esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Calculation of Geometric Parameters
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Meaning** & **Equation** \\ \hline
**Profile parameters** & & \\ \hline MPD (mm) & Mean profile depth & MPD = \(\frac{\sum_{i}\left(\frac{1+\alpha_{i}}{2}-h\right)}{n}\) \\ \(Ra\) (mm) & Roughness average & \(Ra=\frac{1}{n}\cdot\sum\limits_{i=1}^{n}|y_{i}|\) \\ \(Rq\) (mm) & RMS roughness & \(Rq=\sqrt{\frac{1}{n}\cdot\sum\limits_{i=1}^{n}\left(y_{i}\right)^{2}}\) \\ \(Da\) & Arithmetic mean slope & \(Da=\frac{1}{n}\cdot\sum\limits_{i=1}^{n}\left|\frac{\Delta y}{\Delta x}\right|\) \\ \(Dq\) & RMS slope & \(Dq=\sqrt{\frac{1}{n}\cdot\sum\limits_{i=1}^{n}\left(\frac{\Delta y}{\Delta x }\right)^{2}}\) \\ \(La\) (mm) & Average wavelength & \(La=2\pi\times\frac{Ra}{Da}\) \\ \(Lq\) (mm) & RMS wavelength & \(Lq=2\pi\times\frac{Ra}{Da}\) \\
**Height parameters** & & \\ \(Ssk\) & Skewness of height distribution & \(Ssk=\frac{1}{Lq^{3}}\cdot\frac{1}{A}\sum\limits_{A}Z^{3}(x,y)\) \\ \(Sku\) & Kurtosis of height distribution & \(Sku=\frac{1}{Lq^{4}}\cdot\frac{1}{A}\sum\limits_{A}Z^{4}(x,y)\) \\ \(Sp\) (mm) & Maximum peak height & \(Sp=maxZ(x,y)\) \\ \(Sv\) (mm) & Maximum valley height & \(Sv=|minZ(x,y)|\) \\
**Functional parameters** & & \\ \(Sk\) (mm) & Core height & \(Sk=Sk1-Sk2\) \\ \(Spk\) (mm) & Reduced peak height & \(Spk=Sp-Sk1\) \\ \(Sok\) (mm) & Reduse valley height & \(Sok=Sk2-Sv\) \\
**Volume parameters** & & \\ \(Vvv\) (mm\({}^{3}\)/mm\({}^{2}\)) & Dale void volume & \(Vvv=V_{v}(mr2)\) \\ \(Vvc\) (mm\({}^{3}\)/mm\({}^{2}\)) & Core void volume & \(Vvc=V_{v}(mr1)-V_{v}(mr2)\) \\ \(Vmp\) (mm\({}^{3}\)/mm\({}^{2}\)) & Peak material volume & \(Vmp=V_{m}(mr1)\) \\ \(Vmc\) (mm\({}^{3}\)/mm\({}^{2}\)) & Core material volume & \(Vmc=V_{m}(mr1)-V_{m}(mr2)\) \\
**Hybrid parameters** & & \\ \(Sdr\) & Developed interfacial area ratio & \(Sdr=\frac{1}{A}\left\{\frac{\Delta z}{A}\sqrt{1+\left[\left(\frac{\partial z (x,y)}{\partial x}\right)\right]^{2}+\left[\left(\frac{\partial z(x,y)}{ \partial x}\right)\right]^{2}}-1\right\}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Geometric parameters of macro-texture.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 2 Mission Overview
### Inter-Satellite Cross-Validation Method  
#### 2.5.1 iGNSS-R Altimeter Products  
The iGNSS-R altimeter experiment data used for inter-satellite cross-validation in this study were collected from 11 July 2023 to 29 December 2023. The data feature a sampling rate of 5 Hz and an along-track spatial resolution of 40 km. For inter-satellite cross-validation with radar altimeter products, sea surface heights and several error correction parameters were extracted from these data. These parameters include ocean tides, polar tides, solid Earth tides, DAC, and dry and wet tropospheric correction terms.
