried out in a pairwise comparison matrix of diverse alternatives with respect to different criteria using an absolute scale in a hierarchical structure and facilitated a qualitative decision support tool for multidimensional complications [8]. Despite its popularity, AHP does not include vagueness to effectively deal with the uncertainty and imprecision dependency of the decision maker's perception of the crisp values [9]. Such inconveniences have been addressed by using a fuzzy logic approach. In the fuzzy AHP, the evaluation criteria and alternatives are compared on a pairwise basis, and through the linguistic variables, which are represented by triangular fuzzy numbers (TFNs), to solve the ambiguity involving in ranking and prioritizing the alternatives in healthcare decision-making.
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 4 Experimental Configuration
### Directional Antenna  
In our experiment, the IEEE 802.11 ad module was equipped with four end-fire antenna arrays (2 \(\times\) 2 MIMO, two antenna arrays were used for reception and the other two antennas were used for transmission), which consisted of multiple short antenna arrays (an array is illustrated at the front-end, in the direction opposite to the USB host interface shown in Figure 3). In the end-fire antenna array, each antenna for the transmission or reception had different phases by 180 degree. There distance between the two antennas could be changed instead of the dynamic phase configuration performed in a phase array antenna. The Tensorcom 802.11 ad module forms the beams toward the two different bore sights, as shown in Figure 2, for the transmission and reception using the two end-fire antenna arrays. With this, the module can sweep sectors to align the beam directions between the transmitter and receiver as described in the IEEE 802.11 ad standard; accordingly, the module enables the configuration of beams dynamically according to vehicle positions on the roads.
## 3 Results and Discussion
### Modeling of Bentonite Evolution Experiment  
As model bentonite, we used bentonite from the 10 th Khutor deposit described by the structural formula (NaCaMg) (Al\({}_{1.46}\)Fe\({}^{3+}\)\({}_{0.16}\)Fe\({}^{2+}\)\({}_{0.04}\)Mg\({}_{0.34}\))[Si\({}_{3.83}\)Al\({}_{0.17}\)] (OH)\({}_{2}\)[57]. It was assumed in the model that the composition of the pore water, initially in equilibrium with the bentonite was a pH of 8.5, Al 3.17 \(\times\) 10\({}^{-5}\) mol/L, C 3.93 \(\times\) 10\({}^{-3}\) mol/L, Ca 5.01 \(\times\) 10\({}^{-4}\) mol/L, Cl 5.64 \(\times\) 10\({}^{-4}\) mol/L, Mg 3.42 \(\times\) 10\({}^{-4}\) mol/L, Na 1.74 \(\times\) 10\({}^{-3}\) mol/L, S 3.12 \(\times\) 10\({}^{-4}\) mol/L, Si 4.99 \(\times\) 10\({}^{-5}\) mol/L, K 10 \(\times\) 10\({}^{-4}\) mol/L, and Fe 1 \(\times\) 10\({}^{-5}\) mol/L. In the model, ion exchange was also taken into account (CEC = 10 2 meq/100 g). The results of modeling the transformation of smectite from the 10 th Khutor deposit, as well as a comparison with the experimental data, are shown in Figure 10a. The parametrization of the model is given in Table 8, according to [54]. The simulation was carried out in the code PhreeqC 2.18 [64]; the database used was PK PhreeqC Ilnl.dat. Figure 8: Results of the fitting of the Mössbauer spectra of initial (**a**), 1 month\(-\)treated (**b**), and 3 month\(-\)treated (**c**) bentonite samples. Doublets from Fe\({}^{3+}\) are represented by a green line, while from Fe\({}^{2+}\) by a red line. Figure 9: Graphs of thermal analysis of bentonite: (**a**) TG; (**b**) DTG, (**c**) DTA. The results show that the studied bentonite has high stability; the calculated values of the solubility constants in the dissolution equation (see calibrated values) are significantly lower than the literature values [54], including according to the dissolution model [74; 75]. A forecast of the evolution of bentonite as function of chemical composition was also carried out with the obtained parametrization of the model for 450 days. The results are shown in Figure 10b. A relatively small transformation of smectite, a decrease in the content of quartz, and an increase in the content of microcline are clearly visible. The content of albite initially dropped sharply, then, at the end of the first week, an increase in its content in the system was observed. Kaolinite almost completely transformed into other clay materials in the models. A feature of the model behavior of illite was a certain drop in its content, which was replaced by an increase in its amount in the system--a pattern of illitization began to be observed. The results obtained indicate the high stability of the studied bentonite from the 10 th Khutor deposit, which can be recommended for use as a material for engineering safety barriers. Figure 10: Modeling results: (**a**) Comparison of model and experimental data of clay phases evolution. Lines—modeling; squares—experimental data. (**b**) Model of the evolution of bentonite from the 10 th Khutor deposit in this system for 450 days.
