esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Illustrative Examples
### Mathematical Model
#### 5.1.1 Multivariable Thermal Mixing Process  
Consider the multivariable thermal mixing process of Figure 13. The mixer consists of a chamber including hot and cold fluid valves and an exit valve.  
Figure 12: Generation of contour lines.  
Figure 13: Thermal mixing tank process.  
The main objective is to keep \(Q_{s}\) (outlet flow) and \(T_{s}\) (outlet temperature) at the desired values, which are considered the same as that of the tanks \(Q\) and \(T\). The system is supposed to have a hot water with temperature \(T_{1}\) and cold water with temperature \(T_{2}\). Both of them are considered constants. The two inlet flow rates \(Q_{1}\geq 0\) and \(Q_{2}\geq 0\) act as manipulable variables by means of two motorized valves whose dynamic model can be described as follows:  
\[\begin{split} Q_{1}(s)&=\frac{K_{1}}{1+T_{1}s}e^{- \tau_{1}s}U_{1}(s)\\ Q_{2}(s)&=\frac{K_{2}}{1+T_{2}s}e^{-\tau_{2}s}U_{2 }(s)\end{split} \tag{34}\]  
where  
* \(Q_{1}\) and \(Q_{2}\): feed flow rates (m\({}^{3}\)/s);
* \(T_{1}\) and \(T_{2}\) : feed temperatures of the tank, (\({}^{\circ}\)C);
* \(K_{1}\) and \(K_{2}\) : static gain of the system;
* \(\tau_{1}\) and \(\tau_{2}\): system time delay;
* \(U_{1}\) and \(U_{2}\): system inputs;
* The \"\(s\)\" in Equation (34) represents the complex variable in the Laplace transform.  
It is also supposed that there are two measurements. It is further assumed that the measurement of the temperature \(T\) is carried out without delay. However, because of the location and technology of the flowmeter, it is supposed that there is a delay between the output flow and its measurement:  
\[Q_{m}(t)=Q_{s}(t-\tau_{s}) \tag{35}\]  
where  
* \(Q_{m}\): measured outlet flow, (m\({}^{3}\)/s);
* \(Q_{s}\): outlet flow, (m\({}^{3}\)/s);
* \(\tau_{s}\): time delay between the output flow and its measurement.  
Another assumption is that there are thermal losses proportional to the temperature difference between the inside and outside temperatures, which act as a disturbance effect. Thus, the model becomes  
\[\begin{split} Q_{1}+Q_{2}-Q_{s}&=A\frac{dh}{dt}\\ T_{1}Q_{1}+T_{2}Q_{2}-TQ_{s}+K_{p}(T-T_{e})&=A \frac{dTh}{dt}\\ Q_{s}&=\alpha\sqrt{h}\end{split} \tag{36}\]  
where  
* \(K_{p}\): proportionality constant of thermal losses;
* \(T_{e}\): environmental temperature, (\({}^{\circ}\)C);
* \(A\): cross-sectional area of the tank, (m\({}^{2}\));
* \(\alpha\): proportionality constant which depends on the coefficients of discharge and the cross-sectional area;
* \(h\): height of liquid in the tank, (m).
# Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems
## 3 Proposed System Architecture
Furthermore, the blockchain identity of the device can be linked to the integrity of its hardware and firmware by using a physical unclonable function (PUF) [32], and the tool OEM can control firmware updates. Given that we consider the OEM of the tool as a trusted entity and the inspector as a trusted source, our architecture provides the missing component to convert a connected tool into a trustworthy oracle [27], ensuring data integrity by creating immutable, traceable, and non-repudiable records easily verifiable by other stakeholders. Moreover, the smart contracts on the blockchain module provide a decentralized, verifiable, and transparent way to manage several other elements  
Figure 1: Proposed high-level architecture for a mining inspection system. of the inspection process, increasing their trustworthiness. For instance, certifications can be implemented as tokens, generated and managed by the certification authority, and directly linked to a particular operator or device. Another example is implementing a reputation score for the operators and inspectors, based on voting by previous users. Furthermore, the smart contract can automatize several steps of the inspection process. For example, the contract can implement simple logic, such as accepting a measurement for an inspection only if it is coming from a certificated tool or inspector, or more complex business rules, such as requiring more than one measurement from different tools or inspectors. Our proposed system architecture makes noteworthy contributions to the current state of the art. First, it uses modern mobile devices (i.e., mobile app ) to carry out on-site inspections and later automatically transfer them to the cloud module for further processing. Our proposed architecture can work even in remote mining sites with no Internet connection, as the current computing capabilities of modern mobile devices allow to store several hundreds of inspections. Second, it considers IoT-based measuring instruments (i.e., connected tool ) as trusted oracles of a blockchain-based system (i.e., blockchain module ). As a result, the connected tool becomes a practical choice to vouch for the truthfulness of the collected data during the inspection. Combining these technologies creates a cryptographically protected repository of inspection information, where data are immutable and traceable, and the data source can be precisely identified. This approach further improves trust and transparency between the stakeholders involved in the inspection process [3].
