esa-sceva/satcom-chunk-collection huggingface dataset:
# Imaging Diagnostics of Inside of a Building Wall Using Millimeter-Wave Reflectometer
## 2 Reflectometric Measurement  
### Principle  
A Fabry-Perot interferometer consists of two parallel reflecting surfaces, as shown in Figure 1a. When an electromagnetic wave is incident on the surface, the wave is partially transmitted and reflected due to the differences in the refractive index. The transmitted and reflected waves depend on the interference between the multiple reflections at the two reflecting surfaces. When the transmitted beams, \(T_{1}\), \(T_{2}\), ---, are in phase, the sum of the sequences generates a high transmission peak, and the sum of the reflected waves, \(R_{1}\), \(R_{2}\), ---, shows the minimum transmission peak. On the other hand, when the transmitted beams are out of phase, the sum of them shows the minimum value, and the reflected waves show the maximum. In Figure 1a, this interference depends on the wavelength of the electromagnetic wave \(\lambda\), the incident angle to the surface \(\theta\), the thickness of the dielectric material between the reflecting surfaces \(l\) and the refractive index of the material \(N=\sqrt{\epsilon}\) (\(\epsilon\) is the relative permittivity). For the inspection of tile materials attached to the outer wall of the building, we can utilize the principle of Fabry-Perot interferometry. The model of a subject is shown in Figure 1b. The reflectance function is as follows:  
\[I_{r}\propto\frac{2R(1-\cos\delta)}{1-2R\cos\delta+R^{2}} \tag{1}\]  
where \(R\) is the reflectivity of the front and back surfaces of the tile as follows:  
\[R\cong\bigg{(}\frac{1-N}{1+N}\bigg{)}^{2}. \tag{2}\]  
Additionally, \(\delta\) is the phase difference between successive transmitted waves \(T_{1}\), \(T_{2}\), ---, which is as follows:  
\[\delta=\bigg{(}\frac{2\pi}{\lambda}\bigg{)}2 Nl\cos\theta \tag{3}\]  
Therefore, the reflected wave is minimized when the value of \(2 Nl\cos\theta\) is equal to an integral multiple of the wavelength (\(\lambda\), \(2\lambda\), ---). On the other hand, the reflected wave is maximal when it is equal to an odd multiple of half a wavelength (\(\lambda/2\), \(3\lambda/2\), ---). Sincethe thickness of the tile plate is given, the maximum and minimum changes in the reflected wave can be obtained by changing the wavelength of the incident wave.
## 4 Experiment
### I/O Characterization of Android Applications with MOST  
In this section, we describe the analysis of I/O characteristics of real world Android applications. Among the applications we have analyzed, we describe the I/O characteristics of Facebook and Twitter. Figure 14a shows the distribution of file types for the I/Os directed to eMMC partition, which is analyzed with MOST. We grouped the files into six categories: SQLite database (.db), SQLite journal (.db-journal), executables (.so,.apk, and.dex), resources (.dat and.xml), multimedia, and others.  
We found that SQLite and its journal files are responsible for approximately 90\(\%\) of the write I/Orequests in both Facebook and Twitter applications. The reason for such high number of write I/O requests to the local storage is because Facebook and Twitter cache the timeline records in their SQLite table, which is common approach in modern SNS applications to improve the processing speed and to reduce the latency for user requests [30]. We also found that resources (.dat and.xml) and executable files are responsible for about 60\(\%\) of the read I/O requests. Next, we categorized the blocks in the filesystem partition into three types: metadata, journal, and data. Figure 14b illustrates the result of file type distribution. Metadata and journal account for 10\(\%\) and 20\(\%\) of all write I/Os, respectively.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 6 Test and Analysis
### Kalman Filter Algorithm  
The measurement accuracy can be improved significantly after external or system interference is compensated. However, the compensated data are still influenced by measurement jump points and noise errors. As shown in Figure 10, in the 7500 sets of measurement data, there are four obvious jump points. Such incorrect measurement jump points can cause hidden dangers to the safe flight of UAVs. At the same time, in Figure 10, there is a  
Figure 8: Compensation algorithm effect.  
Figure 9: Data error before and after compensation.  
Figure 10: The influence of jumps and noise.  
Using the continuous-discrete model established in this study, the measurement equation step size adopts the sampling period of the sensor, and the error model adopts the uncorrelated white Gaussian noise model. The measurement results and error comparisons before and after filtering are shown in Figures 11 and 12.  
Figure 11: Data comparison before and after filtering.  
compared with the compensated data after filtering. This effectively eliminates the errors caused by jump points and noise.
