esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model Description and Problem Statement
### DQN Model for RAN-Level Slice Admission Control
#### 3.4.1 State Space  
At predetermined time intervals \(t\), the DQN agent observes the system state by constructing a state vector  
\[s_{t}=(\mathrm{SR},\mathrm{RF}),\]  
where the following are defined:  
* SR contains the resource requirements at both the RAN and MEC.
* RF is a binary indicator denoting whether the system has sufficient resources to fulfill the request (1 if sufficient; it is 0 if otherwise).  
Figure 4: Slices admission control algorithm.  
The state vector includes the parameters of Table 2:  
This formulation ensures that the DQN agent is fully informed of the current resource availability and network demands, allowing it to make precise allocation decisions.
## 2 Materials and Methods  
### Materials  
Three types of polyol were used in synthesizing TPUs. The researchers performed grouping based on structure and performed detailed experimental comparisons. The three function groups were polyester, polyether, and polycarbonate. Poly (butylene adipate) diol (PBA) is a linear polyester polyol (Lydye Chemical, Mn = 1000 g/mol). Poly (tetramethyl-lene ether) glycol (PTMG) is a type of linear polyether (Lydye Chemical, Mn = 1000 g/mol), and polycarbonate diol (PCDL) is a type of carbonate polyol (Tosho NIPPOLLAN 981, Mn = 1000 g/mol). Their structural differences could be used to achieve different performances in the PU/TPU industry. The structures of the three types of polyol are provided in Table 1.  
For a polymerization flow diagram of the TPUs, refer to Scheme 1. Three types of polyols and chain extender were used to prepare six types of TPUs. The final polymer product in the diagram is the polymerization product of PTMG and 1,3-BDO.  
Regarding the hard chain section of the TPUs, 1,4-butanediol (Alfa Aesar, Ward Hill, MA, USA) and 1,3-butanediol (Alfa Aesar, Ward Hill, MA, USA) were used. The aromatic diisocyanate 4,4\({}^{\prime}\)-diphenylmethane diisocyanate (MDI) used originated from BASF, of Germany. All the materials were used immediately after they were received. Each TPU formula used the same mole ratio, and the NCO/OH of the hard chain segment is R. To control the influence of hard chain segment on the TPUs, we designed two compounding ratios, R = 1.00 and R = 1.02; this was done to determine whether the increase of hard chain segments would affect structure and performance. For the constituents of the two hard chain segments, see Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Polyols** & **Abbreviation** & **Chemical Structure of the Main Components** \\ \hline Poly(butylene adipate) diol & PBA & HO \\ Poly(tetramethylene ether) glycol & PTMG & H \\ Polycarbonatediol & PCDL & HO \\ \hline \hline \end{tabular}
\end{table}
Table 1: The types of polyol used in synthesizing thermoplastic polyurethanes (TPUs).
## 4 Results  
As the LR method was employed to produce the landslide susceptibility map, it is necessary to analyze the multicollinearity of landslide conditioning factors [91]. Currently, the tolerance (TOL) and variance inflation factor (VIF) are mostly used in multicollinearity analysis [92, 93]. Generally, a TOL \(<\) 0.1 or a VIF \(>\) 10 indicate multicollinearity [72]. In the study, the results indicate that there are no multicollinearities among the 16 landslide conditioning factors (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Collinearity Statistics**} \\ \cline{2-5}  & **Tolerance** & **VIF** & **Tolerance** & **VIF** \\ \hline Elevation & 0.543 & 1.843 & 0.507 & 1.970 \\ Profile curvature & 0.897 & 1.115 & 0.897 & 1.115 \\ Plan curvature & 0.824 & 1.214 & 0.801 & 1.248 \\ Slope angle & 0.764 & 1.308 & 0.759 & 1.317 \\ Slope aspect & 0.912 & 1.096 & 0.904 & 1.106 \\ SPI & 0.524 & 1.907 & 0.464 & 2.157 \\ TWI & 0.830 & 1.205 & 0.834 & 1.200 \\ STI & 0.479 & 2.086 & 0.426 & 2.346 \\ Distance to roads & 0.817 & 1.224 & 0.823 & 1.214 \\ Distance to rivers & 0.952 & 1.050 & 0.924 & 1.083 \\ Distance to faults & 0.926 & 1.080 & 0.731 & 1.368 \\ Lithology & 0.729 & 1.372 & 0.734 & 1.362 \\ Rainfall & 0.741 & 1.349 & 0.802 & 1.247 \\ Soil & 0.805 & 1.242 & 0.687 & 1.456 \\ NDVI & 0.847 & 1.180 & 0.639 & 1.566 \\ Land use & 0.640 & 1.562 & 0.507 & 1.970 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multicollinearity analysis.  
