esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Foundation
### Diagram of the Transportable Radar  
The transportable radar is built by two ISO\({}_{20}\) shelters. In the first one, the auxiliary equipment, such as UPS and generator sets, is located. In addition to the interrogator system, the second shelter is a deployable and retractable tower that contains the radiant system and the pedestal, which is responsible for the continuous rotation of the antenna, a photo of the system has been included in Figure 1.  
The proposed deployable tower and antenna system for the transportable MSSR system are composed of the LVA antenna system and mechanical interface, antenna drive system, lighting system, deployable tower with antenna (11 m high) and lightning protection. The antenna drive system and antenna are integrated into the deployable tower; in the transport configuration, mechanical protection is provided during the pedestal and the antenna is transported. The electronics and power equipment for the transportable MSSR system are housed in a hardened shelter comprising the MSSR interrogator, which is made up of two identical channels. Each channel is connected to an RRF port; through this, the channel with an operational status is connected to the antenna. When the RRF is used to change the channel in operational or reserve status, the operational channel is connected to the ALV of the monopulse antenna, which is mounted on the deployable tower and is monitored by the UCS application installed in the control and monitoringsystems of the MSSR. This can be achieved both locally and remotely by developing the same orders, communication system and power elements. The Mode S transmitter receives the appropriate signals from the data extractor to modulate, amplify and develop the RF transmission of the interrogations for the SUMA channel and for the OMNI channel; the interrogation mode, interleaving, output power and other characteristics are sent from the control and management systems. It is worth noting that the MSSR Mode S system works at full capacity, with a high degree of security and confidence, in an autonomous way, and without the need for operator intervention; this is due to the fact that the system implements a BITE, which runs on real-time and is distributed among the different radar units of both channels. The information obtained from the BITE, as well as the possible alarms, are visualized in the UCS application in both the local and remote management systems. The receiver is divided into three different processing blocks to develop the functions of video processing, real-time channel control and link control. With this, the radar system will be able to detect and process targets ranging between 0.3 and 150 nautical miles, as well as SSR such as Mode S, and targets of more than 66,000 feet in height in the 360\({}^{\circ}\) azimuth when the transponders comply with ICAO annex 10, Volume IV.
## 3 Results and Discussion
### Surface Observation and Elemental Analysis  
The surface morphology of carbon steel immersed in 1 M HCl solutions without and with 40 mg/L TSCA for 1 h at 298 K is shown in Figure 9. In the absence of TSCA inhibitor, the carbon steel surface is rough due to the cover of corrosion products. In contrast, the surface is comparatively smooth in the presence of TSCA. To demonstrate the adsorption of TSCA, an energy diffraction spectrum experiment was carried out on the exposed carbon steel surface. From the two energy diffraction spectra, it can be found that characteristic peaks of N and S arise in the presence of TSCA. This indicates the adsorption of TSCA on the carbon steel surface. The TSCA film inhibits the corrosion of carbon steel, which is in accordance with the electrochemical experiments and adsorption isotherm results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & \(E_{\text{HOMO}}\) **(eV)** & \(E_{\text{LUMO}}\) **(eV)** & \(\Delta E\) **(eV)** & \(\chi\) **(eV)** & \(\gamma\) **(eV)** & \(\Delta N\) \\ \hline TSCA & \(-6.92\) & \(-2.28\) & \(4.64\) & \(4.60\) & \(2.32\) & \(0.65\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Derived quantum chemical parameters for TSCA calculated by the DFT/B3 LYP method.  
Figure 9: SEM micrographs and EDS results of carbon steel immersed in 1 M HCl solutions without TSCA (**a**,**c**) and with 40 mg/L TSCA (**b**,**d**) for 1 h at 298 K.  
In conclusion, TSCA effectively inhibited the corrosion of carbon steel in 1 M HCl solution in light of the weight loss, electrochemical impedance spectroscopy, potentiodynamic polarization, adsorption isotherm, potential of zero charge, and surface morphology results. The TSCA film adsorbed on the carbon steel surface inhibited the cathodic and anodic reactions.
