esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies
## 1 Introduction  
Traditional automotive data collection has focused on low-bandwidth vehicle data such as speed and brake status. However, as autonomous vehicles increasingly seem to be the future of transportation [1], these data collection methods are becoming obsolete. Modern autonomous vehicles require the large-scale collection of high-bandwidth data (e.g., video, point clouds) for algorithm development and validation and verification. Deep-learning networks for common autonomous vehicle perception tasks such as object detection [2; 3; 4], object tracking [5; 6; 7], and trajectory prediction [8; 9; 10; 11] need significant quantities of high-bandwidth real-world data for effective training and testing.  
The finite on-board storage capacity presents a challenge for high-bandwidth data capture that low-bandwidth data logging systems do not encounter. Recently, event data recorders specialized for such high-bandwidth data capture have been explored. The smart black box (SBB) [12; 13] is one such system. The SBB uses pre-defined rules for event-of-interest (EOI) detection and computes the data value according to the detected EOI. The data value is then used to determine data compression factors and as the basis for prioritized data recording.  
This paper expands the smart black box to record high-priority video data and applies the SBB to a real-world driving dataset. Rather than using pre-defined rules for EOI detection as in [12; 13], machine learning-based methods for generalized EOI detection are applied to derive the data value. Raw data are grouped into buffers, compressed, and stored in a priority queue in order to discard low-value data as the storage capacity is filled. The SBB is assessed on real-world driving video. We focus on video data due to the ubiquity of cameras as a sensor in automotive applications.  
This paper offers two primary contributions. Firstly, we apply video anomaly detection (VAD) [14; 15] and online action detection (OAD) [15; 16] as methods for generalizedEOI detection on real-world driving video. To estimate the data value from combined VAD and OAD outputs, we introduce a hybrid value method based on a weighted sum. Secondly, we present an updated SBB pipeline incorporating VAD and OAD and designed for the real-time recording of dash camera video data. We find that while the SBB improves the collection and retention of high-value data, improved EOI detection methods are needed to realize the full potential of the SBB.  
The paper is structured as follows. First, related work is explored in Section 2. Then, an overview of the original SBB [13] is presented, and the changes we made for application to real-world data are discussed in Section 3. Section 4 describes our adjusted data classification system, the updated SBB pipeline, and the new value estimation method. Section 5 presents experimental results on a combined real-world dataset and analyzes the performance of our updated SBB. Section 7 concludes the paper and discusses future work.
# The Effect of Varying Out Beverage Ratios on the Characteristics of Fermented Dairy-Oat Beverages
###### Abstract  
In the context of the growing popularity of plant-based diets and the search for alternatives to traditional dairy products, there is a need to develop new, consumer-appealing products that combine the nutritional value of milk and oats. The aim of this study was to investigate the effects of different proportions of oat beverage in cow's milk on the physicochemical, textural, sensory, and microbiological properties of fermented dairy-oat beverages, as well as to assess the stability of these properties during cold storage. Dairy-oat beverages with varying percentages of oat beverage (0-100%) were prepared and subjected to fermentation using starter cultures of _S. thermophilus_ and _L. delbrueckii_ subsp. _bulgaricus_. Changes in pH, lactose content, color, syneresis, texture, and starter bacteria population were evaluated over 28 days of storage at 6 \({}^{\circ}\)C. The results showed that the addition of oat beverage significantly affected the chemical composition, fermentation process, and quality characteristics of the final product. Changes were observed in the content of protein, fat, lactose, and carbohydrates, a slower fermentation process, changes in color, increased syneresis, and changes in texture depending on the proportion of oat beverage. The optimal proportions of the cow's milk and oat beverage mixture, ensuring the desired sensory, textural, and microbiological stability, were found to be in the range of 25-50% oat beverage addition.  
refmented beverages; oat beverage; lactic acid fermentation; physicochemical properties; texture; syneresis; microbiological stability 2020
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 2 Literature Review
### Long Short-Term Memory (LSTM)  
Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that involves a loop structure between neurons in each layer [12]. LSTM is suitable for sequential or time-series data since it can maintain the continuity of information [39]. LSTM is more popular than the original RNN because the vanishing or exploding gradient and long-term dependency problems in traditional RNN have been overcome in LSTM [1; 3; 4].  
LSTM takes a significantly long time to train, despite these advantages, compared to other DL algorithms [1]. In addition, LSTM only considers the forward information and does not consider the backward information. This issue, however, can be resolved in Bidirectional LSTM [40]. LSTM has caught much attention among researchers, and some of their research works in the phishing detection domain are shown in Table 11 (Appendix A).  
