esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 2 Materials
### Behavior of IPSO  
To verify whether the grouped particles in the IPSO algorithm perform as intended--i.e., the exploration group conducts a global search while the exploitation group focuses near the minimum value--the variance of inter-particle distances was used as the evaluation metric. A higher variance indicates a broader search range, while a lower variance suggests a more focused and localized search.  
\[f=\sum_{i=1}^{n}x_{i}^{2}+(\sum_{i=1}^{n}0.5 ix_{i})^{2}+(\sum_{i=1}^{n}0.5 ix_{i} )^{4} \tag{9}\]  
The first three experiments utilized the standard PSO algorithm with inertia weights set to a constant value of 0.5, as well as Formulas (6) and (7). The fourth experiment examined the variance values of particles under the combined behavior of the IPSO algorithm. The Zakharov function, shown in Formula (9), was selected as the benchmark function to find its minimum value under the initial conditions of 3 dimensions, 30 particles, and 100 iterations. The corresponding data is summarized in Table 1.  
Analyzing these data, the following conclusions can be drawn:  
* The exploration group achieved the highest variance throughout the iterations, demonstrating its capability to perform broad global searches.
* The exploitation group maintained significantly lower variance values, reflecting its concentration near the optimal solution.
* The standard PSO showed a gradual decrease in variance, indicating a more balanced yet less specialized search approach.  
This suggests that the independent design of the exploration and exploitation groups has met expectations. However, deep exploration near the optimal solution in the early stages is of limited use, as the global best position is likely to change rapidly. Similarly, extensive searches in the later stages have a lower probability of finding better solutions and are less conducive to convergence. To address this, the IPSO algorithm introduces a dynamic grouping strategy, allowing particles in the exploration group to gradually transition to the exploitation group as iterations progress. This adjustment effectively eliminates redundant global searches in the later stages. As a result, during iterations 76 to 100, the variance levels of IPSO closely align with those of PSO. This demonstrates that IPSO successfully balances the behaviors of the two groups of particles. Furthermore, the broader exploration in the early iterations enables IPSO to outperform PSO, yielding better search results. This improvement will be further discussed in the next section.
# An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure
## 2 Attempting to Make CTP Cooperative for the Infrastructure Shm
### Etx as a Function of the Signal-to-Interference-and-Noise-Ratio (Sint)  
There is a plethora of link quality metrics that can be encapsulated towards a solution that maximises throughput, as well as minimising packet drops and retransmissions [45]. Thus, the metric ETX emerged, which is the average number of transmissions of data and ACK packets. A node calculates ETX by obtaining the packet reception ratio of a wireless link \(l\) with each of its neighbouring nodes in the data direction, denoted as \(PRR_{data}\). Thereafter, it calculates the packet reception ratio in the ACK direction, denoted as \(PRR_{ack}\), ETX is widely known as the inverse of the probability of Packet Success Delivery given as  
\[ETX_{l}=\frac{1}{PRR_{data}*PRR_{ack}} \tag{1}\]  
As it is clear from (1), a link is perfect if its ETX value is 1. Moreover, the route ETX is the sum of the ETX of every link in the route. Hence, a two-hop route of perfect links has an ETX of 2. As it can be seen, the larger the ETX value the less reliable the link. ETX has several significant features, such as that it impacts throughput since it depends on delivery ratios. Additionally, it detects link asymmetry by employing bidirectional links, it uses precise link loss ratio measurements, and it penalizes routes with more hops, which have lower throughput due to interference between different hops of the same path [46]. In addition, ETX may implicitly lower the energy consumption per packet, since each node selects the receiver with the less ETX to forward the packet.  
