esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Remote Sensing Data  
In this study, all remote sensing data during the period from 2003 to 2005 are eight-day composites at 1-km resolution. MODIS-GPP and -ET datasets were downloaded from Numerical Terradynamic Simulation Group including MOD17 [22] and MOD16 [15] products. GPP products were calculated as follows:  
\[\text{GPP}=\varepsilon_{max}\times f(T_{min})\times f(VPD)\times FPAR\times SW _{rad}\times 0.45 \tag{1}\]  
where \(\varepsilon_{max}\) is the maximum light use efficiency obtained from LOOKUP table on the basis of land cover types [22]. The attenuation scalars \(f(T_{min})\) and \(f(VPD)\) are simple linear regression functions of daily minimum temperature (\(T_{min}\)) and vapor pressure deficit (\(VPD\)). Values of \(T_{min}\), \(VPD\) and were obtained from the Data Assimilation Office (DAO) dataset. ET products were calculated using Penman-Monteith (PM) equation [23] as follows:  
\[\lambda\mathrm{E}=\frac{\Delta\times\mathrm{A}+\underset{a}{\rho}\times C_{p} \times(e_{s}-e_{a})/r_{a}}{\Delta+\gamma\times(1+r_{s}/r_{a})} \tag{2}\]  
where \(\lambda\mathrm{E}\) is the latent heat flux and \(\lambda\) is the latent heat of evaporation; \(\Delta\) is the slope of saturation vapor pressure curve; \(\mathrm{A}\) is available energy partitioned among sensible heat, latent heat and heat fluxes on land surface; \(\rho_{a}\) is the mean air density at constant pressure; \(C_{p}\) is the specific heat of the air; \(e_{s}-e_{a}\) is the vapor pressure deficit of the air; \(r_{a}\) and \(r_{s}\) are the surface and aerodynamic resistances, respectively; \(\gamma\) is the psychrometric constant. In ET products algorithm, ET is calculated as the sum of soil evaporation and plant evapotranspiration. More information about each detailed algorithm is available in [PERSON] _et al._[15].  
Figure 1: Spatial distributions of the eight sites in this study. MF: Mixed forests; EBF: Evergreen broadleaf forests; ENF: Evergreen needleleaf forests.  
LAI/FPAR (MOD15 products) data were downloaded from Land Processes Distributed Active Archive Center [6]. GLASS-LAI data were obtained from the Center for Global Change Data Processing and Analysis of Beijing Normal University [24,25]. The GLASS-LAI retrieval algorithm employs general regression neural networks (GRNNs) to generate a long time-series of global LAI data [24,26]. GRNNs are trained by fused LAI from MODIS and CYCLOPES and the reprocessed MODIS reflectance of the BELMANIP sites during 2001-2003 [25]. The intercomparison of GLASS-LAI and other products, including MODIS, indicates that the quality of GLASS-LAI data has been greatly improved [26]. The 3 \(\times\) 3 homogenous pixels (3 \(\times\) 3 km) around the center of flux towers were extracted for analyses in this study. For missing data owing to bad-quality, linear interpolation and adjacent values are used for gap-filling.
## 3 Methodology
### Rectangle-Aided Location-Aided Routing (RALAR)
The proposed RALAR protocol provides a position estimation subsystem that can be used with any other LAR or even Location-Based Service (LBS) protocol in the VANET environment. In other words, the LAR routing protocol has not been enhanced to be implemented directly in the VANET environment; however, a new layer named the position prediction layer (rectangular zone) has been developed. This is to provide more accurate information that can be used with any other routing protocol based on location information similar to the LAR protocol. In the proposed approach, the LAR protocol is utilized, because it is based on the methodology where it determines the expected and requested zone by using the position of the destination node and source node, respectively. The conventional LAR protocol utilized the received signals from the GPS that may cause error value (due to breaching the outer layer). This means if GPS gives the wrong position value of the node, the exact route for a destination is impossible to find. In this regard, the proposed heuristic RALAR protocol uses the idea of rejecting weak GPS location value and accepting good ones. It can be achieved by using a moving rectangular zone that was designed based on the node's mobility model. Figure 1: Rectangle-aided location-aided routing (RALAR) Explanation. In Figure 2, the process of the proposed RALAR protocol has been drawn. This explains that the proposed RALAR gets the location information from the last estimated position and starts creating the rectangle zone. The latest obtained measurement values from the sensors (i.e., GPS, compass, odometer) were collected. The measurements were also tested to confirm the idea that they were actually located inside the prediction layer (i.e., a rectangular zone). In the case of the measurement, if the values are found to be inside the rectangle, they will be accepted and deemed to be correct, or else it will be rejected and updated for the next value. This process will always be in a continuous state until the system is stopped to finish the algorithm.