## 2 Materials and Methods
### Shielding Properties of Embedded Cu Meshes  
In this study, the \(SE\) of the materials was measured using a special 16.00/6.95 mm air-filled coaxial cell (type II, 50 \(\Omega\), GOST RV 51914-2002). The advantages of this approach are a wide frequency range (from 10 MHz to 7 GHz), the ability to measure at low frequencies and a relatively simple and convenient measurement technique. It is required to ensure reliable electrical contact between the material sample and the conductors of the coaxial path over the entire area of their contact for the correctness of the result. To overcome this difficulty, the specific design of the cell has a groove of varying thickness into which the sample is inserted and clamped. Electro-sealing gaskets made of dense metallized fabric were used for better electrical contact between the sample and the coaxial tract walls. The cell provides the correct result for conductive samples.  
The test bench includes a Keysight FieldFox N9916A (Keysight Technologies, Santa Rosa, CA, USA) network analyzer, coaxial cable assemblies and a cell; we performed a full two-port calibration at the cell inputs. To determine the \(SE\) of a material sample:  
1. measure the spectrum \(S_{21}\) (or \(S_{12}\)) of the cell without a sample (groove thickness-- 0 mm), S\({}_{21\mathrm{OS}}\) (dB),
2. measure the spectrum \(S_{21}\) (or \(S_{12}\)) of the sample cell, S\({}_{21S}\) (dB),
3. calculate the spectrum \(SE\) (dB) by Formula (1), \[SE=S_{21\mathrm{OS}}-S_{21S}\] (1)  
The dynamic measurement range of the \(SE\) unit is 80 dB and the measurement error is \(\pm\)2 dB.
## 3 Case Study
### Transportation Distance  
In the transportation process of an open-pit mine, trucks primarily travel between loading points, unloading points, and battery-swapping stations. The distances between these points are shown in Tables 3-5, with all units in kilometers. Table 6 illustrates the best transportation route between the unloading point and the battery-swapping station.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unloading Point**} & & & & \\ \hline
**Shovel Point** & & **A** & **B** & **C** & **D** & **E** \\ \hline a & 1.5 & 1.1 & 2.0 & 1.2 & 1.7 \\ b & 3.2 & 1.2 & 0.6 & 1.1 & 3.4 \\ c & 1.2 & 3.2 & 2.6 & 1.5 & 1.4 \\ d & 0.3 & 2.3 & 2.3 & 1.8 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The transportation route for the shovel point and unloading point.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Number of Shovel Points & 5 \\ Number of Crushing Stations & 4 \\ Number of Battery-Swapping Trucks & 30 \\ Number of Battery-Swapping Stations & 3 \\ Number of Batteries per Station & 15 \\ Unit Cost per km (Fully Loaded) & 10 CNY/km \\ Unit Cost per km (Unloaded) & 7 CNV/km \\ Energy Consumption per km (Fully Loaded) & 4.58 kWh/km \\ Energy Consumption per km (Unloaded) & 2.78 kWh/km \\ Truck Speed (Fully Loaded) & 17 km/h \\ Truck Speed (Unloaded) & 22 km/h \\ Unit Maintenance Cost per km & 1 CNY/km \\ Truck Loaded Capacity & 300 tons \\ Truck Battery Capacity & 300 kWh \\ Nominal Battery Capacity at Factory Temperature & 25 \\ Temperature Impact Coefficient (k0) & 0.8 \\ Shovel Point Production Task Requirement & 4000 tons \\ Unloading Point A Consumption Capacity & 6000 tons \\ Unloading Point B Consumption Capacity & 7000 tons \\ Unloading Point C Consumption Capacity & 8000 tons \\ Unloading Point D Consumption Capacity & 4000 tons \\ Work Shift Duration & 8 h \\ Average Loading Time & 5 min \\ Average Unloading Time & 3 min \\ Battery Swapping Time & 6 min \\ Minimum Battery-Swap Threshold Coefficient & 25\% \\ Battery Charging Speed & 4.5 kWh/min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter table.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Name** & **Population Size** & **Generations** & **Crossover Probability** & **Mutation Probability** & **Number of Elite Individuals** \\ \hline Value & 100 & 400 & 0.8 & 0.01 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Genetic algorithm parameter configuration.
