esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 5 Experiment and Analysis  
### Experimental Parameter Setting  
This paper verifies the algorithm's effectiveness through MATLAB. The simulation environment is Intel(r)Core(tm) i7-9700U [EMAIL_ADDRESS] GHz with 16 GB RAM. 90-210 nodes are randomly distributed in a farm with a size of 400 \(\times\) 400 (m\({}^{2}\)). The sink is in the middle of the farm, and all sensors are isomorphic. Specific experimental parameters are shown in Table 1:  
In order to simulate the farm environment, the following channel model is adopted in this paper. Through the path loss model, the link connectivity probability between nodes can be calculated. Additionally, the link instability in the farm is simulated by adding random noise.  
\[\begin{split} PL(d)=PL(d_{0})+10h\cdot\log_{10}(\frac{d}{d_{0}})+X _{\sigma}\\ =26.535+36.285\log_{10}(d)+X_{\sigma}\end{split} \tag{12}\]  
This paper compares and analyzes the network performance in four aspects: energy consumption, throughput, delay, and retransmission rate. Reference [19], reference [18], reference [23], and reference [36] are used as comparison algorithms. In this paper, they are represented by Algorithm 1, Algorithm 2, Algorithm 3, and Algorithm 4, respectively.  
The network energy consumption (J) is the sum of energy consumed by all nodes until the simulation end. In this paper, it is recorded as EC. The standard energy consumption per package (J/packet) is defined as:  
\[\text{NEC}=\frac{\text{EC}}{\text{total number of received packets at }\sin\text{k}} \tag{13}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Area (m\({}^{2}\)) & 400 \(\times\) 400 \\ Number of nodes & 90–210 \\ Node communication distance (m) & 100 \\ Packet size (bit) & 1440 \\ Data rate (kbps) & 250 \\ Initial energy (J) & 5 \\ E\({}_{dec}\) (n) \(\cdot\) bit\({}^{-1}\)) & 50 \\ \(\varepsilon_{b}\) (p) \(\cdot\) bit\({}^{-1}\cdot\) m\({}^{-2}\)) & 10 \\ \(\varepsilon_{amp}\) (p) \(\cdot\) bit\({}^{-1}\cdot\) m\({}^{-2}\)) & 0.0013 \\ \(d_{0}\) (m) & 88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
Network throughput is the number of bits of data received by the sink in each round of simulation.  
The average delay is the average delay (s) of a data packet from the source node to the sink:  
\[\text{delay}=\frac{\text{sum of received packets delay at }\sin\text{k}}{\text{number of received packets at }\sin\text{k}} \tag{14}\]  
Retransmission rate:  
\[\text{RR}=\frac{\text{total number of packets received}}{\text{minimum number of transmissions}} \tag{15}\]
# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 4 Results
### Ablation Studies
#### 4.2.1 Varying Architecture  
In this experiment, we evaluated the performance of different architectures. We trained models using one, two, and three layers and using noise augmentation and dropout. Table 7 shows that in most cases, the two-layer model performed better than the others. The results also demonstrate that the noise augmentation plus the recurrent dropout units help in all cases with testing noise, but do not help with non-noise testing.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Layers**} & \multirow{2}{*}{**Noise Aug**} & \multirow{2}{*}{**Dropout**} & \multicolumn{6}{c}{**Testing Noise**} \\ \cline{5-10}  & & & **No-Noise** & **10 cm** & **20 cm** & **30 cm** & **40 cm** & **50 cm** \\ \hline One layer & No & No & 96.44 & 58.44 & 60.66 & 60.0 & 56.44 & 56.22 \\ One layer & No & Yes & 96.44 & 58.44 & 57.77 & 52.22 & 56.88 & 56.88 \\ One layer & Yes & No & 88.22 & 82.22 & 81.55 & 80.88 & 83.11 & 82.0 \\ One layer & Yes & Yes & 94.88 & 88.22 & 87.77 & **89.33** & 87.55 & 88.44 \\ \hline Two layers & No & No & 96.22 & 34.22 & 36.22 & 38.0 & 35.33 & 37.11 \\ Two layers & No & Yes & 96.44 & 39.11 & 39.33 & 36.22 & 37.55 & 37.11 \\ Two layers & Yes & No & 94.44 & 87.11 & 87.77 & 88.66 & 88.44 & 88.66 \\ Two layers & Yes & Yes & 95.55 & **89.33** & **90.44** & 88.44 & **89.33** & 88.44 \\ \hline Three layers & No & No & 92.0 & 27.33 & 30.0 & 28.88 & 28.66 & 24.88 \\ Three layers & No & Yes & **97.33** & 38.0 & 34.88 & 34.44 & 35.77 & 34.88 \\ Three layers & Yes & No & 94.0 & 88.66 & 86.88 & 84.22 & 86.22 & 86.44 \\ Three layers & Yes & Yes & 96.66 & 88.0 & 88.88 & 88.88 & 89.11 & **89.11** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Varying architecture results. Each row represents a trained model with either one, two or three layer; as well as with and without noise augmentation and recurrent dropout. The best result on each column is highlighted in bold.  
