esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
The increase in the RBE-weighted dose could be further amplified by minimizing the gamma and fast neutron components, as these are not selectively targeted to cancerous tissue. In fact, the enhancement, confined to the region beyond 20 keV/\(\upmu\)m, reaches a substantial 68%. It is important to note that the overall extent of enhancement depends on the contribution of the BNC component to the total dose--the higher the contribution of the BNC component, the greater the enhancement due to the selective delivery of boron. As highlighted in the IAEA report on BNCT [3], it is crucial to maintain control over radiation field components that cause non-selective dose absorption. It is important to note that microdosimetry, performed with a homogeneous boron distribution in the cathode walls, represents a simplified version of the real scenario, where the micron-scale distribution of boron could be non-uniform. As highlighted by [PERSON] [30] and demonstrated through Monte Carlo simulations by [PERSON] et al. [8], intra- and inter-cellular heterogeneity in \({}^{10}\)B distribution significantly impacts the biological effectiveness of boron neutron capture therapy (BNCT). Despite not precisely capturing the effects of a heterogeneous distribution, microdosimetry still offers valuable insights into the radiation field and the average impact of boron enrichment. \begin{table}
\begin{tabular}{c c c c} \hline \hline \({}^{10}\)B Concept/ppm & RBE from Equation (3) & Absorbed Dose/Gy & RBE-Weighted Dose/Gy \\ \hline
0 & 1.68 & 1.00 & 1.68 \\
8.5 & 1.87 & 1.08 & 2.02 \\
10 & 1.88 & 1.08 & 2.03 \\
25 & 2.12 & 1.20 & 2.55 \\
30 & 2.19 & 1.23 & 2.68 \\
70 & 2.65 & 1.55 & 4.10 \\
100 & 2.85 & 1.76 & 5.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The RBE, the relative physical dose, and RBE-weighted dose at several \({}^{10}\)B concentrations. Figure 10: (**a**) Microdosimetric \(yd(y)\) distributions measured with 0 ppm of \({}^{10}\)B, and reconstructed for a realistic scenario with 30 ppm of \({}^{10}\)B delivered to the tumor and 8.5 ppm to healthy tissue. (**b**) the \(yd(y)r(y)\) distributions weighted with \(r(y)\), the Tilkidis weighting function at 2 Gy, represented as a dotted line [25]. Furthermore, the microdosimetric characterization conducted using a gas detector with boron-enriched cathode walls reflects a scenario in which boron atoms are primarily externally delivered to the sensitive micrometric target. Even when working with boronated gas, the situation does not change significantly due to the low density of the gas compared to the walls. To explore a scenario where boron is included within the sensitive target, Monte Carlo simulations can be employed.
# A Model-Driven Approach for Conducting Simulation Experiments
## 5 Evaluation  
Applying MDE for simulation experiments has positive effects on the building and sharing of knowledge, the productivity and quality of code, the reusability, as well as the automatic generation of simulation experiments. This will ultimately lead to entire simulation studies being conducted in a more effective and systematic manner.  
We demonstrate the benefits using three real simulation studies from the interdisciplinary research project ELAINE on electrically active implants. These comprise the DES study of a cell signaling pathway by [PERSON] et al. [47; 48], the virtual prototyping study of a neurostimulator by [PERSON] et al. [49], and the FEA study of electric fields in a stimulation chamber by [PERSON] et al. [50]. We believe that well-designed and realistic case studies are the best way to convince modelers to adopt MDE for simulation experiments in their daily practice and to integrate this approach with other toolchains.  
