esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 1 Introduction
### Paper Contributions and Structure  
This paper provides a security assessment, recommendations and solutions to design and implement a secure architecture for systems that manages missions with swarms of heterogeneous autonomous or unmanned vehicles in Smart Farming. In the case of heterogeneous swarms, the diverse nature of the hardware implies that the use of the particular security solutions that manufacturers may offer is not sufficient, and it is necessary to design a holistic solution able to protect the global architecture. Moreover, the security solution must not affect the performance of the vehicles, which are capable of autonomous navigation and must transmit their feedback to the control station in real time. The energy consumption of vehicles must also be taken into account, considering the limited autonomy that most autonomous commercial vehicles usually have (e.g., standard battery-powered drones typically have short endurance, 15 to 90 minutes maximum, due to battery weight limitations [9]).  
The architecture and technologies proposed for managing real-time communications among autonomous vehicles is the result of the research being carried out in the project H2020-ECSEL-AFARCLOUD. This architecture and some of the security recommendations have been tested in 11 site scenarios [10].  
The structure of the paper is as follows. Section 1 introduces the challenges of the research. Next, in Section 2, a communication architecture is proposed for the management of missions involving autonomous vehicles. Moreover, the state-of-the-art of security technologies is presented, related to this type of communication architecture. Then, Section 3 explains the results of the research covering all of the security implementations and recommendations. Following, Section 4 discusses the context in which these results are applicable and most valuable. Finally, Section 5 concludes the paper with a detailed summary of the main findings and cybersecurity recommendations.
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms  
### VAD Algorithm  
The drive signal is a control signal from the outside of the system to the ultrasonic transmitter; thus its start time \(t_{s}\) is known in one signal frame. Assuming that the surrounding environment to be detected is limited by the maximum distance, the maximum distance reached by the ultrasonic signal is a fixed value of \(w_{d}\). Assume that the ultrasonic speed is \(v\) and the sampling rate of the system is \(s\). A signal frame \(f\) can be defined as the number of samples from the drive signal to the farthest target and back to the receiver, such that \(f=2w_{d}s/v\). Therefore, when the subsequent algorithm calculates a target position, it only needs to sample \(f\) after \(t_{s}\) as a signal frame.  
Because the beam shape of the ultrasonic sensor used in this system is a spherical waveform, the ultrasonic signal reflected by the test platform and the signal directly transmitted by the transmitter to the receiver will cause a blind area. The most important factors that determine the region of the blind area are the amplitude and the number of cycles of the trigger signal of the transmitter. As shown in Figure 3, signal 1 is the interference signal which results in the blind area, and signal 2 is the reflected signal of the detected object. When the amplitude and number of cycles of the trigger signal increase, the range of signal 1 and the region of the blind area increase. If the object is in the blind area, signal 2 is indistinguishable because it overlaps with signal 1 to form a single signal. If the object is not in the blind area, the waveform of the interference signal is fixed and stable in the same system. Only by avoiding this segment of the signal, the real available reflected signal of the object can be found.
## 3 Data Preprocessing
### Calculation of the Lag Time of Daily Generating Capacity  
In Section 3.2, a precipitation grid data set covering HC City at 34 points and a precipitation grid data set covering GL City at 28 points are generated. The total daily precipitation in the two regions on the day and the previous 15 days (the sum of the daily precipitation values of 34 points) is taken as the input variable set to be selected, and the increment of electricity generation on the day and the previous day is taken as the output variable set to be selected, denoted as \([P_{t}^{s},\ P_{t-1}^{s},\ P_{t-2}^{s},\ P_{t-3}^{s},\ \ldots,\ P_{t-15}^{s}]\) and \([\Delta E_{t}]\), respectively, where \(t\)-\(i\) represents the \(i\)-th day before the date \(t\). Using Algorithm 1 in Section 2.2 to calculate the correlation between the input variable set to be selected and the output variable set to be selected, the change in the _AIC_ value during the calculation process is shown in Figures 5 and 6, respectively. It can be seen from the figure that the AIC value of HC city continues to decline and reaches the minimum when the 6-th variable  
Figure 4: Precipitation data grid structure in GL. The orange dots on the left represent the actual 12 ground meteorological stations in GL, and the blue squares on the right represent the precipitation grid data points (a total of 28) formed by dividing GL into a 0.3\({}^{\circ}\)\(\times\) 0.3\({}^{\circ}\) (latitude and longitude) grid.  
