esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 4 Experiments
### Comparison of the Results with State-of-the-Art Models  
M-MAMFT achieves the best prediction accuracy on all three publicly available datasets, and the specific experimental results are shown in Table 3. The most significant improvement in prediction accuracy is achieved on the ETH2 dataset. Compared with the DLinear prediction results, the MSE and MAE metrics of the M-MAMFT model decreased by an average of 47.6% and 24.6% from a prediction length of 96 to a prediction length of 720, respectively. In addition, the decrease in the values of the MSE and MAE increases as the prediction length increases, and the most significant decrease ratios are when the prediction length is 720, where the MSE and MAE decrease by 56.1% and 31.2%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Electricity** & **Weather** & **ETTh2** & **BCAT** \\ \hline Initial learning rate & 0.002 & 0.0001 & 0.0001 & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial learning rate settings for the four datasets.  
Figure 4: Selected data from the BCAT dataset.  
Compared with the Dlinear model, the prediction accuracy of M-MAMFT on the Electricity dataset and the Weather dataset is greatly improved. In the prediction of the Electricity dataset, the prediction results of the M-MAMFT model, compared with those of the Dlinear model, show the most significant improvement in prediction per-formance when the prediction length is 96, with the MSE and MAE decreasing by 8.6% and 5.1%, respectively; and from a prediction length of 96 to a prediction length of 720, the MSE and MAE decrease on average by 4.9% and 2.7%, respectively. In the predic-tion of the Weather dataset, the predictions of the M-MAMFT model showed an aver-age reduction of 8.4% and 8.5% in MSE and MAE, respectively, when compared to the predictions of the Dlinear model; the maximum reduction in MSE and MAE was 14.2% and 11.8%, respectively, at a prediction length of 96.  
Our model also achieved the best prediction results on the BCAT dataset. Because there is only one variable in the BCAT dataset, the predictions of the two model versions, M-MAMFT and I-MAMFT, are consistent. When the prediction length is 96-720, the MSE and MAE of our model are on average 0.8% and 1.7% lower than the optimal results of the other models. Although there is only a small advantage on the BCAT dataset, it still demonstrates the superiority of our model's prediction performance.
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 6 Conclusions  
5G networks has an efficient effect in energy consumption and provides quality of experience and amount of devices communication, and 5G will change connected services and devices through higher reliability, connectivity, and cloud storage. IoT applying 5G infrastructure changes application scenario in many fields especially real-time communication between machines, data, and people. IoT with 5G environment provides solutions of network layer, including enhancing quality of service, router and jamming control, and resource optimization, to solve challenges of smart medical healthcare. Medical privacy is important in smart medical healthcare because data leaking brings potential harm to patients and hospital. We propose a FAIDM for medical privacy protection in 5G telemedicine systems which provides federated identity management which provide a secure way to protect medical privacy. To achieve privacy preservation, we provide anonymous identity to constrained nodes for reducing exposure of personal private data. Our scheme provides features below. (i) Proposed scheme provides federated identity management which can manage identity of devices in a hierarchical structure efficiently. (ii) Identity authentication will be achieved by mutual authentication between devices and SBSs/MBSs. (iii) The proposed scheme provides session key to secure transmitted data which is related to privacy of patients. (iv) The proposed scheme provides anonymous identities for devices in order to reduce the possibility of leaking transmitted medical data and real information of device and its owner. (v) If one of devices transmit abnormal data, the proposed scheme provides traceability of anonymous identities for servers of medical institute to check specific device. (vi) the proposed scheme provides anonymous signature for non-repudiation of devices, and records of signatures can be used for periodical audit of medical institute.  
Conceptualization, C.-L.H. and T.-W.L.; methodology, C.-L.H. and T.-W.L.; security analysis, T.-W.L.; writing--original draft preparation, T.-W.L.; writing--review and editing, T.-W.L.; supervision, C.-L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology, Taiwan, grant number MOST 108-2221-E-182-011, Healthy Aging Research Center, Chang Gung University, Taiwan, grant number EMRPD1K0461 and EMRPD1K0481, and Chang Gung University, Taiwan, grant number PARPD3K0011.  
