esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, we proposed a deep learning framework that aims to reduce training costs and enhance computational efficiency for Protein Secondary Structure Prediction (PSSP). The approach integrates autoencoder-based dimensionality reduction with a sub-sequencing strategy, converting full-length protein sequences into smaller, fixed-length segments. This transformation facilitates more efficient model training without sacrificing predictive accuracy.  
Experimental results confirm that dimensionality reduction--particularly at 256D and 128D--significantly decreased training time while preserving critical contextual information within the protein sequence embeddings. Leveraging GPU acceleration further enhanced training speed for lower-dimensional models. In contrast, high-dimensional embeddings often exceeded GPU memory limits, necessitating CPU-based execution and, thus, prolonging training times. While increasing subsequence length improved the model's capacity to capture long-range dependencies, it also raised memory requirements and computational load. These findings underscore the need to balance representation richness with system-level efficiency.  
The proposed framework performed effectively in Q3/Q8 classification tasks, maintaining high F1 scores, even under reduced-dimensionality settings. The 256D-50L configuration, in particular, emerged as the optimal setup--delivering nearly 99% of baseline accuracy while reducing the training duration by up to 43%. These outcomes highlight the efficacy of combining dimensionality compression and sequence segmentation in training deep learning models for large-scale protein analysis.  
In conclusion, the presented method offers a practical and scalable solution for protein secondary structure prediction, especially in environments with limited computational resources. By maintaining a strong trade-off between accuracy and efficiency, the framework contributes meaningfully to the domains of bioinformatics and computational biology. Moreover, the proposed strategy has the potential to serve as a foundational tool for future studies involving more complex structural predictions or multi-task learning in protein science.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 3 Kinematics Modeling and Control Methodology
### Forward Kinematics Analysis
#### 3.2.1 Coordinate Transformation Matrix between Adjacent Tubular  
the pose transformation matrix of Frame \(F_{i-1}\) with reference to Frame \(F^{{}^{\prime}}_{i}\) can be formulated as:  
\[i_{-1}^{i}T=Transl(0,D,0)RotZ(\beta_{i})RotX(\alpha_{i})Transl(0,D,0) \tag{10}\]  
where \(Transl(0,D,0)\) represents the translation function with respect to Y-axis by shifting displacement \(D\). \(RotZ(\beta_{i})\)and \(RotX(\alpha_{i})\) represent the rotation function regarding Z-axis and X-axis, \(\alpha_{i}\) and \(\beta_{i}\) denote respectively the corresponding counterclockwise rotation angle around axis. Equation (10) can be rewritten as homogeneous matrix expression:  
\[i_{-1}^{i}T=\left[\begin{array}{cccc}\cos\beta_{i}&-\cos\alpha_{i}\sin\beta _{i}&\sin\alpha_{i}\sin\beta_{i}&-D\cos\alpha_{i}\sin\beta_{i}\\ \sin\beta_{i}&\cos\alpha_{i}\cos\beta_{i}&-\cos\beta_{i}\sin\alpha_{i}&D+D \cos\alpha_{i}\cos\beta_{i}\\ 0&\sin\alpha_{i}&\cos\alpha_{i}&D\sin\alpha_{i}\\ 0&0&0&1\end{array}\right] \tag{11}\]
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### Evaluations of FDI Attacks
#### 3.2.2 SoC Values of Honest and Malicious Consumers  
Figure 2 presents the SoC values of an honest consumer (#1), while Figure 1 illustrates the SoC values of a malicious consumer (#3) when the system is under FDI Attack #1, with \(C_{\text{max}}=6\) and \(C_{\text{max}}=10\). Figure 3 shows the SoC value of consumers #1 and #3 when there are no FDI attacks in the system. Tables 2 and 3 summarize the impact of the five attacks on the performance of the RL agent in terms of total reward, fairness, power usage, and the average SoC values for both honest and malicious consumers in both scenarios where \(C_{max}=6\) and \(C_{max}=10\) respectively. The baseline row represents the scenario without any FDI attacks.  
