esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-GNSS Data and Products
### Multi-GNSS Data  
The multi-GNSS Experiment (MGEX) [28] was initiated by IGS in 2012, to prepare for the incorporation of the new and modernized systems, which include Galileo, BDS, QZSS, and Navigation with Indian Constellation (NAVIC) systems, as well as the modernized GPS and the recovered GLONASS, and any other space-based augmentation system (SBAS) of interest [14]. A global multi-GNSS stations network, currently comprising more than 170 active stations, has been deployed under the framework of the MGEX campaign and integrated in parallel with the existing IGS network of GPS/GLONASS reference stations. As a minimum, all the MGEX stations support the tracking of GPS constellation as well as at least one of the new Galileo, BDS, or QZSS constellations, and the GLONASS satellites are tracked by most of the stations from MGEX. The majority of the MGEX stations allows for the RT data access in addition to the off-line archival data.  
Meanwhile, various ACs and agencies have been providing the multi-GNSS products [29; 30] and the satellite orbit/clock products for most of the new constellations have also been generated on a routine basis [31], which inspires much interest in using the multi-GNSS observations for PPP technique. The main supporters of MGEX are the CNES, Institut National de l'Information Geographique et Forestiere (IGN), Geoscience Australia (GA), GFZ, Japan Aerospace Exploration Agency (JAXA), DLR, BKG, and the ESA, which contribute to roughly three quarters of the multi-GNSS stations. In early 2016, it was decided to terminate the experimental phase of the MGEX and to pursue IGS multi-GNSS activities as the \"IGS Multi-GNSS Pilot Project\". The term \"MGEX\" will be retained by IGS for this pilot project due to its high recognition achieved so far [32].  
In this study, observational data of globally distributed MGEX stations are processed in operational RT PPP mode to retrieve the tropospheric delays. The geographic distribution of those stations along with their supporting constellations is illustrated in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**IGS RTS** & **Reference Point** & **GNSS** & **Analysis Center** \\ \hline
**IGS01** & APC & GPS & SE Combination \\
**IGS02** & APC & GPS & KF Combination \\
**IGS03** & APC & GPS/GLO & KF Combination \\
**CLK70** & APC & GPS & GFZ \\
**CLK01** & APC & GPS/GLO & BKG \\
**CLK21** & APC & GPS/GLO & DLR/GSOC \\
**CLK16** & APC & GPS & WUHAN \\
**CLK81** & APC & GPS/GLO & GMV \\
**CLK92** & CM & GPS/GLO/GAL/BDS & CNES \\
**GFZC2** & APC & GPS/GLO/GAL/BDS & GFZ \\
**GFZD2** & APC & GPS/GLO/GAL/BDS & GFZ \\ \hline \end{tabular}
\end{table}
Table 2: The International GNSS Service (IGS)-RT stream products from each analysis center.
