esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methods
#### 3.2.7 Design of Ground Truth Label Models  
The ground truth was created from the reference data with its six unique classes, as shown in Table 3. 'Void' represents the absence of information, as described in Section 3.2.6. The other five classes are defined in Section 3.1.2, with three related to thinning. The first claim is that detecting forests that need thinning from remote sensing data is feasible. Since we are not interested in the urgency of the thinnings for this objective, we summarized _thinning 1_ and _thinning 2_ into one class called _thinning_. This ground truth is called _TN_ (thinning necessity), and it differentiates between the classes _void_, _thinning_, _no thinning_, and _other_ (Table 3). To address the second claim regarding the urgency of thinnings, we created another ground truth named _TU_ (thinning urgency) (Table 3). _TU_ was designed to differentiate between urgent thinning (_thinning 1_), not urgent thinning (_thinning 2_), _no thinning_, _other_, and _void_. Furthermore, we constructed two additional ground truths named _masked TN_ and _masked TU_ to focus exclusively on commercial forest (Table 4). The ground truths _masked TN_ and _masked TU_ expect to answer the claims as stated above for _TN_ and _TU_; however all non-commercial forests (mainly protective forest and roads) were excluded. Finally, all ground truth data were created by rasterizing the reference data (vector data) with the GDAL library into images of the size \(512\times 512\times 1\).
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 3 Results  
### Imaging Detectors and Techniques  
SEM uses a variety of detectors and imaging techniques to obtain detailed information on the surface of samples. The main detectors include those for secondary electrons, backscattered electrons, and X-rays, while imaging techniques may involve the use of low voltage, non-vacuum environments, and combined methods to provide a comprehensive understanding of sample characteristics. Among the available detectors, secondary electron (SE) detectors are used to capture detailed images of the sample's surface topography. Secondary electrons are emitted from the surface when the primary electron beam interacts with it, making these detectors particularly useful for obtaining high-resolution images with topographic contrast [8]. Backscattered electron (BSE) detectors, on the other hand, collect electrons that have been reflected back from the sample. These detectors provide information on the composition and structure of the sample, as the BSE contrast depends on the atomic number and density of the material [9]. Finally, X-ray detectors, such as energy-dispersive spectroscopy (EDS) detectors, analyze the chemical composition of the sample by generating an energy spectrum of the emitted X-rays [10]. Regarding the imaging techniques for obtaining surface details, low-voltage imaging is used to improve image quality and reduce sample damage, particularly for sensitive samples such as biological specimens. By lowering the voltage of the electron beam, more detailed images can be acquired without excessive penetration [11]. Another technique involves imaging in non-vacuum environments, such as humid or gaseous atmospheres, using environmental SEM (ESEM). This technique is beneficial for analyzing samples that cannot be prepared in a vacuum [12]. Finally, imaging techniques can be combined with other methods, such as energy-dispersive spectroscopy (EDS) and wavelength-dispersive X-ray spectroscopy (WDS), to obtain detailed information on both the topography and the chemical composition of the sample [13] (Figure 1).
# LoRa Communication Using TVWS Frequencies: Range and Data Rate
## 1 Introduction
This paper aims to study and assess TVWS-based LoRa in terms of coverage and data rate depending on different factors, such as spreading factor, bandwidth, and environmental scenarios. Compared to conventional LoRa, the main difference is the use of TVWS frequency bands instead of ISM band for transmission, with the advantage being increased coverage. The major contributions of this work are summarized as follows:  
* We study and investigate the maximum range and data rate provided by TVWS-based LoRa under different scenarios such as free space, indoors and outdoors: urban and rural. * A new path-loss model for TVWS-based LoRa is proposed and compared with conventional path-loss models and other path-loss models in literature for different scenarios. * The impact of TVWS-based LoRa compared to ISM-based and 2.4 GHz-based LoRa communication is discussed and shows that TVWS is the promising candidate for better and long-distance coverage. The rest of this paper is organized as follows. Section 2 briefly includes an overview of LoRa technology and TVWS with the related works. Path-loss models for different scenarios with LoRa using TVWS are discussed in Section 3. LoRa range and data rate for these transmission frequencies are examined in Section 4. The impact of these evaluations is discussed in Section 5. Section 6 draws the main conclusions.