# Monitoring of Surgically Treated Upper Arm Fracture by Implanted Antenna at 402 MHz
## 3 The Method  
### The Upper Arm Model  
The human upper arm is composed of the humerus surrounded by a group of muscles, followed by fat and skin. Blood supplies and vanes are included between the muscles. Figure 2a shows a cross-sectional view of the upper arm. An approximated cylindrical model of the human upper arm is proposed for the simulation of this monitoring technique, which is shown in Figure 2b. The model is composed of concentric cylindrical layers of bone, muscle, fat, skin, and, finally, the splint layer, which is used to hold the fractured bone in place during the healing period. A metallic fixation plate in Figure 1b is surgically used to fix the fracture in place. The fracture is modelled by an additional layer in the bone. A circular disc layer perpendicular to the bone shaft represents the transverse fracture, and an elliptical disc layer with an angle including the bone axis represents the oblique fractured humerus.  
Figure 1: (**a**) Upper arm fracture, (**b**) fixation plates, and (**c**) surgically treated arm.  
Figure 2: (**a**) Cross-section of the upper arm; (**b**) cylindrical model of the surgically treated fractured humerus.  
As the proposed antenna is a half-wave dipole, and its position is centered at the fracture, the location of the fracture must be away from the shoulder and the elbow joint by one quarter of the wavelength for the antenna to be in a suitable place. In general, the common position of arm fractures is in the middle shaft of the humerus.  
Table 1 summarizes the dimensions of the tissue model [17] in addition to the medical splint and fixation plate dimensions. Normal and fractured humerus models are simulated by CST microwave studio at 402 MHz.
# UAV Charging Station Placement in Opportunistic Networks
## 6 Clustering Approaches
### Dbscan  
The second approach used is DBSCAN, which is a non-parametric clustering algorithm. The idea of DBSCAN is to pack together points with many nearby neighbors and marking those points that lie alone in low-density regions as outliers. DBSCAN is depicted in Algorithm 2.  
```
0:\(X=\{x_{1},x_{2},...,x_{n}\}\) A set of data Points \(\epsilon\) distance \(min\)_Pts_ minimum number of nodes Output:\(C=\{c_{1},c_{2},..,c_{k}\}\) Cluster centroids foreachunvisited\(x\in X\)do  Mark \(x\) as visited; \(N\gets FindNeighbours(x,\epsilon)\) if\(|\ N\mid<min\)Ptsthen  Mark \(x\) as unvisited; else \(C\leftarrow\{x\}\); foreachpoint\(x^{\prime}\in N\)do \(N\gets N\setminus x^{\prime}\); if\(x^{\prime}\) is not visitedthen  Mark \(x\) as unvisited; \(N^{\prime}\gets FindNeighbours(x^{\prime},\epsilon)\); if\(|\ N^{\prime}\mid\geq min\)Ptsthen \(N\gets N\cup N^{\prime}\);  end if if\(x^{\prime}\) is not yet a member of any clusterthen \(C\gets C\cup\{x^{\prime}\}\);  end if  end if  end foreach  end foreach
```  
**Algorithm 2** DBSCAN Clustering Algorithm  
In terms of our scenario, DBSCAN takes the maximum distance between clusters and the minimum number of nodes in a cluster as parameters. Based on these parameters, DBSCAN gives different numbers of cluster centers. The minimum number of nodes in a cluster is set to 3 for the DBSCAN cases.
## 3 Results
### Intercomparison Between L-Band Soil Moisture Retrievals and In-Situ Measurements  
The soil moisture retrievals during the observation period of August 2014 to May 2016 were compared to soil moisture measured by in situ probes. As illustrated in Figure 10, the soil moisture results retrieved by GNSS-R are in good agreement with the measurements by the soil moisture probe. The correlation coefficient between ELBARA-II and GNSS soil moisture is 0.83, and 0.81 between ELBARA-II and in-situ probes. The statistic errors between the retrieved soil moisture and in situ measurements are listed in Table 3. Soil moisture retrieved by both L-band remote sensing methods have the same variation tendency and are sensitive to the influence of precipitation.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 2 M2D2 System Framework
### Novelty and Breakthroughs  
Compared to existing research, M2D2 is distinguished by its far-reaching approach to multi-modal data usage, from baseline modeling (combining reconstruction and contrastive learning) to dual-scale drift detection (short- and long-term), as well as its built-in interpretability, active learning feedback loop, and drift-aware federated framework. This end-to-end design more comprehensively addresses the complexities of large-scale industrial production lines and accommodates diverse anomalies that emerge at varying frequencies and severities. Moreover, the system transcends mere anomaly signaling by offering in-depth explanations--through attention visualization or SHAP/LIME--allowing on-site personnel to swiftly devise targeted interventions. Extended to multi-site or multi-machine contexts, federated learning incorporates drift scoring to dynamically reweigh local updates or exclude highly drifting clients, thereby ensuring that the global model remains stable in the face of heterogeneous or rapidly evolving conditions.  