# Energy Trading Strategy for Heat and Electricity-Coupled Microgrid Based on Cooperative Game
## 2 System Framework  
As shown in Figure 1, the involved parties of CHP-MG energy management are a single MGO and multiple users. The MGO is the electrical and thermal energy provider of MG, which has an independent CHP system and a PV generation system. The CHP system consists of a micro-turbine (MT), a back-up boiler and waste heat recovery system, and a heat supply system. The MT is driven by natural gas, which can directly provide power for users. The waste heat generated by an MT can provide heat for users through a waste heat recovery system and heat energy supply system. If the waste heat produced by an MT does not satisfy the thermal demand of users, the back-up boiler will be used to fill gaps in the heat supply. Similarly, if the electricity generated by the CHP and PV system cannot meet the electrical demand of users, the MGO can balance the redundant or inadequate electricity by trading with the utility grid.  
As an energy consumer of MG, each user has a certain amount of thermal and electrical load. The electric load demand considered in this paper mainly consists of fixed electric load and shiftable electric load. Fixed electric load is used to guarantee the basic power demand of energy consumers, such as lighting, refrigerators, elevators, etc. Shiftable electric load is the load that consumers can regulate the operation time according to the dynamic electricity price and energy consumption benefits, such as air conditioning, dryers, electric vehicles, etc. The thermal demand considered in this paper mainly refers to the hot water and heating supply in winter, which is necessary to ensure the environmentally comfortable users' temperature. Besides, users can reduce part of the thermal demand according to their adaptation to the indoor environment.  
The energy transactions between the MGO and users are achieved by bargaining with each other. To maximize the operation benefit of the MGO, the supply of electric energy and heat energy will be determined according to the bargain prices. In addition, each user can bargain independently with the MGO and determine the energy consumption by shifting electric demand and reducing thermal demand.
## 3 Example Analysis
### Experimental Analysis and Verification
#### 3.3.3 Daily Load Forecasting Results from Visualization and Comparison Experiment
A calculation of the average absolute percentage error of the BLA prediction model and the prediction data of the original Bi-LSTM was performed, and the results are shown in Figure 9.  
It can be seen that the Bi-LSTM errors are larger than the BLA residential electricity prediction model proposed in this topic. To further verify the complexity of the method in this paper, we calculated the parameter amount and the floating-point operations per second (FLOPS) of the BLA model in this paper. After calculation, the number of parameters of the BLA model is 88,148. The model proposed in this paper is mainly composed of Bi-LSTM and self-attention. The input vector dimension of the Bi-LSTM is 7 \(\times\) 48, the hidden state dimension is 50, and the FLOPS is 80 K. The FLOPS of self-attention is 9.8 k. The remaining fully connected layer FLOPS is 44.2. So, the FLOPS of BLA is only 134. Since the model was very fast to train, we trained for 226 epochs in just 427 s on a single V100 GPU. To further verify the effect of the BLA residential electricity consumption prediction model proposed in this paper, a horizontal comparison was performed. We chose the GRU, RNN, and LSTM algorithms commonly used in time series prediction for comparison. By introducing a self-attention mechanism, the BLA model we proposed improves the problem that the above algorithms have for the same time series information weight. The prediction results of the BLA, RNN, LSTM, and GRU models were compared and analyzed through experiments to determine the relative performance of the model proposed in this paper. In the second comparison experiment, to compare the superiority of the method in this paper, we conducted experiments on the same data set and compared the GRU [41], RNN [42], LSTM [43], and DB-LSTM [44] methods. Among them, the DB-LSTM method means that DBSCAN is used for clustering first, and then LSTM is used for prediction. The results are shown in Figure 10.  