## 4 Experimental Validation  
The objective of this section is to verify the influence of initial force on the response from experimental measurements. A mock-up was designed in accordance with the structure described above. The structural response was obtained by exciting the structure using an impact hammer, and measuring the resulting transverse acceleration. The experimental results are compared with an existing spring-mass model available in the literature presented in Section 2.3 and the dynamic bending model of interest in this paper, derived in Sections 2.4 and 2.5.
# Use of Real-Time GNSS-RF Data to Characterize the Swing Movements of Forestry Equipment
## 3 Results
### Binomial Logistic Regression-Element Characterization  
The odds of correctly classifying an element at the 10.0 s transmission interval was decreased 0.262 times when compared to the 2.5 s transmission interval, and was significant at a 95% confidence interval (CI) (Table 3).  
The 5.0 s interval odds of correct classification was not significantly different than the 2.5 s interval. At each of the 18 observed angle intervals between 20 and 360, the odds of correct classification increased 1.004 times for each increasing interval with a CI exceeding 95%. In practice, this means that larger arcs have a higher incidence of correct classification. The location of the GPS transponders was also found to be a significant predictor (95% CI) of correct classification, with the odds of correct classification decreasing 0.476 times when moving from the grapple to the heel location. In practice, connected to arc distance, for the same arc in degrees, the heel moves a shorter distance than the grapple, resulting in more frequent misclassification. Observed element (Swing/Unload or Return) was not found to create differences in the odds of correct classification of cycle element. The relationships between the transponder location, swing angle, GNSS interval, element and proportion of correctly classified elements are further represented in Figures 3 and 4.  
Figure 3: Bar chart representing the proportion of correct classifications of the Swing Unload cycle element at the grapple and heel locations at 18 angle intervals.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Variable** & **Estimate** & **SE 1** & _p-Value_ & **Odds Ratio** & **95\% Confidence** \\ \hline Intercept & 1.748 & 0.212 & \textless{}0.001 & 5.741 & 3.813–8.772 \\ Rate 5 & 0.144 & 0.320 & 0.6525 & 1.155 & 0.621–2.182 \\ Rate 10 & \(-\)1.339 & 0.328 & \textless{}0.001 & 0.262 & 0.138–0.499 \\ Swing Angle & 0.004 & 0.001 & \textless{}0.001 & 1.004 & 1.003–1.006 \\ Location Heel & \(-\)0.744 & 0.124 & \textless{}0.001 & 0.476 & 0.372–0.605 \\ Cycle Element & 0.025 & 0.122 & 0.8374 & 1.025 & 0.807–1.300 \\ Rate 5: Swing Angle & \(-\)0.001 & 0.002 & 0.3789 & 0.999 & 0.996–1.002 \\ Rate 10: Swing Angle & 0.000 & 0.002 & 0.8781 & 1.000 & 0.999–1.003 \\ \hline \end{tabular}  
* Standard error of the variable estimate  
\end{table}
Table 3: Logistic regression coefficients associated with model describing variable impact on whether GPS returned correct element classification as represented by field observations.
# Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite
## 4 Results
### Accuracy of Wind Speed Inversion
#### 4.1.2 Experimental Results of SAR Data with Different Relative Wind Directions  
The second set of experiments determined that the incidence angles of the three SARs were 25\({}^{\circ}\), 35\({}^{\circ}\), and 45\({}^{\circ}\). In the case of noises of 0 dB, 0.5 dB, and 1 dB, respectively, multiple sets of wind speed inversion experiments were conducted by changing the relative wind direction of the three network SARs. The experimental results are shown in Table 4.  
