esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods.  
Keywords:IMFs; multiscale permutation entropy; stacked sparse denoising autoencoder; fault diagnosis +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Assessment of Product Quality
Such elevated temperatures give rise to a number of detrimental changes, which, among other parameters, impact the color. These changes are attributed to both biochemical processes and thermal decomposition [42]. The rapid increase in temperature of the material being microwave-dried can be attributed to the inhomogeneity of the microwave field and changes occurring in the material. In the former, localized overheating of the material, known as \"hot spots\", results in substantial qualitative alterations at these locations. To address this phenomenon, advanced microwave chamber designs are employed, and the dried material's movement is maintained through the microwave field (transmission belt, rotating pan, drum, etc.) [43]. Figure 4: Average water activity, \(a_{w}\), and color difference index, \(dE\), for each process. Additionally, changes in moisture content can also result in the material overheating. This is due to the fact that the moisture affects the dielectric properties of the material, namely, its capacity to absorb and reflect microwaves. When the material contains a substantial amount of moisture and the radiation power is properly adjusted, microwaves can effectively evaporate moisture without causing a sudden increase in the material's temperature. However, as the moisture content decreases during the drying process, the ability of the material to absorb microwaves changes. This can lead to a significant increase in the temperature of the material and unfavorable changes in its color. Therefore, it is very important to regulate the microwave power and exposure time, especially towards the end of the process. An alternative solution is the periodic application of microwaves (intermittent microwave drying) and the introduction of a so-called \"relaxation period\", during which the temperature and humidity gradients will be equalized. This non-stationary hybrid drying is a promising technology that allows operational time to be reduced while maintaining high product quality, including color [35; 39; 44]. The beneficial impact of ultrasound on product color (Figure 4, CVUS) can be attributed to its non-thermal nature. Although ultrasound application accelerated heating of the material (compared to CV), unlike microwave radiation, it did not result in overheating effects (Figure 2c). Consequently, the initial color of the samples was preserved more effectively, and the majority of observed changes can be attributed to the temperature of the drying agent (air) and the high oxygen content in the atmosphere. These findings align well with those reported by [PERSON] et al. [37].
## 2 Materials and Methods
### Optimizer
#### 2.3.2 Shortest-Job-First  
The _Shortest-Job-First_ (SJF) scheduling algorithm inherits some of its behavior from the FIFO scheduler. It executes repeatedly as long as pending changes are present to produce updated schedules that contain all currently known jobs. SJF processes all known jobs as follows. It sorts all \(n\) jobs with respect to their predicted processing time in ascending order and selects the job with the shortest predicted processing time that does not have  
Figure 3: The First-In-First-Out (FIFO) algorithm.  
unscheduled predecessors first. It then assumes this first job to be processed and sorts all remaining \(n-1\) jobs again with respect to their shortest predicted processing time. Therefore, SJF splits the set of jobs into two lists \(\mathcal{S}\) and \(\mathcal{U}\), where \(\mathcal{S}\) contains all scheduled jobs, and \(\mathcal{U}\) contains all remaining jobs. Scheduling a job, i.e., moving it from \(\mathcal{U}\) to \(\mathcal{S}\), might lead to other jobs in \(\mathcal{U}\) being processable and therefore schedulable. Figure 4 shows how jobs are sorted and selected. Job J1 depends on J0 and cannot be scheduled until J0 is scheduled. Jobs J4 and J5 both depend on J3. Jobs that can be scheduled are marked with a check mark, jobs that cannot be scheduled (because of precedence constraints) are marked with a cross. Already scheduled jobs are marked with a star. The fill level of each job corresponds to the predicted processing time, making J7 the fastest job and J6 the slowest. In the first step, all jobs are sorted with respect to their processing time. Then, the fastest schedulable job--here J7--is scheduled, i.e., moved from \(\mathcal{U}\) to \(\mathcal{S}\). As no jobs depend on J7, no precedence constraints are changed. SJF then virtually applies J7 and updates the timing models, as well as the predicted processing times for all remaining, schedulable jobs. SJF then selects the next fastest, schedulable job in the sorted set \(\mathcal{U}\), now being J2, in which the predicted processing time was updated. Since both J4 and J5 depended on J2, their precedence constraints are now lifted and both jobs are schedulable. SJF would then select the next fastest, schedulable job in \(\mathcal{U}\), being J4. This repeats until \(\mathcal{U}=\emptyset\).  
