esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band OFF and K/Ka-Band ON  
Next, the K/Ka-band path is enabled (ON) while the X-band path is disabled (OFF). The following subsections summarise the resulting simulations over 23-28 GHz.
# Forecasting of Power Demands Using Deep Learning
###### Abstract  
The forecasting of electricity demands is important for planning for power generator sector improvement and preparing for periodical operations. The prediction of future electricity demand is a challenging task due to the complexity of the available demand patterns. In this paper, we studied the performance of the basic deep learning models for electrical power forecasting such as the facility capacity, supply capacity, and power consumption. We designed different deep learning models such as convolution neural network (CNN), recurrent neural network (RNN), and a hybrid model that combines both CNN and RNN. We applied these models to the data provided by the Korea Power Exchange. This data contains the daily recordings of facility capacity, supply capacity, and power consumption. The experimental results showed that the CNN model outperforms the other two models significantly for the three features forecasting (facility capacity, supply capacity, and power consumption).  
Keywords:power forecasting; deep learning; CNN; RNN; hybrid model +
Footnote †: journal: Applied Sciences
## 4 Experiments and Results: Performance Analysis
### Simulation Results
#### 4.3.2 Discussions: Real-World Applicability, Scalability, and Privacy Concerns
The use of efficient consensus mechanisms within the blockchain ensures that as the network grows, the time and resources required to validate transactions do not become prohibitive. To further enhance scalability, future iterations of the architecture could incorporate sharding techniques, where the blockchain is divided into smaller, more manageable segments. This would allow for the parallel processing of transactions, thereby significantly increasing throughput and efficiency. Our proposal not only addresses the immediate security and efficiency needs of VANETs, but also takes into consideration crucial aspects like data privacy and scalability. While challenges in these areas exist, ongoing advancements in blockchain technology and vehicularcommunication systems present promising solutions. As such, the proposed architecture stands as a forward-thinking approach, which is poised to adapt and evolve in tandem with the growing and changing landscape of intelligent transportation systems.
## 4 Results and Discussions
### Performance Evaluation of HMC  
Figures 5-7 show the actual value vs. predicted value curves for one-day time-interval of HMC using RNN, LSTM, and GRU, respectively. All of the models predict very well; however, GRU performs slightly better than the other two models. The results are summarized in Table 4.  
Figure 5: HMC actual vs. RNN predicted for one-day interval.  
Figure 6: HMC actual vs. LSTM predicted for one-day interval.  
Figure 7: HMC actual vs. GRU predicted for one-day interval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** \\ \hline RNN & 0.98321 & 0.28826 & 1.03648 & 0.40842 \\ \hline LSTM & 0.98346 & 0.29185 & 1.04387 & 0.40531 \\ \hline
**GRU** & 0.98389 & 0.28329 & 1.01502 & 0.40010 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of HMC one-day interval.  
Figures 11-13 show the actual vs. predicted curves for five-day time intervals of HMC. GRU outperformed the other two models in this timeframe as well. The results are summarized in Table 6.  
Figure 8: HMC actual vs. RNN predicted for three-day interval.  
Figure 10: HMC actual vs. GRU predicted for three-day interval.  
Figure 9: HMC actual vs. LSTM predicted for three-day interval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** \\ \hline RNN & 0.96305 & 0.43949 & 1.58962 & 0.61541 \\ \hline LSTM & 0.96204 & 0.44340 & 1.58702 & 0.62375 \\ \hline
**GRU** & 0.96413 & 0.42743 & 1.54541 & 0.60643 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of HMC three-day interval.
# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 4 Experimental Studies
### Experimental Dataset  
The DAIC-WOZ dataset [12] is mainly video, audio, and questionnaire survey data collected to diagnose mental disorders such as depression and anxiety. This dataset is referenced in the Depression Recognition Task Module in the 2014 and 2016 AVEC competitions. The interviews are conducted through situational dialogues between the virtual interviewer, [PERSON], and the subjects to elicit corresponding emotional responses and record the characteristics in real-time. According to official standards, the entire dataset exposes 189 sample data and is divided into 107 training sets, 35 validation sets, and 47 test sets. Since no official public label indicates whether the subjects in the test set are depressed or not, this paper employs only 142 sample data from the combined training and test sets to ensure the accuracy and validity of the experimental results. Moreover, to safeguard privacy, the DAIC-WOZ dataset does not provide the original video data but instead offers the 2D coordinate data of facial landmarks extracted by OpenFace [30]. Therefore, the experiment in this paper directly utilizes the provided coordinate data of facial landmarks, eliminating the need for facial landmark extraction steps. The detailed classification of depression or non-depression in the sample data is shown in Table 3.  
