esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware Countermeasures Benchmarking against Fault Attacks
## 4 Countermeasures against DFA
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type of** **Countermeasure**} & **Scheme** & **Technology** & **Area** & **Frequency** & **Throughput** & **Fault Coverage** \\  & & & **Overhead** & **Degradation** & **Degradation** & **Corrected (\%)** \\ \hline None & Original & - & 1 & 1 & 1 & 0 \\ \hline HW red. & [35] & Virtex 5 & 1.8655 & 1 & 1 & 100 \\  & [36] & 0.35 μm & 1.3573 & 0.6364 & 0.6363 & 98.22 \\  & [37] & 0.13 μm & 1.36 & NA & NA & 24.96 \\  & [38].1 & Virtex 4 & 1.159 & 0.966 & 0.4838 & 99.99 \\ Temp. red. & [38].2 & Virtex 4 & 1.131 & 0.904 & 0.4546 & 99.99 \\  & [39].1 & Virtex 4 & 1.043 & 0.85 & 0.85 & 12.5 \\  & [39].2 & Virtex 4 & 1.0758 & 0.8342 & 0.8342 & 12.5 \\ \hline  & [40] & Virtex 5 & 1.1566 & 0.739 & 0.7389 & 80.47 \\  & [41] & Virtex 1000 & 1.0781 & 0.7088 & 0.7088 & 18.9 \\  & [42] & NA 1 & 1.731 & 0.6451 & 0.6449 & 33 \\  & [43] & Virtex 4 & 1.167 & 0.886 & 0.4649 & 77.48 \\  & [44].1 & Virtex 5 & 1.2538 & 0.8847 & 0.8848 & 98.22 \\  & [44].2 & Virtex 5 & 1.2039 & 0.868 & 0.8638 & 91.14 \\  & [45] & Spartan 6 & 1.16 & 1 & 1 & 100 \\  & [46] & NA & 1.44 & NA & NA & 37.17 \\ Info. red. & [47] & NA & 1.4 & NA & NA & 48.5 \\  & [48] & NA & 1.35 & NA & NA & 90 \\  & [49] & XCV1000E & 1.77 & 0.8653 & 0.87 & 100 \\  & [50].1 & Virtex II PRO & 1.3145 & 0.9778 & 0.9778 & 60.63 \\  & [50].2 & Virtex II PRO & 1.2845 & 0.9354 & 0.9354 & 60.63 \\  & [50].3 & Virtex II PRO & 1.2406 & 0.9654 & 0.9654 & 60.63 \\  & [51].1 & Spartan 3 & 1.8102 & 1.0016 & 1.0016 & 58.59 \\  & [51].2 & Spartan 3 & 1.5844 & 0.8308 & 0.8308 & 58.59 \\  & [52] & 0.18 μm & 1.18 & 0.65 & 0.74 & 24.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Implementation cost, performance and fault coverage corrected of each scheme.
# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 2 Materials and Methods
### Data  
Data captured by HJ-1A and Sentinel-2 were used for tree species classification. The HSI data of HJ-1A and the MSI data of Sentinel-2A were obtained from the official websites of the China Center for Resources Satellite Data and Application and the United States Geological Survey (USGS), respectively, which is presented in Figure 1.  
The HJ-1A satellite was equipped with an HS imaging instrument with 115 bands. The images have a 100 m spatial resolution. The Sentinel-2A data has 13 spectral bands [34]. It provides abundant data information for the field of land and coastal remote sensing [35]. To supply the gap of the relatively LR of the HSI, we used the bilinear interpolation method to improve the resolution of the HJ-1A/HSI image (both datasets were taken on 20 August 2016) using ENVI 5.1 software to increase the spatial resolution of the HJ-1A/HSI image to 10 m. The experimental HSI data were resampled by the interpolation algorithm so that it had the same 10 \(\times\) 10 m\({}^{2}\) spatial resolution as the MSI on the same ground. The classification of dominant forest species was performed in the study area from second-class data surveyed by the Tahe Forestry Bureau in 2018.  
Because of the large area of the forest, the authors attempted to select the area with the most species as the research object, and the size of the three study areas was 500 \(\times\) 500 \(\times\) 115 pixels and 500 \(\times\) 500 \(\times\) 13 pixels for the HSI and MSI data, respectively. The dominant tree species were birch, larch, spruce, nongolica, willow, and poplar. Table 1 lists the tree species of the present article's three study areas, where the training samples approximately accounted for a third of the total samples.  
Figure 1: Map of the study area.
