esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Studies
### Vehicle Localization
The issue arises from the integration of errors over time. The problem brings about a drift in the position that is reported by the sensor. The error in acceleration generates a linear error in velocity. The error also generates a quadratic error in position. Similarly, an error in gyroscope data causes a quadratic error in velocity and a cubic error in the position [30]. The advantage of an IMU is that it can provide odometry data with a fast update rate when there are large sudden movements across a short time interval. This motivates designers to use it along with VO to form a visual inertial odometry (VIO). Data from the IMU and VO can be fused either loosely or tightly. A loosely coupled approach for visual-inertial systems keeps the visual and inertial framework as independent entities [31], while a tightly coupled approach combines the visual and inertial parameters under a single optimization problem and their states are jointly estimated [32]. Contemporary tightly coupled methods for visual inertial odometry fall into two categories, namely optimization-based methods [33; 34; 35] and filter-based algorithms [36; 37; 38]. In an optimization-based approach, an optimal estimate is calculated using an optimization problem solver. The optimizer tries to minimize the photometric error in order to extract more information from images. Although optimization-based methods provide high accuracy, these algorithms impose high computational costs on the system. Contrary to optimization-based methods, the Kalman filter is the cornerstone of filter-based approaches [39]. Filter-based methods show acceptable efficiency, and the accuracy is comparable to that of optimization-base methods. In this study, we use a filter-based stereo visual-inertial odometry introduced in [40]. It employs a multi-state constraint Kalman filter (MSCKF). For the feature detector, a Fast corner detection [41; 42] is used.
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Network Layer
#### 4.3.4 Wormhole Attacks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference Key Features** & **Advantage** & **Disadvantage** \\ \hline
[68] & Hop difference and local monitoring & Low computing overhead & Low adaptability \\ \hline
[69] & Time Ratio Threshold & Centralized routing protocol & Single of failure on base station \\ \hline
[70] & Round trip time & Multiple paths & Comparison with old method \\ \hline
[71] & Round trip time & Low computing overhead & Comparison with one method \\ \hline
[72] & Node trust optimization model & No additional hardware & No quantitative evaluation for delivery ratio, delay, throughput \\ \hline
[73] & Localization for anchor node & No additional hardware & Comparison with benchmarks not specialized for WSN \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of a recent antiwormhole attack. all its valid locators for the first time. Because an attacker node tries to change the location of the legitimate node, the estimated location is based on the exact distance that is smaller than a minimum threshold. This property is applied to detect the wormhole node. In terms of using location information, [PERSON] et al. [74] proposed novel secure, optimized localization in a large-scale WSN (SOLLW) using a one-class SVM. The SOLLW detects wormhole-infected anchor nodes by identifying the hop count and distance among the anchor pairs through an SVM. This procedure is accomplished by a random distance error factor with the distance estimated by hop parameters. If a wormhole is infected on a specific anchor node, then the estimation of location is not correct because the hop count is not the same as the actual count. This difference is captured by an unsupervised SVM and recovered through linear optimization. In summary, most of the research work is involved in this network layer. Even though some ML approaches have been proposed, they can be improved by optimizing the parameters in their models or employing new ML approaches with new objective functions. Recently, deep reinforcement learning with multi-agents has gained researchers' interests' to support multi-objective functions and good performance for routing.