## 3 System Functional Modules
### Base Station Module Hardware and Software Design
#### 3.3.4 The Base station  
The base station waits for the user to set the node information. The information will be sent to JN5139 module once it is obtained. Then the station waits for the JN5139 module to return information of the processing result, and updates the screen accordingly. This task will be implemented by a loop.  
Figure 11: The base station and the GPRS communication flow.  
(2) Node Uploading Data Processing Tasks  
At this time, the base station waits for JN5139 to upload the node networking data. Then the updated data are stored in the node information list and the data collection list of the base station. After that the information is renewed on the screen. This task will be implemented by a loop.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Carthamus tinctorius L
#### 3.1.5 Antiobesity Potential  
Obesity represents a multifaceted metabolic disorder with a complex origin, characterized by abnormal or excessive fat accumulation that poses a significant health risk. Fat accumulation has been noted to exhibit a strong association with indicators of systemic oxidative stress [30; 31; 32]. The bioactive compounds obtained from safflower, including HSYA and safflower yellow (SY), have been identified as having antiobesity properties. In a study involving diet-induced obese (DIO) mice, HSYA or SY were administered at a dosage of 200 mg/kg per day for 10 weeks. The findings indicated that both SY and HSYA led to significant reductions in body weight gain and fat mass in the DIO mice. Additionally, improvements were observed in glucose metabolism and liver function parameters. [PERSON] et al. [33] postulated that the favorable impacts of these compounds may be linked to elevated expression of antioxidant enzymes in the liver and adipose tissue. Another experimental analysis of SY demonstrated notable effects, including significant reductions in body fat mass, lowered fasting blood glucose levels, and improved insulin sensitivity in high-fat diet-induced obese male mice. These findings were observed following an 8-week regimen of intraperitoneal injections of SY at a dosage of 120 mg/kg daily. The study suggested a potential mechanism involving the promotion of browning in subcutaneous white adipose tissue and the activation of the IRS1/AKT/GSK8 (insulin receptor substrate 1/protein kinase/glycogen synthase kinase 3\(\beta\)) pathway in visceral white adipose tissue [34]. According to [PERSON] et al. [35], in mice with obesity induced by an HFD, oral administration of HSYA also led to decreased fat accumulation, improved insulin sensitivity, and reestablished normal glucose levels. HSYA effectively alleviated inflammation, fortified intestinal structures, boosted short-chain fatty acid production, and rectified gut microbiota imbalance triggered by an HFD [35]. Although HYSA and SY seem to be promising antiobesity drugs, it turns out that supplementation with safflower oil also could bring some advantages. Safflower oil supplementation reduced the abdominal fat in male rats fed with an HFD and undergoing swimming training. In this study, safflower oil had no significant effect on lipid biochemical parameters and blood glucose [36]. In a randomized, double-blind, placebo-controlled clinical trial, participants with metabolic syndrome, without lifestyle modification, who took 8 g of safflower oil for 12 weeks, experienced significant improvements in waist size, systolic and diastolic blood pressure, fasting blood sugar, insulin resistance, and increase in adiponectin level to placebo recipients. The lipid profiles, leptin levels, and serum creatinine were similar in the experimental and control groups [37].
