esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiport Energy Management System Design for a 150 kW Range-Extended Towing Vessel
## 4 Simulation
### Simulation of ESS
#### 4.2.2 Case 2: ESS with the Lithium Battery Only and Satisfied Initial Battery Condition (75% SoC)  
This case study compared the performance of the EMS with and without supercapacitors to show the advantages of using supercapacitors in the ESS. In this study, the capacitors were mainly used to address the sudden power demand and large discharge current. For this purpose, the results for two simulation periods (3.995-4 s and 6.837-6.841 s) were investigated. Figures 19 and 20 show the battery's discharge current waveforms and SoC curves of the two ESS systems, respectively. Please note that the hybrid ESS shown in the two figures means the ESS with both battery and supercapacitors. The following analysis was conducted:  
1. In the period 3.995-4 s, the ESS with lithium battery only maintained a peak current of about 190 A, which was 1.9 times higher than that of the hybrid ESS (100 A).
2. In the period of 6.837-6.841 s, the ESS with lithium battery only maintained a peak current of about 85 A, which was 1.7 times higher than that of the hybrid ESS (50 A).
3. The SoC decrease rate for the ESS with lithium battery only was faster than that of the hybrid ESS before 4 s. At 4 s, the battery's SoC of the ESS with lithium battery only dropped to 74.996%, which was slightly lower than that of the hybrid ESS (74.998%).
4. Both systems showed a consistent SoC decrease rate during 4-6 s. In the subsequent cruise, the sudden power demand decreased the battery's SoC. At 7 s, the battery's SoC of the ESS with lithium battery only fell to 74.985%, which was also lower than that of the hybrid ESS (74.988%).  
Therefore, the hybrid ESS outperformed the ESS with lithium battery only in reducing the impact of sudden power demands. This indicates that the hybrid ESS has a smaller energy flow in the lithium battery. It can also be seen from the SoC curves that the energy consumed by the hybrid ESS was lower than that of the ESS with lithium battery only. It proved that the supercapacitors in the hybrid ESS allowed the battery to discharge more stably and avoided the impact of the high discharge current, so as to reduce the battery's aging rate.
## 1 Introduction
In the case of Measurement-Based Handover Triggering, the propagation delay is based on the measurement result obtained from the reference signal at the satellite's location. However, there exists a disparity between the measurement result obtained when the satellite is located and the result obtained at the reference point after accounting for the actual propagation delay. This discrepancy adds complexity to the handover process and necessitates careful consideration to mitigate its impact on communication system performance. Because of reasons, several papers have presented diverse approaches to address the NTN handover problem [18; 19; 20; 21; 22]. These include proposing a graph framework and determining an optimal path, optimizing the handover process through MIMO (Multiple Input Multiple Output) technology, maximizing benefits by defining a utility function for mobile terminals, detecting load during the handover process and employing channel reservation optimization strategies, as well as introducing papers focusing on optimizing handover by enhancing the random access process. To address the challenges in servicing ground terminal nodes at high altitudes, this paper considers the realistic scenario, taking into account the propagation delay that occurs at high altitudes, and proposes a handover triggering technique based on machine learning to predict the distance and altitude angle between the moving satellite's cell center and the User Equipment (UE). The XGBOOST model, an ensemble algorithm based on gradient boosting, is utilized to predict the distance and elevation angle between the UE and the cell center after the propagation delay based on the UE's location. Using a model that determines whether to trigger handover based on the predicted location can enhance the handover function to ensure continuous service provision at high altitudes and achieve more accurate handover triggering. Section 2 of this paper provides a comprehensive review of the related literature, including performance measurement of NTN handover and evaluations of proposed handover triggering techniques based on mobility enhancement by 3 GPP. Section 3 describes the proposed system model, the dataset used for experimentation, and the implementation methodology. Section 4 elaborates on the performance evaluation metrics used to assess the research results and presents the outcomes of the model's performance. Finally, Section 5 offers a concluding summary of the paper.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 7 Formal Validation  
The proposed protocol was validated using Automated Validation of Internet Security Protocols and Applications (AVISPA) [1]. This is a semi-automated validation tool that permits the verification  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference/Protocol\(\rightarrow\)** & **[PERSON] et al. [7]** & **[PERSON] et al. [6]** & **Proposed Protocol** \\ \hline \hline TOTAL & \begin{tabular}{c} (5 nT\({}_{\text{S}}\) + 11 nT\({}_{\text{P}}\) + 8 nT\({}_{\text{E}}\) + (4 nT\({}_{\text{S}}\) + 9 nT\({}_{\text{E}}\) + 35 nT\({}_{\text{H}}\)) * (12 nT\({}_{\text{E}}\) + 33 nT\({}_{\text{H}}\)) * 10.88 \\ 32 nT\({}_{\text{H}}\)) * 10.88 = 26.43n mJ \\ \end{tabular} & \
\begin{tabular}{c} (5 nT\({}_{\text{S}}\) + 11 nT\({}_{\text{P}}\) + 8 nT\({}_{\text{E}}\) + (4 nT\({}_{\text{S}}\) + 9 nT\({}_{\text{E}}\) + 35 nT\({}_{\text{H}}\)) * (12 nT\({}_{\text{E}}\) + 33 nT\({}_{\text{H}}\)) * 10.88 \\ 10.88 = 15.45n mJ \\ \end{tabular} &
\begin{tabular}{c} (12 nT\({}_{\text{E}}\) + 33 nT\({}_{\text{H}}\)) * 10.88 \\ 10.88 = 2.93n mJ \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Energy cost of protocols.  
