esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Security Analysis
### User/Participant Impersonation Attack  
If an attacker tries to impersonate a legitimate user \(U_{i}\) by sending a request message \(\{ID_{i^{\prime}},M_{0^{\prime}},M_{1}\}\), including \(M_{0}=\mathrm{h}(R_{U}\oplus UA_{i}\oplus UC_{i})\), \(UA_{i}=\mathrm{h}(ID_{i}\oplus X_{5})\) and \(UC_{i}=UA_{i}\oplus\mathrm{h}(r_{i}\oplus X_{5})\), to the RCRS. Attackers cannot obtain user biometrics and cannot calculate \(\mathrm{h}(r_{i}\oplus X_{5})\). In addition, RCRS checks its \(ID_{i}\) through its own database to confirm its legitimacy. Illegal data will interrupt communication. From the above description, our proposed TCVK scheme can resist participant impersonation attacks.
## 3 Algorithm and Strategies of Data Processing  
### Antenna Phase Model Review  
There may exist different antenna definitions to describe the inhomogeneous electromagnetic behavior of antennas in different application aspects. However, an antenna definition consists of a mean antenna offset (PCO) with respect to an antenna reference point (ARP) and azimuth and elevation dependent phase center variations (PCVs) are commonly adopted in the GNSS community [2; 3; 4]. The ARP has been conventionally defined as the intersection of the vertical antenna axis of symmetry with the bottom of the antenna.  
A set of complete antenna phase center models is usually defined in a left-handed local coordinate system. The reference origin is from the ARP. The Up direction parallels the vertical antenna symmetric axis and points to the zenith direction. The north direction actually aligns with the geographic north direction in field measurements within the horizontal plane. The so-called signal frequency dependent 3D mean phase center offset (PCO) is referring to the reference origin (ARP), and finally the elevation  
Figure 2: Robot antenna calibration facility at Wuhan University.  
and azimuth dependent PCVs are therefore referring to the PCO for individual frequencies. Then the total PCCs in any direction of a satellite can be given by:  
\[\Delta\mathbf{r}=\mathbf{r}\cdot\mathbf{u}+\mathit{PCV}(\alpha,\beta) \tag{1}\]  
where \(\mathbf{r}\) and \(\mathbf{u}\) are the 3D PCO vector and the unit vector in the satellite sight, respectively; \(\alpha\) and \(\beta\) are the azimuth and elevation of satellite in the antenna local coordinate system (_cf._ Figure 1). In Equation (1), the PCO impacts are dependent on the elevation mask [6], and the magnitude of the azimuth dependent PCVs are generally small and are neglected directly in the relative antenna PCCs in differential model [2]. However, the azimuth dependent PCV values of some antenna types can reach up to 1-2 cm due to the unsymmetrical PCV patterns of the antennas [8]. This should not be ignored in this situation if highly accurate positions are required.
## 1 Introduction
In the higher dimensional cases, on the other hand, the most fundamental degree of freedom is the radius of a domain, although the degrees of the shape make the description more complicated. As is already mentioned, the quantum domain can be dispersive. Such a feature is associated with its center of gravity, as was discussed in the past studies [14; 15]. Furthermore, the domain growth does not continue forever [16]. Briefly speaking, the quantum domain is affected by other degrees of freedom, for instance, the phonons, which provide incoherence to the growth process, and we expect that they possibly hinder the growth, as a similar mechanism argued for quantum friction [19]. In this article, we focus on a more general case. We start the dynamics after the photoexcitation from an initial state, which is chosen as a certain point in the ground-state phase diagram. We assume that this point is not close to the phase boundary. In these cases, true PIPTs are not expected to occur. However, we think that it is possible to observe a precursor. Here, the precursor is defined as a quantum domain with a small spatial extension. One of its detections is of course via the difference spectrum between the spectra before and after the photoexcitation. The problem will be a small signal because of the relatively small converted fraction. We here propose another detection, which is via the oscillation typical to the small quantum domain. As shown in Figure 1c, this oscillation is interpreted as a breathing mode of the domain. It has at least two advantages in the detection. The first advantage is that the frequency can be located within the optical gap, which makes the identification relatively feasible. As the second advantage, when the system is one-dimensional, the oscillation is equivalent to the so-called Bloch oscillation found for a wave packet in a uniform field, namely, in a Stark ladder [20; 21; 22; 23], which means that the oscillation frequency is basically identical to the step energy, that is, the energy difference corresponding to the one-site growth. Such features are argued in detail in the following sections. Figure 1: Scenarios of photoinduced electronic phase transitions (PIPTs). (**a**) Direct transition from the ground state of the initial phase to that of another phase. In a bulk system, this is strictly prohibited. (**b**) Domain formation on the phase boundary and (**c**) the formation of a small domain in situations far from the phase boundary. In (**b**), the domain grows persistently in the absence of dissipation, while the growth saturates and a breathing oscillation remains in (**c**).
