esa-sceva/satcom-chunk-collection huggingface dataset:
# ADS-B Like UTM Surveillance Using APRS Infrastructure
## 5 Conclusions  
In UTM development, the proper OBU for surveillance data reporting is the key device to support the complete UAV surveillance. This study develops a APRS OBU, based on available LightAPRS modules, capable of reporting the GPS position and UAV 6 DoF acquired from Pixhawk FC. Moreover, the APRS GTS is also developed to relay the 90 byte surveillance data onto the UTM cloud. By conducting 19 flight tests at different sites from 4 km to 40 km away from the CJCU GTS, the GTS collected 1330 packets of data out of 1331 reported from the APRS OBU. Each received data packet in the GTS has a complete 90 bytes for surveillance with position and UAV 6 DoF. The test results verify the availability of the APRS OBU to successfully transmit surveillance data to the UTM Cloud with satisfactory continuity and reliability. Furthermore, the APRS GTS deployment, with a low rate of missing data, covers a 40 km radius at a specific frequency 144.61 MHz.  
Based on the test results mentioned above, the developed APRS ADS-B like system is capable of working in the Tainan City UAV Integrated Pilot Program (IPP), and supports full surveillance for rotor-wing UAVs flying 30 m above ground level to fixed-wing UAVs flying in high altitudes. According to APRS ADS-B like functional capability, its altitude coverage can be extended to 20,000 feet or higher. By distinguishing UAVs from manned aircraft, the UTM can offer services to rotor-wing UAVs flying below 400 feet or fixed-wing UAVs flying higher.  
The development of hierarchical UTM [4] proposes four candidates of ADS-B like [6] on-board unit for UAVs to report data periodically to UTM cloud. Complete flight surveillance can be accomplished with ADS-B like supports. It is the most important progress for UAV flight under effective UTM operation [15].
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 4 Simulation Results
### Time-Frequency Domain Narrowband Anti-Interference Simulation  
This scheme uses Matlab 2017 software to simulate the module of counteracting narrowband interference. Four array antennas were used to conduct an anti-narrowband interference simulation test on the BD2 B3 signal (operating frequency is 1268.52 MHz \(\pm\) 10.23 MHz). The signal amplitude of the generated narrowband interference signal is -70 dBm. The simulation results of anti-single-band narrowband interference and anti-three-band narrowband interference are shown in Figure 20. The test results show that when receiving the B3 frequency point, the receiver can resist up to three narrowband interferences at the same time; when resisting interference from a single frequency band and three frequency bands, the interference-to-signal ratio is \(\geq\)57 dB and \(\geq\)47 dB, respectively (signal level \(-\)133 dBm). Among them, the real-time performance of interference suppression is better than 1 ms.
# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 3 Results  
### Test Setup  
To undertake our experiments, we used a machine with an intel(r) core(tm) i7-6700 HQ, CPU 2.60 GHz, RAM 16 GB, and GPU intel(r) HD Graphics 530. Our model was implemented in Python (Jupyter Notebook 6.0.3 web-based) using the Keras deep learning  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time Interval**} & \multirow{2}{*}{**Time Steps**} & \multicolumn{3}{c}{**Soil Moisture Value (\%)**} & \multirow{2}{*}{**RSME Validation** **(\%)**} \\ \cline{3-3} \cline{5-5}  & & **Measure** & **Forecast** & **Static Error** & **(\%)** \\ \hline
12 h & 144 & 57.80 & 55.70 & 2.10 & 2.595 \\ \hline
8 h & 96 & 57.00 & 54.90 & 2.10 & 2.466 \\ \hline
6 h & 72 & 56.20 & 54.50 & 1.70 & 2.380 \\ \hline
4 h & 48 & 55.90 & 54.70 & 1.20 & 2.216 \\ \hline
3 h & 36 & 54.90 & 54 library [27], having Tensorflow as backend. Our mode takes 30 min to train for 100 epochs, a 0.001 learning rate, a split ratio of 70% for training and 30% for testing, and a 72 batch size.  
To increase confidence in the proposed model's results, a cross-validation step is re-quired. This entails dividing the datasets into K subsets and rotating the validation and training subsets. Finally, the model average performance is calculated by averaging the K-folds' performance. In this paper, we use K-Fold coding to divide our data into five subsets, which means that the holdout method is repeated five times, with one of the five subsets serving as the test set and the other four serving as the training set, each time.