Figure 9: Energy cost comparison.  
of security robustness of authentication protocols by checking the secrecy of key parameters and the vulnerability to intruders.  
AVISPA validation is made through codes written in the high-level protocol specification language (HLPSL). The message exchange of the protocol is translated to HLPSL code, where each entity is defined as a communication agent that performs roles. Each entity has its own role defined, which contains all the parameters exchanged in the messages (States). All parameters that must remain secret are signalized and observed during the code execution. If no secret value is vulnerable to be discovered by intruders, the protocol is considered safe.  
Our validation was made for each of the four phases performed over an insecure channel (HUP, PUP, TP and CP). Figure 10 presents the role for an ordinary device in the PUP phase, named Dpi in the code. Each State symbolizes the messages sent (SND) and received (RCV). Each parameter that must remain secret is signalized with a flag, such as sec_3 and sec_4 in Figure 10. The flag SecureChannel accompany an encrypted message and the parameters that are sent encrypted are signalized as secret (parameter). Figure 11 shows the role of the cloud server in PUP phase.  
Figure 10: Role of D2D device Dpi in PUP phase.  
We use two of the AVISPA's four security evaluation backends in the validation of our proposed protocol: On-the-Fly-Model-checker (OFMC) [23] and Constraint Logic-based Attack Searcher (CL-AtSe) [24]. Figure 12 presents the results of the OFMC analysis of the PUP phase, which concluded that our proposed protocol is safe.  
Our proposed protocol was declared safe according to the analysis.  
Figure 12: On-the-Fly-Model-checker (OFMC) analysis result.  
Figure 13: Constraint Logic-based Attack Searcher (CL-AtSe) analysis result for PUP phase.
## 3 Results
### Semiconductor Manufacturing
\begin{split} y_{W}=25.601+843.010x_{2}\\ {\text{R}}^{2}=0.721,{R^{2}}_{\text{adj}}=0.698,\,\text{s}=138.318, \,\text{PRESS}=328.601\end{split}  \tag{18}\]  
\[ \begin{split} y_{Oxide}=187.296+0.834x_{1}+10.682x_{3}\\ R^{2}=0.688,\,{R^{2}}_{\text{adj}}=0.636,\,\text{s}=14.801, \,\text{PRESS}=4.068\end{split}  \tag{19}\]  
Both responses passed the tests of normality and constant variance. In this study, only the \(x_{2}\) variable had a significant effect on \(y_{W}\). The \(x_{1}\) and \(x_{3}\) variables had a linear relationship with \(y_{Oxide}\). The surface plots were presented in the literature ([PERSON] and [PERSON], 1996). The effect of the adequate equation to present the appropriate contour and response surface plots is evident by comparing the adequate and proposed full equations in the literature ([PERSON] and [PERSON], 1996). [PERSON] and [PERSON] (2016) used the Box-Behnken design and RSM to study the optimization of MEMS devices. The influencing factors were \(x_{1}\), top electrode length (TEL); \(x_{2}\), top electrode width (TEW); \(x_{3}\), torsion spring length; and \(x_{4}\), torsion spring width (TSW). The response y was the pull-in voltage. The full equation, which included \(x_{1},x_{2},x_{3},x_{4},{x_{1}}^{2},{x_{2}}^{2},{x_{3}}^{2},{x_{4}}^{2},{x_{1}} {x_{2}},{x_{1}}{x_{3}},{x_{1}}{x_{4}},{x_{2}}{x_{4}},\) and \(x_{3}x_{4}\), was proposed and used to produce the surface plots. The full equation calculated with the datasets in literature [27] is  
\[ \begin{split}\text{yrv}=27.200-7.550x_{1}-2.717x_{2}-6.208x_{3}+3.2 92x_{4}+0.154{x_{1}}^{2}-0.696{x_{2}}^{2}\\ (<0.001)\ (<0.001)\ (<0.001)\ (<0.001)\ (0.842)\ (0.376)\\ +1.192{x_{3}}^{2}-0.258{x_{4}}^{2}+0.825x_{1}x_{2}+1.800x_{1}x_{3}-0.925x _{1}x_{4}+0.425x_{2}x_{3}\\ (0.147)\ (0.731)\ (0.364)\ (0.062)\ (0.311)\ (0.635)\\ -0.400x_{2}x_{4}+1.350x_{3}x_{4}\\ (0.655)\ (0.148)\\ R^{2}=0.975,\ R^{2}{}_{\text{adj}}=0.945,\text{s}=1.748,\text{ PRESS}=211.08\end{split}  \tag{20}\]  