# An Efficient Multi-Lane SpaceFibre Core for Spacecraft Data-Handling Networks
## 5 Conclusions  
This paper proposed an E-ML-SpFi core based on the SpaceFibre protocol. The architecture of hierarchical error recovery reduces the implementation complexity and execution overhead of the retry mechanism. Benefiting from the well-designed modified word re-ordering block, the core is compatible with different classes of words and various lane configurations, representing a compromise between flexibility and hardware utilization. By using the parallel architecture of alignment operations, the core accomplishes low latency. Implementation results show that the E-ML-SpFi core is fully compliant with the protocol, equipped with a data signaling rate up to 6.25 Gbps. Additionally, the core occupies lightweight hardware resources. Furthermore, the total recovery time in case of a lane failure is reduced up to 30.9%. As a result, the core is a valid baseline to design the emerging spacecraft data-handling network. In future work, the E-ML-SpFi core will experience a long-term comprehensive test in a harsher environment before the core is applied to space missions.  
Conceptualization, J.Z. and J.A.; methodology, J.Z. and J.A.; validation, J.Z.; formal analysis, J.Z. and J.A.; investigation, J.Z.; resources, J.A. and Y.J.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, J.A., J.Z. and Y.J.; visualization, J.Z.; supervision, J.A.; project administration, J.A. and Y.J.; funding acquisition, J.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences, No. XDA15020205.  
The authors declare no conflict of interest.
## 1 Introduction
By applying our method to these two datasets, the results show that there exist six aberrant brain regions and 17 abnormal functional connections between the SZ group and HC group. Among our findings, there was an obvious decrease, as well as increase of both the grey matter volume and the connectivity of brain regions. The decreasing regions mainly appeared in the default mode network (DMN) and salience network (SN), e.g., the grey matter volume of precuneus (PCUN) and caudate (CAU), and the connectivity of these two brain regions, as well as insula (INS) and CAU were significantly reduced. Moreover, all connectivity corresponding with rolandic operculum and insula significantly reduced [26; 27; 28; 29; 30; 31]. The significantly increased grey matter volume of brain regions was mainly distributed in frontal gyrus (FG) and supramarginal gyrus (SMG), and there also existed four with significantly increased connectivity, such as middle frontal gyrus and superior occipital gyrus, as well as middle occipital gyrus and fusiform gyrus, and the corresponding conclusion of increasing also was discussed [28; 29; 32]. To further confirm the significance of the selected abnormal functional connections, we also used complex network analysis. Since the level of response activity in brain regions and the ability of functional connectivity between different brain regions can reflect the degree of brain disorders, the results have the potential to provide evidence for accurate diagnosis and further for precision medicine learning of such kinds of psychiatric diseases.
# BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF
## 3 Data Processing and Validation
### Network and Processing Strategy  
The data from a network of 103 globally distributed multi-GNSS (MGEX) [26] stations in 2022 were utilized for orbit estimation validation, with all stations capable of tracking BDS signals. The distribution of stations is illustrated in Figure 4.  
The validation process involves using the upgraded PANDA software package [27], which includes a real-time orbit estimation procedure on a Linux server. The data processing strategy for orbit estimation is detailed in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Applied Models** \\ \hline _Observation modelling_ & \\ Observation & BDS ionosphere-free combinations of code and phase measurements. \\ Sample rate & 30 s. \\ Elevation mask & 7\({}^{\circ}\). \\ \multicolumn{3}{l}{A priori precision of 0.02 cycles and 1.5 m for raw phase and code,} \\ Weighting & respectively. 1 for \(E>30\), otherwise 2\(\sin(E)\) where \(E\) means the \\ \multicolumn{3}{l}{elevation angle.} \\ \hline _Corrections_ & \\ Satellite phase center & igs14.atx [28]. \\ Receiver phase center & igs14.atx [28]. \\ Phase wind up & Corrected [29]. \\ \multicolumn{3}{l}{Saastamoinen model for wet and dry hydrostatic delay with} \\ \multicolumn{3}{l}{Troposphere a priori model} & Global Mapping Function (GMF) mapping functions without \\ \multicolumn{3}{l}{gradient model [30]} \\ \multicolumn{3}{l}{Station displacements} & Solid Earth tides, pole tides, and ocean tides are corrected \\ \multicolumn{3}{l}{according to IERS Conventions 2010 [31].} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Modeling and parameterization strategy for orbit determination.  