Figure 4: Shortest-Job-First (SJF) processing eight jobs.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Analysis of Results
#### 4.3.2 Network Routing Load (%) vs. No of Nodes  
Figure 7 shows the normalized data load for different routing protocols and network sizes in IoMT networks. A lower overhead value indicates a more efficient routing protocol. The results indicate that the SBAODV protocol has the lowest normalized load of data (overhead) across all network sizes tested. At 20 nodes, SBAODV has an overhead value of 2588, which is 20% to 30% lower than the other protocols, with DADRP being the closest with an overhead value of 2849. As the number of nodes increases, the overhead values for all protocols tend to increase. However, the SBAODV protocol maintains its superiority, with an overhead value of 5101 at 100 nodes, 7% to 23% lower than other existing routing protocols.  
The low overhead value of SBAODV can be attributed to its ability to adapt to network topology changes and dynamically select the best path to the destination based on the available network resources. This enables the protocol to transmit data with the least amount of additional data, which can help reduce the UAVs' energy consumption and enhance the network's efficiency. In contrast, other protocols, such as LPNRP, TPH, RPH, FDTRP, and DADRP exhibit higher overhead values than SBAODV for all network sizes. These protocols are less efficient than SBAODV in transmitting data with the least amount of additional data. This is because of the Stackelberg game theory model SBAODV efficiently detect black hole devices and stops them from transmitting false packets across the network. While LPNRP, TPH, RPH, FDTRP, and DADRP cannot detect black hole attackers, these  
Figure 6: Packet delivery ratio % vs. no of nodes.  
devices transmit many false packets, increasing the network load across the network. From Figure 7, we can clearly observe that the proposed scheme offers the most negligible network load. These results highlight the importance of selecting an efficient routing protocol for IoMT networks assisted by UAVs. Efficient data transmission is critical for improving the quality of healthcare services provided. The low overhead value of SBAODV makes it a suitable choice for routing in these networks.
## 4 Model Prediction Results and Analysis
### Impact of Silt and Clay  
The compressibility of soft ground is influenced by the silt and clay content of the soil. Therefore, to evaluate the influence of silt and clay on model performance, the relationship between the R\({}^{2}\) values across the entire site and individual zones was investigated. The R\({}^{2}\) values of the ML models calculated for each zone in Table 3, were averaged and presented in Table 5.  
Analyzing the prediction results based on hydrogeological characteristics shows that zone 2, located farthest from the coast, has the lowest average R\({}^{2}\) value, while zones 1, 3, and 4, closer to the coast, show higher average prediction accuracy. The average grain size of the tidal flat sediments in the Saemangeum area was the greatest in zones 1 and 4, and the smallest in zone 2. This is consistent with the trend observed in the R\({}^{2}\) values. In particular, in zone 3, an examination of the stratigraphy revealed a unique structure compared with the other zones. A thick gravel layer, 2-10 m in thickness, lay atop the bedrock layer at the bottom. Additionally, a loose sandy soil layer was deeply distributed with a thickness ranging from 1.5 to 15 m. Additionally, as shown in the investigation results presented in Figure 6, the clayey soil became thicker toward the open sea, and zone 2 had the thickest soft clay layer.  
This sedimentation pattern can be attributed to the characteristics of clay and silt. Fine-grained sediments such as clay and silt, with smaller particle sizes, are more influenced by the flow and intensity of ocean currents compared with coarse-grained sediments like sand and gravel. As a result, they are transported further and deposited at greater distances.  
Figure 6: Distribution of the soft clay layer.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **Zone 1** & **Zone 2** & **Zone 3** & **Zone 4** & **Saemangeum** \\ \hline Mean R\({}^{2}\) Value & 0.7299 & 0.4964 & 0.7059 & 0.8447 & 0.6942 \\ \hline \end{tabular}
\end{table}
Table 5: Mean R\({}^{2}\) value by zone.  
Additionally, silt and clay exhibit higher compressibility than sand and gravel, which makes their behavior more challenging to predict.  
Therefore, zone 2, located farthest from the coast, featured a thick deposition of highly compressible silt and clay layers. This increased the data variability, hindered the model's ability to learn the data distribution, and ultimately resulted in the lowest prediction accuracy.
# Power Losses Investigation in Direct 3 \(\times\) 5 Matrix Converter Using MATLAB Simulink
## 4 Model of the MxC Converter in MATLAB Simulink  
The matrix converter simulation was carried out using the Simulink Simscape environment. For the power loss investigation, three models were simulated and investigated.  
