esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Standard Solutions and Calibration
#### 2.2.2 Preparation of the Working Solutions  
Each working solution for DMS, DMDS, and DMTS was prepared by using ultrapure water as the solvent. Each of the three DMSs stock solutions (Section 2.2.1) was diluted step by step to a 100 mg/L working solution. It could only be used within a day.  
Mixed working solution was prepared by using ultrapure water as solvent. The 10 mg/L of mixed stock solution (Section 2.2.1) was diluted stepwise into 10 \(\upmu\)g/L mixed standard solution. And then, it was diluted stepwise using ultrapure water into a series working solutions in the concentration of 10, 50, 100, 200, 500, 750, and 1000 ng/L. These mixed working solutions could also only be used within a day.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Properties** & **Dimethyl Monosulfide** & **Dimethyl Disulfide** & **Dimethyl Trisulfide** \\ \hline Chemical formula & C\({}_{2}\)H\({}_{6}\)S & C\({}_{2}\)H\({}_{6}\)S & C\({}_{2}\)H\({}_{6}\)S\({}_{3}\) \\ Structural formula (chemistry) & & H\({}_{3}\)C & H\({}_{3}\)C & S \\ \(\mathrm{Molar\ mass/(g\cdot mol)^{-1}}\) & 62.13 & 94.2 & 126.26 \\ States of matter & colorless liquid & colorless/yellow liquid & colorless liquids \\ Odors & & Decaying vegetation, septic, swamy \\ Density/(g\(\cdot\)cm)\({}^{-3}\) & 0.846 & 1.063 & 1.202 \\ Melting point/(\({}^{\circ}\)C) & \(-\)98 & \(-\)85 & \(-\)68.05 \\ Boiling point/(\({}^{\circ}\)C) & 35–41 & 110 & 170 \\ Olfactory threshold/(ng/L) & 0.3\(\sim\)1000 [8] & 200–5000 [21] & 10 [22] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic properties of dimethyl sulfide compounds.
# Changes in Meteorological Elements and Its Impacts on Yunnan Plateau Lakes
## 4 Discussion  
### Geographical Heterogeneity of Climate Factor Variation in Highland Lakes in Yunnan  
According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, the period from 1983 to 2012 may be the hottest 30 years in the Northern Hemisphere in the past 1400 years [29]. However, although global warming has become an accepted fact, the underlying changes in climate are characterized by geographical heterogeneity. The rate at which climate changes may differ with time in a single region, and may also vary in different regions within a single period of time [30], with climatic region and elevation being two important geographical factors influencing climate change. Studies have found that the most rapid global warming (>0.4 \({}^{\circ}\)C per decade) in recent decades has occurred in the mid-latitude air temperate regions of the Northern Hemisphere [31]. Moreover, it has been established that changes in climate have been more pronounced at high elevations, with the rate of increase in average annual temperature in the Himalayas, the region with the highest average elevation worldwide, being almost 3.5 times higher than that of the global average, 2.5 times that of China, and 1.5 times that of the Qinghai-Tibetan Plateau [25]. In southwest China, the Qinghai-Tibetan Plateau has been the first to respond to global climate change, followed by the Yunnan-Guizhou Plateau, with the Sichuan Basin and eastern Guizhou being identified as the slowest responder [32]. The plateau lakes in Yunnan have consistency with respect to genesis, but are characterized by varying degrees of diversity in terms of elevation, morphology, climate, and other factors. Whereas the average elevation of the lakes located in the northwest of Yunnan is 2055 m, that of lakes in central Yunnan is 1718 m. In terms of climate, Lake Lugu in northwest Yunnan is classified as a high-elevation temperate climate lake, whereas the lakes located in central Yunnan are low-elevation subtropical climate lakes, and lakes Chenghai and Erhai in northwest Yunnan are located in a transitional region between two climate zones, and their patterns of climate change show clear geographical heterogeneity. During the past 40 years, whereas the air temperature of all assessed lakes in the Yunnan Plateau has undergone a significant upward trend, the rate at which air temperature is rising in the lakes of the northwest of Yunnan has been significantly higher than that of the central Yunnan lakes. Moreover, the lakes in northwest Yunnan have a significantly earlier abrupt climate change than those in central Yunnan.