Figure 2: Thematic maps of landslide conditioning factors.
# Drug-Food Interactions with a Focus on Mediterranean Diet
## 2 Pharmacological Mechanisms of Drug-Food Interactions
### Pharmacokine Drug-Food Interactions (PK-DFIs)
#### 2.1.4 Elimination  
Drug's removal from the body is occurring through excretion processes for an unmetabolized drug or its metabolite. Kidneys are the main organ of drug elimination and drugs (or their metabolites) are filtered through them into the urine and excreted. A secondary pathway is through the bile where the bio-transformed drugs in the liver are excreted in the bile and eliminated through enterohepatic circulation in the feces (Figure 3). Renal elimination of drugs involves the processes of glomerular filtration (drug passive diffusion from blood stream into the urine), proximal tubular secretion, and distal tubular reabsorption from passive diffusion or active transport. For renal elimination, two factors are playing roles for potential DFIs. The first is the urinary pH since drug's ionization--depending on the alkaline or acidic environment--plays a role in its capability to passively diffuse through membranes, trapped in ionized form and excreted, whereas the non-ionized form is re-absorbed. The second factor is the transporter-mediated interactions which can impact drugs' elimination profile or accumulation in renal tubular cells, leading to drug-induced nephrotoxicity. Diet habits play a key role in the acidity or alkalinity of urine pH. Vegetables and fresh fruits are mostly related with alkaline diets, whereas high protein content diets usually acidify the urine [103]. An alkaline urine pH can reduce the excretion rate of weakly basic drugs and an acidic urine pH from diet may decrease the excretion amount for weakly acidic drugs. Mementine and flecainide are two characteristic examples where their elimination profile is changing based on the urine pH conditions [104; 105].
# Distribution and Deposition of Cylindrical Nanoparticles in a Turbulent Pipe Flow
## 5 Results and Discussion
### Penetration Efficiency
#### 5.3.4 Relationship of Penetration Efficiency and Related Parameters  
It is necessary to build a relationship between the penetration efficiency and related parameters in order to effectively characterize the penetration efficiency. As shown in Figures 11 and 12, the penetration efficiency was inversely proportional to the particle aspect ratio \(\lambda\), the ratio of pipe length to pipe diameter \(L\)/\(D\) and the Reynolds number (\(Re=Q/vD\), where \(Q\) is the flow rate, \(v\) is the fluid viscosity and \(D\) is the pipe diameter). So, we combine \(\lambda\), \(L\)/\(D\) and \(Re\) into a dimensionless synthetic parameter \(\eta=D\times 10^{9}/\lambda LRe\). Based on the above numerical data, we could establish the following expression of penetration efficiency PE:  
\[\text{PE}=67.34105+4.43965\ln(\eta-2.65815) \tag{15}\]  
Figure 13 shows the penetration efficiency as a function of the related synthetic parameter. \(\bullet\): expression (15).
## 4 Technical Analysis of MDSEM  
### Problems Existing in WPPCS  
The panel structure includes the development of the semi-monocque shell structure. The strengthening components and the shell of the panel do not need to be riveted, screwed, welded or connected by other methods but are manufactured as a whole, semifinished product. Compared with the semi-monocoque shell structure, the panel structure greatly improves the load-bearing efficiency, fatigue resistance and reliability, effectively reduces the structural quality, and has the advantages of less parts, short process cycle, high strength and stiffness [2, 3, 4].  
Figure 7 is a typical schematic diagram of welded panel parts. The welded panel parts can be divided into stiffener zone and weld zone. The structural feature of the stiffener zone is composed of stiffeners and shells, which are integral. The structural feature of the weld zone is only composed of thickened shells. Obviously, the stiffeners in the stiffener zone cannot extend to the weld seam.  