In the following, we first develop meta models to capture the characteristics of the three modeling and simulation approaches together with our domain modeling experts(stochastic DES, virtual prototyping, and FEA) and discuss how knowledge about simulation experiments can be exchanged within, but also between the different modeling and simulation communities. Next, we demonstrate that based on the base meta models composed with a shared meta model for the experiment type \"global sensitivity analysis\", three complex simulation experiments can be specified in a straightforward manner to solve actual problems in the different application domains (analysis of a cell signaling pathway, the battery of a neurostimulator, and the electric field in a stimulation chamber). Then, we show how our JSON-based format can act as a standard exchange format to facilitate the automatic reuse of simulation experiments, e.g., for the cross-validation of two related cell signaling models. Finally, we show how our approach can be used to automatically generate simulation experiments by extracting information from a workflow.  
Note that all the experiments we describe are only snapshots of the three studies in the form of single simulation experiments. During the simulation studies, of course, further analysis steps with the same or other experiment types (e.g., simulation-based optimization or statistical model checking) are involved, until the initial research questions can be answered. For these further steps, our approach can be applied analogously.
## 4 Numerical Results  
### Feeder and Case Description  
The model was tested on a modified IEEE-33 node distribution network and a four-node transportation network, as shown in Figure 3. The feeder has residential loads connected to nodes 1-22, commercial loads from nodes 22 to 33, and a hospital at node 30. Figure 4 shows the load profiles used for the different load types. The loads and VOLL for all the nodes in the case are shown in Figure 5. The 33-node network is divided into four zones, shown as Z1, Z2, Z3, and Z4 in Figure 3. The distributed generation available in the network includes three solar farms across nodes 18, 22, and 25, and a conventional gas generator of 1 MW at node 33. A candidate node from each zone was selected as a potential FEVCS location. The selected candidate nodes for FEVCS are presented as nodes connected by a road network in the figure. Nodes 18, 22, 25, and 33 were selected as the search window for the optimal location of the FEVCS. It is assumed that the fault is active for 8 h, and during a fault, the upstream substation does not provide power to the network. Furthermore, it is assumed that the FEVCS has more than three charging ports available for the support of grid restoration. Only three representative EVs--several fleets of EVs with the same routing behavior--are considered in all cases to incorporate the variation in usable EV storage with a minimal computational load. Moreover, the size of the chargers can be varied to incorporate the increase or decrease in usable EV storage; the considered FEVCS is made up of 125 kW capacity chargers, vehicles are considered to have usable storage of 300 kWh each, and six fleet vehicles are considered available for grid restoration. To reduce the computational load, two fleet vehicles are assumed to follow the same behavior; therefore, the representative EV considered for the base cases has a storage of 600 kWh and a charger rating of 250 kW. Cases 0, 1, and 2 are cases with zero, one, and two FEVCSs on the distribution network, respectively. The aforementioned cases do not have any BESS in the network. Cases 3, 4, and 5 are cases with zero, one, and two FEVCSs with solar farms supported by BESS. The probabilities of the line damage scenarios after scenario reduction are 0.277, 0.388, and 0.333 respectively. The results of scenario 2 of the second stage are presented as it is the most probable scenario and will have the most influence, while the first stage model decides the location of the FEVCS. The rest of the scenarios do not have faults that interrupt the power flow to cause any load loss. In the analysis below, the SOC has been mentioned in terms of stored energy in kWh.  
Figure 3: Four-node transportation and IEEE-33 distribution network of the test case.
# A Metric for Secrecy-Energy Efficiency Tradeoff Evaluation in 3 GPP Cellular Networks
## 2 System Model
### Physical-Layer Secrecy  
By adopting the common security terminology, the source of information, which is identified as Alice, is the serving sector of the cell under examination. The legitimate receiver [PERSON] is the mobile user within the cell range. An undesired eavesdropper, [PERSON], can move around within the cell boundaries trying to capture the information from Alice to [PERSON]. To restrict the analysis, [PERSON]'s location is chosen on the direction of maximum radiation of his serving antenna, so that [PERSON]'s position can be simply described reporting his absolute distance in meters from [PERSON]. The results presented in the following sections are derived for [PERSON] in the same cell as [PERSON], tuned to Alice-to-Bob wireless frequency. All other surrounding sectors are considered as interferers for both [PERSON] and [PERSON], with signals as described in the previous section.  