The adequate equation calculated by backward elimination regression is  
\[ \begin{split}\text{yrv}=26.773-7.550x_{1}-2.717x_{2}-6.208x_{3}+3. 292x_{4}+1.352{x_{3}}^{2}+1.801x_{1}x_{3}\\ R^{2}=0.962,\ R^{2}{}_{\text{adj}}=0.95,\text{s}=1.659,\text{ PRESS}=100.206\end{split}  \tag{21}\]  
Comparing the \(\text{R}^{2}{}_{\text{adj}}\) and \(s\) values, the adequate equation has better accuracy than the full equation. The adequate equation also exhibits better predictive performance, as indicated by the PRESS criterion. The normality test is failed (\(p=0.0046\)). Further analysis is needed.
## 4 Results and Discussions
### Data Collection and Analysis
#### 4.1.3 Test Setups and Configurations  
Multiple simulations were simultaneously executed across different computers, each configured according to specific setups detailed in Table 2. The first setup, labeled X, ran the base simulator with no sorting algorithm to assist the virtual grocery shoppers. The second setup, labeled DProSA-AG, ran the base simulator with dynamic sorting assistance that used the agglomerative clustering approach to form the proximity clusters. The third setup, labeled DProSA-AP, ran the base simulator with dynamic sorting assistance that used affinity propagation clustering to form the clusters. All setups had the same shopping list contents per iteration, but the contents were shuffled randomly.  
The simulator was built with three configurable settings: total number of items to choose from; range of the shopping list size; and the number of shoppers to run. Given these settings, the simulators were executed across five distinct configurations, each representing a unique scenario, as summarized in Table 3:  
* Scenario 1a determines the algorithms' learning rates when there are fewer items per shopper.
* Scenario 1b determines the algorithms' average learning capacity.
* Scenario 1c determines the algorithms' learning rates when there are more items per shopper.
* Scenario 2 determines the performance of the algorithms in an uncontrolled scenario wherein shopping lists and contents vary per shopper.
* Scenario 5 utilizes the full 555 item pool to show the performance of the algorithms in more diverse item pools.
# Object Detection for Brain Cancer Detection and Localization
## 3 Experimental Analysis
### The Dataset  
We obtained the real-world data that we analyzed from a repository freely available for research purposes [18]. The exploited dataset is composed of 300 brain MRIs. We considered 70% of the dataset for training (210 images), 20% for validation (60 images), and the remaining 10% for testing (30 images). The dataset is labeled with the \"tumor\" label, with the detail related to the bounding box related to the label localization.  
The dataset is composed of brain images relating to different types of brain tumors (i.e., Meningioma, Pituitary, and Glioma), of different dimensions and located in different areas of the brain. Considering that different types of tumors were considered (and that in the dataset they are represented with the same number of images), the images of healthy subjects are equal to 25% of the total images.  
We resized all the images to the dimension of 512 \(\times\) 512 pixels. Regarding the model parameters, we used a batch size of 16 and we set the number of epochs to 50. As optimizer, the stochastic gradient descent (SGD) was exploited. We set the patience parameter to 50, the workers to 8, the maximum number of detections per image to 300, the momentum to 0.937, and the intersection over union (IoU) threshold for Non Maxima Suppression (NMS) to 0.7.  
We utilized the Roboflow web application to perform data augmentation ([[https://roboflow.com/](https://roboflow.com/)]([https://roboflow.com/](https://roboflow.com/)), accessed on 8 August 2023) by randomly rotating the pictures 90\({}^{\circ}\) clockwise, 90\({}^{\circ}\) counterclockwise, and upside down.  