Figure 3: Precipitation data grid structure in HC. The orange dots on the left represent the actual 8 ground meteorological stations in HC, and the blue squares on the right represent the precipitation grid data points (a total of 34) formed by dividing HC into a 0.3\({}^{\circ}\)\(\times\) 0.3\({}^{\circ}\) (latitude and longitude) grid.  
is calculated, while the _AIC_ value of GL city reaches the minimum when the 4-th variable is calculated.  
The above results show that the most significant impact on the fluctuation of the daily power generation of the small hydropower stations in HC is the total daily precipitation set \(\{P_{i-2}^{s}\), \(P_{i-1}^{s}\), \(P_{i-3}^{s}\), \(P_{i-4}^{s}\), \(P_{i-6}^{s}\), \(P_{i-5}^{s}\)}. Additionally, the most significant influence of \(\Delta E_{t}\) on the daily power generation fluctuation of small hydropower station groups in GL is the total daily precipitation set \(\{P_{i-1}^{s}\), \(P_{i-2}^{s}\), \(P_{i-4}^{s}\), \(P_{i-3}^{s}\)}. Therefore, in the subsequent forecast of the daily power generation of the group of small hydropower stations in HC and GL, the precipitation grid data of the 6 days and 4 days before the forecast date will be used as the forecast input variable, respectively.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 3 Methodology
### LST Retrieval from Landsat8
The formula for calculating the surface emissivity is as follows:  
\[\varepsilon_{s}=0.9625+0.0614 VFC-0.0461 VFC^{2} \tag{11}\]  
\[\varepsilon_{b}=0.9589+0.086 VFC-0.0671 VFC^{2} \tag{12}\]  
where \(\varepsilon_{s}\) is the surface emissivity of the natural surface; \(\varepsilon_{b}\) is the surface emissivity of the built-up area. \(B(T_{s})\) can be expressed as:  
\[B(T_{s})=[L_{\lambda}-L\uparrow-\tau(1-\varepsilon)L\downarrow]/\tau\varepsilon \tag{13}\]  
According to [PERSON]'s law, \(T_{s}\) can be expressed as:  
\[T_{s}=\frac{K_{2}}{\ln\left(\frac{K_{1}}{B(T_{s})}+1\right)} \tag{14}\]  
In TIRS Band 10, \(K_{1}\) and \(K_{2}\) are constant: \(K_{1}=774.89\)\(W/\left(\text{m}^{2}\text{-}\upmu\text{m}\cdot\text{sr}\right)\) and \(K_{2}=1321.08K\). It is necessary to verify the accuracy of the inversion for the reason that all kinds of LST inversion methods errors exist. The MOD11A1 LST data of the same day is used as the reference value of HefeiLST. The fishnet is used for grid processing, figuring out the mean value of temperature in each grid and carrying on the correlation analysis. As shown in Table 3, the surface temperature obtained by the RTE method differs very little from the maximum and average values of the MOD1A1 data. At the same time, the correlation coefficient between the two is 0.94, which is very significant at a confidence level of 0.001. The results show that the LST retrieved by the RTE method meets the research needs. Finally, we converted the LST raster data into vector data, and the accordance means--standard deviation method was employed to transform the LST into a thematic map of five thermal categories: low, sub-low, middle, sub-high, and high, as shown in Figure 10. The basis of this grading and the temperature ranges corresponding to the different temperature levels are shown in Table 4.
## 3 Wireless Communication Networks
### Security  
The dynamic and flexibility of RWN represent a challenge for implementation of security schemes due to frequent changes in the network status, e.g., sporadic connectivity, variable number of active WDs, temporal topology, etc. In addition, it is desirable to provide authentication and privacy requirements to avoid vulnerabilities due to malicious eavesdropping devices. Commonly, the schemes require associative mechanisms, security protocols which can be proactive or reactive and involve cross layer functions or coding signals [37; 38].