The WPPCS is a whole structure welded by several frames and several panels on the basis of precise control of the dimension of the welding interface. Figure 8 is a typical WPPCS; the typical local structure in the Figure 8 consists of four parts, which are panel 1, panel 2, panel 4 and frame 3. The four parts are connected into a whole structure by welding. The stiffeners of each panel are discontinuous in the weld zone [2].  
Obviously, due to the limitations of structural characteristics and process constraints, the WPPCS has no stiffeners at the weld zone, and the discontinuous stiffeners between the panels lead to the non-direct continuity of the force transmission path, which is an insurmountable unreasonable structural design [2; 14; 16; 17]. On the other hand, the weight of the frame of the WPPCS accounts for more than 50% and cannot be significantly reduced. The above two shortcomings make it impossible to further improve the lightweight level of the WPPCS, which hinders the application of the panel structure in the MDSEM.
## 4 MAMZI with Phase Noise Stabilization  
In a MAMZI with phase noise, the phases of the system \(\phi_{k}^{*}\) (see Equation (2)) are affected by the phase noise \(\phi_{k}^{noise}\). To stabilize it, an externally manipulable control phase (\(\phi_{k}^{c}\)) will be used. Then, the phases of the system will be modeled as \(\phi_{k}^{*}=\phi_{k}^{c}+\phi_{k}^{noise}\) with \(k\in\{1,2,...,N\}\). Note that if it is possible to observe if \(\phi_{k}^{c}=-\phi_{k}^{noise}\) (for all \(k\)), then \(M_{N}\) converges to the identity matrix. Also, as \(MBS^{T}\cdot I\cdot MBS=I\), then it can be seen that the intensity output from (3) is given by \(l^{out}=[l_{0}..,0]\). Therefore, it is possible to stabilize multi-parameter phase noise on an MAMZI by maximizing one of its outputs, which is obtained when a control phase is the negative magnitude of the phase noise applied to each arm. Then, the proposed phase noise stabilization method is obtained from the following optimization:  
\[argmax\{I_{k}^{out}(\phi_{k}^{c})\} \tag{7}\]  
Figure 2 shows the proposed scheme for the phase noise stabilization process. Here, the plant is the MAMZI whose inputs are the information in the internal phases \(\phi_{l}^{*}=[\phi_{1}^{*},\phi_{2}^{*},...,\phi_{N}^{*}]\), and the outputs encompass the resulting intensity \(I_{l}^{out}\), which depends on the optical interference produced by the phases \(\phi_{l}^{*}\). The input optical intensity (\(I^{in}\)) is a constant.  
In this scheme, the so-called \"phase noise\" (\(\phi^{noise}\)) is modeled as a disturbance, which is compensated by an actuator of the control system that we call \"phase control\" (\(\phi^{C}\)). The controller operates on the error (\(e\)) obtained when comparing the output (\(I_{l}^{out}\)) with a reference value (\(I_{ref}\)). That is, we calculate the error as \(e=I_{l}^{out}-I_{ref}\) for the given control system shown in Figure 2, where \(I_{l}^{out}\) represents the output to be optimized. Note that phases, intensities, errors, and references are vectors of order N.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 5 Simulation Results  
### Benchmark Optimization Problem  
To evaluate the efficacy and performance of the proposed XOR BGSA with repository, it is tested against several benchmark problems. In optimization of benchmark problems, 20 bits are used to decode numerical values into the binary domain. The number of solutions considered (population size) is equal to 25, maximum number of generations are then selected to be equal to 1000. Two sets of comparisons are presented. In the first set of optimization, the proposed XOR BGSA is compared to BGSA [16], IBPSO [17], BPSO [15], BGWO [18] and BDA [19]. The main goal in these simulations is to demonstrate how the modifications proposed in this paper including the addition of XOR to the acceleration equation for BGSA as well as the repository effect its performance. As in the real world, an optimization problem may be applied to a wide range of different functions including unimodal and multimodal problems, the proposed optimization algorithm is tested against both classes of optimization problems. The ability of the proposed optimization algorithm in dealing with problems with multiple local minimas is required to be investigated as well. For having such study, some of the optimization problems considered as the benchmark optimization problems include multiple local minimas. The benchmark problems considered are a collection of functions with single optimum solutions and functions with multiple local optimums. Other than these specifications, it is required to illustrate the performance of the proposed optimization algorithm in dealing with non-differentiable problems. The specifications of benchmark optimization problems are mentioned in front of them for ease of reference. The parameter \(N\) represents the dimension of these optimization problems which is considered to be equal to either \(1,\ 2,\ 5\) and \(10\) to test the optimization algorithms for different dimensions. These dimensions are multiplied by the number of bits taken for decoding real values, which results in optimization in \(20,\ 40,\ 100\) and \(200\) dimensional space. The mathematical formulas for the test functions as well as their specifications are as follows [20].