Roboflow is a platform that facilitates developers in managing computer vision projects by providing data management capabilities. It enables the integration of images along with annotations created on Labelbox and offers the ability to apply various transformations to the images.
# PK-Judge: Enhancing IP Protection of Neural Network Models Using an Asymmetric Approach
## 2 Related Work
### White Box Watermarking  
Weights-Based Embedding: Several methods embed watermark directly in neural network weights [PERSON] et al. [11] embedded static watermarks into model weights via regularization. They are utilizing a secret matrix to facilitate both embedding and extraction. Ownership is verified by comparing the extracted watermark with the original using Bit Error Rate (BER). However, the approach is susceptible to statistical attacks due to increased variance in the watermarked layer [18] and vulnerable to fine-tuning. [PERSON] and [PERSON] [19] proposed exponential weighting for robust watermarking, while [PERSON] et al. [20] introduced a greedy residual approach to embed watermarks with minimal accuracy loss but their approach lead to increased computational complexity and longer training times due to the greedy residual embedding process. [PERSON] and [PERSON] [21] presented RIGA, which hides watermarks covertly in weights. Although these approaches preserve fidelity, they can be computation-heavy when verifying across large model weight matrices. DeepiSign [14] embedded cryptographic hashes in the frequency domain. [PERSON] et al. [22] introduced passport layers as a DNN model protection mechanism. [PERSON] et al. [15] introduced Deep Serial Number (DSN) by training a teacher model, followed by using knowledge distillation to transfer the teacher model's knowledge to student models. [PERSON] et al. [23] integrate license features into models by synchronizing license training with model parameters through gradient optimization and introduce employ random perturbations for data standardization.  
Dynamic Parameters: Another method focuses on embedding signals into dynamic parameters, such as batch normalization statistics or activation distributions. [PERSON] et al. [24] advocated leveraging external feature embeddings to verify ownership passively. [PERSON] et al. [12] (DeepSigns) introduced an activation-distribution watermark. DeepMarks [13] employs a technique where a watermark is embedded within the model's weight distribution's probability density function, employing a method akin to that of [PERSON] et al. for extraction. Ownership is verified by correlating the extracted watermark with the owner's signature through dot product computation. But, this protocol has a large calculation overhead.  
Structure-Level Watermarking: Instead of focusing on weights, some approaches alter or tag the network architecture itself. [PERSON] et al. [25] proposed a lottery-ticket-basedwatermark that prunes and reconfigures subnetwork structures, while [PERSON] et al. (2020) introduced structure pruning or architectural morphing to encode watermarks. Such methods often demand no extra data and avoid performance drops by carefully adjusting layers or channels. Nevertheless, they lack generalization and flexibility across diverse architectures and are less robust to architectural compression or layer-wise transformations.
## 3 System Architecture
### Smart Homes Appliance Models  
For the performance evaluation of the proposed methodology, smart home appliances were classified in three different categories. These categories included deferrable appliances, base load appliances, and non-deferrable appliances. The total number of user appliances was represented by \(A_{n}\). Deferrable appliances, base load appliances, and non-deferrable appliances were represented by \(A_{d}\), \(A_{b}\), and \(A_{nd}\), respectively. According to the user requirements and preferences, all user appliances were scheduled with a 24-h time horizon, as expressed in Equation (1).  
\[h\in H\;\forall\;H=\{h_{1},h_{2},h_{3},\ldots,h_{24}\}. \tag{1}\]  
The execution period for each appliance should be within the earliest starting time (\(\hat{\alpha}\)) and least finishing time (\(\hat{\beta}\)), as represented in Figure 3. The difference between the earliest starting time and the actual starting time is known as the waiting time of the individual appliance. \(\hat{\alpha}\) and \(\hat{\beta}\) for individual appliances were circumscribed by the consumer, as described in Table 1. In the proposed home appliance scheduling scheme, twelve different rudimentary appliances were considered for a smart home, which are mentioned in Table 1.
