esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study for a Brigade-Level EMP Shelter Using the Finite Integrating Method
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protection Level** & **Frequency Range** & **Shielding Standard (dB)** \\ \hline Level 1 & 10 kHz\(\sim\)10 MHz & 20 log \(f\)-60 or higher \\  & 10 MHz\(\sim\)1 GHz & 80 \\ \hline Level 2 & 10 kHz\(\sim\)10 MHz & 20 log \(f\)-80 or higher \\  & 10 MHz\(\sim\)1 GHz & 60 \\ \hline Level 3 & 10 kHz\(\sim\)10 MHz & 20 log \(f\)-100 or higher \\  & 10 MHz\(\sim\)1 GHz & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Radioactive protection performance standards. Figure 4: Simulation conditions (**a**) Case 1—Air condition, (**b**) Case 2—Dry and wet soil conditions. Figure 3: Sensor locations. Figure 2: Developed FIM model. In the simulation results, high electric field strength was measured around the windows for the ground structure, as expected. The incoming high-power EMP refracted by hitting the walls, ceiling, and floor inside the communication room. In the underground structure, the natural attenuation effect of soil was confirmed, and the wet soil with high electrical conductivity showed a better attenuation effect. As shown in Table 5, the wet soil produced a natural attenuation effect of up to 30 dB. Figure 6 shows the electric field strength attenuation by measurement locations over time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Value** & **Air** & **Dry Soil** & **Wet Soil** \\ \hline Density (kg/m\({}^{3}\)) & 1.204 & 1550 & 1800 \\ Thermal conductivity (W/K/m) & 0.026 & 0.2 & 1 \\ Heat capacity (J/K/kg) & 1.005 & 0.8 & 1.5 \\ Diffusivity(m\({}^{2}\)/s) & 2.14872 \(\times\) 10\({}^{-6}\) & 1.6129 \(\times\) 10\({}^{-7}\) & 3.7037 \(\times\) 10\({}^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Input values used in FIM. Figure 5: E1 pulse shape used in simulation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Shielding Effectiveness** & **Air** & **Dry Soil** & **Wet Soil** \\ \hline Room 1 & 13.8 dB & 15.18 dB & 22.15 dB \\ Room 2 & 17.99 dB & 22.85 dB & 31.70 dB \\ Room 3 & 17.45 dB & 23.60 dB & 28.40 dB \\ Room 4 & 18.96 dB & 23.87 dB & 34.24 dB \\ Corridor 1 & 17.45 dB & 19.82 dB & 25.41 dB \\ Corridor 2 & 11.50 dB & 12.11 dB & 19.41 dB \\ Corridor 3 & 21.13 dB & 24.00 dB & 30.22 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Measurement results of natural shielding effectiveness against E1 Pulse. Figure 6: _Cont_.
## 3 Results
### Phase 2
#### 3.2.4 Spread Study  
Dye was found in the target plane in 22/23 hemithoraces and in the subcutaneous space in 1/23. The dye solution was also found in the superficial SVT plane in 18/23 hemithoraces. A median of 3 (0-5) nerves, from RCL2 to RCL7, were stained in the target DSP, whereas a median of 1 (0-5) RCL were found dyed in the superficial plane (Table 1 and Figure 8). In one cadaver, the dye was found in the subcutaneous space in one hemithorax. In the same cadaver, no RCL were found stained in the contralateral hemithorax. Additionally, the thoracicus longus nerve was found stained in 17.39% (4/23) of the hemithoraces. No intrathoracic dye was found.  
Figure 8: Diagram of the number of nerves stained after performing the deep serratus plane block with a mixture of 0.4 mL kg\({}^{-1}\) of methylene blue and iopromide 50:50 in 12 cat cadavers. One hemithorax was excluded (red cross). L, left hemithorax; R, right hemithorax; T2, T3, T4, T5, T6, T7, and T9, spinal nerves; 1–12, cats 1–12.  
Figure 7: Computed tomography transverse images of three cat cadavers after performing an ultrasound-guided deep Serratus plane block. (**A**) Image at the level of the fifth thoracic vertebra. Contrast media pooled in the target area between the serratus ventralis thoracics and intercostales externi muscles. (**B**) Image at the level of the fourth thoracic vertebra. Contrast media in the right hemithorax reach the epaxial muscles. (**C**) Image at the level of the sixth thoracic vertebra showing a combination of A and B patterns. Yellow arrow, contrast media; blue arrow, scapula; D, dorsal; LL, left lateral; RL, right lateral; V, ventral.  