Figure 4: Distribution of stations for precise orbit determination for BDS on 325 th day of 2022. The red points denote the position of stations.
# Analytical Selection of Leakage Inductance for Single-Phase Dual Active Bridge Converters
## 3 Reactive Power Command Generation and Calculation of Leakage Inductance
### Calculation of Leakage Inductance  
In a DAB DC/DC converter, the power transfer is inversely proportional to both the frequency and the leakage inductance. Given that the frequency remains constant, the maximum required leakage inductance for the designed power requirement can be determined via  
\[L_{\text{max}}=\frac{8V_{in}^{2}}{\pi^{2}\omega_{s}\cdot P_{rated}}. \tag{30}\]  
Typically, the single-phase shift (SPS) method [18] is employed to calculate the necessary leakage inductance. However, the proposed approach enables the developed power controller-based strategy to utilize a significantly smaller leakage inductance for the same power requirement. Figure 7 illustrates the comparison of the required leakage inductance for both approaches across various rated power levels. The input and output voltages are taken as 108 V and 250 V, respectively, with a switching frequency of 30 kHz.  
Figure 8 presents a graphical representation detailing how reactive power varies in response to changes in the primary voltage when applied to different leakage inductance values at full load. These values include the optimal leakage inductance (\(L_{emax}\)), as well as both a 10% reduction (0.9 \(L_{emax}\)) and a 10% increase (1.1 \(L_{emax}\)) from the optimal value. The red dashed line indicates the physical limitation of the DC-link voltage. The graph clearly shows that when the converter is operating with a leakage inductance 10% above the optimal level, the primary voltage (indicated by \(\blacksquare\)) required for eliminating the reactive power of the first harmonic components extends beyond the system's physical capacity. Conversely, with a leakage inductance that is 10% below the optimal value, the required primary voltage (indicated by \(\bullet\)) stays within its physical limit. It is important to note that with a leakage inductance that exceeds the designated requirement, it becomes impossible to achieve zero reactive power due to the first harmonic components. Under these circumstances, to ensure compliance with primary side voltage constraints, the command for reactive power is calculated according to the maximum permissible primary voltage, thereby adapting to the physical limitations and maintaining proper converter functionality.  
Figure 7: Leakage inductance for SPS and proposed law across various rated powers.
## 4 Case Study
### Impact on Cost of Peak Regulation with Nuclear Power  
In this part, three schemes are designed for comparative analysis.  
(1) Scheme one  
Nuclear power units do not participate in peak-shaving scheduling and maintain stable operation at full power.  
(2) Scheme two  
Nuclear power units participate in peak-shaving scheduling of the power system according to the output pattern of \"12-3-6-3\". The peak regulation depth of two nuclear power units is set in advance at 30%.  
(3) Scheme three  
The method of peak-shaving scheduling of the power system is the same as in scheme two. The optimization solution is based on the joint optimal peak-shaving scheduling model. The optimization cost results are shown in Figure 3.  
Figure 3: Cost comparison among peak regulation operation types.  
Figure 2: The hourly load demand and wind power prediction.  
As can be seen in Figure 3, scheme three has the lowest total operating cost. At this time, the optimal peak-shaving depth of the two nuclear power units is 0% and 29.8%, respectively. Shorter start-stop operation times are the main reason for the reduced operational costs. For example, compared to scheme one, the one-time stop operation cost of thermal power unit 3 (g3) is reduced in schemes two and three with peak-shaving of nuclear power units.
