esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 2 Materials and Methods
### Remote Sensing Data Collection
#### 2.2.5 Handheld Personal Laser Scanning (PLS\({}_{\text{hh}}\)) Data  
The PLS\({}_{\text{hh}}\) data were collected using a GeoSLAM Horizon (GeoSlam Ltd, Nottinghamshire, UK) on 8 February 2019, during the leaf-off season, without any presence of low vegetation. GeoSLAM Horizon is an inertial measurement unit (IMU)-aided handheld laser scanning system based on the SLAM algorithm. The system consists of a rotating VLP-16 (Velodyne Lidar, San Jose, CA, USA) LiDAR sensor, IMU, data storage, and power supply. It does not have a GNSS sensor and all scans are recorded in the local coordinate system. The VLP-16 operates at a 903 nm wavelength, the angular step size is 0.1\({}^{\circ}\)-0.4\({}^{\circ}\) and it has 3 mrad beam divergence. Data collection across six plots was divided into three sessions. During the first session, only one plot was recorded (Plot 139), during the second session two plots were recorded (Plots 134 and 135) and during the third session, three plots (Plots 143, 141 and 140) were recorded (Figure 1). On average, data collection lasted 4 min per plot, including the time to walk in between the plots. The system operator was unfamiliar with the terrain and did not follow any predetermined path. The trajectory of the scanner can be seen in Figure 1.  
The collected data were processed using the GeoSLAM HUB software, which generated the point cloud. The point cloud was georeferenced in a HTRS96/TM coordinate system using the software CloudCompare (version 2.11.3). Point clouds were georeferenced based on a six-parameter transformation (3 translations and 3 rotations) using spheres placed on the stakes whose coordinates were determined in the 1 st survey. The point cloud from the first session was georeferenced using 3 spheres located on Plot 139 (RMSE = 0.7 cm); the point cloud from the second session was georeferenced using two spheres located on Plot 143 and another two spheres on Plot 140 (RMSE = 2.4 cm); the point cloud from the third session was georeferenced using two spheres on Plot 135 and two on Plot 134 (RMSE = 1.6 cm). The PLS\({}_{\text{hh}}\) plot point cloud on average consisted of 36 million points (\(\sim\) 28,500 points\(\cdot\)m\({}^{-2}\)).
## 3 Results and Discussion
### The Ranking of the Samples Based on the SRD Approach  
The ranking of the samples in terms of the order of observations (abundance of each detected compound) was carried out based on row average ranking as a golden standard (consensus ranking). The obtained results are presented in Figure 5. Based on the results, it can be concluded that there was no particular grouping of the samples regarding their SRD values. However, it can be noticed that the sample Gly/Glu/Sor/W3 was closest to the reference ranking, and the control samples Cont0 and Cont6 were placed furthest from the reference ranking, whereas the Cont3 sample was placed among the other CO\({}_{2}\)-NADES samples. This implied that the control samples Cont0 and Cont6 had a significantly different order of the compounds than the reference ranking and CO\({}_{2}\)-NADES systems. This could also imply that the variations in the change in composition in the control samples were more pronounced than in the case of the CO\({}_{2}\)-NADES samples. All the samples were significantly distant from the Gaussian curve, indicating that the results were not random in nature.  
Figure 4: The score plot (**a**) and loadings plot (**b**) of the PCA of NADES based on headspace composition. (OM—Oxygenated Monoterpenes, MH—Monoterpene Hydrocarbons, NT—Non-Terpenes, OS—Oxygenated Sesquiterpenenes, SH—Sesquiterpene Hydrocarbons).  
The results of 7-fold cross-validation of the SRD procedure are presented in Figure 6 in the form of a Box-Whisker plot. The results are based on normalized SRD values (SRDnorm) obtained in each repeated ranking procedure.  
The CO\({}_{2}\)-NADES and control samples were ordered in the same way as in the SRD graph (Figure 5). For each sample, there were seven SRD values obtained for each repeated SRD analysis. The median, 25%, and 75% of the range, minimum, and maximum values are presented as a Box-Whisker plot. The graph points to the consistency in the SRD analysis of the samples. An extreme SRDnorm value is observable for the sample Gly/Glu/Sor/W3; however, it can be considered insignificant. The vertical red dashed line shows the significant difference between the Cont0 and Cont6 samples in comparison to the  
Figure 5: The ranking of the NADES by SRD and CRRN with row average as a reference ranking. The right \(y\)-axis presents the relative frequencies for the Gauss curve (XX1 = 5% limit, Med = Median, XX19 = 95% limit).  