In order to evaluate the achievable level of secrecy that the system can grant to a mobile user in the depicted cellular system, we adopt the concept of Secrecy Capacity derived from [PERSON]'s notion of perfect secrecy [19], [PERSON]'s wiretap channel [1] and [PERSON] work [20]. As in [20], [PERSON]'s theoretical capacity per unit bandwidth is expressed as  
\[C_{B}=\log\left(1+|h_{B}|^{2}\frac{P}{N_{B}}\right), \tag{3}\]  
where  
\(h_{B}\): is a coefficient inclusive of both the transmit and the receive antenna gains and of path-loss of the Alice-to-Bob channel, being [PERSON] served by [PERSON]; \(P\): is the power level of the sector that is serving [PERSON]; \(N_{B}\): is the power of the equivalent Gaussian noise component perceived by [PERSON]; it includes both thermal noise and interference from the surrounding sectors.  
A similar expression describes [PERSON]'s capacity, while the Secrecy Capacity of [PERSON] can be expressed as  
\[C_{s}=\begin{cases}\log(1+\gamma_{B})-\log(1+\gamma_{E}),&\text{if }\gamma_{B}>\gamma_{E},\\ 0,&\text{if }\gamma_{B}\leq\gamma_{E},\end{cases} \tag{4}\]  
where we called \(\gamma_{B}\) and \(\gamma_{E}\) the SINR that is experienced by [PERSON] and [PERSON], respectively, (i.e., \(\gamma_{B}=|h_{B}|^{2}\frac{P}{N_{B}}\) and \(\gamma_{E}=|h_{E}|^{2}\frac{P}{N_{E}}\)).
## 2 Materials and Methods
### Antioxidant Activity of Protein Hydrolysates (LPH)  
#### 2.4.1 DPPH Radical Scavenging Capacity  
The analysis was carried out according to the method of [PERSON] [23], with slight modifications. First, 0.250 g of LPH from all test groups was dissolved in 10 mL of distilled water. The aliquots of the solutions were diluted 25 times with 80% methanol. After 20 min of incubation at room temperature, they were centrifuged at 8240\(\times\)\(g\) for 10 min at 10 \({}^{\circ}\)C (Beckman J2-21M, USA). The precipitate was discarded and the supernatant was collected and analyzed. Subsequently, 1.2 mL of DPPH solution in methanol (0.2 mM) was mixed with 1.8 mL of methanol and 1.0 mL of the respective sample. The mixture was homogenized and incubated in the dark at room temperature for 60 min. Absorbance was measured at 517 nm against methanol (Libra S 22 UV-Vis spectrophotometer, Biochrom, Holliston, MA, USA). In the control, the sample solution was replaced with 1.0 mL of 80% methanol. Standard solutions of Trolox (concentration from 1.0 to 15 \(\mu\)g/mL) were used as a positive control.  
The following formula is used to calculate the DPPH radical inhibition percentage (%RSA):  
\[\%\text{RSA}(\text{DPPH})=\frac{(\textit{Acontrol}-\textit{Asample})}{ \textit{Acontrol}}\times 100 \tag{1}\]
## 2 Materials and Methods
### Machine Learning Models  
In the paper, 4 machine-learning models--namely, LR, SVMs, KNNs, and DT--are independently applied to classify 2D FBM images according to their corresponding estimates from the MLE. Some important features are described as follows.  
In statistics, multiclass logistic regression is a classification method obtained by generalizing logistic regression to multiclass problems. In multiclass logistic regression, the dependent variable is predicted based on a series of independent variables--namely, features and observed variables. After linearly combining the independent variables and their corresponding parameters, a certain probability model is used to calculate the probabilities of certain results in the predicted dependent variables, and the parameters corresponding to the independent variables are calculated from the training data. These parameters are the regression coefficients in multiclass logistic regression.  