The first model used SiC transistors in the bidirectional switch. Additionally, for the proper current commutation, the direction of the current of every output phase must be known. For this purpose, additional antiparallel diodes were connected at the output of every phase of the matrix converter to reliably detect the current direction.  
The second model used the IGBT transistor in bidirectional switches to compare losses to the SiC one. The current direction diodes were left in the circuit.  
Finally, the third model bidirectional switches were based on IGBT transistors too, but direction diodes were removed. Instead of bidirectional diodes, the voltage drop at the transistor of the bidirectional switch was measured and evaluated. If the value was higher than 2.8 V, according to Table 1, the current was reportedly flowing through the internal diode of the IGBT; thus, current direction was negative. If the voltage drop was below 2.8 V, the current was flowing through IGBT itself and the current was positive.  
This approach requires additional circuits and comparators, but the losses generated at the direction detection diodes can be eliminated, which can lead to higher efficiency.  
For the simulation, indirect control using vector modulation was used. The matrix converter is virtually split into two parts, a virtual rectifier and a virtual inverter. Both parts can be controlled separately with their gains, respectively. The resultant switching pulses are then transferred from the indirect to the direct topology using the following matrix [25]:  
\[\begin{bmatrix}S_{aA}&S_{bA}&S_{CA}\\ S_{aB}&S_{bB}&S_{eB}\\ S_{aC}&S_{aC}&S_{aC}\\ S_{aD}&S_{BD}&S_{CD}\\ S_{aE}&S_{BE}&S_{cE}\end{bmatrix}=\begin{bmatrix}S_{7}&S_{8}\\ S_{9}&S_{10}\\ S_{11}&S_{12}\\ S_{13}&S_{14}\\ S_{15}&S_{16}\end{bmatrix} \tag{11}\]  
where individual switches correspond to the switches in the indirect and direct matrix topology, as shown in Figure 3.  
Additionally, the matrix converter requires an input filter for the proper function and for the current drawn from the grid to be sinusoidal. The filter will have additional losses; thus, the filter was modeled to the simulation too. The chosen filter was the LC damped filter due to its relatively low component count and good performance. The LC filter is unstable around its resonant frequency, which can cause problems during the matrix converter operation, especially during low output powers and step load changes. Multiple damping solutions are known for the LC filter, and for this application, the resistor parallel to the inductor as a damping component was chosen, due to its good damping, low cost, and simplicity. The parameters of the used filter are summarized in Table 2.
# A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning +
###### Abstract  
Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors' knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license.  
IoT; machine learning; software engineering; TDD; testbed Article
# Vibration Properties of Dual-Rotor Systems under Base Excitation, Mass Unbalance and Gravity
## 2 Modeling for a Dual-Rotor System under Base Motions  
As shown in Figure 1, a counter-rotating dual-rotor system is mounted on a moving rigid base (the blue dashed box), which consists of a low-pressure rotor with a low rotating speed \(\Omega_{LP}\) and a high-pressure rotor with a higher rotating speed \(\Omega_{HP}\). The excitations due to the base motions, mass unbalance and gravity are taken into consideration. Three coordinate systems are defined in this paper: an inertial frame of reference \(F_{0}(X_{0},Y_{0},Z_{0})\), which is fixed to the ground; a non-inertial frame of reference \(F_{b}(X_{b},Y_{b},Z_{b})\), which moves with the rigid base; and a local frame of reference \(F_{I}(X_{I},Y_{I},Z_{I})\), which moves with the rotor. All three coordinate systems have relative movement to each other. For the dual-rotor model in this paper, longitudinal vibration and rotor torsion are ignored.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 3 Edge Computing and Deep Learning-Based Malware Detection Method
### Main Functions of the Edge Computing Deep Learning-Based Malware Detection System
#### 3.4.4 Training Data Transmission  
To continuously improve deep learning models, it is necessary to be able to collect the necessary training data from each IIoT and send them to an edge or cloud server. In other words, for training of the deep learning model that requires high-performance computing power, the extracted function is transmitted to the edge server with better processing performance than the edge device for operation.  
