esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Task Execution Cost Analysis
### Energy Consumption  
Since the battery strength of an automobile is restricted, the energy overhead for data uploading of a parked car must be taken into account. Since the energy consumption of a vehicle occurs during the task receiving, sub-task performing, and task result transmitting processes, the total energy overhead for executing task \(t\) is expressed as follows:  
\[E_{t}=\sum_{t=1}^{m}P_{\text{o,send}}T_{u,t}^{up}+\delta f_{v}^{2}T_{u,t}^{ com}+P_{\text{o,rec}}T_{u,t}^{down} \tag{13}\]  
where \(P_{\text{o,send}}\) and \(P_{\text{o,rec}}\) denote the delivery power and the receiving power of the vehicle, respectively; \(\delta\) is a coefficient that relies on the chip architecture of the CPU; and \(m\) is the set of task performers. Considering both the time cost for finishing a task and the energy cost of task execution by parked vehicles, the total task execution cost of task \(t\) is modeled as follows:  
\[\cos t_{t}=\lambda T_{t}+(1-\lambda)E_{t} \tag{14}\]  
where \(\lambda\) represents the weight of the time spent on completing the task.
## 3 Resilience Calculation
### Recovery Model  
The recovery model is necessary in the calculation of resilience [17; 44; 45], but may be quite challenging to be realistically defined since it requires sufficient information from previous earthquakes. Analytical formulations are generally assumed and calibrated on the database of previous earthquakes. In this paper, two hypotheses were considered:  
1. mobilization time [46] (that includes building inspection, site preparation, providing engineering services... ) was neglected and thus the recovery function starts at the time of occurrence (\(t0E\)).
2. a linear recovery function was considered, as suggested by [17], when no sufficient information is available.  
These two hypotheses may be considered the principal limitations of the model and may be object of future work.  
Then, the calculation of repair time has been proposed in literature by several contributions. In particular, ref. [47] proposed a RT model consisting of two components: (1) Repair Scheduling and (2) Resource Scheduling. In addition, ref. [48] proposed a procedure to calculate RT, by considering the worker allocation and repair sequencing procedure. Both these approaches were introduced in order to overcome the well-established FEMA P-58 methodology [49]. Herein RT was calculated by following the simplified assumption that repair time is proportional with the drift ratio (the ratio between the maximum drift due to the seismic loads (\(\delta E\)) and the design drift under static load conditions (\(\delta S\))).  
\[\text{RT}=\gamma\frac{\delta E}{\delta S} \tag{3}\]  
It is worth noting that the coefficient \(\gamma\) needs to be calibrated in order to represent the conditions that range between the case that the building is not repairable or uneconomical to repair the building (RT equal to replacing time) and the case of minor damage that does not compromise the structural operability (RT = 0).
## 3 Results, Analysis, and Simulations
### Simulation Planning Results Based on Link Budget, Coverage, and Capacity Calculations  
Two scenarios are used for the simulations, i.e., outdoor to outdoor (O2O) with LOS and NLOS for 3.5 GHz and 28 GHz. The new sites are allocated according to the cell radius of the coverage distance. This radio planning uses existing sites and non-existent sites. Therefore, a recommendation for new site placement per the coverage area is calculated. The site allocation is set automatically using the Automatic Coverage Planning (ACP) tool available in the Atoll software. The results of the Atoll RNP tool simulation show the number of gnodeB required to cover the target area, the values of the SS-RSRP for coverage and the SS-SINR parameters for quality, and the average throughput data rate for capacity obtained in this research, and these are shown in the following plots.
