esa-sceva/satcom-chunk-collection huggingface dataset:
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
### The TerraSAR-X Mission
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **StripMap** & **4 Beams ScanSAR** & **6 Beams ScanSAR** \\ \hline Number of subswaths & 1 & 4 & 6 \\ Swath width (ground range) & 30 km \({}^{1}\), 15 km \({}^{2}\) & 100 km & 194 to 266 km \\ Nominal L1b product length & 50 km & 150 km & 200 km \\ Full performance incid. angle range & 20\({}^{\circ}\)–45\({}^{\circ}\) & 20\({}^{\circ}\)–45\({}^{\circ}\) & 15.6\({}^{\circ}\)–49\({}^{\circ}\) \\ Data access incidence angle range & 15\({}^{\circ}\)–60\({}^{\circ}\) & 15\({}^{\circ}\)–60\({}^{\circ}\) & 15.6\({}^{\circ}\)–49\({}^{\circ}\) \\ Azimuth resolution & 3.3 m \({}^{1}\), 6.6. m \({}^{2}\) & 18.5 m & 40 m \\ Ground range resolution & 1.7 m–3.49 m & 1.7 m–3.49 m & \(\mathcal{<}\)7 m \\ \hline \multicolumn{4}{c}{HH or VV \({}^{1}\)} \\ Polarizations & HH/VV, HH/HV, VV \({}^{2}\) & HH or VV & HH or VV \\ \multicolumn{4}{c}{\({}^{1}\) single pol, \({}^{2}\) dual pol.} \\ \end{tabular}
\end{table} 
Table 1: TerraSAR-X’s StripMap and ScanSAR mode parameters (modified from [23]). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Spotlight** & **High Resolution Spotlight** & **Staring Spotlight** \\ \hline Number of subswaths & 1 & 4 & 6 \\ \hline Scene extent & 5 km \(\times\) 10 km & 5 km \(\times\) 10 km & 2.5 km \(\times\) 6 km \\ (azimuth x ground range) & & & \\ \hline Full performance incid. angle range & 20\({}^{\circ}\)–55\({}^{\circ}\) & 20\({}^{\circ}\)–55\({}^{\circ}\) & 20\({}^{\circ}\)–45\({}^{\circ}\) \\ Data access incidence angle range & 15\({}^{\circ}\)–60\({}^{\circ}\) & 15\({}^{\circ}\)–60\({}^{\circ}\) & 15\({}^{\circ}\)–60\({}^{\circ}\) \\ Azimuth steering angle & Up to \(\pm\) 0.75\({}^{\circ}\) & Up to \(\pm\) 0.75\({}^{\circ}\) & \(\pm\)2.2\({}^{\circ}\) \\ Azimuth resolution & 1.7 m \({}^{1}\), 3.4. m \({}^{2}\) & 1.1 m \({}^{1}\), 2.2. m \({}^{2}\) & 0.24 m \({}^{1}\) \\ \hline Ground range resolution & 1.48 m–3.49 m & 1.48 m–3.49 m (150 MHz) & \\  & 0.74 m–1.77 m (300 MHz) & 0.85 m–1.77 m \\ \hline \multicolumn{4}{c}{Polarizations} \\ Polarizations & HH or VV \({}^{1}\) & HH or VV \({}^{1}\) & \\  & HH/VV \({}^{2}\) & HH/VV \({}^{2}\) & HH or VV \({}^{1}\) \\ \hline \multicolumn{4}{c}{\({}^{1}\) single pol, \({}^{2}\) dual pol.} \\ \end{tabular}
\end{table} 
Table 2: TerraSAR-X Spotlight mode parameters (modified from [23]). All data acquired from the TerraSAR-X and TanDEM-X missions are stored in the multimission archive of DLR. By the end of June 2017, more than 235,000 individual data takes were ingested corresponding to almost 290 TBytes of SAR raw data. The raw data are processed to SAR Level 1b products following user requests.
