esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 4 Experimental Verification
### Experimental Test 2
#### 4.2.4 Visual Analysis of the Proposed Method  
Similarly, a confusion matrix is used to analyze the fault diagnosis capability of the proposed method visually. Figure 16 is a confusion matrix diagram of the 20 th trial of the HSDAE method on the data set H. It can be seen from the figure that for the four types of fault data, 100% diagnosis accuracy can be achieved on the training set and 99.87% diagnosis accuracy on the testing set. In the testing set, category 0, category 1 and category 3 achieve 100% accuracy, category 2 has four sets of data misclassification, and the recognition accuracy rate is 99.46%. In conclusion, the diagnosis results show that the proposed method can achieve accurate fault diagnosis of rolling bearings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Data Sets** & **E** & **F** & **G** & **H** \\ \hline LGSSAE & Average accuracy & 81.06\% & 85.23\% & 88.30\% & 89.52\% \\  & Standard deviation & 0.0572 & 0.0484 & 0.0215 & 0.0638 \\  & Average accuracy & 97.66\% & 98.71\% & 98.73\% & 99.01\% \\ FDSAE & Standard deviation & 0.0066 & 0.0085 & 0.0109 & 0.0125 \\ DisAE & Average accuracy & 92.38\% & 94.96\% & 96.59\% & 98.10\% \\  & Standard deviation & 0.0485 & 0.0116 & 0.0060 & 0.0031 \\ BNAE & Average accuracy & 71.85\% & 74.11\% & 76.96\% & 85.43\% \\  & Standard deviation & 0.0346 & 0.0386 & 0.0368 & 0.0261 \\ HSDAE & Average accuracy & 99.32\% & 99.60\% & 99.78\% & 99.88\% \\  & Standard deviation & 0.0021 & 0.0012 & 0.0008 & 0.0006 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The experimental results of different comparison methods in the private data set.  
Figure 14: Comparison of t-SNE diagrams of experimental results on data set G. **(a)** T-SNE graph of data set G; **(b)** the t-SNE diagram of HSDAE features without discriminant distance; **(c)** the t-SNE diagram of HSDAE features.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 4 Methodology
### Decoder  
At each decoding step, the decoder generates a probability distribution over candidate nodes based on the set of output node tokens \(\left\{g_{i}^{(L)}\right\}_{i\in\mathcal{V}}\) and the global graph token \(g_{\text{graph}}\), both derived from the GCM-based encoder. Unlike traditional DRL methods that utilize transformer-based decoders with heavy computational complexity, we introduce an attention-driven, computationally lightweight decoder that selects nodes in a step-wise manner, improving efficiency without compromising performance.  
During decoding, the model constructs a context token \(c_{t}\) by concatenating the global graph representation \(g_{\text{graph}}\), the depot (starting node) token \(g_{a_{0}}\), and the token of the last visited node \(g_{a_{t-1}}\) at the current time \(t\). This context token effectively integrates global structural information with localized node-specific features, ensuring a well-informed selection process.  
\[c_{t}=\text{Concatenate}\left[g_{\text{graph}},g_{a_{0}},g_{a_{t-1}}\right]. \tag{18}\]  
To facilitate route construction, the decoder employs a single-head attention-driven compatibility layer, which computes the probability distribution over candidate nodes, guiding the next-step selection in a computationally efficient manner.  
\[\delta_{i}^{t}=\mathcal{C}\cdot\tanh\left(\frac{\left(c_{t}W_{c}^{Q}\right)\left(g_{ i}W_{c}^{K}\right)^{\mathsf{T}}}{d_{g}}\right), \tag{19}\]  
\[p_{\theta}(a_{t}=i|s_{t})=\frac{\exp\left(\delta_{i}^{t}\right)}{\sum_{i}\exp \left(\delta_{i}\right)}, \tag{20}\]  
where \(W_{c}^{Q},W_{c}^{K}\in\mathbb{R}^{d_{g}\times d_{g}}\) are trainable parameter matrices, and \(\mathcal{C}\) is a coefficient to control the compatibility within the range of \([-\mathcal{C},\mathcal{C}]\). The overall decoding process is depicted in Figure 2.
