esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Field Monitoring Project
### Monitoring Parameters and Methods  
During the construction of the Gangkou tunnel, the internal displacements of surrounding rocks were measured by the multi-point extensometers (see Figure 4), the contact pressures between surrounding rocks and primary linings were directly measured by the vibrating-wire pressure cells and the axial forces in rock bolts were obtained by the vibrating-wire bolt force gauges (see Figure 5). In addition, the vibrating-wire steel strain gauges (see Figure 6) were adopted to measure the strains on the upper and the lower flanges of the I-shaped steel arches and then on basis of the assumption that the stresses are distributed linearly in the cross section of the steel, the stresses can be calculated through the stress-strain relationship according to the classical mechanics of materials, as shown in Equation (1).  
\[N_{1}=\frac{1}{2}E_{1}(\varepsilon_{inner}+\varepsilon_{outer})A;M_{1}=\frac{1 }{2}E_{1}(\varepsilon_{inner}-\varepsilon_{outer})W \tag{1}\]  
where, \(N_{1}\) and \(M_{1}\) represent the axial force and the bending moment in the steel arch respectively; \(E_{1}\) denotes the elastic modulus of the I-shaped joint steel, being equal to 210 GPa; \(\varepsilon_{inner}\) and \(\varepsilon_{outer}\) are respectively the measured strain values on the upper and the lower flanges of the I-shaped steel arch and the tensile strain is regarded to be positive while the compressive strain is negative; \(A\) represents the cross-sectional area of the No. 14 I-shaped joint steel which is equal to 21.5 cm\({}^{2}\), \(W\) is the section modulus in bending of the joint steel, being equal to 102 cm\({}^{3}\).  
Figure 4: Multi-point extensometer.  
Figure 3: Schematic diagram of the central heading method in class III rock masses.  
Furthermore, the internal forces in the secondary linings were obtained through the calculation of the strains measured by the embedded concrete strain gauges (see Figure 7). The internal forces in the secondary linings can be calculated by Equation (2). In the practical application, the internal forces can be obtained from the observed strains by computer programming. Detailed monitoring parameters and methods are presented in Table 4.  
\[N_{2}=\frac{1}{2}E_{2}\big{(}\varepsilon^{\prime}_{inner}+\varepsilon^{\prime}_{ outer}\big{)}bh;\ M_{2}=\frac{1}{12}E_{2}\big{(}\varepsilon^{\prime}_{inner}- \varepsilon^{\prime}_{outer}\big{)}bh^{2} \tag{2}\]  
where, \(N_{2}\) and \(M_{2}\) respectively denote the axial force and the bending moment to be calculated; \(E_{2}\) denotes the elastic modulus of the secondary lining; \(\varepsilon^{\prime}_{inner}\) and \(\varepsilon^{\prime}_{outer}\) are respectively the measured strain values on the inner and outer side of the secondary lining; \(b\) and \(h\) respectively represent the per unit length and the thickness of the secondary lining.  
Figure 5: Rock bolt force gauge.  
Figure 6: Layouts of the steel strain gauges.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 2 Intelligent Transportation Systems (ITS)
### Sensing  
Sensing is essentially the detection of certain types of signals in the real world and the conversion of them into readable data. Traffic sensors generate data that supports the analysis, prediction, and decision-making of intelligent transportation systems. There are various sensors for different data collection purposes and scenarios. The most commonly seen traffic sensors in today's roadway networks and transportation infrastructures include, but are not limited to, inductive loop detectors [10; 11; 12], magnetic sensors [13; 14], cameras [15; 16], infrared sensors [17], LiDAR sensors [18], acoustic sensors [19], Bluetooth [20], wi-fi [21], mobile phones [22], and probe vehicle sensors [23; 24; 25]. These sensors measure the feature quantity of some objects or scenarios in transportation systems, such as road users, traffic flow parameters, congestion, crashes, queue length at intersections, and automobile emission.  