## 2 Materials and Methods
### Evaluation Strategies and Statistical Analysis  
#### 2.6.1 Evaluation Strategies  
Several evaluation methods can be used in the field of intensity inhomogeneity correction, and can be categorized as qualitative and quantitative assessments [21]. This study was focused on quantitative assessment, because subjective visual examination of the BFC results (qualitative assessment) could be difficult to interpret if the inhomogeneity were mild, therefore not enabling a proper comparison of the different BFC methods. Among the three methodologies of quantitative assessment (inhomogeneity field, intensity variation, and segmentation based) [21], the latter two were selected, as there was no information regarding the true inhomogeneity field, and the quality of the nonuniformity correction would be derived indirectly. The measures based on intensity variability considered were: the coefficient of variation (CV), as the ratio of standard deviation (\(\sigma\)) and mean (\(\mu\)) intensity for WM (Equation (1)) and GM (Equation (2)); and the coefficient of joint variation (CJV) (Equation (3)), by summing the \(\sigma\) of the intensities of two tissue classes (WM and GM), normalized by the difference between the \(\mu\) intensities for such tissues:  
\[\text{CV}(\text{WM})=\frac{\sigma(\text{WM})}{\mu(\text{WM})} \tag{1}\]  
\[\text{CV}(\text{GM})=\frac{\sigma(\text{GM})}{\mu(\text{GM})} \tag{2}\]  
\[\text{CJV}(\text{WM},\text{GM})=\frac{\sigma(\text{WM})+\sigma(\text{GM})}{| \mu(\text{WM})-\mu(\text{GM})|} \tag{3}\]  
Considering the mean intensity of each tissue to be relatively constant, its standard deviation should be reduced when intensity inhomogeneity is corrected, translated as a decrease in CV and CJV. As the segmentation-based measure, the Dice coefficient (DC) (Equation (4)) was considered to assess the BFC via the results from brain tissue segmentation maps (considering M as the reference map obtained from manual BE, and A the maps obtained from automated BE methods), defined as:  
\[\text{DC}(\text{M},\text{A})=\frac{2|\text{M}\bigcap\text{A}|}{|\text{M}|+| \text{A}|} \tag{4}\]  
Therefore, the DC describes the similarity level between the segmentation results, and it ranges from 0 (no overlap) to 1 (perfect match).
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 1 Introduction
### Our Contributions  
In this work, we present the first LWE-based verifiable threshold multi-party fully homomorphic encryption scheme (VTMFHE) with active security, addressing the aforementioned challenges. Our scheme builds upon the GSW encryption scheme proposed by [PERSON] et al. [4]. We achieve security against malicious adversaries by integrating Shamir secret sharing, share resharing, commitments, and NIZK proofs. The main contributions are summarized as follows:To address the high complexity and overhead in asynchronous settings of existing TMFHE schemes, as well as the rapid growth of smudging noise during decryption, we constructed an LWE-based TMFHE scheme using Shamir secret sharing. This scheme is compact, as the size of the ciphertexts or the evaluated ciphertexts remains independent of the evaluation functions and the number of parties, N. Furthermore, we propose an improved share resharing technique that transforms secret key shares in the scheme into additive shares. Each party only needs to perform the resharing protocol once to store a constant state, enabling any subset of at least t parties to locally execute precomputed additive key recombination during decryption. This supports flexible t-out-of-N threshold structures and allows non-interactive key reconstruction in the decryption phase. As a result, partial decryption results from such a subset can be combined in a single round of communication to produce the decryption of the evaluated ciphertext, enabling some parties to remain offline or to generate new keys for dynamically changing participant sets in different computation tasks.
2. To address the issue of existing TMFHE schemes lacking active security, we constructed a verifiable distributed key generation encryption algorithm and threshold decryption protocols using commitment and NIZK, demonstrating how to integrate these components into our proposed VTMFHE scheme. During the implementation of the scheme, the following operations can be effectively verified for correct protocol execution and parameter usage: (a) generation of the encryption public key, (b) generation of ciphertexts by each party using the encryption algorithm, and (c) computation of partial decryption results by parties using the correct secret key shares in threshold decryption. Finally, we have demonstrated, through hybrid experiments, that our scheme is secure against malicious adversaries.
# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 1 Introduction  
The volume of network traffic is increasing exponentially, largely driven by the growing use of over-the-top (OTT) media services [1]. These video applications generate significant network traffic characterized by large packet volumes, vast data bytes, and irregular packet arrival intervals. As a result, video traffic has become a critical focus of analysis in Network Traffic Monitoring and Classification (NTMC). The benchmark Internet traffic flow dataset BRASIL: Characterizing Network-based Applications was introduced in 2005 and classified Windows Media Player and Real applications under the multimedia category [2]. Similarly, popular benchmark datasets such as ISCXVPN2016, ISCXTor2016, and CIC-Darknet2020 categorize video traffic under labels like Streaming and Video-Stream [3, 4, 5]. However, categories such as Web Browsing, Chat, Transfer, Audio-Stream, and VoIP may include traffic flows related to video, but the boundaries between applications and categories remain unclear, requiring further validation for accurate classification. In modern networks, video traffic resists straightforward categorization, it is intertwined with other traffic categories in diverse characteristics, including network protocols, architectures, application types, and traffic distribution characteristics, creating a complex profile. While some datasets include video-related traffic, these samples typically represent only a small portion of the total dataset. To address this, researchers have developed proprietary datasetstailored for specific traffic categories. This paper reviews existing network traffic datasets that contain video traffic and introduces a novel algorithm to generate a proprietary Internet video traffic dataset. The proposed approach aims to improve the performance of classification metrics in the video traffic category within NTMC.
# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
## 2 Methodology  
### Peak Load Shifting Control Strategies  
There are two operating modes for BESS in power systems. During low load periods, the BESS switches to a charging state and acts as an electrical load, absorbing excess energy from the load absorption system. During peak load periods, the BESS switches to the discharging state and acts as a generator, discharging excess energy into the system. Developing a reasonable BESS charging and discharging control strategy can help to improve the peak-shaving efficiency and BESS utilization.
###### Abstract  
Fog computing accredits by utilizing the network edge while still rendering the possibility to interact with the cloud. Nevertheless, the features of fog computing are encountering several security challenges. The security of end users and/or fog servers brings a significant dilemma in implementing fog computing. The computational power of the resources constrains Internet of Things (IoT) devices in the fog-computing environment. Therefore, an attacker can easily attack. The traditional methods like attribute-based encryption (ABE) techniques are inappropriate for resource-constraint devices with protracted computing and limited computational capabilities. In this regard, we investigate a multiauthority ciphertext policy-attribute-based encryption (MA-CP-ABE) method that enables multiauthority attribute revocation and computation outsourcing. Moreover, the encryption and decryption processes of resource-constraint IoT devices are outsourced to the fog nodes. In this way, it also reduces the computational burden of the resource-constraint IoT devices. Hence, we propose MA-CP-ABE for encryption and decryption, attribute revocation and outsourcing by reducing the computational burden and securing the system. We compare the computational offloading approach with the existing techniques to prove that the proposed approach outperforms the existing approaches. The proposed method reduces the operation time for the encryption and decryption process. We outsource cryptography operations to the fog node, reducing the end user's computational cost. Eventually, simulated outcomes are used to assess the algorithm's computational cost.  
fog computing; ciphertext policy; attribute-based encryption; cryptography Article
## 6 Conclusions  
This paper presents a DSM control technique by incorporating NB-PLC on residential, commercial, and industrial loads. The DSM is applied to SACs by using SEP 1.0. Temperature sensitivity analyses are presented to help understand the influence of atmospheric temperature variations on power consumption profiles of various types of consumers. The characteristics of NB-PLC are investigated for LV and MV networks in detail using the measurements taken at LV channels, MV channels, hybrid channels (involving transformer bridge), and solely transformer responses. The presented SNR profiles will help to assess the feasibility and performance of NB-PLC over LV channels, MV channels, and transformer bridge. It is verified from field test results that the implementation of DSM control technique significantly reduces the power consumption in peak hours. The control commands can be sent by MCR to adjust the thermostat temperature of SACs. Similarly, SACs can send daily, weekly, monthly, and annual power consumption profiles to MCR. The setup of proposed system is compatible with various other appliances in context of future smart homes. This research is novel in the sense that it assesses the feasibility of DSM services using NB-PLC in South Asian countries like Pakistan.  