# Characteristics of Plane Gate Vibration and Holding Force in Closing Process by Experiments
## 3 Results and Discussion
### Correlation between Gate Vibration and Holding Force
#### 3.3.2 Correlation in Closing Process
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Water Depth (m)**} & \multicolumn{5}{c}{**Gate Opening Ratio \(e\)**} \\ \cline{3-6}  & & **0.1** & **0.2** & **0.3** & **0.5** & **0.8** \\ \hline \multirow{4}{*}{Lateral} & 16 & \(-0.257\) ** & \(0.015\) & \(-0.225\) ** & \(0.053\) ** & \(-0.043\) ** \\  & 18 & \(-0.127\) ** & \(0.169\)** & \(0.142\) ** & \(0.016\) & \(-0.053\) ** \\  & 20 & \(-0.134\) ** & \(-0.156\) ** & \(-0.256\) ** & \(0.185\) ** & \(-0.173\) ** \\  & 23 & \(-0.249\) ** & \(-0.177\) & \(-0.088\) ** & \(0.173\) ** & \(0.458\) ** \\ \hline \multirow{4}{*}{Vertical} & 16 & \(-0.472\)** & \(0.057\)** & \(0.049\) ** & \(0.085\) ** & \(0.108\) ** \\  & 18 & \(-0.031\) ** & \(0.228\) ** & \(0.187\) ** & \(-0.267\) ** & \(0.053\) ** \\  & 20 & \(-0.189\)** & \(-0.109\) ** & \(-0.546\) ** & \(-0.095\) ** & \(-0.082\) ** \\  & 23 & \(-0.186\)** & \(-0.201\)** & \(0.093\) & \(-0.166\) ** & \(-0.125\) ** \\ \hline \multirow{4}{*}{Horizontal} & 16 & \(0.169\)** & \(-0.005\) & \(-0.009\) & \(0.002\) & \(-0.041\) ** \\  & 18 & \(-0.027\) & \(-0.054\)** & \(0.248\) ** & \(-0.066\) ** & \(-0.014\) \\ \cline{1-1}  & 20 & \(-0.03\)** & \(0.001\) & \(-0.123\) ** & \(-0.179\) ** & \(-0.034\) ** \\ \cline{1-1}  & 23 & \(-0.018\) & \(-0.022\) * & \(0.014\) & \(-0.022\) * & \(-0.225\) ** \\ \hline \hline \end{tabular} Note. * denotes a significant difference (\(p<0.05\)), ** denotes a significant difference (\(p<0.01\)).  
\end{table} 
Table 4: Correlation between gate vibration and holding force in fixed gate opening. than at the middle in fixed gate opening. To facilitate the layout and not damage the structure of hydro-elastic gate, however, three acceleration sensors were installed near the gate lug in this study. Thus, the vibration near the lug might be unable to fully represent the characteristics of horizontal vibration. Nevertheless, the structure and operation mode between the curved gate and plane gate are both different. The reliability of the above results applied to a moving plane gate in this paper is uncertain. We have thus far not been able to quantity the uncertainty through the existing results. The following research will focus on the characteristics of horizontal vibration at different gate heights in closing process.
## 2 System Model
### Spatial Jitter Effect  
This section is intended to introduce an acceptable model for FSO links' non-zero boresight spatial jitter, which can be probabilistically estimated by the following PDF, [37]:  
\[f_{I_{P,n}}\left(I_{P,n}\right) = \frac{\xi_{n}^{2}}{A_{0,n}^{\xi_{n}^{2}}g_{n}}I_{P,n}^{\xi_{n}^{2 }-1},\ \ 0\leq I_{P,n}\leq A_{0,n}g_{n} \tag{4}\]  
where \(\xi_{n}=w_{z,\omega,n}/2\sigma_{S,n}\) is the ratio of the \(n\)-th equivalent beam radius at the receiver, \(w_{z,\omega,n}=\left[0.5\sqrt{\pi}w_{z,n}^{2}erf(v_{n})/v_{n}\exp\left(-v_{n}^ {2}\right)\right]^{1/2}\), to the standard deviation of the pointing error displacement at the receiver, \(\sigma_{S,n}\), with \(w_{z,n}\) being the waist of the Gaussian beam at the receiver's plane, depending on the initial beam waist at the transmitter, the refractive index structure parameter, the operational wavelength, and the optical link length [38; 39; 40], while \(A_{0,n}=\left[erf(v_{n})\right]^{2}\), \(v_{n}=\sqrt{\pi}D_{n}/2\sqrt{2}w_{z,n}\), and \(erf(\cdot)\) stands for the error function [37]. In addition, \(g_{n}=\exp\left[2\left(2\sigma_{S,n}^{2}-\sigma_{x,n}^{2}-\sigma_{y,n}^{2}- \mu_{x,n}^{2}-\mu_{y,n}^{2}\right)/w_{z,\omega,n}^{2}\right]\) with \(\mu_{x,n}\), \(\mu_{y,n}\), \(\sigma_{x,n}\), and \(\sigma_{y,n}\) being the means and the standard deviations of the Gaussian distributions, describing the horizontal and elevation filters of the \(n\)-th beam, respectively. For zero boresight spatial jitter, \(\mu_{x,n}=\mu_{y}\), \(n=0\), while \(\sigma_{x,n}=\sigma_{y,n}=\sigma_{S,n}\), resulting in \(g_{n}=1\). The strength of the SJ is regulated by \(\xi_{n}\), meaning negligible impact at large values [41].
