esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Formulation
#### 3.2.2 Formulation  
The proposed problem can be formulated as the following mixed integer program (MIP): Minimize:  
\[f=\sum_{\forall i\in I}\sum_{\forall k\in K}\sum_{\forall m\in M}\sum_{\forall p \in P_{m}}\left\{q_{i}\cdot y_{i}^{k}\cdot y_{m}^{k}\cdot\{[t_{m}^{k}-t_{i}^{k}] +h_{i}^{p}\cdot[DT(p)-T_{w}-t_{m}^{k}]\}\right\} \tag{1}\]  
\[\text{S.t.}:\underset{\forall i\in I}{\Sigma}y_{i}^{k}\geq 1,\forall k\in K \tag{2}\]  
\[\underset{\forall k\in K}{\Sigma}y_{i}^{k}=1,\forall i\in I \tag{3}\]  
\[\underset{\forall j\in I}{\Sigma}x_{jm}^{k}=1\forall k\in K\forall m\in M \tag{4a}\]  
\[\underset{\forall j\in I}{\Sigma}x_{mj}^{k}=0\forall k\in K\forall m\in M \tag{4b}\]  
\[\underset{\forall j\in I}{\Sigma}x_{jm}^{k}=0\forall k\in K\forall m\in D \tag{5a}\] \begin{table}
\begin{tabular}{c l} \hline \hline
**Indices** & \\ \hline \(i,j,m\) & Node(demand point, bus depot and rail station) index \\ \(k\) & Feeder bus index \\ \(p\) & Subway schedule trip index \\ \hline
**Sets** & \\ \hline \(I\) & Set of demand points \\ \(K\) & Set of feeder buses \\ \(D\) & Set of bus depots \\ \(M\) & Set of rail transit stations \\ \(P_{m}\) & Set of subway schedule trips at rail station \(m\); \(\forall m\in M\) \\ \hline
**Parameters** & \\ \hline \(q_{i}\) & Number of passengers at the demand point \(i\); \(\forall i\in I\) \\ \([l_{i}\cdot e_{i}]\) & The boarding time window of the demand point \(i\); \(\forall i\in I\) \\ \(h_{i}^{p}\) & The subway schedule trip \(p\) of rail station \(m\) chosen by \\ \(d_{ij}\) & Passengers at demand points \(i\); \(\forall m\in M\), \(\forall i\in I\), \(\forall p\in P_{m}\) \\ \(d_{ij}\) & Distance from the node \(i\) to node \(j\); \(\forall i,j\in I\cup D\cup M\) \\ \(t_{ij}\) & Travel time from the node \(i\) to node \(j\); \(\forall i,j\in I\cup D\cup M\) \\ \(DT(p)\) & Depature time of subway schedule trip \(p\) at rail station \(m\); \\ \(T_{w}\) & Walking time for passengers from the drop-off point of the rail station to the subway platform \\ \(T_{\text{max}}\) & Maximum travel time of feeder bus route \\ \(D_{\text{max}}\) and \(D_{\text{min}}\) & Maximum and minimum travel mileage of feeder bus \\ Q & Maximum capacity of feeder bus \\ \(H\) & A very large fixed value \\ \hline
**Decision Variables** & \\ \hline \(x_{ij}^{k}\) & Whether the node \(i\) precedes the node \(j\) on the vehicle k, or not; \\ \(\forall i,j\in I\cup D\cup M\), \(\forall k\in K\) \\ \(y_{i}^{k}\) & Whether the node \(i\) is covered by the vehicle k, or not; \\ \(t_{i}^{k}\) & The time of vehicle k arriving the node \(i\); \(\forall i\in I\cup D\cup M\) \\ \(q_{i}^{k}\) & Number of passengers at node \(i\) assigned to vehicle k; \\ \(\forall i\in I\cup D\cup M\) \\ \(U_{ik}\) & An auxiliary (real) variable for sub-tour elimination constraint in vehicle k; \(\forall k\in K\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters and variables in the mathematical model.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background  
RFI is a phenomenon where unwanted electromagnetic signals disrupt the operation of electronic systems. In aviation, the consequences of such interference are particularly critical, as industry relies heavily on precise and reliable communication, navigation, and surveillance systems. Understanding the theoretical underpinnings of RFI is essential for developing effective mitigation strategies to ensure aviation safety and efficiency.  