Figure 6: The results of 7-fold cross-validation of the SRD procedure.  
other samples based on the Wilcoxon Matched Pairs Test at the 0.05 confidence level. This is a confirmation of the previously mentioned assumption that Cont0 and Cont6 possessed a significantly different order of the compounds content than the reference ranking and CO\({}_{2}\)-NADES samples.
###### Abstract  
Nodal rotations are produced by bending and shear effects and bending rotations can be easily calculated using Euler-Bernoulli's stiffness matrix method. Nevertheless, shear rotations are traditionally neglected, as their effects are practically negligible in most structures. This assumption might lead to significant errors in the simulation of the rotations in some structures, as well as the wrong identification of the mechanical properties in inverse analysis. Despite its important role, no other works studying the calculation of shear rotations in deep beams were found in the literature. To fill this gap, after illustrating the errors of commercial software regarding calculating the rotations in deep beams, this study proposed a simple and intuitive method to calculate shear rotations in both isostatic and statically redundant beams. The new method calculates the shear rotation for all segments separately and introduces the result to the total rotation of the structure. This method can be applied to find the shear rotation in a redundant structure as well. A parametric study was carried out to calculate slenderness ratios to determine in what structural systems the shear rotations can be neglected. In addition, the errors in the inverse analysis of deep beams were parametrically studied to determine the role of shear rotation in different structural systems. Finally, to validate the application of the method in actual structures, a construction stage of a composite bridge was analyzed.  
structural system identification; observability method; shear rotation; [PERSON]'s beam theory; direct analysis; inverse analysis +
Footnote †: journal:  
Applied Sciences
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 5 Numerical Results and Analysis
### Computation Complexity Analysis  
This subsection provides a time-consuming simulation for the complexity of the proposed algorithm. The benchmark algorithm used the RIS-DDPG algorithm, the SCA algorithm, and the SDR algorithm in the same way as in Section 5.2. The simulation condition is \(N=36\), the number of iterations for the SCA algorithm is 100, the iteration termination condition for the SDR algorithm is an error of less than 0.1, and the other simulation conditions are the same as in Section 5.2. Since both RIS-TD3 and RIS-DDPG can be learned offline and run online, we take the simulation time spent per episode in the testing phase after training as the algorithm consumption time. We recorded the time consumed by each algorithm for 10 simulations and averaged them to obtain the algorithm time-consuming simulation results shown in Table 2.  
As can be seen from Table 2, the simulation time consumed by the RIS-TD3 algorithm proposed in the paper for one episode (200 steps) is comparable to that of the RIS-DDPG algorithm, with an increase of only 0.03 s. This is due to the fact that the proposed algorithm uses six DNNs while the RIS-DDPG algorithm only uses four DNNs. However, combining the results of 5.2, the RIS-TD3 algorithm obtains a median GRCD gain of 50 at \(N=36\) compared with that of RIS-DDPG, so the algorithm achieves a significant performance gain with a small time cost. Furthermore, the time consummation of the traditional SCA algorithm is 1.3 times longer than that of the proposed algorithm for only 100 iterations, but the average GRCD obtained is lower than that of the proposed RIS-TD3 algorithm. Finally, the traditional SDR algorithm needs the longest simulation time and has the lowest median GRCD performance. This indicates that the complexity of the proposed algorithm is significantly lower than that of the conventional algorithm.
## 2 Related Research  
### Intelligent Transportation System  
Cyber-physical system technology will be applied to road traffic systems to improve safety, efficiency, and security, accelerating the realization of intelligent transportation systems for future smart cities [8].  
Connected vehicle technology (CV) is an important cyber-physical system technology that can bring about a breakthrough improvement in the safety and efficiency of road traffic systems. In fact, it is possible to develop efficient intersection control algorithms by applying CV technology and contribute to reducing traffic congestion [9; 10]  
One of the representative research topics using cyber-physical systems is the development of intersection communication protocols [11]. In particular, the development of an intersection communication protocol that efficiently controls the flow of vehicles in complex traffic situations where autonomous vehicles and existing vehicles coexist can be said to be very important. In light of this importance, various studies have been conducted to develop intersection control protocols. Another example is the development of a traffic congestion mitigation protocol that focuses on the problem of traffic congestion on highways and develops a protocol to reduce traffic congestion by utilizing communication functions between vehicles.  
