esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results
### Experimental Setup
#### 3.1.1 Data Set  
In order to better compare the segmentation effect of transformers on remote-sensing data sets at different scales, the experimental data were obtained from the state-of-the-art airborne image data sets Vaihingen data set and Potsdam data set, provided by ISPRS. The Vaihingen data set and Potsdam data set have the same type of features and different scales.  
Vaihingen data set: Vaihingen is a relatively small village located in Germany with a number of detached buildings and small multistory buildings. The Vaihingen data set contains 33 remote-sensing images of different sizes covering an area of 1.38 km\({}^{2}\) in Vaihingen. The spatial resolution of the top image and DSM is 9 cm, and each image has four bands: near-infrared, red, green, and blue. We cropped 33 remote-sensing images of different sizes into small images with a size of 224 \(\times\) 224, and the cropped images were divided into a training set, a validation set, and a test set according to the ratio of 8:1:1. The percentage of each type of feature in the data set is shown in Figure 9.  
Potsdam data set: Potsdam is a city with a long history of large buildings, narrow streets, and a dense settlement structure. The Potsdam data set contains 38 remotely sensed images of the same size (6000 \(\times\) 6000) covering an area of 3.42 km\({}^{2}\) in Potsdam. The spatial resolution of the top image and DSM is 5 cm, and each image has four bands: near-infrared, red, green, and blue. Similarly, we cropped 38 remote-sensing images of the same size into small images with a size of 224 \(\times\) 224, and the cropped images were divided into a training  
Figure 9: Proportion of each type of feature in the Vaihingen data set.  
set, a validation set, and a test set according to the ratio of 8:1:1. The percentage of each type of feature in the data set is shown in Figure 10.
# Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition
## 3 Method
### Multi-Scale Spatial-Temporal Feature Interaction (MSTI)  
From a human action kinematic perspective, human motion exhibits multi-scale temporal characteristics, encompassing both rapidly changing local details (e.g., finger movements) and slower-evolving global patterns (e.g., trunk motion). Consequently, multi-scale temporal convolution is designed to extract these hierarchical motion features. Furthermore, human movement requires coordinated interactions among multiple joints. For instance, a throwing motion demands precisely timed coordination of the shoulder, elbow, and wrist joints [41]. Thus, modeling individual joints solely through temporal convolution is insufficient; it is essential to simultaneously account for kinematic interactions across different joints.  
To this, we introduce a multi-scale spatial-temporal feature interaction (MSTI) module to capture the correlations between sub-actions at different granularity levels and enhance the skeleton motion information interactions among joints, as illustrated in Figure 5. Specifically, the feature \(\mathbf{X}_{MTCA}\in\mathbb{R}^{C_{1}\times T\times V}\) first undergoes an average pooling operation along the joint dimension, producing a global representation \(\mathbf{X}_{global}\in\mathbb{R}^{C_{1}\times T\times 1}\). Then, both \(\mathbf{X}_{MTCA}\) and \(\mathbf{X}_{global}\) are processed by multi-scale temporal convolution (MSTC) to capture multi-scale temporal information. The MSTC consists of six branches, each of which applies a 2D convolution for channel reduction. The first four branches employ temporal convolutions with different dilation rates of 1, 2, 3, and 4, respectively. The fifth branch incorporates a \(3\times 1\) max-pooling layer, while the sixth branch retains the original input. Finally, all the outputs from the six branches are concatenated along the channel dimension.  
Then, a learnable vector \(\mathbf{V}\in\mathbb{R}^{1\times V}\) is introduced to facilitate spatial information interaction among joints. These features are then combined to fuse spatial-temporal information among joints, obtaining \(\mathbf{X}_{out}\in\mathbb{R}^{C_{1}\times T\times V}\), as formulated below:  
\[\mathbf{X}_{out}=\mathrm{MSTC}(\mathbf{X}_{MTCA})+\mathrm{MSTC}(\mathbf{X}_{ global})\times\mathbf{V} \tag{19}\]
## 2 Experiment  
The experimental setup for the investigation of cascade SRRS is shown in Figure 1. The laser source for both WLC seed excitation and SRRS pumping was a self-made two-stage, double-pass Yb:YAG chirped-pulse amplifier, providing transform-limited ~1.2 ps output pulses with an energy of up to 20 mJ at a repetition rate of 100 Hz after the grating compressor [26].  