RF interference in aviation systems may result from several technical mechanisms. One such mechanism is receiver desensitization, where strong out-of-band signals suppress the sensitivity of adjacent weak signals, such as those from navigation beacons. Intermodulation occurs when two or more signals mix in a non-linear component (e.g., an amplifier), generating spurious frequencies that fall within the receiver band. Additionally, cross-modulation may arise when an unwanted signal modulates the amplitude of a desired carrier, leading to distorted reception. These effects are particularly relevant in densely packed RF environments around airports, where multiple emitters coexist in proximity.
# Numerical Modeling of Venous Outflow from the Cranial Cavity in the Supine Body Position
## 3 Results  
In total, we performed flow simulations in 14 models of the cerebral venous outflow. The geometry of these models, particularly the cross-sectional area and length of the upper stenosis, morphology of the valve located downstream, flow volumes, and the degree of shift from the jugular to the vertebral pathway, is presented in Table 1.  
Our previous flow simulations in the model, in which the IJV did not exhibit any strictures, revealed that 84% of the fluid flew through the IJV. In the current study, with a longer time of simulated flow, we obtained a similar result (90%). In our 3-dimensional models with two alternative outflow pathways, a shift from the jugular to the vertebral pathway was interpreted as the sign of hemodynamically relevant flow resistances evoked by stenotic lesions in the IJV (nozzle-like upper stricture or stenotic abnormal valve located downstream). We also interpreted the degree of such a shift as the level of hemodynamic relevance of the stenosis. Quantitative results of the simulations are presented in Table 1.  
We found that a significant shift from the jugular to the vertebral pathway can be evoked by both upper and lower lesions (Table 1). Regarding the nozzle-like strictures located upstream, flow resistance resulting in the shift of the flow towards the vertebral outflow route seemed to be more significant if such a stenosis was shaped in the following ways:  
* Narrower (Models 6 and 9);
* Longer (Model 8);
* Eccentrically positioned (Models 7, 10, and 13);
* Irregularly shaped (Models 5 and 12).  
Regarding stenoses of the jugular valve located downstream, the shift was more pronounced if the distance between stiff valve leaflets was smaller (Model 3 vs. Model 4). Abnormally shaped valve forming a septum (Model 11) resulted in the most significant flow disturbances. Some qualitative examples of the flow pattern in the settings of different strictures are shown in Figures 8-11.  
Figure 8: Model of the cerebral venous outflow comprising the internal jugular vein (**above**) and the vertebral venous plexus (**below**); flow is from the left to the right. There is a significant stenosis at the beginning of the jugular pathway and stenotic valve downstream.  
Figure 11: Model of the cerebral venous outflow comprising the internal jugular vein (**above**) and the vertebral venous plexus (**below**); flow is from the left to the right. Upper part of the jugular pathway is unrestricted, while downstream there is a septum nearly completely obstructing the outflow.  
Figure 10: Model of the cerebral venous outflow comprising the internal jugular vein (**above**) and the vertebral venous plexus (**below**); flow is from the left to the right. Upper part of the jugular pathway is unrestricted and there is a stenotic valve downstream.  
Figure 9: Model of the cerebral venous outflow comprising the internal jugular vein (**above**) and the vertebral venous plexus (**below**); flow is from the left to the right. There is a significant stenosis at the beginning of the jugular pathway and no valve downstream.
## 2 Preliminaries
### Ring Learning with Errors  
The Ring Learning with Errors (RLWE) assumption [23] with parameter \((N,Q,\chi)\) is a central cryptographic assumption used in lattice-based encryption schemes. Given a set of polynomially many samples \((a_{i},b_{i}=s\cdot a_{i}+e_{i})\in R_{Q}^{2}\), where \(a_{i}\) is uniformly sampled from \(R_{Q}\), and both \(s\) (the secret) and \(e_{i}\) (the noise) are drawn from an error distribution \(\chi\) over \(R_{Q}\), the RLWE assumption asserts that it is computationally hard to distinguish the \(b_{i}\)'s from uniformly random elements of \(R_{Q}\).  