At this point, the relationship of ETX with the SINR will be provided. The Rayleigh channel [47] is considered for the transmissions. For a wireless link \((i,j)\), the Packet Reception Ratio (PRR) \(PRR_{i,j}\) is defined as the ratio of the number of packets received by node \(j\) over the number of packets sent by node \(i\). This is the \(PRR_{data}\) and similarly, \(PRR_{j,i}\) is the \(PRR_{ack}\). It can be expressed by approximation as:  
\[PRR_{i,j}=(1-\xi)^{l}, \tag{2}\]  
where l is the packet length in bits. The Bit Error Rate (BER), which we denote as \(\xi_{i,j}\), is given by the following formula [48]  
\[\xi_{i,j}=\frac{1}{2}\left(1-\sqrt{\frac{\gamma_{i,j}}{1+\gamma_{i,j}}}\right), \tag{3}\]  
where \(\gamma_{i,j}\) is the SINR of the transmission from node \(i\) to node \(j\). \(\gamma_{i,j}\) is given by  
\[\gamma_{i,j}=\frac{H_{i,j}p_{i}}{\sum\limits_{t\
eq i,t\
eq j}p_{t}H_{t,j}+N_{ 0}}, \tag{4}\]  
where \(H_{i,j}\) and \(H_{t,j}\) are the channels gains of the transmitter and the interferer, respectively, \(p_{i}\) and \(p_{t}\) are the transmission powers of the sender and the interferer, and \(N_{0}\) is the noise. As we can see, the SINR increases, then the PRR increases, and the ETX decreases, reaching the value 1, when communication is flawless.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 3 Description of the INSPIRE-SAT 7 Mission and Mission Operations
### The Space Segment
#### 3.2.4 The Li-Fi Payload  
The Li-Fi payload aims to demonstrate the performance of Li-Fi technology in real space conditions through a point-to-point communication of 5 Mbps. The Li-Fi payload integrates the last generation of Li-Fi chipset. Oledcomm and LATMOS wish to prove the use of this Li-Fi chipset during a successful space-based mission in operational environment. The Li-Fi payload consists on a power stage, a micro-controller able to manage the I2C protocol and to drive the ASIC. The optical stage will consist of a light-emitting diode (LED) and a photodiode, allowing a unidirectional communication. The main goal is to demonstrate that Li-Fi communication is not only feasible in space, but also that it is possible to achieve similar performance to traditional connections using copper harnesses. Main characteristics of the INSPIRE-SAT 7 Li-Fi are given in Table 5.  
The 5 Mbps data rate is imposed by the Li-Fi modem. Indeed, the ASIC used allows to reach speeds of the order of Gbps. Within the framework of the mission and in order to validate the performances stated in Table 5, periodic measurements will be carried out. During these data exchanges, the speed, latency and jitter are measured and transmitted to the INSPIRE-SAT 7 OBC.
## 2 Superiorities of Near-Space Radar Platform  
As shown in Figure 1, while compared to current satellite and airplane platforms, near-space platform has many superiorities for microwave remote sensing applications:
## 2 Numerical Methods and Experimental Procedure
### Signal Analysis
#### 2.6.2 Acoustic Emission Parameters  
In traditional AE, many different features are extracted [50; 51] from the signals to structure them using classification algorithms and to associate each signal class with a specific damage mechanism. These features are the so-called \"AE features or descriptors\". Figure 7 shows a typical experimental AE signal to illustrate these parameters. The beginning of the signal used to calculate a descriptor is  
Figure 6: Finite element modelling of surface lead break and experimental procedure.  
determined as the point where a fixed threshold is exceeded. The threshold values are chosen with respect to the experimental signals and are fixed as \(10^{-7}\) m/s for the particle velocity. To determine the end of the signal, we employ an energetic criterion (Figure 7a). For each point in the waveform, the cumulative energy computed from the first threshold crossing is compared to the energy contained in a time interval (10 \(\upmu\)s) following that point. If this energy is less than 0.5% of the cumulative energy, then the corresponding point represents the end of the signal. We calculated more than 20 parameters [50] for each AE signal (Figure 7), but only several of them are presented here for the sake of clarity. Among the temporal features derived from the evolution of the signal with time, one can cite the amplitude, the duration, the rise time, etc. The rise time (RT) is the time between the first threshold crossing and the time of the maximum peak. Zero crossing rate (ZCR) shows how many times the signal crosses the temporal axis with respect to signal duration. One can also extract features from the Fourier Transform of the signal. \(FC\) is the Fourier Transform centroid of the signal given by [44].  
\[FC=\frac{\sum_{i=1}^{max}f_{i}A_{i}}{\sum_{i=1}^{max}A_{i}} \tag{10}\]  
with \(A\) being the numerical particle velocity. The spectral spread, denoted \(SS\), gives an indication of the bandwidth of the spectral content of a signal. This is actually the standard deviation of the \(FT\), and is given by:  
\[SS=\sqrt{\frac{\sum_{i=1}^{max}\left(f_{i}-FC\right)^{2}A_{i}}{\sum_{i=1}^{max }A_{i}}} \tag{11}\]  
Figure 7: \(Cont.\)  
The partial powers _PPi_ represent the percentage of energy contained in each frequency interval. The partial power begins at 100 kHz due to the interest domain of AE.
