esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Two Models for Calculating the Satellite Repeat Period
### Orbit Offset Coefficient Method  
The satellite operation period must be obtained before calculating the satellite repeat period. The satellite operation period means the period of the satellite around the orbit of itself one time. Based on [PERSON]'s third law, the operation period can be determined as:  
\[T_{SOP}=2\pi\sqrt{\left(R+H\right)^{3}/GM}, \tag{1}\]  
where GM (=3.986005 \(\times\) 10\({}^{14}\) m\({}^{3}\)/s\({}^{2}\)) is Earth's gravitational constant, \(R\) denotes the mean radius of Earth, and \(H\) is the height of the satellite from the ground. Then, the number laps of the satellite around the Earth for one solar day can be calculated by:  
\[n=\frac{24\times 60}{T_{SOP}}, \tag{2}\]  
where \(T_{SOP}\) is counted in minutes, and the \(24\times 60\) means the time of a solar day united in minutes. Thus, the approximate satellite repeat period united in solar days can be obtained by:  
\[D=\frac{d}{n-n_{\text{int}}}, \tag{3}\]  
where \(n_{\text{int}}\) = Integer(n). \(D\) means the approximate days of the satellite repeat period and united in solar days. Additionally, d denotes the coefficient of orbit offset, which is the orbital offset relative to the previous day [24]. In the practical calculation, if the satellite orbital offset is westward, the value of d is negative. If the offset is eastward, the value of d is positive. Since the value of d is only given by satellite designers and is unknown for users, another method is adopted to calculate the approximate satellite repeat period.
## 4 Discussion
### Pockmarks Spatial Distribution and Morphometry
On the other hand, steep sidewalls probably indicate that pockmarks are in some way active or are being kept clear of sediment. [PERSON] et al. [95] suggested that slopes exceeding the angle of ropose were indications of pockmark activity. [PERSON] et al. [83] proposed pockmark depth more closely relates to activity status, with deeper pockmarks likely to have been active for longer periods of time or to have experienced a more intense venting of gas. Similarly, [PERSON] et al. [91] interpreted the relatively deeper and steeper-sided pockmarks within the Oslojord field to be recently active and wider shallower ones as older or inactive for a long time. Based on the depth-width ratio of PGPF, the large, deep and steep-sided pockmarks of the southern sector could be considered the most active area in the entire PGPF. Side-scan sonar showed very low backscattering inside the pockmarks, suggesting that the pockmarks were infilled with muddy deposits. The low backscatter of the floor, walls and rims suggest that the Patras pockmarks generally lack carbonate clasts and chemosynthetic communities, which are usually associated with pockmarks. Carbonate clasts are recorded by high to very high backscatter amplitudes because of the strong differences in densities and acoustic velocities between the carbonate clasts and the surrounding mud and their small relief [96; 97; 98]. Moreover, methanotrophic communities (e.g., shells of chemosynthetic bivalve beds) contribute to enhancing the amplitude of the signal [99; 100]. ROV images recovered from a deep, steep-sided pockmark of the southern sector of the PGPF confirmed the presence of fluid mud inside the pockmark and the absence of benthic ecosystems related to fluid seepage. The absence of such benthic ecosystems may be related to the fine-grained character of the sediments. [PERSON] et al. (2010) observed scarce fauna in the stiff clay at the base of a pockmark in the Fladen Ground area of the North Sea, while abundant megafaunal communities were observed inside several pockmarks of the North Sea with coarse-grained sediments on their floors ([PERSON] et al., 2011). A lack of benthic ecosystems related to pockmarks has also been reported ([PERSON] et al., 2011) in a shallow water pockmark field (15-40 m) in the Bay of Concarneau (French Atlantic coast). Furthermore, in Inner Oslofjord, Norway, the analysis of macrofaunal assemblages inside and outside numerous pockmarks showed no influence on the composition of the fauna at any taxonomic level ([PERSON] et al., 2011). In the PGPF, the ROV inspection also showed the existence of small holes (10-20 cm in diameter) scattered on the muddy floor of the examined pockmark. Most probably, these holes result from bioturbation (e.g., benthivorous fishes), though the possibility that they are related to gas leakage should not be excluded.
# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 3 Proposed Method
### Data Processing  
Inspired by [13], which suggests that human heart rate varies rhythmically over a continuous period, we store video frames composed of continuous T frames as spatiotemporal maps. This input feature facilitates easier learning of physiological signal information by neural networks. In [25], it is mentioned that the face contains the richest physiological signal information. Therefore, we first identify and locate the facial region within the video sequence, subsequently storing the ROI sequences composed of facial regions as spatiotemporal maps.  