## 3 Results and Discussion
### Operation Characteristics of FE-TCMOS
Figure 5: **(a)** The voltage transfer curves of conventional complementary metal-oxide-semiconductor (CMOS) and ternary complementary metal-oxide-semiconductor (TCMOS). The third V\({}_{out}\) state is formed in TCMOS by voltage dividing depending on IOFF difference between TPMOS and TNMOS. **(b)** The circuit schematic diagram of 7-stage inverter chain. **(c)** The switching time of FE-CMOS and FE-TCMOS with respect to the number of inverter stages. The switching time of FE-CMOS is pico-sec order, whereas FE-TCMOS is micro-sec order. The dashed line represents ferroelectric switching delay, which is larger than the switching time of FE-CMOS. Figure 6: **(a)** The switching time comparison between TCMOS and FE-TCMOS. The switching speed difference between them is negligible. **(b)** The butterfly curves of FE-TCMOS with respect to V\({}_{DD}\). **(c)** The comparison of static noise margin between FE-TCMOS and TCMOS. The improvement of SNM in FE-TCMOS is extracted as the increase in percentage, compared to TCMOS.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 3 Results
### Results of Feature Frequency Extraction by PCA  
In this study, the dielectric constant \(\varepsilon^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\) of the straw compost samples at 113 frequency points in the frequency band of 1-100 kHz were analyzed through PCA using MATLAB software for the correlation coefficient matrix of three sets of data for \(\varepsilon^{\prime}\), \(\varepsilon^{\prime\prime}\), and \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) combined variables. The results are presented in Table 2.
## 3 LDAF Background and Overview
### LDAF Architecture
#### 3.1.4 Server and Client  
The LDAF framework is comprised of server and client entities. The LDAF server embodies the establishment and management of the end device connections, message encoding, service execution, and connections to external APIs. The LDAF client applies a set of client libraries for the LDAF server connections and message encoding and enables end device application logic to communicate with the framework.  
When the LDAF server is initialized, the Service.js library scans the subdirectory where all available services are defined and implemented. A particular service is usually implemented as a main library (main.js), with the specific application logic and a service definition file (serviceDef.js). Service definition files specify message type object. It also specifies (binary) message-content encoding mechanism, including corresponding message definition files (e.g., proto). Upon initialization, connections to the external APIs are established, too.  
We applied two message types for the Ethereum--based smart-grid switch and counter. As seen in Figure 3, one is for notifications about new blocks added to the blockchain ('newBlock') and the other ('newEvent') for new event notifications sent from the smart contract to the device.  
When a new client connects to a selected service on an LDAF server, they compare client and server service definitions, which must match to ensure correct encoding and decoding of messages. Both service definition files (serviceDef.js) and message definition files (e.g., proto) must be present on the client's side, too. We provide a generic client library (Client.js), which establishes the connection to the server and receives, transcodes, and transmits messages.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
### Analysis of Research Question 4: Network Architecture
#### 4.3.2 Network Topologies and Their Impact on Diffusion  
The network topology significantly influences the diffusion of information in IoT and Space IoT environments. Table 10 summarizes key topologies and their relevance to diffusion dynamics.  
Table 10 highlights the direct implications of network structures on diffusion efficiency. For instance, scale-free networks facilitate rapid diffusion via hubs but are highly sensitive to targeted disruptions, which is critical for IoT infrastructures like smart grids. Similarly, mesh networks provide redundant paths, ensuring fault tolerance in industrial IoT environments but at the cost of increased network overhead.  
Understanding these interactions helps in designing efficient diffusion strategies tailored to IoT and Space IoT environments, where network dynamics and communication constraints must be considered.
