esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to Classical Computing Device: Resonant Tunneling Diode
### Implementation of Condition 1 and Condition 2  
We consider that the RTD depicted in Figure 2 corresponds to the modified design of the device that accommodates a large number of \(N\) electrons simultaneously inside. In this particular device, enlarging the lateral area \(A\) is enough to enlarge the number of electrons inside the RTD. We can reasonable assume that the contact _prepares_ the wave packets of each electron in a similar way so that the **condition 1** of our protocol in Equation (1) is easily satisfied. Certainly, a point that requiresfurther discussion is how to ensure that the many-body wave function of electrons in the active region of this modified device can be approximated by the non-interacting wave function in Equation (1). In the Appendices A and B some qualitative indications are mentioned. A different solution for minimizing the undesired Coulomb and exchange interactions will be discussed in next section, for quantum computing.  
To satisfy the second condition we have to ensure that all electrons inside the device contribute equivalently to the measured value of the electrical current. A detailed discussion of the conditions that have to be satisfied by the quantum device to ensure this point is provided in the Appendix C. We anticipate here that such discussion is greatly simplified by associating to each electrons a quantum (Bohmian) trajectories, whose positions and velocities are well-defined even in absence of a measurement, in addition to the orthodox wave function. Then, the electrical current due to the simultaneous contribution of all electron leads to the following expression,  
\[I_{exp}(t)=\sum_{i=1}^{N(t)}I^{i}(t)=\sum_{i=1}^{N(t)}q\frac{v_{x}^{i}(t)}{L}, \tag{7}\]  
As discussed in the Appendix C the above expression assumes that the lateral dimensions of the (two terminal) electron device are much larger than the longitudinal one, and that the contacts are formed by metals with a fast screening time in comparison with that on the active region. The **condition 2** of our protocol to define the current operator as in Equation (2), requires to define the output instantaneous value of the current as,  
\[I_{T}(t)=\frac{I_{exp}(t)}{N(t)}, \tag{8}\]  
The value of \(N(t)\approx N\) can be assumed to be proportional to the enlarged lateral area, and the value \(I_{T}(t)\) computed from \(I_{exp}(t)\) after knowing the ratio of the modified/unmodified areas. The detailed discussion on the definition of the current and the implications of **condition 2** is given in Appendix C.
## 3 Results and Discussion
### Effect of Soil Salinity on Biomass Yield and Sodium Content in F. arundinacea
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Plots** & **Na\({}^{\star}\) Content in Dry Biomass** & **Soil Salinity** \\  & **(mg\(\cdot\)g\({}^{-1}\))** & **(g NaCl\(\cdot\)L\({}^{-1}\))** \\ \hline FA\_1 & 2.72 \(\pm\) 0.30 & 6.85 \(\pm\) 0.18 \\ FA\_2 & 2.45 \(\pm\) 0.11 & 6.24 \(\pm\) 0.09 \\ FA\_3 & 7.17 \(\pm\) 1.18 & 5.76 \(\pm\) 0.38 \\ FA\_4 & 6.45 \(\pm\) 0.27 & 4.75 \(\pm\) 0.21 \\ FA\_5 & 8.38 \(\pm\) 0.21 & 4.02 \(\pm\) 0.15 \\ FA\_6 & 7.71 \(\pm\) 0.73 & 2.82 \(\pm\) 0.25 \\ FA\_7 & 8.57 \(\pm\) 0.48 & 1.49 \(\pm\) 0.06 \\ FA\_8 & 8.66 \(\pm\) 0.88 & 0.70 \(\pm\) 0.06 \\ \hline Mean \(\pm\) SD & 6.51 \(\pm\) 2.37 & 4.08 \(\pm\) 2.10 \\ V [\%] & 36.44 & 51.55 \\ \hline \hline \end{tabular} SD—standard deviation, V—variability coefficient (n = 8 study plots).  