Figure 1: Overall structure of the proposed SS-Net.  
Around the face, there are non-skin areas such as hair and background that do not contain an rPPG signal. To reduce this noise, we employ the Dlib [26] face detector to detect 68 facial landmarks. We filter out noisy areas based on specific coordinate points.  
Particularly, as illustrated in Figure 2, for a video segment consisting of T frames, we illustrate the processing using the first frame of the video segment. First, we employ Dlib to detect facial landmarks and locate ROIs according to the principles mentioned above. Then, the ROI region is divided into \(w\times h\) blocks, resulting in \(N=w\times h\) blocks. Each pixel within each channel of each block undergoes average pooling, yielding results denoted as \(b_{1}^{N}\in R^{3}\), indicating the first block among \(N\) blocks with three channels. After processing all ROI blocks in this frame, we concatenate \(b_{n}^{N}\), \(n\in[1,N]\) to obtain \(f_{1}\). We process the remaining video frames frame by frame to obtain \(f_{2}\dots\), \(f_{T}\), where \(T\) denotes the total number of frames in the video segment. Finally, we concatenate \(f_{t},t\in[1,T]\) in chronological order to obtain the spatiotemporal map \(M_{temp}\in R^{T\times N\times 3}\). By transposing \(M_{temp}\), we obtain \(M\), where each row of the spatiotemporal map represents the temporal evolution of the average pixel values of an ROI block, and each column represents the average pixel values of different ROI blocks at a given time point.
## 4 Overview of GNSSs
### Quasi-Zenith Satellite System
#### 4.5.3 QZSS Ground Segment  
The MCS is the focal point for the execution of QZS-1 navigation operations and performs the following functions:  
* Estimating the orbit and clock offset;
* Generating navigation messages;
* Remote control;
* Monitoring the quality of the navigation signal;
* Generating status or warnings.  
Monitoring stations have been located in Japan and in Asia and Oceania to receive QZSS and GPS signals for precise orbit and clock estimates. Some of these stations outside Japan have been established in cooperation with international organizations. The monitoring stations receive L-band navigation signals from the QZSS and GPS constellations and then transmit the raw data to the MCS via a ground or satellite communications link.  
The monitoring station (TCS) is responsible for QZS-1 satellite bus operations. This includes, but is not limited to, the following functions:  
* Performing orderly operations;
* Remote control;
* Planning and maintenance;
* Integration of all system operations.  
The main ground station, located on Okinawa, ensures continuous operation of the QZSS, which is constantly visible there. Due to regular typhoons during the summer season, the station has been equipped with two redundant high-gain 7.6 m diameter antennas and a protective radar dome to ensure uninterrupted operation even in severe weather.  
The station, responsible for time synchronization, measures the difference between the satellite's on-board clock and a highly stable ground reference point, and performs data transmission operations. Based on the TMS measurements, UTC (Coordinated Universal Time) time offset parameters and a set of GPS-QZSS time offsets (QOTO) are generated for the QZS-1 navigation message [92].
## 2 Measurement Campaign
### Scenario Analysis  
A map of the acquisition geometry for the measurement campaign is shown in Figure 1 with the broadcasting transmitter Donaueschingen (Tx) marked in black, the location of the LORA-11 receiver at Dubendorf airfield marked as Rx and the analyzed flight path of the PC-12 shown as a solid red line. The blue straight line indicates the single element half power beam width (HPBW) of LORA's receiving antennas. The color coding in Figure 1 highlights the local topography.  
The selected transmitter of Donaueschingen (whose main parameters are reported in Table 1) is situated at a distance of 54.4 km from the receiver location.  
To assess the transmitter signal level at the receiver location, digital terrain elevation data from the ASTER GDEM data set [22] were analyzed with FHR's DARWIN program [23; 24]. This software tool calculates propagation losses according to the recommendations in [25]. Figure 2 shows the expected signal attenuation along the propagation path between transmitter Donaueschingen (left hand side in Figure 2) and the LORA-11 receiver stationed at the airfield Dubendorf (right hand side in Figure 2). As is evident from Figure 2, the mountainous area prevents optical line of sight between the two sites. Specifically, the prediction tool estimated an additional 21.4 dB of attenuation losses with respect to optical line-of-sight propagation. Nevertheless, thanks to diffracting wave radio propagation effects, transmitted signal reception at the airfield and subsequent reconstruction was still possible.  