# AVSS: Airborne Video Surveillance System
## 6 Evaluation
### Simulations
#### 6.1.1 Number of UAVs  
Figure 6 a,b are the presentation of placement scores. Figure 6a shows the global score increments of each placement, thus local score. In all graphs, \(x\) axis is the number of UAVs that have been placed, and \(y\) axis is the score. Focusing area scenario is shown in Figure 3b, which has 7 focusing areas. It can be observed that the local score drops significantly at 8 th UAV, which shows that the efficiency of placement more than the number of focus area is considerably less efficient. Our algorithm does not include any external measures to limit the number of UAVs even when more is available, but other metrics such as the energy or the noise can be factored in to find the optimal number of UAVs to be placed. Considering only surveillance quality, more UAVs are always better.
# Demand-Response Control in Smart Grids
## 6 Theoretical and Experimental Comparison between Vickrey and Dutch Auctions
### Theoretical Comparison between Vickrey and Dutch Auctions  
We summarize the comparison between Vickrey and Dutch auctions in Table 5. This comparison includes the number of messages sent, by whom the price was determined, bidder collusion, vulnerability to a lying auctioneer, and undesirable private information revelation, based on [35].  
The Dutch auction has a relatively high communication cost in comparison with the Vickrey auction. This is because the auctioneer uses an outcry communication strategy that is necessary to broadcast B decreasing prices to all engaged bidders Cn, which could result in many messages. Each Deutch auction requires a total of messages equal to  
\[\mathrm{N_{Msg}}=\mathrm{Cn}(\mathrm{B}+1)+1 \tag{17}\]  
where  
\(\mathrm{N_{Msg}}\) is the number of messages sent per auction,  
Cn is the number of participating consumers, and  
B is the total number of bids sent by the auctioneer to all consumers.  
In theVickrey auction, publishing the current highest bid to other participants is not required because bidders are limited to a single bid and the bids are sealed. The total number of messages required per Vickrey auction is equal to  
\[\mathrm{N_{Msg}}=2\mathrm{Cn}+1 \tag{18}\]  
The second criterion is whether the agent determines the price by itself or from others. In Vickrey auctions, the agent's bid determines whether or not he wins the auction, but not the price. However, in Dutch auctions, the winning agent determines the price. In certain circumstances, this ensures trustful bidding and avoids counter speculation.  
Although the Vickrey auction presents some advantages in comparison with Dutch auction, such as a lower communication weight, it suffers from some drawbacks such as bidder collusion, vulnerability to lying auctioners, and the necessity to reveal sensitive information, as summarized in Table 5.  
The solution to bidder collusion vulnerability is to identify the coalition parties.  
To address lying auctioners, we need to add trusted third-party auction servers that reveal the highest bid to the seller after closing. The proposed mechanisms for avoiding lying by the auctioneer include cryptographic signatures on bids and the use of trusted third-party auction servers.  
Figure 5: A class diagram of the auction framework.
## 1 Introduction
### Justification and Objectives  
As mentioned earlier, Morocco is currently a key country for solar development, and as a consequence some studies dealing with irradiation prediction and characterization focused on that country can be found in the literature. For instance, [PERSON] et al. [55] carried out an assessment on the different solar irradiation prediction models for the northern Moroccan city of Tetouan. In reference [56], two empirical models were analyzed for 24 different cities across Morocco. [PERSON] et al. [42] conducted a study on the variability of solar irradiation data on different locations in the country. Proving the above-mentioned trend of employing satellite data for the solar resource validation, [PERSON] et al. [43] compared satellite and ground measured data for northern African countries, including Morocco. Likewise, references [31; 32; 33; 34; 35] have also been focused on the irradiation forecasting in several cities of Morocco, employing different methodologies. Given the high potential for solar energy development in the MENA region and, particularly in Morocco, this paper will focus on this country to ease the deployment of solar energy projects. As concluded in the above paragraphs, the prediction and characterization of solar irradiation using satellite data provides accurate and satisfactory results. Moreover, as highlighted in Section 1.1, state of the art, the prediction and characterization of solar irradiation rely mostly on either the use of complex models (i.e., Collares-Pereira) or on complex mathematical techniques (such as ANN-based algorithms). This mathematical complexity might hamper their use by businesses and project developers when assessing the solar resource. In this regard, the first objective of the paper is to introduce a comprehensive and simple methodology to assess the solar resource for a project, based on the determination of its probability distribution function (PDF) for a specific location, assuming that the knowledge of statistical techniques may be more widely extended than other more complex mathematical techniques. A second objective of the paper is to illustrate the selection of the best satellite database available for a determined location, focusing on the case of several cities across Morocco. To the best of the authors' knowledge, no studies have been found where a new database for solar irradiation values that depends on historical data is provided for Morocco. Thus, it is considered to be the first study covering this gap in the scientific literature. This
