esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results
### Evaluation Results of SBAS Ionospheric Corrections
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**WAAS**} & Station & ALGO & FAIR & MDO1 & NIST & PRDS & QUIN \\  & Code signals & P1+P2 & P1+P2 & P1+P2 & C1+P2 & P1+P2 & P1+P2 \\ \hline \multirow{2}{*}{**EGNOS**} & Station & BRST & METS & PADO & PTBB & SOFI & VILL \\  & Code signals & C1/P1+P2\({}^{1}\) & P1+P2 & P1+P2 & P1+P2 & C1+P2 & P1+P2 \\ \hline \multirow{2}{*}{**MSAS**} & Station & C1+P2 & DAEJ & GND & MIZU & STK2 & TSKB \\  & Code signals & C1+P2 & C1+P2 & C1+P2 & P1+P2 & C1+P2 & C1+P2 \\ \hline \multirow{2}{*}{**GAGAN**} & Station & HYDE & IISC & LHAZ & & & \\  & Code signals & C1+P2 & P1+P2 & C1+P2 & & & \\ \hline \multirow{2}{*}{**SDCM**} & Station & ARTU & MOBK & NOVM & NRIL & TIXI & YAKT \\  & Code signals & P1+P2 & P1+P2 & P1+P2 & P1+P2 & P1+P2 & P1+P2 \\ \hline \hline \end{tabular}  
* For the BRST station, C1& P2 are used from 1 January 1 to 17 June 2018, and P1 and P2 are used from 18 June to 30 June 2018.  
\end{table} 
Table 3: The code signals used at each selected IGS station. values for the stations located in MSAS and GAGAN service areas are significantly larger than others. The average RMS values of EGNOS, WASS, and SDCM ionospheric corrections are 0.491, 0.505, and 0.523 m, respectively. While MSAS and GAGAN suffer about 3 decimeters worse accuracy with RMS values of 0.795 and 0.858 m, respectively. Therefore, ionospheric corrections provided by EGNOS, WAAS, and SDCM are more accurate than GAGAN and MSAS in term of RMS. Figure 16 shows the bias and the STD values of GPS broadcast ionospheric correction errors at each station. The bias values in all selected station are positive. Most of the bias values are significantly larger than those bias values computed with SBAS ionospheric correction errors at the same station. All STD values are larger than the corresponding STD values calculated with SBAS ionospheric correction errors. The average RMS values over the stations in the WAAS, EGNOS, MSAS, GAGAN and SDCM service areas are 1.482, 1.556, 1.626, 1.333 and 1.896 m, respectively. It indicates that the accuracy of GPS ionospheric correction is much worse than SBAS ionospheric correction. Figure 14: The disturbance storm time (Dst) and Kp indices during the period from 1 January 2018 to 30 June 2018. Figure 15: The bias and standard deviation (STD) values of SBAS ionospheric correction errors for each selected IGS station.
# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 2 Materials and Methods
### Image Space Post-Processing
#### 2.2.4 Selection of Cases for K2S Dataset  
Of the 816 potential cases, the target was selecting 300, with the intent of maintaining sufficient cases for training reconstruction and segmentation models, maintaining some variety of anomalies in included cases, and ensuring a reasonable memory footprint given computational constraints. Radiologists with three (J.L.) and four (F.G.) years of experience developed 5-point LIKERT scales to assess segmentation quality: (1) unusable; (2) poor, with some mislabeling of bones or cartilage; (3) useable, with some major issues, but correct labeling of bone or cartilage; (4) good, with some minor but acceptable issues; (5) (near) human-like. Examples of cartilage segmentation LIKERT scores for the 5 classes are seen in Figure 4, and for bone in Figure 5. Segmentation LIKERT scores were calculated for bone and cartilage from videos of the segmentations that cycled through all sagittal slices. Cases with acceptable segmentation quality for both cartilage and bone were selected as the K2S dataset. Cartilage LIKERT scores for K2S were as follows: 5:14; 4:175; 3:110; 1:1. Bone LIKERT scores were as follows: 5:112; 4:179; 3:9.  
Figure 4: 1–5 LIKERT cartilage segmentation scores overlaid on ground truth knee scans. In this example, the LIKERT of 5 indicates human-like segmentation; the LIKERT of 4 shows a slight underestimation of patellar and tibial cartilage; the LIKERT of 3 is assigned due to minor underestimation of patellar and tibial cartilage, with soft tissue detected as femoral cartilage; the LIKERT of 2 is assigned due to missing mask areas for patellar and tibial cartilage, with femoral cartilage overestimation; the LIKERT of 1 is missing a tibial cartilage mask.