# Image Restoration Using a Fixed-Point Method for a TVL2 Regularization Problem
## 3 Fixed-Point Method for TVL2 Problem (4)
Thus, we want to propose a different approach for finding an approximate solution to the linear system in line 8 of Algorithm 2. We first split the coefficient matrix \(A^{T}A\) into  
\[\|G(w_{1})-G(w_{2})\|_{2} =\|T(Lw_{1})-T(Lw_{2})\|_{2}\]4  
where \(\delta\) is a positive constant and \(D=\mathrm{diag}(A^{T}A)\) is a diagonal part of \(A^{T}A\). Then, the ill-conditioned linear system can be solved using the following iterative method:  
```
1:Given : observed image \(f\), positive parameters \(\alpha,\ \beta,\ \gamma,\ \lambda\) and \(\kappa\in(0,1)\)
2:Initialization : \(a^{0}=0,b^{0}=0\) and \(u^{0}=f\)
3:for\(k=0\) to \(maxit\)do
4:\(\hat{a}^{k+1}=\left(\ I-\mathrm{prox}_{\frac{k}{\pi}\|.\|_{2}}\right)\left(u^{k }+a^{k}\right)\)
5:\(a^{k+1}=\kappa a^{k}+(1-\kappa)\hat{a}^{k+1}\)
6:\(\hat{b}^{k+1}=\left(\ I-\mathrm{prox}_{\frac{k}{\pi}\theta}\right)\left(Bu^{k }+b^{k}\right)\)
7:\(b^{k+1}=\kappa\hat{b}^{k}+(1-\kappa)\hat{b}^{k+1}\)
8: Solve \(A^{T}Au^{k+1}=A^{T}f-\alpha\gamma a^{k+1}-\lambda B^{T}b^{k+1}\) for \(u^{k+1}\)
9:if\(\frac{\|u^{k+1}-u^{k}\|_{2}}{\|u^{k+1}\|_{2}}<tol\)then
10:endif
11:endfor
```  
**Algorithm 3** Fixed-point method for TVL2 problem (4)  
**Inner Solver**  
Choose \(y_{1}=u^{k}\)  
**for\(\ell=1\) to \(maxl\)**  
Solve \((A^{T}A+\delta D)y_{\ell+1}=\delta Dy_{\ell}+A^{T}f-\alpha\gamma a^{k+1}- \lambda B^{T}b^{k+1}\) for \(y_{\ell+1}\)  
**end for**  
\(u^{k+1}=y_{\ell+1}\),  
where \(u^{k}\) refers to the restored image computed at the previous \(k\)th step, and the optimal parameter \(\delta>0\) is chosen by numerical tries. Semi-convergence analysis for Inner Solver has been studied by [PERSON] and [PERSON] [23]. Algorithm 2 is an iterative method that converges to a solution to TVL2 problem (4) as \(k\rightarrow\infty\), so we do not have to get an accurate solution to the linear system in line 8 of Algorithm 2. For this reason, we have used \(maxl=1\) for Inner Solver. Notice that the linear system in Inner Solver is equivalent to solving the following least squares problem:  
\[\min_{u}\left\|\left( \begin{array}{c}f\\ \frac{1}{\sqrt{\delta}}D^{-\frac{1}{2}}(\delta Dy_{\ell}-\alpha\gamma a^{k+1}- \lambda B^{T}b^{k+1})\\ \end{array} \right)-\left( \begin{array}{c}A\\ \sqrt{\delta}D^{\frac{1}{2}}\\ \end{array} \right)u\right\|_{2}^{2}. \tag{29}\]  
Hence, the linear system in Inner Solver is solved by applying the CGLS to (29).
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 3 Main Architecture  
Our goal is to design a multiobject tracking system, named TraPeHat. An overview of the system is shown in Figure 2; our tacker is an online tracker. When our system receives a new frame, it works as follows.  
**Step 1**: Detect and track each pedestrian's head in the current frame, as well as detect each pedestrian's body.
**Step 2**: Integrate the information above. Specifically, pair the head bounding boxes with the full-body bounding boxes by determining whether they belong to the same pedestrian. If they do, we link these boxes to determine their relationship.  
Figure 2: The main architecture of our proposal, Tracking Pedestrians with Head Tracker (TraPeHat).  
**Step 3**: According to the matching results in Step 2, the head bounding boxes in the head motion tracklets are replaced with the body bounding boxes, thus generating the final desired pedestrian body motion tracklets.