By observing Figure 10, it is not difficult to find that the model in this paper is more accurate than the other four models when clustering is the second class. The MAPE is 3.70%, 1.17%, 4.99%, and 1.62% lower than that of the GRU, RNN, LSTM, and DB-LSTM methods, and the prediction error is significantly reduced. There are two main reasons for this. First, our proposed model is based on a bidirectional recurrent neural network, which is highly robust for modeling time series data. Second, we introduce an attention mechanism on top of this. It enables the model to assign different weights to temporal features and extrinsic features simultaneously, thereby highlighting the key features of the  
Figure 9: Comparison of MAPE between the Bi-LSTM model and the BLA model under different cluster numbers. input data that play a key role in the residents' load forecasting process, which helps to improve the accuracy of short-term load forecasting.
# Micro-Slit Cutting in an Aluminum Foil Using an Un-Traveling Tungsten Wire
## 5 Conclusions  
In this study, a novel cutting method for aluminum foil is proposed where an un-traveling tungsten micro-wire is utilized as the tool. It only needs one feeding motion without tool traveling or spinning. According to simulation and experiments, conclusions could be drawn:  
For an aluminum foil with a certain thickness, a tungsten wire with a moderate diameter can fulfill the cutting process. The wire first feeds at a low velocity to contact the foil edge and generate enough tension. Due to the sensitive plasticity of aluminum, plastic strain in the form of a breach will occur.  
For a single slit, the width machined under initial velocity shows no difference from the width machined under secondary velocity, which is a little smaller than tool diameter. Among all slits, the width standard deviation is less than 3 \(\upmu\)m with an average width of around 47 \(\upmu\)m, which shows great width consistency.  
The roughness of the slit side wall is mainly determined by secondary velocity. The roughness increases fast with secondary velocity and a smoother surface can be obtained when the initial velocity is close to the secondary velocity. The best surface roughness is 0.110 \(\upmu\)m.  
Conceptualization, J.Z. and X.F.; methodology, X.F. and N.Q.; software, J.Z.; validation, J.Z.; investigation, J.Z.; resources, N.Q.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, X.F. and N.Q.; visualization, J.Z.; supervision, N.Q.; project administration, N.Q.; funding acquisition, N.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 51675274.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Modeling of the Total Variation (TV)-Based Noise Reduction Algorithm  
A TV-based noise reduction algorithm that can reduce different types of noise in medical images while preserving edge information was designed.  
The general equation for the image degradation process with noise components can be expressed as:  
\[\text{A}(x,\ y)=h(x,y)**PSF(x,y)+N(x,y)\ (**:2D\text{ {convolution}}) \tag{1}\]  
were, A(\(x\), \(y\)) is the degraded image; \(h(x,y)\) is the original image; \(PSF(x,y)\) is the shift-invariant point spread function; and \(N(x,y)\) is the noise. A regularization function is generally used to solve the unknown term \(h(x,y)\). Based on the image degradation process, the proposed algorithm can be expressed as [11]:  
\[V_{TV}[u_{s}]=\int_{\Omega}|\
abla u_{s}|dxdy\ (\
abla u_{s}=\sqrt{{u_{s_{x}}}^{2}+ {u_{s_{y}}}^{2}}) \tag{2}\]  
where, \(V_{TV}\) is the total variation; \(\
abla u_{s}\) is the gradient; \(u_{s_{x}}\) and \(u_{s_{y}}\) are \(\frac{\partial_{u_{s}}}{\partial_{x}}\) and \(\frac{\partial_{u_{s}}}{\partial_{y}}\), respectively.  