A small portion (\(\sim\)100 \(\upmu\)J) of the laser energy was used to generate WLC in the undoped YAG rod. The first thin-film polarizer (TFP 1), together with a half-wave retardation plate (\(\uplambda\)/2), formed an attenuator of the incident pulse energy, and the iris aperture selected the central beam part with an initial diameter of 6.4 mm. The crystal length and a focusing lens (F1) with a focal length of 100 mm were chosen to obtain stable WLC spanning to \(\sim\)1.4 \(\upmu\)m. The WLC radiation spectrum was selected with a long-pass filter (F) FELH1050 (Thorlabs Inc., Newton, NJ, USA) and collimated using a lens (F2) with a focal length of 400 mm. The collinear propagation of the WLC seed and pump beams was provided using a custom-made dichroic mirror (DM) transmitting at 1030 nm and reflecting in a wavelength range of 1100-1400 nm, while precise temporal overlap was ensured by two plane silver mirrors (M2 and M3) in the delay line. A second attenuator formed by a thin-film polarizer (TFP 2), together with a half-wave retardation plate (\(\uplambda\)/2), was used to vary the pump energy supplied to the Raman cell. A quarter-wave retardation plate (\(\uplambda\)/4) provided the circular polarization of the pump to suppress SVRS. The WLC seed and pump beams were focused using a lens (F3) with a focal length of 750 mm to the center of the Raman cell. Both SVRS and SRRS occurred in a high-pressure gas cell that was 1 m long with MgF\({}_{2}\) windows 5 mm thick and filled with compressed hydrogen at a variable pressure from 1 MPa to 5 MPa.  
Spectral measurements were carried out using a NIRQuest512-2.5 spectrometer (Ocean Optics, Orlando, FL, USA) with an integration time of 100 ms corresponding to 10 pulses. To do this, a small portion of the Stokes beam was split off by a fused silica wedge, and a diffuser was placed in front of the multimode fiber at the spectrometer input to ensure uniform spatial distribution. Beam intensity profiles were measured using a WinCamDLCM-C CMOS profiler (DataRay Inc., Redding, CA, USA). Pulse energy was measured using a LabMax-TOP console with J-10 MB-HE energy sensors (Coherent Corp., Saxonburg, PA, USA). The combined energy of SRRS and SVRS was separated from the pump radiation using a FELH1050 long-pass filter, while the SRRS was rejected using a FELH1350 long-pass filter (Thorlabs Inc., Newton, NJ, USA). The compression of the Stokes pulses was verified using SF11 prisms with an apex angle of 59\({}^{\circ}\) spaced 80 cm apart. A Glan-Taylor prism sampled p-polarized Stokes radiation for characterization using homemade second harmonic generation frequency-resolved optical gating (SHG-FROG).
###### Abstract  
This study analyzes 105 coal ash samples from Jurassic and Carboniferous coals from five mines in Kazakhstan, Lenin, Saradyr, Bogatyr, Maikuben, and Shubarkol, focusing on the inorganic elemental compositions, their occurrence, and industrial and environmental implications. Methods include coal ash yield and volatile matter analysis, mineralogical characterization via low-temperature ash X-ray diffraction (LTA-XRD), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). High-temperature ash (HTA) was analyzed using energy-dispersive X-ray fluorescence (EDXRF), highlighting XRF's potential for rapid multi-elemental analysis. Nine major elements (Al, Si, P, S, Fe, K, Ca, and Ti) and eleven trace elements (As, Cu, Cr, Zn, Pb, V, Ga, Mn, Ni, Y, Yb, and Zr) were identified in HTA samples through EDXRF. SEM and dendrogram analysis confirm their co-occurrence with quartz, kaolinite, pyrite, and accessory minerals such as chalcopyrite, zircon, rutile, and REE-bearing apatite. The elemental content of samples enhances industrial suitability by reducing emissions. Only Yb shows slight enrichment for economic benefits, along with La, Ce, and Nd, while concentrations of potentially toxic elements indicate minimal environmental risk. EDXRF demonstrates its efficiency for large-scale investigations, with all samples analyzed in a few days using automated overnight measurements. This approach shows promise for future studies focusing on trace elements, including REE.  