\end{table} 
Table 2: Salt concentration in plants and soil on experimental plots of tall fescue. Figure 2: Correlation between soil salinity and air-dry biomass yield of tall fescue on experimental plots in the field cultivation. It was interesting that in our field experiment a relatively strong negative correlation was found between the salt content in the soil and above-ground plant parts of _F. arundinacea_ (Figure 3). However, a detailed analysis of this graph shows that the sodium content in plants changes abruptly, staying at a similar level in quite a wide range of soil salinity: 0.70-1.49, 2.82-5.76 and 6.24-6.85 g NaCl:L\({}^{-1}\). It confirms the results of the pot experiments with irrigation using saline water [43]. At the lowest level of water salinity, the sodium content in plants of _F. arundinacea_ 'Fawn' clearly increased compared to the non-saline control, while at higher concentrations of NaCl, it strongly decreased, remaining at a similar range. In addition, in these studies, an inverse relationship was found for potassium ions in tall fescue.
## 3 The Sound Source Monitoring Combing Stereo Vision and Beamforming  
The stereo vision system and microphone array are used to collect optical and acoustic information at the same time in the space where the sound source is located. The target area of the sound source obtained by the former is used as the search domain for the beamforming sound source 3D localization, thereby narrowing the searching range and reducing the amount of computation. Specific steps are as follows:  
1. Use binocular cameras and microphone arrays to acquire images and sound signals at the same time.
2. Segment the sound signals corresponding to the frame rate of the depth image, that is, the time matching of the image and the sound.
3. Segment the target from the depth image, and obtain the 3D information of the target surface.
4. Convert the 3D information of the target surface to the acoustic array coordinate system to complete the spatial matching of the two systems.
5. The 3D information of the target surface after coordinate conversion is used as the beam-forming reconstruction surface, and the focused beamforming is performed to reconstruct the sound field of the target 3D surface. The sound pressure reconstruction point where the maximum output is located is the three-dimensional coordinate of the sound source.
6. Import the depth of the three-dimensional coordinates into the sound intensity scaling method to obtain the sound power of the hemisphere on the side of the sound array.
## 3 Proposed Scheme  
To explain our proposed method more clearly, first, we will briefly describe the conventional SOR method [27], AOR method [30], and Chebyshev acceleration method [31],sequentially, and then propose a two-stage CAOR scheme to balance the performance and complexity trade-off by performing fewer iterations to further accelerate convergence.
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 5 Results and Analysis
### Task Execution of Legitimate Nodes
The results shown in Figure 7 verify that the tasks executed by TETES are more than the other three schemes and it executes 41% of tasks when a number of offloaded tasks is 10 and the fog node has fixed processing capacity. However, the percentage of executed tasks in TETES decreases to 26% with the increase in the number of offloaded tasks to 20, however, it is more than the other three schemes. This is due to the fixed processing capacity of the fog node as it can not execute more tasks. On the other hand, STF, LTF, and FCFS execute 30%, 19%, and 27% of offloaded tasks when the number of offloaded tasks is 10 and decreases to 19%, 9.5%, and 14% respectively when a number of offloaded tasks rise to 20.
## 4 Principles and Solving Steps of the Multiplier Path Following Method
### Principles
#### 4.1.2 Introduction of the Relaxed KKT Condition  
According to the KKT conditions of the quadratic programming problem, a sufficient and necessary condition for \(\mathbf{T}\) to be the optimal solution is the existence of a Lagrange multiplier \(\mathbf{y}=(y_{1},y_{2},\cdots,y_{\mathrm{m}})^{\mathrm{T}}>0\) and a slack variable \(\mathbf{w}=(w_{1},w_{2},\cdots,w_{\mathrm{m}})^{\mathrm{T}}>0\) such that  
\[\left\{\begin{array}{l}\mathbf{Qx}+\mathbf{C}-\mathbf{A}^{\mathrm{T}}\mathbf{y}=0\\ \mathbf{Ax}-\mathbf{w}-\mathbf{b}=0\\ y_{i}w_{i}=0\ (i=1,2,\cdots,m)\\ \mathbf{y}\geq 0,\mathbf{w}\geq 0\end{array}\right. \tag{17}\]  
where \(\
abla_{\mathrm{T}}\Phi=\frac{\partial\Phi}{\partial\mathbf{T}}\); \(\mathbf{Q}=\mathbf{H}+\beta\mathbf{C_{\mathrm{e}}^{\mathrm{T}}\mathbf{C_{\mathrm{e}}}}\); \(\mathbf{C}=\mathbf{c}-\mathbf{C_{\mathrm{e}}^{\mathrm{T}}(\mathbf{v}+\beta\mathbf{f})}\); \(m\) is the number of rows in the coefficient matrix of inequality constraint equation \(\mathbf{A}\).  
