esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Stage Deep Learning Framework for Video Surveillance
## 1 Introduction
This process enables an in-depth analysis of the spatial attributes of the footage, laying the groundwork for subsequent sophisticated analysis. 3. Integration of BiLSTM with Multi-Head Attention for Temporal Dynamics: The second phase combines BiLSTM with multi-head attention, focusing on the temporal relationships between frame feature vectors. This synergy enhances the model's ability to identify anomalies, leading to a marked improvement in detection accuracy. 4. Customized Transformer Module for Enhanced Pattern Recognition: In the final phase, a specially adapted Transformer module is employed. This module is proficient in handling relative positional embeddings and recognizing long-range dependencies, thereby boosting the processing efficiency. It is specifically tailored to anomaly detection in video data. Figure 1: Trends in accrual costs by major criminal crimes of prosecution in 2012–2021. (Source: “2022 Crime Analysis” by the Supreme Prosecutors’ Office of Korea). Superior Detection Capabilities: Through comprehensive testing and evaluation, our model demonstrates superior performance over existing leading-edge techniques, particularly in handling the complexities and variances found in CCTV footage. Practical Real-World Application Potential: The proposed model exhibits significant potential for real-world applications, such as in security monitoring and automated surveillance systems. This represents a significant leap forward in the realms of video anomaly detection and deep learning technology. The rest of the paper is organized as follows. Section 2 provides the contextual background for this research while exploring the existing literature and prior studies. Section 3 begins with an explanation of the general Transformer mechanism in Section 3.1; subsequently, Section 3.2, a comprehensive explanation of the proposed overall architecture, is presented, starting with an in-depth overview. Following this, Sections 3.2.1-3.2.3 sequentially delve into each stage, providing detailed descriptions. Section 3.2.4 then offers a detailed exposition of the distinctions from the general Transformer mechanism outlined in Section 3.1. Finally, Section 3.3 concludes with an explanation of the loss function used. Section 4 begins with an explanation of the dataset used in the research and the preprocessing stages. It continues through the final result analysis. In Section 4.1, the introduction of the dataset is provided, followed by the presentation of the experimental environment in Sections 4.2 and 4.3. Subsequently, Sections 4.4 and 4.5 conduct a detailed analysis of the performance comparison of the proposed model. Lastly, Section 4.6 conducts an ablation study for additional validation. Finally, in Section 5, we summarize the key aspects of the study, outline our contributions to the field, and suggest directions for future research.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Smart Metering  
Smart metering systems allow consumers to play an active role in the operation of electricity markets, as they are the gateway to access the new grid. In particular, they utilize applications which allow interfacing with home automation systems, remote access to energy billing data, collection of additional information on grid operation, power quality and downtime, as well as information on consumption and consumption-based pricing, firmware updates etc. [4; 9; 41].  
Smart metering is based on the IEC 62056-x and IEC 61334-x standards which support the most well-known wired and wireless communication technologies, including IPv6 [7]. It is based on open and flexible specifications, and efforts are made towards the creation of a user-friendly management environment [57; 58] as well as to ensure the protection of user privacy and secure data transmission (cryptography integration, blockchain, etc.). Standardization and compliance of equipment and software with specifications of DLMS/COSEM technology is also supported [59].