The methylene blue was also observed dorsally between serratus dorsalis thoracicis and the LD muscles, staining the RDL of the rami dorsales of the spinal nerves between T2-T7 (Figure 7). A median of 2 (0-6) RDL were dyed, specifically RDL2 (17.39%), RDL3 (39.13%), RDL4 (43.48%), RDL5 (39.13%), RDL6 (43.48%), and RDL7 (30.43%) (Figure 9).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Location of the Staining** \\ \hline
**Nerve** & **Deep Plane** & **Superficial Plane** \\ \hline RCL2 & 17.39\% (4/23) & 8.70\% (2/23) \\ RCL3 & 52.17\% (12/23) & 26.09\% (6/23) \\ RCL4 & 78.26\% (18/23) & 43.48\% (10/23) \\ RCL5 & 91.30\% (21/23) & 65.22\% (15/23) \\ RCL6 & 78.26\% (18/23) & 26.09\% (6/23) \\ RCL7 & 8.70\% (2/23) & 0\% (0/23) \\ RCL8 & 0\% (0/23) & 0\% (0/23) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number and percentage of RCL stained in the cat cadavers either in the deep or the superficial plane of the serratus ventralis thoracicis following DSP injection of a 0.4 mL kg\({}^{-1}\) mixture of iopromide and methylene blue. RCL; rami cutanei laterales.  
Figure 9: Distribution of dye in the deep serratus ventralis plane after the ultrasound-guided injection of a mixture of methylene blue and iopromide 50:50. RDL, rami laterales of the rami dorsales of the spinal nerves; RCL, rami cutanei laterales of the intercostales nerves; D, dorsal; Cr, cranial; Cd, caudal; V, ventral.
# Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation
## 2 Parameter Estimation Problem and System Poles  
In this section the parameter estimation problem that is applied to the induction machine nonlinear model is introduced. The optimal sampling time is computed with the aim to improve the solution of this problem. Subsequently, the procedure to determine linear systems poles is presented as a fundamental step in determining the induction machine optimal sampling time.
## 1 Introduction
### Related Works
In addition to directly predicting the severity of an accident, XG Boost was also used to study traffic safety authorities from the Ministry of Transportation Security (MOT) in Riyadh, Saudi Arabia, including 13,546 motor vehicle crashes reported on 15 rural roads between January 2017 and December 2019. It found that crash type, weather, road surface condition, type of site damage, lighting conditions, and vehicle type were sensitive variables in predicting the severity of an accident [21]. Overall, machine learning-based models have achieved a wealth of results in the prediction of road traffic accidents, but there are still some drawbacks. The first is that the effect of missing data on the forecasting outcome is not perfectly accounted for. The missing data set contains missing crash and traffic flow data. Traffic information during an accident is indispensable for analyzing the impact of an accident [22]. Therefore, thereis a need to address the lack of missing traffic data. Second, the aforementioned research primarily focuses on predicting road accidents. It should be noted that the proposed model and method may not be suitable for tunnel accident analysis and prediction because the tunnel has a traffic environment different from that of the city road and freeway, and there is a need to consider the impact of additional traffic conditions on crashes. To overcome the aforementioned issues, this paper proposes a data-filling method based on GCN-LSTM to predict and fill missing traffic flow data. Second, this article encodes data from 200 traffic accidents recorded in Yingtian Street Tunnel in Nanjing and chooses the independent variables after coding. The classifier and regression model is based on BP neural network and random forest and is used to predict traffic congestion and crash duration, respectively. Therefore, this article has the following three main contributions:  
1. All models are built based on real-time data recorded during accidents, such as the time of occurrence, duration, type of accident, and type of vehicles involved, rather than obtained through simulation or modeling. 2. For missing traffic flow data, we use graph convolution long short-term memory (GCN-LSTM) network models to predict and fill in the gaps. Compared with direct deletion or using the mean value filling method, our approach is more accurate. 3. We build both a BP neural network and a random forest model to predict road conditions and accident duration after accidents. By comparing the prediction results, we demonstrate that both the random forest model and neural network have good predictive performance, and the random forest model is more robust in predicting the duration of accidents.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Vertebrates
#### 2.2.2 Galago (Baby Monkey) Inspired Jumping Robots  
Galago can execute spectacular jumps. A 0.25 kg galago can execute 2.25 m vertical jumps, which means that the displacement of the centroid during the flight phase is approximately six body lengths (tail not included). The muscles of the hind limbs of the galagoes account for 25% of the entire body weight and produce powerful propulsion. The peak power output calculated during take-off is around 2200 W/kg, and this value is much higher than that of the tested power output of muscle fiber bundles in animals, around 1000 W/kg [154; 155; 156]. Peaks in power output likely imply elastic recoil of the complex aponeurotic system of the vastus muscle, which works as a mechanical power amplifier. Mechanical power amplification means that elastic strain energy is stored during the initial phase of jumping, to be then released at an increased rate during take-off.  