Conceptualization, B.M. and S.G.; methodology, B.M., M.S. and J.N.; validation, M.N.I., A.U.R. and I.R.; data curation, M.B., M.S., J.N. and H.H.; investigation, M.B., M.N.I., I.R. and H.H.; writing--original draft preparation, B.M., A.U.R. and J.N.; writing--review and editing, M.N.I., A.U.R., S.G. and I.R.; visualization, H.H., M.B. and M.S.; supervision, S.G.; Funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (IF-PSAU-2021/01/18103). The authors also thank Natural Sciences and Engineering Research Council of Canada (NSERC) and New Brunswick Innovation Foundation (NBIF) for the financial support of the global project. These granting agencies did not contribute in the design of the study and collection, analysis, and interpretation of data.  
The authors declare no conflict of interest.
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 2 Materials and Methods
### Method  
#### 2.2.1 Alternating Temperature Setting and Sample Maintenance  
In this study, three groups of alternating temperature processes were designed: T1 is a cyclic process of heating to constant temperature to cooling, which simulates the sunrise-to-sunset process in a natural desert environment. T2 is a cyclic process of cooling to constant temperature to heating, which simulates the sunset-to-sunrise process in a natural desert environment. Meanwhile, T3 is a process of constant temperature throughout as a control group.  
The range of the alternating temperature was set to the highest temperature of 40 \({}^{\circ}\)C and the lowest temperature of 20 \({}^{\circ}\)C. The constant temperature was set to 30 \({}^{\circ}\)C throughout the whole process. The alternating temperature was set as a cycle of 12 h, and there were 8 cycles for a total of 96 h. The temperature variation of the three experimental groups during a cycle is shown in Figure 4.  
The cementing material and aeolian sand were preheated, and the temperature was controlled during the sample preparation process. This ensures that the initial reaction temperature was achieved as soon as possible after the material was mixed, and the effect of the temperature difference from the outer to the inner layer of the aeolian sand column sample was minimized as much as possible. Since urease must be at a temperature of \(-20\) \({}^{\circ}\)C to maintain activity without loss and the mass of urease added was extremely small (2.5 mg), the temperature of added urease will have little influence on the temperature of all materials; therefore, the urease was not preheated.  
The preheating steps were as follows: Before the materials were mixed, anhydrous calcium chloride, urea, distilled water, and aeolian sand from each temperature experimental group were placed in a temperature maintenance chamber (YH-60B, produced by Shanghai Meiyu Instrument Co., Shanghai, China) with a constant temperature of 20 \({}^{\circ}\)C (T1), 40 \({}^{\circ}\)C (T2), and 30 \({}^{\circ}\)C (T3), all with 20% humidity, for 24 h.  
The temperature maintenance steps were as follows: After the preheated steps were conducted for each material (except urease), the samples were prepared according to the description in Section 2.1 and immediately placed in the temperature maintenance chamber, which had been adjusted to the starting temperature (T1: 20 \({}^{\circ}\)C, T2: 40 \({}^{\circ}\)C, and T3: 30 \({}^{\circ}\)C). The samples were maintained according to the temperature curves shown in Figure 4.  
Figure 4: Temperature variations of the three experimental groups during a cycle (12 h).
## 5 Discussion and Findings
### Predicting Injuries  
The ML algorithms deployed to predict injuries showed considerable success, with the best-performing model achieving a commendable recall score of 0.60. This means that the algorithm correctly identified 60% of the injuries among injured players, demonstrating its usefulness in predicting such occurrences. Notably, the analysis revealed that 'Squad' emerged as the most crucial feature in predicting injury, with an average feature importance of 0.23 across models and an even more pronounced significance of 0.29 in the best model, a fine-tuned XGBoost model. 'Average Minutes per Match', with an average feature importance score of 0.16, and 'Age', with an average importance rating of 0.14, were close behind. These findings shed light on the critical importance of squad dynamics and player age in comprehending and forecasting injuries in professional football.