## 3 Multi-Objective OPP Model
### The Constraints Considering the Network Observability  
Realizing complete network observability is the constraint of the OPP model. The constraint can be calculated as:  
\[\mathbf{M}=\mathbf{AX}+\mathbf{O}+\mathbf{G}\geq\mathbf{b}, \tag{19}\]  
where \(\mathbf{M}=[m_{1},m_{2},\ \cdots\ m_{N}]^{\rm T}\) is the measurement redundancy vector during the normal operation of each D-PMU and \(\mathbf{O}=[o_{1},o_{2},\cdots,\ o_{N}]^{\rm T}\); \(\mathbf{b}=[b_{1},b_{2},\ \cdots\ b_{N}]^{\rm T}\) is an \(N\)-dimensional column vector used to ensure that each bus is observable. \(\mathbf{G}\) is the measurement redundancy added by SCADA, which is defined as:  
\[\mathbf{G}=\left[\sum_{i=1}^{S_{1}}{(1-g_{1})g_{i}},\sum_{i=1}^{S_{2}}{(1-g_{2})g_ {i}},\ \cdots\ \sum_{i=1}^{S_{n}}{(1-g_{n})g_{i}}\right]^{\rm T}, \tag{20}\]In the existing OPP model, the buses in the network are not distinguished. Distribution networks may have multiple load buses. Considering the characteristics of different buses, the modified constraints are as follows:  
\[{b^{\prime}}_{i}=\left\{\begin{array}{ll}2&\text{forD-PMU outage}\\ 1&\text{fornormal condition}\\ 0&\text{forunimportant bus}\end{array}\right.. \tag{21}\]  
Some important buses should be prioritized as the highest, such as hospitals, electrified railways and head offices. D-PMU outages can lead to the unavailability of measurements. In this study, the measurement redundancy of important buses is increased. These buses are observed at least twice, meaning that they can be observable under emergency conditions such as D-PMU outages. In contrast, some unimportant buses, such as affiliated workshops and public loads in small towns and rural areas, are not observed by D-PMUs in order to reduce the number of D-PMUs required. If some important buses need to be observable under emergency conditions, such as D-PMU outages, the values are taken as 2. If some unimportant buses do not need to be observable, the values of \({b^{\prime}}_{i}\) are taken as 0.  
If the differences between buses are considered, \({b^{\prime}}\) can replace \({b}\) to build a model. Although applying Equation (21) cannot provide a solution with the fewest D-PMUs, it can allow the D-PMUs to be placed more reasonably.
## 3 Experimental Validation
### Switch Schedule and Reconfiguration of the OW-DCN
Thus, when contention occurred, the switch schedule and the retransmission changed due to the priority changes of each transmission link, as shown in Figure 5d. The improved performance of the proposed OW-DCN is also demonstrated by the reduced number of contentions and zero packet loss (shown in Figure 5f). Moreover, it should be noted that this reconfiguration automatically operates under the management of the SDN control plane without any manual operation.
## 2 Materials and Methods
### Image Segmentation  
When thinking beyond perfect image object candidates (_i.e._, an ideal correspondence between an image object and a real-world entity), two failure modes expected in segmentation are over-segmentation and under-segmentation [37; 40; 63]. The former is generally acceptable, although it could be problematic if the geometric properties of image object candidates are used in the classification step. The latter is highly unfavorable because the resulting segments represent a mixture of elements that cannot be assigned a single target class [37; 47]. Thus, better classification accuracy hinges on optimal segmentation, in which the average size of image objects is similar to that of the targeted real-world objects [40; 64; 65; 66; 67].  
Image segmentation is inherently a time- and processor-intense process. The processing time exponentially rises with respect to the scene size, spatial resolution, and the size of image segments. We employed two algorithms to make the segmentation process more efficient, a multi-threshold segmentation (MTS) algorithm and a multiresolution segmentation (MRS) algorithm within the eCognition Developer 9.0 (Trimble, Raunheim, Germany) software package. The former is a simple and less time-intense algorithm, which splits the image object domain and classifies resulting image objects based on a defined pixel value threshold. In contrast, the latter is a complex and time-intense algorithm, which partitions the image scene based on both spectral and spatial characteristics, providing an approximate delineation to real-world entities. In the initial passes of segmentation, we employed the MTS algorithm in a self-adaptive manner for regionalizing the entire scene into manageable homogenous segments. The MRS algorithm was then tasked for targeted segments for generating progressively smaller image objects in a multiscale architecture.
