esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods for Manipulation Protection and Detection
### [PERSON] et al.[36]  
According to the authors, one of the most intriguing recently proposed approaches to active image integrity assurance is the study \"Proactive Image Manipulation Detection via Deep Semi-Fragile Watermark\" by [PERSON] et al [36]. This method is based on the concept of a transparent watermark embedded in the image in the form of a mask, which can later be extracted to detect potential modifications--see Figure 6. A key element of the solution involves two neural network models responsible for generating and recovering the watermark. The watermark itself is another image, transformed into a feature matrix and added to the original image. The algorithm's concept resembles data-hiding techniques such as \"image-in-image\" [56; 57]. However, the decoding component of the hidden image has been modified so that any changes in the watermark indicate the regions of the image that have been manipulated.  
The authors proposed using the UNet architecture [58] for both models in their solution. These networks are trained in a supervised process utilizing two loss functions: hiding loss, which focuses on maintaining the high transparency of the watermark, and revealing loss, which ensures the accuracy of extracting the hidden image while accounting for manipulations introduced during training. The manipulation process is simulated by an attack module consisting of two groups of operations. The first group includes natural processing operations commonly occurring in transmission pipelines, such as JPEG compression [59] and Gaussian blur. Artifacts introduced by these operations should  
Figure 6: [PERSON] et al. [36] high level architecture.  
not be interpreted by the decoding network as distortions and must be ignored during the detection of manipulated regions. The second group of the attack module simulates intentional manipulations of selected image regions, such as inpainting. For each such manipulation, a binary mask is generated to indicate the attack region, which assists in validating the detection results by the decoding component.  
This algorithm represents an advancement over previous methods because it avoids the inclusion of specific manipulations in the training pipeline, which could potentially limit the ability to generalize across various sabotage operations. Instead, it randomly selects and modifies a region within the container image to simulate the effect of manipulation, using this modification as the ground truth label during training. The output of the attack module is therefore a manipulated image and a binary mask indicating the altered regions. The task of the decoding network is to recover the hidden image from the carrier image (manipulated or unmanipulated). The original hidden image is then subtracted from the recovered image to extract the manipulated regions--assuming correct decoder performance, the manipulated areas should have values significantly different from zero.
# Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
## 4 Experiments  
### Dataset  
Our experiment utilized the first benchmark dataset for structured radiology report generation, Rad-ReStruct, which was constructed based on the semi-structured coding of the IU-Xray [33] dataset. By systematically integrating 3720 standardized chest X-ray images with 3597 reports, it formed a medical knowledge system containing over 180,000 fine-grained question-answer pairs, or over 180,000 sample quantities. The dataset construction process adopted a two-layer coding architecture: first, based on the semi-structured findings annotated by medical experts (utilizing 178 controlled vocabularies from MeSH [34] medical subject headings and RadLex [35] radiology terms, covering anatomy, pathological signs, foreign bodies, and attribute descriptions), the term combinations in the original unstructured reports (e.g., \"infiltration/lung/upper lobe/left/patchy/mild\") were parsed; then, through full-patient data mining, a three-level decision tree-style report template was constructed, with the top level determining the existence of abnormalities (e.g., \"Is there any opacity in the lungs?\"), the middle level locating anatomical and pathological features (e.g., \"Are there any signs of pneumonia in the lungs?\"), and the bottom level describing morphological attributes (e.g., \"What are the boundary characteristics of the abnormal area?\"), and the term combinations not found were removed to form a streamlined template. This template innovatively introduced a clinical logic constraint mechanism, containing 96 medical entity categories, dynamically marking questions as single-choice or multiple-choice types, while retaining a \"no selection\" option to simulate real diagnostic scenarios. The data were divided following an 80-10-10 stratified strategy, with patient ID hash mapping ensuring data isolation across subsets. As the sole dataset currently providing medical images, structured reports, and hierarchical question-answer triples, Rad-ReStruct established a new benchmark for explainability in radiology report generation tasks through clinical logic constraints and strict evaluation protocols, and it is also the only dataset that can provide medical images, hierarchical question-answer pairs, and is ideally suited our experimental design.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 10 Routing and Energy Allocation in Rechargeable WSNs with Multiple Sources and Destinations
### Solution Method  
A heuristic method named DualNet was proposed that obtains an infeasible upper bound and a feasible lower bound and iteratively solves the problem until it converges to the optimal solution infinity. First an upper bound was obtained on the value of the objective function at the optimal solution of the problem after a long period of time (theoretically infinity). The solution that gives the upper bound is obtained by an infeasible energy allocation i.e., energy allocation that is higher than the average replenishment rate. The energy allocation (and hence the routing solution) are the same over all time slots and is more than the average replenishment rate, yielding infeasiblity. Using the energy allocation obtained, a routing sub-problem that is strictly convex and computationally easier than the original problem is obtained because of the decoupling of the time component. This requires solving the problem every time slot.  