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
## 2 Islanded Smart Microgrid and False Data Injection Attack  
### Islanded Smart Microgrid (ISMG)  
There are a lot of places and islands which could not connect to the EPGs because of long distance, security, climate, etc., but there is a lot of equipment and people in these places because of work, recreation, etc. These places have their own control and security on the systems, and must guarantee the protection, security, welfare, entertainment, etc., of the people who are in there. Therefore, the concept of ISMG is raised. The ISMG has emerged with the appearance of Internet of Things (IoT) and Information and Communication Technology (ICT) in the islands to optimize the performance of urban services and develop the standards of living and improve the quality of life for many citizens.  
Interaction between local communities and urban infrastructure is currently possible through ISMG technology. Additionally, municipal assets are now managed more efficiently, and public data is collected and used, which will solve various urban issues such as traffic and energy problems, and can create new value to monitor and control various events occurred in islands. Creation services in ISMG have different models in a variety of areas including energy, education, medicine, environment, traffic and safety, and roughly 70% of the currently offered services are focused on three subjects: energy, safety and traffic. It can be difficult to know which routes are being revealed on an ISMG because technologies and data are interconnected like a spider, and the appearance of the city varies depending on what technologies have been used. Besides, a threatening virus attack might be dangerous enough to paralyze urban practices. Thus, this study suggests ways according to SVD and FFT to detect attacks in the EPG of an ISMG.
## 3 System Design and the Proposed SEAC-Based Resource Allocation Methodology
### Performance Feedback Mechanism
#### 3.3.2 Cumulative Rewards in the N-Step Actor-Critic Framework  
The N-step actor-critic framework computes the cumulative reward \(G_{t}\) over \(N\) steps to balance immediate rewards and long-term performance.  
\[G_{t}\!=\!\sum_{k=0}^{N-1}\gamma^{k}r_{t+k}+\gamma^{N}V(s_{t+N}) \tag{4}\]  
where  
* \(G_{t}\) is the cumulative reward at time step \(t\);
* \(\gamma\) is the discount factor;
* \(r_{t+k}\) is the immediate reward at time step \(t+k;V(s_{t+N})\) is the estimated value of the state at time step \(t+N\);
* \(N\) represents the number of steps used in the \(N\)-step actor-critic method, where cumulative rewards are calculated over \(N\) time steps to balance between immediate and future rewards.  
The immediate rewards provide a short-term measure of success for each action taken, focusing on the success of individual transmissions and collision avoidance. These rewards are accumulated over N steps to form the cumulative reward \(G_{t}\), which evaluates the long-term impact of actions on the resource allocation efficiency and overall network performance. This dual approach ensures that the model not only makes effective immediate decisions but also optimizes for future states.
## 4 Research Challenges in Agricultural Vehicle Path Tracking Control
### Controller Development
The research of Stanley controller in agricultural machinery automatic navigation still needs further study. The traditional sliding mode control method has successfully solved the path-tracking problem of agricultural vehicles from interference suppression and convergence. However, the oscillation problem of sliding mode control also has a negative impact on the controller. Therefore, in path tracking control research of agricultural machinery, it still needs to be further studied to reduce the oscillation or find alternative solutions. In model predictive control research, the linearized predictive model has better real-time performance, but there is also a problem of weakening the controller's ability to respond to reference path curvature and heading changes, which needs to be studied and balanced in the future.