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 2 System Model and Problem Formulation
### Theoretical Foundations
#### 2.2.2 Large Deviation Theory of Binary Hypothesis Testing  
In the BCOC system model, the classification task at the receiver can be formulated as a binary hypothesis testing problem. Specifically, let \(H_{1}\) denote the hypothesis that the source sequence originates from the subsource \(X_{1}\), i.e., the input of classifier is the i.i.d. sequence \(Y_{1}^{m}\); and let \(H_{2}\) denote the hypothesis that the source sequence originates from the subsource \(X_{2}\), i.e., the input of classifier is the i.i.d. sequence \(Y_{2}^{m}\). Under this setting, classification errors can be categorized into two types.  
* Type I error (\(E_{1}\)): Classify hypothesis \(H_{1}\) as \(H_{2}\).
* Type II error (\(E_{2}\)): Classify hypothesis \(H_{2}\) as \(H_{1}\).  
Since the source sequence is randomly generated from either \(X_{1}\) or \(X_{2}\) with prior probabilities \(\alpha_{1}\) or \(\alpha_{2}\), the overall classification error probability \(P_{e}\) is given by  
\[P_{e}=\alpha_{1}Pr(E_{1})+\alpha_{2}Pr(E_{2}). \tag{6}\]  
According to large deviation theory, \(P_{e}\) follows an exponential decay law when the sequence length \(m\) is sufficiently large. More specifically, \(P_{e}\) is characterized by the Chernoff information [30], given by  
\[P_{e}\doteq e^{-mC(P_{Y_{1}},P_{Y_{2}})}, \tag{7}\]  
where the Chernoff information between probability distributions \(P_{Y_{1}}\) and \(P_{Y_{2}}\) is defined as  
\[C(P_{Y_{1}},P_{Y_{2}})=-\min_{0\leq\alpha\leq 1}\log\left(\sum_{y}P_{Y_{1}}^{ \alpha}(y)P_{Y_{2}}^{1-\alpha}(y)\right). \tag{8}\]  
Chernoff information quantifies the statistical distinguishability between two probability distributions. A larger Chernoff information value indicates greater separability between the distributions, leading to a lower classification error probability in the asymptotic regime, as indicated by (7). Consequently, enhancing classification accuracy can be formulated as improving the Chernoff information in (8).
# Particle-Velocity-Based Mixed-Source Sound Field Translation for Binaural Reproduction
## 5 Simulation Study
### Simulation Results
#### 5.2.3 Translated Position  
We also evaluate the performance of each method when the translated position moves away from the receiver region. Figures 10 and 11 show the results of \(\zeta_{V}\) and \(|\mathbf{r}_{V}|\) at 300 Hz, and the results of \(\zeta_{E}\) and \(|\mathbf{r}_{E}|\) at 1500 Hz, respectively, as a function of translation distance along the positive \(y\)-axis. Note that we plot Figures 10 and 11 at different frequencies because \(\mathbf{r}_{V}\) is a good predictor for low frequencies, whereas \(\mathbf{r}_{E}\) is a good predictor for high frequencies, as mentioned in Section 5.1. From Figures 10 and 11, we show that all the methods have little error at the origin where the receiver is positioned; however,  
Figure 8: The directional error \(\zeta_{V}\) and the magnitude of \(\mathbf{r}_{V}\) with respect to frequency at the translated position of (0.2, 0.3, 0) m. (**a**) Directional error \(\zeta_{V}\); (**b**) magnitude of \(\mathbf{r}_{V}\).  
Figure 9: The directional error \(\zeta_{E}\) and the magnitude of \(\mathbf{r}_{E}\) with respect to frequency at the translated position of (0.2, 0.3, 0) m. (**a**) Directional error \(\zeta_{E}\); (**b**) magnitude of \(\mathbf{r}_{E}\).  
the performance worsens as the translation distance increases for all the methods. Compared to the sparse solution of the sound-pressure-based expansion, the sparse solution of the particle-velocity-based expansion has less directional error and this advantage becomes more significant for a further translated position, which leads to a better direction reproduction, especially for the translated position far away from the receiver region. The results of Figures 10b and 11b also indicate that the sparse solution of the particle-velocity-based expansion can provide a better quality of localization for the listener than the sparse solution of the sound-pressure-based expansion. Once again, we notice that the least squares solution and the closed-form solution have same performance, and both are worse than the the sparse solutions of the particle-velocity-based expansion and the sound-pressure-based expansion.  