Figure 7: Precision and recall curves for the models presented in Table 6 evaluated on a testing set with Gaussian noise of zero mean and 40 cm of standard deviation.
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
## 4 Coupling Analysis of the Electromagnetic-Temperature Field
### Coupling Characteristic Analysis Based on Unidirectional Coupling Method
#### 4.1.2 Establishment of the Electromagnetic Analysis Model  
The establishment of the electromagnetic analysis model includes the definition of material, the determination of excitation source, and boundary conditions. Silicon steel sheet material DW465-50 was used as the core material of the motor stator and rotor, and the input BH curve was used as the constitutive relation of magnetic field; winding material was copper; rotor bracket, casing, and shaft material were steel; the PM material was NdFeB (material grades: N3 UH), its magnetizing direction was set in the cylindrical coordinate system, and the constitutive relation of magnetic field was set as residual magnetic flux density. The insulation layer was made of insulating material. At the same time, the conductivity of copper winding material was the value of conductivity at 60 \({}^{\circ}\)C, while the conductivity of other components was the value of conductivity at 20 \({}^{\circ}\)C. The core region of the stator and rotor was bounded by Ampere's law, while the other regions were bounded by the Gauss' law. Then, the current of the stator winding was set up. Figure 9 shows the schematic diagram of a three-phase winding configuration.  
According to Figure 9, the input and output boundaries of the winding current are set, and the winding turns, the conductor cross-section, and the current values under rated conditions are given.
# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 2 Data Introduction and Processing Methods
### Data Processing Methods
The current global map can be built using the median or mean values for specific parameters routinely every five days (i.e., the revisiting period of the satellite). Then, the regional background map over the EQ influential zone can be extracted from the global background map. After that, the difference map was built by calculating the difference values in each grid between the current map and the background map over the EQ influential area (every five days within 25 days before and 5 days after the mainshock) to depict the abnormal variations during seismic activity. The particle integration comparison  
The HEPP observations within the appropriate time window and inside the EQ influential zone were selected. The electrons were categorized into two energy ranges: 0.1-0.3 MeV and 0.3-3.0 MeV. Then, the electron flux was calculated by integrating it over scattering angles. According to previous research, the variation of electron flux greater than 0.5 orders of magnitude is regarded as anomalous flux relative to the \"no-anomalous\" period in the same region, which is rated as highly significant [38]. It is noted that only seismic events that occurred away from the South Atlantic Anomaly (SAA) region or the outer radiation belts can be analyzed using the electron flux comparison method.
## 5 Matching Analysis of Modelled Parameters
### Parameters' Calculation and Analysis
#### 5.2.1 Braking Deceleration  
In the process of the virtual trial, the target deceleration data for the cast-steel group was set as initial conditions, and the curve is shown as the yellow line in Figure 2. Under the condition that the train's braking system remains unchanged, the braking pressure \(F_{Ni}\) applied to the friction sub by the train braking devices at all brake models remains unchanged, and the target deceleration curve for the carbon-ceramic group can be calculated by combining Equations (3) and (4), as shown by the blue line in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Braking Conditions** & **Values** & **Unit** \\ \hline Initial speed & 160, 200, 250, 300, 350, 360, 380 & km/h \\ \hline
**Braking mode** & **Emergency braking, Segmented emergency braking, Maximum common braking** & / \\ \hline Train load & AW\_0, AW\_1, AW\_2, AW\_3 & / \\ \hline Line’s slope & 8–17 & \%o \\ \hline \hline \end{tabular}
\end{table}
Table 5: Braking condition parameters of high-speed trains.  