## 5 Further Discussion
To evaluate the effects of the threshold balancing techniques and the spike neurons, we conducted the experiments on the MNIST dataset for the following 10 combinations: the proposed balancing technique + soft-IF pair, the spike-norm + soft-IF pair, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Neural Encoding** & **Training Approach** & **Learning Type** & **Accuracy (\%)** & **Latency (Time Steps)** \\ \hline
[21] & Rate-based & Gradient-based & Supervised & 99.59 & 100 \\
**our work** & Rate-based & Conversion & Supervised & **99.33** & **64** \\
[36] & Rate-based & Biological + gradient & Semi-supervised & 99.28 & 200 \\
[24] & Rate-based & Conversion & Supervised & 99.19 & \(>\)500 \\
**our work** & Rate-based & Conversion & Supervised & **99.14** & **4** \\
[37] & Rate-based & Gradient-based & Supervised & 98.89 & 30 \\
[15] & Temporal-based & Biologically based & Semi-supervised & 98.4 & 30 \\
[38] & Rate-based & Biological + gradient & Supervised & 97.20 & 9 \\
[12] & Rate-based & Biologically based & Unsupervised & 95 & 350 \\
[16] & Rate-based & Biologically based & Semi-supervised & 91.1 & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the recent SNN models for the MNIST dataset. the spike-norm + IF pair, the Act-Norm channelwise + IF pair, the act-norm + IF pair, the robust-norm + soft-IF pair, the act-norm + soft-IF pair, the robust-norm + IF pair, the proposed balancing technique + IF pair, and the act-norm channelwise + soft-IF pair. Here, IF indicates the integrate-and-fire neuron shown in Figure 11, soft-IF indicates the soft-reset IF neuron shown in Figure 11, spike-norm indicates the spike-based normalization technique [25] of using the maximum of the weighted sums of spikes over the latency, act-norm channelwise indicates the threshold balancing technique [27] of using the maximum activations in the ANN models, and robust-norm [28] indicates the threshold balancing technique [28] of using a scaled maximum activation in the ANN models. Figure 12 shows the performance of each threshold balancing technique and neuron model pair for the same SNN architecture on the MNIST dataset. Please refer to Appendix G for more detail. As seen in Figure 12, most experiments have shown better performance for the combination with the soft-reset IF neuron model than the combinations with the IF neuron model. This seems to be attributed to the soft-reset IF neuron model better approximating the ReLU activation in the CNN than the IF neuron. The combination of the proposed threshold balancing technique and the soft-reset IF model showed the best performance over the examined latencies.
## 4 Experimental Results
### Time Analysis  
Figure 10 depicts a timing analysis of the CH selection approach. The proposed method took less time than the existing CH selection methods. The period would be extended as the number of CHs increases. The proposed schedule, as shown in Table 8, yielded a reduced (65 s) execution time of 5 CH compared to the existing methods.
## 2 Data Description and Data Preprocessing  
### Bridge Overview and Data Set Composition  
This study utilizes acceleration monitoring data from a large-span arch bridge. The bridge in question is a dual-cantilever steel box girder tied-arch bridge with a main span of 470 m and side spans of 110 m each. Fifteen acceleration sensors were installed on the bridge, as depicted in Figure 1. For the purpose of anomaly detection, acceleration monitoring data from April 2016 and December 2018 were selected, with a sampling frequency of 50 Hz. The raw data were segmented into fixed-length, time-series samples of one hour each. A total of 21,960 data sequences were obtained (61 days \(\times\) 24 h \(\times\) 15 channels). Each data sample has a length of 180,000 \(\times\) 1 data points. Using manual labeling, labels were assigned to each sample sequence, categorizing all samples into six distinct classes. Table 1 provides a detailed description of each class along with the corresponding sample counts. It is evident from the table that'minor' anomalies and'missing' data are the predominant anomaly types, whereas other anomaly types are less frequent, with noise-related anomalies only observed in April 2016. Figure 2 provides a detailed temporal distribution of anomalies for each data category.  
Figure 1: Placement diagram of acceleration sensors for a long-span arch bridge.
## 3 Results
This analysis is essential for optimizing material properties, including the sintering temperature, coating behavior, and overall performance of the glass, particularly in applications such as coatings for Ti6 Al4V. Figure 8, above, provides the coefficient of thermal expansion (CTE) trend of the glass composition series based on the computational results from the MD simulations. The coefficient of thermal expansion (CTE) quantifies how a material's volume changes with temperature. The graph presented in Figure 8 illustrates that as temperature increases, the system's volume expands, and as temperature decreases, the volume contracts. The slope of this graph indicates the rate of volume change with temperature; a higher CTE results in a steeper slope, signifying more pronounced volume changes. The CTE for each composition is calculated based on their computational assessment by the following general equation:  
\[\alpha=\frac{\Delta L}{(L_{0}\times\Delta T)} \tag{5}\]  
where \(\Delta L\) is the change in length of the test specimen due to heating or cooling, \(L_{0}\) represents the original length of the specimen at room temperature, and \(\alpha\) is the linear expansion coefficient. Thermodynamically, computation analysis permits the assessment of CTE measurement through measurement of the strain against the temperature profile by determining volume change with temperature change at constant pressure and is shown as follows:  
\[\alpha_{V}=\frac{1}{V_{0}}\frac{\delta V}{\delta T_{P}} \tag{6}\]  
Additionally, we converted the CTE with regard to volume change at constant pressure to adapt to the CTE in response to change in length through the following equation:  
\[\alpha_{L}=\frac{1}{L}\frac{\delta V}{\delta T}=\frac{1}{3}\alpha_{V} \tag{7}\]  
The CTE was determined through regression analysis of temperature-strain data from simulations at temperatures below the \(T_{\mathrm{g}}\). The outcome of the CTE calculations is presented in Table 5.  
