esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Working Principle of Robots  
As shown in Figure 1, the fastener-nut disassembly and assembly robot mainly comprises a master control system, a carrying platform, a robotic arm, a rotating sleeve, an electromagnet, and a monocular camera. The camera is positioned above the fastener by a bracket at a certain height from the fastener.  
The working principle of the robot is shown in Figure 2. After the robot starts working, it first performs initial displacement adjustment by moving a short distance to ensure that the target spiral rail spike is within the camera's field of view. Subsequently, the robot enters a visual positioning state, where the camera collects the position information of the spike as a control input. The robot controller drives the servo motor to guide the transport platform to the target position, ensuring that the center axis of the sleeve is aligned with the center axis of the spiral spike and achieving robot positioning. At the same time, the electromagnet is activated to secure the robot in the target position. Then, the robotic arm descends until the sleeve completely envelops the nut. At this point, the sleeve begins to rotate under the drive of the motor. By controlling the forward and reverse rotation of the  
Figure 1: Fastener-nut disassembly and assembly robot.  
motor, the nut can be assembled or disassembled. Throughout the process, a torque sensor monitors the torque changes and determines whether the nut assembly or disassembly has reached the expected completion state based on the preset torque threshold. Finally, the robotic arm rises, the electromagnet is turned off, and the system is reset to its initial state. At this point, the single operation cycle is completed, and preparations for disassembling and assembling the nuts of the following rail sleeper begin. The robot operates cyclically until all the fastening nuts in the designated maintenance area have been disassembled and assembled. Additionally, the robot features a symmetrical dual-robotic-arm structure design, which allows it to simultaneously disassemble and assemble the fastening nuts on both sides of the rail during each operation, thereby improving operational efficiency.  
In the entire workflow, visual positioning serves as a critical input parameter for robot motion control, and its positioning accuracy directly determines the success rate of the robot's task execution. Therefore, research on precise visual positioning algorithms for spiral rail spikes in rail environments holds immense significance for enhancing the efficiency and accuracy of robot operations in complex conditions.
## 4 Results Analysis and Discussion  
This study focuses on detecting abnormal ECG patterns using the LADTree model. The efficacy of the proposed LADTree model is compared to traditional ML models using error rate and accuracy analysis as benchmarks. Training and testing procedures are carried out in two different ways: scenario 1 involves percentage splitting, in which the dataset is partitioned into training and testing sets based on a specified percentage, and scenario 2 uses K-fold cross-validation, in which the dataset is divided into K subsets for training and testing iteratively. These different criteria enable a thorough evaluation and comparison of the LADTree model's performance with known ML techniques in the context of CKD prediction using ECG data.
## 2 Proposed Method
### Joint Detection and Classification Network  
#### 2.2.1 Architecture  
The output layer of the main network was formed with pitch labels and a special non-voice label. These labels were handled as a set of classes in the same level. Although voice detection and pitch estimation in the melody extraction task have a close relationship, they require different levels of abstraction, as mentioned in Section 1. In pitch estimation, the network predicts a continuously-varying value of pitch at each frame, although it uses contextual information from neighboring frames. In voice detection, the network predicts the sustained binary status of voice from textures that can be obtained from a wider context, such as vibrato or formant modulation. Therefore, simply adding a non-voice label to the target pitch labels has limitations in extracting the characteristics of voice activity.  