From the above analysis, we conclude that the least squares solution of the particle-velocity-based expansion is identical to the closed-form solution of the sound-pressure-based expansion, whereas the sparse solution of the particle-velocity-based expansion has better overall performance than the sparse solution of the sound-pressure-based expansion.
## 3 Physical Description of the On-Chip, Optical Wireless Link
### Optical Antennas
It is worth pointing out that the multi-level numerical approach proposed in this paper is virtually applicable to every optical antenna configuration, such as multiple-feed arrays of plasmonic antennas [38] or dielectric antennas [19; 39] and to every multilayered environment.
# Energy Saving in Wireless Sensor Networks via LEACH-Based, Energy-Efficient Routing Protocols
## 4 Threshold LEACH with Sleep-Awake Scheduling Protocol
### Overview of T-LEACH\({}_{\text{SAS}}\) Operation
During their allocated time slots, and if the value of sensed data exceeds a predefined threshold, the sensor nodes wake up, transmit their data to their cluster head, and then, go back to sleep. Otherwise, if the value of sensed data does not exceed the specific threshold, the nodes stay asleep. During each round, the cluster heads collect the data that have been transmitted to them from their cluster members, perform data aggregation to remove redundancies, and then, transmit the aggregated data to the base station, further reducing energy consumption and enhancing data transmission efficiency. Nodes and cluster heads update their energy levels after each round, and if any node depletes its energy, it is identified as a dead node and is, henceforth, excluded from any participation in the overall protocol operation during all future rounds. This continuous monitoring and adaptation of energy usage, coupled with threshold-based data transmission and sleep-awake scheduling, significantly enhance the network's overall energy efficiency and prolong its operational life.
## 5 Conclusions  
The main goal of our research was to determine whether vibration monitoring on a gripper could be used for adaptive control, and therefore, if changes during the milling process would be detectable. To answer this question, tests were conducted to find if changes of parameters would produce differing measurements. The fast Fourier transform was used to process measured signals.  
The most dominant frequencies (133 Hz and higher multiples) seemed to originate from tool cutting into material. When single-sided spectrum maxima were compared, the tool, spindle rotation, and feed rate seemed to affect the result.  
The variance analysis and Duncan tests revealed a significant effect of tool, material, and spindle rotations. The feed rate analysis did not show conclusive results where the probability of similarity was over 5%.  
Overall, vibrations measured at pneumatic gripper processed with fast Fourier analysis seemed to be sufficient as a potential signal for adaptive control during milling, similarly to [22; 23]. As in [23], the roughness of specimens should be measured and paired with vibrations in a further study, to provide data for adaptive control model.  
The results and procedures of this study will also serve for measurements of amplitude spectrum during nesting milling strategies in ongoing research. The limitation of the investigated measuring and processing system was that if the signal is to be evaluated in real time, it will require higher computational demands. The authors also intend to use this system in the development of a smart pneumatic gripper for the woodworking industry, where the modified procedures described in the article can be used.  