The lower bound solution is obtained by assigning a feasible energy value in each time slot for each node. The energy assignment for a node is the minimum of either the average harvested energy or the available battery energy (including the instantaneous replenishment for a given time slot). This assignment is done by each node on its own and hence is a distributed energy assignment.  
Using the energy assignment values the routing subproblem that obtains the lower bound, is again a similar routing subproblem to that of the upper bound subproblem.  
Dual decomposition was used to solve the problem which enabled a distributed implementation of the scheme. Each source node solves two problems, one to determine the amount of data to inject in the network at a given time slot \(t\), \(x_{s}\left(t\right)\), the other subproblem to determine the routes and their flows, \(w_{ij}\left(t\right)\). All the nodes that are not sources of data, and only responsible for relaying data over multiple hops, solve the routing problem only. The dual variables are computed using the subgradient algorithm.
# A Frame-to-Frame Scan Matching Algorithm for 2D Lidar Based on Attention
## 2 System Overview  
The ASM algorithm can be divided into three modules: the attention area selection module, the pose solving module, and the results verification and key frame selection module. The overall system framework is shown in Figure 1.  
The ASM algorithm takes 2D lidar data and wheel odometry as its input. The wheel odometry is used to determine the robot's movement distance relative to the previous keyframe, and when the distance exceeds the threshold, a new key frame is generated, and the algorithm enters the matching process. First, the attention area selection module selects the area that the matching algorithm needs to pay attention to while ignoring all other areas; secondly, the algorithm uses the laser data obtained from the attention area to match the reference frame and to obtain the pose of the current key frame; Finally, the coincidence degree of the attention area is evaluated according to the matching results, and if the coincidence degree exceeds the threshold, then the matching pose is used as the final pose; otherwise, the odometer pose is used as the final pose.  
The pseudo code of the proposed method is shown in Algorithm 1. The three modules are further described in Sections 3-5.  
```
Input: reference frame lidar data \(\text{ld}_{\text{rf}}\), reference frame pose \(P_{\text{rf}}\), reference frame attention area \(AA_{\text{rf}}\), wheel odometry pose \(P_{o}\), now frame lidar data \(ld_{\text{rf}}\) \(\delta\)-distance threshold for keyframe insertion. if\(|P_{\text{rf}}-P_{o}|>\delta\)then  Calculation the attention area of now frame \(AA_{\text{rf}}\)  Calculation the pose of now frame \(P_{\text{rf}}\) using the lidar data covered by attention area; ifLegalPoseCheck(\(P_{\text{rf}}\)) == falsethen return;  end ifisReferenceFrameNeedUpdate() == falsethen return;  end if\(ld_{\text{rf}}=ld_{\text{rf}}\) \(P_{\text{rf}}=P_{\text{rf}}\) \(AA_{\text{rf}}=AA_{\text{rf}}\);  end