For most sensor signals, while there is a lot of valuable information that can be mined from them, the conversion process is straightforward and can be completed based on some simple rules. For example, loop detectors measure the change in the inductance when vehicles pass over them for traffic volume and occupancy detection; Bluetooth sensors capture the radio communication signal with a device unique identifier, i.e., the media access control address (MAC) so that they can estimate the number of devices (usually associated with the number of road users) or travel time; acoustic sensors generate acoustic wave to detect the existence of objects at a certain location, without the ability to tell the object type. For some sensors, such as camera and LiDAR, the conversion of the raw signals (i.e., the digital images and the 3D point cloud) to useful data can be quite complicated, and thereby advanced algorithms have been applied widely to the conversion of LiDAR and camera signals.  
Figure 2: The structure of the content in this review paper.
# Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree
## 3 Tree Physiological Activity Monitoring System
### TPAM System Evaluation  
Since bioimpedance can be modeled as a combination of a resistor and a capacitor, the TPAM system was evaluated using an idealized resistor and capacitor. Figure 7 shows the measurement results of the TPAM system obtained using ideal components.  
Figure 7a shows the resistance value measured using the TPAM system. The x-axis represents the resistance values measured with a standard device (34470A, Keysight, Santa Rosa, CA, USA), and the y-axis represents the values measured with the TPAM system. When measuring in the TPAM system, the frequency of the injection current was changed to 8 kHz, 16 kHz, 32 kHz, and 64 kHz. Even if the frequency of the injection current changes, the measured value remains approximately the same, which is in good agreement with the original characteristics of the resistor. The variation in the resistance value measured according to the frequency is approximately \(\pm\)30 \(\Omega\) at most. In the 1 k\(\Omega\) to 15 k\(\Omega\) range, it showed a full-scale nonlinearity of 2.1% and in the 1 k\(\Omega\) to 8 k\(\Omega\) range, it  
Figure 6: Data acquisition and flow in TPAM system: (**a**) overall flow configuration and (**b**) GUI configuration screen.  
showed a full-scale nonlinearity of 1.1%. The straight line is a curve fitting the line of the resistance value measured with the reference device (Keysight 34470A).  
Figure 7b shows the capacitance value measured using the TPAM system. The x-axis represents the capacitance values measured with a standard device (Keysight 34470A) in a logarithmic scale, and the y-axis represents the values measured with the TPAM system in a logarithmic scale. When measuring in the TPAM system, the frequency of the injection current was changed to 8 kHz, 16 kHz, 32 kHz, and 64 kHz. In theory, the impedance of a capacitor is inversely proportional to the capacitance, so when drawn on a logarithmic scale, it is expressed as a straight line with a negative slope. When the capacitance increases from 0.33 nF to 3.3 nF, the measured impedance values in the TPAM system plot as a negative straight line on a logarithmic scale, as expected from theoretical predictions. Also, theoretically, capacitance is inversely proportional to frequency, so the capacitance should be halved every time the frequency is doubled. Looking at the TPAM capacitor measurement results in Figure 7b, you can see that when the frequency value of the injection current is doubled, the impedance value is reduced to 1/2. The straight line in Figure 7b represents the curve-fitted impedance value based on the reference capacitor and frequency.
## 2 Literature Survey  
Delay QoS routing is a challenging issue in WVSNs. Traditional approaches for wireless sensor networks are not efficient or even feasible in a WVSN due to three reasons: first of all, visual data are much bigger and complicated than scalar data, and processing and transmitting visual data will consume much more system resources than scalar data. Secondly, WVSNs need efficient collaborative image processing and coding techniques that can exploit correlation in data collected by adjacent camera sensor nodes. Video coding/compression that has low complexity, produces a low output bandwidth, tolerates losses, and consumes as little power as possible is required. Third, it needs to reliably send the relevant visual data from the camera sensor nodes to aggregation nodes or the sink node in an energy-efficient way [1,2]. This issue is related to the transmission technique that consists of the routing assignment to provide energy efficient and stable routes that meet the end-to-end QoS guarantees [2, 3, 4].  