## 2 Materials and Methods
### Data Preprocessing  
The acquired signals were filtered with a low-pass Butterworth filter to eliminate noise. Third degree, 50 Hz sampling frequency, and 5 Hz cut-off frequency parameters were used for filtering process. This step was applied to increase the accuracy of the signals and improve the data quality. In addition, shifting (shifting on the sample axis) process was performed with a cross-correlation method to find the alignment between two signals. This method was applied to evaluate the degree of overlap of signals, to facilitate analysis, and to ensure that artificial intelligence models produce more accurate results. Finally, the acquired signals were optimized by separating them into training and test data.
## 4 A Deep Learning-Based CSI Feedback for New Radio
### System Model  
While the authors of [12] adopted a multiple-input single-output (MISO) system, i.e., a transmitter deploying an antenna array of \(N\) antennas with only one antenna at the receiver side, here, we consider a 5G NR-compliant MIMO system, with \(M\) receiving antennas. For the \(c\)-th sub-carrier at the \(m\)-th receiving antenna, the received signal can be expressed as  
\[y_{m,c}=\mathbf{h}_{m,c}^{H}\mathbf{v}_{c}x_{c}+z_{m,c} \tag{4}\]where:  
* \(\mathbf{h}_{m,c}\) is the complex channel vector relative to the \(c\)-th sub-carrier and to the \(m\)-th receiving antennas of size \(N\times 1\);
* \(\mathbf{v}_{c}\) is the complex precoding vector of size \(N\times 1\);
* \(x_{c}\) is the complex data symbol transmitted on the \(c\)-th sub-carrier;
* \(z_{m,c}\) denotes the additive noise on the \(c\)-th sub-carrier relative to the \(m\)-th receiving antenna.  
The precoding vector \(\mathbf{v}=\{\mathbf{v}_{c}:c=1,\ldots,\widetilde{C}\}\), with \(\widetilde{C}\) the number of sub-carriers of the OFDM system, is computed by the BS once it receives the CSI feedback information \(\mathbf{H}_{c}=[\mathbf{h}_{1},\ldots,\mathbf{h}_{M}]\in\mathbb{C}^{N\times M}\). The amount of coefficients the UE must feedback is then \(N\times M\times\widetilde{C}\), which can easily become too demanding from a complexity point of view, since resource elements must be dedicated to the channel reporting, instead of data packets (i.e., a capacity drop). The \(M\)\(y_{m,c}\) symbols received per sub-band are then combined at the receiver side and the final estimated symbol \(\hat{y}_{c}\) on the \(c\)-th sub-carrier is obtained as follows:  
\[\hat{y}_{c}=\mathbf{u}_{c}^{H}\mathbf{y}_{c} \tag{5}\]  
where \(\mathbf{u}_{c}\) is an \(M\times 1\) combiner and \(\mathbf{y}_{c}=[y_{1,c^{\prime}},\ldots,y_{M,c}]^{T}\) is an \(M\times 1\) vector containing all the received symbols on the \(c\)-th sub-carrier.
## 2 Region, Data, Method  
### Network of Stations  
The Kamchatka Peninsula is one of the most seismically active regions of the Earth due to its location at the junction of the Pacific oceanic plate and the continental North American and Eurasian tectonic plates. Here, strong earthquakes occur within the Kamchatka fragment of the Kuril-Kamchatka seismic focal zone and the western fragment of the Aleutian seismic focal zone as a result of Pacific oceanic plate movement in the northwest direction (Figure 1). Since 2011, a network of digital broadband seismic stations (Table 1) has been operating here. This provided the technical conditions for studying the variations in microseismic oscillations and evaluating the potential of such data to search for precursors of strong earthquakes. The geometric dimensions of the network are characterized by the maximum distances between stations, 1450 km in the N-S direction (between SKR and KMSK stations) and 560 km in the W-E direction (between PAL and BKI stations). The average distance between two adjacent stations is 120 km (median 110 km) with a range of values from 10 to 240 km.
## 4 Experimental Evaluation
### Results and Performance Analysis
For both receivers, BER increases as ROP diminishes. However, the NNWSC-based receiver consistently shows a lower BER across all ROP conditions. Nonetheless, the performance advantage of the NNWSC gradually diminishes, especially at low ROP levels, where its BER rises sharply, narrowing the gap with the conventional receiver. For the conventional receiver, the BER level of S2 is notably higher due to the pronounced effects of IBI and ISI. In contrast, the NNWSC-based receiver demonstrates an effective mitigation of IBI and ISI, leading to minimal performance differences among the three sub-bands. Figure 10: The relationship between the BER and ROP attenuation per sub-band. Overall, the experiments demonstrate that the NNWSC-based receiver exhibits a significant advantage under high ISI and IBI conditions, effectively suppressing both types of interference and substantially reducing BER. Notably, when signal power is strong, the NN's learning capability allows it to handle complex interference more effectively, showcasing a clear performance advantage in challenging environments.