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 4 Experiments
### Evaluation Based on Internal Comparison  
In the STS feature extraction, the size of time bin used to allocate track points into time bins and a representative point is selected from each time bin. Then each representative point is mapped to a fixed point on the road by the distance threshold. As mentioned before, these two parameters are determined by the demand of applications. If the trajectories are sampled at a higher rate, smaller time bin sizes and distance thresholds could be selected to achieve more accurate prediction. The sampling time interval of the courier dataset is 2 s, and 30 s of the MTA bus dataset. Therefore, in the courier  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Feature Extraction** & & **Model Building** \\ \hline \multirow{3}{*}{Internal Comparison} & \multirow{3}{*}{STS*} & \multirow{3}{*}{Time bin size} & \multirow{3}{*}{8/30 s} & \multirow{3}{*}{16/60 s} & \multirow{3}{*}{32/90 s} \\  & & Distance Threshold & & 5/50 m & 10/100 m & 15/150 m \\ \cline{1-1} \cline{3-5} \cline{5-5} \cline{5-5} \cline{5-5} \cline{5-5} \cl  
dataset, the time bin size is set to 4 s, 8 s, and 16 s separately, and the distance threshold is set to 5 m, 10 m, and 15 m. In the bus dataset, time bin size is set to 30 s, 60 s, and 90 s. The average distance of two sampled points is 82.2 m, so the distance threshold of the bus is set to 50 m, 100 m, and 150 m. Then features are imported to the same LSTM model, with fixed \(maxLength=40\). Different prediction precisions are recorded in Table 6 (the courier dataset) and Table 7 (the MTA bus dataset).
# SnoreLab Application in the Assessment of Obstructive Sleep Apnea Syndrome: A Pilot Study
## 4 Discussion  
\"Sleep medicine\" is one of the ENT subspecialities with the highest number of medical apps for patients; however, the number of articles that validated these applications is much lower [5]. Most applications in this category primarily focus on recording and analyzing sleep patterns, and providing graphical representations of various sleep phases and snoring intensity. Previous studies have shown that smartphones can conduct pre-screening examinations with comparable quality to traditional PSG. Conducting PSG via smartphones necessitates additional hardware implementation, such as a pulse oximeter, accelerometer, and external microphone [13, 14]. However, research assessing whether these applications not only diagnose or characterize but also improve OSA remains limited [15, 16, 17]. Additionally, the growing prevalence of consumer sleep technology, including snoring monitoring apps, has sparked privacy concerns. The AASM has addressed this issue by issuing a statement and guidelines [18]. There remain significant doubts about the effectiveness of these apps, which can register only snoring without simultaneously monitoring other vital parameters. The SnoreLab \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Demographic** \\ **Features** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Simple** \\ **S**noring (\(n=6\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mild OSAS** \\ **(\(n=7\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Moderate OSAS** \\ **(\(n=2\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Severe OSAS** \\ **(\(n=4\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Overall Cohort** \\ **(\(n=19\))** \\ \end{tabular} } \\ \cline{2-2} \cline{6-11}  & & \(n\) & \% & \(n\) & \% & \(n\) & \% & \(n\) & \% & \(n\) & \% \\ \hline Sex & & & & & & & & & \\ M & 4 & 66.7 & 5 & 71.4 & 2 & 100 & 4 & 100 & 15 & 78.9 \\ F & 2 & 33.3 & 2 & 28.6 & 0 & 0 & 0 & 0 & 4 & 21.0 \\ \hline Comorbidities & & & & & & & & & & \\ DM & 1 & 16.7 & 1 & 14.3 & 1 & 50 & 1 & 25 & 4 & 21.0 \\ Dyslipidemia & 0 & 0.0 & 2 & 28.6 & 2 & 100 & 2 & 50 & 6 & 31.6 \\ HBP & 2 & 33.3 & 2 & 28.6 & 2 & 100 & 4 & 100 & 10 & 52.4 \\ Obesity (BMI \(\geq\) 30) & 3 & 50.0 & 1 & 14.3 & 0 & 0 & 3 & 75 & 8 & 42.1 \\ \hline Age and timing of diagnosis & \(\mu\) & SD & \(\mu\) & SD & \(\mu\) & SD & \(M\) & SD & \(\mu\) & SD \\ \hline Age & 49.5 & 9.2 & 51.71 & 9.3 & 61 & 3 & 45.3 & 13.3 & 50.9 & 10.5 \\ Timing of symptoms (years) & 4 & 0.8 & 9 & 6.5 & 4 & 0.0 & 6 & 0.8 & 6 & 4.3 \\ \hline \hline \end{tabular} Abbreviations: OSAS = obstructive sleep apnea syndrome; DM = diabetes mellitus; HBP = high blood pressure; BMI = body mass index; \(n\) = number; SD = standard deviation; \(\mu\) = average; \% = percentage of patients for each subgroup.  