###### Abstract  
To obtain the viscoelastic parameters of asphalt mixtures and analyze the effect of temperatures and modifiers on viscoelastic properties of asphalt mixtures, the creep compliances of the neat asphalt mixture (AM), compound diatomite and basalt fibers reinforced asphalt mixture (DBFAM), and styrene-butadiene-styrene modified asphalt mixture (SBSAM) were tested and calculated by the static creep tests. And the creep compliances of the three asphalt mixtures at \(-20\)\({}^{\circ}\)C, \(-10\)\({}^{\circ}\)C, and \(0\)\({}^{\circ}\)C are deducted by the time-temperature equivalence principle (TTEP) and Arrhenius equation. Further, the relaxation modulus of the three asphalt mixtures from \(-20\)\({}^{\circ}\)C to \(50\)\({}^{\circ}\)C at \(10\)\({}^{\circ}\)C increments are calculated by the convolution integral and Simpson method. Subsequently, the Burgers model, the generalized Kelvin model, and the generalized Maxwell model are applied to analyze the viscoelastic properties of the three asphalt mixtures at different temperatures. The results show that the generalized Kelvin model and the generalized Maxwell model are superior to the Burgers model in describing the variation of viscoelastic properties of asphalt mixtures with loading time. At low temperatures, asphalt mixtures have excellent properties in resisting permanent deformation and releasing internal stress. Besides, the addition of SBS modifier and compound diatomite and basalt fibers modifier can significantly raise the viscosity \(\eta_{1}\) and the elastic modulus E\({}_{1}\) of the asphalt mixture, respectively.  
Keywords: asphalt mixture; viscoelastic properties; static creep tests; creep compliance; relaxation modulus; Burgers model; generalized Kelvin model; generalized Maxwell model +
Footnote †: journal: Applied Soft. 2019, 9, 4246; doi:10.3390/app9204246
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 4 Datasets and Metrics
### Surface Defect Datasets  
In the field of industry, a dataset plays a crucial role in the research of generative model-based defect inspection methods. Unlike common detection datasets, defect datasets have smaller sample sizes with fewer categories and prefer to use grayscale images. According to the summary of the selected methods in the last section, the extensively used defect datasets are summarized in Table 7, specifically as follows: AITEX [114], DAGM [115], KolektorSDD1 [116], KolektorSDD2 [117], MT (Magnetic Tile) [118], MVTec [119], NEU series (NEU-CLS [120], NEU-DET [24], and NEU-CLS-64 [72]), RSDDs [121], X-SDD [122], and YDFID-1 [123]. These datasets cover a wide range of defect inspection scenarios of manufacturing product surfaces.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Year** & **Brief Description** & **GM** & **Objective** & **Task** & **LM** \\ \hline \multirow{3}{*}{[108]} & \multirow{3}{*}{2022} & Introduce channel attention mechanism to improve detail recovery ability in the raindrop removal area & AE-WGAN & Bearing & CLS & WSL \\  & & Fuse prior knowledge data to generate defects with varied features and regard the generated defects as teacher samples to fine-tune the inspection model & Mask2 Defect & Automobile parts; MT & CLS SEG & WSL \\  & & Design an improved GAN with an AE architecture to address the paucity of requisite defective samples & GAN & Fabric & CLS & WSL \\  & & Combine a GAN and a residual AE for defect segmentation in semi-supervised learning manner & GRD-Net & MVTec & CLS SEG & WSL \\  & & Build a network model based on encoder–decoder–encoder and incorporate an attention feature fusion module in the decoder & F-GAN & MVTec & CLS & USL \\  & & Combine DCCVAE and ACWGAN-GP for image generation and recognition of ship coating defects & DCCVAE-ACWGAN-GP & Ship coating & CLS & USL \\ \hline \hline \end{tabular}  
* Notions: GM = generative model, CLS = classification, SEG = segmentation, LM = learning mode, USL = unsupervised learning, WSL = weakly-supervised learning.  
\end{table}
Table 6: Summary of studies based on multi-models for surface defect inspection.
# One-Photon-Interference Quantum Secure Direct Communication
## 1 Introduction
Our OPI-QSDC protocol is a new one-way MDI-QSDC protocol that operates without relying on quantum memory. Moreover, we harnesses single-photon interference, as utilized in twin-field QKD protocols [41; 42; 43; 44; 45; 46; 47; 48; 49] that have broken the repeaterless quantum communications bound known as the Pirandola-Lauremaa-Otaviani-Banchi (PLOB) bound [50]. This bound has been extended into bipartite quantum interactions [51] and quantum network universal limitations [52]. In the OPI-QSDC protocol, [PERSON] and [PERSON] are able to achieve single-photon interference at the intermediate node Charlie by utilizing phase-locking techniques [53], when using weak coherent light sources. This doubles the transmission distance compared to other one-way QSDC protocols [34]. We analyze the security of our protocol using quantum wiretap channel theory and simulate its performance, demonstrating its ability to break the PLOB bound. The remainder of this paper is organized as follows. In Section 2, we describe the detailed steps of the proposed OPI-QSDC protocol, while in Section 3, we analyze its security. Then in Section 4, we present our numerical analysis of performance. Finally, we give a conclusion in Section 5.