## 3 The Proposed Framework
### Anomaly Detection
#### 3.2.1 Features Extraction  
The proposed methodology consists of using volumes of three consecutive frames. For each frame \"\(F_{t}\)\" we used the volume F: \(\{F_{t};F_{t-1};F_{t-2}\}\) as input. As applied in [18], we did not use the complete CNN \"from end to end\", but we used the four initials 3D residual blocks (16 convolutional layers) as feature extractors. The architecture of this network is presented in Figure 3. Thus, instead of getting an output of one feature vector, the resultant map is a matrix of 841 feature vectors of 256 dimensions. In this matrix, each raw represents a small patch in the input frame. This is explicitly shown in Figure 4.  
Figure 3: The four initial 3D residual blocks of Res3D.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 4 Results and Discussion  
### Moisture Uptake  
The moisture uptake could be classified into two distinct regions (Figure 4) in line with the Fickian curve: region A with a notable increase during the initial exposure period until complete saturation in region B. The pure sandwich specimen (GSP) exhibited a higher moisture uptake than the GSV and GSH samples, with absorption rates of 4.8%, 4.4%, and 4.3%, respectively (Table 3). This difference is due to the low resistance to diffusion once the matrix starts to plasticise, unlike the other samples that require more extensive epoxy plasticization over the exposure period.  
The reduced moisture absorption in the GSH samples indicates that moisture primarily diffused in the in-plane direction, with minimal through-thickness effects (edge effects) in the set-up. This is more similar to sealing the edges before conditioning, which limits the arbitrary influx of moisture through the edges, thereby representing the real-life application of FRPSS in service. All samples absorbed moisture until reaching saturation. To connect the acoustic emission (AE) feature with the level of moisture absorption, the average AE velocity was plotted against the moisture content for both specimen types. The data showed that AE velocity declined until saturation, decreasing from 1788.6 m/s to 1335.5 m/s (31% drop) and from 2496.6 m/s to 1485.2 m/s (48% drop) for Type A and Type B, respectively. Further details on the use of acoustic emission for moisture monitoring were discussed in a previous work by the authors [11].
## 6 Implementation and Evaluation
### Experimental Evaluation
#### 6.3.4 Number of Packets  
Another key performance indicator is the amount of energy consumed by the network to complete the communication task. The main two contributing factors to the energy consumption are the efficiency of the RDC and the numbers of packets sent. The RDC mostly depends on the MAC protocol and is beyond the scope of this article. We use the number of packets sent inside the LLN as an indicator for the amount of energy consumed by the network as exact numbers for energy consumption are hardware dependent. In this subsection we first present the theoretical model for calculating the number of packets and then present the results obtained from our simulations.
# Grasp Detection Combining Self-Attention with CNN in Complex Scenes
## 4 Method
### Loss Function  
Given the input image I, the model predicts a set of grasp pixel heatmaps \(\mathrm{G_{i}}=\{\mathrm{Q_{i}},\mathrm{Sin2\theta_{i}},\mathrm{Cos2\theta_{i}}, \mathrm{W_{i}}\}\), and the ground truth map \(\widehat{\mathrm{G}_{i}}=\left\{\widehat{\mathrm{G}_{i}},\sin 2\widehat{ \theta_{i}},\mathrm{COS2\widehat{\theta_{i}}},\widehat{\mathrm{W_{i}}}\right\}\) is set to the same format as the grasping CNN's outputs. We trained the model by calculating the \(\mathrm{Smooth_{L1}}\) loss between the prediction and labels, treating the grasp detection problem as a regression problem, which is beneficial for algorithm implementation. The loss function is defined as follows:  
\[\mathrm{L}\left(\widehat{\mathrm{G}_{i}},\mathrm{G_{i}}\right)=\frac{1}{\mathrm{ N}}\mathrm{\sum_{i}^{N}}\mathrm{\sum_{m\in\{q,\cos 2\theta,\sin 2\theta,w\}}} \mathrm{Smooth_{L1}}\left(\widehat{\mathrm{G}_{i}^{m}}-\mathrm{G_{i}^{m}}\right) \tag{8}\]  
where \(\mathrm{Smooth_{L1}}\) is defined as  
\[\mathrm{Smooth_{L1}}(\mathrm{x})=\left\{\begin{matrix}0.5\mathrm{x}^{2}& \mathrm{if}\ |\mathrm{x}|<1\\ |\mathrm{x}|-0.5&\mathrm{otherwise}\end{matrix}\right.\]
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 5 Results  
### Design of Tested Scenarios  
The algorithms are coded in C++, and the experiments are conducted using an Intel Core i7-6700 HQ 2.60 GHz CPU under a 64-bit Windows 10 system with 16 GB RAM.  