# Study on Mechanical Behavior of a New Three-Dimensional Sensor Under Rock Rheology
## 2 Three-Dimensional borehole Full Stress Monitoring Theoretical Analysis
\begin{aligned} \sigma_{\rho}{}^{\prime}=cos^{2}(\theta-V) \sigma_{x}+sin^{2}(\theta-V)\sigma_{y}+2 cos(\theta-V)sin(\theta-V)\tau_{xy}\\ \sigma_{\theta}{}^{\prime}=sin^{2}(\theta-V)\sigma_{x}+cos^{2}(\theta-V) \sigma_{y}-2 cos(\theta-V)sin(\theta-V)\tau_{xy}\\ \sigma_{z}{}^{\prime}=\sigma_{z}\\ \tau_{\theta z}{}^{\prime}=cos(\theta-V)\tau_{xy}+\sin(\theta-V)\tau_{yz} \\ \tau_{\theta z}{}^{\prime}=-sin(\theta-V)\tau_{xz}+cos(\theta-V)\tau_{yz} \\ \tau^{\prime}_{\rho\theta}=\left(cos^{2}(\theta-V)-sin^{2}(\theta-V) \right)\tau_{xy}+cos(\theta-V)sin(\theta-V)(\sigma_{y}-\sigma_{x})\end{aligned} \right. \tag{21}\]  
From this, the compensation values of the measured stress components can be obtained, which are recorded as radial stress compensation \(\Delta\sigma^{\prime}{}_{\rho}\), polar stress compensation \(\Delta\sigma^{\prime}{}_{\theta}\), axial stress compensation \(\Delta\sigma^{\prime}{}_{z}\) and radial shear stress compensation \(\Delta\tau^{\prime}{}_{\rho\theta}\):  
\[\left\{ \begin{aligned} \Delta\sigma^{\prime}{}_{\rho}=\Big{[}\frac{ \sigma_{x}+\sigma_{y}}{2}\Big{(}1-\frac{d^{2}}{\rho^{2}}\Big{)}-cos^{2}(\theta- V)\sigma_{x}-sin^{2}(\theta-V)\sigma_{y}\Big{]}+\big{(}\frac{\sigma_{x}-\sigma_{y}}{2} \Big{(}1-4\frac{d^{2}}{\rho^{2}}+3\frac{d^{4}}{\rho^{4}}\Big{)}cos2\theta\\ -2 cos(\theta-V)sin(\theta-V)\tau_{xy}\big{)}+\tau_{xy}\Big{(}1-4 \frac{d^{2}}{\rho^{2}}+3\frac{d^{4}}{\rho^{4}}\Big{)}sin2\theta\\ \Delta\sigma^{\prime}{}_{\theta}=[\frac{\sigma_{x}+\sigma_{y}}{2} \Big{(}1+\frac{d^{2}}{\rho^{2}}\Big{)}-cos^{2}(\theta-V)\sigma_{x}-sin^{2}( \theta-V)\sigma_{y}]+[-\frac{\sigma_{x}-\sigma_{y}}{2}\Big{(}1+3\frac{d^{4}}{ \rho^{4}}\Big{)}cos2\theta\\ +2 cos(\theta-V)sin(\theta-V)\tau_{xy}]-\tau_{xy}\Big{(}1+3\frac{d^{4}}{ \rho^{4}}\Big{)}sin2\theta\\ \Delta\sigma^{\prime}{}_{z}=-v[2\frac{(\sigma_{x}-\sigma_{y})\rho^{2 }}{\rho^{2}}cos2\theta+4\tau_{xy}\frac{\rho^{2}}{\rho^{2}}sin2\theta]\\ \Delta\tau^{\prime}{}_{\rho\theta}=\Big{[}\frac{\sigma_{x}+\sigma_{y}}{2} \Big{(}1+\frac{d^{2}}{\rho^{2}}-\frac{3d^{4}}{\rho^{4}}\Big{)}sin2\theta+\tau _{xy}\Big{(}1+\frac{2d^{2}}{\rho^{2}}-\frac{3d^{4}}{\rho^{4}}\Big{)}cos2\theta \Big{]}-[cos^{2}(\theta-V)-sin^{2}(\theta-V)\tau_{xy}\\ +cos(\theta-V)sin(\theta-V)\big{(}\sigma_{x}-\sigma_{y}\big{)}]\end{aligned} \right. \tag{22}\]  
It can be seen from the above formula that only the four stress components of \(\sigma_{\rho}{}^{\prime}\), \(\sigma_{\theta}{}^{\prime}\), \(\sigma_{z}{}^{\prime}\) and \(\tau^{\prime}{}_{\rho\theta}\) of each measuring point in the eight measuring points are needed to express the size, direction and inclination angle of the six stress components at the center point of the borehole. The Working Principle and Mechanical Properties Analysis of the Three-Dimensional Borehole Full Stress Monitor
# Fake Base Station Detection and Link Routing Defense +
## 3 Background and Primer
### Digital 5G-AKA and Non-Access Stratum (NAS) Setup  
In 5G, the user equipment establishes the NAS-layer connection with the core network after the mutual authentication and key agreement (known as 5G-AKA) between them, as shown in Figure 2a (blue-shaded), which uses public key cryptography. 4G does conduct AKA between the user equipment and core network, similar to 5G, but uses symmetric cryptography, unlike 5G. 5G therefore provides stronger security than 4G. The earlier generations before 4G, i.e., 2G and 3G, do not implement or support AKA. In 5G-AKA, the user equipment encrypts its subscriber identity using the core network public key that is installed into its USIM card and sends it to the core network in the registration request message. After receiving the registration request from the user equipment, the core network verifies the user equipment subscriber identity using its private key and sends an authentication request to the user equipment with authentication parameters. The user equipment authenticates the core network using the authentication parameters and sends an authentication response (success or rejection) to the core network. After mutual authentication between the user equipment and the core network, they negotiate ciphering and integrity-protection algorithms for subsequent communication using the security mode procedure. After this procedure, both the RRC- and NAS-layer connections use ciphering and integrity-protected algorithms.  