## 3 Applying Bioinspired Networking
### Localization  
Location awareness is an important property of networked sensor systems for applications such as environmental monitoring and animal tracking. Location information of the sensor nodes is used to detect and record events and to relate these events to position. Furthermore, location awareness can be used to route packets by using geometrically aware routing.  
Traditionally localization techniques involve the periodic transmission of beacon signals [1]. The localization process has two phases. In the first phase, _i.e._, the ranging phase, the nodes determine the distance between the target nodes and the neighboring beacons. In the second phase, the target nodes estimate their positions based on the ranging information. Many systems over the years have addressed the problem of automatic location sensing mainly by using techniques such as received signal strength, angle of arrival, time of arrival, and round trip time for the ranging phase.  
In contrast, \"beacon-free\" systems rely solely on a corporate localization of nodes, where nodes work together in a peer-to-peer manner to sense and subsequent form a map of the network [40].  
[PERSON] and [PERSON] [30] proposed a localization method for wireless sensor networks based on PSO. A comparison with a localization method based on simulated annealing was given. It was reported that the PSO-based method provided better localization performance compared to simulated annealing for wireless sensor network localization. Similar work was reported by [PERSON], [PERSON] and [PERSON] [31] and a better performance of the PSO algorithm compared to conventional stochastic methods was demonstrated. [PERSON] and [PERSON] [17] reported on the use of PSO and a bacterial foraging algorithms (BFA) for the distributed localization of deployed nodes in a sensor network. The two bioinspired approaches were compared. Moreover, the authors used the PSO and BFA algorithms for segmentation of terrain images for and optimal and autonomous deployment of sensor network nodes.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 6 Perspectives
### Improvements in Spatiotemporal Resolution  
Most satellite remote sensing data are not available at high spatial and temporal resolutions simultaneously, meaning that they cannot fully meet the requirements of precision agriculture. Due to the characteristics of different crop growth stages and spatial heterogeneity of soil conditions (e.g., micro-typography, texture, moisture, nutrient status), frequent observations at a high spatial resolution are needed during the entire crop growth period to determine the precise timing and location of irrigation or fertilization [280]. Concerning drought monitoring, precision agriculture has exerted higher technical demands [281; 282]. Faced with the challenge, researchers have experimentally applied the unified system of satellites, unmanned aerial vehicles (UAVs) [283; 284], and ground-based sensor networks [285] to the monitoring of agricultural drought [286]. Well-designed space-air-ground coordination can provide more accurate information on spatially heterogeneous farmland conditions. We can thus perform precision irrigation to stem water deficits before damage and losses are caused. With regards to continuous observation, geostationary satellites and virtual satellite constellations are also going to play more significant roles [287].
## 2 Theory Introduction
### Frequency Domain Representation  
The most mature model for the quantitative description of oscillator phase noise is the power law spectrum model established according to the experimental results and the internal characteristics of the oscillator as Figure 3, that is, the oscillator phase noise spectral density can be expressed as [18]  
\[S_{\phi}(f)=\sum_{i=0}^{4}\frac{h_{i}}{f^{i}}=h_{0}+\frac{h_{1}}{f}+\frac{h_{2}} {f^{2}}+\frac{h_{3}}{f^{3}}+\frac{h_{4}}{f^{4}} \tag{7}\]  
Among them, the corresponding noise components are as follows: \(h_{0}\) is called white phase modulation noise (WPM), \(h_{1}\) is called flicker phase modulation noise (FPM), \(h_{2}\) is called white frequency modulation noise (WFM), \(h_{3}\) is called flicker frequency modulation noise (FFM) and \(h_{4}\) is called frequency modulation random walk noise (RWFM). Among the five noise components, except WPM, the other four are non-stationary processes. An important property of 1/f noise is self-similarity or scale invariance. The meaning of Equation (7) in the time domain means that the phase noise inside the oscillator can be expressed as the superposition of five mutually independent power law spectral noise components.  
The research and application of the power law spectral model in this paper is reflected in two points:  
1. According to a set of measured SSB phase noise points, through the fitting algorithm, the power law coefficient can be solved, and Formula (7) can be established. In this way, the phase noise characteristics of this signal can be clearly described from the frequency domain. If the transfer function of a signal in a communication measurement system is known, various errors caused by phase noise after the signal containing phase noise is transmitted can be solved. This analysis method is carried out in the frequency domain and is described in Section 3.
2. Assuming that Equation (7) has been established and the frequency domain characteristics of the phase noise of the signal are known, an algorithm can be designed to generate the time domain of the phase noise corresponding to the corresponding power law spectral components based on the weight coefficients of each power law spectral component noise. The time-domain phase noise sequence can be obtained by superimposing each component in the time domain. This is very critical and useful.  
Figure 3: Typical Ka signal phase noise test results. (Rohde Schwarz FSW50 Phase Noise Analyzer).  