# Call Model and Test-Verification Methods for PS-LTE Core Equipment
## 3 Field Test Scenarios for MCPTT and eMBMS
### MCPTT Field-Test Scenarios
* KPI 1 restricts the required time to acquire permission to talk to within 300 ms.
* KPI 2 restricts the end-to-end call connection time (invite to 200 OK) to within 1000 ms.
* KPI 3 restricts the end-to-end media transmission delay time (mouth-to-ear latency) to within 300 ms when encryption is not applied. The approximate delay of KPI 1 is calculated as approximately 200 ms using the network section from UE to peer entity through eNB, including (60 + 10 ms) \(\times\) 2 (two-way delay) + UE L7 delay time (60 ms). The test is conducted at a fixed point of the wireless environment medium electric field, which does not include additional delays such as terminal mobility and RAN sharing. KPI 1, 2, and 3 should be measured for each terminal using a measuring instrument, and the average value is derived after repeating the process more than 50 times to ensure statistical significance of the result. The test environment is shown in Figure 6, and groups are set for each manufacturer so that they are not affected by other groups during the test. Local selection calls that can call all the terminals within a specific area should not be operated during the test. The resulting measurements of delay budget in group voice calls are shown in Table 4, and it can be observed that the results of testing at a fixed point of reference at a signal received power (RSRP) of \(-63\) dBm satisfies KPI 1/2/3. Figure 5: Flow Diagram of Creating an MCPTT Channel.
# Experimental Study on Scour Resistance Performance Enhancement of Chongqing Red Clay
## 3 Results and Discussion
### Anti-Dispersion Analysis
Experimental results indicate that cement content variations (6-14%) exert minimal influence on the short-term anti-dispersion performance of solidified soil, constrained by the time-lagged nature of cement hydration and water-to-solid ratio effects. As shown in Figure 10, when the water-to-solid ratio decreases from 0.6 to 0.4, the suspended solids content sharply declines from 350 mg/L to 30 mg/L (a 91.4% reduction), and the pH value decreases from 11 to 8.6 (a 21.8% reduction). Figure 11c demonstrates that specimens with lower water-to-solid ratios exhibit dense and smooth solidified soil surfaces and high water clarity with distinct interfaces, while those with higher ratios show blurred interfaces and turbid water, indicating greater particle dispersion. This phenomenon may occur because high water-to-solid ratios result in excess free water exceeding the adsorption capacities of red clay particles, cement particles, and hydration consumption. The surplus free water occupies interparticle voids, forming a \"lubrication layer\" that weakens van der Waals forces and electrostatic attraction, promoting particle dispersion. Conversely, low water-to-solid ratios enable full adsorption of free water by red clay particles, cement particles, and hydration products, reducing interparticle spacing and significantly enhancing cohesion. The denser accumulation of hydration products like C-S-H gels under low ratios forms a continuous network structure that effectively encapsulates red clay particles, improving the anti-dispersion performance of solidified soil. Experimental results indicate that at a water-to-solid ratio of 0.55, the suspended solids content reaches 150 mg/L (approaching but risking exceeding the specification limit of \(<\)150 mg/L) with a pH of 10.3. Reducing the ratio to 0.5 achieves 97 mg/L suspended solids and pH 9.1, fully meeting specifications while maintaining pumpable 1-h flowability. A further reduction to 0.45 yields 140 mm 1-h flowability, failing construction requirements. Thus, a water-to-solid ratio of 0.5 optimally balances flowability and anti-dispersion performance, preventing both cohesion loss due to excessive free water and insufficient flowability from inadequate free water.