Figure 2: Comparison of target braking reduction speed between carbon-ceramic group and cast-steel group.  
Because the average frictional coefficient of carbon-ceramic brake discs is higher than that of cast-steel brake discs, the trend of target braking deceleration is the same for both groups; however, the carbon-ceramic brake disc provides a higher value of braking deceleration than cast-steel brake disc for the train under the same speed conditions.
## 5 Simulation and Experiment
### Simulation
#### 5.1.1 Simulation and Analysis of ILEACH and TGA Algorithms
Figure 14: Completion time by the average amount of data of cluster heads. (**a**) Small amount of data cases; (**b**) large amount of data cases. Figure 15: Completion time by UAV flight speed. (**a**) 32 Mbits; (**b**) 64 Mbits. In scenario 3, we evaluated the performance of TGA-MOVING-DCMDR varying with sensor scale. We set the flying speed to 12 m/s, and the average amount of data in cluster heads was 64 Mbits. The scale of sensor nodes varies from 100 to 400. As shown in Figure 16, the number of cluster heads selected by ILEACH increases with the number of sensors, respectively, 13, 22, 30, 36, and the corresponding clustering ratios are 13%, 11%, 10%, and 9%. The completion time of the UAV data collection task in the four simulation groups increases with the number of sensor nodes. In the case of the same number of nodes, the completion time of the method TGA-MOVING-DCMDR is always less than the other three methods. The results show that the optimization scheme in this paper can be used in different scale scenarios.
## 4 Results and Discussions
### Circularity Results
#### 4.3.2. Circularity Comparison Results  
The three samples--T2.5, MKT, and KFT--were used for the circularity comparison mainly because the other sample (SFKF5) did not achieve the desired compressive and tensile strengths. The durability results for the proposed equation is obtained from previously published results [40]. The circularity index results for the three mix designs, giving equal importance to reuse, repair, and recycling, are shown in Figure 7a. A circularity index closer to 1.0 indicates a higher potential for circularity in the product mix. The results indicate that the KFT sample achieves the highest circularity score of 0.44, while both T2.5 and MKT have a circularity score of 0.184.  
For all three samples, the repair stage contributed the most to the circularity index. This is primarily due to the significant durability improvements in the novel mixes compared to the control mix. This suggests that the novel mixes would last longer than conventional mixes, thereby reducing repair and maintenance costs.  
A Monte Carlo simulation with 5000 iterations was performed to investigate the influence of weighting factors (the importance of each \"R\" stage within material circularity) on the circularity index. The corresponding results are presented in Figure 7b. These results further emphasize that the KFT mix can achieve better circularity performance compared to the other mix designs proposed in this study. Additionally, the findings highlight that, for the given mix designs, focusing on improving durability characteristics with an optimized high cement replacement ratio can lead to better circularity performance.  
Figure 7. Circularity index results for the three mix designs.
## 2 Design of Seismic Test Mockup  
### Configuration and Dimensions  
In this study, the seismic test mockup is designed to be able to simulate the actual nuclear component installed in the in-structure building. This will be consistent with the purpose of validating the strain-based seismic design method developed in previous studies [9; 13]. To do this, the metal component nozzle, which is known as one of the seismically fragile components in nuclear steam supply systems (NSSS), was selected as the reference for the test mockup. Specifically, the hot leg surge line nozzle connecting the hot-leg pipe and the surge line piping system, which is installed at the elevation of 136 ft in the in-structure building, is referenced in this test mockup design.  
The main design concept of the test mockup is to configure the piping layout, resulting in the maximum accumulated plastic strain at the safe-end region connecting the nozzle and the piping system in the same condition as the actual hot-leg surge line nozzle in NSSS. Figure 1 presents the designed overall configuration and seismic directional axis of the test mockup. As shown in the figure, the test mockup consists of a target nozzle, horizontal and vertical pipes, one elbow, and two added masses. The total height of the test mockup is 1132 mm, and the total mass is about 158 kg. Table 1 reveals the summary of the design parameter values of the test mockup.  
The used material of the test mockup is Type 316 stainless steel, which has an elastic modulus of 190 GPa, a density of 7970 kg/m\({}^{3}\), and a Poison's ratio of 0.27 at room temperature.