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
###### Abstract
Currently, just a few authors have mentioned and solved the problem of robot motion with the minimum energy consumption criterion [22,23,24,25,26,27,28]. It is necessary to reduce the energy consumption of the DDWMR to perform more missions efficiently with a limited energy supply. Several methods which have been published are able to deal with the finite energy of batteries, such as focusing on motion planning to reduce energy consumption [22,23], selecting robot's components which use less energy [24], using energy management techniques in the WMR [25], generating speed profiles to reduce energy consumption, avoiding sudden accelerations and decelerations of the WMR [26], prolonging the time of operation by increasing the number of batteries or by diverting the robot back to the charging station [27], or improving the quality of the battery [28], etc. Among these methods, the optimal path planning to reduce the energy consumption is more effective and widely applied than the others. In general, the optimal path is the shortest path with several sharp turns [22]. These sharp turns force the robot to decelerate and accelerate as required by that path, which leads to the consumption of extra energy. To tackle that problem, [PERSON] and [PERSON] [22] have used Bezier curves to smooth the path which will minimize the energy consumption. In [23], [PERSON] et al. have used Dubins path to smooth the path so that the energy consumption of the robot is reduced. From the above-mentioned studies, it can be derived that motion planning without considering the energy consumption of the robot is not efficient planning in the energy sense. Therefore, a better solution would be to integrate both optimal path planning and minimum energy robot motion control. If an optimal path has been already planned, an optimal motion controller is needed for the robot so that it can go to the destination with the minimum energy consumption. The main goals of this paper are: (1) To propose an optimal fuzzy logic controller using the genetic algorithm (GA_FLC) to reduce the energy consumption of the DDWMR more effectively than the other algorithms; (2) To create a software in Google Colab(r) and Jupyter platforms using Python language in order to execute the full optimization process for the Fuzzy Logic Controller (FLC); (3) To publish this software as an open-source code for all the users who want to use and improve it. The rest of this paper is organized as follows. The problem is described in Section 2. The optimization of the FLC is introduced in Section 3. Result collection and final discussion are presented in Section 4. The conclusion is stated in Section 5.
### Purpose, Contribution, and Paper Structure  
The purpose of this paper is to provide an ESO-based robust controller for performing accurate rehabilitative motions. For the upper limb system model, the flexion and extension movements are given by a sinusoidal trajectory, which is an input to the exoskeleton. The proposed control method precisely tracks these inputs, improving the repeatability of joint movement and aiding the patient's recovery during the early phases of rehabilitation. In addition to the fact that patients' musculoskeletal problems differ and may occur while performing such exercises, which can interfere with tracking and consequently the rehabilitation process, the proposed method actively removes these disturbances while tracking.  
A trajectory tracking control for two-link ULRRE has been devised in this work. A finite-time stable tracking differentiator based on the ADRC control law has improved trajectory tracking accuracy. The exoskeleton model is obtained using the Euler-Lagrange method. The two units, LESO and FTSTD, estimate the system's states and eliminate disturbances as lumped disturbances, including modeling uncertainties and external disturbances. FTSTD generates signals by differentiating the input, resulting in an output that increases gradually rather than abruptly, improving accuracy and decreasing overshoot. The proposed work has been compared with recently developed ESO-based trajectory tracking control strategies such as ADRC, NLADRC, and I-ADRC by performing a simulation study. The proposed method's efficacy has been evaluated using various performance indices with 20% parameter variation and constant external disturbance. Simulation results indicate an improvement in trajectory tracking accuracy with disturbance rejection and robustness against parameter variation.  
The rest of the paper is organized as follows: Section 2 focuses on ULRRE modeling. Section 3 details the ESO-based and FTSTD-based ADRC control strategies. The closed-loop stability is described in Section 4. Simulation results are shown in Section 5. Discussion is given in Section 6. Finally, in Section 7, the conclusion is reached.