## 4 Problem Formulation and Methodology
### Proposed Methodology
#### 4.2.2 Topic Modeling
For each token \(a_{i}\in\{1\ldots|d|\}\) : Select a topic \(z_{i}\sim\text{Mult}\left(\theta_{d}\right)\) Draw \(x_{i}^{d}\sim\text{Bernouli}(\pi_{zi})\) If \(x_{i}=0\): Select the regular topic \(r\) from \(d=a_{i}\sim Mult\big{(}\varphi_{zi}^{\prime}\big{)}\) If \(x_{i}=1\) : Select the seed topic \(from\)\(d=a_{i}\sim Mult\big{(}\varphi_{zi}^{\prime}\big{)}\)
```  
**Algorithm 1**Topic Modeling with SLDA  
Since discriminative power carried by the \(s\) practically determined the classes of training instances, it was noted that the diversity of the datasets, which was not focused on a certain topic, would divert the correlations between the semantics of the instances and personality traits. Initially, all the seed words collected during our preliminary study were incorporated to guide the modeling process. However, the initial training yielded awkward outcomes where the majority of the data points were oriented to either Neuroticism or Extraversion. This is due to the variation in the number of seed words that belong to each \(t\) in our training. Thus, our team decided to tune the number of seed words in \(S\) by repeating the training until a global equilibrium is optimized to the optimal coherence score, and at the same time, the generated topics are interpretable and meaningful to the category of the seed words. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notion** & **Description** \\ \hline \(D\) & Total number of documents in each dataset \\ \(T\) & Total number of topics. \\ \(V\) & The vocabulary of attributes \\ \(S\) & The vocabulary of seed words \\ \(A\) & A regular attribute in the document \\ \(S\) & A seed word in the document \\ \(\Theta d\) & The topic distribution of document \(d\) \\ \(\Phi t\) & The word distribution of topic \(t\) \\ \(\delta_{a,t}\) & The probability of attribute a being a latent feature for category \(t\) \\ \(\alpha\), \(\beta\), \(\gamma\) & Dirichlet Priors \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of notions. To minimize the wrong topics categorization, the SLDA was run multiple times where each run used instances without replacement. In this study, the analysis was run twice and thrice for _myPersonality_ and _Sentiment140_, respectively, to improve the categorization. The variation in the number of running times was due to the variation in terms of the size of datasets. The multiple iterations of SLDA showed the improvement in terms of reliability of modeling the topics according to aggregated categories by minimizing arbitrary post hoc selection of topics. Typically, multiple iterations using Gibbs Sampling on LDA variant models may cause inconsistency because of random sampling [34]. This inherent problem also persists in our experiment, but the effects are almost insignificant for the categorization tasks described here. Consequently, once SLDA generated the relevant topics for each training instance, the instances that contained fewer than five words were eliminated because the categorization seems to be irrelevant to the predicted classes.