[PERSON] et al. recently proposed the galago-inspired jumping robot \"Salto\" [157; 158]. Through the optimization of the multi-linkage parameters [159], the obtained eight-bar mechanism can achieve a series of requirements: (i) the foot trajectory can always be kept in a vertical line; (ii) due to zero momentum, the trajectory of the center of mass is not deflected during the jump; (iii) the ground contact force is always maintained at a constant value by considering the series of elastic actuators. \"Salto\" can realize pitch angle righting using an inertial tail and can perform the parkour similar to humans. Vertical jumping agility is a new proposed concept and is measured by vertical distance per second, corresponding to the average vertical speed that a system can attain with repeated jumps. This parameter is very important in natural environments, since it decides how quickly a predator can catch its preys or how quickly a prey can escape from its predators. \"Salto\" is the most agile jumping robot ever built. The improved version, \"Salto-1P\", [160; 161] can perform controlled yawing and rolling movements of the body using two propellers and an inertial tail and can perform precise continuous jumping. During the design of \"Salto\" series jumping robots, researchers focused mainly on the similarity of the jumping mechanism and performance with real animals.
# Edge and Cloud Computing in Smart Cities
## 4 Application Domains
### Cybersecurity and Threat Detection  
As edge-cloud infrastructures expand, ensuring robust cybersecurity is critical to mitigate risks associated with data breaches, unauthorized access, and cyberattacks. AI-driven anomaly detection enables real-time threat identification at the edge, preventing security breaches before they escalate [158; 159; 160]. FL enhances cybersecurity by enabling real-time threat intelligence sharing without exposing raw data, improving collaborative defense mechanisms across distributed networks. Blockchain-based security frameworks introduce decentralized trust models, ensuring tamper-proof authentication and secure data exchanges [161; 162; 163]. The integration of zero-trust architectures enforces continuous authentication and access verification, reducing vulnerabilities in edge-cloud ecosystems. These advancementscollectively strengthen cybersecurity resilience, ensuring the integrity, confidentiality, and availability of edge-cloud services [164; 165]. Table 4 provides a summary of various application domains in edge-cloud computing, categorizing them based on their primary objectives, computational challenges, key performance metrics, edge-cloud dependencies, and critical constraints. The table encapsulates how different sectors leverage edge-cloud frameworks to enhance efficiency, minimize latency, and optimize resource management.
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 3 Results and Discussion
### Effect of Adsorbent Dosage  
The effect of the surface-modified PAN fiber dose on the adsorption capacity of the surface-modified PAN fibers for Cu(II) was evaluated, and the results are illustrated in Figure 7. As the surface-modified  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Langmuir Model**} & \multicolumn{4}{c}{**Freundlich Model**} \\ \hline \(\mathbf{K_{L}}\) & \(\mathbf{q_{m}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{K_{F}}\) & **1/n** & \(\mathbf{R^{2}}\) \\ (I/mg) & (mg/g) & & **(I/g)** & & \\ \hline
0.703 & 99.06 & 0.679 & 47.06 & 0.153 & 0.928 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of Langmuir and Freundlich models for adsorption of Cu(II) on surface-modified PAN fibers. Here, \(\mathbf{K_{L}}\), \(\mathbf{K_{F}}\), \(\mathbf{q_{m}}\), n, and \(\mathbf{R^{2}}\) denote the Langmuir and Freundlich equilibrium adsorption constants, maximum sorption capacity of metal ions on adsorbent, Freundlich constant, and determination coefficient, respectively.  