The SVM algorithm was originally designed for binary classification problems. When dealing with multiclass problems, it is necessary to construct a suitable multiclass classifier. For each class, there is a 2-class classifier (one-vs.-rest)--the current class and other classes. Therefore, we can transform a multiclass problem into \(n\) 2-class problems, where \(n\) is the number of classes.  
The KNN classification algorithm is among the simplest ones for data mining. Its procedure for determining the class of the unknown sample is as follows: (1) take all the known samples as references; (2) calculate the distances between the unknown sample and all the known samples; (3) select the \(K\) known samples that are closest to the unknown sample; (4) vote by a majority; and (5) classify the unknown sample according to the category with a large proportion of the \(K\) known samples.  
The DT is a multi-classification model that uses the tree model to make decisions; it is simple, effective, and easy to understand. Its two main advantages are that the model is readable and the classification work is considerably fast. DT learning mainly consists of three steps--feature selection, decision tree generation, and decision tree pruning.
## 3 Model and Methodologies  
To address the bike-rebalancing problem, this study proposes a comprehensive approach that integrates demand forecasting, inventory management, and vehicle routing optimization. The effectiveness of a multi-objective MIP model for the PDVRP in bike rebalancing hinges on accurate target inventory levels, which in turn depend on reliable demand distribution predictions. To achieve this, we employ a sophisticated methodology that encompasses several key components.  
This study's methodology is structured in two principal phases. The first phase involves implementing a probabilistic demand-forecasting model based on DeepAR, employing an autoregressive recurrent neural network. This model examines intricate patterns within historical bike-sharing data to project future demand distributions. We then apply inventory management techniques to determine optimal inventory levels that sustain desired service standards, thereby establishing a solid foundation for subsequent optimization.  
The second phase integrates these preparatory elements into a multi-objective framework for the PDVRP, tailored specifically to bike-rebalancing scenarios. To efficiently explore Pareto-optimal solutions in this complex optimization context, we propose a hybrid genetic algorithm (HGA) as an enhanced alternative to a conventional multi-objective MIP model. This HGA synergistically combines Genetic Algorithm (GA) principles with MIP solving techniques. Our integrated approach aims to deliver a robust and practical solution to the multi-faceted challenges inherent in bike-sharing system rebalancing.  
The objectives of the PDVRP in this paper are to minimize travel distances and balance bike inventory levels across all bike station clusters. Inventory balance is measured by the sum of net deviations between the target and actual bike inventory after rebalancing. These objectives cannot be directly derived from a single solution consisting of vehicle travel sequences. While travel distance can be directly obtained, net deviations require solving an additional optimization problem. Achieving an optimal allocation plan without considering travel paths is ineffective in this problem structure. This structure does not allow the use of straightforward approaches such as simple genetic algorithms. Other complex methodologies, such as scalarization-based multi-objective optimization approaches like MOEA/D [32] and collaborative neurodynamic approaches [33], may not be appropriate for our problem. Furthermore, to the best of our knowledge, there is no standard exact solution method for this type of multi-objective problem.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 2 Attacks on VSS
### Steganography Attacks  
Steganography involves a method to use the unused or less important information bits of the user content (such as images, videos, network traffic). Two types of common steganography attacks are hiding the malicious code in the genuine application and by a command and control (C & C) communications channel [30; 31].  
A common technique in many malware droppers is to append data to the end of the file or utilize unused portions of the file format [32]. In any method of steganography, it is hard to detect malicious code coming through user files in a network. Malicious payloads can be embedded into a set of PNG files. The PNG files can then be compiled into a legitimate application, along with a function that would extract and drop the malware onto the system.  
In command and control protocol attacks, the \"Domain Name Server (DNS) and Hyper Text Transfer Protocol (HTTP)\", can be used to embed the malicious code in response to a request from a client.  