The relaxed KKT conditions, derived by substituting \(\mathbf{YWe}=\mu\mathbf{e}\) for \(y_{i}w_{i}=0\ (i=1,2,\cdots,m)\), are as follows:  
\[\left\{\begin{array}{l}\mathbf{QT}+\mathbf{C}-\mathbf{A}^{\mathrm{T}}\mathbf{y}=0\\ \mathbf{AT}-\mathbf{w}-\mathbf{b}=0\\ \mathbf{YWe}=\mu\mathbf{e}\\ \mathbf{y}\geq 0,\mathbf{w}\geq 0\end{array}\right. \tag{18}\]  
where \(\mathbf{Y}=\mathrm{diag}(y_{1},y_{2},\cdots,y_{\mathrm{m}})\); \(\mathbf{W}=\mathrm{diag}(w_{1},w_{2},\cdots,w_{\mathrm{m}})\); \(\mathbf{e}=(1,1,\cdots,1)_{\mathrm{m}}^{\mathrm{T}}\); \(\mu\) is the center path parameter; and \(\mu=\delta\frac{\mathbf{v}^{\mathrm{T}}w}{m}>0\), \(0<\delta<1\).  
For each center path parameter \(\mu\), Formula (18) has the unique solution \(\{T(u),\mathbf{y}(u),\mathbf{w}(u)|\mu>0\}\), named the center path. The path following method attempts to approach the optimal solution gradually through iterating along the direction of the center path.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 5 Conclusions  
On the whole, we aimed to address the problem that traditional line segment detection methods easily fail in the presence of complex illumination and noise interference. To further improve the performance of space target segment detection, we propose a robust line segment detector for space applications called ST_LSD in this paper. The proposed detector can effectively solve the phenomenon of line segment fracture caused by complex illumination and noise interference. The image data are collected through a semiphysical simulation platform, and the MNS-LS dataset is constructed precisely to verify the performance of ST_LSD. Our quantitative and qualitative evaluation results show that ST_LSD is superior to traditional detectors in terms of detection accuracy and visual effect under complex illumination and noise interference.  
There are still some limitations to the proposed approach. It can be seen from the experimental results that the speed of ST_LSD needs to be further improved. In future studies, we suggest three extensions to the work presented in this paper: (1) optimize the active grouping and line segment verification of ST_LSD to be more suitable for real-time application scenarios; (2) expand and optimize the existing MNS-LS dataset to meet more mission requirements; and (3) extend ST_LSD's algorithm to detect the circles, which  
Figure 15: The computation times of different methods on dataset images.  
occur frequently in space targets, to adapt to more and more extensive space on-orbit service missions.  
Conceptualization, [PERSON] ([PERSON]) and C.H.; Methodology, [PERSON] ([PERSON]) and L.W.; Software, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Data curation, H.L. and R.D.; Writing--original draft preparation, X.Z. (Xingxing Zhang) and C.H.; Writing (review and editing), X.Z. (Xingxing Zhang) and L.W.; Validation, X.Z. (Xingxing Zhang). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province, grant number BK20180465, and the Shanghai Aerospace Science and Technology Innovation Foundation, grant number SAST 2021-026, SAST 2020-019.  