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Evading Immune Response
#### 2.3.1 FasX  
Group A _Streptococcus_ (GAS) such as _Streptococcus pyogenes_ is a human pathogen responsible for a broad spectrum of diseases including streptococcal pharyngitis (strep throat) and the highly invasive and often deadly necrotizing fascitis (flesh-eating syndrome) [116; 117]. It produces a 205-nt sRNA, FasX, that plays a major role in immune evasion of the pathogen. It is regulated by the FasBCA system, with serotype-specific differences in basal expression potentially linked to single nucleotide polymorphisms (SNPs) in the _fasX_ promoter. FasX reduces pilus expression by binding to the pili mRNAs inhibiting their translation [118] (Figure 1d). GAS pilus expression is tightly regulated, as it can enhance adhesion and biofilm formation but also increase bacterial susceptibility to neutrophil extracellular traps [118]. The interaction between FasX and its target pili gene is serotype-specific. For example, in serotypes M1 and M2, FasX targets genes encoding the minor pilus protein (M1.cpa and M2.113), while in serotype M6 and M28, it regulates genes encoding the major pilus protein (M6.tee6 and M28.fctA). FasX also negatively regulates the expression of the fibronectin-binding proteins Prtf1 and Prtf2, which promote adhesion and internalization. FasX represses Prtf1/2 expression through posttranscriptional regulation by base pairing with the Prtf1 and Prtf2 mRNAs within their 5 UTR, specifically overlapping the ribosome binding sites. This duplex formation impedes ribosome access, thereby inhibiting mRNA translation [45].  
Conversely, FasX increases streptokinase (a plasminogen activator that produces serine protease plasmin, which promotes bacterial metastasis) expression by stabilizing its mRNA [119]. These regulatory actions of FasX contribute to the pathogen's ability to cause disease by contributing to the transition from colonization to dissemination across multiple GAS serotypes, as demonstrated in a bacteremia model using human plasminogen-expressing mice [119]. Reduced pilus expression decreases GAS adherence to host tissues, while increased streptokinase expression promotes tissue barrier breakdown and immune evasion (Figure 1d).
## 1 Introduction
The first phase acquires global information for the BS, preparing necessary conditions for finding the shortest hop paths from each node to the BS in second phase. During reconfigurable routing, the data is first routed through the shortest hop paths. Then, a reconfiguration is initiated by the node whose residual energy falls below a threshold. An SOS message is sent by this dying node to the BS for further processing. Upon receipt, the BS classifies those nodes affected by the dying one into different types, and reconfigures them respectively by replying messages of reconfiguration. This instant interaction between the BS and sensor nodes allows effective adjustment of the network virtual topology, which prevents the dying node from jeopardizing the data transmission of other nodes and contributes to the network performance. The rest of this paper is organized as follows: Section 2 presents related works, while Section 3 introduces the proposed RRP in detail. Section 4 explains the simulations experiments. Section 5 provides insights into the analysis of the simulation results. Section 6 discusses the impact of message exchange on energy consumption during reconfiguration. Finally, the conclusions are presented in Section 7.
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
## 2 DFEC Ranging Principle
### Proposed Method
Since each signal is generated by the same PLL loop control, it can be obtained that the signal transmission clock difference is the same, and the receiver clock at the receiving end is also the same. It can be found that by making the difference of the distance equations of the signals of two frequencies, we can eliminate the influence of the clock difference. Here we only give the difference equation when the frequency L1 signal is used as the reference. From this we get the following formula. \[\Phi_{B_{1}}-\Phi_{L_{1}}=\|\mathbf{r}_{t}^{2}-\mathbf{r}_{u}\|-\|\mathbf{r}_{ t}^{1}-\mathbf{r}_{u}\|-\lambda_{B_{1}}N_{B_{1}}+\lambda_{L_{1}}N_{L_{1}}+\xi_{ \phi_{B_{1};L_{1}}}. \tag{5}\]  
From the above formula, we can find that the distance difference between the signals becomes independent of time, and only related to the real distance of the signal and the ambiguity of the whole cycles. As mentioned above, we can use the principle of phase chase. The intra-cycle phase is calculated to calculate the true whole-circle distance value. Therefore, the above equation can also be expressed by the sum of the whole-phase phase distance value and the phase distance value within a period, thereby avoiding the problem of ambiguity of the whole cycles. Similarly, assuming that we know the initial phase of two frequency signals, we re-express the formula 4 as  
\[\left\{ \begin{array}{l}\Phi_{L_{1}}=\lambda_{L_{1}}*N+\Delta\phi_{L1}-\Delta \phi_{i,B1}+\xi_{\phi_{L_{1}}}\\ \Phi_{B_{1}}=\lambda_{B_{1}}*N+\Delta\phi_{B_{1}}-\Delta\phi_{i,B_{1}}+\xi_{ \phi_{B_{1}}}\end{array} \right. \tag{6}\]  
Wherein, N is the whole cycles, \(\Delta\phi_{L_{1
## 3 Motivation for the SSI Wallet Survey
### Further Usage and Analysis  
We observed that several wallets disabled Android backups, allowed clear-text traffic, and used older methods such as the message-digest algorithm 5 (MD5), secure hash algorithm 1 (SHA1), and data encryption standard (DES) and older secure sockets layer (SSL)/transport layer security (TLS) versions or applied custom crypto methods. In addition, we were able to gain access to the SQLite database of Data Wallet and find [PERSON] and [PERSON] in the log files. For esatus Wallet, we noticed a connection to Azure. However, we were not sure what, for example, the \"Fitness Activity\" is used for. In one case (Gataca Identity), we observed debugging functionality and user certificates were allowed. More interestingly, in the case of VIDwallet, we found a Facebook token and key in the source code and our pictures taken of IDs on the file system.  