# Document Image Shadow Removal Based on Illumination Correction Method
## 3 The Dark Illumination Correction Net
### Overall DICNet Structure  
The DICNet structure is shown in Figure 2, and mainly includes three blocks: feature extraction, shadow detection block, and the illumination correction block. The feature extraction module first extract visual features from the input images; then, the shadow detection block obtains the soft shadow mask of the input image to accurately capture shadow intensity information, which provides a key aid for the subsequent shadow correction. The illumination correction block further learns the illumination intensity requiring correction with the assistance of the soft shadow mask. It supplements the illumination intensity in the shadow area and ultimately outputs a document image without shadows. Overall, DICNet achieves shadow removal in document images based on optical principles and ensures an accurate and high-quality removal effect through two key steps: shadow detection and illumination correction.  
The feature extraction block includes four convolution layers with the ReLU activation function and SSCABlock. It first extracts features from the input images. Then, the DICNet is divided into two branches: one for shadow mask detection and the other for dark illumination correction based on the soft shadow mask.  
The shadow detection block consists of one convolution layer, one SSCABlock, and three convolution layers in sequence, and the ReLU activation function is used. The shadow detection block outputs a soft shadow mask \(\mathbf{M}\). At the same time, the outputs of shadow detection and feature extraction blocks are fused as the input of the illumination correction block.  
The illumination correction block consists of two convolution layers, one SSCABlock, two convolution layers, and one SSCABlock in sequence. It outputs an illumination-corrected image \(\mathbf{B}\).  
The shadow-free image outputted by DICNet can be calculated as follows:  
\[\mathbf{O}=\mathbf{I}+(\mathbf{1}-\mathbf{M})\otimes\mathbf{B} \tag{3}\]  
Figure 2: DICNet structural diagram.  
where \(\otimes\) denotes channel-weighted operation, i.e., \((\mathbf{1-M})\) is not produced with each \(\mathbf{B}\) channel; \(\mathbf{O}\) is the output shadow-free image; \(\mathbf{I}\) is the input shaded image; and \(\mathbf{1}\) is the matrix where all elements are 1.  
The parallel shadow detection and illumination correction branches are tightly coupled through shared feature extraction and cross-branch guidance mechanisms. Specifically, the shadow detection branch provides soft shadow masks that dynamically guide the illumination correction branch to focus on shadow regions while preserving non-shadow content: a critical requirement for document images where text and details must remain intact.
# Enhancing E-Grocery-Delivery-Network Resilience with Autonomous Delivery Robots
## 3 Problem Definition
### Deterministic Model
#### 3.2.2 Time Constraints  
This model primarily accommodates the emergency situation where late deliveries could lead to disruption to a person's normal life. To maximize the likelihood of not delaying a person's life necessities, groceries should be received by the customers within the specified hours without impacting their normal life. We used a hard deadline constraint of customers instead of a soft deadline. However, this was different from traditional emergency logistics with a very tight time window. For our problems, late deliveries were translated to soft deadlines, and we included a violation penalty to represent the loss due to late arrival.  