Not applicable.  
The authors acknowledge the above funds for supporting this research and all editors and reviewers for their helpful comments and suggestions, which greatly improved this manuscript.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### The Structure of the Proposed Model  
The architecture of the time-sensitive service transmission model based on the optical interconnect parallel subnet comprises two main components, as illustrated in the diagram below: the model construction part and the algorithm-solving part.  
It is described in detail in Figure 4.  
1. The introduction of the optical interconnect parallel subnet aims to overcome the limitations of traditional Low Earth Orbit constellation routing for the transmission of time-sensitive services. The latter faces some obstacles that are difficult to overcome at the transmission level. Detailed explanations of the optical interconnect parallel subnet can be found in Section 3.2.  
Figure 3: Flowchart of reinforcement learning principles.  
2. The introduction of the CQF (Credit-based Queueing and Forwarding) gate control scheduling in TSN (Time-Sensitive Networking) technology aims to enhance the stability and controllability of time-sensitive service forwarding and processing in LEO (Low Earth Orbit) satellite networks. Detailed explanations of the CQF gate control schedule can be found in Section 3.3.
3. The mathematical modeling of routing in LEO satellite networks based on CQF scheduling is conducted because the various factors and constraints of this issue align with classical industrial processing control problems, making it amenable to transformation. Details of the mathematical modeling are discussed in Section 3.4.
4. The introduction of reinforcement learning for solving the satellite routing scheduling problem is aimed at leveraging scenarios that align with the application conditions of reinforcement learning, thereby reducing the likelihood of traditional solutions getting stuck in local optima. Detailed information on the utilization of reinforcement learning is provided in Section 3.5.
5. The performance of the time-sensitive service transmission model based on the optical interconnect parallel subnet is compared with traditional routing scheduling algorithms and conventional solving methods to demonstrate its advantages. Specific experimental results are presented in Section 4.
## 6 Benefits, Challenges, and Limitations of CFD in the Food Industry
### Cost and Hardware Requirements  
The implementation of CFD in the food industry is often constrained by significant cost and hardware requirements. High-performance computing systems are necessary for simulating complex food processes, especially when dealing with large 3D models or transient phenomena. The computational power required for these simulations translates to increased costs in terms of software licensing, the maintenance of hardware infrastructure, and energy consumption [151; 156].
# Distributed Software Build Assurance for Software Supply Chain Integrity
## 7 Related Work  
### Remote Code Attestation  
Our work is related to remote code attestation that verifies the integrity of the running code on the users' system, adopting several hardware-based, software-based, and hybrid techniques. Hardware-based attestation techniques [18; 19; 20] involve hardware components, e.g., trusted platform module (TPM), software guard extensions (SGX), and software-based attestation techniques [21; 22; 23; 24], that involve a challenge and response protocol in real-time, time synchronization, empty memory space filling, etc. An advancement of these techniques, hybrid attestation techniques [25; 26; 27], integrates both hardware-based and software-based techniques, minimizing the hardware cost with embedded processing components, e.g., field-programmable gate array (FPGA), memory protection unit (MPU) and micro-controller unit (MCU), etc. Our scheme goes beyond the research in remote code attestation in two ways: first, our scheme verifies the integrity of a software in different stages of the supply chain; second, our scheme does not rely on the centralized authority by using a distributed blockchain.  
Figure 6: Bandwidth performance in assurance resolutions per second in comparison between our scheme and remote server locations.