## 3 Results  
### Evaluation of Regression Model  
The regression model's performance is summarized in Table 3, where we evaluated the model across five different folds (A to E) to ensure robustness and reliability. The metrics presented are MSE, RMSE, and the correlation coefficient (r). The results highlighted an average MSE of 14.833 degree2, indicating the variance between the predicted values and the actual values across all folds. The RMSE, providing a sense of the average error margin, stood at 3.820 degrees. The correlation coefficient, averaging at 0.981, underscored a highly positive correlation between the predicted and actual values, suggesting the model's effectiveness in capturing the underlying relationship. In examining the performance across the five folds in Table 3, it was observed that the values for folds 2 and 5 exhibited greater MSE and RMSE values compared to folds 1, 3, and 4. Notably, fold 2 presented the highest MSE at 20.035 degree2 and RMSE at 4.476 degrees, followed by fold 5 with an MSE of 18.83 degree2 and RMSE of 4.339 degrees. Despite this, the correlation coefficient for fold 2 was lower than that of fold 5, indicating a variance in prediction accuracy and reliability across folds.
## 3 Results
### Wireless Local Area Network Latency Comparison  
The potential wireless communication networks are put to the test to verify their effectiveness in the edge processing of the GPR B-scans. For the comparison, a B-scan file of 2.7 megabytes is transferred using each Wi-Fi technology. A packet size of 32 bytes with a 0.5-millisecond delay was found to be optimal for this transmission case [34]. In the two setups, the access point and Wi-Fi hotspot are located at a distance of approximately 6 m apart from each other stations in another room of the building, with multiple objects placed between them. For each of the five configurations, the latency for each packet is plotted over the time delay to that packet and is shown in Figure 7.  
In Figure 7, the latency is shown to be the lowest in the HaLow Wi-Fi setup (0.0062 ms) and the highest in the 2.4 GHz Orbic Wi-Fi hotspot setup (3.5 ms). Additionally, the HaLow Wi-Fi setup shows the lowest number of outliers compared with the other setups, indicating higher stability.  
For comparison, Figure 8 depicts the resulting latencies versus time in one graph. While the maximum latency times can be interpreted with this graph, further statistical analysis is required to analyze the latency data in terms of consistency and performance.  
Figure 6: A digitally rendered representation of F-scans in AR displayed over (**a**) snow-covered ground and, (**b**) uncovered ground.  
Figure 7: Latency test of B-scans transmission over (**a**) Wi-Fi HaLow, (**b**) 2.4 GHz HTC Wi-Fi hotspot, (**c**) 5 GHz HTC Wi-Fi hotspot, (**d**) 2.4 GHz Orbic Wi-Fi hotspot, and (**e**) 5 GHz Orbic Wi-Fi hotspot.  
Probability density histograms for each case are depicted in Figure 9 with each setup nominally following a log-normal distribution. From this figure, it can be interpreted visually that higher probability density in low latencies indicates better performance. By this standard, Wi-Fi HaLow shows its superiority in consistency and low latency compared with the other four cases. While Figure 9a shows several outliers for the Wi-Fi HaLow setup, the low probability density of these outliers indicates the stability of the Wi-Fi HaLow.  
The numerical statistical values, such as mean, median, standard deviation, and total latency, are compared in the five network setup cases in Table 2. In this table, the HaLow setup has the lowest values for the four parameters. The HaLow is followed in performance by the Orbic setup and then by the HTC setup.  
Figure 8: Comparison of latency while transmitting B-scans over Wi-Fi HaLow, 2.4 GHz HTC Wi-Fi hotspot, 5 GHz HTC Wi-Fi hotspot, 2.4 GHz Orbic Wi-Fi hotspot, and 5 GHz Orbic Wi-Fi hotspot.  
Figure 9: Comparison of probability density in terms of latency while transmitting B-scans over (**a**) Wi-Fi HaLow and, (**b**) 2.4 GHz HTC Wi-Fi hotspot, (**c**) 5 GHz HTC Wi-Fi hotspot, (**d**) 2.4 GHz Orbic Wi-Fi hotspot, and (**e**) 5 GHz Orbic Wi-Fi hotspot.