In this study, each customer has a deadline of order \(tl_{f}\), which means that an ADR should arrive at the customer \(f\) no later than \(tl_{f}\). If an ADR is delayed \(\delta_{fk}^{\mathit{ADR}}\) at customer \(i\), the arrival time at customer \(j\) might be delayed. In this study, we assumed that a delay happening at \(i\) would be independent from \(j\). In other words, one delay at a customer would not result in delay at another customer. The loading time of each customer at a micro hub was denoted as \(\sigma_{f},f\in F\). The speed of the vehicle type \(v\) was \(u^{v}\).  
\[T_{ik}^{\mathit{cum}}=0\;\forall\;i\in O,k\in K_{\mathit{sum}} \tag{14}\]  
\[T_{ik}^{\mathit{ADR}}+\tau_{ij}^{\mathit{ADR}}-T_{jk}^{\mathit{ADR}}\leq \left(1-\psi l_{ijk}^{\mathit{ADR}}\right)L\forall\;i,\;j\in N_{2},\;k\in K_{ \mathit{ADR}} \tag{15}\]  
\[0\leq T_{ik}^{\mathit{ADR}}\leq\sum_{j\in N_{2}}\psi l_{ijk}^{\mathit{ADR}}L \forall\;i\in F,\;k\in K_{\mathit{ADR}} \tag{16}\]\[0\leq T_{ik}^{ADR}-\delta_{ik}^{ADR}\leq tl/\sum_{j\in N_{2}}y_{ijk}^{ADR}\forall i \in F,\;k\in K_{ADR} \tag{17}\]  
\[T_{ik}^{cum}+\rho_{ij}^{cum}-T_{jk}^{cum}\leq\left(1-x_{ijk}^{cum}\right)L \forall i,\;j\in N_{1},\;k\in K_{cum} \tag{18}\]  
\[0\leq T_{ik}^{cum}\leq\sum_{j\in N_{1}}x_{ijk}^{cum}L\forall i\in S,\;k\in K_ {cum} \tag{19}\]  
Constraint (14) limits all vans leaving the depot at time 0. Constraint (15) restricts the time continuity based on the visiting sequence of nodes in the second echelon. Constraint (16) ensures that if an ADR does not visit a node, the visit time is zero. Constraint (17) represents that if an arrival time at a customer is after the deadline, a delay has occurred (\(\delta_{ik}^{ADR}=1\)). Constraint (18) restricts the time continuity based on the visiting sequence of nodes in the first echelon. Constraint (19) places the limit that if the van does not visit a node, the visit time is zero.
# Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography
## 3 Experiment  
### Simulation Experiments  
This paper uses Head data as simulation data to validate and evaluate the proposed geometric parameter calibration method. The 360-projection data were generated with a step size of a 1-degree interval, and projection data with definite geometric parameter deviation and projection data without geometric parameter deviation were generated by changing parameters. The reconstructed image of the calculated geometric parameters is compared with the reconstructed image of the actual set truth value to verify the validity and accuracy of the proposed method.  
As can be seen from Table 2, the calibration results of the proposed method for geometric parameters are closer to the true value parameters. Figure 4 shows the 2D slices obtained by 3D reconstruction of the true parameters, uncorrected parameters, parameters calculated by [PERSON] et al.'s and RANSAC method, and parameters calibrated by this method. It can be seen from the figure that there are no obvious geometric artifacts in the calibration results of the three methods. Compared with the uncorrected image, the quality of the reconstructed image is significantly improved. To better demonstrate the correction effect, Figure 5 shows the pixel values of the red line position in Figure 4. As shown in Figure 5, due to the slight deviation between RANSAC, the proposed method, and the true value parameters, the reconstruction results basically coincide with the true value results. In addition, the method proposed in this paper is closer to the pixel values of corresponding points in the results of truth-value parameter reconstruction.  
Figure 4: The Head data reconstruction results with different geometric parameters.  
Figure 5: Comparison results of voxel values in red line position in Figure 4. (**a**) The pixel value corresponding to the (I) position in Figure 4. (**b**) The pixel value corresponding to the (II) position in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{u_{0}}\) **(Pixel)** & \(\mathbf{\eta}\) **(\({}^{\circ}\))** \\ \hline Truth parameters & 255.000 & 2.000 \\ \hline Parameters Calculated by Xiao & 255.000 & 2.137 \\ \hline Parameters Calculate by RANSAC & 255.012 & 1.951 \\ \hline Parameters Calculated by our method & 254.997 & 1.995 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The calibration parameters in the Head data compared with the true value, [PERSON] [18], RANSAC and ours.