Kazakhstan; major elements; trace elements; coal; XRF +
Footnote †: journal: Applied sciences
# Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry
## Materials and Methods  
### Study Site and Institu Data  
Obros Gyalos is a small protected cove (Figure 1) located at the prefecture of Chania in the island of Crete; it lacks a proximity to rivers and anthropogenic activities. The cove has been selected for the creation of a SCUBA diving park of Crete due to its unique seascape morphology. There was a detailed bathymetric survey among the activities for its establishment during summer of 2017 [19]. In total, 9954 bathymetric points have been collected; these have been split into two parts for calibration and validation. Prior to the random spatial split using the \"_Subset features_\" tool in ArcGIS 10.5, which randomly splits the dataset into two parts based on percentages, an aggregation of the in-situ data has been performed to match the spatial resolution of the satellite data (3-m). Points have been converted to 3-m pixels using the MEAN function (the mean of the attributes of all the points within the cell) of the corresponding tool \"_Point to Raster_\" in ArcGIS 10.5. After that, the resulting raster dataset has been converted into points, producing the final dataset of 4756 points, split into 2854 points for training and 1902 points for validation.
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 2 State of Art of Estimation Models for Solar Radiation  
### Estimation Models for Global Solar Radiation  
The global solar radiation is the sum of beam solar radiation and diffuse solar radiation. Most of the research on global solar radiation is based on two different time scales: daily and hourly.
## 5 Analysis Results
### Vertical Displacement of Bridge Pile Foundation  
Long piles are always used in bridge foundation, as opposed to short piles, and the soil has a negative friction effect on the pile body when the vertical displacement of soil is larger than the pile foundation settlement, which generates additional axial force inside the pile. When the ultimate bearing capacity of the pile is insufficient, this leads to further subsidence of the pile and poses a threat to the upper structure. In contrast, the direction of the negative friction is cut to the pile surface. In the case of the horizontal deformation of the pile, negative friction produces an additional bending moment, further reducing the safety of the pile. In the present case of long piles (i.e., pile foundation with a long pile greater than the excavation depth of the foundation excavation), the vertical displacement of the surrounding soil caused by the foundation excavation is dual. The pile body has the effect of friction resistance in two directions when settlement occurs at a certain depth. Above the boundary line, soil settlement was greater than the pile settlement, and the soil had negative friction on the pile. Below the boundary line, there was an upward friction resistance to the pile body, resulting in an upward pulling force. Therefore, the long piles adjacent to the deep foundation excavation may settle or lift during foundation excavation. As shown in Figure 15, the pile in the present case is a long pile with negative friction resistance; thus, the pile foundation presents a linear settlement trend.  
Figure 15 shows the deformation curves of the top of piles A, B, C and D under different working conditions. In Figure 15, with increasing excavation depth of the foundation pit, the vertical displacement at the top of the pile increases, and the maximum vertical displacement at the top of the pile occurs when the excavation is completed. The maximum displacements of piles A, B, C and D were 0.24, 0.45, 0.58, and 0.3 mm, respectively.  
The dynamic load had a significant influence on the vertical displacement of the pile foundation, which cannot be ignored, based on the comparison of the vertical displacement of the four piles under working condition 6 (applying a train dynamic load when excavating to the bottom). The maximum displacements of the pile top of piles A, B, C and D under dynamic load were 0.37, 0.64, 0.79, and 0.43 mm, respectively. Compared with condition 5 (ex excavation to the bottom without the train load), the increases were 54.1, 42.2, 36.2, and 43.3%, respectively. However, although the increases were large, the overall vertical displacement of each pile was small, and the influence of train vibration could be ignored.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 5 Conclusions
In future research, we plan to focus on pre-processing and optimize the mAP metric for post-processing object detection tasks, as well as further improve the detection accuracy of the algorithm model through knowledge distillation. Conceptualization, Y.Z. and Y.L. ; methodology, Y.L. and Y.Z. ; software, Y.L. ; validation, Y.L. ; investigation, C.L. and Y.Z. ; writing--original draft preparation, Y.Z. and Y.L. ; writing--review and editing, C.L., Y.L. and Y.Z. ; visualization, Y.L. ; supervision, Y.Z. and C.L. ; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Natural Science Foundation of Heilongjiang Province (LH2023F003) for financial support. Not applicable. The raw data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study. The authors declare no conflict of interest.