## 3 Proposed Protocol
### Data Forwarding Algorithm
#### 3.3.3 Forwarding the Data Packet  
Denote by \(sR_{1}...R_{m}x\) the Euclidean routing path constructed by \(s\) using the algorithm described above, where \(x\) coincides with \(t\) if \(s\) is a \(t\)-aware node, and \(x\) is a vertex of \(\mathcal{C}(t)\), otherwise. Then, the coordinates of \(R_{1},...,R_{m},x\) are inserted into the packet header as virtual anchors that will guide the packet. If \(s\) is a \(t\)-aware node, then the packet is forwarded toward the virtual anchors using greedy algorithm until reaching \(t\). Otherwise, the packet is forwarded toward the virtual anchors using greedy algorithm until reaching the first \(t\)-aware node, hereafter we call this node as _sub-destination_ and denote as \(t^{\prime}\). Then, \(t^{\prime}\) determines the sub-base path, denoted as \(\mathcal{B}(t^{{}^{\prime}})\), which is an Euclidean path from \(t^{\prime}\) to \(t\) that bypasses \(\mathcal{H}(t^{{}^{\prime}})\), \(\mathcal{H}(t)\) and \(\mathbb{C}(\overline{t^{\prime}},\overline{t})\) and satisfies either one of the following conditions:  
* \(\mathcal{B}(t^{{}^{\prime}})\) is the shortest path from \(t\) to a vertex of \(\mathcal{C}(t)\). * \(\mathcal{B}(t^{{}^{\prime}})\) is \((\Gamma_{2}\times|\mathcal{L}(t^{\prime},t)|)\)-bounded path, where \(\mathcal{L}(t^{\prime},t)\) is the shortest path from \(t^{\prime}\) to \(t\) and \(\Gamma_{2}=Max\left\{1,\frac{L_{0}(1+\epsilon)}{\frac{\theta_{1}}{\sin\frac{(n- 2)\pi}{2n}}\left(L_{0}+\frac{\epsilon_{C(t)}}{2}\right)+\frac{\epsilon_{C(t)} }{2}}\right\}\). Let us denote \(\mathbb{A}(t^{{}^{\prime}})\) as the union of \(\mathcal{H}(t^{{}^{\prime}})\), \(\mathcal{H}(t)\) and \(\mathbb{C}(\overline{t^{\prime}},\overline{t})\), then \(t^{\prime}\) constructs the so-called sub-Euclidean routing path (denoted as \(\mathcal{R}(t^{\prime},t)\)) which is the image of \(\mathcal{B}(t^{{}^{\prime}})\) through the transformation \(F(\mathcal{B}(t^{{}^{\prime}}),\mathbb{A}(t^{{}^{\prime}}),\alpha_{2})\), where \(\alpha_{2}\) is a parameter defined by  
\[\alpha_{2}=Max\left\{1,\frac{(1+\epsilon)\,L_{0}-|l_{t^{\prime}}|}{|\mathcal{B }(t^{\prime})|}\right\}\]  
where \(l_{t^{{}^{\prime}}}\) is the routing path from \(s\) to \(t^{\prime}\). Finally, all the vertices of \(\mathcal{R}(t^{\prime},t)\) are inserted into the packets as virtual anchors. The packet then is greedily forwarded towards these virtual anchors gradually until reaching the destination \(t\).
## 3 Results
### CHSP Induces Apoptosis in Ovarian Cancer Cells through the p53-Dependent Intrinsic Pathway  
Annexin V and propidium iodide (PI) staining was performed for flow cytometry analysis. As shown in Figure 2, an increase in cell apoptosis in OVCAR-3 cells was induced in a concentration-dependent manner after CHSP treatment. At 20 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of CHSP, the apoptosis rate was 44.21%. Similar to the results of cell viability above, there was also no significant apoptosis rate of normal cells (IOSE364). In this study, caspase-3/7 enzymatic activities were detected by using a Caspase-Glo 3/7 Assay kit. As shown in Figure 2c, compared to controls, the caspase-3/7 enzymatic activities were maximally increased to 3.57-fold in OVCAR-3 cells when treated with 20 \(\mathrm{\SIUnitSymbolMicro g}\)/mL CHSP for 24 h. Moreover, it was identified that there is an up-regulation in pro-apoptotic proteins including Bax (1.73-fold), BAD (1.53-fold), cleaved PARP (1.5-fold), and Puma (2.9-fold), shown in Figure 3.  