```  
**Algorithm 1**The Proposed Algorithm.
## 5 The SDRE Integral Type Servo Controller  
### SDRE Integral Type Servo Controller Theory  
In order to perform command following, the SDRE controller can be implemented as an integral servomechanism as demonstrated in [13]. The implementation of the integral servo can be accomplished by augmenting the state \(\mathbf{x}\) as  
\[\mathbf{\check{x}}=\begin{bmatrix}\mathbf{x}_{T}^{T}&\mathbf{x}_{N}^{T}&\mathbf{x}_{I}^{T} \end{bmatrix}^{T} \tag{29}\]  
where \(\mathbf{x}_{I}\) is the vector integral state of \(\mathbf{x}_{T}\). It is desired for the vector components of \(\mathbf{x}_{T}\) to track a reference command \(\mathbf{r}_{c}\). The augmented system then becomes  
\[\mathbf{\dot{\check{x}}}=\mathbf{\tilde{A}}(\mathbf{\check{x}})\mathbf{\check{x}}+\mathbf{\tilde{B }}(\mathbf{\check{x}})\mathbf{u} \tag{30}\]  
\[\mathbf{\tilde{A}}(\mathbf{\check{x}})=\begin{bmatrix}\mathbf{A}(\mathbf{x})&\mathbf{0}\\ \mathbf{E}&\mathbf{0}\end{bmatrix}\mathbf{\tilde{B}}(\mathbf{\check{x}})=\begin{bmatrix}\mathbf{B }(\mathbf{\check{x}})\\ \mathbf{0}\end{bmatrix} \tag{31}\]  
The SDRE integral type servo controller is then given by  
\[\mathbf{u}=-\mathbf{\tilde{R}}(\mathbf{\check{x}})^{-1}\mathbf{\tilde{B}}(\mathbf{\check{x}})^{T }\mathbf{\tilde{P}}(\mathbf{\check{x}})\mathbf{\check{x}} \tag{32}\]  
\[\mathbf{\hat{x}}=\begin{bmatrix}(\mathbf{x}_{T}-\mathbf{r}_{c})^{T}&\mathbf{x}_{N}^{T}&(\mathbf{x }_{I}-\int\mathbf{r}_{c}\mathrm{d}t)^{T}\end{bmatrix}^{T} \tag{33}\]  
In order for the SDRE to have a solution, the pointwise detectability condition must be satisfied. This is accomplished by penalizing the integral states with the corresponding non-zero diagonal elements of \(\mathbf{\tilde{Q}}(\mathbf{\check{x}})\).
# Optimal Concordant Tests
## 2 Methods
### Optimal Concordant Test
Repeat this process for \(B\) times (e.g., \(B=10^{5}\)) to get the empirical null distribution of the test statistic, which will be used to approximate the \(p\)-value of \(T_{CLRT}\):\(p=\#[T_{i}\geq T_{CLRT}(p_{1},\cdots,p_{n})]\,/\,B\), where \(T_{i}\) (\(i=1,\cdots,B\)) is the test statistic from the \(i^{th}\) resampling, and \(T_{CLRT}(p_{1},\cdots,p_{n})\) is the observed test statistic from the data. For \(T_{CLRT}\), we have the following result [10]:  
**Proposition 3**. : _Under the conditions specified in (5), the CLRT-based test, \(T_{CLRT}\), is asymptotically UMP._We now propose an optimal concordant test. Denote \(T_{CLRT}^{L}=T_{CLRT}(P_{1},P_{2},\cdots,P_{n})\), and \(T_{CLRT}^{R}=T_{CLRT}(1-P_{1},1-P_{2},\cdots,1-P_{n})\), and then define the following concordant test statistic:  
\[T_{CLRT}^{C}=\max\Big{(}T_{LRT}^{L},T_{LRT}^{R}\Big{)}. \tag{7}\]  