By comparing the SoC value of honest consumer #1 where there is no FDI attacks given in Figure 3 (left one) to the SoC values in case the system is under attack given in Figure 2 when \(C_{max}=6\), it can be seen that attack #1 harm the honest consumers by reducing their SoC values. The same conclusion can be observed in Table 2 for the other FDI attacks. By comparing the SoC value of malicious consumer #3 given in Figure 3 (the right one) when there are no FDI attacks to the SoC values in Figure 1 when consumer #3 launches attack #1, it can be seen that consumers can benefit from the FDI attacks by increasing their battery SoC value. The same conclusion can be observed in Tables 2 and 3 for the other FDI attacks. The table shows that the attacks lead to a decrease in the average SoC for honest consumers and an increase in the SoC of malicious consumers, highlighting how the attacks shift energy distribution unfairly in favor of malicious consumers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Layer** & **Layer Type** & **Number of Units** & **Activation Function** \\ \hline \multirow{3}{*}{Actor} & Layer 1 & Linear & 64 & ReLU \\ \cline{2-5}  & Layer 2 & Linear & 64 & ReLU \\ \cline{2-5}  & Layer 3 & Linear & 10 & Sigmoid \\ \hline \multirow{3}{*}{Critic} & Layer 1 & Linear & 128 & ReLU \\ \cline{2-5}  & Layer 2 & Linear & 128 & ReLU \\ \cline{2-5}  & Layer 3 & Linear & 1 & Linear \\ \hline \hline \end{tabular}
\end{table}
Table 5: The hyperparameters for the Actor-Critic architectures.  
Figure 2: SoC values of Consumer 1 (honest) at different times and \(C_{max}\) of 6 and 10 when the system is under FDI attack #1.  
The experimental results show that attackers have higher SoC values, as under-reporting SoC increases their chances of receiving more charging power compared to honest consumers. Since the total available power for charging is limited, a larger share is allocated to malicious consumers, leaving less for the honest ones, which explains their lower SoC values. The disparity between malicious and honest consumers worsens when the available charging power is lower, i.e., when \(C_{\max}=6\) compared to \(C_{\max}=10\).
# Propagation Property of an Astigmatic sin-Gaussian Beam in a Strongly Nonlocal Nonlinear Media
## 2 Theoretical Formulation
This is consistent with those of Gaussian and higher-order Gaussian beams in SNNM [1]. Moreover, the calculations also indicate that the edge dislocation nested in the input sin-Gaussian beam always remains during propagation. However, for sufficiently large values of \(\beta_{x}\) and \(\beta_{y}\), the intensity pattern changes periodically and is independent from the input power \(p_{0}\), which can be seen from Figure 2. In the following we only consider the case of small \(\beta_{x}\) and \(\beta_{y}\). Figure 2: Evolution of intensity distributions of sin-Gaussian beams with the scaled propagation distance \(z_{s}\) for \(\beta_{x}\) = 4, \(\beta_{y}\) = 3. The other parameters are \(R_{0x}\) = \(R_{0y}\) = \(\infty\) and \(z_{p}\) = \(z_{R}\). On the other hand, for \(\beta_{x}\
eq 0\), \(\beta_{y}\
eq 0\) and \(R_{0x}\
eq R_{0y}\) (i.e., \(\alpha_{x}\)\(\
eq\)\(\alpha_{y}\)) corresponding to beams with astigmatism, Equation (8) turns into:  
\[\int_{-\infty}^{\infty}\exp\Bigl{(}-\alpha x^{2}+\beta x\Bigr{)}\ =\ \exp\biggl{(}\frac{\beta^{2}}{4\alpha}\biggr{)}\mathrm{Re}(\alpha)>0 \tag{7}\]0  
For sufficiently small \(\frac{z_{R}\beta}{\sqrt{2}z_{p}}\), Equation (16) can approximate to:  
\[\int_{-\infty}^{\infty}\exp\Bigl{(}-\alpha x^{2}+\beta x\Bigr{)}\ =\ \exp\biggl{(}\frac{\beta^{2}}{4\alpha}\biggr{)}\mathrm{Re}(\alpha)>0 \tag{7}\]1  
which just represents a vortex beam and agrees with the numerical results. Finally, it should be pointed out that, for \(\lambda=500\) nm and \(w_{0}=1\) mm, one has \(z_{R}\approx 6.28\) m and the curvatures \(R_{0x}\) and \(R_{0y}\) approximate to \(z_{R}\), which is realizable in experiment. When the sin-Gaussian beam is produced with a sine-amplitude modulated grating, \(\mid\beta_{x}\mid=\mid\beta_{y}\mid<1/3\) implies that the spatial frequency of the sine-amplitude modulated grating should be below \(0.05 lp/mm\) and is ultra-low, which seems to be a challenging task in manufacturing technology nowadays.