To compare the image performances of different noise reduction algorithms, conventional methods incorporating the median and Wiener filters were designed.  
Figure 1: Schematic diagram illustrating the sample preparation for confocal laser scanning microscopy (CLSM) imaging.
## 6 Results
### Results from the Numerical Simulations
#### 6.2.1 Further Analysis of the Experimental Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sensor Setup \#1**} & \multicolumn{2}{c}{**Sensor Setup \#2**} & \multicolumn{2}{c}{**Sensor Setup \#3**} \\ \cline{2-7}  & **Cases 1–9** & **Cases 10–17** & **Cases 1–9** & **Cases 10–17** & **Cases 1–9** & **Cases 10–17** \\ \cline{2-7}  & **Type 1** & **Type 2** & **Type 1** & **Type 2** & **Type 1** & **Type 2** \\  & **Errors [\%]** & **Errors [\%]** & **Errors [\%]** & **Errors [\%]** & **Errors [\%]** & **Errors [\%]** \\ \hline MFCCs & 4.44 & 0.00 & 2.22 & 1.25 & 2.22 & 0.00 \\ \(f_{\text{cut-off}}=\frac{1}{4}\ f_{\text{s}}\) & 3.33 & 0.00 & 1.11 & 0.00 & 1.11 & 0.00 \\ \(f_{\text{cut-off}}=\frac{1}{8}\ f_{\text{s}}\) & 5.56 & 0.00 & 3.33 & 1.25 & 3.33 & 0.00 \\ Log-Scale & 5.56 & 1.25 & 3.33 & 2.50 & 1.11 & 1.25 \\ Linear & 3.33 & 0.00 & 1.11 & 1.25 & 1.11 & 0.00 \\ ECO-MMFCCs & 3.33 & 0.00 & 1.11 & 0.00 & 1.11 & 0.00 \\ MO-MMFCCs & 2.22 & 25.00 & 3.33 & 30.00 & 1.11 & 27.50 \\ LO-MMFCCs & 10.00 & 25.00 & 0.00 & 23.75 & 2.22 & 25.00 \\ BFCCs & 1.11 & 0.00 & 0.00 & 2.50 & 1.11 & 0.00 \\ GTCCs & 2.22 & 0.00 & 2.22 & 1.25 & 2.22 & 1.25 \\ TECCs & 0.00 & 7.50 & 0.00 & 11.25 & 0.00 & 6.25 \\ AR coefficients & 13.33 & 0.00 & 12.22 & 0.00 & 6.67 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results according to the sensor setup considered.
## 3 Network Model
### Transmission Model
#### 3.2.1 Irregular Radio Model  
The sensor transmission is usually modeled as a disk centered at the node with its radius being its communication range, as shown in Figure 3a. However, the reception signal power attenuation relies on \(R^{-\alpha}\), where \(R\) is the transmission range and \(\alpha\) is the loss constant that depends on the wireless medium with typical values between two and four, and it can vary from device to device [34; 35]. In fact, it has been found that the radio communication range is highly probabilistic and irregular [36; 37], especially in the indoor environment [38], like Figure 3b. The works in [37; 39] make it evident that the radio is irregular, even time-varying while stationary, in the MICA2 sensor platform. Most simulators do not have irregular radio models, because of the complexity of analyzing irregular radio models. Our previous study [40] proposes a probabilistic approach to describe the connection between nodes, since the radio model is irregular. More specifically, the network connection probability is modeled as a Cox process, and the numeric simulations are performed to evaluate the network connection probability.  
It is worth noticing that [34] states that the WSNs cannot simply be modeled as a geometric graph where each node connects other nodes within its communication range. For wireless networks, only the maximum outgoing edge contributes to the energy consumption, but other nodes with a shorter communication range can be connected more or less for free, which makes studying energy-related issues in WSNs quite different. A wired network is a link-based or edge-based metric, while a wireless network is a node-based metric [34].  
Figure 3: (**a**) Regular radio model; (**b**) Irregular radio model.