In any time series communication measurement system simulation model, the time domain noise sequence can be superimposed on the pure carrier to simulate the signal with phase noise generated by the actual link. This method of analysis is performed in the time domain and is highlighted in Section 4.
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 4 Hardware Setup
### UAVs-LS System Setup  
As shown in Figure 9, the UAV-LS system consists of a decision-making sub-system, a RTK-GPS positioning sub-system, a wireless communication sub-system, and a UAV subsystem. Specifically, sensor information is sent from UAVs to the GCS through a wireless communication subsystem periodically. The information includes precise latitude and longitude coordinates obtained by the RTK-GPS positioning unit and the relative ground height provided by the barometer. Since UAVs and GCS are two different platforms running different operating systems, the interactive message exchange is enabled by using the MAVLink protocol [24]. At the GCS, a series of decisions are made and collision-free trajectories are planned, which are sent to the UAVs through the wireless communication. The autopilot controller on UAVs adjusts UAV locomotion modes to track the reference trajectories.  
Figure 8: Prototype of the quadrotor developed for the UAV-LS system.
# New Four Points Initialization for Digital Image Correlation in Metal-Sheet Strain Measurements
## 2 Theoretical Bases
### Superficial Strain Estimation  
To estimate the level of superficial strain, the engravings of speckle, circular or square marks on the sheet are used, which involves an electrochemical process in which an electrolyte is applied as a reagent on the surface of the sheet to engrave a thin pattern so that when the sheet is painted, the marking is imperceptible. The use of a circles pattern was chosen because it is considered a non-uniform mark deformation. In this proposed method, the facet sizes (window sizes) for the correlation function are defined by the distance between the circles' centroids. The advantages and disadvantages of a circle pattern in contrast with a speckle pattern are show in Table 1.  
After the sheet is subjected to the sausage process, the circles are distorted in the form of ellipses and the distances between their centroids are modified; the latter are used in the digital images to determine the deformation states in the testing metal sheet. Figure 3a,b shows the changes of the circular grid with the deformation of the metal sheet. A simple way to estimate the deformation in the centroid of each ellipse is by averaging the distance with its four neighboring centroids \(\hat{C}_{k-1,l}\), \(\hat{C}_{kJ-1}\), \(\hat{C}_{k+1,l}\) and \(\hat{C}_{k,l+1}\), as expressed by Equation (6):  
\[\Delta f_{k,l}=\frac{d\Big{(}\hat{C}_{i,j},\hat{C}_{i-1,l}\Big{)}+d\Big{(}\hat {C}_{i,j},\hat{C}_{i,j-1}\Big{)}+d\Big{(}\hat{C}_{i,j},\ \hat{C}_{i+1,j}\Big{)}+d\Big{(}\hat{C}_{i,j},\hat{C}_{i,j+1}\Big{)}}{4} \tag{6}\]  
where \(d\Bigg{(}\Bigg{[}\begin{array}{c}x_{1}\\ y_{1}\\ z_{1}\end{array}\Bigg{]},\Bigg{[}\begin{array}{c}x_{2}\\ y_{2}\\ z_{2}\end{array}\Bigg{]}\Bigg{)}=\sqrt{\left(x_{1}-x_{2}\right)^{2}+\left(y_{1 }-y_{2}\right)^{2}+\left(z_{1}-z_{2}\right)^{2}}\).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Characteristics** & **Speckle Pattern** & **Circle Pattern** \\ \hline The normalized correlation matches well with the patterns. & YES & NO \\ Mark deformation is assumed as irregular. & NO & YES \\ A pixel can represent a mark. & Some cases & YES \\ Independence of the distance between the cameras and the specimen. & Some cases & YES \\ \hline \hline \end{tabular}
\end{table}
Table 1: The advantages and disadvantages of circle pattern versus speckle pattern.
## 3 Data Sources and Research Methodology  
### Data Sources and Processing  
Various remote sensing data were used in this study, including digital elevation model data, precipitation and air temperature, the leaf area index (LAI), the normalized difference vegetation index (NDVI), GPP, and ET. Among them, the elevation data were obtained from the NASA Earth Science Data Web site ([[https://nasadaacs.eos.nasa.gov/](https://nasadaacs.eos.nasa.gov/)]([https://nasadaacs.eos.nasa.gov/](https://nasadaacs.eos.nasa.gov/)); accessed on 20 September 2023), and they are high-precision topographic data acquired by ALOSs.  