## 2 Methodology
### Scattered Decision-Level Fusion  
#### 2.2.1 Overview  
In this section, we develop a new fusion method for our NDT problem based on concepts from KDE. Here, the role of the vector-valued data sample \(\mathbf{x}_{i}\) is taken by the two-dimensional hit location \(\mathbf{d}\) as detected by a single sensor \(S_{i}\) during surface inspection. As defined in Equation (1), the _joint density_ that includes the hits from all sensors can be computed from _partial densities_ that include only the hits from a single sensor. We extend this property to a more general framework of density-based fusion. Specifically, we introduce two modifications: (1) Each partial density is computed in the respective local coordinate system, using sensor-specific KDE parameters; and (2) To merge the partial densities into the joint density, the outer sum from Equation (1) is generalized to an arbitrary fusion rule \(F\). Our approach is outlined by the following steps:  
1. Define a grid of discrete locations \(\mathbf{p}\) where the fused density should be evaluated. These locations are defined in the common coordinate system after registration, so that they refer to the same location on the specimen for all individual inspection methods. Map these locations to each local coordinate system using the coordinate transforms \(T_{S}\) obtained during spatial registration. See Figure 3 for an illustration of this step.  
2. For each individual sensor, compute the spatial density \(\hat{f}_{s}\left(\mathbf{p}\right)\) of single-sensor hits \(\mathbf{d}\) and evaluate the density at the mapped locations \(T_{S}\left(\mathbf{p}\right)\): \[\hat{f}_{S}\left(\mathbf{p}\right)=N\left(\mathbf{h}_{s},\Delta^{S}\right)\sum_{d\in D \left(S\right)}w_{d}K_{\mathbf{h}_{S}}\left(T_{S}\left(\mathbf{p}\right)-\mathbf{d}\right)\] (2) The normalization constant \(N\left(\mathbf{h}_{S},\Delta^{S}\right)\) and the bandwidth \(\mathbf{h}_{S}\) are sensor-specific parameters. \(N\) depends on the kernel bandwidth and on the sampling distances \(\Delta^{S}=\left(\Delta^{S}_{x},\Delta^{S}_{y}\right)\) (pixel dimensions). \(w_{d}\) is a per-hit weighting factor.
3. For each evaluation point \(\mathbf{p}\), fuse the partial density values \(\hat{f}_{S}\left(\mathbf{p}\right)\) from the different sensors using a fusion rule \(F\): \[\hat{f}\left(\mathbf{p}\right)=F\left(\left\{\hat{f}_{S_{1}}\left(\mathbf{p}\right), \ldots,\hat{f}_{S_{N}}\left(\mathbf{p}\right)\right\}\right)\] (3)  
We will now provide a detailed explanation of these steps. The grid defined in Step 1 determines the resolution at which the fused density will be sampled. The grid size depends on the kernel bandwidths, because smoother densities computed from larger kernels facilitate coarser sampling grids to reduce the computational complexity. For optimal resolution however, we propose setting the grid size according to the sampling distance of the finest-sampled individual sensor. In the second step, the partial densities are computed as explained next.
## 3 Interference Mitigation Schemes
### MAC Approach  
Interference between WBASNs can be mitigated by switching their working channel or manually scheduling their superframe. In this approach, a MAC protocol is explored to maximize throughput and minimize transmission power while mitigating interference.
# Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks
## 7 Conclusions
In wideband measurement, we can distinguish between multipath components with an 1 ns resolvable delay bin. Moreover, the PLM proposed model will be investigated in underground and proximity parking lot environments. Conceptualization, A.M.A.-S., T.A.R., A.D. and E.H.; Data curation, A.M.A.-S. and M.N.H. ; Formal analysis, A.M.A.-S., M.N.H., A.D. and E.H.; Funding acquisition, T.A.R. ; Investigation, A.M.A.-S., T.A.R. and M.N.H. ; Methodology, A.M.A.-S., T.A.R. and M.N.H. ; Project administration, T.A.R. ; Resources, A.M.A.-S., T.A.R. and E.H.; Software, A.M.A.-S.; Supervision, T.A.R. ; Validation, A.M.A.-S. and T.A.R. ; Visualization, A.M.A.-S. and A.D.; Writing--original draft, A.M.A.-S.; Writing--review & editing, A.M.A.-S., T.A.R., M.N.H., A.D. and E.H. The authors would like to thank the Research Management Centre (RMC) at Universiti Teknologi Malaysia for funding this work under grant number Q. JI30000.21A2.04E21 and acknowledge the grant number A. J090601.5357.07085 by HICOE-WCC managed by the Universiti Teknologi Malaysia. The authors declare no conflict of interest.
# Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
## 3 Results
### Macelignan Inhibited Glutamate-Induced Apoptotic Cell Death in HT22 Cells  
The apoptotic morphology of the HT22 cells was monitored after treatment with or without glutamate and pretreatment with macelignan. As shown in Figure 4a, Hoechst 33342 was used to stain chromatin to discriminate apoptotic cell death induced by glutamate. Compared with the control cells, the condensed and fragmented chromatin, shown in bright blue, was observed after the cells were exposed to glutamate, which was indicative of apoptosis. However, this effect was ameliorated by treatment with 5 and 10 \(\upmu\)M macelignan. To further confirm whether macelignan reduced apoptotic cell death in HT22 cells, double staining with annexin V and PI was performed (Figure 4b,c). The results demonstrated a significant increase in the number of annexin V/PI-positive apoptotic cells after exposure to glutamate (61.0%); however, this effect was significantly reversed by treatment with 5 \(\upmu\)M (22.0%) and 10 \(\upmu\)M (19.3%) macelignan. These results indicated that macelignan might prevent HT22 cells from undergoing glutamate-induced apoptosis.  
Figure 3: Effect of macelignan on ROS generation in HT22 cells. Cells were pretreated with Mac (5 and 10 \(\upmu\)M) for 2 h, followed by exposure to glutamate (5 mM) for 8 h. (**a**) Mac (5 \(\upmu\)M, 1.92-fold; 10 \(\upmu\)M, 1.62-fold) inhibited the generation of ROS in glutamate-treated cells; (**b**) NAC, the positive control, inhibited the generation of ROS in glutamate-treated cells; (**c**) fluorescence images of HT22 cells exposed to glutamate were captured using fluorescence microscopy. Scale bar, 50 \(\upmu\)m. Value represents the mean \(\pm\) S.D. (\(n=3\)). # \(p<0.05\) in comparison to the untreated cells, * \(p<0.05\) in comparison with glutamate-treated cells (ANOVA, Tukey’s test). Mac, macelignan; NAC, N-acetylcysteine; CTL, control; Glu, glutamate.
## 3 Calculation of Lightning Current Distribution Characteristics
### The Influence of Soil Resistivity  
The distribution of lightning current after the flashover of line insulators is studied by varying the soil resistivity \(\rho\). The lightning peak current I\({}_{0}\) is fixed at 50 kA. The different values of soil resistivity considered are 200 \(\Omega\) m, 500 \(\Omega\) m, 800 \(\Omega\) m, 1000 \(\Omega\) m, 1200 \(\Omega\) m, and 1500 \(\Omega\) m. Figure 6 illustrates the lightning current distribution for T\({}_{0}\) and T\({}_{\rm R1}\) towers.  
Figure 5: The influence of lightning peak current on the lightning current distribution of T\({}_{0}\) and T\({}_{\rm R1}\) towers.  
Figure 6: The influence of soil resistivity on lightning current distribution of T0 and TR1 towers.  
The results indicate that as soil resistivity increases, the mine current entering the tower through T\({}_{0}\) and T\({}_{\text{RI}}\) decreases. Additionally, the waveform and amplitude of the mine current entering the tower through T\({}_{0}\) and T\({}_{\text{RI}}\) remain the same. This phenomenon occurs because, following a line insulator flashover, the increase in soil resistivity leads to an increase in the grounding resistance of the tower grounding grid. This increase in resistance partially obstructs the flow of lightning current into the ground through the tower grounding grid. Simultaneously, the increase in soil resistivity also hampers the flow of lightning current through the tower grounding grid into the surrounding soil area.