**Author Contributions:** Conceptualization, P.K. ([PERSON]) and R.K.; methodology, P.K. ([PERSON]); software, A.H.; validation, P.K. ([PERSON]); formal analysis, A.H.; investigation, P.K. ([PERSON]) and P.K. ([PERSON]); resources, R.K.; data curation, A.H.; writing--original draft preparation, A.H.; writing--review and editing, P.K. ([PERSON]); visualization, A.H.; supervision, P.K. ([PERSON]); project administration, P.K. ([PERSON]); funding acquisition, P.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by \"APVV-20-0403 FMA analysis of potential signals suitable for adaptive control of nesting strategies for milling wood-based agglomerates\" and project VEGA 1/0791/21 \"Research of non-contact method of analysis of small and dust particles arising in the production process with a prediction of negative effects of dust particles\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the Operational Program Integrated Infrastructure for the project: National Infrastructure for Supporting Technology Transfer in Slovakia II-NITT SK II, co-financed by the European Regional Development Fund.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 3 Method: LEACH-MTC Based on Moving Target State Prediction
### Cluster Head Selection Strategy Based on Equilibrium  
In this section, round is defined as a base unit related to data transmission in network. In each round, after the working node set \(G_{k}^{\prime}\) is formed, cluster head selection is performed for all working nodes. In the cluster head selection stage, in order to reduce the probability that nodes with little residual energy or far from the base station become cluster heads, an improved strategy is designed. This strategy takes into some factors including target movement state, the distance between working node and the target, the residual energy rate of working node, and the distance between working node and the base station. Assuming \(s_{i}\) is a working node, we can obtain its the cluster head selection threshold \(T_{w}^{\prime}(s_{i})_{k}\) at time \(k\):  
\[T_{w}^{\prime}(s_{i})_{k} =\left\{\begin{array}{cc}\kappa*p_{k}+\mu*\Delta p_{k}&s_{i}\in G ^{\prime}\\ 0&others\end{array}\right. \tag{25}\] \[\Delta p_{k} =\Delta T(s_{i})_{k}-\frac{1}{n_{k}}\sum_{i=1}^{n_{k}}\Delta T(s_ {i})_{k}\] (26) \[\Delta T(s_{i})_{k} =\alpha E_{avg}(s_{i})_{k}+\beta(1-\omega_{st}(s_{i})_{k})+\gamma (1-\omega_{ss}(s_{i})_{k}) \tag{27}\]  
where \(p_{k}\) and \(\Delta p_{k}\) represent the expected probability and the compensation probability at which the node \(s_{i}\) becomes the cluster head, respectively. \(n_{k}\) represents the number of surviving nodes in the working node set \(G_{k}^{\prime}\). \(E_{avg}(s_{i})_{k}\) represents the residual energy rate of the node \(s_{i}\). \(\omega_{st}(s_{i})_{k}\) and \(\omega_{ss}(s_{i})_{k}\) represent the distance factor of the node \(s_{i}\) to the target, the node \(s_{i}\) to the base station, respectively. \(\kappa,\mu,\alpha,\beta\) and \(\gamma\) represent the weighting coefficient of the cluster head selection threshold, respectively.  
Therefore, after the cluster head selection threshold is determined using the Equation (25), the cluster can be formed by the LEACH protocol framework. Every cluster with working nodes which is self-organized, and the monitoring data of the nodes are processed and transmitted by the cluster head nodes.
# Analysis of Satellite Compass Error's Spectrum
## 4 Discussion
It is necessary to take into account the effects of appearing obstructions, and the user should be aware of the effects of the nearby obstructions of the operation of the satellite compass. The mentioned jumps in the heading measurement for a particular compass cannot be clearly determined with regards to time and amplitude. These changes, although similar and occurring almost at the same time in different compasses, are not identical. Sometimes they are slightly shifted in time for different compasses, relative to the change in constellation. This is clearly seen in Figure 9, where synchronous registrations made in the same place are shown, and sharp changes occur at different times. For example, after 1700 and just before 2200, the sharp changes can be observed in all three registrations at almost the same moments, but their amplitudes differ. There is no rule that one particular compass always shows larger changes. These changes can be considered as a typical filtration effect used in the integrated system. This kind of effect also appears in the coordinate indications referred to by the GNSS receiver, which is a separate element of the tested device. However, the code principle of a position measurement results in fundamental differences from the phase principle of angle measurement. Therefore, during measurements, especially in motion, there were several cases observed in which the heading indications were less accurate or even incorrect, while the coordinate values were correct. Such cases occur when, due to the presence of the obstructions, the configuration of the satellites has decreased to four, while setting the heading requires observation of signals from five satellites. Practitioners using such devices should pay attention to this fact. It is common to observe the values of DOP and (HDOP) - Horizontal Dilution of Precision as indicators of the quality of the receiver's work, while this refers to the position and is not true in relation to the heading (Figures 8 and 9). It seems that for applications, such as hydrographic vessels, it would be advisable to propose a new indicator to facilitate the control of heading information quality in satellite compasses.