In standard RLWE-based constructions, the error distribution \(\chi\) is defined as the spherical discrete Gaussian distribution \(D_{\mathbb{Z}^{N},\sigma}\) over the ring \(R=\mathbb{Z}[X]/(\Phi_{k}(X))\), where \(\Phi_{k}(X)\) is the \(k\)-th cyclotomic polynomial and \(N=\varphi(k)\) denotes its degree. In the context of RLWE, this degree \(N\) is also referred to as the dimension of the RLWE assumption. The distribution \(\chi\) is \(B_{\chi}\)-bounded, meaning that with overwhelming probability, the \(l_{\infty}\)-norm of a sampled element--viewed as a coefficient vector in \(\mathbb{Z}^{N}\)--is at most a fixed bound \(B_{\chi}\), where \(B_{\chi}\ll Q\). Specifically, each element sampled from \(\chi\) is represented as a polynomial in \(R_{Q}\) whose coefficients are independently drawn from the same one-dimensional discrete Gaussian distribution \(D_{\mathbb{Z},\sigma}\) with mean zero and some standard deviation \(\sigma>0\).
## 4 Evaluation over Use Cases
### ICT Gateway
#### 4.1.7 Monitoring and Communication  
As explained previously, the communication and auditing phase deals with the security changes that may occur during the lifecycle of the system. Following the steps of Figure 5, the methodology is supported in this phase by the auditing framework and the monitoring ontology developed in [45], which is intended to detect security issues based on a set of blueprints derived from the design phase and manually specified by the user. As shown in Figure 9, one of the blueprints used in the auditing framework is the updated MUD generated from the treatment phase, which contains a specific configuration (e.g., limited connections) that must be monitored to keep the system in a secure state according to the test results. If there is a violation of the MUD policies during the monitoring process, the auditing framework requests a re-evaluation process or a mitigation action, which could imply a modification of the MUD file. Additional details on this can be found in [45; 46] and therefore are not included in this article.
## 4 Detailed Problem Description
### Customers' Orders and Storage Requests  
All customer orders, denoted as output requests, \(k\in R^{\text{out}}\), possess the following attributes: (i) an arrival time, \(\rho_{k}\); (ii) a required product identified by a unique code, \(c_{k}\); (iii) a specified required quantity, \(w_{k}\); (iv) a minimum quality level of the material, \(q_{k}\); and (v) an indicator specifying the I/O point used, \(s_{k}\). Distinguishing customer orders \(k\in R^{\text{out}}\) from storage orders (input requests \(k\in R^{\text{in}}\)) involves an additional attribute, \(t_{k}\). In the former case, \(t_{k}\) holds a value of zero, whereas in the latter case, it holds a value of one. The interpretation of the remaining attributes varies based on \(t_{k}\). For instance, in a customer order, \(c_{k}\), \(w_{k}\), and \(q_{k}\) signify the product, quantity, and required quality level, respectively. Conversely, in a storage request, they represent the product, weight, and quality of the incoming bundle, respectively.  
In an SLC-AS/RS, processing customers' orders differs slightly from traditional AS/RS setups for pallets. Typically, customers specify a particular product type, along with a quantity in kilograms and a desired quality level. The fulfillment of this request necessitates one or more bundles, ensuring that the overall quantity and quality of retrieved bundles closely match the required specifications. This resembles a knapsack problem embedded within the larger issue at hand.
## 3 Approaches for Imbalanced Datasets  
### Dataset  
In this study, we select the dataset of CCF prediction as a case study of the imbalanced dataset obtained in [17]. The description of this dataset is shown in Table 1. In September 2013, this dataset included transactions that were collected within 2 days and created by credit cards through European cardholders. The imbalanced CCF dataset has 284,807 transactions and 31 columns. These columns consist of 28 numerical features, named V1 to V28, a feature of \"Time\", a feature of \"Amount\", and a label of \"Class\". The label of \"Class\" is divided into two classes, such as positive class (frauds), denoted as 1, and negative class (no-frauds), denoted as 0. The type of all of these features is expressed as an object, and there are no missing values in this dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Descriptions** & **Details** \\ \hline Number of columns & 31 \\ Number of features & 30 \\ Labels & Class 0 and class 1 \\ Number of rows & 284,807 \\ Feature type & Object \\ Missing values & None \\ No frauds vs Frauds & 99.828\% vs. 0.172\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data description.  
As shown in Figure 2, this CCF dataset is highly imbalanced so we can define the positive and negative classes as the minority and the majority classes. It is true that the imbalanced ratio (IR) is the most prevalently employed measure, which illustrates the imbalanced extent of a dataset.  