The efficiency and safety of the road traffic system can be improved by utilizing cyber-physical system technology, but research on security technology is also active. Since external security attack elements are steadily increasing to the extent that affects the function of vehicle operating devices, various cyber physics system studies are being conducted to increase the security of vehicles. Of course, various internal intrusion detection systems (IDS) have been developed, but most of them have not successfully responded to some security attacks because of the probabilistic characteristics of such systems.
# Thermal and Energy-Efficiency Assessment of Hybrid CLT-glass Facade Elements
## 5 Full-Size Numerical Analysis of the Live-Lab Facility
### Thermal Characterization and Performance Assessment
#### 5.3.2 Thermal Characterization of the Facade and Door Openings  
Finally, the EnCert-HR model was used to determine the thermal performance of the hybrid facade element object of analysis. Based on the Technical Regulations in use, the overall impact of linear thermal bridges was also considered by increasing the predicted \(U\) value by an \(U_{\text{TM}}=0.05\) W/m\({}^{2}\)K increment. The derived thermal indicators are presented in Table 6.  
Figure 12: Internal condensation—water vapour pressure in the building part.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & **Vapour** & **Saturated** & **Internal Air** & **External Air** & **Surface** & **Temperature** \\  & **Pressure in** & **Vapour** & **Temperature** & **Temperature** & **Temperature** & **Factor** \\  & **Space** & **Pressure** & **Temperature** & **Temperature** & **Temperature** & **(Equation (1))** \\ \hline  & \(p_{i}\) & \(p_{sat}\) & \(T_{int}\) & \(T_{out}\) & \(T_{sl,min}\) & \(f_{Rsi}\) \\  & **(kPa)** & **(kPa)** & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & \(f_{Rsi}\) \\ \hline Jan & 1.075 & 1.344 & 20.0 & 1.0 & 11.4 & 0.547 \\ Feb & 1.119 & 1.399 & 20.0 & 2.9 & 12.0 & 0.532 \\ Mar & 1.218 & 1.522 & 20.0 & 7.1 & 13.3 & 0.480 \\ Apr & 1.396 & 1.745 & 20.0 & 11.7 & 15.4 & 0.446 \\ May & 1.778 & 2.222 & 20.0 & 16.8 & 19.2 & 0.750 \\ Jun & 2.058 & 2.572 & 20.0 & 20.3 & 21.6 & - \\ Jul & 2.058 & 2.572 & 20.0 & 21.9 & 21.6 & - \\ Aug & 2.058 & 2.572 & 20.0 & 21.3 & 21.6 & - \\ Sep & 1.737 & 2.171 & 20.0 & 16.3 & 18.8 & 0.675 \\ Oct & 1.376 & 1.720 & 20.0 & 11.4 & 15.1 & 0.430 \\ Nov & 1.204 & 1.504 & 20.0 & 6.5 & 13.1 & 0.489 \\ Dec & 1.084 & 1.355 & 20.0 & 1.4 & 11.5 & 0.543 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Surface condensation risk assessment for the building part.  
For the _U_-value for CLT-glass facade elements in Table 6, a relatively high parameter is found compared to glass facade systems in general, with a recommended _U_-vale in the range of 1.6 W/m\({}^{2}\)K (see also [28]). This latter estimate, however, could be justified by the lack of geometrical details in the full-size EnCert-HR model, thus requiring further local investigations.
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 4 Approach
### Mapping Tokens  
The CodeBERT model provides a standardized tokenizer, BertTokenizer, that takes the preprocessed source code as input and outputs a list of integers. The BertTokenizer maps each token to an integer specified in the vocabulary file, including sub-word tokens, out-of-vocabulary tokens (<UNK>), and other special tokens (
 and 
). If padding is enabled, 0 will be padded before or after the generated integers to ensure that all generated integer lists have the same length.
# Accurate Algorithms for Spatial Operations on the Spheroid in a Spatial Database Management System
## 3 Validation of Results  
At this point, firstly we must prove in a quantitative way that the coordinates shown in Table 1 are approximate and do not define the true intersection of two geodesics. Then, in Section 4, the new proposed algorithm is presented, and we will assess its error with this same quantitative methodology.  
We can check the correctness by means of the direct and inverse problems of geodesy. [PERSON] [31; 32; 33] deals with the problem directly on the ellipsoid by using numerical integration and there are some libraries like the GeographicLib suite [13] which is optimized to deliver accuracy close to machine precision.  