In wireless visual sensor networks for real time video surveillance, sensor nodes need to capture and forward the packets to the sinks within an acceptable delay under the limited resource constraints, including embedded vision processing, data communication, and battery energy issues. It is a challenging issue since it needs to meet the end-to-end delay for each application and at the same time optimize the system resource utilization by minimizing the average system delay so that more real-time applications can be granted access in the near future. Existing research on wireless sensor networks address the link scheduling techniques [5], channel assignments [6, 7, 8], routing algorithms [9,10], or resource allocations [11, 12, 13]. Some of the well-known routing protocols, such as _Ad hoc_ On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), Optimized Link State Routing (OLSR), and Destination Sequenced Distance Vector (DSDV) are the protocols applied to _Ad hoc_ networks, such as MANETs, WMNs, VANETS, _etc._, that consist of hosts interconnected by routers without a fixed infrastructure that can be arranged dynamically [14]. Even though like in the MANETs, there is no centralized AP or base station in WVSN, sensor nodes are also the relay nodes to forward the data to the sink node. However, without addressing the delay QoS, these approaches are not applicable to real-time applications. In the following, we survey existing approaches on shortest path routing, QoS routing, and routing algorithms. To the best of our knowledge, there is no research addressing the end-to-end delay from the user perspective and at the same time optimizing the system resource utilization by minimizing the average system delay.
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
## 4 Coupling Analysis of the Electromagnetic-Temperature Field
### Coupling Characteristic Analysis Based on Unidirectional Coupling Method
#### 4.1.4 Analysis of Unidirectional Coupling Results  
After establishing the electromagnetic analysis model and the temperature analysis model, the unidirectional coupling analysis of the electromagnetic-temperature field can be carried out, and the changes in the two fields can be obtained. Figure 13 below shows the temperature distribution of the IWM at 12,000 s by unidirectional coupling calculation.  
Figure 12: Casing surfaces.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Thermal Conductivity**} & \multirow{2}{*}{**Density (kg/m3)**} & \multicolumn{2}{c}{**Specific Heat**} \\  & & **(W/(m\({}^{\circ}\)C)** & & **Capacity (J/(kg\({}^{\circ}\)C))** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Stator and rotor core \\ \end{tabular} } & DW465-50 & 40/40/0.95 & 7700 & 426 \\  & copper & 379 & 8900 & 390 \\  & NdFeB & 6.16 & 7800 & 460 \\ \multirow{2}{*}{\
\begin{tabular}{c} Insulation layer \\ Housing \\ Air \\ \end{tabular} } & Insulation material & 0.3 & 1300 & 1340 \\  & 45 steel & 50.2 & 7850 & 480 \\ \multirow{2}{*}{
\begin{tabular}{c} Air \\ \end{tabular} } & Air & 0.0267 & 1.29 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Material properties of components.  
From Figure 13, it can be observed that the highest temperature appeared in the stator winding, and the lowest temperature distributed in the motor shaft. And the highest temperature of each part can be obtained, as shown in Table 6.
# Energetic and Structural Studies of Two Biomass-Derived Compounds: 6- and 7-hydroxy-1-indanones
## 1 Introduction  
Accurate data about thermochemical and thermophysical properties of biomass-derived compounds are essential for the evaluation of the corresponding chemical behavior. These data are very relevant when controlling experimental investigations to produce value-added chemical products from biomass. Moreover, data can be used in the development of schemes for the prediction of the homologous properties of related compounds [1, 2], in particular, modeling studies for the prediction of bio-oil composition [3]. In this context, our research group has been involved in an extensive experimental and theoretical thermodynamic study on key biomass-derived compounds, namely, vanillyl alcohol [1], levoglucosan [4], cellulose allomorphs [5], \(\alpha\)-D-xylose [6], and more recently, cyclopentenones [7], indanones [2], and other classes of relevant compounds extracted from biomass.  