## 4 Experimental Evaluation
### Scenario 2: Distance between STA and AP  
The communicable distance is essential information in a wireless network because fluctuating RSSIs or interruptions occur as the communication distance increases. Section 4.3.1 explains a measurement environment for evaluating communicable distance. Then, Sections 4.3.2 and 4.3.3 examine the communication performances of the two network layers.
# Optimum System for Diagnosing Power Quality in Electrical Microgrids
## 2 Materials and Methods
### Measurement Requirements  
DiagElect is designed for various PQ analyses, ranging from energy monitoring to the detection and classification of both stationary and non-stationary PQDs. To ensure DiagElect is suitable for these analyses, voltage and current measurements taken by the SDMD must comply with several standards [10; 39; 40]. These standards require sampling voltage and current at rates of 128, 256, or 512 samples per cycle across 10 cycles, using high-speed analog-to-digital converters (ADCs) with a minimum sample rate of 12.8 kSPS and a signal-to-noise ratio (SNR) exceeding 86 dB. In addition, simultaneous measurements from different nodes are essential to accurately assess and compare pseudo-stationary disturbances such as distortion and imbalance, which may fluctuate over time. Moreover, measurements at each node must be synchronized to facilitate precise calculations of power components. This synchronization is achieved using a network time protocol (NTP) server [41], ensuring that data collection is coordinated across multiple points.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 4 Analysis of Eavesdropping Attacks in UASNs
### Link Criteria  
We first define the link condition in UASNs. We denote the transmission power by \(P_{t}\). The signal-to-noise ratio at receiver denoted by \(SNR\) can be calculated according to the aforementioned underwater acoustic channel model in Section 2 as follows:  
\[SNR=AG\frac{P_{t}}{\int\limits_{B}A(d,f)df\int\limits_{B}N(f)df} \tag{10}\]  
where \(B\) is the bandwidth used in acoustic communications. In order to investigate the relationship between frequency \(f\) and \(SNR\) better, we normalize the bandwidth. Then the Equation (10) can be simplify to  
\[SNR=AG\frac{P_{t}}{A(d,f)N(f)} \tag{11}\]  
Figure 4: The beam pattern of an unshaded uniform liner array hydrophone with 10 isotropic hydrophone elements.  
Equation (11) is a general equation in both IUSNs and AUSNs. But note that in IUSNs, \(AG=1\), while in AUSNs, \(AG=M\). Equation (11) can be expressed in terms of dB by \(SNR_{dB}\) as  
\[SNR_{dB}=10\log AG+10\log P_{t}-10\log A(d,f)-10\log N(f) \tag{12}\]  
Generally, a link can be successfully established if and only if  
\[SNR_{dB}\geq\Delta_{0}(dB) \tag{13}\]  
where \(\Delta_{0}\) is the minimum signal-to-noise ratio at receiver. In particular, we let the signal-to-noise ratio equal to \(\Delta_{0}\), and then have the minimum transmission power \(P_{t}\) in dB  
\[10\log P_{t}=\Delta_{0}+10\log A(d,f)+10\log N(f)-10\log AG \tag{14}\]  
We denote the minimum transmission power in IUSNs by \(P_{t}^{i}\) and that in AUSNs by \(P_{t}^{a}\). Then, we can express the relationship between \(P_{t}^{i}\) and \(P_{t}^{a}\) from Equation (14) with different value of \(AG\) as  
\[10\log P_{t}^{a}=10\log P_{t}^{i}-10\log M \tag{15}\]  
In this paper, we assume that both AUSNs and IUSNs apply power control scheme to save power. In other words, the nodes in AUSNs and IUSNs use the minimum transmission power \(P_{t}^{i}\) and \(P_{t}^{a}\), respectively. We can see from Equation (15) that the transmitters in AUSNs can use less transmission power to establish link under the same network topology and environment conditions compared with the transmitters in IUSNs.