This ratio is considered as a ratio of the sample size of the majority class \(N_{maj}\) over the sample size of the minority one \(N_{min}\), as shown in (1). If the IR value is equal to 1, the dataset is exactly balanced between two classes. If the IR value is bigger than 1, we have an imbalanced dataset [18]. In the CCF dataset of this paper, the minority class (frauds) occupied only 0.172% of all transactions, as illustrated in Figure 2.  
\[\text{IR}=\frac{N_{maj}}{N_{min}} \tag{1}\]  
After analyzing the dataset, we recognized that the feature \"Amount\" has values from 0 to 25,691.16, which is illustrated in Figure 3. However, if the features are of relatively the same scale and/or close to the normal distribution, the ML algorithms will have good performances or converge faster. Therefore, standardization is used to eliminate the mean and scale to unit variance to alleviate the wide range of the feature \"Amount\", so that approximately 68% of the values lie in between \((-1,1)\)[19]. A new feature, named \"normAmount\" is added to this dataset. Additionally, the \"Time\" and \"Amount\" features do not need to build models, and thus we will drop these features. We obtained the dataset of 30 features and the \"Class\" label.  
Figure 2: Fraud class histogram with the imbalanced dataset.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results
### Comparison Results
#### 3.2.1 Comparison on DanceTrack  
As shown in Table 1, we compared our proposed method with SORT [4], DeepSORT [5], CenterTrack [27], FairMOT [7], QDTrack [22], TransTrack [38], TraDes [14], ByteTrack [6], and OC-SORT [10] on the DanceTrack dataset. The results show that our method outperforms others, achieving the best performance across all evaluation metrics. Compared to the baseline model, HOTA improves by 10.2%, MOTA by 3.0%, IDF1 by 4.8%, DetA by 10.7%, and AssA by 8.9%. This highlights the effectiveness of our method in handling missed detections and the complex motions of tracking objects. The results were directly obtained from the official evaluation server of DanceTrack.
# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 4 Experimental Results and Analysis
### Extraction of Vegetation and Water Body in the Pavia Center Data Set

Table 5: Interaction characteristic coefficients of water body. information, BAND47 and BAND48 have a better recognition effect. Analogous to the expression form of the normalized index, BAND48 can be expressed as:  
\[\text{BAND48}=\frac{\text{band66}-\text{band94}}{\text{band66}+\text{band94}}= \frac{R-NIR}{R+NIR} \tag{10}\]  
Compared with NDVI, it can be proved that the interactive relationship between BAND47 and BAND48 can be used as an interactive vegetation index. In order to display the results more intuitively, BAND32 and BAND48 are used as the water body index and vegetation index to extract the water body and vegetation in the Pavia Center data set, and the results of the water body extracted by BAND32 are compared with the results of NDWI, which was extracted by the laboratory. Results of vegetation extracted by BAND48 are compared with NDVI, as shown in Figures 5 and 6:  
BAND32 can effectively extract water body and is similar to the extraction results of NDWI; BAND48 can also effectively extract vegetation and is similar to the extraction results of NDVI. To make the result clearer, the accuracy of the correctly extracted pixels in the binary image is counted. The pixel value 0 represents black (background), and the pixel value 255 represents white (target features). The quantitative results of the water body are shown in Table 7. The quantitative results of vegetation are shown in Table 8. The accuracy of BAND32 and BAND48 for the correct extraction of water body and vegetation is higher than that of NDWI and NDVI. Figure 5: Extraction results of water body and partial enlarged image: (**a**) Ground truth information; (**b**) NDWI extraction result; (**c**) BAND32 extraction result.