## 5 Experimental Results and Discussion
### Case II: External Vibration with the Varying Frequency  
To test the applicability of the proposed fuzzy controller for the external vibration with the varying frequency, an external excitation of 8 Hz was applied to the main structure at \(t=0\) s. Then, we changed the frequency of the excitation from 8 to 16 Hz at \(t=3\) s. Figure 23 shows the measured time response of the structure's displacement. Figure 23 validates that the proposed fuzzy controller has the adaptive ability to suppress vibration efficiently and rapidly, even if the frequency of external vibration is changed. Figure 23 shows that the VRR of the proposed control at the frequency of 8 Hz is almost 86.5% with the electric field output of 2 kV/mm, which is consistent with the previous result in Figure 20. The electric field output of 1.8 kV/mm is obtained by using the proposed controller and the VRR of 16 Hz is almost 29.4%. Therefore, the experimental result shows that the proposed controller validates the vibration reduction ability for the external vibration with the two frequencies of 8 and 16 Hz. From the above experimental results, we found the fact that the proposed ATVA has the superior ability of absorbing vibration for this plate at the first and third vibration modes (8 Hz and 77 Hz). For the second mode, the VRR of the ATVA is reduced from 81.5% (for the first mode) to 40.9%. Except for the vibration frequency of 8 Hz, the proposed type-2 fuzzy controller has better VRR than the type-1 fuzzy control.
## 4 Time Differenced Filter Algorithm
### Observation Equation  
As illustrated in Figure 11, the ionosphere-free combination pseudorange noise of Xiaomi Mi 8 is much larger than the ionospheric delay and broadcast group delay. Meanwhile, considering that pseudorange outlier percentages of raw GNSS observations are as high as 5.10%-43.86% in urban environments, the ionosphere-free combination may lose a lot of usable pseudorange observations. Therefore, single frequency positioning strategy is appreciated in Xiaomi Mi 8 data processing. The GNSS pseudorange observation can be formed as follows:  
\[p^{j}=\rho-cdt^{s}+cdt_{r}+d_{qph}+d_{ion}^{j}+d_{trop}+\varepsilon_{p^{j}} \tag{5}\]  
where, \(P\) is the pseudorange observations, the superscript \(j\) is the frequency label of the observations, \(d_{qph}\) is the error in the signal propagation path that originates from GNSS broadcast ephemeris orbit and clock, \(d_{ion}\) is the ionospheric delay, which is corrected by the ionospheric delay correction model of each GNSS system, \(d_{trop}\) is the tropospheric delay, corrected by the Saastamoinen model [35], and \(\varepsilon_{p^{j}}\) is the pseudorange noise including the multipath error. Then the observation equation in the linearized form is given by  
\[\textbf{Z}_{t_{i}}=\textbf{B}_{t_{i}}\delta\textbf{X}_{t_{i}}+\textbf{v}_{t_{i}} \tag{6}\]  
where \(\textbf{Z}_{t_{i}}\) is the construction of pseudorange measurement residual vector, \(\textbf{B}_{t_{i}}\) is the observation design matrix, \(\delta\textbf{X}_{t_{i}}\) is the correction to the initial position, which is predicted in Equation (3) and used in the linearization of the observation equation, \(\textbf{v}_{t_{i}}\) is the observation noise vector, which can be represented by a vector of Gaussian white noises with \(E(\textbf{v})\) = 0 and \(E(\textbf{vv^{T}})\) = **R**. **R** is obtained from the aforementioned C/N0-dependent weighting model.
studied as a concentrated source of the essential fatty acid ALA for hen diets, resulting in eggs enriched with ALA and, to a much lesser extent, with DHA [11; 12; 13]. In humans, the metabolic pathway for n-3 PUFA synthesis comprises the conversion of ALA firstly into EPA and, at a later stage, to DHA, catalyzed by desaturases and elongases. Unfortunately, limited yields of EPA and DHA are obtained through their in vivo synthesis from ALA, making indispensable their consumption to meet the recommend dietary intakes [12]. Therefore, adding EPA and DHA to hen diet is a much more profitable way to enrich eggs in n-3 PUFA in comparison with flaxseed supplementation. In this sense, fatty fish oil, namely from sardine and menhaden, has been used as DHA and EPA sources for eggs enrichment [14; 15; 16]. Several studies reported higher levels of DHA in eggs from hens fed with fish oil, suggesting a large conversion of EPA into DHA [14; 16; 17; 18; 19]. One of the main constraints to the supplementation of the hen diet with fish oils is the appearance of off-flavors, identified as \"fishy taste\", at fish oil concentration of about 1.5%, which results in yolk DHA levels below 100 mg/egg [17; 20]. Higher levels of n-3 PUFA have been reached with microalgal feed supplementation. Microalgae are unicellular organisms, mainly found in oceans and in fresh or brackish water, that can be cultured either autotrophically or heterotrophically [21]. Autotrophic microalgae stand out as a more sustainable source of n-3 PUFA, since the heterotrophic production requires the intake of organic carbon sources provided from terrestrial crops [21; 22]. Moreover, microalgae are an interesting source of bioactive compounds, such as polyphenols and carotenoids, for food and feed purposes [23]. _Nannochloropsis_ is a marine microalgae genus highly efficient in the conversion of carbon dioxide into neutral lipids, with high content of EPA ranging from 1.1% to 12% DW across species [24]. EPA content of ca 3 to 5.5. (% DW) was reported for _Nannochloropsis oceanica_[25; 26; 27].  