# Metrics Related to Confusion Matrix as Tools for Conformity Assessment Decisions
## 3 Results and Discussion
### Guard Banding by Confusion Matrix
For \(w\)\(\epsilon\)\([-0.001548,\ 0.001003]\) hold \(kappa_{Im}\geq kappa_{Im}\). Analogously, for the MCC statistic, it holds that for \(w\)\(\epsilon\)\([-0.0025,\ -0.001808]\) and for \(w\)\(\epsilon\)\(\langle 0.001282,\ 0.0025]\) worth \(MCC_{Im}<MCC_{In}\). For \(w\)\(\epsilon\)\([-0.001808,\ 0.001282]\), it holds that \(MCC_{Im}\geq MCC_{In}\). For both metrics, the subscript \(In\) represents the label for the initial model, and the subscript \(Im\) represents the label for the improved model. It naturally arises that it is possible to make use of these changes in the behavior of metrics to determine the optimal length of the guard band. The list of possible values of the guard band, together with the corresponding parameters, is demonstrated in Table 2.  
By comparison of the data from Table 2, it can be concluded that the most favorable situation for minimizing the producer's risk for the initial model is at the point of change, as \(MCC_{In}=MCC_{Im}=0.6614\). Therefore, the optimal guard band for the initial model in the case of minimizing the global risk of the producer is equal to \(w=-0.001808\) mm. In this case, the producer's risk is the smallest and equal to \(R_{P}=2.85\%\), but the consumer's risk is the highest for all proposed lengths of the guard bands. The most favorable situation with the improved model is for \(precision=recall=F1=0.9991\). Then, the producer's and consumer's global risk are the smallest and amount to \(R_{C}=R_{P}=0.086\%\). Therefore, when minimizing the producer's risk, in the improved model, it is optimal to set the length of the guard band to \(w=-0.0004649\) mm. In practice, consumer risk minimization models are more common because the manufacturer wants to deliver the highest-quality product to the customer. A low-quality product damages the manufacturer's reputation and increases its costs due to product returns. For the model of consumer risk minimization, in the initial model, the recommended optimal length of the guard band is \(w=0.001282\) mm. This is exactly the length of the guard band for which the MCC curves for the initial and improved model intersect and holds \(MCC_{In}=MCC_{Im}=0.6444\). This same length of the guard band is the optimal length in the improved model, too. With the consumer risk minimization model, in that case, the global producer's and consumer's risks are the lowest and the amounts \(R_{P}=0.38\%\) and \(R_{C}=0.019\%\). Although the lengths of the guard band are smaller in the intersection points of the kappa metric compared to the values of the guard band in the intersection points of the MCC metric, the optimal is to take the length of the guard band that corresponds to the intersections of the MCC curves, because in that case, the values of the risks are lower. Generally, recent research shows that the MCC metric has better properties than the kappa statistic, [40,50].
## 3 Disturbance-Rejection Controller Design Based On Linear Extended State Observer (LESO)
### Stability Analysis  
We used Lyapunov stability theory to analyze the stability of the system with disturbance \(d\) and control law (27).  
**Theorem 1**.: _Given Systems (1) and (2) under disturbance \(d\), for target attitude \([\xi_{d},\omega_{d},\dot{\omega}_{d}]\), with feedback control law (27), attitude error \(\tilde{\xi},\tilde{\omega}\) and disturbance estimation error \(\tilde{d}\) are globally asymptotically stable._  
Proof.: We rewrite System (23) to the state space form  
\[\begin{split}\dot{x}&=Ax+Bd^{\prime}\\ \tilde{d}^{\prime}&=Cx+d^{\prime}\end{split} \tag{29}\]  
where \(x=\begin{bmatrix}x_{1}\\ x_{2}\end{bmatrix},A=\begin{bmatrix}0&-\omega_{o}^{2}\\ 1&-2\omega_{o}\end{bmatrix},B=\begin{bmatrix}-\omega_{o}^{2}\\ 0\end{bmatrix},C=\begin{bmatrix}0&-1\end{bmatrix}\), and we can see \(z_{2}=-x_{2}=Cx\).  
Assume disturbance \(d^{\prime}=J^{-1}d\) is constant. From Equation (22), we know the direct current gain from \(d^{\prime}\) to the estimation of the disturbance \(z_{2}\) is 1, so we get \(-CA^{-1}B=1\).  