# Soft-Output Detector Using Multi-Layer Perceptron for Bit-Patterned Media Recording
## 2 BPMR Channel Model  
Figure 1 shows the BPMR system with soft output MLP detector. In this channel, to fit numerical pulse without media noise, we use a 2D Gaussian pulse response which is considered as a square island length of 11 nm, a thickness of 10 nm, an element thickness of a magneto-resistive (MR) read-head of 4 nm, an element width of the MR read-head of 15 nm, a gap distance of 6 nm, and a fly height of 10 nm [13]. The 2D Gaussian pulse response is given as  
\[H(z,x)=A\cdot\exp\left\{-\frac{1}{2c^{2}}\left[\left(\frac{z}{PW_{z}}\right)^{ 2}+\left(\frac{x}{PW_{x}}\right)^{2}\right]\right\} \tag{1}\]  
where \(A\) is the normalized peak amplitude, the constant \(c\) is the relationship between the standard deviation of a Gaussian function and PW50, \(z\) and \(x\) are the cross- and down-track directions, and \(PW_{z}\) and \(PW_{x}\) are the PW50 of the cross- and down-track pulses, respectively. Here, the parameters related to the 2D Gaussian pulse response are as follows: \(A=1\), \(c=1/2.3548\), \(PW_{z}=24.8\) nm, \(PW_{x}=19.4\) nm. The 2D discrete time readback signal is given by  
\[r_{j,k}=\sum_{m=-M}^{M}\sum_{n=-N}^{N}a_{j-m,k-n}\cdot h_{m,n}+n_{j,k} \tag{2}\]  
where \(M\) and \(N\) are the length of the interference from neighboring islands in the cross- and down-track directions, respectively, \(a_{j,k}\in[-1,1]\) is the \(k\)-th recorded user data bit of the \(j\)-th track, \(h_{m,n}\) is the channel response coefficient, and \(n_{j,k}\) is the electronic noise modeled as an additive white Gaussian noise with zero mean and variance \(\sigma^{2}\). We set \(M=1\) and \(N=1\). The channel response coefficient \(h_{m,n}\) can be written as  
\[h_{m,n}=H(m\cdot T_{z}+\Delta_{TMR},\ n\cdot T_{x}) \tag{3}\]  
where \(T_{z}\) is the track pitch, \(T_{x}\) is the bit length, and \(\Delta_{TMR}\) is a read-head offset. We set \(T_{z}=T_{x}=18\) nm at 2.0 Tb/in\({}^{2}\) and \(T_{z}=T_{x}=14.5\) nm at 3.0 Tb/in\({}^{2}\), respectively, and \(\Delta_{TMR}=(TMR_{z}\times T_{z})/100\), where \(TMR_{z}\) is the percentage of the TMR. When the read-headis not able to continue at the center of the main data track, the system performance is degraded due to TMR.
## 2 Methodology
### Evaluation of the Selected Features and the SVM Model  
To evaluate the goodness of our selected features and the classification performance of the SVM model, we adopt several metrics and analysis methods for the evaluation.  
The Kruskal--Wallis test by ranks was applied on each feature, which is a non-parametric method for determining whether three or more independent groups of equal or different sample sizes are the same or different on some variable of interest [70; 71]. This test showed if there is a significant difference between the three terrains for each feature. Furthermore, to evaluate the statistical difference between each two terrains, the paired t-test, a statistical procedure used to determine whether the mean difference between two sets of observations is zero [45], was used after a Kolmogorov-Smirnov (KS) test, ensuring that the features in each class obey a Gaussian distribution.  
For the SVM model, four kind of indexes, i.e., average accuracy, confusion matrix, sensitivity and specificity, are used to evaluate the classification performance. The average accuracy (_acc_) of the SVM model is defined as  
\[acc=\frac{N_{c}}{N_{total}}\times 100\% \tag{1}\]  
where \(N_{c}\) is the number of correctly classified events, and \(N_{total}\) is the total number of the test events. Here, the test events mean all the applied test set data in the ten-fold cross-validation procedure.  
The confusion matrix \(Q\) is used to better quantify the specifics of the classification [19], which is defined in this paper as follows:  
\[Q=\left[\begin{array}{ccc}q_{11}&q_{12}&q_{13}\\ q_{21}&q_{22}&q_{23}\\ q_{31}&q_{32}&q_{33}\end{array}\right] \tag{2}\]  
where the element of the confusion matrix is defined as  
\[q_{ij}=\frac{p_{ij}}{p_{total,i}}\times 100\% \tag{3}\]  
where \(p_{ij}\) is the number of samples of the \(i\)th terrain that are identified as the \(j\)th terrain, and \(p_{total,i}\) is the total number of samples of the \(i\)th terrain. The elements on the diagonal present the classification accuracy and the others show the errors.  
In addition to accuracy, the calculation of sensitivity and specificity is beneficial for evaluating the SVM model's performance in binary classification problems [72]. They are defined as follows:  
\[SEN=\frac{TP}{TP+FN} \tag{4}\]  
\[SPE=\frac{TN}{TN+FP} \tag{5}\]  
where SEN and SPE denote sensitivity and specificity, and TP, TN, FP, FN are defined as follows (Table 4):In this work, when considering one terrain as the positive value, the other two terrains are seen as the negative values, and therefore three sensitivities and specificities are calculated when taking each terrain as the positive value in turn.