Similar to DNN and CNN, LSTM can be implemented individually [20; 41; 42; 43; 44; 45], incorporated with traditional machine learning techniques [46; 47], or combined with other DL algorithms in a hybrid model for an improved performance in detecting malicious websites [10; 11; 31; 33; 35; 36]. Among the studies of LSTM-based phishing detection models, a majority of them specified the parameter settings for neural network architecture, number of epochs, and learning rate; but ignored the dropout rate and batch size [31; 41; 42; 44; 47]. Moreover, only certain parameters were optimized during the fine-tuning process [32; 42]. To evaluate the overall performance of LSTM models, four popular metrics were used, being accuracy, precision, recall, and F1-score [31; 35; 41; 44; 45]. Other measures training time, detection time, error rate, detection cost, number of epochs per second, etc. [33; 42; 46; 47].
## 5 Sample Preparation and Loss Measurement  
### Sample Preparation  
Using SLM, two coils are printed for the same slot geometries using two different materials. The technology SLM can accelerate the development of innovative customized components. Additionally, it can allow for material optimization in order to reduce raw material consumption.  
In the first prototyped sample, a single-strand coil is printed with eight flat solid turns using pure-copper, as shown in Figure 9. The gaps between the conductors are reduced so that the slot fill factor is increased to 77.96%. This remarkably high fill factor allows for reduced DC losses in this copper coil. The coil is then heat treated inside a controlled atmosphere chamber to relive any residual stress and to boost its electrical conductivity. Finally, the turns are insulated using epoxy resin that withstand a temperature up to 180 \({}^{\circ}\)C. The copper coil weight is 183.6 g. The electrical conductivity of the copper coil is measured compared to commercial copper. It is found that the printed copper coil has 100% IACS (International Annealed Copper Standard).  
In the second prototyped sample, a semi-stranded coil is designed, as shown in Figure 10. The four upper turns are divided into four transposed strands. Unlike conventional transposed roebel bar, the strand transposition is ingeniously made at the coil front end winding (not inside the slot), as previously explained in Figure 3. As a result, a high fill factor of 70.6 is achieved. This fill factor is considered to be much higher than that in the conventional fully stranded coils, which is typically between 35-45%, as reported in [29]. The coil is 3D printed using SLM of aluminum alloy (AlSi10 Mg), as  
Figure 8: Comparison between the thermal profile at the worst case scenario under the same cooling conditions.  
shown in Figure 11. The coil has a perfect finishing with very precise dimensions. This material combines good conductivity and ultralight weight, which are suitable for high frequency machines. A post-process heat treatment is also used to enhance the electrical and mechanical properties. Finally, the strands and turns are electrically insulated using class H air-drying polyester varnish (Synthite AC-43 180 \({}^{\circ}\)C). The coil weight is 55.2 g, which is only 30.1% of the weight of the 3D printed copper coil.  
Figure 10: A novel semi-stranded coil design with transposed strand locations.  
Figure 9: Reference coil case: 3D printed pure copper coil and assembly with a ferromagnetic core.
## 3 Methods
### Confidence Distribution Encoder  
We aim to map match information, represented by \(X_{c}\in\mathbb{R}^{N\times 7}\), to a probability distribution within \([0,1]\), the details as shown in Figure 6. Here, \(X_{c}\) includes both geometric and additional information, with 7 channels representing position and other details. Then, the match information \(X_{c}\) is processed by the confidence distribution encoder. Initially, a series of ResNet blocks [30] with shared weights transforms \(X_{c}\) to a higher-dimensional space. Subsequently, the group layer from CLNet [31] aggregates the local consensus features. A \(1\times 1\) MLP kernel is used to derive the probability distribution \(p_{l}\) of these aggregated features. The adjacency matrix is constructed by multiplying \(p_{l}\) with \({p_{l}}^{T}\). Enhanced local features are then input into a Graph Convolution Network (GCN) [32] to integrate the global match features, with this process iterated to strengthen the global features and reduce incorrect matches. Finally, a \(1\times 1\) MLP kernel is applied to produce the confidence probability distribution \(p_{con}\).
# Seismic Behavior of Triple Tunnel Complex in Soft Soil Subjected to Transverse Shaking
###### Abstract  
Combining multiple tunnels into a single tunnel complex while keeping the surrounding area compact is a complicated procedure. The condition becomes more complex when soft soil is present and the area is prone to seismic activity. Seismic vibrations produce sudden ground shaking, which causes a sharp decrease in the shear strength and bearing capacity of the soil. This results in larger ground displacements and deformation of structures located at the surface and within the soil mass. The deformations are more pronounced at shallower depths and near the ground surface. Tunnels located in that area are also affected and can undergo excessive distortions and uplift. The condition becomes worse if the tunnel area is larger, and, thus, the respective tunnel complex needs to be properly evaluated. In this research, a novel triple tunnel complex formed by combining three closely spaced tunnels is numerically analyzed using Plaxis 2D software under variable dynamic loadings. The effect of variations in lining thickness, the inner supporting structure, embedment depth on the produced ground displacements, tunnel deformations, resisting bending moments, and the developed thrusts are studied in detail. The triple tunnel complex is also compared with the rectangular and equivalent horizontal twin tunnel complexes in terms of generated thrusts and resisted seismic-induced bending moments. From the results, it is concluded that increased thickness of the lining, inner structure, and greater embedment depth results in decreased ground displacements, tunnel deformations, and increased resistance to seismic-induced bending moments. The comparison of shapes revealed that the triple tunnel complex has better resistance against moments with the least amount of thrust and surface heave produced.  