## 4 Case Study of Lateral Distance Measurement
### Dynamic Test  
In a tunnel section, a vehicle lateral location measurement experiment was carried out, and the dynamics and practicability of the algorithm were verified by the actual data. The vehicle drove in the left lane, and the test found that the distance of the vehicle from the left wall was about 5.74 m, the distance from the right wall was about 6.21 m, and the total width of the tunnel was about 11.96 m. According to experience, the design width of the tunnel is about 12 m, so the accuracy of the distance measured from the LiDAR to the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Number** & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} \\
**Left** & **Right** & **Left** & **Right** & **Left** & **Right** \\ \hline Algorithm & 0.697 & 1.301 & 1.193 & 0.801 & 1.565 & 0.435 \\ results/m & 0.710 & 1.315 & 1.197 & 0.821 & 1.575 & 0.450 \\ Error/m & 0.013 & 0.014 & 0.004 & 0.020 & 0.010 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test of lateral distance measurements.  
Figure 6: Calculate vehicle coordinates according to lane line points.  
walls on both sides of the tunnel meets the requirements. The distance between the vehicle and the walls on both sides cannot remain unchanged during the process of driving, so the distance between the vehicle and the walls on both sides fluctuates within a certain range. At the same time, the measured tunnel width is relatively stable, and the fluctuations are also in the centimeter-level range.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Geographical Areas and Weather Data
#### 2.1.2 Regional Network of Meteorological Stations  
ARPA Sardegna collects the measurements sent by the Regional Meteorological Network (Rete Unica Regional--RUR). The network comprises 109 thermo-pluviometric stations and 77 meteorological stations, totaling 186 stations (Figure 2, right) distributed throughout the island, typically installed near population centers. In addition to the sensors, the network includes the instrumentation required for data transmission via radio or GPRS/UMTS, a series of radio repeaters, and the two data acquisition stations located in Cagliari and Sassari. The thermo-pluviometric stations provide measurements of air temperature (\({}^{\circ}\)C) and precipitation (mm/h), while the meteorological stations, in addition to the aforementioned measurements, also supply relative humidity (%), global solar radiation (W/m\({}^{2}\)), and wind speed (m/s) at 10 m above the soil. While the calibration and maintenance of the station instruments is outsourced to specialized third parties, the Meteoclimatic Department of ARPA Sardegna is responsible for the quality control of the above measurements and for their processing into hourly and daily time series. The quality control of weather measurements adopts standard and automated data validation criteria such as physical consistency (i.e., the recorded values must fall within the expected range) and climatological consistency (as above, but relative to the typical climate of the region under consideration), temporal consistency (i.e., variations between consecutive readings), and persistence of measurements. On the other hand, spatial consistency check is performed manually and almost daily by comparing the measurements of neighbouring stations. All of the above quality control procedures generate validation flags that are attached to the data, thus denoting the level of reliability of the measurements. The data along with the corresponding validation codes are then stored in a relational database. Dedicated software therefore extracts only the validated data and prepares the files to be delivered daily to CRS4 via an FTP server. Consequently, barring sensor and transmitter malfunctions, thousands of measurements are collected daily by CRS4.
## 3 Mobility Methodology
### The PMSS of NDN with C-IoT and AI
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flow of Transmission** & **Process Status** & **Description** \\ \hline Process 1 & Before transmission & Before handover transmission between the producer and the consumer, consumers transmit I\_packets for requesting data to the mobile producer. The mobile producer checks the available content with SR1. If the content is not available, then it searches with other SRs in their neighborhood. \\ Process 2 & Broadcast & Interest packet time is the time to retrieve the requested content and the time it takes to send it from the source to the destination. \\  & & The I\_packet goes through the nearby RS until the mobile producer is reached. Processing data from the consumer to the mobile producer is similar to the processing of data in KITE operation behavior. While the handover process starts, the mobile producer cuts off the connection with RS1 and tries to find a greater signal with another RS from another zone. From the RS, a new naming prefix and a new mobile interest packet are created while reaching a new RS. \\ Process 4 & FIB update & I\_packet. Routing information is important to control the broadcast domain to make sure that no collision of I\_packets occurs between the domain. \\  & & After this process is completed, the consumer resends a new interest packet to obtain new information from the producer. To maintain connectivity, NDN uses the best route strategy to reduce collision and forward the I\_packet to the mobile producer’s new location. \\ Process 6 & Cloud update & Cloud update \\ Process & Cloud update & The mobile producer replies to the D\_packet to the consumer through the NDN router. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The PMSS’s operation.