Some of the spatial databases like Oracle Spatial or PostGIS implement the direct and inverse geodesy problems and expose them through SQL functions. All these solutions make these calculations with high precision and yield identical results.  
For this test, we only need a library or software that can solve the direct and inverse problems of geodesy. Of the many available solutions, we used the GeographicLib library through PostGIS [20] (PostGIS Geography Support Functions functions ST_Azimuth and ST_Distance for the inverse problem, and ST_Project for the direct problem). We also tested GeographicLib with a Java implementation that provides the same results.
# Cellulose Nanofibril (CNF) Films and Xylan from Hot Water Extracted Birch Kraft Pulps
## Materials and Methods
### Hygroscopic Properties of CNF Films  
The water sorption isotherms of the CNF films were determined by dynamic vapour sorption (DVS) using a DVS-1 Video (Surface Measurement Systems, London, UK) apparatus. About 2-5 mg of film were placed in the microbalance of the DVS system, and the film was first dried at 25 \({}^{\circ}\)C by lowering the relative humidity (RH) to 0% with nitrogen gas (flow rate 200 mL/min) until the mass change was lower than 0.002 mg/min over a 10 min period. Once dried, the RH in the chamber was increased step-wise to 19, 36, 54, 75 and 90% while keeping the temperature at 25 \({}^{\circ}\)C. In each step, the RH was kept constant until the mass of the film reached an equilibrium (mass change lower than 0.002 mg/min over a 10 min period). A full sorption cycle, including adsorption and desorption, was performed for each sample. Three samples from each CNF film were measured.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 3 CDL: Cooperative Downlink Listening
### Limitations and Problems of LoRaWAN Downlink Communication  
To minimize power consumption, end devices in class A perform uplink-oriented data transmission as already shown in Figure 4. This approach has an advantage of minimizing power consumption by allowing the device to wake up at some events or scheduled time, transmit data, and then go to sleep mode. However, this approach has a problem with downlink communication from network server to each end device. That is, for the reception downlink from network server, each end device should wait until its own uplink data transmission to network server is completed, and the downlink communication is only available by RX window1 and window2 followed by uplink transmission. Furthermore, in the approach, downlink broadcasting is not possible.  
In general, end devices transmit sensing data periodically to the network server. In this case, time synchronization between devices and the network server is required. Such time synchronization tasks should be not often but periodically executed. In the case of LoRa standard (class A), for all end devices to be synchronized with network server, the only way is to use its own RX window followed by individual uplink data transmission. However, this results in significant traffic increase in the network as shown in Figure 5.  
This problem might make not only traffic increase but also storage wastage in the network server. Each node transmits sensing data periodically at the scheduled time and the network server stores all uplink data from the end device managed. As a matter of fact, after that application, the server is responsible for performing various analyses based on the stored data. Even though the same sensing value for the long time is kept, the network server should store all data from the end device. This can lead to the unnecessary increase of storage in the network server, and it also makes the application server difficult to monitor the sensor field in the near real-time, because data analysis is based on the storage accumulated in the network server. All of these problems are caused by the limitation of downlink availability.
## 4 Discussion of Test Results
### Image Acquisition and Cracking Recognition
#### 4.3.2 Crack Recognition in CT Images  
The digital image processing (DIP) technology was used to process the slice images of ECC obtained by CT scanning [23], so as to identify the internal cracks and analyze the distribution of cracks. This study utilized image processing toolkit in MATLAB to process the obtained CT images.  
Figure 14: Reference fatigue lives of different OSBD pavement materials.  
Figure 15: Three-dimensional CT scanning and reconstruction of ECC cracked specimen.  
Crack recognition in CT images was realized by image enhancement, filtering, denoising, threshold segmentation and other technical means [24; 25]. Specimens were scanned every 1 mm along the depth direction, and 26 images were obtained from top surface of the specimen to the bottom.  
Figure 16 shows the original CT image slice and crack recognition result on the top surface of the specimen. It can be observed that the fatigue cracking behavior of ECC was multi-crack propagation, and there was no single failure section. Multiple cracks were initiated and propagated into visible cracks in the bending-tension region. The length, width and extent region of cracks on top surface were obviously greater than those on the deeper depth sections.