triple tunnel complex; soft soil; seismic response; finite element analysis Article
# AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse
## 4 Implementations of an AMR-Assisted Picker-to-Parts System
### Separated Handover Locations for Sub-Aisles  
In the first implementation, we consider a warehouse with two blocks of parallel aisles, with a handover location assigned to each sub-corridor. Each sub-aisle is assigned a specific order picker, who can load the items collected exclusively in the handover location mentioned above. Both the handover locations of each aisle are placed in the perpendicular main corridor, as depicted in Figure 2.  
Each AMR, starting from the depot located on the left of the middle aisle, visits the handover locations of the sub-corridors clockwise. According to this configuration, the AMR first visits the corridors of the upper block and, later, those of the lower block. Once the tour is over, the AMR returns to the starting depot. Note that the AMR can eventually turn back to the depot when the right-most aisle containing an order is reached. If the number of aisles per block is n, the warehouse will consist of \(2n\) sub-aisles and \(2n\) handover locations, each one operating for a single sub-aisle.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 5 Performance Evaluation
### Communication Cost  
In order to analyze the communication overhead of each scheme under the same conditions, we assume that the elements in \(Z_{q}^{*}\) are the same length as the messages, both 160 bits, \(|Z_{q}^{*}|=160\) bits, \(|m|=160\) bits. The bilinear pairing group is \(|G_{1}|=2|p^{\prime}|=1024\) bits, and the elliptic curve group is \(|G|=2|p|=320\) bits.  
Table 5 compares the signcryption output length as the communication cost with other schemes. It can be seen that our scheme and [24] have the lowest communication overhead, both of which are 640 bits. Compared with [27], our scheme is reduced by \((2208-640)/2208=71.01\%\); compared with [28], it is reduced by \((4416-640)/4416=85.51\%\); compared with [25], it is reduced by \((960-640)/960=33.33\%\). Therefore, our scheme has lower communication overhead than the others and further saves the bandwidth of V2G networks.  
Figure 5: Computation cost of the CSO in this scheme.  
Figure 6: Comparison of computation costs for the CSO [24; 25; 27; 28].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Length of Signcryption (Bits)** & **Length of Aggregate Signcryption (Bits)** \\ \hline
[27] & \(2|G_{1}|+|m|=2208\) & \((n+1)|G_{1}|+n|m|=1184n+1024\) \\
[28] & \(\left|Z_{q}^{*}\right|+4|G_{1}|+|m|=4416\) & \(n\left|Z_{q}^{*}\right|+\mathbf{n}|m|+(2n+2)|G_{1}|=2368n+2048\) \\
[24] & \(\left|Z_{q}^{*}\right|+|G|+|m|=640\) & \(\left|Z_{q}^{*}\right|+n|G+n|m|=480n+160\) \\
[25] & \(\left|Z_{q}^{*}\right|+2|G|+|m|=960\) & \(\left|Z_{q}^{*}\right|+2n|G|+n|m|=800n+160\) \\ Ours & \(\left|Z_{q}^{*}\right|+|G|+|m|=640\) & \(\left|Z_{q}^{*}\right|+n|G|+n|m|=480n+160\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Communication cost comparison among CLASC schemes.
## 4 Experiments
### Data Preparation  
We obtained 22 data sets with classification tasks from OpenML. The list of data sets with basic characteristics is presented in Table 1.  