## 4 Results and Analyses
### Effect of ACF on Hydrothermal Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case ID**} & \multicolumn{2}{c}{**Thermal Conductivity**} & \multicolumn{2}{c}{**Heat Capacity**} & \multicolumn{2}{c}{**Hydraulic Conductivity**} \\ \cline{2-7}  & \(\lambda_{\pi}\) **(W/m/\({}^{\circ}\)C)** & \(\lambda_{f}\) **(W/m/\({}^{\circ}\)C)** & \(C_{u}\) **(10\({}^{6}\) J/m\({}^{3}\)/\({}^{\circ}\)C)** & \(C_{f}\) **(10\({}^{6}\) J/m\({}^{3}\)/\({}^{\circ}\)C)** & \(K_{x}\)\({}^{u}\) **(10\({}^{-6}\) m/s)** & \(K_{y}\)\({}^{u}\) **(10\({}^{-6}\) m/s)** \\ \hline Reference case & DSQX & DSQX & DSQX & DSQX & DSQX \\ \hline \multirow{4}{*}{ACF of thermal conductivity} & DSNX & DSNX & DSQX & DSQX & DSQX & DSQX \\  & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\  & DSMK & DSMK & DSQX & DSQX & DSQX & DSQX \\  & DCSX & DCSX & DSQX & DSQX & DSQX & DSQX \\  & DLSX & DLSX & DSQX & DSQX & DSQX & DSQX \\  & DBIN & DBIN & DSQX & DSQX & DSQX & DSQX \\ \hline \multirow{4}{*}{ACF of heat capacity} & DSQX & DSQX & DSNX & DSNX & DSQX & DSQX \\  & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\  & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\  & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\  & DSQX & DSQX & DBIN & DBIN & DSQX & DSQX \\ \hline \multirow{4}{*}{ACF of hydraulic conductivity} & DSQX & DSQX & DSQX & DSQX & DSNX & DSNX \\  & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\  & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\  & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\ \hline \multirow{4}{*}{ACF of hydraulic conductivity} & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\  & DSQX & DSQX & DSQX & DSQX & DSQX & DSQX \\ \cline{1-1}  & DSQX & DSQX & DSQX & DSQX & DSQX & DBIN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parametric influence of different ACFs of hydrothermal properties. Figure 6: Effects of different ACFs of variable hydrothermal parameters on uncertain thermodynamic properties: (**a**) Thermal conductivity; (**b**) heat capacity; (**c**) hydraulic conductivity. curtain temperature is in the middle. When DSNX is used, the SD of the freezing curtain temperature is the smallest. For the thermal conductivity, the maximum and minimum SD of freezing curtain temperature during the freezing process are 2.27 \({}^{\circ}\)C and 0.89 \({}^{\circ}\)C, respectively. For the spatial variability of heat capacity, the maximum and minimum SD of freezing curtain temperature during the freezing process are 2.66 \({}^{\circ}\)C and 1.02 \({}^{\circ}\)C, respectively. For the hydraulic conductivity, the maximum and minimum SD of freezing curtain temperature during freezing is 2.70 \({}^{\circ}\)C and 0.94 \({}^{\circ}\)C, respectively. Therefore, different correlation functions have different variability effects on the freezing curtain temperature. The most significant impact comes from DBIN, followed by DSQX, with DSNX having the smallest effect.
## 1 Introduction
Typical findings indicate a notable reduction in vibration response and slight alterations in a structure's natural frequency and damping [33; 34]. [PERSON] et al. [35] introduced a method to predict changes in a structure's modal parameters due to passive individuals, requiring accurate knowledge of the empty structure's modal characteristics, where each passive individual is modeled by their apparent mass and integrated into the structure without restrictions on the degrees of freedom. In their study, [PERSON] et al. [36] investigated frequency variations in a vehicle-bridge interaction system subjected to a moving mass, which can be significant from the aspect of the movement of a service vehicle on a pedestrian bridge. They employed a three-dimensional model that incorporated the effects of inertial and centrifugal forces from the moving mass into the bridge's motion equations. [PERSON] et al. [37] suggested that the mass of pedestrians, along with any additional weight they carry in urban environments, could significantly contribute to the vibrations experienced by a bridge. In addition to the effects of the additional mass on the dynamic characteristics of bridges, the influence of the stiffness of different structural parts on the dynamic properties of a timber bridge was experimentally analyzed [38]. Human-structure interactions were also analyzed in the case of aluminum footbridges [39]. Recently, studies have focused on the vibration control of pedestrian bridges using dynamic vibration absorbers [40; 41; 42]. This paper introduces a procedure that relies on analytical solutions to straightforwardly determine the natural frequencies of a beam pedestrian bridge with additional mass, which, to the best of the authors' knowledge, has not been derived before. It applies to various positions of pedestrians or service vehicles on a bridge, where their mass is modeled either as uniformly distributed or concentrated. In one bridge example, the importance of the additional load mass effects on the fundamental frequency of a beam pedestrian bridge and its time-history response due to the dynamic moving pedestrian load is highlighted.