5G and 4G AKA build some resistance against fake base stations. More specifically, even if the fake base station can get the victim user to connect initially, the user can attempt to connect to the backend core network. In 5G AKA, the core network sends a digitally signed message after RRC as a part of the mutual authentication between the core network and the user (including the subscription verification of the user). Once it has received the digitally signed message, the user can verify the digital signature to authenticate the connection. However, such a defense consumes the processing and energy resources of the mobile user in a disproportionately large manner so that the threat impact and the resource consumption (the digital communications after RRC and radio medium access control (MAC) and communicating to the backend, which is multiple hops away) is significantly larger and disproportionate to the attacker effort (SIB1 injection at RRC).
# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
## 3 Methodology
### An Image Processing Module  
#### 3.2.1 A Defogging Filter  
The dark channel prior defogging filter is an effective image-defogging algorithm that removes fog effects by estimating the dark channel prior to an image, thereby restoring the image's clarity and contrast [13]. Based on the atmospheric scattering model, a fog image can be constructed as follows:  
\[I(x)=J(x)t(x)+A(1-t(x)) \tag{1}\]  
where \(I(x)\) represents the fog image, and \(J(x)\) denotes the scene radiance (clean image). \(A\) is the global atmospheric light, and \(t(x)\) is the medium transmission map, defined as follows:  
\[t(x)=e^{-\beta}d(x) \tag{2}\]  
where \(\beta\) represents the scattering coefficient of the atmosphere, and \(d(x)\) denotes the scene depth.  
Select the top 0.1% brightest pixels in the dark channel image and use the corresponding pixels in the original image to estimate the atmospheric light \(A\). For each pixel in the input image, select the minimum value among all color channels within a local window. This forms the dark channel image.  
\[J^{dark}(x)=\min_{y\in\Omega(x)}\biggl{(}\min_{c\in\Omega(x)}I^{c}(y)\biggr{)} \tag{3}\]  
where \(J^{dark}\) is the dark channel image, \(\Omega(x)\) represents the local window around pixel \(x\), and \(I^{c}(y)\) is the pixel value of color channel \(c\).  
The transmission map \(t(x)\) describes the portion of light that is not scattered and reaches the camera. It is estimated as follows:  
\[t(x)=1-\omega J^{dark}(x)/A \tag{4}\]  
In this context, \(\omega\) is a hyperparameter that is optimized through backpropagation by the multi-scale adaptive network to improve the performance of the defog filter for foggy image detection.
## 3 Results
### Interannual Dynamics  
The long-term trends of MODIS, BESS, and VPM GPP products in China over the eight climate zones from 2001 to 2015 are shown in Figure 6. From the perspective of the whole country of China, both BESS and VPM GPP data exhibited an increasing tendency in the past 15 years compared to the MODIS GPP. The correlation between BESS and VPM GPP attained an r = 0.85. Especially, GPP of all these products increased significantly with time in the Northeast China regions (I and II) and in the northwestern area (III). Meanwhile, the correlations between BESS and VPM GPP were apparently higher, ranging from 0.81 to 0.92, compared to correlations between MODIS and VPM GPP, of r = 0.35 to 0.89. In addition, Figure 6 shows that MODIS GPP did not capture the interannual variations over half of China's area, including the Northeast China region (I and II) and the Southern China area (VII and VIII). However, the rising trends had strong correlations between BESS and VPM products values  
Figure 4: Seasonal dynamics of multiyear average MODIS GPP, BESS GPP, and vegetation photosynthesis model (VPM) GPP at an 8-day interval from 2001 to 2015 across China. DOY represents “day of the year”. The error bars mean the standard error.  
Figure 5: Gray rectangles and red dots show the intercomparison between the MODIS and BESS GPP products against the VPM GPP estimates at an 8-day period, respectively. The dashed line represents the 1:1 line, and the red and gray lines represent the linear fits.  
(r = 0.74-0.86). Overall, BESS GPP is more consistent with VPM GPP than MODIS GPP on the annual time scale.