Figure 2: The comparison diagram of the inversion wind speed and the actual wind speed (**a**: 0 dB noise; **b**: 0.5 dB noise; **c**: 1 dB noise; **l**: no wind direction input; 2: wind direction input).  
It can be seen from the table that when the noise is less than 1 dB, the method in this paper can extract relatively accurate wind speed information from the networked SAR satellite data of different relative wind directions. The comparison between the inversion wind speed and the actual wind speed is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise** & **Relative Wind Direction** & **Actual Wind Speed** & **RMSE1** & **RMSE2** \\ \hline
0 dB & 45\({}^{\circ}\) & 1–30 m/s & 0.50 m/s & 0.00 m/s \\ \hline
0 dB & 90\({}^{\circ}\) & 1–30 m/s & 0.23 m/s & 0.00 m/s \\ \hline
0 dB & 180\({}^{\circ}\) & 1–30 m/s & 0.06 m/s & 0.00 m/s \\ \hline
0 dB & 240\({}^{\circ}\) & 1–30 m/s & 0.31 m/s & 0.00 m/s \\ \hline
0.5 dB & 45\({}^{\circ}\) & 1–30 m/s & 1.58 m/s & 2.52 m/s \\ \hline
0.5 dB & 90\({}^{\circ}\) & 1–30 m/s & 0.89 m/s & 1.25 m/s \\ \hline
0.5 dB & 180\({}^{\circ}\) & 1–30 m/s & 1.16 m/s & 1.08 m/s \\ \hline
0.5 dB & 240\({}^{\circ}\) & 1–30 m/s & 1.15 m/s & 1.07 m/s \\ \hline
1 dB & 45\({}^{\circ}\) & 1–30 m/s & 2.63 m/s & 2.78 m/s \\ \hline
1 dB & 90\({}^{\circ}\) & 1–30 m/s & 1.24 m/s & 2.41 m/s \\ \hline
1 dB & 180\({}^{\circ}\) & 1–30 m/s & 1.42 m/s & 2.12 m/s \\ \hline
1 dB & 240\({}^{\circ}\) & 1–30 m/s & 2.05 m/s & 2.12 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results (constant incident angle).  
Figure 3: The comparison diagram of the inversion wind speed and the actual wind speed **(a: 0 dB noise; **b**: 0.5 dB noise; **c**: 1 dB noise; 1: no wind direction input; 2: wind direction input).  
It can be seen from the figure that, as with Figure 2, when the noise is greater than 0 dB, most of the wind speeds extracted by this method are higher than the actual wind speed. This phenomenon is caused by the positive correlation between the SAR backscatter coefficient and wind speed. The addition of noise increases the backscatter coefficient of SAR, which makes the wind speed result higher than the actual wind speed.
###### Abstract  
During mechanical ventilation, a disparity between flow, pressure and volume demands of the patient and the assistance delivered by the mechanical ventilator often occurs. This paper introduces an alternative approach of simulating and evaluating patient-ventilator interactions with high fidelity using the electromechanical lung simulator xPULMTM. The xPULMTM approximates respiratory activities of a patient during alternating phases of spontaneous breathing and apnea intervals while connected to a mechanical ventilator. Focusing on different triggering events, volume assist-control (V/A-C) and pressure support ventilation (PSV) modes were chosen to test patient-ventilator interactions. In V/A-C mode, a double-triggering was detected every third breathing cycle, leading to an asynchrony index of 16.67%, which is classified as severe. This asynchrony causes a significant increase of peak inspiratory pressure (\(7.96\pm 6.38\) vs. \(11.09\pm 0.49\) cmH\({}_{2}\)O, \(p<0.01\)) and peak expiratory flow (\(-25.57\pm 8.93\) vs. \(32.90\pm 0.54\) L/min, \(p<0.01\)) when compared to synchronous phases of the breathing simulation. Additionally, events of premature cycling were observed during PSV mode. In this mode, the peak delivered volume during simulated spontaneous breathing phases increased significantly (\(917.09\pm 45.74\) vs. \(468.40\pm 31.79\) mL, \(p<0.01\)) compared to apnea phases. Various dynamic clinical situations can be approximated using this approach and thereby could help to identify undesired patient-ventilation interactions in the future. Rapidly manufactured ventilator systems could also be tested using this approach.  