## 1 Introduction
[33] reported a non-significant mean difference of less than 1 mmHg (95% CI = \(-\)9.8 to 8.5) in the limb occlusion pressure between the right and left legs. To the contrary, [PERSON] et al. [32] reported a large and significant difference in the AOP between the dominant and non-dominant legs of males (21 \(\pm\) 28 mmHg; \(p\) = 0.009) but not of females (13 \(\pm\) 27 mmHg; \(p\) = 0.053). Since differences in the AOP between individuals is attributed primarily to differences in limb circumference [5; 25; 26; 32; 51; 26], one might presume that differences in AOP between the two limbs within individuals may also be due to differences in limb circumference. Nevertheless, there were large differences in AOP between legs reported by [PERSON] et al. [32] despite an average differences in leg circumference of less than 1 cm. In addition, AOP was greater in the smaller leg in 40% of the participants. Thus, differences in AOP between limbs may be due to other factors, such as differences in the cuff bladder position between the two limbs [52]. Few studies have reported the reliability of AOP measurements. A recent study reported high interrater (ICC = 0.894 to 0.984) and test-retest reliabilities (ICC = 0.737 to 0.985) of the brachial artery AOP [30]. Other studies have reported acceptable repeatability of AOP measurements of the brachial artery [29] and the femoral artery [45]. [PERSON] et al. [35] reported a time effect of measuring brachial artery AOP over four measurements over two days but the mean difference between measurements was relatively small and probably of little practical significance. A recent review of the literature regarding BFR during exercise concluded a need for increased clarity of methodology used during BFR during exercise [53]. The primary purpose of this study was to compare the AOP of the superficial femoral artery (SFA) in the dominant and non-dominant legs in young healthy men and women when using a continuous and incremental cuff inflation protocol. The secondary purpose of this study was to evaluate the reliability of AOP measurements using the continuous and incremental cuff inflation protocols. We hypothesized no significant difference in AOP between the sexes or between the dominant and non-dominant legs, no significant differences in the AOP measured using continuous or incremental cuff inflation protocols, and reliable measurements of AOP.
## 1 Introduction
### Scope and Method of Research  
In this study, an effective technique is proposed to detect main cracks on a concrete structure by applying imaging processing algorithms appropriate to images extracted from the octree structure of TLS data. The proposed technique aims to overcome the pre-mentioned drawbacks while fully taking advantage of the contactless measurement of TLS. To respond to the drawbacks of the data capacity, resolution, and accuracy of crack recognition, a data compression scheme is proposed based on octree data structure, which has been applied to various research fields such as computer graphics, robotics, etc. [31; 32; 33; 34; 35; 36] and proved to be a reasonable approach in terms of memory usage and search speed [37; 38]. Moreover, an improved algorithm using a 3D significant eigenvector-based shape descriptor allows the octree method to provide a better image recognition [39].  
The proposed method is summarized by the following work procedures. First, a shape information model that can maintain the shape change in structure throughout the laser scan data was constructed. The shape information model uses an octree data structure through point-cloud processing and voxelization, and it can efficiently downsample and visualize a large-scale scan data. To respond to the diversity of operation environments in detecting cracks, a combination of image processing algorithms, such as K-means clustering [40], subtraction with median filtering [41], binarization [42], and morphological operations were proposed. The crack detection procedure involved the following steps: (1) segmentation for preprocessing, (2) classification of an image into 3 categories (i.e., background, structural joints, and sediments, and surface) using K-means clustering according to the color similarity, (3) conversion to gray scale and image enhancement using median filtering and improved subtraction, (4) deletion of non-crack parts on the surface by K-means clustering results, (5) detection of major crack objects on the surface based on the Otsu's binarization method, and (6) highlighting crack objects by morphological operations. The overall workflow is presented in Figure 1.  
Figure 1: The workflow of crack detection by terrestrial laser scanning (TLS) measurement.  
Then, the proposed technique is validated on a spillway wall of a concrete dam structure in South Korea. The test wall is larger than 100 m\({}^{2}\) and has many cracks with various shapes. The wall also has many structural joints and contaminated by serious sediments, which are expected to significantly impede the detection of cracks on the wall surface. The wall is scanned by a Leica ScanStation C5 model at 32 m distance. Along with the original scanned data, the compressed data up to 30%, 50%, and 80% are prepared by the proposed octree data structure. Then, the proposed crack detection procedure is applied to original and compressed images, and the results from the images are compared with the visual inspection result to validate the performance of the proposed technique.