Not applicable.  
This study did not report any data.  
Figure 8: Computational complexity of receiver/gateway node with varying number of devices.
## 2 Materials and Methods
### Data Preprocessing
#### 2.3.1 Data Augmentation (DA)  
Audio data augmentation methods use different techniques to increase the model's generalization ability and add variety to the dataset. The following data augmentation techniques were implemented using the Librosa tool [26] considered in the following:  
1. Time Shift: Shifting the audio signal in time by a certain amount (positively or negatively) makes the model insensitive to the location of the sound. This technique is used when information dependent on the timing of the sound should not be important to the model and makes the model robust to sound samples at different timings.
2. Time Stretch: Changing the length of the sound without changing its speed, that is, speeding it up or slowing it down, allows the model to be robust to different speeds of sound. This helps the model be flexible to different tempos of sound and allows it to make accurate predictions despite changes in speed.
3. Pitch Shift: Changing the pitch of the sound, i.e., frequency shifting, makes the model robust to different tones. Pitch shifting allows models that are not particularly sensitive to changes in pitch to be more generalizable to a wide range of tones.
4. Add White Noise: Adding a low level of random white noise to the audio signal allows the model to perform better in noisy environments. This technique helps the model better adapt to real-world environments, except when the audio is clean.
5. Slice: Generating shorter audio samples by randomly taking a section from a long audio file allows the model to focus on specific audio segments. This allows the model to learn the information in long audio files more effectively and focus on important details in specific short segments.  
The parameters of the methods used are shown in Table 3.
###### Abstract  
This paper proposes a standard applications of Radio Frequency Identification (RFID) system to manage vehicles at electronic toll gate system without compromising the vehicle speed. The existing electronic toll gate system has low latency as each car needs to wait in a queue for passing the gate. Installation of reader antenna at each lane makes the system expensive. We propose a two-ray ground model for using minimal reader antenna to define required antenna height and downtilt angle to cover the total gate area. We estimate that reader antenna at a height of 15 m and 45.4\({}^{\circ}\) downtilt angle are required for optimal signal reception from the car at a distance of 35-38 m. Modified Pulse Protocol algorithm is suggested for mitigating reader collision and increase throughput and utilization of the system by 14.71% and 15.2% respectively. The probability distribution function of the contention window is modified to triangular function. The M-ary Trimming Q-tree protocol is suggested to resolve the tag collision by trimming the idle nodes from the query tree, which reduces identification time. The total time slot decreases with higher elements, and the total throughput also increases to 98%. The \"no-lane\" strategy established a fast, efficient and cost effective vehicle toll collection system.  
RFID; downtilt angle; throughput; utilization; modified pulse protocol; M-ary trimming query tree 2025  
27 February 2025  
25 March 2025  
Accepted: 27 March 2025  
Submitted: 31 March 2025  
**Citation**: [PERSON];[PERSON]; [PERSON]; [PERSON]; [PERSON] RFID Antenna Deployment Model and Anti-Collision Algorithms to Enhance Vehicle Toll System  
Embance Vehicle Toll System
# Fuzzy Regulator for Two-Phase Gas-Liquid Pipe Flows Control
## 2 The Two-Phase Gas-Liquid Mixtures Flow
### Flow Type Recognition  
From a variety of industry-oriented imaging solutions' points of view, the electrical capacitance tomography (ECT) [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26] applied to the two-phase gas-liquid mixtures visualization [27] and the phase distribution calculation [28] is getting popular, especially when flow key parameters are required. Industry demands notably include efficient non-invasive automatic phase fraction calculation and flow structure recognition in the vertical and horizontal pipelines. This can be solved by using non-deterministic fuzzy-logic-based techniques for the analysis of volumetric images. The authors developed the automated two-phase gas-liquid flow type recognition based on a fuzzy evaluation of a series of reconstructed 3D ECT volumetric images [29] and the raw 3D ECT measurement data [30]. The set of fuzzy-based features was calculated for flow substructure classification. As a result of this analysis, the obtained features were used to classify given diagnostic information (reconstructed images or raw measurement data) into one of the known flow regime structures.  