Figure 6: Effect of solution pH on adsorption of Cu(II) ions. Here, q denotes the adsorption capacity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Materials**} & \multicolumn{1}{c}{**Conditions**} & \multicolumn{1}{c}{**q (mg/g)**} & **Reference** \\ \hline Synthetic hydroxyapatite & Initial concentration = 6355 mg/L; pH = 3 & 50.2 & [12] \\ Bentonite & Initial concentration = 3177 mg/L; pH = 5 & 27.6 & [13] \\ Lime sand brick & Initial concentration = 30 mg/L; pH = 5 & 7.0 & [27] \\ \hline \hline \multirow{4}{*}{Surface-doped PAN fiber} & Initial concentration = 1000 mg/L; pH = 6 & 143.5 & [25] \\ \cline{1-1}  & Initial concentration = 10 mg/L; pH = 6 & 45.0 & [8] \\ \cline{1-1}  & Initial concentration = 1000 mg/L; pH = 4 & 150.6 & [2] \\ \cline{1-1}  & Initial concentration = 500 mg/L; pH = 4 & 112.3 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the adsorption capacity (q) of different fibrous ion exchangers and adsorbents for Cu(II) adsorption.  
PAN fibers were added to the Cu(II) solution, the mass of adsorbed Cu(II) per unit mass of fiber gradually decreased and the removal percentage increased. The decrease in adsorption capacity per unit mass was attributed to the concentration of Cu(II) being insufficient to match the increasing number of adsorption sites caused by the addition of fibers, and therefore some adsorption sites were unsaturated [28]. The maximum percent removal reached 97.63% when the dose of 0.1 g adsorbent was added to 40 mL of 100 mg/L Cu(II) solution.
###### Abstract  
Recently, drones, have been utilized in many real-life applications including healthcare services. For example, providing medical supplies, blood samples, and vaccines to people in remote areas or during emergencies. In this study, the maximum coverage facility location problem with drones (MCFLPD) was studied. The problem is the application of drones in the context of the facility location and routing. It involves selecting the locations of drone launching centers, which maximizes patient service coverage within certain drone range constraints. In this study, a heuristic named the maximum coverage greedy randomized heuristic (MCGRH) is developed. The idea of the algorithm is to first choose some facilities to open at random from among those that can handle the most weight of the patient demands. After that, patients are assigned to the closest opened facility with the capacity to serve them. Finally, drones are assigned to patients based on the least amount of battery consumed between the patient and the facility. Extensive testing of MCGRH indicated that it ranks efficiently alongside other methods in the literature that tried to solve the MCFLPD. It was able to achieve a high coverage of patients (more than 80% on average) within a very fast processing time (less than 1 s on average).  
maximum coverage; facility location problem; routing and scheduling; drones; combinatorial optimization; heuristics; metaheuristics; healthcare
## 3 Results
### Dose Linearity  
Figure 2a shows that the intensity of the signal generated by the CZT detector increases linearly when the delivered dose is increased from 50 to 1000 cGy, indicating a linear correlation between the signal intensity and the delivered dose. The slope of the fitted curve is 1.019, with an R2 value of 0.9987, demonstrating an excellent linearity of the detector within the measured dose range. Furthermore, measurements in the low-dose region (Figure 2b) were performed to assess the precision of the measured values. The same linear fit was used for both high-dose and low-dose ranges. Data measured from 10 to 50 cGy also revealed excellent linearity, with a standard deviation close to zero.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Energy (MV)** & \begin{tabular}{c} **Field Size** \\ **(cm\({}^{2}\))** \\ \end{tabular} & **SSD (cm)** & **Dose (cGy)** & \
\begin{tabular}{c} **Dose Rate** \\ **(MU/min)** \\ \end{tabular} &
\begin{tabular}{c} **Measuring** \\ **Depth (cm)** \\ \end{tabular} \\ \hline Dose linearity & 6 & 10 \(\times\) 10 & 100 & 0–1000 & 400 & 10 \\ Dose rate & 6 & 10 \(\times\) 10 & 100 & 200 & 100–600 & 10 \\ dependence & 6, 10, and 15 & 10 \(\times\) 10 & 100 & 200 & 400 & 10 \\ Angular dependence & 6 & 10 \(\times\) 10 & 100 & 200 & 400 & - \\ SSD dependence & 6 & 10 \(\times\) 10 & 80–120 & 200 & 400 & 10 \\ Field size & 6 & 5 \(\times\) 5 to & 100 & 200 & 400 & 10 \\ dependence & 6 & 25 \(\times\) 25 & & & & \\ Reproducibility & 6 & 10 \(\times\) 10 & 100 & 200 & 400 & 10 \\ PDD & 6, 10 & 10 \(\times\) 10 & 100 & 500 & 400 & 0–25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement conditions used in this study.