## 1 Introduction
[23] established a 3D model for pipelines and foundation pits, and studied the pattern of pipeline deformations at different foundation pit excavation parameters. [PERSON] et al. [24] analyzed the influence of foundation pit excavation on the deformation and internal force of adjacent subway tunnels through finite element simulation based on engineering examples, and optimized the foundation pit structure accordingly. In summary, many achievements have been made in the research on the deformation of adjacent existing pipelines and soil caused by foundation pit and tunnel excavation, which provides theoretical support for solving practical engineering problems. However, most existing research and simulations were based on the assumption that the pipeline is in full contact with soil during deformation [25; 26; 27]. In fact, buried pipelines interact with the surrounding soil. They cannot be in full contact with the soil, especially in soft soil areas, where the soil is easily deformed in a manner that makes pipelines difficult to achieve complete contact with the soil. Therefore, there is a need to further examine how the excavation of deep foundation pits in soft soil affects nearby pipelines. To this end, this paper investigates the impact of foundation pit excavation on nearby aviation oil pipelines in the aviation oil pipeline construction project for Xinxingcun Station of Tianjin Metro Line 4 in China. With the help of finite element software Midas, the paper simulates layered excavation and bench excavation, and carries out a comparative analysis of how the two construction methods influence the vertical settlement of adjacent pipelines and the deformation of nearby soil. On that basis, it discusses the non-coordinated deformation mechanism for the pipelines and soil in the excavation process of the deep foundation pits for the stations, and clarifies the pattern of changes in the pipeline-soil gaps. The patterns of the deformation of the soil and pipelines around the excavated foundation pit are observed in the field and verified by comparing the numerical simulation results with the actual results. Lastly, the influence law of the changes in pipeline foundation and underlying soil layer on pipeline settlement caused by two excavation methods is discussed.
## 2 Materials and Methods
### Analysis of PAHs  
The contents of PAHs were determined using a method established in the total diet study of The Korean Ministry of Food and Drug Safety (KMFDS) with minor modifications (KMFDS, 2019). Ten grams of the WOF samples were weighed in a 250 mL glass bottle. As internal standard compounds (ISTD), a mixture of CHR-d12 and B[a]P-d12 were prepared at 10.0 \(\upmu\)g/mL (_w/v_) in methylene chloride and 10 \(\upmu\)L of the internal standards mixture was spiked into the sample. The analysis of PAHs was carried out using a 7890B GC with a 5977A MS (Agilent Technologies). Separations were performed on the fused silica capillary column of DB-5 ms (30 m length \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness, J&W Scientific). Helium was used as the carrier gas at a constant flow rate of 1 mL/min. The initial GC oven temperature was maintained at 80 \({}^{\circ}\)C for 1 min, raised to 245 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min and increased to 270 \({}^{\circ}\)C at 30 \({}^{\circ}\)C/min and held for 15 min. The post-run temperature was kept at 270 \({}^{\circ}\)C for 10 min. The injector and transfer line were heated at 300 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively, and the injection volume was 1.0 \(\upmu\)L with splitless injection mode. The EI mode was applied at 70 eV. The ions selected for the qualification and quantitation of 8 PAHs and 2 ISTDs were as follows: B[a]A (\(m/z\) 228 and \(m/z\) 226, 229), CHR (\(m/z\) 240 and \(m/z\) 236, 241), B[b]F (\(m/z\) 252 and \(m/z\) 250, 253), B[b]K (\(m/z\) 252 and \(m/z\) 250, 253), B[a]P (\(m/z\) 252 and \(m/z\) 250, 253), I[c,d]P (\(m/z\) 276 and \(m/z\) 277, 274), D[a,h]A (\(m/z\) 278 and \(m/z\) 279, 276), B[g,h,i]P (\(m/z\) 276 and \(m/z\) 277, 274), CHR-d12 (\(m/z\) 240 and \(m/z\) 236, 241), and B[a]P-d12 (\(m/z\) 264 and \(m/z\) 263, 265). The amounts of PAHs were determined by comparing their peak areas to those of the ISTDs, CHR-d12, and B[a]P-d12.
## 3 Results and Discussion
### Dissolved Phase
#### 3.2.2 Metal(loid)s in the Benthic Chamber  
The two sites experimented different behavior, especially in terms of dissolved oxygen (O\({}_{2}\)) concentrations and Eh values in the benthic chamber. Site VN1 showed O\({}_{2}\) levels (1.97-3.33 mg/L) and positive Eh values (8.7-87 mV). Conversely, VN3 was characterized by anoxia (O\({}_{2}\) < 0.1 mg/L) and reducing conditions, where Eh values were clearly negative, ranging between \(-\)356 and \(-\)394 mV. This wide dissimilarity could be attributed to the location of the sampling sites in the fish farm in terms of hydrodynamics, since the water circulation is driven by the wind and water exchange with the open lagoon only occurs through the sludge gates [12].  