Authors demonstrated in their works that recognizing the two-phase vertical and horizontal flow substructures is possible using the information provided by the spatial analysis of the large set of three-dimensional images and measurement data generated by a non-invasive 3D ECT system. The flow substructures recognition was successfully performed using fuzzy logic inference with very high classification accuracy as good as the human expert work. The classification process of the two-phase flow substructures provides valuable information to construct more accurate two-phase flow maps [12] and reliable industrial process control and diagnosis. Due to the efficiency of the proposed flow evaluation technique and fast 3D image reconstruction [31], the CUDA development library was implemented [32]. Therefore, this process was done in online mode and was limited only by the 3D ECT hardware that could provide input measurement data with a speed of 12 fps.  
The signal of recognized flow type will be given as an input to the regulator module presented in this article. Moreover, the recognition block, together with the regulator, is closed within one feedback loop.
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 5 Experimental Evaluation and Discussion  
This section mainly introduces the experimental results. The training hyperparameters are shown in Table 2. We compare the proposed method with heuristic scheduling algorithms, such as PSO and GA algorithms. In addition, we compare PCA\(-\)DDQN with reinforcement learning algorithms, such as the DDQN and Q\(-\)learning algorithm. We consider evaluating the performance of the proposed method, including cycle life and cumulative reward during learning, SOC curve variation during convergence, and the generalizability of the algorithm.
## 2 Methodology
### Model Evaluation  
The ROC curve is a tool used to evaluate the performance of binary classification models, and it is also widely adopted in LHA studies. The vertical axis of this curve represents the true positive rate (\(TPR\)) of the model, indicating the ability to identify the evaluation units with landslides. Its horizontal axis is the false positive rate (\(FPR\)), which reflects the situation wherein evaluation units with landslides are wrongly classified as non-landslide evaluation units. \(TFR\) and \(FPR\) can be obtained by Equations (7) and (8), respectively. The more closely the curve approaches the upper-left corner, the better the model's ability to distinguish positive and negative samples.  
\[TPR=TP/(TP+FN) \tag{7}\]  
\[FPR=FP/(FP+TN) \tag{8}\]  
where \(TP\) is the number of correctly predicted landslide samples, \(FN\) is the number of non-landslide samples misjudged by the selected model, \(FP\) is the number of landslide samples misjudged by the selected model, and \(TN\) is the number of correctly predicted non-landslide samples.  
The predictive performance of the model is generally quantitatively analyzed using the area under the ROC curve (AUC). Based on the AUC value, the model performance can be classified into the following levels [5; 11; 41]: very low (0.5-0.6), low (0.6-0.7), moderate (0.7-0.8), good (0.8-0.9), and excellent (0.9-1.0). Generally, the AUC value is calculated by Equation (9) [20]:  
\[AUC=(\Sigma TP+\Sigma FN)/(TP+TN+FP+FN) \tag{9}\]  
In addition, the distribution characteristics of LHIs can be used to analyze the performance of LHA models [11; 42]: (1) the coverage of a landslide hazard level usually increases as the hazard level lowers, and the zones with high landslide hazards should constitute a very small proportion of the whole area; (2) landslides are mainly distributed in zones with high hazard levels, and they rarely occur in low-hazard zones.
## 4 Discussion
### Spatial and Temporal Distribution of Chl-a
#### 4.2.2 Temporal Variation of Chl-a  
The concentration of Chl-a in the coastal sea surface of the Beibu Gulf in Guangxi exhibited strong seasonal changes, with the following ranking being apparent: summer \(>\) autumn \(>\) spring and winter (Figure 12).  