Note that the null distributions of \(T_{LRT}^{L}\) and \(T_{LRT}^{R}\) are identical. For the concordant test, \(T_{CLRT}^{C}\), we have the following property. **Theorem 2**. : _Let \(X=\left(X_{1},\ \cdots,X_{n}\right)\in\left(0,1\right)^{n}\) be a random vector with independent components, and let \(T_{CLRT}^{L}=\underset{\alpha>0,\epsilon<1}{\max}\Big{[}n\alpha ln(1-c)+c \sum\limits_{i=1}^{n}F_{G(\alpha)}^{-1}(1-X_{i})\Big{]}\), \(T_{CLRT}^{R}=\underset{\alpha>0,\epsilon<1}{\max}\Big{[}n\alpha ln(1-c)+c\sum \limits_{i=1}^{n}F_{G(\alpha)}^{-1}(X_{i})\Big{]}\), and \(T_{CLRT}^{C}=max\big{(}T_{CLRT}^{L},T_{CLRT}^{R}\big{)}\). For any \(A\in\mathbb{R}\), let \(P_{L}=P\big{[}T_{CLRT}^{L}>A\big{]}\), \(P_{R}=P\big{[}T_{CLRT}^{R}>A\big{]}\), and \(P_{C}=P\big{[}T_{CLRT}^{C}>A\big{]}\). Then, \(P_{L}+P_{R}-P_{L}P_{R}\leq P_{C}\leq P_{L}+P_{R}\)._  
Proof. : First notice that \(f(X)=-\underset{\alpha>0,\epsilon<1}{\max}\Big{[}n\alpha ln(1-c)+c\sum\limits _{i=1}^{n}F_{G(\alpha)}^{-1}(1-X_{i})\Big{]}\) and \(g(X)=\underset{\alpha>0,\epsilon<1}{\max}\Big{[}n\alpha ln(1-c)+c\sum\limits _{i=1}^{n}F_{G(\alpha)}^{-1}(X_{i})\Big{]}\) are both nondecreasing functions of associated random variables, \(X\). The rest of the proof is similar to that for Theorem 1. \(\square\)  
In addition, we have the following result for \(T_{CLRT}^{C}\). **Corollary 2**. : _Suppose \(X\sim U(0,1)^{n}\); for \(A>0\), let \(p_{A}=P\big{[}T_{CLRT}^{L}>A\big{]}\), and then \(2p_{A}-{p_{A}}^{2}\leq P\big{[}T_{CLRT}^{C}>A\big{]}\leq 2p_{A}\)._  
From Corollary 2, the \(p\)-value of the concordant test, \(T_{CLRT}^{C}\), defined in (7) can be estimated by the upper bound, \(2p_{A}\), where \(p_{A}\) is the \(p\)-value from the CLRT-based test, \(T_{CLRT}^{L}\), and can be estimated using the resampling method described previously. Again, when the true \(p\)-value is small, this approximation is very accurate.
# PET/CT Radiomics in Lung Cancer: An Overview
## 3 Applications
### Classification between Primary and Metastatic Lesions; Histological Subtyping  
Detailed lesion characterisation has important implications for the management of patients with lung cancer. Differential diagnosis between primary and metastatic lesions, for instance, is crucial for stratification as well as for establishing the optimal treatment strategy [70]. Likewise, correct identification of histological subtype has a strong influence on the outcome and determination of the most appropriate therapy [71; 72].  
In a retrospective study on a fairly large cohort of patients (\(n=545\)), PET texture features were able to differentiate between primary and metastatic lung lesions, whereas CT features were not [48]. PET radiomic features were also found to correlate with histological subtype (specifically adenocarcinoma vs. squamous cell carcinoma) in [21; 44], whereas, in [49; 52], radiomics signatures based on CT texture features were significantly associated with tumor histology.