The experimental scenarios are designed as follows: observation missions are generated according to a random distribution. They are divided into two categories according to the density of distribution: locally distributed tasks (3\({}^{\circ}\) N-53\({}^{\circ}\) N, 73\({}^{\circ}\) E-133\({}^{\circ}\) E) and globally distributed tasks (60\({}^{\circ}\) S-60\({}^{\circ}\) N). From each type, 360, 720 and 1080 tasks with O-VTWs within the planning cycle are randomly selected to form the set of examples, respectively. Set the estimated consumption storage \(m_{j}\) and the priority \(p_{j}\) of the task \(t_{j}\) to a randomly sampled integer in [50; 100]. The time discount coefficient \(\lambda\) for task profit is \(10^{-5}\). Set the scheduling period to 1.5 h. The decay function of task benefits over time represented by  
Figure 8: Constellation communication topology. (**a**) Original constellation communication topology. In addition to communicating with directly adjacent satellites, the satellite may be able to communicate with more distant satellites; (**b**) constellation communication topology under the single-chain strategy. Each satellite only communicates with directly adjacent neighboring satellites; (**c**) constellation communication topology under the single-chain strategy in case of partial communication link damage. When a neighboring satellite directly adjacent to the satellite fails, the satellite can communicate with a more distant neighboring satellite.  
Equation (1) is shown in Figure 9, enabling the final profit to be approximately 95% of the original profit if the task is completed at the end of the planning cycle.  
The constellations are set to a scale of 30, 60 and 90 satellites. The constellation configuration parameters are Walker-5 (30/3/1, 600 km, 60\({}^{\circ}\)), Walker-5 (60/3/1, 600 km, 60\({}^{\circ}\)) and Walker-8 (90/3/1, 600 km, 60\({}^{\circ}\)), respectively. For global mission scenarios, set the maximum available storage of one satellite during the planning period to 750; for the regional mission scenarios, in order to highlight the time constraints, the storage constraints are appropriately relaxed by setting the maximum available storage for satellites during the planning cycle to 1125.
## 2 Materials and Methods
### Remotely Acquired Images and Data
#### 2.3.5 High-Resolution Topographic Profiling  
To optimize the quality of the result of our topographic analysis, we used two different software to extrapolate profiles from the Digital Surface Model, which led to obtaining 1 cm resolution over area 1 and a resolution of 3 cm in area 2 (Figure 7a-d). In particular, serial profiling across the coseismic ruptures of both CAB and PTP fault was performed through the Esri ArcGis v.10.x \"3 DAnalyst\" and through the \"Create Sections\" tool of Move 2019. From both the sets of profiles, we estimated the fault-scarp height (Figure 7a-d).  
To compare our digital field data with those obtained by aero-photogrammetric 3D model, several fault attitudes along the CAB were manually collected, and the height of the scarps measured (Figure 7b).  
The \"surface control\" is essential to estimate the errors between analogical (real outcrop) and digital (virtual outcrop) measurement data.  
In the field, a meter stick was used to estimate all along the fault trace, the height of the scarp, associated throw, and net displacement, both on the long term and coseismic free face. The slope dip angle was measured using a clinometer (Figure 7b).  
Then, we compared the morphological height, the strike, the dip, and dip azimuth of the fault plane, measured in the field, with those obtained from the virtual outcrop 3D model.  