In this paper, the DAIC-WOZ dataset was utilized, and a sliding window approach was implemented to sample the original data. The window size was set to 120 s, with an overlap size defined as 1/6 of the window size. The original data consisted of video samples at a rate of 30 Hz. During the data cleaning process, a filter was applied to exclude video frames with a face confidence lower than 0.7 from the original video. This filtering process aimed to mitigate the influence of significant face deflection or flipping on the experimental results.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 2 Background Review  
Automatic dependent surveillance broadcast (ADS-B) is increasingly utilized within the aircraft industry. Since both aircraft need to be piloted or remotely controlled to avoid any misinformation that could cause a collision, there is a higher demand for a security solution to solve and mitigate most of the threats and vulnerabilities ADS-B suffers.  
Automatic dependent surveillance-broadcast (ADS-B) is an air traffic control system based on information such as position, identity, time, heading, and velocity received by ground sensors and transmitted periodically via an aircraft with the intent of surveillance. ADS-B messages are acquired from the OpenSky network. The reason was to make aviation data, such as air traffic information, available to the public. Aircraft transponders broadcast ADS-B messages, and all nearby receivers on the same communication channel or within the transmission range could capture these messages and the information contained within them.
## 1 Introduction
We evaluate the average network latency, the average network reliability, the average/maximum controller-switch latency, and the running time of our proposed algorithms on real network topologies of various kinds, and the results are compared with the existing SACA method [24]. The main contributions of this paper are briefly highlighted as follows. * A Simulated Annealing Partition-based K-Means (SAPKM) algorithm is proposed to ameliorate the joint placement problem in the SDN-enabled ISTNs. By adopting SAPKM, performance indicators can be accomplished in the sub-domains instead of the whole network, which dramatically reduces the complexity of the joint placement problem. Besides, selecting the centroids of the sub-domains as initialized nodes further decreases the number of redundant iterations. * The joint placement problem in terms of reliability maximization with network latency constraints is analytically formulated. With the appropriate deployment for gateways and controllers, the proposed algorithm will shed light on other performance metrics including controller-switch latency, load balancing, and even the trade-offs among multiple objectives. * Considering the intrinsic properties of network topologies, i.e. the structure and the density of the internal nodes, experiments are performed on real network topologies varying in size and structure. * Compared with the existing algorithms, simulation results indicate the merits of the proposed algorithm in shortening network latency, enhancing network reliability with much lower complexity, especially for adapting to large topologies with multiple gateways and controllers to deploy. All the adaptive traits are propitious for studying the online joint placement problem for dynamic networks in the future. The rest of the paper is organized as follows: Section 2 presents the related works in recent years. Section 3 introduces the general architecture of the SDN-enabled integrated satellite-terrestrialnetworks and formulates the joint gateway and controller placement problem mathematically with constraints. Section 4 introduces the proposed algorithm SAPKM and analyzes the complexity of it. Section 5 presents the numerical results, which show the superiority of SAPKM in both performance and complexity compared with previous works. Finally, concluding remarks are drawn in Section 6.
## 3 Measurement of Elastic Displacement  
### Elastic Displacement Increment  
Excited by external loads, an elastically supported shafting will generate rigid and elastic displacement. The rigid displacement manifests as the system's displacement relative to the global fixed coordinate system, while the elastic displacement reflects the composite elastic deformation caused by the interaction between the bearings and the supporting foundation. The vector sum of the rigid displacement and elastic displacement is the absolute displacement at a specific point within the system, and the absolute displacement difference is the relative displacement of a designated point in the system. Although the absolute displacement reflects the shafting's displacement changes relative to the hull, it cannot accurately reflect the relative positional changes between components of the shafting, thereby making it inadequate for the elastic alignment analysis. In contrast, the relative displacement, although effectively capturing inter-component positional changes, lacks the capacity to precisely quantify system state variations induced by external environmental factors. To address positional changes and associated mechanical analysis challenges induced by elastic deformation, [PERSON] et al. [22] proposed the concept of elastic displacement increment (EDI) to evaluate bearing displacement, as shown in Figure 4.  
The EDI uses the connecting line of any two bearings to denote the rigid displacement, and the vectorial distance from other points to this line represents their elastic deformation. Taking the rear stern bearing as an example, Figure 4a illustrates the EDI of the bearing. Figure 4b shows the EDI of any point on the supporting foundation. This method measures bearing displacement by accounting for elastic deformation of supporting foundations, yet exhibits two critical limitations:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bearing** & **A** & **B** & **Measured Load** & **Calculated Load** & **B \(-\) A** \\ \hline Rear stern bearing & 14.89 & 17.41 & 16.15 & 16.69 & 2.52 \\ Front stern bearing & 6.82 & 7.92 & 7.37 & 7.18 & 1.10 \\ Thrust bearing & 7.83 & 9.79 & 8.81 & 8.22 & 1.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured and calculated load of each bearing (unit: kN).  