Each data set was divided into training and diagnostic parts, with the diagnosis data set including at least 100 observations. We fit a logistic regression model, a support vector machine, and XGBoost to each data set. Following that, three adversarial attacks were launched against the diagnosed part of each data set. All calculations were conducted in Python using the Adversarial Robustness Toolbox package [32] and Permute Attack repository [18]. In the next step, we calculated diagnostic attributes for each data set and attack based on proper surrogate models. To create the surrogate models, we utilized an ensemble of 2500 approximate reducts with \(\epsilon\) equal to 0.05. Figure 2 shows the distribution of obtained Cohen kappa scores between the original prediction and the approximated predictions made by the surrogate model; for every data set and model type, the quality condition of the surrogate model was met. All scores are above 0.9 and the median of these values is equal to 1. \begin{table}
\begin{tabular}{l r r r} \hline \hline
**Data Set Name** & **Number of Instances** & **Number of Attributes** & **Number of Classes** \\ \hline Bioresponse & 3751 & 1776 & 2 \\ churn & 5000 & 20 & 2 \\ cmc & 2000 & 47 & 10 \\ cae-9 & 1080 & 856 & 9 \\ dna & 3186 & 180 & 3 \\ har & 10,299 & 561 & 6 \\ madelon & 2600 & 500 & 2 \\ mfeat-factors & 2000 & 47 & 10 \\ mfeat-fourier & 2000 & 76 & 10 \\ mfeat-karhunen & 2000 & 47 & 10 \\ mfeat-zernike & 2000 & 47 & 10 \\ nomao & 34,465 & 118 & 2 \\ optdigits & 2000 & 47 & 10 \\ pendigits & 10,992 & 16 & 10 \\ phoneme & 5404 & 5 & 2 \\ qsar-biodeg & 1055 & 41 & 2 \\ satimage & 6430 & 36 & 6 \\ semeion & 1593 & 256 & 10 \\ spambase & 2000 & 47 & 10 \\ wall-robot-navigation & 5456 & 24 & 4 \\ wdbc & 569 & 30 & 2 \\ wilt & 4839 & 5 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic characteristics of data sets used in experiments. The result of approximator learning is that the data set contains values of eight diagnostic attributes for each instance of the diagnosis data set and attack. To provide suitable data for training a classifier, we aggregated these attributes within cross-sections defined by data set, model, and attack. For each diagnostic attribute, a set of descriptive statistics was calculated: mean, minimum, maximum, lower quartile, median, upper quartile, and range. For those aggregates, we also added some data set characteristics such as the number of observations, the number of classes in the classification problem, and the balanced accuracy calculated based on predictions and targets. There were a total of 59 features analyzed. Table 2 presents the first 15 rows and 7 columns of the aggregated data set utilized for attack detection and isolation models estimation. For the attack detection problem, column attack was replaced by a binary variable equal to 0 for _org_ value, meaning lack of attack, and 1 otherwise.
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Optimization of Each Layer in the Base Region
#### 3.2.2 Increasing the Potential of the SCH Layer Facilitates Carrier Transport
found that the introduction of the SCH2 layer brings significantly less bandwidth improvement than the SCH1 layer [54], which can be explained by the effect of the SCH layer on the carrier transport. The impact is mainly in the SCH1 layer. The main advantage of introducing the SCH2 layer is that it can increase the output optical power and reduce the threshold current. The report stated that the modulation bandwidth of GAIN-SCH-TL can be increased to approximately 100 GHz when combined with existing bandwidth enhancement techniques (e.g., the use of MQW structures and adjustment of QW positions).
## 1 Introduction
The presented solution has several advantages, such as its capacity for the simultaneous processing of data from multiple plants, integrating all the plant's measurement sensors, adapting to a variety of typologies and structures of measured data, calculating element performance parameters and aggregating and averaging data at different time intervals. The system is scalable to increase the number of plants to be analysed and allows the data to be homogenised. This facilitates the joint processing of their data for their possible aggregation and joint management, with a view to their integration into the grid, within the concept of the Virtual Power Plant (VPP) [31; 84; 85; 86]. Although the current version of the tool is not prepared for real-time analysis, it is possible to store within it monitoring data, for example on a daily and/or weekly basis, and to conveniently carry out data processing, close to the concept of Edge Computing [84], in order to supervise the operation of the plants under analysis. After this introduction, which has described the context and objectives of the work to be established, Section 2 describes the methodology, which shows how this tool has been developed, Section 3 shows the developed databases, the application that allows access to the information, and an example of the analysis of the data stored in the management system. Finally, Section 4 includes the conclusions of the work.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
###### Abstract  
Wide attention has been drawn to the application of space networks (SN) in recent years. Compared with terrestrial networks, SN have a set of unique characteristics, e.g., a long propagation delay, time-varying channel quality, and dynamic link connection, which causes the inapplicability of mature terrestrial networking technologies. Therefore, the focus of SN application is to innovate and break through a series of key networking technologies and protocols. Network emulation is critical for evaluation and verification in the research of networking technologies and protocols. This paper concentrates on designing an emulation platform which provides a realistic, flexible, and extensible experimental environment for the SN. The architecture of platform includes four components, namely the logical plane, control plane, data plane and measurement plane. Container technology is adopted to flexibly symbolize network nodes. The emulation method of dynamic connection relationships and time-varying link characteristics between pairwise nodes are considered so as to realize the real space environment and arbitrary topology. Furthermore, an extensible structure is described to emulate large-scale scenarios and access external emulation resources. Finally, experiment results show that the proposed platform is capable of emulating diverse, dynamic, and complex SN scenarios with high precision.  
space network; emulation platform; container technology; extensible structure Article