# Motion of Submerged Body in a Frozen Channel with Compressed Porous Ice
## 4 Numerical Results and Discussion
### A Compressed Viscoelastic Ice Plate
Therefore, to clearly demonstrate the damping effect caused by porosity, dimensionless ice deflections will be shown for values of \(K\) in the range of \(10^{-9}\)-\(10^{-7}\) m\({}^{2}\), where minimum values correspond to highly porous ice, and maximum values correspond to rubbish ice. Figure 7a shows the dimensionless deflections of porous ice along the central line of the channel for different values of the permeability coefficient \(K\). The blue line shows the results for \(K=0.1\cdot 10^{-8}\) m\({}^{2}\), green, for \(K=0.7\cdot 10^{-8}\) m\({}^{2}\), red, for \(K=0.1\cdot 10^{-7}\) m\({}^{2}\), and black, for \(K=0.7\cdot 10^{-7}\) m\({}^{2}\). It is clear that increasing the porosity parameter dampens the waves both before and after the submerged body. Visually, increasing the porosity parameter slightly reduces the wavelength. Dimensionless strains for this case are shown in Figure 7c along the central line of the channel. Figure 7b,d show the dimensionless deflections of viscous ice and the corresponding dimensionless strains along the central line of the channel for different values of \(\tau\), describing the viscosity of the ice cover. The blue lines show the results for \(\tau=0.07\) s, red, for \(\tau=0.1\) s, green, for \(\tau=0.135\) s, and black, for \(\tau=0.2\) s. The ice deflections and strains decrease significantly in the area of the submerged body's location and rapidly dampen away from it with increasing \(\tau\). Additionally, it can be observed that with increasing viscosity, the wavelength slightly increases, which is opposite to the effect of porosity. For the ice cover compressed across the channel, the velocity \(U=3\) m/s is not supercritical. In this case, the ice deflection is localized above the submerged body, and the effects of porosity and viscosity have no significant influence on it. It was found that in the presence of viscosity, waves in front of the submerged body dampen much faster than waves behind it. For porous ice, wave attenuation occurs uniformly both in front of and behind the body. This is clearly illustrated in Figure 8. In the considered case, the amplitudes and shapes of the deflections of porous and viscous ice covers visually coincide near the submerged body for \(K=0.7\cdot 10^{-8}\) m\({}^{2}\) (blue line) and \(\tau=0.135\) s (red line). Note that the maximum strains in both cases are reached slightly in front of the body along the central line of the channel. These strains will be the largest for porous ice. Next to these maximum strains will be strains in front of the submerged body for the porous ice and strains behind it for the viscous ice.
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 3 Performance Evaluation
### Performance Evaluation for the Delay-tolerant service
As mentioned above, since the heavy traffic load that needs many frames for the transmission is instantaneously imposed on the satellite network in the case of high burstiness, the end-to-end delay increased, resulting in the reduced maximum goodput.
# Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
## 5 Uncertainty Detection and Processing
### Conflict Detection and Processing of Monitoring Data  
The reliability of the data collected by the sensors is first determined using belief functions. When the level of reliability is low, the data from these sensors cannot participate in the data fusion. When the reliability is within the threshold range, the fusion weight is calculated by the belief function proposed in [PERSON] (1977) in order to perform multi-sensor data fusion and therefore improve the stability of the fusion results. This study integrates the [PERSON] (1977) belief function and the fuzzy-index belief function ([PERSON] et al. (2016)), in order to develop a sensor data-fusion belief function to deal with uncertain data in Scenario 2.  
Step 1: Development of the belief function  
Considering the large-scale of construction sites, there are large value differences in terms of TSP sensors at different points. If the difference between \(x_{i}\) and \(x_{j}\) is larger than the fusion upper limit of the belief function, the belief degree will turn to 0, making the result too absolute. In order to avoid the loss of information, the sensor data-fusion belief function is established.  
\[b\big{(}x_{i},x_{j}\big{)}=K\times e^{-|x_{i}-x_{j}|\cdots},K\in[0,1] \tag{2}\]  
Among them, \(K\) represents the magnitude of belief, denoted as 1 for the purpose of simplicity of calculation. When \(\big{|}x_{i-}x_{j}\big{|}<20\), \(b\big{(}x_{i},x_{j}\big{)}\) is approximately 0, which is consistent with actual situations on construction sites.  
Step 2: Establishment of the belief matrix  
Figure 7: Identification process of abnormal data.  
\(n\) represents the number of sensors, and the belief matrix is established according to the proposed belief function.  
\[\mathbf{B}=\begin{bmatrix}b_{11}&b_{12}&\cdots&b_{1n}\\ b_{21}&b_{22}&\cdots&b_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ b_{n1}&b_{n2}&\cdots&b_{nn}\end{bmatrix}\]  
Step 3: Fusion of the weighted sensor data  
The degree to which the \(i\)-th sensor is supported by other functions is in Equation (3).  
\[\sup(x_{i})=\sum_{j=1}^{n}b_{ij},i\
eq j \tag{3}\]  
After normalizing the belief function, the weight of the sensors can be obtained in Equation (4).  
\[w_{i}=\frac{\sup(x_{i})}{\sum_{i=1}^{n}\sup(x_{i})} \tag{4}\]  
According to the weights of \(n\) sensors, the reading value of fused sensor can be obtained in Equation (5).  
\[x=w_{1}x_{1}+w_{2}x_{2}+\cdots w_{n}x_{n} \tag{5}\]