Figure 8: Graph of change in volume versus temperature change in Kelvin for glass composition series. Data were accumulated from computational MD simulation where systems were annealed to 2000 K.  
The relationship between the number of rings per base and the number of ring members in a glass complex provided multifaceted aspects of their structural chemistry. Figure 9 illustrates the relationship between the number of rings per base and the number of ring members in glass complexes, which are coordination compounds with amorphous structures. Here, the base is the central atom in the network, while the rings per base represent the number of ring formations around each base, and the number of ring members denotes the number of atoms in each ring. This relationship, known as ring size distribution, is a medium-range structural property influenced by metal-ligand coordination.
## 7 Experimentation and Analysis
### Algorithm Performance Validation
#### 7.1.1 Algorithm Convergence Accuracy Comparison
The former determines the best performance the algorithm can achieve, and the latter determines the consistency of the optimal solution in multiple iterations. The closer the two are, the better the convergence performance and the higher the accuracy of the algorithm. It is worth noting that the optimal fitness value of the GLSOAG on F12 is slightly higher than that of SOA by 1.84021 \(\times\) 10 \({}^{-1}\), but the difference between the optimal fitness value and the mean fitness value of the GLSOAG is smaller than that of SOA by 2.433841. Overall, except for the sub-optimal result on F8 with a slight difference, the GLSOAG has a certain advantage in all other test functions. The maximum accuracy difference amounts to \(1\times 10\)[30].
## 7 Use of Biomaterials in the Release of Extracellular Vesicles for Treatment of Cutaneous Wounds
### Decellularized Biomaterials  
Wound dressings based on decellularized biomaterials have received much attention in regenerative medicine, due to their natural origin [121]. Recently, the development of hydrogels based on porcine small-intestinal submuc (SIS), obtained by decellularization, has been described. This biomaterial is mainly composed of collagen, and is enriched in factors with the ability to promote tissue regeneration [122]. Such hydrogels were prepared with SIS, to which catecholamine was added to improve their properties. In addition, fusion peptides, consisting of collagen-binding peptides linked to CP05 peptide (CRHSQMTVT-SRL), were generated through genetic engineering. The latter recognizes the surface protein CD63, abundantly present on extracellular vesicles. Therefore, this modification allows the MSC-EV to remain anchored to the biomaterials, increasing their retention capacity. Thus, after seven days of maintenance of the hydrogel loaded with hUCMSC-derived EV in a culture medium, EV release was more than doubled in the hydrogel containing fusion proteins [123]. Moreover, this hydrogel increased the proliferation and migration of NIH3T3 fibroblasts in vitro, as well as angiogenesis of EA.hy926 endothelial cells, with respect to hydrogel without fusion proteins. On the other hand, application in wounds of a diabetic-rat model accelerated healing, promoting the formation of granulation tissue, collagen and skin appendages [123].  
Decellularization of the human amniotic-membrane has also been used for the preparation of scaffolds for tissue and organ repair [124]. As with other non-cellular approaches, there are no ethical problems in its use. It is readily available without the high cost, being biocompatible. It also shows low immunogenicity, has adequate mechanical properties and good cell adhesion. Recently, EV derived from adipose-tissue MSC were incorporated into scaffolds, obtained from human acellular amniotic-membrane. They were used as dressings, for the treatment of diabetic skinulcers [125]. The MSC-EV used favored proliferation and migration of human dermal-fibroblasts, as well as angiogenesis of HUVEC in vitro. Dressing consisting of human acellular amniotic-membrane plus MSC-EV was applied every other day, three times in total, in an in vivo model of diabetic mouse, induced with streptozotocin. The results showed that with respect to the use of human acellular amniotic membrane dressing or VE in PBS separately, it accelerated healing. That was accomplished through a greater immunomodulatory effect, induction of angiogenesis and promotion of extracellular matrix production [125]. This confirms the high therapeutic potential of dressings, designed from human acellular amniotic-membrane, together with MSC-derived EV, for the treatment of chronic ulcers.