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 1 Introduction
Because the eigenvector represents the spatial structure of the multipath propagation, spatial filtering by the first eigenvector can reduce the effects of noise, but a large number of antennas is needed to resolve the multipath components more precisely. In device-free localization (DFL) techniques, the performance is significantly influenced by the strong line-of-sight (LOS) path and is prone to bias depending on the person's location [18]. To cope with these problems, this study developed an indoor DFL system based on commodity WiFi devices, which achieves a reasonable performance by using machine learning for the spatio-temporal features of wireless channels. The technical contributions of this study are as follows. First, a 2.4 GHz-band three \(\times\) three MIMO channel sounder was developed where the channel bandwidth was extended to approximately 68 MHz by concatenating the CSI taken at six consecutive WiFi channels to achieve high resolution in the delay time domain. Second, we developed an indoor location estimation method using the spatio-temporal features of multipath propagation characteristics, which were obtained by separating the multipath components into three angle taps and two delay taps. This can reduce the dependency of the performance on the change in the LOS path. Third, a support vector machine (SVM) was applied to identify a person's location. The remainder of this paper is organized as follows. In Section 2, a channel-sounding system based on IEEE 802.11n wireless LANs is presented. Next, we present an indoor location estimation method in Section 3. The identification results of the sub-area where a person is present via two measurement campaigns are presented in Section 4. This demonstrates the performance of the proposed system and its feasibility. Finally, Section 5 concludes the paper.
## 1 Introduction
Indeed, we recently demonstrated that by small modifications of the transmit beamforming (i.e., introducing small delays or phase shifts between different MLT beams), the near-field pressure can be adjusted to remain within safety regulations [15]. Finally, in order to test the feasibility of MLT for volumetric imaging, a qualitative [16] and a quantitative [17] study were recently performed where the 2D findings were extrapolated to 3D. It was shown, by in silico and in vitro experiments, that a 16 MLT-4 MLA with transmit and receive apodization (Tukey \(\alpha=0.5\)) generates volumetric images within a single heartbeat with an appropriate image quality for functional cardiac imaging [17]. Despite these fast imaging solutions, it is important to note that the typical volumetric ultrasound recording remains pyramidal, implying that a significant portion of the reconstructed image lines are not relevant to analyze 3D myocardial dynamics. Therefore, a straightforward way to reduce the number of transmit events is by limiting the FOV to the anatomically relevant space only, i.e., to a conically shaped volume, which captures the myocardium throughout the cardiac cycle. This reduced FOV can then be combined with the aforementioned parallelized scan sequences in order to further increase frame rate. Although fully automatic solutions towards myocardial segmentation exist, translating that information in a fast ultrasound scan sequence is not obvious. The aim of this study was therefore to (1) develop a methodology to automatically define the characteristics of the conically shaped FOV given a segmented myocardial volume, and (2) to set up an appropriate MLT scan sequence to scan the associated FOV as fast as possible. Ideally, the FOV definition and its associated parallel scan sequence would be implemented in real-time and customized per patient.
## 4 Reliable and Secure Probability Analyses
### Formulation of the CEO Problem  
Both \(x_{\text{UR}_{1}}\) and \(x_{\text{UR}_{2}}\) are derived from the common source. Their mutual correlation can be represented using a bit-flipping model as \(x_{\text{UR}_{1}}=x_{\text{UR}_{2}}\oplus e\), where \(e\) indicates the probability of bit-flip between \(x_{\text{UR}_{1}}\) and \(x_{\text{UR}_{2}}\). The equalities \(H(x_{\text{UR}_{1}})=H(x_{\text{UR}_{2}})=1\) and \(H(x_{\text{UR}_{1}}|x_{\text{UR}_{2}})=H(x_{\text{UR}_{2}}|x_{\text{UR}_{1}})= H(e)\) hold, leading to \(H(x_{\text{UR}_{1}},x_{\text{UR}_{2}})=1+H(e)\). Here, the function \(H(e)=-e\log_{2}(e)-(1-e)\log_{2}(1-e)\) defines the binary entropy.  
Relying on the Slepian-Wolf theorem, the correct reconstruction of \(x_{\text{S}}\) via joint decoding at D is possible when the source rate pair \((R_{\text{UR}_{1}}^{s},R_{\text{UR}_{2}}^{s})\) for \(x_{\text{UR}_{1}}\) and \(x_{\text{UR}_{2}}\) lies within the regions indicated as 1 and 2 in Figure 5. Yet, given the permission for errors in the primary stage due to the assumption of imperfect source-relay links, the second stage's analysis aligns with the CEO problem's dynamics.  