## 2 Methodology
### Equivalent Fatigue Damage Constitution
#### 2.3.4 Fatigue Residual Strain  
Under fatigue loading, concrete's fatigue residual strength and elastic modulus continuously decrease, while the residual strain accumulates with the increase of loading cycles. Fatigue residual strain after \(n\) cyclic loading is usually fitted from fatigue experiment data [37], which consists of complicated equations with many uncertain parameters. Therefore, a simplified method is developed to estimate fatigue residual strain.  
At fatigue failure, the nonlinear loading branch of the equivalent stress-strain curve is shown as a thin solid line (a) in Figure 13, and the fatigue residual secant modulus calculated from Equation (29) is shown as a dashed line (b) in Figure 13, in which fatigue residual strength envelope curve is shown as the thick solid line.  
Figure 13: Sketch of estimating fatigue residual strain at fatigue failure \(\epsilon_{\text{res}}^{\text{f}}\) from the maximum fatigue strain \(\epsilon_{\text{max}}^{\text{f}}\) and fatigue residual secant modulus \(E_{\text{s}}^{\text{f}}\).  
From Figure 13, the fatigue residual strain at fatigue failure is calculated from the limit state parameters at fatigue failure as:  
\[\varepsilon_{\text{res}}^{\text{f}}=\varepsilon_{\text{max}}^{\text{f}}-\frac{ \sigma_{\text{r}}(N_{\text{f}})}{E_{\text{s}}^{\text{f}}}, \tag{31}\]  
where \(\sigma_{\text{r}}(N_{\text{f}})=\sigma_{\text{max}}\) and \(\varepsilon_{\text{max}}^{\text{f}}\) are the residual strength and maximum fatigue strain at fatigue failure, which can be obtained from the fatigue residual strength envelope curve, and \(E_{\text{s}}^{\text{f}}\) is the fatigue residual secant modulus at fatigue failure.  
Assuming fatigue residual strain evolves similarly to the maximum fatigue strain [30], the damage variable can also be described by residual strain as in Equation (32):  
\[D_{\text{f}}=\frac{\varepsilon_{\text{res}}^{\text{n}}-\varepsilon_{\text{res} }^{\text{1}}}{\varepsilon_{\text{res}}^{\text{f}}-\varepsilon_{\text{res}}^{ \text{1}}}, \tag{32}\]  
where \(\varepsilon_{\text{res}}^{\text{f}}\) is the residual strain at fatigue failure; \(\varepsilon_{\text{res}}^{\text{n}}\) is the fatigue residual strain at \(n\)th cyclic loading; \(\varepsilon_{\text{res}}^{\text{1}}\) is the residual strain at the first cyclic loading, which could be taken as 0 for simplicity.  
By transforming Equation (32), fatigue residual strain at \(n\)th cyclic loading is derived and expressed in Equation (33) as  
\[\varepsilon_{\text{res}}^{\text{n}}=\varepsilon_{\text{res}}^{\text{1}}+D_{ \text{f}}\Big{(}\varepsilon_{\text{res}}^{\text{f}}-\varepsilon_{\text{res}}^ {\text{1}}\Big{)}. \tag{33}\]  
Substituting Equation (20) into Equation (33), we can rewrite fatigue residual strain at any cycle ratio as  
\[\varepsilon_{\text{res}}^{\text{n}}(n/N_{\text{f}})= \varepsilon_{\text{res}}^{\text{1}}+a\big{(}\frac{b}{b-n/N_{\text {f}}}-1\big{)}^{1/c}(\varepsilon_{\text{res}}^{\text{f}}-\varepsilon_{\text{ res}}^{\text{1}}). \tag{34}\]