# Challenges for Teaching Wireless Communications Standards at the Graduate Level
## 4 Laboratory Experiences for Telecommunications Standards' Education
### Laboratory Experiences by Using MATLAB  
The University has a Total Academic Headcount license for MATLAB(r) tools, a trademark of The MathWorks, Inc., used by professors and researchers for educational and research goals and by the students for the improvement of their knowledge in the telecom area. MATLAB offers many tutorials in the Communications Toolbox, which cover for example the physical layer aspects of the most common standards, UMTS, LTE, LTE-Advanced, and IEEE 802.11b. Moreover, many interesting simulation examples can be found on the site [[https://it.mathworks.com/matlabcentral](https://it.mathworks.com/matlabcentral)]([https://it.mathworks.com/matlabcentral](https://it.mathworks.com/matlabcentral)) developed by the user community for an open exchanging of MATLAB and Simulink files and models.  
By using the MATLAB LTE System toolbox, it is easy to show the physical processing chain of LTE, e.g., time-frequency resources grid, explaining the position in the sub-carriers and slots of the main physical downlink channels, and the reference signals with different positions according to the multiple antenna scheme used.  
A complete transmitter-channel-receiver chain can be simulated showing how to generate data as random bits, the effect of modulation, and the mapping to every symbol in a subframe. Then, the frame with primary and secondary synchronization signals and reference signals is formed, OFDM modulated, and passed through a fading channel with the addition of Additive White Gaussian Noise (AWGN). The students can understand the impact of the fading channel on the received grid, and different models of the fading channels are available in MATLAB with different delay profiles and related powers, for pedestrian, urban, vehicular, and MIMO propagation conditions compliant with 3 GPP Series 36.  
According to the methodology shown in Section 3.2, different projects can be developed by students to understand the LTE physical layer, e.g., the techniques that dynamically change transmission parameters with the varying channel conditions measured at the UE terminals, i.e., the link adaptation techniques. The Channel Quality Indicator (CQI), Precoder Matrix Indicator (PMI), and Rank Indicator (RI) measurements reported in the feedback for eNodeB allow dynamically changing the system parameters such as the system bandwidth, the MIMO transmission modes,  
Figure 4: DL and UL throughput in different link conditions.  
the number of transmission layers, the precoding matrix, Modulation and Coding Schemes (MCSs), and the transmission power.
# Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks
###### Abstract  
Ultra-dense wireless cellular networks have been envisioned as a promising technique for handling the explosive increase of wireless traffic volume. With the extensive deployment of small cells in wireless cellular networks, the network spectral efficiency (SE) is improved with the use of limited frequency. However, the mutual inter-tier and intra-tier interference between or among small cells and macro cells becomes serious. On the other hand, more chances for potential cooperation among different cells are introduced. Energy efficiency (EE) has become one of the most important problems for future wireless networks. This paper proposes a cooperative bargaining game-based method for comprehensive EE management in an ultra-dense wireless cellular network, which highlights the complicated interference influence on energy-saving challenges and the power-coordination process among small cells and macro cells. Especially, a unified EE utility with the consideration of the interference mitigation is proposed to jointly address the SE, the deployment efficiency (DE), and the EE. In particular, closed-form power-coordination solutions for the optimal EE are derived to show the convergence property of the algorithm. Moreover, a simplified algorithm is presented to reduce the complexity of the signaling overhead, which is significant for ultra-dense small cells. Finally, numerical simulations are provided to illustrate the efficiency of the proposed cooperative bargaining game-based and simplified schemes.  
ultra-dense wireless cellular networks; cooperative game; energy efficiency; power coordination +
Footnote †: journal: Sensors