## 4 Discussion
### Volatile Architecture and Aroma Differentiation  
Aroma constitutes a critical quality parameter for NGBT, accounting for 25% of its sensory evaluation score [48; 49]. Distinct aroma profiles arise from the combinatorial effects of volatile compounds at varying concentrations [50]. This study identified 97 volatile compounds across chemical classes--including 3 acids, 23 alcohols, 9 aldehydes, 6 benzoids, 21 esters, 2 heterocyclics, 7 ketones, and 26 alkenes--with significant quantitative differences among the five NGBT cultivars. Critically, compounds exhibiting OAV \(>1\) and VIP \(>1\) serve as primary aroma contributors [36; 51]. Among the profiled volatiles, 8 dual-criterion markers (OAV \(>1\) and VIP \(>1\)) were identified as key drivers of cultivar-specific aromas. These markers provide mechanistic insights into the olfactory differentiation of NGBTs and offer actionable targets for optimizing manufacturing processes to align with market preferences.  
Geraniol, a monoterenoid alcohol associated with rose-like floral and honey-sweet notes, is biosynthesized via the methylerthitol phosphate (MEP) pathway and regulated by terpene synthase gene _CsTPS_ expression [52; 53]. Its significant enrichment in JSH establishes this compound as a signature aroma marker for this cultivar. Conversely, linalool--imparting a citrus-floral character [54; 55; 56]--dominated YH]'s volatile profile,consistent with its role in conferring distinctive sensory attributes as reported in prior studies on large-leaf tea cultivars.  
Notably, sensory evaluation highlighted YHJ's pronounced nutty aroma, yet correlation analysis revealed a disconnect: Benzaldehyde--the sole volatile with reported nutty descriptors [57, 58]--exhibited low abundance across all cultivars, insufficient to explain YHJ's high sensory score. This suggests the involvement of non-quantified Maillard reaction products formed during thermal processing, known to generate nutty-roasty notes in black tea.  
The aroma composition of NGBTs reflects intricate interactions within terpenoid metabolic networks, particularly monoterepene biosynthesis. Our findings establish a chemical basis for understanding the unique flavor chemistry of NGBTs and provide genomic targets for breeding cultivars optimized for desirable aroma traits.
## 5 Data Analysis
### Sorting and Building the Model Database  
To build the model database, it was necessary to sort and categorize the data; therefore, it was practical to eliminate some confusing data, which would not help design the model. Examples of these data were section number, street name, maintenance percent, many sections for one street, repeated data for one type of maintenance, and one traffic level for the same pavement.  
The important data were:  
* Highway class (main street),* Traffic level (low, high),
* M&R strategy (no maintenance, basic routine maintenance, overlay, and reconstruction),
* Pavement age (years).
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 6 Simulation
### Simulation Results and Analysis  
Based on the above evaluation criteria, we compare our proposed FAF-LSTM with conventional Federated Averaging algorithm and independent training LSTM. For the models obtained under different training modes training by four nodes, their accuracy is shown in Table 1. While the independent LSTM models trained by Node I to Node IV obtain the accuracy of 0.9401, 0.2209, 0.6540 and 0.9416, and the Fed-Avg LSTM models [24] obtain the accuracy of 0.2287, 0.2225, 0.2170 and 0.2209, our proposed FAF-LSTM models arrive at an accuracy of 0.9648, 0.9315, 0.9680 and 0.9738. The accuracy of models trained by different nodes has slightly better performance.  
Furthermore, we evaluated the accuracy of these models as the scale of flow increased, the result is shown in Figure 3. Compared to the test result of three different training modes on four nodes, independent LSTM can obtain higher accuracy in Node I and Node IV which have sufficient training data, but this training mode performed terribly in Node II and Node IV which lack enough data. When we compared FedAvg LSTM, it did not have a better result, while the FAF-LSTM model we proposed always has the best performance among the four nodes.  