In this study, _N. oceanica_ was used as source of n-3 PUFA for hen feed supplementation during 4 weeks. Several parameters were analyzed aiming at evaluating the nutritional profile (protein, lipids, ash, and n-3 PUFA content), physical characteristics (yolk colour, shell thickness and shell colour, egg protein quality (HU) and egg total weight) and shelf life for 28 days at room temperature.
## 3 Results and Discussion
### Analysis of Heavy Metal Contents in Residues before and after Planting  
The heavy meatal content and migration behavior in residues are very important for later residue disposal [37]. Therefore, the soil ratio conditions with the strongest inhibitory effects on mung bean growth were selected for further analysis in the present study. The changes in the heavy metal content in the soil before and after planting were analyzed. Figure 5 shows the proportions of heavy metals in the soil after planting compared to the heavy metal content in the soil before planting.  
Figure 5: Proportions of heavy metals in the soil before and after planting: (**a**) high-temperature oxidation residue, (**b**) pyrolytic residue, and (**c**) thermal-washing residue.  
Compared to the pyrolytic residue and high-temperature oxidation residue, the large variations in Cu, Pb, Zn, Cd, Cr, and Ni in the thermal-washing residue might be attributed to the strong adsorption of these metals by mung beans. Furthermore, the mung beans exhibited a stronger ability to accumulate As and Hg in the high-temperature oxidation residue than in the thermal-washing residue. However, Cr and Ni uptake was very weak in the mung beans that were cultivated in the high-temperature oxidation residue. Cd exerts strong genotoxic effects at low concentrations [29] and can therefore partially or fully inhibit vital enzyme activities, such as those that are responsible for ATP production [40]. Therefore, the high-temperature oxidation residue induced the highest levels of plant membrane damage and aging. Pb is highly persistent in soil, and although Pb is less toxic than Cd, it can alter biodiversity and disrupt ecological functions [41]. Moreover, Pb ions can be biomagnified through the food chain and can cause severe neurotoxicity in humans when ingested [42]. Interestingly, the mung beans exhibited the highest Pb uptake rates among all of the heavy metals.  
The hazards of heat treatment residues on the environment and plants not only depend on their heavy metal content but also on the toxicity of organic compounds and their interactions with the heavy metals in the residue. The thermal-washing residue was found to contain \(n\)-hexadecane, which had an inhibitory effect on Cd toxicity [20]. Therefore, the Cd content was the highest when the plants were exposed to the thermal-washing residue, which also explained the low toxicity of this residue treatment.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 4 Location Determination with Mutual Base Station Authentication  
Next, we consider the modification of the described protocol that allows for ensuring the anonymity of the vehicle while determining the distance to the base station, further referred as P-AF-BS protocol and shown in Figure 6.  
In the previous section, we discussed the protocols for vehicle-to-base station communication by transferring the corresponding vehicle _id_ and location, yet this approach is vulnerable to a number of security issues. An attacker controlling a malicious BS could gain access to the localization of any vehicle, control its movement, or deceive the vehicle by transmitting the wrong distance, which might lead to unpleasant consequences. Security of such a protocol might be significantly improved by not allowing the base station access to precise vehicle location. There are two possible ways of delivering this improvement:  
* The distance is calculated on the side of the vehicle, all the operations are performed in a special secure computing module;
* The distance is calculated on the side of the base station, while the vehicle actions are limited to sending requests and receiving answers.