The malware detection method proposed in this study is a distributed edge deep learning detection method, which is different from the existing centralized deep learning malware detection method. From smart factory IIoT sensor data, the data to be analyzed are collected and sent to an edge server for training of the deep learning model to create a local model. Additionally, a global model is created by integrating the local model information sent to the cloud server to monitor the performance of the deep learning model and improve its performance. A trained model is then installed at the edge (G/W) of the edge node to detect malware.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 1 Introduction
### Field Measurements  
Field measurements are widely accepted as a reliable way of investigating wind characteristics such as wind speed, peak factor, and turbulence profiles. [PERSON] et al. [5] presented an analysis and discussion of wind effects on a high skyscraper during the passage of Typhoon Nida. The coherence of spectral power density and wind velocities were explored using wind records [22]. [PERSON] et al. summarized the wind field parameters during Typhoon \"Fung-Wong.\" [23]. The variable characteristics of the wind speed profiles and related pulsation parameters were investigated by a measurement system established on a mountain [24]. [PERSON] et al. [25] analyzed the wind parameters of the Typhoon Bailu and discovered that the logarithmic model was more compatible with the recorded wind speed profile than other generally described profiles. A complete investigation of wind characteristics was conducted based on records during 10 typhoons [26]. [PERSON] et al. [27] explored the data recorded during Typhoon Haima passage and calculated the wind-induced pressures and structural responses of the 600 m high PingAn Finance Center (PAFC) in Shenzhen. [PERSON] et al. [28] examined wind field characteristics of Typhoon Morakot and revealed that the autocorrelation coefficient reduced as the time interval increased. The wind properties of several typhoons, including the peak factor and turbulence intensity, were statistically examined [29, 30]. The full-scale wind field parameters were estimated and described using record data from a bridge's structural health monitoring system [31]. [PERSON] et al. [32] studied the characteristics of wind and discovered that the integral turbulence scales increased with the height and time intervals.  
In this paper, up to 42 h of wind speed data from Typhoon Morakot was obtained by using two WJ3 anemometers installed on the roof of a building at Wenzhou University. Then, the wind characteristics were studied near the ground surface. The findings can be utilized as a data reference in the prevention of wind disasters and to expand the wind database in this region.
## 4 Multihoming and Interworking
### GLI-Split with Classic IPv6 Nodes
#### 4.3.2 Enhanced Behavior of GLI-Gateways  
Upgraded GLI-nodes directly register at the local mapping system and for classic IPv6 nodes, the registration is done, e.g., by an enhanced DHCP (see Section 3.2.2). Hence, the local mapping system knows which hosts inside its domain are upgraded and which are classic IPv6. When a packet arrives at a GLI-gateway, the gateway asks the local mapping system for a local destination GLI-address of that packet. The local mapping system returns the requested address and upgrade information to the gateway. The gateway can thus behave differently, depending on whether it forwards packets to upgraded or classic hosts.
# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 2 Related Work
### Traditional Knowledge Graph  
Research on traditional knowledge graphs primarily focuses on their representation. Knowledge graph representation aims to embed the entities and relations of a knowledge graph into a low-dimensional embedding space, enabling tasks such as link prediction and knowledge inference. Current methods of knowledge graph representation are typically classified into translation-based models, bilinear-based models, and neural network-based models. Among translation-based models, TransE [12] is a foundational method, relying on the principle that \(h+r\approx t\), where the embedding of the head entity, when translated by the relation, approximates the embedding of the tail entity. However, TransE struggles with modeling complex relations, which led to the development of more advanced models such as TransH [18], TransR [19], and TransD [20]. In bilinear-based models, RESCAL [21] calculates the correlation of triples by multiplying a relation matrix with the head and tail entity vectors. To reduce model complexity, DistMult [13] simplifies the relation matrix to a diagonal form, while ComplEx [22] extends DistMult by employing a complex vector space, enabling better handling of non-symmetric relations. Neural network models utilize deep learning techniques like CNNs and GNNs to learn entity and relation embeddings. ConvE [23] leverages CNNs to capture the intricate interactions between entities through entity and relation embeddings. CompGCN [24] and RGCN [25], both extensions of GCN [26], represent different methods of knowledge graph modeling. CompGCN enhances entity-relation interactions by applying compositional operations on relations, integrating both entity-level convolutional aggregation and relation-specific modeling. RGCN focuses on learning entity representations by aggregating information from neighboring nodes across different relations, making it particularly effective for handling complex multi-relational knowledge graphs.  
However, traditional knowledge graph representation methods are limited in their ability to effectively utilize features from multimodal data. Consequently, there has been an increasing amount of research dedicated to the development of multimodal knowledge graphs [27; 28]. These studies integrate multimodal data into knowledge graphs, fusing features from various modalities to enhance the overall quality of the graph.