The average accuracy of Node I to Node IV is shown in Figure 4, it can be observed that our proposed FAF-LSTM model has a distinct advantage over the FedAvg LSTM and independent LSTM.  
The ROC is shown in Figure 5. As the AUCs of FAF-LSTM are 0.8159, 0.7451, 0.8040 and 0.8688, the AUCs of Fed-Avg LSTM are 0.5508, 5457, 0.5491, 0.5495, and the AUCs of independent LSTM are 0.6513, 0.4095, 0.6468, 0.6472, respectively. The FAF-LSTM is slightly better in all these evaluation criteria.  
Generally, compared with independent LSTM, Fed-Avg LSTM cannot improve the performance of each model. But our proposed FAF-LSTM has improved the accuracy and AUC value. The average improvements are shown in Table 2.  
As can be seen from Figures 4 and 5, Tables 1 and 2, the proposed schemes in this paper not only improve the performance of a single model but also improve the performance of each model as a whole.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Mode** & **Node I** & **Node II** & **Node III** & **Node IV** \\ \hline Independent LSTM & 0.9401 & 0.2209 & 0.6540 & 0.9416 \\ Fed-Avg LSTM & 0.2287 & 0.2225 & 0.2170 & 0.2209 \\ FAF-LSTM & 0.9648 & 0.9315 & 0.9680 & 0.9738 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation accuracy on different training modes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **Compare with Independent LSTM** & **Compare with Fed-Avg LSTM** \\ \hline Accuracy Increase & 39.22\% & 334.36\% \\ AUC Increase & 24.92\% & 47.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Improvement compared with different training modes.  
Figure 4: Average accuracy of four nodes.  
Figure 3: Accuracy of Node I to Node IV.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 3 Adaptive Downward/Upward Protocol
### Overview of RRD+ Mechanism
#### 3.1.1 Movement Direction Monitoring  
RRD+ uses variation of Received Signal Strength Indicator (RSSI) to monitor movement direction. Nodes obtain RSSI values from acknowledgement (ACK) messages and control messages. A node manages two RSSI values for each parent node, Old RSSI value and New RSSI value. Old RSSI is retrieved from the previous ACK or control message, and New RSSI value is obtained from currently received ACK or control message. According to the variation of New RSSI value with regards to Old RSSI value, RRD+ estimates and monitors the movement direction of nodes. When a New RSSI value is lower than an Old RSSI value, RRD+ considers that the node is moving away from its parent node. Otherwise, RRD+ considers that the node is moving closer to its parent node.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 2 Methods
### Forces Acting on the Hull During Berthing
#### 2.1.1 Wind Pressure and Wind Moment  
The wind pressure (\(F_{W}\)) acting on the hull during berthing is typically calculated based on the wind-exposed area using [PERSON] empirical formula, as shown in Equation (1), which considers wind direction and angle.  
\[F_{W}=\frac{1}{2}\rho_{a}C_{a}\Big{(}A_{T}\cdot cos^{2}\theta+A_{L}\cdot sin^{ 2}\theta\Big{)}V_{a}{}^{2}\frac{1}{1000}, \tag{1}\]  
where \(F_{W}\) represents the wind pressure, \(\rho_{a}\) represents the air density, \(C_{a}\) represents the wind pressure coefficient, \(A_{T}\) represents the frontal wind-exposed area, \(A_{L}\) represents the lateral wind-exposed area, \(\theta\) represents the relative wind direction, and \(V_{a}\) represents the wind speed.  
Although the wind pressure coefficient is determined through wind tunnel experiments, this study used the wind pressure coefficients for each ship type proposed by [PERSON], as shown in Figure 1[24]. The lateral component of wind pressure creates a turning moment around the hull's center of gravity, rotating the bow. The center of wind pressure (\(a\)) can be approximated by Equation (2), and the wind moment (\(M_{W}\)) can be calculated using Equation (3).  
\[a=(0.291+0.0023\theta)L, \tag{2}\]  
\[M_{W}=F_{W}\cdot sin\theta\cdot(0.5L-a), \tag{3}\]where \(M_{W}\) represents the wind moment, \(F_{W}\) represents the wind pressure, \(\theta\) represents the relative wind direction, and \(L\) represents the length overall (LOA).