We then get  
\[\begin{split} z_{2}&=Cx\\ &=C(e^{At}x(0)+\int_{0}^{t}e^{A(t-\tau)}B\;d^{\prime}\;d\tau)\\ &=C(e^{At}x(0)+e^{At}A^{-1}Bd^{\prime}-A^{-1}Bd^{\prime})\\ &=Ce^{At}(x(0)+A^{-1}Bd^{\prime})-CA^{-1}Bd^{\prime}\\ &=Ce^{At}(x(0)+A^{-1}Bd^{\prime})+d^{\prime}\end{split} \tag{30}\]  
From Systems (23) and (30), we get  
\[\tilde{d}^{\prime}=d^{\prime}-z_{2}=-Ce^{At}(x(0)+A^{-1}Bd^{\prime}) \tag{31}\]  
Assume the upper bound of \(d^{\prime}\) is \(||d^{\prime}||\leq\tilde{d}\), and choose  
\[\begin{split} c&=a_{0}+\frac{a_{0}\tilde{d}^{\prime}} {||x(0)||}\\ &-\omega_{o}<\lambda<0\end{split} \tag{32}\]  
where \(a_{0}>0\), which satisfies \(||\tilde{d}^{\prime}||\leq c||x(0)||e^{\lambda t}\), so \(\tilde{d}^{\prime}\) can exponentially converge to 0. Based on the Lyapunov converse theorem [33], there exists a Lyapunov function \(V_{2}\), which satisfies  
\[\begin{split} a_{1}||\tilde{d}||^{2}\leq V_{2}\leq a_{2}|| \tilde{d}||^{2}\\ \dot{V}_{2}&\leq-a_{3}||\tilde{d}||^{2}\end{split} \tag{33}\]  
where \(a_{1},a_{2},a_{3}>0\).  
Define a new Lyapunov function \(V_{3}=V_{1}+V_{2}\), and then we get  
\[\dot{V}_{3}\leq||\tilde{\omega}|||\tilde{d}||-k_{2}||\tilde{\omega}||^{2}-a_{ 3}||\tilde{d}||^{2} \tag{34}\]  
Then, select \(k_{2}\geq\frac{1}{4a_{3}}\), and \(\dot{V}_{3}\leq 0\).
## 2 Mechanical Analysis Model for TSWs  
### Background  
The Jubankeng tungsten mine, located in Lianping County, Guangdong Province, China, is a major underground source of high-quality wolframite ore. The deposit comprises steeply dipping, thin to extremely thin ore bodies, including both single-vein and vein-belt types. Since commencing operations in the early 1980s, the mine has developed 12 levels ranging in depth from 280 m to 805 m.  
The surrounding rock consists of metamorphic sandstone and hard rock types such as slate, brilliant porphyry, and gabbro. These rocks feature mostly wavy and flat-closed joints with relatively few horizontal joints. Some joints are filled with gravel, quartz, and mud. Overall, the rock mass is moist, with occasional water seepage. Figure 1 presents a comprehensive 3D model of the Jubankeng mine.  
Currently, the mine uses a medium-deep hole sublevel drill and bending method for segmental ore extraction. Stopes are aligned with the ore veins and measure approximately 50 m in length and height. Sill pillars range from 2.5 to 3.0 m, inter-chamber pillars from 3.0 to 8.0 m, and crown pillars from 2.0 to 5.0 m. In the case of narrow-vein mining, such as for gold or tungsten, the stope width is small and the roof generally experiences no significant deformation or failure [18; 19]. However, the temporary stope wall (TSW) is subjected to asymmetric, localized loads and has a large exposed area, making it particularly vulnerable to instability due to nearby gosafs. Figure 1b shows a typical residual ore recovery project at the Jubankeng mine. Figure 1c illustrates a case where the planned stopes completely overlap with existing gosafs, while Figure 1d shows a case where such overlap is only partial.  
Hence, a key consideration in the recovery of residual resources is determining the optimal spatial relationship between planned stopes and gosafs to ensure the structural stability of the recovery area.