p 2  
ventilators can be deployed to decrease the work of breathing and to deliver a high concentration of oxygen into the lungs. A mechanical ventilator is essentially a medical device combining actuators, sensors, digital electronic and software to fulfill a predefined ventilation strategy [8; 9; 10]. The basic structure and main functional building blocks of a typical mechanical ventilator are depicted in Figure 1. Assisted mechanical ventilation should be ideally fully adaptive to a patient's respiratory behavior by providing limited and fully synchronous respiratory support. Otherwise, asynchrony between the patient needs and the output of the ventilator arises [11; 12].
## 1 Introduction
In this study, the sand was treated with three different proportions of fly ash (5%, 10%, and 15%) based on the dry weight of soil and a constant cement content of 3% as an activator. The study concluded that the OMC decreased after the addition of 5% of fly ash in the presence of 3% cement from 13.4% to 12.4% and then increased to 14.35% with the 15% fly ash content. An organic clay soil (organic content of 36.9%) with a high liquid limit of 85.2%, low unit weight, and high water content of 87.12% was mixed with Class **C** and Class **F** fly ash to investigate the effectiveness of fly ash in the stabilization of organic soil [8]. In this study, fly ash contents of 5%, 10%, 15%, and 20% were used based on the dry weight of the soil. The results showed a noticeable enhancement in MDD and OMC. This study concluded that as the fly ash content increases the MDD increases and the OMC decreases. The effect of fly ash on the properties of expansive soil were studied by [PERSON] and [PERSON] [9]. High-plasticity expansive soil (CH according to USCS) was mixed with 0%, 5%, 10%, 20%, 25%, 30%, and 40% Class **F** fly ash. The study concluded that as the fly ash portion in the mixture increases, the MDD increases and the OMC decreases. Based on the literature review, it appears that a typical percentage of fly ash used in the soil stabilization/treatment studies for different usages is about 20%, except for one study where the maximum percentage of fly ash used was 40%. This study explored the possibility of using a higher percentage (up to 60%) of fly ash in soil to enhance the engineering properties that can be used in subgrade construction and in other engineering applications. The use of fly ash to enhance the engineering properties of clayey sand provides a beneficial use of wastes and reduces the cost of waste management (landfilling) and environmental pollution.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology
### Quality of Service Measures
#### 4.2.1 Latency  
Latency is a measure of the time it takes for a message to travel from its source to its destination. We consider latency to measure QoS in order to ensure timely delivery of important messages. Lower latency values are indicative of faster communication and more efficient network operations.  
The average latency \(L_{i}\) from node \(i\) to all other nodes is expressed as follows:  
\[L_{i}=\frac{1}{N-1}\sum_{j\
eq i}T_{i,j} \tag{4}\]  
where \(T_{i,j}\) is the time taken for a message to travel from node \(i\) to node \(j\), and \(N\) is the total number of nodes in the network.  
In Equation (4), the summation \(\sum_{j\
eq i}T_{i,j}\) calculates the total travel time for messages from node \(i\) to all other nodes \(j\), and \(N-1\) measures the number of other nodes (excluding node \(i\) itself).
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 3 Proposed Method
### Optimization Methods  
In the training process of CNN, when there are too many parameters in the model but the training data is limited, it is easy to produce the phenomenon that the gap between training error and the test error is too large, that is, the overfitting problem. Therefore, Dropout strategy and parameter norm penalty are adopted to avoid overfitting, and the moving average model is adopted to make the model more robust.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 5 Results and Analysis  
As a result of the computations from CST MWS, optimized dimensions, return loss curves, reflection phase, gain, directivity, effective permittivity, and permeability of the proposed frequency RMS were analyzed in this section.