As an example, we consider User A (house or office owner) who approaches the IP camera. After it detects User A's face successfully, it stores the image and captures another image without a face (named a cover image). The steganography technique is now used to send a \"stego image\" (combined image where the actual image is hidden in the cover image). This \"stego image\" is stored in the home server, which then will be processed using the reverse steganography technique to retrieve the original image with the face.  
Another User B (attacker) intercepts data transmission between the IP camera and the home server and captures all the data which have \"stego image\" along with other captured images. At this point, the attacker can perform three categories of attack; namely, stego-only attack, known cover attack and known message attack. Any image can be represented with a pixel value (a bunch of ones and zeroes). The cover image will also have a string of bits, but their sequence of bits will be different on the LSB or the MSB side when compared to the corresponding face image. Any change on the LSB bits of the face image will not alter it significantly, whereas changes in the MSB bits will significantly degrade the quality of the face image. An attacker can use statistical analysis for the detection of changes in LSB bits or human visual perception to detect the changes in the MSB bits to detect the face image from the cover image. User B can then use this decrypted face image to attack the home security system and gain access to the home or office.
## 4 Experiments and Results
### Data Pre-Processing
#### 4.1.1 Data Augmentation  
In this study, we augment the number of samples in the dataset with nine data enhancement operations, including Rotation, Salt-and-pepper noise addition, Brightening, Darkening, Enlargement, Vertical flip, Horizontal flip, Gaussian noise addition, and Translation. The schematic of the data augmentation is shown in Figure 12a. The final result is shown in Figure 12b. After data enhancement, the number of samples in the dataset is augmented to 6949, and the number of different samples is basically even.  
Figure 11: Schematic diagram of the transfer learning method.  
Rotation is a transformation that rotates an image by a specified angle around its center; salt-and-pepper noise is a type of image noise that is characterized by random white and black pixels. In the process of the noise addition for each pixel in the image, if the pixel is not randomly selected, its value in the new image will be the same as its value in the original image. If the pixel is randomly selected, its value in the new image will be 0 or 255, and its color will be black or white. Brightening is a transformation that increases the brightness of an image. Darkening is a transformation that decreases the brightness of an image. Enlargement is a transformation that increases the size of an image. Vertical flip is a transformation that flips an image vertically. Horizontal flip is a transformation that flips an image horizontally. Translation is a transformation that shifts an image by a specified amount on the x-axis and y-axis. In addition, Gaussian noise is a type of image noise that is characterized by random values that follow a Gaussian distribution. The addition of Gaussian noise increases the complexity of the image and makes it more similar to the noise present in actual rock images. This allows for better simulation of real-world scenarios and improves the robustness of the model to noise. In rock classification, this method allows the model to adapt better to different environments, thereby improving recognition accuracy. The formula for adding Gaussian noise to an image is:  
\[\mathrm{I}^{\prime}(x,y)=\mathrm{I}(x,y)+\mathrm{N}(\mu,\sigma^{2}) \tag{7}\]  
where, \(\mathrm{I}\) is the original image, \(\mathrm{I}^{\prime}\) is the brightened image, \(\mathrm{N}\) is the function of gaussian noise, \(\mu\) is the mean, and \(\sigma^{2}\) is the variance. In this study, \(\mu\) is set as 0 and \(\sigma^{2}\) is set as 0.01.
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 5 Concrete Applications of Quantum Stream Cipher
### Optical Satellite Communication  
The Y-00 quantum stream cipher, which was developed for fiber-optic communications, can also be applied to satellite communications. In satellite communication applications, the rate of operation is an important factor because communication performance depends on the weather conditions. With QKD, it is difficult to keep communications up and running except on clear-air nights. In the case of Y-00, communication by any satellite system can be almost ensured when the weather is clear. In case of bad weather, the effects of atmospheric turbulence and scattering phenomena need to be considered. We are currently analyzing the performance of the system in such cases at 10 Gbps operation [30].