# Magneto-Inertial Fusion and Powerful Plasma Installations (A Review)
## 5 Theoretical Studies and Prototypes
During the ALPHA project, the PLX-\(\alpha\) team presented a concept in which a liner with cold D-T fuel and a liner with argon with high Z-pinch were formed. After the high-Z liner fully formed around the D-T liner, pulsed laser waves were fired into the exploding D-T liner to create a magnetic field, allowing the spherical inner liner to function as a target. MIF, with laser jets, has a strong potential due to the relatively high repetition rate of one to two cycles per second, which closely resembles a continuous source of fusion energy. It is possible to use a thick-liquid inner wall, such as a flowing layer of liquid metal or molten salt, as the main heat carrier to remove heat from the fusion reaction chamber, while reducing damage to the vessel structure from neutrons and serving as a medium for extracting tritium to maintain the D-T fuel cycle in the reactor. The key challengesfor developing a commercial reactor are achieving uniform liner pressure, forming a magnetized plasma target, and developing reactor-type plasma guns [93]. ZAP Energy aims to generate fusion energy using Z-pinch stabilized by a sheared flow. One of the latest inventions is the FuZE-Q reactor. This device eliminates the need for the expense and complexity of installing magnetic coils, replacing them with pulses of electrical current along the column of high-conductivity plasma. Because Z-pinch plasma is unstable, it decays within tens of nanoseconds without uniform compression. The new device allows for an increase in the current pulse from 500 kA to 650 kA, which will enable a gain factor of Q = 1 by 2026 [94].
## 5 Results
### LIBS Data Analysis  
The acquired LIBS data enable both qualitative and quantitative elemental analysis of the investigated sample. In the wavelength range of the ARCHES LIBS instrument from 531 to 776 nm, several major and minor rock-forming elements are available. Figure 10 displays two LIBS spectra acquired with the ARCHES LIBS: one from an unknown rock at the testbed at PEL and the other from a presumably igneous rock with a volcanic context at the foothills of Mt. Etna during the demonstration mission. Each spectrum represents the average of ten laser shots from a single position on the sampled rock.  
The spectrum acquired indoors at PEL exhibits strong calcium (Ca) emission, moderate emission of sodium (Na) and potassium (K), and silicon (Si) emission. The spectrum obtained at Mt. Etna shows strong emission of the alkali metals Na, K, and lithium (Li), and low emission of Ca and Si. Hydrogen (H) is present in both spectra, along with the nitrogen (N) triplet, resulting from the breakdown of ambient gas contributing to the laser-induced plasma. Both spectra feature emission lines of observable elements with sufficient signal-to-noise ratio (SNR), allowing further analysis, such as classification or the derivation of quantitative elemental values. For the latter, reference LIBS data were obtained with the ARCHES LIBS module from a set of geological samples provided by ESA, and the results will be presented in an upcoming publication.  
Figure 10: LIBS spectra recorded with the ARCHES LIBS instrument on rocks during tests at PEL and the demonstration mission on Mt. Etna. Each spectrum represents the average of ten laser shots from a single position on the sampled rock. The continuum signal was removed using a moving minima algorithm, and each spectrum is normalized with its respective sum for easier comparison of the spectra’s relative line intensities.  
Figure 9: Picture sequence, showing the LRU2 coupling its robotic arm to the LIBS modular payload box on its back and placing the instrument onto a target for the measurement. In the background, LRU1 equipped with cameras can be seen.