## 4 Experiment and Result Analysis
It should be noted that in the experiments on the ESP20 dataset, the ENN was trained with a noiseless raw signal merely and tested with signals of various SNRs.
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## Appendix A Proof of Convergence to a Stable Solution for DAP-CDFs
### Proof 2: Stability Reached in Finite Time  
We wish to show that there is a value for \(r\) from which on the CDFs \(F_{u}^{r}(\tau)\) no longer change with increasing \(r\) values. For this, assume that every link has a maximum delay \(t_{\max}\) for which delivery to the corresponding neighbor is guaranteed. This implies that all link PDFs satisfy the following condition:  
\[\forall uv\in E:\forall\tau\geq t_{\max}:f_{uv}(\tau)=0. \tag{10}\]  
Next, note that in a connected network, at least one path from any given node \(u\) to the sink must have a finite number of hops. The deadline achievement probability PDF following that path is given by the convolution of all the link PDFs along the path. If the path has \(n\) hops, then by Equation (10) and the properties of the convolution, it is guaranteed that the DAP-CDF of \(u\) is equal to \(1\)\(\forall\tau\geq nt_{\max}\). Therefore, for all \(r\) such that \(rt_{\min}\geq nt_{\max}\), the CDFs \(F_{u}^{r}\) must be stable over their entire domain of \(\tau\).  
Considering the above fact for all nodes in the network, it is apparent that all DAP-CDFs eventually reach a stable state. The number of rounds needed to achieve stability is at most \(r=\frac{nt_{\max}}{t_{\min}}\), with \(h\) being the number of hops of the longest considered path in the network. This finishes the proof of convergence to a stable solution.
## 3 Materials and Methods
### Data Descriptions  
Materials for emotion analysis used in this work are the database of the Physiological Signals (DEAP) [39], which is a collection of EEG signals from 32 subjects, ranging from 19 to 37 years old with equal numbers of males and females. A total of 40 videos with a length of 63 s are watched and listened to by the subjects to stimulate various emotions, which are measured by valence, arousal, liking, and dominance scales based on participant self-report. EEG signals are recorded with 40 AgCI wet electrodes during the period of video time with a sample frequency of 512 Hz.
### Remote Sensing Estimation of Gross Primary Productivity and Light Use Efficiency  
#### 2.4.1 Analysis Boundary  
All remotely sensed measures were masked to an approximate cropland ecosystem boundary which was defined as a 10 \(\times\) 10 matrix of Landsat pixels surrounding the flux tower, giving an area of approximately 300 m \(\times\) 300 m. This area was chosen across sites because flux tower height was generally set at a level which best captured the immediate field and eliminated surrounding landscapes [44], and this area generally fell within the target fields while enabling consistent spatial sampling from each site. This area was also consistent with former studies on flux tower sites which have used both Landsat and MODIS resolutions and therefore enhanced comparability of error across approaches [22; 24; 27; 45]. The mean of values across the 10 \(\times\) 10 matrix were taken for all remotely sensed variables to generate the final dataset with the number of observations shown in Table 1.  
More recent flux tower initiatives have captured meteorological variables which enable calculation of flux footprints for use in future studies [46; 47].
## 3 Results and Discussions
### Photodegradation on BHA  
#### 3.3.1 Effect of Doped Amount of Rare Earth  
Different rare-earth elements and doped amounts affected the photocatalytic activity of RE/TiO\({}_{2}\) on BHA. As shown in Figure 7, the removal efficiency of BHA by direct photolysis was very low and the adsorption ability of RE/TiO\({}_{2}\) to BHA was negligible. Compared with pure TiO\({}_{2}\), La and Ce indicate significant effects on BHA photodegradation while Gd and Yb only have tiny promoted effects on the photodegradation. Along with the overall trend, the photocatalytic activity of RE/TiO\({}_{2}\) on BHA enhanced with the increased amount of doped rare-earth element and then was weakened with the further increased amount, especially for La and Ce doped composites. In a word, the photodegradation ability of 0.75%La/TiO\({}_{2}\), 0.20%Ce/TiO\({}_{2}\), 0.70%gd/TiO\({}_{2}\), and 0.50%Yb/TiO\({}_{2}\) are up to the highest compared with pure TiO\({}_{2}\) or other kinds of the same doped materials, which are in consistence with the analysis of UV-Vis DRS.  