In situations where sequences received by \(\text{UR}_{1}\) and \(\text{UR}_{2}\) contain discrepancies, both \(\text{UR}_{1}\) and \(\text{UR}_{2}\) relay these imperfect sequences to D as per the LF configuration. Define \(e_{1}^{\prime}\) and \(e_{2}^{\prime}\) as the distortion measures of \(\Pr(x_{\text{UR}_{1}}\
eq\hat{x}_{\text{UR}_{1}})\) and \(\Pr(x_{\text{UR}_{2}}\
eq\hat{x}_{\text{UR}_{2}})\) correspondingly, where \(\hat{x}_{\text{UR}_{1}}\) and \(\hat{x}_{\text{UR}_{2}}\) symbolize the estimations of \(x_{\text{UR}_{1}}\) and \(x_{\text{UR}_{2}}\). The anticipated Hamming distortion across \(\mathcal{M}\) symbols is articulated as follows:  
\[E\Bigg{[}\frac{1}{n}\sum_{n=1}^{\mathcal{M}}d\big{(}x_{\text{UR}_{k}},\hat{x} _{\text{UR}_{k}}\big{)}\Bigg{]}\leq e_{k}^{\prime}+\eta,\quad k\in(1,2), \tag{25}\]  
with the error propagation likelihood characterized by  
\[d\big{(}x_{\text{UR}_{k}},\hat{x}_{\text{UR}_{k}}\big{)}=\begin{cases}1,& \text{if}\quad x_{\text{UR}_{k}}\
eq\hat{x}_{\text{UR}_{k}},\\ 0,&\text{if}\quad x_{\text{UR}_{k}}=\hat{x}_{\text{UR}_{k}}.\end{cases} \tag{26}\]  
Here, \(\eta\) is a minute positive constant, and the function \(E(\cdot)\) is the expectation mechanism. Finally, the destination retrieves an estimate of the information sent from the source using majority rule decoding [42] Section 4.1, or an best decision approach [43].
###### Abstract  
Surface solar irradiance (SSI) is a crucial component in climatological and agricultural applications. Because the use of renewable energy is crucial, the importance of SSI has increased. In situ measurements are often used to investigate SSI; however, their availability is limited in spatial coverage. To precisely estimate the distribution of SSI with fine spatiotemporal resolutions, we used the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A) equipped with the Advanced Meteorological Imager (AMI). To obtain an optimal model for estimating hourly SSI around Korea using GK2A/AMI, the convolutional neural network (CNN) model as a machine learning (ML) technique was applied. Through statistical verification, CNN showed a high accuracy, with a root mean square error (RMSE) of 0.180 MJ m\({}^{-2}\), a bias of \(-0.007\) MJ m\({}^{-2}\), and a Pearson's \(R\) of 0.982. The SSI obtained through a ML approach showed an accuracy higher than the GK2A/AMI operational SSI product. The CNN SSI was evaluated by comparing it with the in situ SSI from the Ieodo Ocean Research Station and from flux towers over land; these in situ SSI values were not used for training the model. We investigated the error characteristics of the CNN SSI regarding environmental conditions including local time, solar zenith angle, in situ visibility, and in situ cloud amount. Furthermore, monthly and annual mean daily SSI were calculated for the period from 1 January 2020 to 31 January 2022, and regional characteristics of SSI around Korea were analyzed. This study addressed the availability of satellite-derived SSI to resolve the limitations of in situ measurements. This could play a principal role in climatological and renewable energy applications.  
surface solar irradiance (SSI); GK2A/AMI; machine learning 2022141840. [[https://doi.org/10.3390/rs14081840](https://doi.org/10.3390/rs14081840)]([https://doi.org/10.3390/rs14081840](https://doi.org/10.3390/rs14081840))
## 5 System Overview  
The proposed TWIN-ADAPT system comprises an adaptive continuous learning-driven _digital twin_ specifically designed for anomaly classification on real-time streaming data from a cleanroom's cyber-physical system components such as the Vacuum Pump, Fumehood and Photolithography Unit. Each component's monitoring model (i.e., the anomaly classifier model or the learner) is represented by its digital twin, comprising both offline and online operational stages. Figure 8 presents an overview of the TWIN-ADAPT framework.  