[PERSON] et al. [28] describe specific application programming interface (API) calls, which indicate feature inclusion.  
Figure 4: Screenshots of selected wallets found in the Google Play Store 3/3. (**a**) esatus Wallet asking about future behavior with verifier [30]. (**b**) VIDwallet with issues complying with requested claims [30]. (**c**) VIDwallet receiving claims about a throwaway email address.  
* Root detection: The app checks if it can execute the su command or if any root-enabled apps are on the smartphone.
* Integrity check: It verifies that the app has not been tampered with and was installed from a verified app store.
* Custom keyboard: Since keyboards store inputs independently whether they are sensitive or not, passwords and similar information from a wallet should not be included. One way of mitigating this is by using custom keyboards.
* Biometric authentication: The use of biometrics or two-factor authentication (2 FA) as a type of multi-factor authentication (MFA).
* Screenshot disabled: Malicious apps and users can make screenshots of credentials, passwords, and similar. Hence, disabling this feature can help.
* Hardware secure module: The key-revealing information is stored in a secure enclave to provide a higher level of security.
* Random generator: A secure random generator used for cryptography.  
The authors specified those for crypto wallets. However, these indicators should also apply to SSI wallets. We evaluated the selected SSI wallets based on these API calls. Most do not include these features besides secure random generation and biometric authentication, as shown in Table 2.
# Experimental Study on Dynamic Effects of a Long-span Railway Continuous Beam Bridge
## 6 Finite Element Model  
### Bridge Model  
Finite element analysis is performed to help understand the experimental results, and study the impact effect of trains running at high speeds on the bridge, as will as the impact effect of different types of vehicles on the bridge. The FE model for the dynamic impact analysis of the high-speed train-bridge system was formed and embedded in the popular finite element software ANSYS (ANSYS Inc., Pittsburgh, PA, USA) [31]. A finite element model of the Yexihe Bridge is established by the software MIDAS (POSCO E&C, Pohang-si, Korea), as shown in Figure 13. The main girder, pier and pile foundation are simulated by beam element. According to the actual geological conditions, the length of the pile foundation takes the actual length. For all piers, the lateral and vertical relative displacement between the pier top and main beam is restrained. Pier 1 is a fixed bearing, and the longitudinal relative displacements between Pair 1 and main beam is restrained. The main girder is meshed by 268 (finest mesh) elements can give us optimal combination of accuracy and efficiency. The piers and the pile caps are divided into 47 (coarsest mesh) elements, and the pile foundation is divided into 48 elements. The materials of the main girder, the pier and the pile foundation are the C55, C25, and C30 concrete, respectively. The elastic modulus of the C55, C25, and C30 concrete are 36, 30, and 32 GPa, respectively. The Poisson ratio and unit weight of the concrete are 0.2 and 25 kN/m\({}^{3}\), respectively.  
The vibration equation of the bridge can be expressed as:  
\[[\mathbf{M_{b}}]\Big{\{}\tilde{\mathbf{X_{b}}}\Big{\}}+[\mathbf{C_{b}}]\Big{\{}\dot{\mathbf{X_{ b}}}\Big{\}}+[\mathbf{K_{b}}]\{\mathbf{X_{b}}\} = \{\mathbf{F_{b}}\} \tag{5}\]  
where \([\mathbf{M_{b}}]\), \([\mathbf{C_{b}}]\) and \([\mathbf{K_{b}}]\) are the mass, damping, and stiffness matrices of the bridge, respectively; \(\Big{\{}\tilde{\mathbf{X_{b}}}\Big{\}}\), \(\Big{\{}\tilde{\mathbf{X_{b}}}\Big{\}}\) and \(\mathbf{\{X_{b}}}\) are the nodal acceleration, velocity, and displacement vectors of the bridge, respectively; \(\{\mathbf{F_{b}}\}\) is the external force vector acting on a bridge. Rayleigh damping was adopted in the finite element model.