\end{table} 
Table 1: Demographic features of the patients’ cohort.
## 2 Preparation
### Data
#### 2.2.6 Aerial Laser Scanning Data  
ALS data were acquired on 4 and 13 September 2012 at the speed of 180 km/h and height of 1500 m in the clear sky. The ALS system was mounted with laser scanner LMS-Q560 (RIEGL Laser Measurement Systems GmbH, Horn, Australia), GPS, and Inertial Navigation System (INS). The working spectral wavelength was 1550 nm with the beam divergence angle as 0.5 mrad. The recording time interval and the point density were 1 ns and 2 points/m\({}^{2}\) with the laser pulse frequency as 50 kHz, respectively. The vertical and horizontal accuracy of this ALS system were 0.15 m and 0.50 m, respectively.
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 3 Proposed Methods
### Level of Protection and Attacker Assumptions
#### 3.2.2 Raw Data Protection  
In this section, we describe the implementation of encrypted data, assuming input \(X\), a vector \(\alpha\), and a linear operator \(M\), satisfying Equation (1).  
\[(\alpha^{-1}\times\alpha)\times F(X)=\alpha^{-1}\times F(\alpha\times X) \tag{1}\]  
Take the convolutional algorithm commonly used in the DNN network as an example. If each weight in the convolution kernel \(R\) is multiplied by \(\alpha\), which is a scalar, the output is \(\alpha R\). It can be seen that it is consistent with Equation (1). In this case, we use Equation (1) as the \((\alpha^{-1}\times\alpha)\times R=\alpha^{-1}\times(\alpha R)\). This property is also applicable to other linear operators. We use Equation (1) as the central principle for parameter conversion, to protect user privacy while using the GPU for acceleration. The overall system flow is shown in Figure 2.  
The overall system flow is shown in Figure 3.  
As can be seen from the figure, we first encrypt the user file before sending it to the GPU to protect the privacy of the user file.  
Figure 3: Details about implement of the protection, including encryption, decryption, and validation.
# Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD
## 4 Results and Analysis  
For GPS-based POD orbit, the orbit overlapping consistency is adopted to evaluate the orbit accuracy. The BDS-based POD orbits accuracy is evaluated by comparison with GPS-based POD orbit, and the GPS/BDS-2/3 combined orbit solution is analyzed by the overlapping consistency and comparison with the GPS-based POD orbit.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 4 Additional Research
### Energy and Capacity Adequacy
#### 4.1.1 Adequacy Planning Tools  
New planning tools must accurately consider the stability, reliability, and flexibility of grid operation [82]. Most current planning tools seek to optimize the generation mix by solving a cost minimization problem, while assessing generation and transmission adequacy. Given the scenarios of transmission capacity expansion, the problem aims to only minimize the total investment cost that optimizes generation capacity by reference to the relevant technologies and their costs [83]. The ability to detail investment for transmission capacity expansion will then become limited as the VRE proportion approaches 100% [84]. Moreover, to render the problem tractable, the current planning tools employ rather simplified representations of generator details. The tools do not consider all relevant aspects of generator operation and, thus, the path toward 100% renewable electricity should be demonstrated using iteration between planning and operation, thus multi-year time-series scheduling at high temporal resolution. Another challenge is the increasing need for energy sector coupling, thus maximizing demand flexibility in, and the welfare of, the entire energy system by incorporating electricity demand with other energy sectors. A further need is how energy sector coupling can ensure storage on the seasonal time scale, assisting to resolve the energy and capacity adequacy issue. Adapted and enhanced tools are needed that incorporate new constraints on existing planning models or the ability to link planning models with more detailed analytical tools. This is because the traditional tools do not consider the impacts of important enablers of the zero-carbon power grid: transmission expansion, distributed generation, energy storage, demand participation, and sector coupling.