The highest metal(loid) concentrations during the benthic chamber experiments were found at site VN1, excluding As, Cu, Hg and Mn which were higher at VN3 (Figure 5). Excluding As, Mn, Pb and V, which remained constant during the experiment at VN1, the maximum values generally occurred at T\({}_{1}\) (2 h) and T\({}_{2}\) (4 h) samplings and then decreased until T\({}_{4}\) (8 h). Only Zn showed an increase until the end of the experiment. These trends may be justified by the opposite behavior of dissolved O\({}_{2}\) and Eh (Figure 5), since the precipitation of metal(loid)s in oxidized form, likely associated with Fe oxides, would have occurred. The increase of O\({}_{2}\) at VN1 suggests that the dissolved O\({}_{2}\) production by photosynthesis prevailed over the microbial respiration. On the contrary at VN3, where nearly all metal(loid)s remained rather constant, Cu slightly varied over time and Pb and Zn showed a maximum at T\({}_{1}\) (2 h) and T\({}_{3}\) (6 h), respectively (Figure 5). This can be a consequence of the permanent reducing conditions that characterised this site during the experiment. Indeed, the constant anoxia has likely prevented the alternation of the precipitation and dissolution processes, which usually influence the physicochemical form of the metal(loid)s, favoring the presence of the dissolved form, and, thus, eliminating the variability that characterised the behavior of metal(loid)s.
## 4 Analyzing Experimental Results
### Comparison Experiments  
For the sake of highlighting the advantages of our proposed model, we had selected some current mainstream target detection methods for comparison experiments, including YOLOv3, YOLOv4-Tiny, and YOLOv7-Tiny. In Figure 8, we compared the four evaluation metrics of these several models.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **P** & **R** & **mAP** & **Para.** & **Gflops** \\
**Yolov5s** & **0.851** & **0.833** & **0.867** & **7,012,822** & **15.8** \\ \hline +BoT & **0.912** & **0.929** & **0.949** & 6,685,910 & 15.4 \\ +DW+BoT & 0.891 & 0.902 & 0.928 & 5,267,826 & **13.6** \\ +DW+BoT+SimAM & 0.895 & 0.902 & 0.932 & 5,267,826 & 13.8 \\ +DW+BoT+SimAM+DIoU & 0.893 & 0.876 & 0.93 & 5,267,826 & 13.8 \\ +DW+BoT+SimAM+SIoU & 0.896 & 0.914 & 0.947 & **5,267,826** & 13.8 \\ \hline \hline \end{tabular}  
* P stands for precision, R for recall, and Para. for the number of model parameters.  
\end{table}
Table 3: Comparison of ablation experiment results.  
In this study, to make our proposed model more convincing, the comparison experimental environments we performed in were consistent and had the same parameter configurations. From Figure 8, we can conclude that our proposed algorithm model outperformed the existent popular one-stage detection algorithms mentioned in this paper. In respect to crack detection accuracy P, R, and mAP, it also had the least number of parameters.
## 2 Overview of EV and PV Systems
### Electric Vehicles
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Level** & \begin{tabular}{c} **Supplied Voltage** \\ **Range (V)** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Current (A)** \\ \end{tabular} & \begin{tabular}{c} **Output Power** \\ **Level (kW)** \\ \end{tabular} & \begin{tabular}{c} **Estimated Charge** \\ **Time (hours)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} AC charging \\ (on-board chargers) \\ \end{tabular} } & Level 1 & \begin{tabular}{c} 120 Vac \\ (1-phase) \\ \end{tabular} & up to 16 A & up to 1.92 kW & 7–17 h \\  & Level 2 & \begin{tabular}{c} 208–240 Vac \\ (1-phase) \\ \end{tabular} & up to 80 A & up to 19.2 kW & 0.4–7 h \\  & Level 3 & \begin{tabular}{c} 208–240 Vac \\ (1 and 3-phase) \\ \end{tabular} & up to 400 A & up to 96 kW & Less than 0.5 h \\ \hline \multirow{3}{*}{\begin{tabular}{c} DC charging \\ (off-board chargers) \\ \end{tabular} } & Level 1 & \begin{tabular}{c} 200–450 VDC \\ 2 \\ \end{tabular} & up to 80 A & up to 36 kW & 0.4–1.2 h \\  & Level 2 & \
\begin{tabular}{c} 200–450 VDC \\ 3 \\ \end{tabular} & up to 200 A & up to 90 kW & 0.2–0.4 h \\ \cline{1-1}  & Level 3 &
\begin{tabular}{c} 200–600 VDC \\ \end{tabular} & up to 400 A & up to 240 kW & 0.1–0.2 h \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SAE J1772 US Standard for EV/PHEV charging power levels ([PERSON], 2010; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018)The DC charging systems are installed at fixed locations and are built with dedicated wiring [22]. In addition, this charging systems are known as fast charging systems since they can handle higher power levels, charging the electric vehicles in less time compared with the level 1 AC chargers. Although each EV battery pack requires an specific power level supply, modern DC charging stations have implemented a system that identifies the voltage level of the battery pack and adjusts to that [22]. Depending on the flow of the power allowed by the charger, two types of charging systems can be found--unidirectional or bidirectional [27]. When the power only flows in one direction from the grid/PV into the battery, it is called unidirectional. On the other hand, when the power also flows from the battery to the grid, it is called bidirectional, under the concept of V2G, as mentioned in Section 1[15; 27].