Overall, M2D2 combines multi-modal feature integration, short- and long-term drift detection, interpretability, active learning, and drift-aware federated coordination into a system with adaptive, self-improving capabilities. This integrated design not only matches the demands of high-complexity domains such as advanced semiconductor manufacturing, Industry 4.0, and smart factories but also establishes a forward-looking foundation for future extensions, including DT integrations, PdM strategies, and beyond.
## 1 Introduction
The columns in the first story of the story-isolation structure are generally independent columns, which independently support the floor as shown in Figure 1. Several studies have shown that this type of frame structure can significantly increase the seismic capacity by reducing the lateral acceleration and inter-story displacement response of the superstructure above the isolation layer while having no impact in increasing the seismic capacity of the substructure below the isolation layer [16; 26]. Thus, the isolation layer could have a large displacement under a strong earthquake excitation, causing a P-Delta effect [27] on the column and producing extra damage to the building. Therefore, the lateral stiffness of the first story columns are expected to be large enough to limit the displacement and stress of the isolation rubber bearing and thus to avoid damage to the rubber bearings when they have a large deformation. This stiffness, however, should be limited to a relatively moderate value as too large a column section with high stiffness would decrease the space size on the first floor and increase the engineering cost. In addition, the stability of the isolation system could be another concern as it may deteriorate when the building is subjected to strong earthquakes in multiple directions. Figure 1: Typical earthquake damage to buildings with weak substructure: (**a**) Strong earthquake in Turkey, 1999 [11]; (**b**) strong earthquake in Wenchuan, China, 2008 [17]. This study proposes a new structural design of the first-story isolation system in an RC frame building, where the RC columns connecting with isolation rubber bearings are inter-connected by beams. The coupled beam-column connections are potentially used to increase the seismic capacity of the whole RC building by increasing the integrated stiffness of the coupled columns and the stability of the isolation system. A 7-floor RC frame building with the coupled beam-column connections was designed according to a typical prototype structure and was then modeled by using the Finite Element Analysis (FEA). The inter-story displacement and the acceleration of each floor were analyzed. A scaled model test was then carried out in the lab to verify the simulated results. This study aims to establish a new structural design of the first-story isolation system with the coupled beam-column connections in an RC frame building, and to verify its good seismic performance.
# Stabilization of Road Embarkments on Peat Soils Using Oil Shale Ash and Pozzolanic Additives
## Materials  
### Peat Soil  
Soil sampling took place in January 2019 at the Jarveslja Training and Experimental Forest Centre of the Estonian University of Life Sciences (58\({}^{\circ}\)16\({}^{\prime}\) N, 27\({}^{\circ}\)19\({}^{\prime}\) E) in four different locations. The soil at the test site is peat with a layer thickness of 0-3 m. Below this, in turn, lies a moraine layer and Devonian sediments. Due to the geology, it is a very good place for the formation of peat soils (the water-holding layers below the moraine layer). Additionally, this small area shows high variation in soil types [15]. The humidity and contents of organic and inorganic matter in percent by weight are shown in Table 1.  