Figure 5: Band structure of RE/TiO\({}_{2}\) (2 \(\times\) 2 \(\times\) 1 supercell).  
Figure 6: TDOS of RE/TiO\({}_{2}\) (2 \(\times\) 2 \(\times\) 1 supercell) (**a**) and PDOS (**b**) of Gd/TiO\({}_{2}\) (2 \(\times\) 2 \(\times\) 1 supercell).  
The addition of rare earth would result in expanding the lattice of TiO\({}_{2}\), which has been proved by computation. The expansion might lead to crystal defects and distortion, ultimately affecting the energy band structure and weakening the recombination of photogenerated electrons and holes. However, too many defects would become the recombination center if excess rare-earth element was doped. So, there always exists an optimum doped amount [39].
## 3 Multi-Time Scale Dispatching Framework of Active Power and Reactive Power Based on MPC
### Day-Ahead Stage  
In this paper, the time scale \(\Delta\)T for the day-ahead optimal dispatch of the distribution network is set to 1 h. In the day-ahead optimization stage, the system inputs the day-ahead forecast data of wind, PV, and load, and minimizes the operation cost and voltage deviation of the distribution network, using mixed-integer second-order cone programming. The purpose of day-ahead optimal dispatch is to determine the operation state of the slow-motion reactive power devices, such as OLTCs and CBs as well as the charging and discharging state of VPSSH and BES. These determinations are used as fixed parameters in the intra-day rolling stage. Due to manufacturing technology and lifespan limitations, the number and speed of OLTC and CB operations are restricted, and the charge and discharge cycles of VPSSH and BES are also limited.
## 4 Analysis of Field Test Results
### Machine Learning Residual Estimation
#### 4.3.2 Residual Monitoring Results  
After applying the inflation factors, the \(K\) factor of 4.4172 was multiplied to the thresholds, as shown in (21), to calculate the residual monitoring thresholds with a \(10^{-5}\) uncertainty level. We evaluated the performance of the proposed monitoring methodology using the test data. Figure 13 shows example cases of residual anomaly monitoring for GPS satellites PRN 31 and 32. The blue line illustrates the SBDD residuals, while the magenta dots denote the thresholds obtained using the machine learning-based residual modeling engine, which takes satellites' C/No and elevation and azimuth angles of the satellites as input arguments. When the elevation angle or the C/No increases, the expected receiver noise decreases and the threshold becomes tighter. When only the C/No drops due to the effects of a multipath, the threshold takes this into account and loosens the threshold despite no significant change in the elevation. Therefore, the proposed methodology not only tightly bounds the residuals but also considers multiple parameters for more robust anomaly monitoring. Figure 14 shows the residual monitoring results for multiple PRN combinations. From the results, it is evident that the thresholds consistently tightly bound the SBDD residuals under nominal, fault-free conditions.  
Figure 13: Demonstration of the residual anomaly monitoring for satellites PRN 31 and 32 using test data and model constructed from the training data. The threshold decreases with increasing elevation angle and C/No, and increases for azimuths with potential presence of multipath.  