The observed deviation between the height values of the fault scarp directly measured in the field and those extracted from virtual outcrop never exceed 2 cm (compare the deviations between the curves and the colored dots of Figure 8b), confirming the reliability of the reconstructed digital topography.  
Figure 7: Georeferenced orthomosaic of CAB. (**a**) Digital Surface Model (DSM) of CAB and trace of topographic profile (from 1 to 8); (**b**) manual measuring of the scarp height associated to the CAB; (**c**) topographic profiles obtained using 3D Analyst extension of ESRI ArcGis; (**d**) topographic profiles obtained through move software.
## 4 Results
### Dynamic versus Static Measurements Results and Validation
#### 4.2.3 Resulting Full 3D Hill-Charts  
The resulting efficiency 3D hill-charts obtained by the classical static point-by-point and by the dynamic measurement methods are provided in Figures 11 and 12. From a qualitative point of view, the results obtained by the dynamic method are in a good agreement with the ones obtained by the static method for both tested cases. Indeed, a satisfactory match between the two resulting 3D hill-surfaces (see the superposition of the fitted surfaces in Figure 13) validates the alternative proposed dynamic measurements method. The differences \(\varepsilon_{\text{S-D}}\) between the two hill-chart surfaces, computed with Equation (10), are mainly due to the quality of the interpolation and due to the number and the position of the available operating points. A more refined grid of operating points for classical static point-by-point method along with a more adapted surface interpolation method should reduce these artificial differences.  
Figure 10: Resulting 4-quadrant characteristic curves of the PAT obtained by the dynamic measurements method: (**a**) speed-discharge factor characteristic curve; (**b**) speed-power factor characteristic curves.
## 3 Network Architecture and Topology
### Network Architecture Classification  
The architectures of real ITS systems can be obtained by mapping the subsystems depicted in Figure 2 onto physical tiers of devices. This leads to largely differentiated systems according to the way that it is done. Two top level decisions should be made; firstly, a flat _vs._ a hierarchical network must be selected and, secondly, for the latter case, a homogenous _vs._ heterogeneous use of wireless technologies. The next sections describe different alternatives which can be found in any of these cases.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Device Layer
#### 3.3.6 Encryption Module  
Encryption modules at the hardware level are a technology specifically designed to realize data encryption and decryption operations on physical devices. These modulesimplement encryption algorithms through hardware rather than software, enhancing both security and performance. A good example is the trusted platform module, or TPM [53]--a hardware encryption module that is widely deployed in computing devices. A TPM can securely store encryption keys, digital certificates, and other sensitive data. Through these features, the TPM is capable of delivering strong hardware-level security services. Hardware-level security mechanisms provide a higher level of protection than software encryption because the hardware-level mechanisms are more difficult to compromise by remote hackers or software vulnerabilities. By integrating TPMs during the design and manufacturing phases of a device, users and organizations can establish a strong security foundation in IoT environments.
## 3 Results and Discussion
### Cold Plasma Diagnostics  
Optical emission spectroscopy (OES) is widely used for plasma diagnostics. OES enables an understanding of the degradation mechanisms and helps to optimize the decontamination process. Excited states are produced mostly by collisions with energetic electrons, which can be measured by OES. Dominant plasma reactive species were detected by OES in the UV/VIS wavelength (180 to 850 nm) region (Figure 3). It was evident that the emission was in the near UV region (300-400 nm), that can explain the purple color of the ACP photons. In the spectrum, N\({}_{2}\) spectra were dominant, as the peaks in the wavelength region of 310-450 nm are the vibrational band of neutral and ionic emission of nitrogen molecule [32]. The peak in 296 nm corresponds to OH as one of the reactive species [33]. Some of the reactive oxygen species such as atomic O could not be identified, probably due to the particle collisions with N\({}_{2}\) and O\({}_{2}\), resulting in the quenching of O (\({}^{3}\)P) and O (\({}^{5}\)P) energy [34] and production of ozone and nitrogen oxide species. The results demonstrate the fact that the major reactive species in ACP were ROS and RNS.