## 4 Systematic Performances Analysis of UWSNs
### Systematic Quar-Performance Calculation Model of UWSN
#### 4.2.3 Measurement Formula of Durability  
Durability is denoted as \(\Psi\), which is determined by the lifetime of the nodes in an UWSN. The lifetime of the network is the interval from the moment that the network starts working to the moment that \(m\) nodes shut down due to energy exhaustion, which means that the lifetime of the network is determined by the first \(m\) nodes whose energy is exhausted. The \(m\) is a parameter that is determined by network redundancy. The lifetime of a single node is determined by the total energy and working energy consumption speed. Furthermore, on its cruising path, an UUV can substitute for some nearby sensor nodes to carry on the data transmission function, therefore, the lifetime of those sensor nodes will be extended. The durability of the network can be measured by Equation (9):  
\[\Psi=\alpha\cdot[\text{Min}_{m}\bigg{(}\frac{e_{b}}{p_{b}}\bigg{)}+\beta\cdot \frac{e_{u}}{p_{u}}] \tag{9}\]  
In the equation, \(\text{Min}_{m}\) returns the \(m\)-smallest element in the set; \(\beta>0\) represents the UUV's contribution to the lifetime of nodes, which is associated with the cruising path of the UUV. \(\alpha\) is the capability adjustment coefficient. \(p_{u}\) is the UUV's energy consumption, including primarily the moving energy consumption, communication energy consumption and detection energy consumption, among which the moving energy consumption is the main part; \(p_{b}\) is the energy consumption of the sensor nodes, including primarily the communication energy consumption and detection energy consumption. The communication energy consumption has a direct proportional relationship to the cube of the communication radius; the detection energy consumption has a direct proportional relationship to the cube of the detection radius, shown by Equation (10):  
\[p_{b}=\alpha\cdot r_{b}^{3}+\beta\cdot d_{b}^{3} \tag{10}\]  
In the equation, \(\alpha\), \(\beta\) are coefficients, and \(\alpha\) is associated with the communication rate of the current nodes. The energy consumption of the radio communication process is not considered in Equation (10), mainly because that the energy consumption of the radio communication is minimal compared with that of the acoustic communication, and the data communication by the electromagnetic waves is also relatively less.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
###### Abstract  
Given the growing number of devices and their need for internet access, researchers are focusing on integrating various network technologies. Concerning indoor wireless services, a promising approach in this regard is to combine light fidelity (LiFi) and wireless fidelity (WiFi) technologies into a hybrid LiFi and WiFi network (HLWNet). Such a network benefits from LiFi's distinct capability for high-speed data transmission and from the wide radio coverage offered by WiFi technologies. In this paper, we describe the framework for the HWLNet architecture, providing an overview of the handover methods used in HLWNets and presenting the basic architecture of hybrid LiFi/WiFi networks, optimization of cell deployment, relevant modulation schemes, illumination constraints, and backhaul device design. The survey also reviews the performance and recent achievements of HLWNets compared to legacy networks with an emphasis on signal to noise and interference ratio (SINR), spectral and power efficiency, and quality of service (QoS). In addition, user behaviour is discussed, considering interference in a LiFi channel is due to user movement, handover frequency, and load balancing. Furthermore, recent advances in indoor positioning and the security of hybrid networks are presented, and finally, directions of the hybrid network's evolution in the foreseeable future are discussed.  
light fidelity (LiFi); wireless fidelity (WiFi); visible light communication (VLC); hybrid network; HWLNet; handover; load balancing 2023