During the offline stage, data from the CPS sensors of that component are aggregated to create a historical dataset, which is utilized to train an (offline) initial model using LightGBM. This model undergoes refinement through hyperparameter optimization using Particle Swarm Optimization (PSO), ensuring that it is finely tuned to the unique characteristics of each CPS component's operational data. In the online stage, the digital twin model processes live data streams that are continuously generated by the cyber-physical system (sensors) of the cleanroom component. Initially, the LightGBM model trained during the offline phase is employed to monitor these data streams for anomalies. Upon the detection of concept drift in the streaming data--signifying operational changes or new anomaly patterns--by the optimized adaptive and sliding windowing method, the proposed continuous learning model collects new data samples through the adaptive window. These samples are reflective of the latest operational states of the CPS. The model is then retrained on these new data to accurately align with the latest patterns, ensuring that each component's digital twin dynamically adapts to maintain effective anomaly classification and continuous operational monitoring in the non-stationary environment of cleanrooms.
## 1 Introduction
### The Value Proposition of 5G
#### 1.2.2 5G: Higher Speed and Lower Latency  
Speeds displayed in Table 1 are only approximate. However, they show a continuous drive towards providing customers with faster speed rates. The mobile network generation that is currently being deployed is 5G, although there are still many GSM (2 nd Generation) networks in production with a switch-off date beyond 2024 [8]. The 3 rd Generation Partnership Project (3 GPP) industry consortium [9] develops the communication protocol specifications for 5G. Telecommunication providers have measured in real life a 5G data transmission speed of 3 Gbps (3000 million bits per second) with a 3 milliseconds (ms) air latency. More habitual figures are in the range of 500 Mbps and 30 ms latency (including both air and edge server connection time gaps). As an example, the reaction time for the human brain on an image perceived by the eye is around 10 ms [10]. 5G providers expect to improve these figures in the coming years.
## 6 Numerical Example
### Step 2: Split the Binary Message into \(k\) Parts:  
Assume \(k=3\).  
The publisher computes the parts \(P_{1},P_{2},P_{3}\) as follows.  
\(P_{1}\) and \(P_{2}\) are randomly chosen.  
\(P_{3}=M\oplus P_{1}\oplus P_{2}\).  
For example, we consider:  
\(P_{1}=00101101\) (45 in decimal).  
\(P_{2}=11010011\) (211 in decimal).  
Then, the publisher computes \(P_{3}\):  
\[P_{3}=01001000\oplus 00101101\oplus 11010011=10110110\]
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 4 Conclusions  
In this paper, the quality of HY-2D satellite-borne GPS data was analyzed, and the receiver was able to observe six or more navigation satellites more than 78.6% of the time. Compared with the \(L_{2}\) frequency data, the variation in elevation angle had a greater impact on the \(L_{1}\) frequency carrier-phase observations. The elevation angles were consistently below 40\({}^{\circ}\) for some of the observations, resulting in a large multipath error and severe fluctuations. The multipath effect, data integrity rate, and cycle slips proved that the HY2 receiver independently developed in China had a good performance and stable operation. Three external methods including carrier differential validation, inter-satellite differential validation, and SLR 3D validation were proposed, and the feasibility of these methods was verified based on the RD orbit of HY-2D. The orbits of HY-2D were precisely determined using the RD method; the carrier-phase residual was used as the internal validation method, and the residual RMS value was 0.0078 m. The DORIS validation and SLR range validation were used as the external validation methods with RMS values of 0.0085 m/s and 0.0495 m, respectively. The proposed SLR 3D validation based on the SLR range validation obtained errors in the x, y, and z directions, and the RMS values were 0.0266, 0.0332, and 0.0269 m, respectively. The results were comparable to the accuracy of SLR range validation but more intuitive. The proposed carrier differential validation and inter-satellite differential validation were mainly carried out using satellite carrier-phase data that was not used in POD. One or two GPS satellites were selected from the satellite-borne GPS data for external validation, and the remaining observations of GPS satellites were used for POD. The RMS values of the carrier differential validation and inter-satellite differential validation were 0.0068 and 0.0106 m, respectively. Experimental results demonstrated that the three proposed methods can be used as external validation methods and that they are reliable.  
Conceptualization, writing--original draft preparation, writing--review and editing and methodology, [PERSON], [PERSON] and H.G.; software, [PERSON]; validation, [PERSON] and H.G.; formal analysis, [PERSON] and Y.J.; resources, [PERSON] and Y.J.; data curation, M.L. and H.P.; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (Grant Nos. 42174041 and 41774001), the Autonomous and Controllable Special Project for Surveying and Mapping of China (Grant No. 816-517), and the SDUST Research Fund (Grant No. 2014 TDJH101).  
Our sincere thanks go to the National Satellite Ocean Application Center for providing satellite-borne GPS data for HY-2D; CODE for providing GPS satellite orbits, clocks and Earth rotation parameters; the ILRS for providing SLR station data and SLR tracking data; and the IDS for providing DORIS data.  
The authors declare no conflict of interest.