# Finite Elements for Higher Order Steel-Concrete Composite Beams
## 2 Recall of the Analytical Model
### Kinematics  
The beam kinematics is characterised by null displacements in the X direction, namely the beam transverse bending and torsion are not included in the model. Furthermore, the slab-girder detachment is considered to be prevented by the shear connection, which is assumed to be stiff in the vertical direction. The displacement field of the composite beam is approximated by superimposing warping components, due to shear flows in the elements, to the overall longitudinal displacements and bending rotations [30].  
Figure 2: (a) Cross-section geometry; (b) local abscissa of the \(i\)-th wall.  
With reference to the cross section of Figure 2a, adopting subscripts \(c\), \(s\), and \(r\) for referring to the concrete slab, the steel girder, and the slab reinforcements, respectively, the following admissible displacement field is considered:  
\[w_{c}(x,y,z)=\ \mathbf{w}_{c}(z)\mathbf{\cdot}\mathbf{a}_{c}(x,y) \tag{1}\]  
\[w_{s}(\xi_{i},z)=\mathbf{w}_{s}(z)\mathbf{\cdot}\mathbf{a}_{s}(\xi_{i})\ \ \ i=1,\ \ldots,\ n \tag{2}\]  
\[v(x,y,z)=v_{0}(z) \tag{3}\]  
where \(w_{c}\) is the longitudinal displacement of the slab and the included steel reinforcements (steel-concrete perfect bond assumption), \(w_{s}\) is the longitudinal displacement of the steel girder, and \(v_{0}\) is the axis deflection. Furthermore,  
\[\mathbf{w}_{c}^{T}(z)=\left[\begin{array}{cc}w_{c0}(z)&\Phi_{c}(z)&f_{c}(z)\\ \end{array}\right] \tag{4}\]  
\[\mathbf{w}_{s}^{T}(z)=\left[\begin{array}{cc}w_{s0}(z)&\Phi_{s}(z)&f_{sv}(z)&f_ {sh}(z)\\ \end{array}\right] \tag{5}\]  
are vectors grouping together the generalized longitudinal displacements \(w_{c0}\) and \(w_{s0}\) measured at the origin of the \(Z\) axis, the rotation \(\Phi_{c}\) and \(\Phi_{s}\) with respect of the \(X\) axis, the slab warping intensity \(f_{c}\), and the steel girder warping intensities \(f_{sv}\) and \(f_{sh}\). Furthermore,  
\[\mathbf{a}_{c}^{T}(x,y)=\left[\begin{array}{cc}1&y&\psi_{c}(x)\\ \end{array}\right] \tag{6}\]  
\[\mathbf{a}_{s}^{T}(\xi_{i})=\left[\begin{array}{cc}1&y(\xi_{i})&\psi_{sv}(\xi_ {i})&\psi_{sh}(\xi_{i})\\ \end{array}\right] \tag{7}\]  
appearing in Equations (1) and (2), are geometric vectors in which \(\psi_{c}\) is a known warping function of the concrete slab, which is assumed to be constant within the thickness, and \(\psi_{sv}\) and \(\psi_{sh}\) are the known steel girder warping functions. Slab and steel girder warping functions are derived through the procedure proposed by [PERSON] et al. [24], assuming each member as a thin-walled element separated from the others and subjected to self-equilibrated elementary loading schemes of pure shear and longitudinal constant flow at the shear connection. Finally, from Equations (1) and (2), the slab-steel girder interface slip may be readily obtained as follows:  
\[\Gamma(z)=\mathbf{w}_{s}\mathbf{\cdot}\mathbf{\overline{a}}_{s}-\mathbf{w}_{c}\mathbf{\cdot}\mathbf{ \overline{a}}_{c} \tag{8}\]  
where the symbol \(\mathbf{\overline{\bullet}}\) denotes quantities calculated at the interface.