Figure 4: Schematic diagram of the EDI. (**a**) EDI of the rear stern bearing (\(\Delta_{1}\)); (**b**) EDI of any point on the supporting foundation (\(\Delta x\)); (**c**) EDIs under different deformation states are equivalent.  
1. The EDI may yield substantial deviation between calculated and actual displacement values when used to measure the displacement change of a certain point;
2. The EDIs at a given point under different system deformation states may be equivalent, so it cannot correctly reflect the deflection of both the shafting and its supporting foundation, as shown in Figure 4c.
# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Architectural Perspective  
The majority of AFV systems broadly adopt a three-stage pipeline architecture similar to the Fact Extraction and VERification (FEVER) shared task [26], as identified and commented on by many researchers [26; 27; 28; 29; 30; 31; 32]. These three stages (also called sub-tasks) are document retrieval (evidence retrieval), sentence selection (evidence selection), and recognizing textual entailment or RTE (label/veracity prediction). The document retrieval component is responsible for gathering relevant documents from a knowledge base, such as Wikipedia, based on a given query. The sentence-retrieval component then selects the most pertinent evidence sentences from the retrieved documents. Lastly, the RTE component predicts the entailment relationship between the query and the retrieved evidence. Although the above framework is generally followed in AFV, alternative approaches incorporate additional distinct components to identify credible claims and provide justifications for label predictions, as shown in Figure 3. The inclusion of a justification component in such alternative approaches contributes to the system's capacity for explainability within the AFV paradigm.  
The majority of AFV systems are highly dependent on deep neural networks (DNNs) for the label prediction task [7]. Furthermore, in recent years, deep-learning-based approaches have demonstrated exceptional performance in detecting fake news [33]. As mentioned in Section 2, there is, however, an inherent conflict between the performance of AI models and their ability to explain how they make decisions. However, although existing AFV systems lack inherent explainability [7], it would be foolish to overlook the potential to use these less interpretable deep models for AFV, as these models possess the ability to achieve state-of-the-art results with a remarkable level of prediction accuracy. However, this also indicates that model-based interpretation approaches may not be a suitable solution for AFV systems; the reason being that these methods require the involvement of simple and transparent AI models that can be easily understood and interpreted.  
Therefore, considering the architectural characteristics of state-of-the-art AFV systems, a potential trade-off solution to achieve explainability may involve incorporating post hoc measures of explainability, either at the prediction level or dataset level, while still leveraging the capabilities of less interpretable deep transformer models. The subsequent subsections delve into the attempts made in the literature to incorporate post hoc explainability in terms of methods and input within the context of AFV.  
Figure 3: Overview of stages in Automated Fact Verification. This figure depicts the primary stages—document retrieval, sentence selection, and recognition of textual entailment—along with optional components for assessing the check-worthiness of claims and providing justifications.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 6 Practical Challenges in Underwater SLAM
### Computational Resources
#### 6.2.1 Model Quantization, Pruning, and Knowledge Distillation  
DL model compression techniques are crucial for hardware-constrained environments. For instance, model quantization is a compression method that reduces the memory footprint, computational cost, and power consumption of deep learning models by converting high-precision weights and activations into lower-precision formats [98]. In the context of SLAM, a quantized self-supervised local feature approach has been introduced by Li et al. [99] for indirect VSLAM, using an orthogonal transformation to improve feature efficiency. Moreover, pruning can be used in conjunction with quantization in SLAM to remove less important parameters, such as weights, neurons, or layers, without significantly affecting performance [100].  
Another compression technique is KD, where a smaller model (student) is trained to replicate the behavior of a larger, more complex model (teacher), improving efficiency while maintaining accuracy. In semantic SLAM, KD has been used to enhance real-time performance in dynamic environments [101]. For instance, a multi-level KD approach has been proposed by Chen et al. [102] to create a lightweight segmentation model, allowing an independent semantic segmentation thread that processes only keyframes, reducing delays. Additionally, a static semantic keyframe selection strategy was proposed for underwater VSLAM by Yang et al. [103] to minimize the impact of dynamic objects, while dynamic probability propagation further refines pose optimization.
## 3 Experimental Bench and Theoretical Model
### Experimental Bench
#### 3.1.2 Theoretical Calculation of Air Compressor  
To address the issue of high energy consumption and poor stability in existing centrifugal air compressors for hydrogen vehicles, a study focused on developing an optimal air compressor with a lower energy consumption, higher isentropic efficiency, and a larger mass flow rate is conducted.  