# Anomaly Detection for IOT Systems Using Active Learning
## 5 Results
### Core Contributions of this Study  
The primary contributions of our study, titled \"Anomaly Detection for IoT Systems Utilizing Active Learning\", are as follows:  
* Development of an effective anomaly detection model using active learning: Our study has demonstrated that active learning is a valuable technique for developing precise and efficient anomaly detection models in IoT systems. The model can iteratively select the most informative data points by employing the uncertainty sampling technique. This approach enhances the model's accuracy and decreases the requirement for a large number of labeled data points during the training process.
* Evaluation of the model on a real-world IoT dataset: Our model is evaluated on the UNSW-NB15 dataset, a publicly available dataset containing real network traffic data from an IoT device. The approach presented in this study demonstrates superior performance compared to various state-of-the-art anomaly detection techniques, with an accuracy rate of 99.7%.
* Investigation of the impact of different feature selection methods: This study examines how feature selection tactics affect our model's performance. PCA, mutual information-based, and correlation-based feature selection are used to evaluate the model's efficacy. Our analysis shows that reciprocal feature selection yields the greatest results.
* Identification of the most significant features for anomaly detection in IoT systems: Finding the most important features for IoT anomaly detection requires packet lengths, bytes sent, and packet send rates. Our research shows that these traits can detect unusual IoT traffic patterns.
* Demonstration of the potential of active learning for future IoT applications: This study suggests using active learning in Internet of Things applications. We recommend active learning for creating robust and effective anomaly detection models for Internet of Things applications in the near future. Active learning can substantially reduce the cost and time of constructing Internet of Things anomaly detection models. This is achieved by reducing the labeled data for training.
## 4 Results
### Comparison of Algorithm Performance
#### 4.3.3 Handover Delay Comparison  
Following the handover management in terrestrial networks, the whole handover management scheme will be divided into three phases: handover measurement, handover judgment, and handover execution [41].  
(1) Handover measurement phase: the star-carrying base station instructs the grid terminal to upload measurement information periodically.  
(2) Handover judgment phase: the star-carrying base station compares the handover measurement information uploaded by the grid terminal with the preset handover threshold value, judges whether the handover conditions are met, and selects candidate satellites according to the deployed handover algorithm.  
(3) Handover execution stage: the star-carrying base station initiates the handover request, and under the control of the ground core network, the grid terminal, the source satellite and the target satellite interact with each other in terms of control signaling and data.  
We compared the total handover process delay and transmission delay of three algorithms. As shown in Figure 11, the average results from various experiments indicate that our GNN+DQN-based handover strategy has a lower transmission delay than the DRL-based strategy and, relative to other handover strategies, it minimizes the total delay of the handover process over the entire communication duration. This is attributed to the three phases of the handover process, including the delay caused by the handover execution (affected by the handover frequency) as well as the data/signaling transmission delay of the star-ground link. Our scheme comprehensively considers two key factors--transmission delay and handover frequency--and optimizes both across the entire communication duration to reduce the number of frequent handover times as much as possible and, at the same time, reduces the transmission delay. The maximum elevation angle-based strategy consistently selects the satellite with the highest elevation angle, thereby minimizing transmission delay due to the shortest star-ground transmission distance. However, it overlooks other factors, leading to frequent handover and increased total delay.  