Some data came from [[https://appeears.earthdatacloud.nasa.gov/](https://appeears.earthdatacloud.nasa.gov/)]([https://appeears.earthdatacloud.nasa.gov/](https://appeears.earthdatacloud.nasa.gov/)), including data on GPP, ET, NDVI, and LAI spanning the years 2001-2020, and the corresponding product names are MOD17A2, MOD16A2, MOD13Q1, and MOD15A2H. Data on GPP, ET, and the LAI are 8-day data with a spatial resolution of 500 m, and the NDVI dataset consists of 16-day data with a spatial resolution of 250 m. The GPP and ET data were synthesized into annual-scale 8-day data using the maximum value synthesis method by Arcgis 10.8, while the NDVI and LAI data were synthesized and processed into annual-scale 8-day data. As the ET data correspond to MOD16A2 in MODIS, the values of some special areas were replaced by 32,762-32,766, and there were no actual ET values, so some areas took a null value.  
The VPD data share the same time series as the aforementioned datasets and were derived from the Terra Climate, with a spatial resolution of 0.05\({}^{\circ}\).  
Temperature and precipitation data were provided by the Tibetan Plateau Research Institute of the Chinese Academy of Sciences ([[https://www.tpdc.ac.cn/](https://www.tpdc.ac.cn/)]([https://www.tpdc.ac.cn/](https://www.tpdc.ac.cn/)); accessed on 20 September 2023) with a spatial resolution of 1 km. For the comparative analysis, this research also utilized annual precipitation and temperature data from six meteorological stations in the Li River Basin, spanning the years 2001 to 2020. The data were sourced from actual measurements.  
The land cover data were obtained from the annual land cover data with a spatial resolution of 30 m of China from 1990 to 2022 published by [PERSON] [24]. In this study, based on the land cover data in 2020, using Arcgis 10.8, we chose four main land cover types (cropland, forests, shrubland, grasslands) as the main objects from the seven land cover types.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 3 Proposed Method  
### System Overview  
The overall framework is depicted in Figure 1. Firstly, MSFE is employed to extract shallow features from the original input silhouettes, which can extend the receptive field and capture the gait spatial and temporal features within different regions. Next, a two-pathparallel structure including FLSFE and MSTFE is designed to obtain multi-scale spatial-temporal features. FLSFE is devised to extract combined features by encompassing both global and local gait information in the spatial domain and use a novel convolutional block (R-conv) by the special design of residual operation. Simultaneously, MSTFE is implemented to learn more discriminative motion representation between long-term and short-term features in the temporal domain and integrate the muti-scale temporal features in an attention-based way. Subsequently, both the spatial and temporal features are integrated to generate muti-scale spatial-temporal representations. HPP [4] is applied to complete feature mapping process, ensuring comprehensive and discriminative features. Furthermore, a view embedding module is introduced to use view prediction learning to calculate the best view and incorporate view information explicitly into the spatial-temporal fused feature representations to gain the final features, which can effectively alleviate the effect of viewpoint variations. Finally, joint losses of cross entropy loss and the triplet loss [4; 7] are selected to train the proposed network.
## 2 Related Studies
### Human Face Identification  
Deep learning and machine learning models can be trained for human face recognition using large-scale datasets of human faces, leading to high-performance computing resources that have improved [10; 11]. Metric learning has been widely used to train deep learning models for face identification [4; 5; 12; 13]. The loss functions used in deep metric learning for the human face identification were contrastive loss [14] and triplet loss [6], which impose a Euclidean margin on the feature. One such model, deep face [15], used explicit 3D face modeling to revisit the alignment and expression stages in the pipeline of face recognition that consisted of four stages. Deep Face achieved accuracy levels of 96.75% and 92.4% for the labeled faces in the wild (LFW) and the YouTube faces (YTF) datasets, respectively. In addition, FaceNet [6], which directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity, was presented. In FaceNet, end-to-end training has shown that simplifying the setup and directly optimizing the loss associated with the task at hand improves performance. FaceNet achieved 99.63%, and 95.12% accuracy levels for the LFW and YouTube Faces DB datasets, respectively. Deep learning models for human face recognition such as SphereFace [7], CosFace [8], Ring loss [16] and ArcFace [9] with improved performances have also been proposed with new loss functions. In SphereFace, angular softmax, which can be viewed as imposing discriminative constraints on a hypersphere manifold, was proposed [7]. CosFace reformulated the softmax loss as a cosine loss by L2 normalizing both features and weight vectors to remove radial variations [8]. ArcFace proposed additive angular margin loss to simultaneously improve intraclass compactness and interclass discrepancy and obviously enforce a more evident gap between the nearest classes [9]. ArcFace achieved accuracy levels of 99.83% and 98.02% for the LFW and YTF datasets, respectively. In addition, global-local GCN [17], which removes noise from the human face dataset, and GroupFace [18], which learns latent groups, were proposed to improve the performance of human face recognition. Many studies related to deep learning have been conducted in various fields to improve the performance of human face identification.