In sampling areas 1 and 4, the thickness of the peat layer and the deposition depth of the mineral subsoil are slightly more than one meter. In areas 2 and 3, the peat layer is thinner. In area 2, the subsoil is approximately 85 cm deep, and in area 3, it is less than half a meter deep.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test Site** & **Depth, cm** & **Humidity, \%** & \
\begin{tabular}{c} **Organic** \\ **Matter, \%** \\ \end{tabular} &
\begin{tabular}{c} **Mineral** \\ **Matter, \%** \\ \end{tabular} \\ \hline \multirow{4}{*}{1} & 0–22 & 181.7 & 34.4 & 65.6 \\  & 22–47 & 218.5 & 53.8 & 46.2 \\  & 47–111 & 146.9 & 22.2 & 77.8 \\  & 111–130 & 87.6 & 9.4 & 90.6 \\  & 0–47 & 300.0 & 53.2 & 46.8 \\
2 & 47–77 & 192.4 & 55.6 & 44.4 \\  & 77–90 & 84.5 & 17.2 & 82.8 \\  & 0–10 & 130.4 & 25.6 & 74.4 \\
3 & 10–37 & 200.3 & 42.0 & 58.0 \\  & 37–52 & 331.0 & 72.4 & 27.6 \\  & 0–10 & 254.6 & 82.6 & 17.4 \\  & 10–37 & 246.0 & 59.9 & 40.1 \\
4 & 46–86 & 243.6 & 80.8 & 19.2 \\  & 92–114 & 100.8 & 24.7 & 75.3 \\  & 122–144 & 44.7 & 4.9 & 95.1 \\ \hline \end{tabular}
\end{table}
Table 1: Humidity, organic, and mineral matter concentrations (% of dry weight) of soil samples from the Jarveslja test site.
## 5 Experiments
### Uncertainty Estimation  
To evaluate the estimated uncertainty, we visualized the distribution of uncertainty under different noises. Specifically, we introduced Gaussian noise with varying standard deviations to the test samples and obtained different uncertainty distributions. Then, we conducted a comparative analysis between these distributions. The results are shown in Figure 3 (to improve clarity, any uncertainties in the cognitive scores that exceed 1 are set to 1 in the visualization).  
From Figure 3, we can analyze that the diagnostic uncertainty and ADAS-Cog13 uncertainty of the samples generally increase with the addition of Gaussian noise with different standard deviations. This is statistically manifested through a reduction in the count of low-uncertainty results and an increase in the count of high-uncertainty results, which is visually represented by a rightward shift in the overall distribution. These observations indicate the effectiveness of our model in estimating uncertainty, as it can effectively capture anomalous data and reflect low confidence in the result by assigning high uncertainty. This experiment also demonstrates the practical significance of our proposed model, which can immediately identify and submit patients with abnormal clinical data to decision makers for further judgment in real-world applications.  
Figure 4 illustrates the change in the accuracy of the test sample after changing the uncertainty threshold. It shows that the lower the threshold value is set, the more accurate the results obtained by the model. In particular, when the threshold is set to 0.1, there is a huge improvement in the performance of all three metrics. This implies the trusted decisions are supported on the basis of the output (prediction and its corresponding  
Figure 3: The uncertainty distribution of ADAS13 and diagnosis after adding noise. (**a**) ADAS-Cog13. (**b**) Diagnosis.  
uncertainty) of our model. In practice, decision makers can obtain higher accuracy and reduce the risk of misclassification by moderately adjusting the uncertainty threshold.
# Planar Microwave Resonant Sensors: A Review and Recent Developments
## 4 Prototype Examples
### Differential-Mode Sensors
#### 4.4.2 Complex Permittivity Measurement of Liquids  
Determination of the complex permittivity of liquids from the measurement of the cross-mode transmission coefficient in differential-mode sensors was first reported in [62], where the sensing elements were OCSRRs. In this subsection, we report a similar sensor, but the sensing elements are DB-DGSs [64]. The device layout and photographs of the top and bottom view (including the fluidic channels) are depicted in Figure 29. The considered reference (REF) liquid is pure DI water, whereas different solutions of isopropanol in DI water constitute the MUT liquids. The measured cross mode transmission coefficients for the different solutions (where the volume fraction of isopropanol is the varying parameter) are depicted in Figure 30a. From this figure, the variation of the maximum of the cross mode transmission coefficient (magnitude) and the variation in the frequency where the maximum appears, as compared to the REF sample, are obtained. The results are depicted in Figure 30b. These are the two variables required to determine the complex permittivity of MUT liquids (consisting of the real and the imaginary parts). From the results of pure DI water, pure isopropanol, and a mixture of 50% isopropanol in DI water, a mathematical model linking the complex permittivity with the above-cited output variables was inferred. Then this model was used to obtain the complex dielectric constant of the different mixtures of isopropanol in DI water from the measured values of the output variable. The results are depicted in Figure 31, where it can be appreciated the good agreement with the results given by the Weiner model.  