The purpose of this study is to contribute to enlarging the thermodynamic database of this class of compounds, as well as to establish some energy-structure relationships for the prediction of the same properties and to evaluate their reactivity. Thus, we present a detailed experimental and computational study on the energetics of the 6- and 7-hydroxy-1-indanone, whose structural formulae are depicted in Figure 1. The experimental data are determined from calorimetric measurements focusing on the determination of the standard molar enthalpy of formation in the gaseous phase, an essential thermodynamic parameter associated with molecular energy. This property was also obtained by G3(MP2)//B3 LYP calculations, using several working reactions. This composite method was also used to perform a conformational analysis of the studied compounds. In addition, we addressed the energetic effects associated with the presence of a hydroxyl group on the core of the benzenic ring of 1-indanone, and the hydrogen bond network was also evaluated.  
In the literature, several studies have been conducted involving 7-hydroxi-1-indanone [8; 9; 10], since it is a prototype in the excited-state intramolecular proton transfer process (ESIPT, Figure 2). These important characteristics of 7-hydroxi-1-indanone and its derivatives confer several applications, namely, as laser dyes, probe sensors, and molecular devices [11; 12]. Thus, in this study, the gas-phase intramolecular proton transfer process of 7-hydroxy-1-indanone was evaluated.
# Carbon Nanotube-Based Composite Filaments for 3D Printing of Structural and Conductive Elements
###### Abstract  
In this publication, we describe the process of fabrication and the analysis of the properties of nanocomposite filaments based on carbon nanotubes and acrylonitrile butadiene styrene (ABS) polymer for fused deposition modeling (FDM) additive manufacturing. Polymer granulate was mixed and extruded with a filling fraction of 0.99, 1.96, 4.76, 9.09 wt.% of CNTs (carbon nanotubes) to fabricate composite filaments with a diameter of 1.75 mm. Detailed mechanical and electrical investigations of printed test samples were performed. The results demonstrate that CNT content has a significant influence on mechanical properties and electrical conductivity of printed samples. Printed samples obtained from high CNT content composites exhibited an improvement in the tensile strength by 12.6%. Measurements of nanocomposites' electrical properties exhibited non-linear relation between the supply voltage and measured sample resistivity. This effect can be attributed to the semiconductor nature of the CNT functional phase and the occurrence of a tunnelling effect in percolation network. Detailed I-V characteristics related to the amount of CNTs in the composite and the supply voltage influence are also presented. At a constant voltage value, the average resistivity of the printed elements is 2.5 \(\Omega\)m for 4.76 wt.% CNT and 0.15 \(\Omega\)m for 9.09 wt.% CNT, respectively. These results demonstrate that ABS/CNT composites are a promising functional material for FDM additive fabrication of structural elements, but also structural electronics and sensors.  
additive manufacturing; polymer matrix composite; structural electronic; fused deposition modeling; conductive composites; 3D printing +
Footnote †: journal: Applied Sci.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Scenarios
#### Scenario 1: No Reuse  
In this scenario, no mobile node is reused as the problem that occurred in the network is permanent. Specifically, the congested node runs out of battery due to heavy congestion. The mobile node sent to solve this problem will need to stay there until the end of the simulation. As a result, if any other congestion occurs in the network, the sink node will need to send another mobile node. This scenario represents the Dynamic MobileCC Algorithm 1, where the mobile node is just placed in the network and works as a static node, and no reuse is possible.  
In Figure 6, we present the topology of this scenario. Here, two mobile nodes are employed as a result of the permanent failure of congestion node 3. Mobile node 21 is required in the network, and at each usability check, meaning that the mobile node checks if it is still needed, it gets a negative answer, which results in it being active during the whole experiment. When congestion occurs at node 8, the sink node can only choose from \"near sink nodes\", and for this reason, it sends mobile node 22 to help with the new congestion occurrence.