The DARWIN program was also used to investigate whether the terrain affected free space propagation of the target echo reception, as this might directly affect the resulting bistatic RCS measurements. Onboard IMU recordings were used to analyze the point-to-point wave propagation path between the transmitter-to-target and target-to-receiver paths. This analysis showed that line-of-sight clearance of more than 0.6 of the first Fresnel zone diameter was present through the entirety of the analyzed flight trajectory on both the transmitter-to-target path and the target-to-receiver path. According to [25], diffraction losses can therefore be assumed to be negligible.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Location (WGS-84) & 47\({}^{\circ}\)53\({}^{\prime}\)17\({}^{\prime\prime}\) N \\  & 8\({}^{\circ}\)34\({}^{\prime}\)37\({}^{\prime\prime}\) E \\ DVB-T signal carrier frequency & 482 MHz \\ Transmitting polarization & H \\ EIRP & 46 dBW \\ Azimuth transmitting pattern \(G_{tx}\) & omnidirectional \\ Antenna height above ground & 122 m \\ Ground elevation & 917 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of transmitter in Donaueschingen.  
Figure 1: The scenario geometry measured and presented in this paper.
## 5 Attack Assumptions and Implementation
### Problem Formulation  
Consider that an adversary may wish to disrupt the plant when the delivery time exceeds, such as stopping the PLC or making a crash on extension devices. Unfortunately, the adversary does not know how to specify to the PLC which variable is meant by \"Stop Button\" or \"Actuator X\". Most PLCs do not necessarily label their I/O devices with semantically meaningful names such as \"Heater\", \"Cooler\", \"Valve\", etc. Instead, PLC uses memory addresses to read/write values from/to sensors and physical devices. We refer to this set of address names as \(\mathcal{V}_{\mathcal{M}}\). Since the adversary does not know the semantics of the names in \(\mathcal{V}_{\mathcal{M}}\), we prefer to use the set of input variables \(\mathcal{V}_{\mathcal{A}}=\{StartButton,Actuator1,Actuator2,\ldots\}\) as an alternative.  
Here raises the challenge, how can the adversary find a correct mapping between \(\mathcal{V}_{\mathcal{M}}\) and \(\mathcal{V}_{\mathcal{A}}\) to construct the malicious payload? To find a correct mapping from \(\mathcal{V}_{\mathcal{A}}\) to \(\mathcal{V}_{\mathcal{M}}\), ICS-ARC requires a piece of common-sense facts about most plants, such as \"When the Start Button is pressed, some actuators start\" and \"All actuators are OFF until the Start Button is ON\". These statements are encoded into a behavioral specification of the target plant. When the PLC control logic is available, ICS-ARC will attempt to locate the device addresses that behave the same as such behavioral specifications under the rule of the PLC control logic.  
For the PLC control logic implementation, we construct a model \(\mathcal{M}\) from the control logic \(\text{Var}(\mathcal{M})=\mathcal{V}_{\mathcal{M}}\), and we perform a checking analysis to find the mapping \(\mu:\mathcal{V}_{\mathcal{A}}\rightarrow\mathcal{V}_{\mathcal{M}}\). We assume the \(\mu\) has a correct mapping, and all properties can be mapped under the control logic. For example, the above property, \"When the Start Button is pressed, some actuators start\" will be checked as \"When \(u_{1}=\top\), then \(y_{1}=\top,y_{2}=\top,\cdots,y_{n}=\top\)\", under the mapping  
\[\mu=\{\text{StartButton}\mapsto u_{1},\text{Actuators}\mapsto\begin{array}{c} y_{1}\\ y_{2}\\ \vdots\\ y_{n}\end{array}\} \tag{3}\]  
Thus, for every temporal logic formula \(\mathcal{A}\) (\(\text{Var}(\mathcal{A})\subseteq\mathcal{V}_{\mathcal{A}}\)), if \(\mathcal{A}\) mapped by \(\mu\) holds over the \(\mathcal{M}\), we would obtain,  
\[\mathcal{M}\models\mu/\mathcal{A} \tag{4}\]  
which means the \(\mathcal{V}_{\mathcal{A}}\) can map the \(\mathcal{V}_{\mathcal{M}}\).
## 4 The IDCP Algorithm
### The Algorithm
#### 4.2.1 Node Selection  
IDCP iterates to optimize the task-scheduling decision of the initial solution. Therefore, an order in which nodes are rescheduled needs to be determined first. Since the critical path is crucial to the solution quality, we prioritize the nodes on the critical path for scheduling.  