It is remarkable that the device is able to resolve volume fractions of at least 5%, i.e., better than the volume fraction resolved with the frequency splitting sensor reported in Section 4.3, and identical to the resolution of the differential sensor reported in [62]. It should be highlighted that the sensor of Figure 29 can be also used as single-ended sensor, and, in this case, the sensitivity is excellent, as discussed in [64]. Finally, let us emphasize that, as compared to the sensor reported in [62], in the sensor reported in this subsection, the fluidic channels are placed at the opposite side of the strip lines, thereby being simpler from the fabrication viewpoint.  
Figure 29: Layout (**a**) and photograph (top view (**b**) and 3D view (**c**)) of the DB-DGS-based sensor. From [64], reprinted with permission.
## 2 Methodology
### Simulation of MERO-Measured TOA OSR and OLR Fluxes
The OSR scene type is determined by the discrete bin method: each of the parameters shown in Table 1 is discretized into several bins and one scene type is defined by one possible combination of bins of every of these parameters [20]. For example, the scene type 1 is comprised of the following parameter bins: (1) surface type: ocean; (2) cloud conditions: clear; (3) wind speed: \(<\)3.5 m s\({}^{-1}\); (4) solar zenith angle: \(0^{\circ}\)-\(10^{\circ}\); and (5) viewing zenith angle: \(0^{\circ}\)-\(10^{\circ}\). Similarly, the \(P_{i}\textsuperscript{OLR}\) in Equation (1) is also derived through ADMs according to the OLR scene type. The OLR scene type is also defined as a possible combination of related parameter bins; all of the related parameters and their discretized bins are listed in Table 2. However, these parameters are different to that of the OSR scene types listed in Table 1. For example, the \(P_{i}\textsuperscript{OLR}\) is related with the vertical temperature change (VTC); the VTC under clear cloud conditions can be derived by subtracting the air temperature at the location where the pressure is 300 hPa lower than the surface pressure from the surface skin temperature; whereas VTC under cloudy cloud conditions could be obtained by subtracting the effective cloud temperature from the underlying surface skin temperature. All the atmospheric parameters listed in Tables 1 and 2 could be derived from the CERES SYN data sets and by the MERO pixel positioning method we proposed based on our previous work using the cubic spline interpolation [18]. The TOA radiative anisotropic of the whole pixel (\(P\textsuperscript{OSR}\) and \(P\textsuperscript{OLR}\) in Equation (1)) could be derived through the area weighting averaging of that of all the sub grid points whereby the Equation (3):  
\[P^{k}=\frac{\sum\limits_{i=1}^{N}P_{i}^{k}A_{i}}{A}\quad k=\text{OSR or OLR} \tag{3}\]where \(N\) is the total number of sub grid points within the pixel, which can be de derived by Equation (4):  
\[N=\left(\frac{S^{\text{PS}}}{S^{\text{PA}}}\right)^{2} \tag{4}\]  
where \(S^{\text{PS}}\) is the assessed spatial resolution of a MERO; the \(S^{\text{PA}}\) is the spatial resolution of the true OSR and OLR data used in this study (0.1\({}^{\circ}\) longitude \(\times\) 0.1\({}^{\circ}\) latitude, about 10 km at equator). Figure 3.0 Geometries of the viewing zenith angle (\(v_{i}\)), the relative azimuth angle (\(r_{i}\)), the solar zenith angle (\(s_{i}\)), and Earth’s viewing angle (\(\omega_{i}\)) of a MERO sensor towards a sub grid point \(i\) within a pixel on the Earth TOA (top of the atmosphere). Figure 3.1 The correlation of a detector array and a pixel array of a MERO system. A pixel is discretized into sub grid points (such as pixel 8\({}^{\circ}\)); each sub grid point represents an Earth TOA area of 0.1\({}^{\circ}\) latitude \(\times\) 0.1\({}^{\circ}\) longitude.