## 6 Conclusions  
In the additive manufacturing processes of powder bed fusion, the temperature of the work space is determined by and reflects the actual values of process parameters, which will result in different quality. To predict the quality of parts based on the temperature field information in SLS, the concept of the temperature field's key features is proposed in this paper. The key feature data are extracted from three aspects: (1) features along with the scanning trajectory; (2) features on the single-layer powder coating; and (3) features of the three-dimensional structure. The mathematical model and data structure of key feature data are established. Based on the PCA method and SVM algorithm, a prediction model based on temperature field images is built after feature extraction, dimension reduction, and parameter optimization. Finally, a practical experiment is carried out to verify the proposed methods. The experimental results show that the optimized prediction model has an accuracy of 88.7% and an F value of 89.3%. It should be noted that the proposed methodology is applicable not only to the SLS process but also to other powder bed fusion processes such as selective laser melting (SLM). In future work, we will continue to explore the correlation between the key features of the temperature field and quality indexes (e.g., porosity), and a real-time analysis model for online monitoring.  
Figure 9: Building and verifying process of the predictive model. PCA: principal component analysis.  
**Author Contributions**: Conceptualization, Z.C.; Data curation, X.Z. ([PERSON]); Formal analysis, Z.C.; Investigation, X.Z. ([PERSON]) and X.Z. ([PERSON]); Methodology, Z.C.; Project administration, Z.C.; Software, X.Z. ([PERSON]); Supervision, Z.C. and J.S.; Visualization, X.Z. ([PERSON]); Writing - original draft, [PERSON] ([PERSON]); Writing - review & editing, Z.C. and J.S.  
**Funding**: This research was funded by National Natural Science Foundation of China, grant number 61803023 and the Fundamental Research Funds for the Central Universities, grant number FRF-TP-16-005A1.  
**Conflicts of Interest**: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Experiment  
### VMD Processes the Data  
When decomposing the magnitude data into six modes, it is important to note that the VMD of the magnitude data is not performed on the entire magnitude dataset at once, as this would lead to information leakage. To avoid information leakage, a rolling time window approach is adopted, selecting a total of four time windows: 6, 12, 18, and 24. Here, using a time window size of 12 as an example, for the first window (i.e., the first 1-12 data points), all data are retained. Then, the time window is rolled, and for the second time window (i.e., the 2-13 data points), only the decomposition results of the last data point within the time window are retained, and so on, until all data are decomposed. The process is similar for other time windows. However, when performing VMD, there is an issue of boundary effects on both sides of the data. We mitigate this impact by using mirror extension.  
Mirror extension involves adding a mirrored part of the signal at both ends, making the signal symmetrical during analysis and reducing the impact of boundary effects on the results. As shown in Figure 3, the decomposed data are smoother than the original data, making it easier for deep learning models to learn their patterns.
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 1 Introduction
In this study, the reflectivity of BDS/GPS/GAL reflected signals derived from the Level 1 data of GNOS-II reflectometry are utilized for soil moisture estimation. The information about GNOS-II Level 1 data, SMAP reference data, and in situ observation data is provided in Section 2. The intercalibration and retrieval method are described in Section 3. Observations from different systems were calibrated to establish a general retrieval model. Since the GNSS-R observations are sensitive to roughness and topography, the model is developed for each ease grid of 36 km, and the vegetation effect is considered for different land classifications. Results and discussion are presented in Section 4. The retrieval accuracy was evaluated by comparing to both SMAP data and in situ measurements. Finally, conclusions are drawn in Section 5.
# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
###### Abstract  
Detecting small targets in infrared imagery remains highly challenging due to sub-pixel target sizes, low signal-to-noise ratios, and complex background clutter. This paper proposes PSHNet, a hybrid deep-learning framework that combines dense spatial heatmap supervision with geometry-aware regression for accurate infrared small-target detection. The network generates position-scale heatmaps to guide coarse localization, which are further refined through sub-pixel offset and size regression. A Complete IoU (CIoU) loss is introduced as a geometric regularization term to improve alignment between predicted and ground-truth bounding boxes. To better preserve fine spatial details essential for identifying small thermal signatures, an Enhanced Low-level Feature Module (ELFM) is incorporated using multi-scale dilated convolutions and channel attention. Experiments on the NUDT-SIRST and IRSTD-1k datasets demonstrate that PSHNet outperforms existing methods in IoU, detection probability, and false alarm rate, achieving IoU improvement and robust performance under low-SNR conditions.  
infrared small-target detection; deep learning; enhanced low-level features 2025 15 7629