The Chl-a concentration in summer and autumn was mainly affected by the climate [49; 50]. In summer, the Beibu Gulf receives abundant rainfall. The wet season ranges from July to September, and the average monthly precipitation exceeds 100 mm, accounting for approximately 55-70% of the total annual rainfall. River runoff is the largest in summer, and many rivers along the coast flow into the Beibu Gulf, taking agricultural and industrial wastewater into the gulf. The inflows have strong diffusion force, carrying large amounts and high concentrations of nutrients. As the sea level is raised by the runoff, the resultant concentration gradient strengthens the water exchange and increases the thickness of the mixing layer. There are high temperatures in summer, with the highest SST being up to 34.1 \({}^{\circ}\)C. Under such environmental conditions, phytoplankton proliferate in large numbers. Therefore, the concentration of Chl-a was the highest in the summer, and the area with a  
Figure 12: Distribution of Chl-a concentrations in the summer and autumn of 2018.  
high value was concentrated in the estuarine area of the region. The southwest monsoon prevails in summer, generating a coastal component of the wind force, and it flows along the coast, driven by the monsoon [51]. The runoff raised the sea level and further promoted southwest flow along the coast by the gradient in the sea level. Therefore, in summer, nutrients along the Beibu Gulf are transported from east to west. The Chl-a concentration declined gradually from east to west. In autumn, rainfall decreases, and runoff into the sea decreases. The river-diluted water contracts to the shore, and the concentration of Chl-a decreases compared with that in the summer.  
In spring and winter, the river runoff was the smallest, the terrestrial nutrient input was lower, the sea surface temperature was the lowest (19.0-24.0 \({}^{\circ}\)C), the growth of phytoplankton was inhibited, and the Chl-a concentration in the sea surface was the lowest. The wind is light over the Beibu Gulf in spring, and the effect of the Beibu Gulf monsoon on aquatic mixing is greatly reduced, causing a smaller mixing layer. Additionally, controlled by the reduction in flow, the Chl-a concentration is generally low in the spring and winter in the Beibu Gulf. The climate data of the sampling point (GX10 referenced as an example) on the sensing date of the satellite is shown in Table 8.
## 1 Introduction  
In recent years, the convergence of Information Technology (IT) and Operational Technology (OT) domains has brought significant advancements in automation, central ization, and networked control systems. This convergence, particularly within critical infrastructure and manufacturing sectors, offers substantial benefits, including enhanced resource management and improved operational efficiency. The ability to control and monitor systems across vast geographical distances has revolutionized how services are delivered, making them more efficient and responsive. However, integrating OT protocols with IT infrastructure has also introduced new vulnerabilities that were not initially accounted for in the design of OT protocols [1]. Their original isolation from the Internet resulted in simple and effective protocol designs. However, this lack of cybersecurity features now results in the need for efforts to layer on security features, often resulting in vulnerabilities, insufficient protection mechanisms, complexity increase, and a negative impact on OT protocol performance. Vulnerabilities, in particular, often stem from the differing security models and communication requirements between IT and OT systems. As a result, OT systems that were originally isolated and designed for closed environments are now exposed to a broader range of cyber threats. Many organizations have turned to network monitoring tools adopted from the IT domain to mitigate these risks, including firewalls, intrusion detection systems, and anomaly detection schemes. While these tools have improved overall cybersecurity postures, OT protocols remain vulnerable to various sophisticated attacks [2].  
The increasing integration of OT devices within the Internet of Things (IoT) and Industrial IoT (IIoT) networks has further compounded these challenges [3]. The proliferation of IoT devices has enabled seamless communication and easier commissioning of networks, thanks to the reliability of IT communication infrastructures. However, the growing complexity of these networks introduces new challenges related to network latency, the availability of communication links, and the specific characteristics of devices used in safety-critical communications [4]. As OT systems become more interconnected with IT networks, ensuring continuous availability and reliability of communication channels becomes crucial. Failures in communication can lead to cascading effects, especially in environments where real-time data exchange is critical for system stability and safety. Therefore, exploring new methods for testing, evaluating, and securing these integrated networks is essential for ensuring that performance and safety requirements are met without compromise.