## 3 Results and Discussion
### Variabilities of the Raman Band Intensities
Intensities at 1500 cm\({}^{-1}\) recorded with the portable Raman spectrometer yield a mean of 13.4 \(\times\) 10\({}^{4}\) with moderate variation. The intensities observed in the spectrum of the benchtop spectrometer were slightly higher, although the absolute intensity would not affect the results with respect to the maturity index. Overall, the intensities displayed a lower intra-spot variability of the multiple measurements as indicated by the error bars. The inter-spot variations ranged for both instruments from 1.1 \(\times\) 10\({}^{4}\) to 1.6 \(\times\) 10\({}^{4}\) at 1500 cm\({}^{-1}\), again with a lower variation in the case of the benchtop instrument. Near the stalk, no significantly higher carotenoid concentration was found than at the other measuring points. Inter- and intra-spot variability would greatly influence the results of a field measurement. However, the low variability due to the selection of the test spot position and instrument and the good precision within replicate measurements favor on-site use. It should also be noted that the intra-spot deviation was smaller than the inter-spot variation, as would be expected. Overall, the variations observed with both instruments were in a comparable and acceptable range. The performance of the portable spectrometer seemed sufficient to take advantage of the benefits of portability for field use, e.g., on a plantation.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 2 System Model
### RAN Model
Under NOMA, the signal \(Y_{k}^{f}(t)\) received at the \(k\)-th EN at the \(f\)-th frequency is  
\[Y_{k}^{f}(t)=X_{k}^{f}(t)+\alpha X_{k-1}^{f}(t)+\alpha X_{k+1}^{f}(t)+\beta A_ {k}(t)U_{k}^{f}(t)+Z_{k}^{f}(t). \tag{2}\]  
According to the circulant Wyner model, in (1) and (2), we assume that \([k-1]=M\) for \(k=1\) and \([k+1]=1\) for \(k=M\), in order to guarantee symmetry. For both OMA and NOMA, the power constraints for the \(k\)-th eMBB and URLLC users are defined within each radio resource frame respectively as  
\[\frac{1}{n_{F}n_{T}}\sum_{t=1}^{n_{T}}\sum_{f=1}^{n_{F}}\mathbb{E}\left[\left| X_{k}^{f}(t)\right|^{2}\right]\leq P_{B}, \tag{3}\]  
and  
\[\frac{1}{n_{F}}\sum_{f=1}^{n_{F}}\mathbb{E}\left[\left|U_{k}^{f}(t)\right|^{2 }\right]\leq P_{U}, \tag{4}\]  
where the temporal average are taken over all symbols within a codeword. Models (1) and (2) can be written in matrix form as  
\[\mathbf{Y}(t)=\mathbf{X}(t)\mathbf{H}+\beta\mathbf{U}(t)\mathbf{A}(t)+ \mathbf{Z}(t), \tag{5}\]  
where matrix \(\mathbf{Y}(t)=[\mathbf{y}_{1}(t),\;\mathbf{y}_{2}(t),\;...,\;\mathbf{y}_{M}( t)]\in\mathbb{C}^{n_{F}\times M}\) gathers all the signals received at all the \(M\) ENs over all the \(n_{F}\) frequencies, and the \(k\)-th column \(\mathbf{y}_{k}(t)\in\mathbb{C}^{n_{F}\times 1}\) denotes the signal received across all the radio frequencies at the \(k\)-th EN. The channel matrix \(\mathbf{H}\in\mathbb{R}^{M\times M}\) is circulant with the first column given by vector \([1,\;\alpha,\;0,\;...,\;0,\;\alpha]^{T}\); matrices \(\mathbf{U}(t)\in\mathbb{C}^{n_{F}\times M}\) and \(\mathbf{X}(t)\in\mathbb{C}^{n_{F}\times M}\) collect the signals transmitted by URLLC and eMBB UEs, respectively; and \(\mathbf{Z}(t)\in\mathbb{C}^{n_{F}\times M}\) is the overall noise matrix. Finally, \(\mathbf{A}(t)\) is a diagonal matrix whose \(k\)-th diagonal element is a Bernoulli random variable distributed as \(A_{k}(t)\sim\mathcal{B}(q)\), \(\forall k=1,\ 2,\...,\ M\).