## 3 Results
### Main Steps in FEA of Human-Seat Interactions
#### 3.2.2 Body Geometry  
After gathering data using the various data acquisition methods stated in the previous section, generating the body geometry is the next step. Papers studied for this review have been divided into two categories. The first category includes papers that used a full-body geometry model [22; 23; 24; 25; 26; 17; 28; 29; 30; 33; 34; 35; 36; 37; 38; 39; 40], and the second category includes the ones that used a specific section of the body [23; 24; 25; 26; 27; 23; 25; 27; 31; 32; 36; 37; 38; 39; 41; 42; 43; 44]. Within those groups, there are models that include mechanical properties [23; 24; 25; 27; 23; 25; 27; 36; 37; 38; 39; 42; 43; 44], and they are also divided into anatomical [23; 24; 25; 21; 23; 25; 27; 39; 42; 43; 44] and phenomenological setups [23; 27; 29; 35; 40]. Anatomical models represent a human model based on its physiological characteristics, where the details of certain body parts such as organs and specific bone structures are portrayed. On the other hand, phenomenological human models are used to show a single characteristic, such as the pressure distribution, of the subject being studied. Depending on the type of results needed, the models can describe a specific part or the entire body.  
Full-body geometry, as shown in Figure 3, consists of a skeletal model that shows the pelvis, femur, tibia, cervical, spine, and the head, and a detailed soft tissue model contains the muscles, ligaments, fat, and skin. Nevertheless, not all full-body models contain this level of detail. Three papers have used a full-body model that consists of a volumetric model that mimics the outer shape of the human body without representing the anatomical parts [2; 31; 40]. This is because the authors in the studies have only investigated a certain behavior such as the pressure distribution of the model interacting with a seat or other padded furniture.  
Figure 3: Full-body human FE model, including the bony structure, soft tissues, and skin [1].  
On the other hand, there are papers that have focused on a certain body section such as the thigh and buttocks section as seen in Figure 4, where the focus was on studying the stress and strain contact forces between the human and the seat pan for comfort and discomfort analysis [1; 15; 16; 17; 18; 19; 21; 23; 24; 25; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 41; 42; 43; 44]. The level of detail included accurate models of the iliac wings, sacrum, coccyx femora, soft tissues, and skin. Another use of accurate thigh and buttocks models is the study of the internal stress on the ischial tuberosities in the development of pressure ulcers [20; 23; 25; 26; 32; 45].
# Model Experimental Study on De-Icing Method of Bridge Pylon Beam Based on Electric Heating
## 2 Model Test Design
### Design Ide of the Heating System  
In roadway de-icing applications, heating cables are usually pre-buried into the structure during road construction, or a slot is created to embed the heating cables in the existing roadway. However, this paper focuses on the study of existing bridges, and the method of pre-burial during construction is clearly not in line with the focus of this paper. If the beam is slotted, it will be more difficult to construct. The difference between road and beam is that the road surface is often occupied by human and vehicular activity, whereas the surface of the beam basically does not involve human activity; so, a heating system with carbon fiber heating cables as the heat source can be considered for direct placement on the surface of the beam.  
If the CFHCs are set directly on the surface of the beam to heat the beam, the heating effect may not be obvious due to the influence of the surrounding environment. Therefore, after setting the CFHC, a layer of stainless steel sheets can be laid on the surface of the heating cable to extend the heat transfer range of the CFHC and directly change the icing surface of the beam from the original concrete surface to the metal surface at the same time. Finally, a layer of heat-insulated film can be set on the contact surface of the heating cable and the beam to concentrate the heat transfer to the metal surface and enhance the heat transfer efficiency again. (The specific layout can be seen in Figure 8, section I-I)  
The external size of the heating system was 1 m \(\times\) 0.1 m \(\times\) 0.01 m. Three K-type thermocouples, named A, B, and C, were placed inside the heating system to record the temperature changes, and the spacing of each was 0.25 m, as shown in Figure 8. The layout spacing of the CFHC was 8 cm; the upper CFHC was 2 cm away from the top of the heating system; and the corresponding thermal power density was 576 W/m\({}^{2}\). The thermocouples were all 40 mm away from the two CFHCs. As the thickness of the metal plate was only 0.5 mm, the temperature difference between the inside and outside of the metal plate was negligible. The I-I section in Figure 8 shows the internal details. The CFHCs and thermocouples were stuck to the front surface. The aluminum film was close to the beam face to insulate the heat. The shaded area in Figure 8 shows the adjusted layout of the CFHC.
# Magnetic Field Effect of Near-Field Radiative Heat Transfer for SiC Nanowires/Plates
## 3 Results and Discussion
### Effect of Structure Parameters  
#### 3.2.1 Filling Ratio  
The filling ratio \(f\) is defined based on the volume fraction of SiC. The heat transfer coefficients for SiC nanowires under different magnetic field intensities at \(d=10\) nm were simulated as plotted in Figure 7. Note that the maximum filling ratio for the nanowires was 0.3.  