As in previous statement, the critical path changes dynamically during the scheduling process, which means nodes on the critical path are not fixed. To reduce the time complexity of the algorithm, we randomly select nodes from the critical path for scheduling.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel without Assistance
#### 3.2.3 Coherent Integration only with Semi-Parallel Implementation  
From the previous cases, it is easy to generalize to the case where \(N_{\rm A}\) accumulators tests \(N_{\rm A}\) delays in parallel. The processing time for one frequency bin is  
\[\begin{array}{l}T_{\rm FB}=KN_{\rm FFT}N_{\rm S}\lceil\frac{N_{\rm S}}{N_{\rm A }}\rceil+2(N_{\rm FFT}+L)-(N_{\rm P}+N_{\rm Z})\\ =KN_{\rm S}\lceil\frac{N_{\rm S}}{N_{\rm A}}\rceil(2N_{\rm P}+N_{\rm Z})+3N_{ \rm P}+N_{\rm Z}+2L\\ =KN_{\rm S}\lceil\frac{N_{\rm S}}{N_{\rm A}}\rceil(2N_{\rm P}+N_{\rm Z})+3N_{ \rm P}+N_{\rm Z}+2L\\ =N_{\rm P}\Big{(}2 KN_{\rm S}\lceil\frac{N_{\rm S}}{N_{\rm A}}\rceil+3\Big{)}+N _{\rm Z}\Big{(}KN_{\rm S}\lceil\frac{N_{\rm S}}{N_{\rm A}}\rceil+1\Big{)}+2L. \end{array} \tag{13}\]  
It can be checked that Equation (13) becomes Equation (9), with \(N_{\rm A}=N_{\rm S}\), and becomes Equation (11) with \(N_{\rm A}=1\).  
Regarding the amount of memory necessary, the memory-based accumulators require  
\[\begin{array}{l}M=N_{\rm A}2N_{\rm P}B_{\rm C}\ {\rm bits},\\ =N_{\rm A}2N_{\rm P}(B_{\rm R}+\lceil\log_{2}(KN_{\rm S})\rceil)\ {\rm bits}, \end{array} \tag{14}\]  
where \(B_{\rm C}=B_{\rm R}+\lceil\log_{2}(KN_{\rm S})\rceil\) as previously. In the same way, Equation (14) becomes Equation (10), with \(N_{\rm A}=N_{\rm S}\), and becomes Equation (12) with \(N_{\rm A}=1\).
## 2 Experimental Procedures
### X-ray Powder Diffraction (XRD)  
Changes in mineralogical compositions of converter slag before and after the reaction with SCD were analyzed by X-ray powder diffraction (XRD). Converter slag samples were placed in the X'Pert3-Powder X-ray diffractometer (PANalytical, Malvern, UK) equipped with Cu-K\(\alpha\) radiation at working voltage 40 kV and current 30 mA. Scan was performed using X'Celerator detector from 5\({}^{\circ}\) to 70\({}^{\circ}\) (29) with a step size of 0.026\({}^{\circ}\). Total time required to scan the sample was 1 h.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Oxide** & **CaO** & **Fe2O3** & **SiO2** & **MgO** & **Al2O3** & **MnO** & **P2O5** & **TiO2** & **SO3** & **CrO3** \\ \hline OPC & 66.17 & 2.52 & 17.82 & 4.15 & 4.83 & 0.17 & 0.19 & 0.31 & 2.61 & - \\ \hline Converter slag & 54.11 & 19.55 & 13.20 & 5.90 & 1.55 & 2.31 & 2.05 & 0.68 & 0.20 & 0.34 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of Ordinary Portland Cement (OPC) and Converter slag (%).  
Figure 1: Photographic images of converter slag (**a**) before and (**b**) after reaction with supercritical carbon dioxide (SCD).
## 3 Results  
### Characterization of Raw Materials and Product Yield  
Table 1, presenting the proximate and ultimate analysis of raw materials, indicates that these were all rich in volatile matter, ranging between 77% and 85%, while each contained different amounts of ash, with FOR having 0.5% and RDF about 15%. Sulfur and Nitrogen contents of all fuels were very low to undetectable, implying insignificant toxic emissions during thermal treatment processes. The calorific value on a dry basis, which was related to the carbon, hydrogen, oxygen (dry ash free, daf) and ash concentrations of the fuels, was higher for the RDF material (\(\sim\)26 MJ/kg).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Sample** & **Volatile Matter** & **Fixed Carbon** & **Ash** & **C** & **H** & **N** & **O** & **S** & **HHV (MJ/kg)** \\ \hline PCO & 76.9 & 17.5 & 5.6 & 47.8 & 6.7 & 0.2 & 39.6 & 0.05 & 19.9 \\ FOR & 84.5 & 15.0 & 0.5 & 46.0 & 6.5 & - & 47.0 & - & 17.3 \\ RDF & 84.8 & 0.3 & 14.9 & 52.8 & 8.2 & 0.3 & 23.8 & - & 26.3 \\ \hline \end{tabular}
\end{table}
Table 1: Proximate and ultimate analysis of raw materials (% dry).  