# On Cross-Layer Interactions for Congestion Control in the Internet
## 5 Performance Evaluation
### Data Center Scenario, Common TCP
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **DT** & **PIE** & **CoDel** \\ \hline \multirow{3}{*}{UC} & NReno & 814 (69) & 169 (41) & 173 (10) \\ \cline{2-5}  & Cubic & 859 (47) & 168 (26) & 172 (4) \\ \cline{2-5}  & C-TCP & 845 (39) & 167 (23) & 162 (14) \\ \hline \multirow{3}{*}{MC} & NReno & 873 (33) & 166 (30) & 222 (28) \\ \cline{2-5}  & Cubic & 900 (1) & 167 (22) & 216 (28) \\ \cline{2-5}  & C-TCP & 877 (17) & 167 (24) & 204 (18) \\ \hline \multirow{3}{*}{HC} & NReno & 899 (1) & 168 (59) & 724 (207) \\ \cline{2-5}  & Cubic & 899 (1) & 168 (47) & 716 (224) \\ \cline{2-5}  & C-TCP & 899 (8) & 169 (52) & 732 (214) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean queue length and standard deviation of queue length [pkts] in the Data center scenario with common TCPs, UC—uncongested, MC—mildly congested, HC—heavily congested network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **DT** & **PIE** & **CoDel** \\ \hline \multirow{3}{*}{UC} & New Reno & 0.02 & 0.08 & 0.06 \\ \cline{2-5}  & Cubic & 0.03 & 0.06 & 0.06 \\ \cline{2-5}  & C-TCP & 0.00 & 0.02 & 0.02 \\ \hline \multirow{3}{*}{MC} & New Reno & 0.40 & 1.64 & 1.45 \\ \cline{2-5}  & Cubic & 0.13 & 1.76 & 1.54 \\ \cline{2-5}  & C-TCP & 0.34 & 1.56 & 1.43 \\ \hline \multirow{3}{*}{HC} & New Reno & 0.67 & 6.53 & 5.78 \\ \cline{2-5}  & Cubic & 0.48 & 6.98 & 6.46 \\ \cline{2-5}  & C-TCP & 0.47 & 6.86 & 6.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Packet drop ratio [%] in the Data center scenario with common TCPs, UC—uncongested, MC—mildly congested, HC—heavily congested network. smaller. However, it should be verified via simulation if tuning only the interval parameter is enough. **Summary:**  
* Replacing DT with PIE or CoDel considerably improves the achievable fairness, the experienced delay, as well as delay variations for all evaluated TCP congestion control mechanisms. * PIE provides satisfactory and comparable results regarding fairness, drop ratio, and queue stability in all network load scenarios. * TCP Compound provides satisfactory fairness and transmission efficiency even with DT in the uncongested and mildly congested network. * **The following combinations provide the best results in terms of satisfactory fairness, delay, and drop ratio at the same time: CoDel with C-TCP (for uncongested and mildly congested network) and PIE with any TCP (during heavy congestion). **
## 4 Simulation and Analysis
### Signal Preprocessing  
To test the performance of the denoising methods, a piece of pure sound signal series was necessary. As machinery sound collected directly from industrial field was always polluted by the environment background noise, the most convenient method to obtain a clean signal was by synthetizing using simulation. In this paper, a piece of simulated train acoustic signal with sampling frequency of 8192 Hz and length of 12,880 taken from Matlab 2015b Signal Processing Toolbox was extracted as the original pure signal \(y\). Then Gaussion white noise \(n\) was added into the original signal. The SNR was introduced to measure the content of noise in the noisy signal \(x\), which could be presented as follows:  
\[\text{SNR}=10\lg\frac{P_{y}}{P_{n}} \tag{19}\]  
where \(P_{y}\) and \(P_{n}\) were effective power of pure signal and noise respectively. The original train sound signal and the noise-added signal with SNR = 5 dB were presented in Figure 4.  
Figure 3: Flow of the proposed empirical mode decomposition-improved fruit fly optimization algorithm (EMD-IFOA).
# Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
## 1 Introduction
It offers a streamlined manufacturing process and cost savings by printing the loop antenna structure solely on one side of the substrate. In comparison, the loop antenna described in [46] necessitates printing on both sides. 2. It can produce three distinct loop modes (at 3.6, 4.0, and 5.1 GHz), resulting in a broader operating band width compared to [46]. 3. The CC demonstrated is an impressive 37-40 bps/Hz, just marginally below the theoretical upper limit of 46 bps/Hz. Both the design methodology of the MLAE and the MIMO performance with an array of eight MLAEs are confirmed through simulation and experimental measurement.