## 3 Methods for Precise Relative Orbit Determination
### Xi'an Research Institute of Surveying and Mapping (CHS) Approach  
The accuracy of POD and PROD for LEOs based on CHS is centimeter-level and millimeter-level, respectively [36]. The CHS solutions of POD and PROD for TH-2 are also based on reduced-dynamic approach and batch least-squares estimation method. Differences to the NDT approach mainly include the following. Within the CHS software,  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **NDT** & **CHS** \\ \hline Global Navigation Satellite & Double-differenced ionosphere-free code and phase observations; 10 s sampling; & Double-differenced ionosphere-free code and phase observations; 30 s sampling; igs08.4 tx \\ System (GNSS) & igs14\_2108.4 tx PCO and phase center variations & PCO and PCVs correction of GNSS \\ measurement model & (PCVs) correction of GNSS transmitter antennas; & transmitter antennas; \\ GeoForschungsZentrum (GFZ) GPS orbits and & (CCDs) final GPS orbits and & 30 s clocks; Relativity correction \\  & GRACE Gravity Model 02C (GGM02C) gravity (100 \(\times\) 100); solid-earth, pole, and ocean tides & European Improved Gravity Field Model of the Earth by New Techniques (EIGEN2) gravity (120 \(\times\) 120); solid-earth, pole, and ocean tides (IERS 2000); luni-solar-planetary (Jet Propulsion Laboratory development ephemeris 405 (JPL DE405)) & European Improved Gravity Field Model of the Earth by New Techniques (EIGEN2) gravity (120 \(\times\) 120); solid-earth, pole, and ocean tides (IERS 2000); luni-solar-planetary (JPL DE405) \\  & Jacchia 71 density model, C\({}_{\rm D}\) is estimated per 3 h; ball, C\({}_{\rm R}\) is estimated; piecewise linear accelerations in T and N directions, estimated per 15 min; maneuvers (constant thrust arc) & No drag model; Empirical CODE Orbit Model (ECOM) 9; pseudo-stochastic R, T and N pulses at 6-min intervals; maneuvers (additional set of pulses) \\ Reference frames & 2008 reference frame; IERS Standard Rapid earth orientation parameters (EOPs); International Astronomical Union (IAU) 2000A; Least-squares Ambiguity Decorrelation Adjustment (LAMBDA) & ITRF 2008 reference frame; CODE final Earth rotation parameters (ERPs); IAU 2000A; \\ Ambiguities resolution & & \\ \hline \end{tabular}
\end{table}
Table 2: Strategy of National University of Defense Technology (NDT) and Xi’an Research Institute of Surveying and Mapping (CHS) software packages for baseline determination.  
piecewise constant empirical accelerations are performed in the R, T, and N directions over 6 min intervals. Orbital maneuvers are considered as a series of velocity pulses in the R, T, and N directions at specified epochs. In addition, the ambiguities are resolved to their integer values by the search strategy [37] instead of LAMBDA method. Considering the use of different software and dynamical models, the NDT and CHS solutions have a certain degree of independence despite the use of common observations. This enables an accurate evaluation of PROD results through an inter-agency comparison.
## 3 Existing Pathfinding Techniques
### Informed Search or Heuristic Approach Algorithms
#### 3.2.2 Greedy Best First Algorithm  
The Greedy Best First Search is another popular heuristic Algorithm that always has a track of a border in order to find the goal node. As the name suggests, the Algorithm 4 will always try to pick the best track at that particular moment. This Algorithm makes use of the concepts of both breadth first search and depth first search Algorithms in order to achieve more efficiency [48]. Using this concept, the most promising node can be chosen. The node which is nearest to the destination node can then be expanded and an estimate cost is made using the heuristic function [49]. For Greedy Best First Search: G(n) = 0 and the total cost required to reach the target node is: F(n) = H(n) + G(n) resulting in F(n) = H(n). Considering the Search Path Sequence vertices to be S_GBFI[], the Algorithm is same as above A* Algorithm with g(q) = 0.  