Figure 1: Effect of Chinese hickory seed skin Carya cathayensis (CHSP) on cell viability of ovarian cancer cells (OVCAR-3, A2780/CP70) and normal ovarian cells (IOSE 364). \({}^{*}p<0.05\) as compared to control. \({}^{**}p<0.01\) as compared to control.
## 3 Results
### Data Description and Analysis
#### 3.1.2 Sentinel-2 and Laboratory Spectral and Sentinel-1 Soil Moisture Information Analysis  
The mean reflectance of soil samples was lower for Sentinel-2 spectra than for laboratory spectra (Figure 5a). Some mean S2-indices were lower than mean Lab-indices, while others were higher or nearly the same (Figure 5b). As expected, the former also varied more, since surface conditions, especially soil moisture, varied among sampling points and dates. Some S2-indices (e.g., NBR, BSI) were more sensitive (i.e., variable) than the others. The same was true for Lab-indices, although this variability seemed to be non-significant.  
The S2-indices explained more than 51% of total inertia on the first two dimensions of the MFA (Figure 6). Most S2-indices contributed to approximately the same degree (>0.75) to dimension 1 but contributed to varying degrees (0.10-0.75) to dimension 2. Indices BSI and GVMI contributed the least to dimension 1 and among the most to dimension 2.  
Most correlations between the Lab-indices and SOC content were significant at \(p<0.01\) and had absolute values greater than 0.40 (Table 5). Weaker correlations between the Lab-indices and SOC content (r = 0.20-0.30) were significant only at \(p<0.05\). Indices with r > 0.70 with SOC content were calculated using wavelengths of 665-832 nm, except for NBR2, which used wavelengths of 1614-2202 nm.  
Figure 5: Sentinel-2 vs. laboratory spectral information for the 58 soil samples: (**a**) Mean \(\pm\) 1 standard deviation of spectra; (**b**) Boxplots of the spectral indices calculated. Whiskers indicate 1.5 times the interquartile range. See Table 2 for definitions of the spectral indices.
## 2 Materials and Methods
### Dataset  
#### 2.2.1 Meteorological Data  
The meteorological data were obtained from the China Meteorological Administration (CMA). The data underwent quality checks and screening, resulting in data from 122 valid stations in SWC being used. Each station recorded daily mean, maximum, and minimum temperatures, as well as daily cumulative precipitation from 1980 to 2019. Any missing data were filled in with linear interpolation. The ECIs were calculated based on daily meteorological data.  
Table 1 displays the indices utilized in this study, along with their definitions. The standardized indices and compound extreme climate indices (CECIs) used were the standardized precipitation index (SPI), standardized temperature index (STI), compound dry-warm index (CDW), and compound dry-cold index (CDC), which were chosen based on previous studies [23]. The ECIs were calculated using the RClimDex package in R [26; 27]. Based on a correlation analysis, highly linearly correlated ECIs were combined into a single indicator via weighted averaging. This was to avoid diluting the indicators with common characteristics during the significance test at a later stage. The following indices are optimized on the basis of the indices calculated by the RClimDex package: the weighted average of the values of TN90P and TX90P was defined as the warm day index (WDI); TN10P and TX10P as the cold day index (CDI); TNN, TXN, TNX, and TXX as the extreme temperature index (ETI); R10 MM, R20 MM, and R25 MM as the rainfall day index (RDI); R95P and R99P as the heavy precipitation index (HPI). The definitions of the other indices follow the definitions in RClimDex (Table 1). Finally, we investigated their impacts on ecosystems and their temporal stability in conjunction with the standardized index, CECIs, and ECIs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indices** & **Definitions** & **Unites** \\ \hline Standardized precipitation index (SPI) & Standardized indices of precipitation for 40 consecutive years in SWC & — \\ Standardized temperature index (STI) & Standardized indices of temperature for 40 consecutive years in SWC & — \\  & Values of the joint SPI and STI distributions, calculated based on the & \\ Compound dry–warm index (CDW) & copula function, includes joint values for dry–warm and wet-cold & — \\  & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Index definitions and units.