# Special Issue on Future Intelligent Transportation System (ITS) for Tomorrow and Beyond
## 2 Contributions
[21], the power management strategy of interdependent MG and EV fleets is presented in ref. [22]. This strategy is integrated with a novel EV charging/discharging scheduling algorithm to reduce the expenses of PV-based charging station. With the advancement of EVs on roads and parking stations, the EV aggregator can be used to provide energy-efficient and cost-effective charging and discharging solutions. Batteries installed in the EVs can act as an energy storage system, shifting load demand from peak to off-peak hours and lowering the electricity bills. Predictive mobility is a major component that can help traffic operators assess traffic performance in smart cities. Using historical data from GPS, the authors incorporated the self-attention long short-term memory (SA-LSTM) model with a Butterworth low-pass filter to estimate the journey time on the road segments [23]. Initially, the LSTM is defined as a standard in this study. Due to the relatively high mean absolute error (MAE) of 27.12 min per 100 km, the SA-LSTM model together with the Butterworth low pass filter was implemented to reduce the MAE to 12.15 min per 100 km. Visual odometry (VO)-based localization algorithms have been created as a result of the information in images. The ability of a mobile robot or vehicle to locate itself is a critical component in the advancement of autonomous robotics and vehicles. The use of a gated recurrent unit (GRU) network trained on pose data acquired by an accurate sensor is proposed in ref. [24] as a new pose estimation method. Reconstructing the rotation matrix with a yaw angle that is the fusion of the yaw angles calculated from the proposed GRU network and previous VO approaches improves performance in terms of translation error and rotation error. The network was trained by using the KITTI dataset [25], and the performance of the KITTI sequences increased by 1.426% in terms of translation error and 0.805 deg/100 m in terms of rotation error. The future ITS will remain as an important aspect of urban planning and future cities, as it will help to enhance road and traffic safety, transportation and transit efficiency, energy efficiency, and pollution reduction. We believe that the articles in this Special Issue offer significant understanding of different infrastructures within the transportation system under different situations to ensure the efficiency and safety of the transportation system. Conceived, S.H.N. and D.H.; designed, S.H.N. ; writing--original draft preparation, S.H.N. and H.J. ; writing--review and editing, S.H.N., H.J., I.K. and D.H. All authors have read and agreed to the published version of the manuscript. This work was supported by the Institute for Information communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-00440, Development of Artificial Intelligence Technology that continuously improves itself as the situation changes in the real world). The authors declare no conflict of interest.
## 3 Calculation of Propagation Time and Attenuation Rate Using the Amiet Method
### Distribution of Propagation Time and Attenuation Rate  
Figure 4 shows the distributions of the propagation time and the attenuation rate in the \(X_{n}-Y_{n}\) plane (\(Z_{n}=0\)) calculated using the derived equations. The position of the sound source \((x_{0},y_{0},z_{0})\) was set to be \((0,0,0)\), and the Mach number inside the wind tunnel \(M=0.3\) and sound speed \(c=340\) m/s.  
The distributions of both propagation time and attenuation rate extend downstream by the convection in the wind tunnel. In addition, the attenuation rate of upstream drops significantly outside the wind tunnel by the effect of refraction at the shear layer. Therefore, the equations of the propagation time and attenuation rate are reasonable.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Performance Evaluation Metrics
#### 4.1.2 Average Channel Utilization (\(\bar{U}_{j}\))  
The average channel utilization for \(j^{th}\) communication channel (\(\bar{U}_{j}\)) can be calculated as given in Equation (20). The channel utilization measures the degree of occupancy of a given channel for communication.  
Figure 4: Network topologies showing the solution of the optimizer for data collection in Hybrid architecture. **(a)** Ad hoc topology; **(b)** Chain topology.  
\[\bar{U}_{j}\ =\ \frac{100}{\mathcal{K}}\sum_{m=1}^{\mathcal{K}}\frac{\mathit{ total\ channel\ utilization\ time\ per\ data\ cycle}}{\mathit{total\ time\ of\ the\ data\ cycle}} \tag{20}\]  
A high score for channel utilization (\(\bar{U}_{j}\)) indicates that the channel is frequently utilized in the network. A lower channel utilization value is desirable.