# Free Surface Flow Simulation by a Viscous Numerical Cylindrical Tank
## 2 Mathematical Formulation  
In this study, the water and air flow movements were considered based on the two-phase incompressible fluid model in a cylindrical computational domain based on the one-fluid assumption. With that assumption, the discontinuous properties through the free surface, such as density and viscosity, are smoothed over a transition region of the finite thickness near the free surface, so the fluid motion of two fluids could be described as the flowing equations:  
\[\frac{1}{r}\frac{\partial ru}{\partial r}+\frac{1}{r}\frac{\partial v}{ \partial\partial}+\frac{\partial w}{\partial z}=0 \tag{1}\]  
\[\begin{array}{rl}\frac{\partial U}{\partial t}=&-\frac{1}{r}\frac{\partial( ruU)}{\partial r}+\frac{1}{r}\frac{\partial(vU)}{\partial\partial r}+\frac{ \partial(wU)}{\partial\partial r}\\ &+\frac{1}{r}\frac{\partial}{\partial r}(r\partial\frac{v}{\partial r})+\frac {1}{r}\frac{\partial}{\partial\theta}(v\frac{v}{\partial\theta})+\frac{ \partial}{\partial z}(v\frac{\partial U}{\partial\theta})+\frac{\partial}{ \partial z}(v\frac{\partial U}{\partial z})+S_{U}\end{array} \tag{2a}\]  
\[u:S_{U}=-\frac{1}{\rho}\frac{\partial P}{\partial r}-\frac{v}{r}(\frac{2}{r} \frac{\partial v}{\partial\theta}+\frac{u}{r})+\frac{v^{2}}{r}+\frac{1}{\rho }f_{br} \tag{2b}\]  
\[v:S_{U}=-\frac{1}{\rho}\frac{1}{r}\frac{\partial P}{\partial\theta}+\frac{v} {r}(\frac{2}{r}\frac{\partial u}{\partial\theta}-\frac{v}{r})-\frac{uv}{r}+ \frac{1}{\rho}f_{b\theta} \tag{2c}\]  
\[w:S_{U}=-\frac{1}{\rho}\frac{\partial P}{\partial z}-g+\frac{1}{\rho}f_{bz} \tag{2d}\]  
In the above equations, \(U=(u,v,w)\) is the velocity vector; \(S_{U}\) is the source term; \(P\) is the pressure; \(\rho\) and \(v\) are the density and kinematic viscosity; \(F_{b}=(f_{br},f_{b\theta},f_{bz})\) is the vector of the body force due to the surface tension; \(t\) is time; and \(g\) denotes the gravity acceleration.  
The free surface was contracted by the VOF method through marking both fluids with a scalar indictor \(F\) defined as the fraction of a control volume occupied by the liquid (\(0\ll F\ll 1\)). Using the definition of the volume fraction, this work represents the density and viscosity in the momentum equations as  
\[\rho=F\rho_{w}+(1-F)\rho_{a} \tag{3a}\] \[v=Fv_{w}+(1-F)v_{a} \tag{3b}\]  
where the subscripts \(w\) and \(a\) denote the water and air fluid. The free surface transport equation in terms of the volume fraction could be written as  
\[\frac{\partial F}{\partial t}+\frac{1}{r}\frac{\partial ruF}{\partial r}+ \frac{1}{r}\frac{\partial vF}{\partial\theta}+\frac{\partial wF}{\partial z}=0 \tag{4}\]With the surface normal represented by the gradient of the volume fraction, the unit normal vector for the free surface was given by  
\[n=\frac{\
abla F}{|\
abla F|} \tag{5}\]  
According to the Continuum Surface Force method proposed by [PERSON] et al. (1986), the smoothed body force used in the momentum equations was given by  
\[F_{b}=\sigma\kappa n\
abla F, \tag{6}\]  
where \(\sigma\) and \(\kappa\) are the coefficient of surface tension and the curvature of the interface respectively.