## 4 Equivalent Consumption Minimization Strategy (ECMS)
### Method 2: PI Controller  
The second investigated method for obtaining the Lagrange multiplier is a feedback control, where \(\lambda\) is used to control the \(SoC\)[35, 44] (see Figure 7). A PI controller is used, since the integrating part enables initializing \(\lambda\) to a value within an appropriate range (see Figure 6). In addition, the dynamic behavior of \(\lambda\) can be influenced by means of the time constant and gain of the controller. If the current state of charge \(SoC_{k}\) falls below the setpoint \(SoC_{k}^{\text{SP}}\), the PI controller increases \(\lambda_{k}\), and the battery charges. Otherwise, \(\lambda_{k}\) will be decreased, and the battery discharges (compare Figure 6). The parameters of the PI controller are determined empirical in such a way that the initial \(\lambda_{0}\) is chosen appropriately, and an oscillating behavior of \(\lambda_{k}\) is almost avoided. Due to this, frequent changes in the transmission speeds and modes during vehicle operation are avoided.  
The setpoints for the controller \(SoC^{\text{SP}}\) are the result of a predicted function, which describes the state of charge as a function of the current vehicle position \(z_{\text{veh},k}\). For this purpose, the three approaches shown in Figure 8 are considered. Figure 8a shows a linear discharge characteristic, beginning with the initial state of charge at the start position and ending with the lower boundary of the state of charge at the end position of the trip. For this kind of prediction, only the distance of the intended trip to be driven is required. In Figure 8b, the linear discharge characteristic is adapted when the state of charge exceeds the current characteristic due to recuperating the braking energy. This adaptation is carried out in every execution step of the ECMS algorithm. The last approach for the prediction, shown in Figure 8c, is based on an optimized discharge characteristic, which is obtained by applying DP on a predefined driving cycle. As in the case of the Hamilton-Jacobi-Bellmann equation presented in the previous section, this kind of prediction requires a representative driving cycle and time-consuming optimization in advance.  
Figure 6: The Lagrange multiplier \(\lambda\) as a function of the state of charge \(SoC\) and the vehicle’s position \(z_{\text{veh}}\), determined by the Hamilton–Jacobi–Bellman equation (Equation (25)).  
Figure 7: Control of the \(SoC\) with the control variable \(\lambda\) and predicted setpoints \(SoC^{\text{SP}}\).  
The method based on the Hamilton-Jacobi-Bellmann equation as well as the control-based method can be used for the determination of the Lagrange multiplier required for the ECMS algorithm shown in Figure 5. In order to apply one of these methods, either a driving cycle or the distance of the intended trip must be predicted. In the following section, the ECMS is applied by means of both methods. Each ECMS implementation is applied on different predictions and is analyzed in terms of the resulting fuel consumptions.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 5 Experimental Results and Analysis
### Simulation Settings  
This article considers setting up four RSUs in the cooperation area, and the cache capacity of each RSU is 256. Suppose there are 1000 video files in the network, the file size is a random number [33] in \(\{1,3,5,7\}\) and it follows the Zipf distribution [34] model with a coefficient of 0.55. We set the transmission delay of sending file \(i\) from the central server to RSU \(r_{m}\) to be 10-20 ms, and the transmission delay between RSUs within the cooperation range is 2-4 ms, and the default delay of transmitting file \(i\) between RSUs is equal. In this paper, random search is used to set the parameters of the established TCN prediction model, which effectively avoids the curse of dimensionality. We set the parameters of the TCN prediction model by Random Search [35], which samples the search space instead of brute-forcing all possible parameter sets, thus avoiding the curse of dimensionality. The specific parameters are shown in Table 1 below:
# Mapping Automated Cyber Attack Intelligence to Context-Based Impact on System-Level Goals
## 3 Proposed Method
### Mapping Attack Types to Distributions  
Figure 1 represents a sample distribution, which demonstrates how the probability of the attacked device being operational drops as the confidence of the occurring attack increases over time.  
Identifying the correct type of distribution that fits different attack types can be challenging, as there are many parameters that may need to be taken into consideration. DREAD [30] and CVSS [17] take a qualitative approach to assessing the severity of vulnerabilities. We include two qualitative metrics (see Table 1): (i) the difficulty in detecting the attack--this may be based on the attack itself being a known surreptitious attack (hard) or something more obvious, such as DoS (easy); and (ii) the damage potential--this is based on the device itself, not the wider damage potential. We need to make a decision on whether the specific attack on a specific device will completely prevent the device from fulfilling its task (high) or just have a minor impact (low). Context is required here and therefore requires input from the process owner. For instance, interfering with input/output data flow on a safety critical device may have high damage potential, while a printer i/o may be low. Note the focus of damage is on the device itself (component), not the wider system.