From Figure 7, we found that the heat transfer coefficient of the nanowire structure obviously increased with increasing magnetic field. This is because the magnetic field leads to a significant increase in the photon tunneling phenomenon of the material structure. The SiC plates are not  
Figure 6: Contour of the transmission coefficient for (**a**) SiC plates and (**b**) SiC Nanowires at \(d=10\) nm.  
Figure 7: Heat transfer coefficient versus filling ratio of nanowires at a gap distance \(d=10\) nm.  
shown here because they remained constant and slightly changed as the external magnetic field strength increased.
## 2 Materials and Methods  
### Study Areas  
Three agricultural test sites, Macarese, Grosseto, and Jolanda di Savoia, in Italy (Figure 1a), and a site in Iran (Figure 1b) were selected as case studies for crop mapping.  
The Jolanda di Savoia farm is in northeast Italy (latitude 44\({}^{\circ}\)52\({}^{\prime}\)59\({}^{\prime\prime}\)N, longitude 11\({}^{\circ}\)58\({}^{\prime}\)48\({}^{\prime\prime}\)E), with an altitude of about 1 m.a.s.l. and mean annual precipitation of 691 mm. This region has a warm and temperate climate with an annual mean temperature of 13.6 \({}^{\circ}\)C [50]. The soils mostly show clayey and silty textures. The area belongs to the Bonifiche Ferraresi S.p.A., the largest farm in Italy, encompassing about 3850 ha (Figure 1c). The sowing time of winter cereals varies between October and December, while for summer crops it varies between April and June The Maccarese S.p.A. farm (latitude 41\({}^{\circ}\)52\({}^{\prime}\)18\({}^{\prime\prime}\)N, longitude 12\({}^{\circ}\)14\({}^{\prime}\)05\({}^{\prime\prime}\)E), with an altitude of 8 m.a.s.l. and a long-term average annual precipitation of 812.9 mm, is in central Italy near Rome (Figure 1d). The farming area has soil texture ranging from sandy to clay loam [50]. The area has a typical coastal Mediterranean climate (hot summer Mediterranean climate, Cas in Koppen classification), with an average minimum temperature in the winter of 5.0 \({}^{\circ}\)C and an average maximum temperature in summer of 27.4 \({}^{\circ}\)C. The rainiest seasons are autumn and winter, and the sowing time for winter cereals varies between October and December, while for summer crops it varies between April and June, according to farm management needs and precipitation patterns [50].  
The Grosseto test area (Figure 1e) is in central Italy (latitude 42\({}^{\circ}\)50\({}^{\prime}\)19\({}^{\prime\prime}\)N, longitude 11\({}^{\circ}\)02\({}^{\prime}\)18\({}^{\prime\prime}\)E, altitude 10 m.a.s.l.) and has a long-term average annual precipitation of 655 mm. The Grosseto area has a Mediterranean climate with very mild wet winters and very hot dry summers.  
The Mirza Koochak-Khan (MKK) farming and industrial lands (Figure 1f) are in the Khuzestan province in southwest Iran (30\({}^{\circ}\)55\({}^{\prime}\)15\({}^{\prime\prime}\)N; 48\({}^{\circ}\)15\({}^{\prime}\)35\({}^{\prime\prime}\)E) with a mean altitude of 5 m.a.s.l., and a long-term average annual precipitation of 266 mm. The MKK soil is mostly characterized by silty clay loam and clay loam textures.
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods
### Analysis
#### 3.6.2 Focus Group  
At the end of the experimental session, an informal focus group was held with all the participants in order to obtain their personal opinions and impressions of the experiment. One of the authors moderated the session and was in charge of guiding the group's conversation, paying special attention to the topic of the study, and collaborative communication through mesh OppNets in a professional audiovisual environment. The session lasted 2 h.
## 4 Real-Scale Vehicle Tests and Result Analysis  
The feasibility of the application of the ACS-SSI algorithm in fault identification and diagnosis of the dump truck suspension systems has been proven by a series of dynamic simulations. However, in the actual operation of the dump truck, there are many non-stationary, nonlinear dynamic characteristics and other mechanical vibration events aliased in the vibration response, which will affect the effectiveness of the diagnostic algorithm. To this end, it is necessary to further study the feasibility of the application of the ACS-SSI algorithm in the fault diagnosis of dump truck suspension systems through real-scale vehicle tests.