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 3 Methodology
### Anomaly Detector Model  
In this phase, the architecture represents a feedforward neural network designed for anomaly detection using image embeddings as the input. The model begins with an input layer that processes these embeddings, which are typically extracted from the swin model. Figure 3 shows the layered architecture, where input image embeddings (dimensional vectors) are given to the input layer. The anomaly detector class takes image embeddings as the input, with a default feature size of 1024. The model consists of three sequential layers, each containing a fully connected (Linear) layer, batch normalization (BatchNorm1d), and a ReLU activation function to introduce non-linearity. The first layer reduces the input dimension from 1024 to 512, followed by a second layer that maintains the dimension at 512, and a third layer that reduces it further to 256. The final output layer is a linear transformation that maps the 256-dimensional feature representation to a single scalar logit. The forward method processes an input tensor through these layers sequentially, producing an output shape, where each sample gets a single logit value. This output is used for binary classification, where we can get a probability for determining whether an input is normal or anomalous.  
Figure 3: Detector model layered architecture.
## 3 Preliminaries  
### Differential Privacy  
The concept of differential privacy (DP) originates from the indistinguishability principle in cryptographic semantic security, implying that an adversary cannot discern between the encrypted outcomes of distinct plaintexts. For example, the inclusion or exclusion of a single record has a negligible impact on query outcomes under DP, thereby safeguarding the record's privacy.  
**Definition 1** (\(\epsilon\)-DP [5]).: _For a randomized algorithm \(K\) and a set \(S\) of all possible outputs of \(K\), a randomized algorithm is said to satisfy \(\epsilon\)-DP if algorithm \(K\) satisfies the following Formula (1) for any two neighboring datasets \(D\) and \(D^{\prime}\) that differ by at most one user._  
\[P[K(D)\in S]\leq exp(\epsilon)\times Pr[K(D^{\prime})\in S], \tag{1}\]  
_where \(Pr[\cdot]\) represents the risk of privacy disclosure and is controlled by the randomness of algorithm \(K\)._  
In Definition 1, the parameter \(\epsilon\) is referred to as the privacy budget, quantifying the level of privacy protection. Specifically, a smaller value of \(\epsilon\) signifies stronger privacy guarantees, whereas a larger \(\epsilon\) implies weaker privacy protection.  
**Definition 2** (Global sensitivity [5]).: _For any function \(f:D\to R^{d}\), the global sensitivity of \(f\) is defined as_  
\[\Delta f=max_{D,D^{\prime}}\|f(D)-f(D^{\prime})\|_{p}, \tag{2}\]  
_where \(R\) represents the real number space; \(d\) represents the query dimension of function \(f\); and \(p\) represents the norm distance used by \(\Delta f\), which is usually measured by \(L_{1}\)._
## 5 Experiments  
### Images Acquirement  
To estimate the depth of objects in image, we must acquire two images (image \(A\) and image \(B\)). Figure 5 shows the process. Firstly, image \(A\) is acquired at the place where the distance between the object and the camera is \(u\). Next, while the camera's intrinsic properties remain unchanged, the camera is moved along the optical axis to the place where the object distance is \(u+d\), and the image \(B\) is acquired. To obtain the actual distance of the objects, the objects to be measured were sitting at specified locations. In Figure 5a, the values of \(x_{1}\), \(x_{2}\), \(y_{1}\), \(y_{2}\), \(d\) and \(u\) can be obtained by using manual measurement; then, we can depend on the values, geometry and size of the objects to compute the actual depth of objects. For example, in Figure 5a, the object \(obj1\) is a cylinder. Assume its diameter is \(D\), then the actual depth of obj1 is \(((u+y_{2}+D/4)^{2}+{x_{1}}^{2})^{1/2}\). Similarly, the actual depth of \(obj2\) and \(obj3\) are \(u\) and \(((u+y_{1}+y_{2})^{2}+{x_{2}}^{2})^{1/2}\), respectively.  
Ordinarily, it is very difficult for the camera to move rigorously along the optical axis. In practical operation, the camera can be installed on a horizontal guide rail or platform on which a straight line has been drawn, with the camera then being moved along the guide rail or the straight line on the platform. This makes the moving direction approximately coincident with the optical axis. In our experiments, the camera is put on a horizontal platform which consists of several identical lab benches and moved along a straight line which had been drawn on the platform beforehand. The scene is showed in Figure 5b.