## 4 Experimental Analysis
### Tracking Performance  
Our experiments determined that a standard deviation (\(\sigma\)) of 3 for the Gaussian function, as outlined in Equation (9), optimally enhanced the blurring effect on the training image \(f_{i}\), resulting in improved outcomes. Furthermore, the learning rate (\(\alpha\)) was optimized to 0.22 to enhance the filter performance.  
To assess the real-time tracking performance, the PSR (peak sidelobe ratio) was recorded for each frame. The PSR evaluates the clarity and accuracy of the adaptive processing output by contrasting the peak's statistical properties, ideally located at the target position, with those of the surrounding sidelobe areas. For analysis, a 10 \(\times\) 10 window centered at the latest position \((x_{new},y_{new})\) was defined, with the remaining area designated as the sidelobe area. The PSR for each frame was calculated as follows:  
\[PSR=\frac{P_{max}-\mu_{psr}}{\sigma_{psr}} \tag{22}\]  
where \(\mu_{psr}\) is the mean value of the pixel value in the sidelobe area, and \(\sigma_{psr}\) is the standard deviation of the pixel value in the sidelobe area. We conducted tracking tests on 153 videos from vehicle collision searches, sourced publicly on the Internet, devoid of any personally identifiable information or visible license plate details, thus ensuring adequate privacy protection. Following testing, a PSR threshold below 6 was considered indicative of tracking failure. This necessitated either halting the online updates or reinitializing the target.  
Taking a vehicle collision video as an example, we recorded the PSR in 70 video frames, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Weather** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** & **AP (\%)** \\ \hline Cloudy & 50.42 & 98.74 & 67.55 & 92.77 \\ Night & 48.55 & 97.89 & 65.92 & 89.97 \\ Rainy & 50.68 & 98.92 & 67.76 & 93.18 \\ Sunny & 52.11 & 98.99 & 67.88 & 94.56 \\ \hline \end{tabular}
\end{table}
Table 4: Test results of the UA-DETRAC dataset under four weather conditions.  
Figure 7: _Cont._  
Calculations indicated that the average PSR was 9.91. Although the PSR significantly dropped at the moment of collision, it remained above 8, suggesting that the tracking performance of this method is effective.
# Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction
## 3 Results and Discussion
Although in this research, the X-ray energy spectrum independent of detector response was calculated for the tube voltage of 230 kV only, using the proposed approach, it is also possible to calculate the X-ray spectrum for every desired tube voltage as well as every external filter with different materials and thicknesses. As an example, the calculated X-ray energy spectrum shown in Figure 10 was applied to beam hardening artifact reduction of a printed steel cylinder (2.5 mm radius and 19 mm height), scanned with the FleXCT scanner using a tube voltage of 230 kV and a 1 mm thick copper filter. A slice of the steel cylinder, reconstructed by the traditional Simultaneous Iterative Reconstruction Technique (SIRT) and Polychromatic SIRT (PSIRT) [19], is shown in Figure 11. All methods were implemented in MATLAB, making use of the ASTRA toolbox to handle forward and backward projections on the GPU [40]. As can be seen from this figure, by employing PSIRT, beam hardening effects have been almost completely compensated. Figure 10: The energy X-ray tube spectra of the FleXCT system calculated via Monte Carlo via simulation for a tube voltage of 230 kV. (1) Beryllium window only, no additional filter, (2) Beryllium window and 1 mm copper filter.