## 4 Compared Method  
### Image (2-D Representation) Construction from 1-D Vibration Data  
The raw vibration data were in one-dimensional form. The transformation of 1-D bearing raw vibration data into 2-D images is a straightforward task. The amplitude of each sample is the intensity of the corresponding pixel in the corresponding image.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **f1-Score** \\ \hline A & 0.9932 & 0.9932 & 0.9932 \\ \hline B & 0.9920 & 0.9920 & 0.9920 \\ \hline C & 0.9946 & 0.9946 & 0.9946 \\ \hline D & 0.9949 & 0.9949 & 0.9949 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity analysis of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Dataset** & **Training Accuracy** & **Testing Accuracy** & **Validation Accuracy** & \multicolumn{3}{c}{**Average Time**} & \multicolumn{3}{c}{**Loss**} \\ \cline{5-10}  & & & & & **Train** & **Test** & **Train** & **Test** & **Validation** \\ \hline
1-D CNN & A & 100\% & 99.38\% & 99.33\% & 119 \(\upmu\)s & 72 \(\upmu\)s & 2.0740e-07 & 0.0201 & 0.0068 \\ \hline
2-D CNN & A & 100\% & 96.27\% & 96.0\% & 168 \(\upmu\)s & 79 \(\upmu\)s  
The transformation between the amplitude of a sample and the corresponding pixel is represented by the following equation [46]:  
\[P[i,j]=A[(i-1)*m+j] \tag{6}\]  
where \(i=1:n;j=1:m;P[i,j]\) is the intensity of the corresponding pixel \((i,j)\) in the \(m\times n\) vibration image. \(A[.]\) is the amplitude of the sample in the vibration signal. The number of pixels in the vibration image equals the number of data points in each vibration sample. Since we took 1600 data points in each sample of vibration signal, we constructed the 40 \(\times\) 40 equivalent 2-D image representation. In our case, \(m=n\).  
Here, the 1-D vibration data were divided into \(N\) equal samples, with 1600 data points in each sample. This means that each sample will be an image of 40 \(\times\) 40 pixels. Therefore, there will be \(N\) images of dimension \(40\times 40\). Suppose that \(S_{1}=[x_{0},x_{1},x_{2},x_{3},\ldots,x_{m},\ldots,x_{n}]\) is one sample of the 1-D signal (say \(S\)) among \(N\) samples.  
The 2-D representation of the first image matrix \(y_{1}\) is:  
\[y_{1}=\left[\begin{array}{ccc}y_{11}&y_{12}\cdots&y_{1m}\\ y_{21}&y_{22}\cdots&y_{2m}\\ \ldots&\ldots&\ldots\\ y_{m1}&y_{m2}&y_{mm}\end{array}\right]=\left[\begin{array}{ccc}x_{0}&x_{1} \ldots&x_{m}\\ \ldots&\ldots&\ldots\\ \ldots&\ldots&x_{n}\end{array}\right]=\left[\begin{array}{ccc}x_{0}&x_{1} \ldots&x_{40}\\ \ldots&\ldots&\ldots\\ \ldots&\ldots&x_{40}\end{array}\right] \tag{7}\]  
Figure 7 shows each 2-D represented sample from the dataset 'A' for ten different health condition bearings [3].
## 3 Framework of Forecasting AOG Models in Aviation
### Role of Real-Time Data Integration in Forecasting AOG Occurrences  
Real-time data processing and IoT technologies have transformed the way airlines predict and manage maintenance and spare parts, shifting from scheduled maintenance to condition-based approaches [29]. By monitoring aircraft components in real-time, airlines can predict when parts are likely to fail and replace them proactively, reducing AOG incidents and improving efficiency. Sensors onboard aircraft continuously collect data from critical systems, such as engines, electronics, and hydraulics, tracking factors like wear, temperature, and pressure [30]. These data are sent to ground-based systems, where advanced analytics and machine learning identify patterns and predict potential failures. This approach enables airlines to manage spare parts strategically, ensuring availability at the right time and minimizing operational disruptions. Real-time monitoring also improves inventory management and reduces downtime caused by part shortages. Predictive analytics alert airlines to potential issues early, allowing maintenance during scheduled stops and preventing delays or cancelations [21]. Over time, big data analytics refine forecasting models by analyzing data from multiple aircraft, identifying failure patterns, and improving prediction accuracy, leading to smoother operations and better resource planning.