Figure 10: Comparison box plot of satellite user data rate.
## 4 Future Research Directions
### Open Research Direction for DSM  
#### 4.1.1 Reliability of the Power System and Communication Networks Used in DSM  
The reliability of the power system and the reliability of the communication media used in the DSM schemes are imperative and indispensable parts of smart grid technology. The reliability of the power system mainly depends on the consumers' demands, i.e., a system will be reliable if the total consumption of all consumers is under the threshold provided by the supplier. If the demanded load exceeds the threshold level, then the whole system will go down. Owing to this, it is mandatory to consider the power system's reliability during the design and development of the stages of an efficient DSM scheme. Advance load forecasting techniques can be used to improve the reliability of the power system. Similarly, the communication links used for energy management are also of great importance. For a robust and reliable DSM scheme, the reliability of the communication networks is also as valuable as power system reliability. In future research, detailed consideration is needed for the reliability of the power system as well as that of the communication network along with consumers benefits.
## 4 Experimental Setup and Analysis of Results
### Evaluation Indicators  
In this paper, the performance of the proposed model is evaluated via the word error rate (WER), a commonly used metric for assessing the performance of AVSR models [42]. WER measures the word-level difference between the system's output text and the reference text, providing an indicator of recognition accuracy. It is calculated as follows:  
\[WER=\frac{S+D+I}{N}\times 100\% \tag{24}\]  
where \(S\) represents the number of substitution errors, \(D\) represents the number of deletion errors, \(I\) represents the number of insertion errors, and \(N\) denotes the total number of words in the reference text. The system generates text that represents its interpretation of the video's content, which is then compared to the reference text containing the correct content. The output text is aligned with the reference text at the word level, and differences are analyzed to identify insertion, deletion, and substitution errors. The sum of these errors is divided by the total number of words in the reference text to calculate the WER. A lower WER indicates better recognition performance by the system.
# Ad Hoc Data Foraging in a Life Sciences Community Ecosystem Using SoDa +
## 4 Discussion
### Complex Query Construction  
The application design system discussed in Section 3.4 can be used to design fairly complex BioFlow extract and SQL select queries. The extract statements are regarded as resource access statements, and thus are not true querying instruments. Once the resources are accessed and data extracted using extract, they can be queried with the full power of SQL's select constructs. Typical SQL queries involve sub- and correlated queries, as well as aggregate functions. While designing query builder GUIs for SQL's aggregate functions is not too difficult, seamlessly incorporating all these features into the query builder is difficult, especially when the users are assumed \"naive\" and have no true experience in direct complex SQL query writing.  
Figure 11: Part 1: Stepwise differential expression analysis using DEApp: (**a**) shows the data input step of the online differential gene expression analysis tool DEApp [36]. Once the input data and their format are chosen, a filtering operation can be carried out at step 2, as shown in (**b**).  
Designing no-coding SQL interfaces has a long and rich research history [37, 38]. While there are numerous SQL query builders, both academic and commercial, most assume that users are familiar with complex SQL syntax and semantics and aim to aid and expedite query building. In particular, they are difficult to use when the query interleaves aggregate functions and correlated subqueries to a too deep level [39]. Designing a query builder for the general public, or biologists, for arbitrary database and intended arbitrary query has its challenges. The recent emergence of LLM-based Text2 SQL efforts [40] probably performs better than most no-coding interfaces. Our interface too has limitations, but we are currently exploring how to enhance our interface with LLM-powered SQL query generation by fine tuning an LLM to BioFlow and Needle.  
Figure 12: Part 2: Stepwise differential expression analysis using DEApp: DEApp allows computing differential gene expression analysis using multiple algorithms and different filter conditions that users are able to select using the interface in (**a**). Finally, a comparison of the expression analysis using multiple methods can be compared, as shown in (**b**). The important observation is that all of these four steps within DEApp are in sequence and have to be followed in succession, creating a dependence or order.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm
#### 3.4.5 Asynchronous Messaging  
The asynchronous nature of Telegram messaging is important for maintaining the system's real-time performance. Here is how it works:  
* _Non-blocking Operations_: Sending messages via the Telegram Bot API is handled asynchronously, meaning the system does not wait for the message to be delivered before continuing with other tasks. This non-blocking operation allows the fall detection algorithm to continue processing video frames and detecting potential falls without interruption.