```
Step 1: Place the starting node into the OPEN list. Step 2: If the OPEN list is empty, Stop and return failure. Step 3: Remove the node n, from the OPEN list which has the lowest value of h(n), and places it in the CLOSED list Step 4: Expand the node n, and generate the successors of node n. Step 5: Check each successor of node n, and find whether any node is a goal node or not. If any successor node is goal node, then return success and terminate the search, else proceed to Step 6. Step 6: For each successor node, Algorithm checks for evaluation function f(n), and then check if the node has been in either OPEN or CLOSED list. If the node has not been in both list, then add it to the OPEN list. Step 7: Return to Step 2
```  
**Algorithm 4** Greedy Best First Search Algorithm.
# Change and Driving Factor Analysis of Eco-Environment of Typical Lakes in Arid Areas
## 2 Materials and Methods
### Calculation Method  
#### 2.4.1 Mann-Kendall Trend Analysis  
The Mann-Kendall test is a nonparametric test method. This method does not require its samples to follow a normal distribution, and a few abnormal values do not interfere with the results. The calculation is relatively simple, which makes it a widely used trend analysis method at present [39].  
Construct a time series \(X\); successively, compare \(X=\{x_{1},x_{2},x_{3},\ldots,x_{n}\}\) (where n is the sequence length); and obtain the Mann-Kendall statistical calculation result, \(S\), as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sgn(x_{j}-x_{i})\, \tag{1}\]  
where \(x_{i}\) and \(x_{j}\) are random variables and \(sgn(x_{j}-x_{i})\) is represented by \(sgn(\alpha)\):  
\[sgn(\alpha)=\begin{cases}+1,\alpha>0\\ 0,\ \alpha=0\\ -1,\alpha<0\end{cases}, \tag{2}\]  
The standardized test statistic, \(Z\), is as follows:  
\[Z=\begin{cases}\frac{S-1}{\sqrt{Var(S)}},S>0\\ 0,\ S=0\\ \frac{S+1}{\sqrt{Var(S)}},\ S<0\end{cases}, \tag{3}\]  
In the Mann-Kendall test, it is generally assumed that there is no obvious change trend of time series \(X\). The value range of test statistic \(Z\) is \((-\infty,+\infty)\), and confidence interval \(\beta\) is specified. If \(|Z|>Z_{1-a/2}\), the original assumption is not tenable, and time series \(X\) hasan obvious change trend. When \(Z\) is positive, it means that time series \(X\) shows an upward trend; otherwise, it shows a downward trend.
# Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges
## 6 Evaluation Metrics  
Recently, the community has achieved a common standard ISO (IEC 30107-3:2016) [78] to evaluate presentation attack detection (PAD), and especially morphing attack detection (MAD) systems. In this standard, the capability of the attack detection is measured with the following errors: attack presentation classification error rate (APCER) and bonafide presentation classification error rate (BPCER). This measure can be defined as follows:  
* Attack presentation classification error rate (APCER) is defined as the proportion of presentation attacks that have been classified incorrectly (as bonafide) [78]: \[\text{APCER}_{\text{PAI}_{\text{s}}}=1-\frac{1}{|\text{PAI}|}\sum_{\omega=1}^{ |\text{PAI}|}\text{RES}_{\omega},\] where \(|\text{PAI}|\) is the number of presentation attack instruments (PAI) and \(\text{RES}_{\omega}\) takes the value 1 if the presentation \(\omega\) is assessed as attack and 0 if it is evaluated as bonafide. A PAI is defined as a used object or biometric trait in a presentation attack.
* Bonafide presentation classification error rate (BPCER) is defined as the proportion of bonafide presentation incorrectly classified as presentation attacks [78]: \[\text{BPCER}_{\text{PAI}_{\text{s}}}=1-\frac{1}{|\text{BF}|}\sum_{\omega=1}^{ |\text{BF}|}\text{RES}_{\omega},\] where \(|\text{BF}|\) is the cardinality of bonafide presentations and \(\text{RES}_{\omega}\) takes the value 1 if the presentation \(\omega\) is allocated as an attack and 0 if it is analyzed as bonafide. An APCER vs. BPCER detection error trade-off (DET) curve and the equal error rate (EER) where both errors are identical, provides a comparison among MAD systems.