The thresholds are conservative and tend to over-bound the residuals most of the time, but may be too tight for certain epochs. Prior to applying the inflation factor, the false alarm rate, which represents the proportion of the residuals that exceeded the computed threshold, was \(1.1\times 10^{-3}\), which is significantly higher than the assigned false alarm requirement. However, after applying the IF to the threshold according to Equation (21), warnings were issued for 22 samples out of a total of 3,328,150 residuals in the 24 h test dataset, even though no actual anomaly event occurred. This corresponds to a false alarm rate of \(6.61\times 10^{-6}\), which is slightly lower than the desired level of \(10^{-5}\). It is suspected that this is due to choosing a larger value from the two tails in Section 4.3.1 in order to calculate a more conservative IF, and also computing IF for HPF and LPF separately and combining them together assuming data independence.  
Figure 14: SBDD residuals and fault thresholds for nominal conditions using test data and trained model.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Periodic Drop Attacks  
The function of Jellyfish periodic drop attacks in our simulations is to drop the packets periodically for 3 s every 7 s. In this section, our primary concern is evaluating the effectiveness of our recently developed protocol in the presence of Jellyfish periodic drop attacks. The performance comparison values are given in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline OMNET++ version & 5.2.6 \\ INET version & 4.2.2 \\ Veins version & 5.0 \\ Environment & Urban \\ SUMO version & 1.3.1 \\ Simulation area & 2500 m \(\times\) 2500 m \\ Simulation time & 500 s \\ Number of nodes & 100 \\ Number of attackers & 0, 3, 6, and 9 \\ Max node speed & 14 m/s \\ Mobility model & Erlangen \\ MAC protocol & IEEE 802.11p \\ Transmission range & 250 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 8: Mobility traffic scenario.  
Figure 9: Simulation scenario.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Attackers** & **Metrics** & **SecE-V2X** & **[20], 2018** & **[37], 2017** \\ \hline  & PDR (\%) & 95.34 & 93.80 & 91.60 \\
0 & EED (ms) & 6.80 & 7.11 & 8.64 \\  & Throughput (packets/s) & 638.15 & 596.33 & 534.55 \\ \hline  & PDR (\%) & 93.21 & 88.76 & 79.63 \\
3 & EED (ms) & 7.48 & 7.94 & 12.21 \\  & Throughput (packets/s) & 608.22 & 556.65 & 452.11 \\ \hline  & PDR (\%) & 89.67 & 76.72 & 64.67 \\
6 & EED (ms) & 8.31 & 9.64 & 16.83 \\  & Throughput (packets/s) & 551.29 & 460.54 & 346.86 \\ \hline  & PDR (\%) & 76.83 & 69.75 & 55.29 \\
9 & EED (ms) & 10.48 & 13.29 & 21.34 \\  & Throughput (packets/s) & 433.53 & 398.41 & 282.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance values comparison under jellyfish periodic drop attacks.
## 3 Methodology
### Inversion of the S-Wave Velocity Structure  
The discrete generalized inversion method was applied to determine the S-wave velocity structure. The problem was expressed as follows:  
\[Y=AX \tag{3}\]  
The vector \(Y\) of length \(n\) corresponds to the difference between the measured phase velocities and the calculated velocities for the initial model. The vector \(X\) of length \(m\), which is the solution to be sought, corresponds to the first-order correction to an initial model.  
In this study, we determined S-wave velocity structures by employing the surface-wave inversion technique [22]. For each site, we constructed an initial model with assigned values of thickness, S-wave velocity, and Poisson's ratio for each layer. This initial model was assumed to be a half-space structure with an S-wave velocity that is the maximum phase velocity at the lowest used frequency divided by 0.92. According to the well-logging data, the Poisson's ratio was estimated, and 0.4 was used for all layers. The total layer number was set to 80. The estimation of layer thickness was as follows: If a maximum frequency of 5 Hz and a phase velocity of approximately 450 m/s were used, the minimum wavelength was approximately 90 m. According to a rule of thumb, the highest resolution of thickness is about one-third of the minimum wavelength. Therefore, we could solve a layer with a thickness of 30 m. The depth resolution of the structure was determined according to the minimum frequency used and its corresponding phase velocity. For inversion, the damping value is adopted 1.0. We stopped the inversion process when the discrepancy of S-wave velocities between neighboring inversions was less than 0.001 km/s at each layer.