The isentropic compression work of the air compressor is calculated as Equation (1) based on isentropic compression work \(W_{s}\), adiabatic index \(k\), gas constant \(R\), and intake temperature \(T_{1}\)[31]:  
\[W_{s}=\frac{k}{k-1}RT_{1}\left[\left(\frac{p_{2}}{p_{1}}\right)^{\frac{k-1}{k}} -1\right] \tag{1}\]  
The shaft power \(W_{tot}\) represents the total power consumption, which is expressed by Equation (2):  
\[W_{tot}=P\times\eta_{mo} \tag{2}\]  
where \(P\) is the power displayed on the DC power cabinet and \(\eta_{mo}\) is the motor efficiency.  
Isentropic efficiency \(\eta\) can be expressed as Equation (3) [14]:  
\[\eta=\frac{W_{s}}{W_{tot}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline No. & Equipment and Appliances & Limit & Precision \\ \hline
1 & Filter & \(\geq\)0.003 \(\mu\)m & / \\
2 & Centrifugal compressor & 20,000–90,000 rpm, 0.01–0.162 kg/s & / \\
3 & Compressor controller & 180–445 VDC & \(\pm\)200 rpm \\
4 & DC power cabinet & 500 V & \(\pm\)1\% \\
5 & Automatic valve & 0–1 & / \\
6 & Temperature transducer & 300 \({}^{\circ}\)C & \(\pm\)1 \({}^{\circ}\)C \\
7 & Pressure transducer & 0.6 MPa & \(\pm\)0.2\% \\
8 & Vortex flow meter & 30–500 m\({}^{3}\)/h & \(\pm\)1\% \\ \hline \end{tabular}
\end{table}
Table 1: Detailed information on the equipment and appliances of the air compressor.  
Figure 3: Experimental setup of air compressor: (**a**) actual bench and (**b**) schematic diagram.
## 5 Experimental Study
### Comparative Test Research on No-Load Variable Speed  
The speed curves of the motor control system under the no-load variable speed based on traditional PI and fuzzy PI when a step signal is input are provided in Figures 20 and 21, respectively. The target speed change process is 1000-500-1200-800 (r/min). The two fragments of speed with the mutation from 0 to 1000 r/min and the mutation from 1200 to 800 r/min are studied for comparison. When the electric motor speed changes from 0 to 1000 r/min, the overshoot of the PI control and fuzzy PI control are 40 r/min and 20 r/min, respectively. Moreover, the times to steady state are 600 ms and 420 ms, respectively. When the electric motor speed changes from 1200 to 800 r/min, the overshoot of the PI control and fuzzy PI control are 35 r/min and 10 r/min, respectively. Moreover, the times to steady state are 35 ms and 260 ms, respectively. When the electric motor control system enters a steady state, the speed fluctuation of the PI control and fuzzy PI control are about 1.5/100 and 1/100, respectively. It can be seen that under the same operating conditions, the electric motor speed response and the overshoot are faster with fuzzy PI control. At the same time, the speed fluctuation is also significantly smaller after entering the steady state.  
The speed curves of the motor control system under the no-load variable speed based on traditional PI and fuzzy PI when a ramp signal is input are provided in Figure 22. It  
Figure 21: Speed curve of fuzzy PI under no-load variable speed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cylinder Diameter/mm** & **Piston Rod Diameter/mm** & **Cylinder Stroke/mm** \\ \hline
63 & 25 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of executive cylinder.  
Figure 20: Speed curve of traditional PI under no-load variable speed.  
can be seen that two control methods can follow the target ramp input well. Between of them, the fuzzy PI control method is more ideal, although there is some overshoot. The traditional PI has a slow following response.
## 1 Introduction
OTDR and DAS are used, for example, for oil well monitoring [8], intrusion detection [9; 10], or even for rock slide detection [11]. Vibrations of train movements near the railway track can also be monitored using a DAS device, where in most cases, fiber cables are already installed there for communication means. Several methods for train tracking using DAS signals have been proposed [12; 13; 14]. However, these algorithms were never evaluated for longer recordings and for different ground and installation conditions. All the presented methods are based on the variance of the measured DAS signals. In this contribution, we propose a method that is based on machine learning methods to detect vibrations, which is more flexible and can be more easily adapted to different recording conditions. Furthermore, the algorithm which is the basis for [15; 16] has a five-second lag in time. The goal of this manuscript is to provide a detailed description and evaluation of an algorithm that can be used for real-time train position monitoring. We will give the details on the data acquisition of DAS test data used for this investigation in the following section. The algorithmic details, as well as a discussion of the accuracy of the proposed method will be given in the later sections. We will further provide an outlook for future work and improvements.