# Numerical Simulation of Piles in a Liquefied Slope Using a Modified Soil-Pile Interface Model
## 1 Introduction
In general, the weakening of soil strength due to liquefaction occurs as a consequence of reduced confining pressure caused by the generation of excess pore pressure. Nevertheless, it is worth noting that rapid weakening can also manifest due to soil instability, depending on the initial stress state [29]. Noteworthy studies by [PERSON] and [PERSON] [30] and [PERSON] et al. [31] reported instances of such rapid weakening observed in liquefaction tests under dynamic loading. However, the employment of interface springs incorporating a constant friction angle merely accounts for the effect of confining pressure reduction and does not adequately address the rapid decline in strength associated with soil instability. Furthermore, while the determination of the constant friction angle through additional static pile pushing analysis was recommended [32], its compatibility with the advanced soil model for liquefaction remains unverified. Therefore, a modified pile-soil interface model was developed to consider the interaction between piles and a liquefiable finite slope. The model linearly decreases the friction angle of the interface normal spring according to the excess pore pressure ratio of the corresponding ground element. The proposed model was validated by comparing it with centrifuge model test results conducted by [PERSON] et al. [33] on piles installed in liquefied slopes. To accurately simulate the centrifuge tests, a rigorous numerical model was applied, including soil modeling, structural modeling of the deck and piles, and modeling of liquefied ground. Based on the constructed analysis model, parametric studies were performed by varying the slope inclinations and the amplitude of input motions.
# Optimized Sonar Broadband Focused Beamforming Algorithm
###### Abstract  
Biases of initial direction estimation and focusing frequency selection affect the final focusing effect and may even cause algorithm failure in determining the focusing matrix in the coherent signal-subspace method. An optimized sonar broadband focused beamforming algorithm is proposed to address these defects. Initially, the robust Capon beamforming algorithm was used to correct the focusing matrix, and the broadband signals were then focused on the optimal focusing frequency by the corrected focusing matrix such that the wideband beamforming was transformed into a narrowband problem. Finally, the focused narrowband signals were beamformed by the second-order cone programming algorithm. Computer simulation results and water pool experiments verified that the proposed algorithm provides a good performance.  
Keywords:broadband beamforming; coherent signal-subspace method; RCB algorithm; second-order cone programming +
Footnote †: journal:
# Analysis of Vertical Earth Pressure Acting on Box Culverts through Centrifuge Model Test
## 2 Vertical Earth Pressure Acting on Box Culverts  
The first solution for computing the vertical earth pressure acting on a box culvert was proposed by [8]. The vertical earth pressure acting on the top section of a box culvert is related to the relative movement between the central soil over the structure and the adjacent soil columns, and the vertical earth pressure acting on the bottom section of a culvert is the sum of the earth load on the top slab, frictional force on the sidewalls, and dead load of the structure. The vertical earth pressure acting on a box culvert largely depends on the type of construction (embankment or trench installation) because the relative settlement of the exterior soil prism and the central soil prism near the box culvert behaves differently depending on the type of construction. This relative settlement can be added to or removed from the weight of the central soil prism above the box culvert, and friction or shear stress between the exterior and the central soil prisms can be generated.  
In embankment installation, the relative settlement of the exterior soil prism is larger than that of the central soil prism above the box culvert, as shown in Figure 1a. The arch shaped deformation acts on the central soil prism, meaning that a negative arch is formed that increases the earth pressure on the substructure. Conversely, in trench installation, the relative settlement of the central soil prism is larger than that of the exterior soil prism, and deformation takes the form of an inverted arch, which is a positive arch. In this case, the direction of the friction or shear stress between the exterior and the central soil prisms is reversed, and the earth pressure is reduced by the corresponding stress, as shown in Figure 1b.