A comparison between the yield of pyrolysis products as a function of temperature is shown in Figure 2. When the temperature increased, the yield of biochar dropped, as more volatiles were produced due to the thermal decomposition of biomass materials. Figure 2 shows that the highest amounts of biochar, liquid condensates and gases were generated by RDF, FOR and RDF samples, respectively.  
Figure 2: Yield of pyrolysis products of (**a**) PCO, (**b**) FOR and (**c**) RDF.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
###### Abstract  
This paper presents a comparative benchmarking of different beamforming techniques for robust multipath and spoofing mitigation in handheld receivers equipped with a small number of antenna elements. In the field of Global Navigation Satellite Systems (GNSSs), multipath and spoofing have become major sources of signal degradation, whereby the presence of closely spaced replicas of the signal of interest hampers the receiver's performance. Spatial filtering through the use of antenna arrays is one appealing approach to combat these effects. However, the upward trend in device miniaturization poses a serious concern for spatial filtering in handheld devices such as tablets or smartphones, particularly for the case of decimeter-wave signals such as in GNSSs. The terminal size constraints lead to deploying arrays with very few antennas, thus degrading the mitigation performance of the above impairments. This work explores the feasibility of performing robust multipath and spoofing mitigation in GNSS receivers with antenna arrays of very small sizes. Simulation results on four-antenna rectangular and two-antenna linear array distributions are provided, showing that the reduction in array size can be counteracted by the use of advanced beamformers.  
array processing; beamforming; handheld; multipath mitigation; spatial filtering; spoofing mitigation +
Footnote †: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression
## 5 Conclusions and Future Work  
In this research, a picture of a brain hemorrhage was taken and entered into the patient's records. The input image was transformed using a DWT \"branch\" wavelet. The transformed image was then compressed using \"Haar\" wavelet compression. The image was compressed to 83.3333% and decompressed and reconstructed to obtain the embedded brace image. This downloaded brace image was then used to extract patient information from the original image. After decompression, the resolution of the studied image was never disturbed, and since the brace image was lossless, the extracted patient data were unchanged. The optimization obtained from the test significantly contributed to the preservation and long-term archiving of the medical image without compromising the patient's privacy. The compression technology produced an amazing lossless image after decompression, which was useful for retrieving patient information. The method developed in this research ensured that medical images were archived optimally and increased privacy. JPEG formats' compression ratio percentages were slightly higher than those of PNG formats. When image size increases, that is, for high-resolution images, the compression ratio lies between 7% and 7.5%, and the compression percentage lies between 30% and 37%. The proposed model increases the expected compression ratio and percentage compared to other models. The average compression ratio lies between 7.8% and 8.6%, and the expected compression ratio lies between 35% and 60%. Computing time is reduced with the proposed method relative to other methods. For high-resolution images, the expected computing time lies between 4 ms and 5 ms.  
In the future, the Internet of Medical Things, medical imaging strategies, soft computing with evolutionary operators, and other hybrid image processing strategies will be applied further to reduce the complexity of the proposed model and to develop better recommender systems for large-scale, high-quality medical images.  
Conceptualization, X.X., R.R. ([PERSON]), O.I.K. and R.M.; methodology, S.K.R.; software, S.K.R., G.M.A.; validation, R.M., S.K.R. and R.R. ([PERSON]); formal analysis, R.M.; investigation, R.R. ([PERSON]), G.M.A.; resources, S.K.R.; data curation, X.X., G.M.A.; writing--original draft preparation, G.M.A.; writing--review and editing, [PERSON] ([PERSON]); visualization, O.I.K.; supervision, R.M., R.R. ([PERSON]); project administration, X.X.; funding acquisition, O.I.K., G.M.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (no. 62172095) and the Natural Science Foundation of Fujian Province (nos. 2020J01875 and 2022J01644).  
Not applicable.  
Not available.  
The authors declare no conflict of interest.