# Study on Single-Event Transient Hardness of Semi-Enclosed Gate NMOS
## 3 Analysis of Simulation Results  
### Simulation and Analysis of Single-Event Transients in Conventional NMOS vs. Semi-Enclosed NMOS  
From the current transient curve in Figure 4, it can be concluded that the pulse width of the traditional NMOS is 72 ps, while that of the semi-enclosed gate NMOS is 70 ps. The peak transient drain current of the traditional NMOS is 2.206 mA, compared to 2.102 mA for the semi-enclosed gate NMOS. This clearly demonstrates that both the pulse width and peak current of the semi-enclosed gate NMOS are smaller than those of the traditional NMOS,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Gate Bias Voltage (V) & 0 \\ Drain Bias Voltage (V) & 1 \\ LET(MeV\(\cdot\)cm\({}^{2}\)/mg) & 20 \\ Heavy Particle Incident Angle (\({}^{\circ}\)) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation conditions.  
Figure 3: Schematic diagram of heavy particle incidence position.  
indicating that the semi-enclosed gate NMOS exhibits superior suppression effectiveness against SET compared to the traditional NMOS.  
Further analysis of the drain charge characteristics of both NMOS types, as shown in Figure 5, reveals that, at 100 ps, the traditional NMOS loses a significantly larger amount of drain charge than the semi-enclosed gate NMOS. By 110 ps, as the devices recover to their steady states, the traditional NMOS also gains substantially more charge than the semi-enclosed gate NMOS. Since current arises from the movement of charges, which alters the charge quantity, a larger and more rapid change in charge quantity results in a higher current. Consequently, the current in the traditional NMOS is greater than that in the semi-enclosed gate NMOS.
# Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System
## 5 Subject Matter Validation  
### Methodology  
#### 5.1.1 Participants  
To validate the HMI design, interviews were conducted with six subject matter experts. The participants chosen came from a range of backgrounds. Three worked on the infrastructure and OLE side, two on the rolling stock side, and one human factors expert whose work focuses on the railway condition monitoring and maintenance processes. Of the infrastructure-focused participants, two worked at a strategic level on aspects of OLE, with the other managing the day-to-day operation of an intercity railway system. The final participant from the infrastructure side was a rail systems engineer specialising in railway electrification. From the rolling stock side, one participant worked as a project engineer for a rolling stock leasing company working with train operating companies to improve the reliability and safety of their trains, and the other managed the performance of a fleet operating on an intercity railway system. Two participants had direct experience of the Metro system under exploration, and all others had sufficient rail experience (including of metro operations) to understand the principles being presented, which was part of the selection process for interviewees. Participant experience in the rail sector ranged between 8 and 40 years.
## 3 Results
### Alpha Wave Detection Efficiency  
The effectiveness of the alpha wave detection system was assessed through tests conducted with three participants: one child and two adults, identified as Subjects 1, 2, and 3, respectively, with an average age of \(18\pm 7.81\) years. Before the experiment, informed consent was obtained from the participants or their legal guardians, as appropriate. The tests involved alternating periods of 10 s with eyes closed and 10 s with eyes open, repeated three consecutive times, resulting in 60 s per test. Detection occurred every 100 ms during the experiment, yielding 600 detections per test. Each participant completed three tests, resulting in 1800 detections per participant. The results were compared with the actual state of the participants during the tests, and the system's performance was evaluated based on the number of correct detections, as detailed in Table 1.  
Subject 1 showed a minimum value during Test 1, achieving a performance of 76%, while Subject 2 recorded the maximum value during Test 1, reaching a performance of 94%. Overall, Subject 2 demonstrated high levels of relaxation.  
The results demonstrate an average detection rate of 85%, reflecting a satisfactory identification of alpha waves and meeting the essential criteria for implementation in the serious game detailed in this article.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{3}{c}{**Detection Efficiency (\%)**} & \multirow{2}{*}{**Subject Average (\%)**} \\  & **T1** & **T2** & **T3** & \\ \hline
1 & 76 & 80 & 85 & 80 \\
2 & 94 & 92 & 90 & 92 \\
3 & 88 & 77 & 83 & 83 \\ \multicolumn{4}{c}{Overall Average of Participants} & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Alpha wave detection performance.  
Figure 11: Frequency spectrum from 8 to 13 Hz.