## 4 Discussion
### Effects of Heating Location  
Figure 12 depicts the effects of heating location on the \(V_{g}\) and \(R_{gw}\) within 1080 days. When adopting local wellbore heating (LWH) and CH, separately heating the 15 m wellbore located in the TPL, the \(V_{g}\) after 1080 days of production is 3.982 \(\times\) 10\({}^{6}\) ST m\({}^{3}\). Under the same heating power of 100 W/m, compared with WWH&CH, the \(V_{g}\) just decreases by 1.8%. This also indicates that in the Class-1 type hydrate reservoir, when adopting vertical well depressurization production, the secondary hydrates generated in the reservoir around the TPL are the key factor affecting production capacity. The cumulative heat input of  
Figure 11: \(EROL_{custom}\) curve with different heating powers within 1080 days.  
Figure 10: Gas and water production curve with different heating powers within 1080 days. (**a**) Gas production rate and cumulative gas production. (**b**) Water production rate and gas-to-water ratio.  
LWH&CH is only 21.42% of that of WWH&CH. After 1080 days of production, the \(R_{gw}\) in the above cases remains around 200, and there is no significant difference.  
As shown in Figure 13, the \(EROI_{custom}\) curve of local wellbore heating (LWH) is significantly higher than that of whole wellbore heating (WWH), which indicates that LWH heating has a better energy output-to-input ratio.
## 3 Fundamental Experiments
### Experiment 2: Different Moment Arms  
The measurement range of the developed torque sensor with a limited capacity of the load cell is determined by the moment arm length of the load cell. In this section, the output voltage is measured by varying the moment arm length. Since the applied torque is the same, the measured output voltage should be inversely proportional to the moment arm length. The weight of 120 g was fixed at a distance of 100 mm from the motor axis on the weight arm to apply the maximum torque of \(\tau_{app}=117.7\) mNm. The result of this experiment using the three different moment arms of the load cell (\(d_{LC}=50\), 100 and 200 mm) is shown in Figure 10. In the range of 0\({}^{\circ}\) to 720\({}^{\circ}\), the computed RMSE for the three cases were 1.2454, 1.2226 and 1.2444 mNm, respectively, and the ratio of the RMSE to the maximum applied torques corresponds to 1.06, 1.04 and 1.06 %, respectively. In the range of 1\({}^{\circ}\) to 179\({}^{\circ}\), on the other hand, the resultant errors were greatly reduced. The computed RMSE were 0.3897, 0.3231, and 0.3498, and the RMSE to the maximum torque ratio reduced to 0.33%, 0.27% and 0.30%, respectively. Resultantly, this experiment showed similar measured torque error regardless of the moment arm length of the load cell. Therefore unfortunately, it is difficult to claim that short moment arm guarantees high measurement accuracy from these results alone. Additionally, in Figure 10b, the data curves intersect near the origin to show the resultant graphs of the elongated eight-shaped loops, similar to Figure 9b.  
The slope of the curves around the origin in Figure 10a,c is almost proportional to the sensitivity of the sensor. In other words, if the amplitude of the sinusoidal output curve changes significantly, the sensitivity of the measurement is high. As the moment arm length becomes longer, the measurement sensitivity becomes higher. To obtain much higher sensitivity using the proposed sensor, the moment arm length of the load cell should be further reduced. The extremely sensitive torque sensor can be realized by using the oblique model described previously. The aforementioned case of \(d_{LC}=6.608\) mm will be 8 times more sensitive than the most sensitive case of \(d_{LC}=50\) mm in the figure. However, in this case, as expected, the length of the moment arm is not properly maintained, and accurate torque is difficult to measure.  
Figure 9: The torque measured by three different applied sinusoidal torque input: 12 g weight 100 mm apart, 120 g weight 100 mm apart, and 400 g weight 300 mm apart. Dash-dotted lines represent fitted sine curves. The \(x\)-axis is (**a**) the input rotation angle and (**b**) the converted torque.
# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
###### Abstract  
This review examines the integration of blockchain technology with the IoT in the Marine Internet of Things (MIoT) and Internet of Underwater Things (IoUT), with applications in areas such as oceanographic monitoring and naval defense. These environments present distinct challenges, including a limited communication bandwidth, energy constraints, and secure data handling needs. Enhancing BIoT systems requires a strategic selection of computing paradigms, such as edge and fog computing, and lightweight nodes to reduce latency and improve data processing in resource-limited settings. While a blockchain can improve data integrity and security, it can also introduce complexities, including interoperability issues, high energy consumption, standardization challenges, and costly transitions from legacy systems. The solutions reviewed here include lightweight consensus mechanisms to reduce computational demands. They also utilize established platforms, such as Ethereum and Hyperledger, or custom blockchains designed to meet marine-specific requirements. Additional approaches incorporate technologies such as fog and edge layers, software-defined networking (SDN), the InterPlanetary File System (IPFS) for decentralized storage, and AI-enhanced security measures, all adapted to each application's needs. Future research will need to prioritize scalability, energy efficiency, and interoperability for effective BIoT deployment.  
blockchain-enabled IoT (BIoT); edge computing; fog computing; internet of underwater things (IoUT); marine blockchain solutions; marine internet of things (MIoT) +
Footnote †: journal: Computer Physics Communications  
1
Footnote 1: [[https://doi.org/10.3390/computers13120329](https://doi.org/10.3390/computers13120329)]([https://doi.org/10.3390/computers13120329](https://doi.org/10.3390/computers13120329))  
Footnote †: journal: Computer Physics Communications
## 4 Discussion of Results
### Concerning the Integrated GIS and BIM Platform (Phase 2)  
Before integrating the GIS and BIM model, it is imperative to verify that all essential elements have been incorporated into the BIM model [38]. In developing the integrated GIS and BIM simulation environment, challenges arise due to the need to use multiple software programs simultaneously, with format incompatibilities preventing seamless integration. For this reason, electronic links are sometimes used to incorporate functionalities from external tools into the GIS and BIM model. An analysis of the GIS-3D and BIM integration process reveals several key technical aspects, highlighting both the strengths and challenges associated with their interoperability:  
* _Enhanced integration capabilities_: GIS-3D and BIM together provide a robust framework for integrating valuable information that supports quantitative analysis of buildings and their built environment. This integration results in semantically rich models that can be applied across various domains, enhancing the decision-making process.
* _Advanced database functionality_: The integrated platform serves as an advanced database, facilitating the management and analysis of the 3D semantics of buildings. This capability allows for models that convey effect-response information, elucidating how buildings interact with their surrounding built environment.
* _Technical inefficiencies_: Despite the advantages, there are notable technical inefficiencies in integrating GIS-3D and BIM. The existing literature indicates a lack of comprehensive theoretical studies that address how to effectively combine the strengths of both models. Much of the focus has been on navigation and visualization aspects of GIS-3D data, particularly in relation to CityGML, rather than on BIM-specific models.
* _Representation and interoperability_: The integration of GIS and BIM models hinges on effective 3D representation and interoperability. Different formats exist for storing and exchanging 3D geometry in both environments, with CityGML and IFC being among the most widely used and standardized.
* _Semantic definition of geometry_: Within the context of GIS and BIM integration, the geometry of models is intrinsically linked to their semantics. This relationship is articulated through levels of detail (LoDs) in GIS and levels of development (LODs) in BIM. It is important to clarify that these terms are often conflated; LODs should be understood as levels of development rather than mere levels of detail.
* _Challenges with higher detail levels_: Higher detail levels, such as LoD 3 and LoD 4, which encompass architectural details, are infrequently achieved. The modeling of these levels requires a variety of datasets that must be collected using diverse technologies and often entails significant manual effort. As a result, most urban-scale buildings are typically represented at most in LoD 2, limiting the granularity of information available for analysis.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms
### Ground-Based Platforms  
Ground-based surveys can be performed by either humans (foot-based) or vehicles [43]. Despite the fact that foot-based surveys (e.g., using handheld or backpack equipment) provide excellent spatial resolution, they require greater data collection time for large areas, which may be impracticable (e.g., due to radiation safe exposure limits) [17].