* _Real-Time Performance_: By ensuring that the alerting mechanism does not block the main processing loop, the system maintains its real-time performance. This is essential for continuous monitoring and the timely detection of falls.
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 2 Experimental Method
### Data Acquisition  
The MyoScan sensor was used to collect the sEMG signals (up to 1600 uV) from the subject's four target muscles. The sEMG was filtered in the range of 10-400 Hz, and a 50 Hz notch filter was set to remove the line interference. The collected raw sEMG signals then were amplified and transmitted to a computer for data analysis through the FlexComp Infiniti System (Thought Technology Ltd., Montreal, QC, Canada), which contains a digital sEMG system with 10 channels and a video collection system [19]. It can ensure the synchronism of the sEMG signal and the video data. The sampling rate of sEMG signals were 2048 Hz. The video data was acquired simultaneously at a resolution of 60 frames per second, using an external high definition high speed camera when each trial was performed. The camera was placed perpendicular to the walking direction of the treadmill, and the distance between the camera and the treadmill was adjusted to ensure that the entire running belt of the treadmill was in the camera's field of view [24].
## 2 Overview
### Collimation  
The collimation allows for four distinct collimation lengths of 20, 14, 8, and 4 m. All of the collimation slits can be set between the full beam width of \(3\times 3\) cm\({}^{2}\) and fully closed with independently moving blades from top, bottom, and both sides. Changing the collimation length means that at the chosen collimation length an aperture is inserted into the beam. All the neutron guides from the moderator to the aperture are left transporting the neutrons to the aperture. All of the neutron guides afterwards are removed from the beam. In the case of SKADI, the removed guides and slits are replaced by \(35\times 35\) mm\({}^{2}\) passive blinds made from boron carbide [12]. Those remove stray neutron propagating parallel to the main beam and reduce the background. The neutron guides will be placed on sledges to be moved in and out of the beam while maintaining the alignment.  
Additionally to the classical pin-hole setup, a very small-angle neutron scattering (VSANS) setup with fixed slit-collimators will allow for accessing Q-values down to several times \(10^{-5}\)A\({}^{-1}\)[13].
###### Abstract  
In this study, a finite element, fully three-dimensional solid modeling method was used to study the mechanical response of a steel-cored aluminum strand (ACSR) with a mid-phase jumper under wind load. A whole model (simplifying an ACSR into a solid cylinder) and a local model (modeling according to the actual structure of an ACSR) of the mid-phase jumper were established. First, the movement of the mid-phase jumper of the tension tower under wind load was studied based on the whole finite element model, and the equivalent Young's modulus of the whole model was adjusted based on the local model. The results of the whole model were then imported into the local model and the stress distribution of each strand of the ACSR was analyzed in detail to provide guidance for the treatment measures. Therefore, the whole model and the local model complemented each other, which could reduce the number of model operations and ensure the accuracy of the results. Through the follow-up test to verify the results of the finite element simulation and the comparison of the simulation and fatigue test results, the causes of the broken strand of the ACSR were discussed. Although this modeling method was applied to the stress and deformation analysis of a mid-phase jumper in this study, it can be used to study the bending deformation of rope structures with a complex geometry and the main bending deformation. In addition, the effect of the friction coefficient on the bending of the mid-phase jumper was studied.  
finite element modeling; aluminum conductor steel-reinforced cable; bend deformation; stress; friction coefficient; wind loads; fatigue fracture +
Footnote †: journal: Applied Physics Letters B
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 3 System Model and Framework Overview  
In this section, we first introduce the system model used in this work. Next, we propose a personalized privacy protection framework for mobile crowdsensing, with a detailed workflow presented in Figure 1.