## 4 System Level Perspectives of JCRS RF Hardware
### Full-Duplex and Self-Interference Cancellation  
Full-duplex operation is essential in dense networks, where spectral efficiency will be an important key performance indicator (KPI). However, self-interference cancellation is the major obstacle for enabling full duplex operation, particularly for monostatic operation in V2X modules and baseband cellular station.  
Self-interference cancellation is essential in enabling full-duplex operation. The transmitter signal, which leaks to the receiver chain through different mediums, overlaps the received signal. It can be cancelled in different domains. Antenna isolation/cancellation techniques have been already covered in previous sections. The leaked signal can also be cancelled in RF, analog, or digital domains. Figure 6 shows the cancellation scheme in the RF domain. Multiple taps are used to modulate the known transmitter signal and subtract from the received signal, thus filtering the received signal. In practice, all three domains are deployed, thus reducing the dynamic range requirements for each stage. Self-interference in the digital domain exhibits a higher cancellation capability as the non-linear components and phase equalization are considered. Dynamic range requirements are dependent on both external and internal specifications, such as bandwidth, hardware non-idealities, and digital signal processing gain.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
###### Abstract  
With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist-Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials.  
compressed sensing; sensing method; reconstruction algorithm; complex network +
Footnote †: journal: Applied Sciences
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 5 VLSI Implementation of Dual-Mode LDPC Decoder for 5G NR Systems
### Architecture of Dual-Mode LDPC Decoder  
The proposed overall decoding architecture with layered decoding is shown in Figure 6. This architecture is composed of an input buffer, an output buffer, a posterior message memory, an extrinsic message memory, a network, a de-network, an early termination, and 64 arithmetic units (AUs). The AU contains a prior message unit (PMU), a check node unit (CNU), a variable node unit (VNU), and first in first out (FIFO) buffer. The posterior message memory and extrinsic message memory are composed of a two-port register-based memory. For the posterior message memory, the quantization of posterior messages is eight bits (one sign bit plus seven total bits) and the maximum number of posterior messages is \(384\times 26\leavevmode\
obreak\ =\leavevmode\
obreak\ 9984\). Hence, the total number of bits of the posterior message memory is \(8\times 9984\leavevmode\
obreak\ =\leavevmode\
obreak\ 79,872\). Because the decoder only reads 64 consecutive posterior messages from a single block at a time, we divided the posterior message memory into 26 memory blocks and 64 memory banks for each memory block. For the extrinsic message  
Figure 5: Simulation results of different algorithms in 5G NR (**a**) rate-11/13 BG 1 and (**b**) rate-5/7 BG 2.  
memory, the quantization of the extrinsic message is five bits. To reduce the bandwidth of the extrinsic message memory, the extrinsic messages are stored as individual signs in a layer: \(min^{1 st}\), \(min^{2 nd}\), and \(index_{min}\). As shown in Figure 7, we divided individual signs into nine bits and ten bits because \(w_{r}~{}=~{}19\) and \(10\) in BGs 1 and 2, respectively. The \(min^{1 st}\), \(min^{2 nd}\), and absolute value of the extrinsic message are scaled so the data length is 4 bits. According to the maximum \(w_{r}~{}=~{}19\), the \(index_{min}\) is stored as five bits. The maximum number of layers is \(4\times 384~{}=~{}1536\), but there are 32 bits to store extrinsic messages for each layer, so the total number of bits of the extrinsic message memory is \(32\times 1536~{}=~{}49,152\). Because the parallelism is 64, we combine 64 consecutive extrinsic messages into a string. Because the base columns and \(w_{r}\) in BG 2 are smaller than those in BG 1, some posterior and extrinsic memories are unused. Hence, the gated clock technique is adopted to reduce the power of the unused memory.