In order to address the discrepancy between the pitch estimation and voice detection, we propose a joint detection and classification (JDC) network. We set up the JDC network so that the two tasks shared the modules. Instead of building it with new modules, we maintained the existing main network and added a branch for dedicated singing voice detection. As shown in Figure 1a, the JDC network shared ConvBlock, ResBlock, and PoolBlock in the bottom, but had a separate Bi-LSTM module for each task. In particular, the voice detection task took the combined features of the shared modules from the main network. The use of multi-level features stemmed from the idea that voice detection may require observing diverse textures in different levels of abstraction. The outputs of ResBlock were max-pooled to match the output size, and then, they were concatenated. The Bi-LSTM layer predicted the probabilities that there was a singing voice from the concatenated features in a sequence-to-sequence manner via the softmax function. We call this an auxiliary network. The features from convolutional blocks were learned jointly using the main and auxiliary network, and the loss function was combined with that derived from the main network to form the final loss function. The detail is described in the next section.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### Area Estimates and Error Estimates  
We used poststratified estimators for the area and area error estimators [57], as derived and suggested by [PERSON] et al. [58]. The estimators use the confusion matrix and the area estimates of the categories based on an output map, that is, the pixel level estimates of the categories. The stratified estimators of the proportion of a category \(k\) is  
\[\hat{p}_{k}=\sum_{i=1}^{L}W_{i}\frac{n_{ik}}{n_{i_{i}}}, \tag{8}\]  
where \(n_{ik}\) is the \((i,k)\) element of the confusion matrix, observed counts on the columns and classified on the rows; \(n_{i_{i}}\) is the row sum of the row \(i\); \(L\) is the number of the categories; and \(W_{i}\) is the proportion of the area mapped as category \(i\). The area estimate of category \(k\) is  
\[\hat{A}_{k}=A\times\hat{p}_{k}, \tag{9}\]  
where \(A\) is the total area mapped.  
The standard error for the poststratified estimator of the proportion of area (Equation (8)) is estimated by  
\[S(\hat{p}_{k})=\sqrt{\sum_{i=1}^{L}\frac{W_{i}\hat{p}_{ik}-\hat{p}_{ik}^{2}}{ n_{i_{i}}-1}}, \tag{10}\]where \(\hat{p}_{ik}=W_{i}\frac{n_{ik}}{n_{i}}\), and standard error of the the area by  
\[S(\hat{A}_{k})=A\times S(\hat{p}_{k}). \tag{11}\]  
An approximate 95% confidence interval is obtained as \(A_{k}\pm 1.96\times S(\hat{A}_{k})\).
## 4 Results
### Data Sets and Setups  
In this study, the power load data set includes 35,043 data, in which a sampling frequency is 15 min. The dataset was normalised to eliminate the magnitude differences. And the dataset was divided into a training set and a test set, with a test set size of 3000.  
In the process of hyper-parameter optimization of machine learning model, when there are boundary restrictions of hyper-parameter in the BO, the methods to optimize the acquisition function are L-BFGS-B, TNC, SLSQP, TC and so on. Among them, L-BFGS-B and TNC are the most commonly used. We choose these two methods as the comparison methods, and the corresponding BO framework are named BO-L-BFGS-B and BO-TNC respectively.  
The PSO algorithm can guarantee the convergence of the algorithm when the parameter \((\omega,c_{1},c_{2})\) satisfies the condition \(-1<\omega<1,0<c_{1}+c_{2}<4(1+\omega)\)[40]. \(c_{1}\) and \(c_{2}\) affect the expected value and variance of the particle position. The smaller the variance is, the more concentrated the optimization result is, and the better the stability of the optimization system is. Other related research results show that the constant inertia \(\omega=0.7298\) and acceleration coefficient \(c_{1}=c_{2}=1.49618\) have good convergence characteristics [41], and the PSO parameters in this experiment are set according to this.  
To ensure the comparability of the experiment, all the train and test are run in the same code package of Python; The surrogate model is a Gaussian process with mean function 0 and covariance function Matern-52, and the kernel function and hyper-parametric likelihood are optimized by maximizing logarithmic likelihood; The acquisition function is UCB, and optimization algorithm is randomly initialized with 5 observations in each experiment.  
In this experiment, RNN and LSTM are selected as the basic model of power forecasting. The search space of hyper-parameters is shown in Table 1. The optimal hyper-parameters are selected by BO-PSO, BO-L-BFGS and BO- TNC algorithms, and the corresponding model are named RNN-BO-PSO, RNN-BO-L-BFGS-B, RNN-BO-TNC, LSTM-BO-L-BFGS-B and LSTM-BO-TNC respectively, with 50 iterations. Repeattraining 10 times for each model independently to ensure the objectivity of the results. The training epochs for the RNN and LSTM models are 100. Based on the comprehensive analysis of the results, the hyper-parameter values of RNN and LSTM models are finally determined. The step flow of BO-PSO method to optimize hyper-parameters is shown in Figure 4.
## 2 Materials and Methods
### Fabrication of Ion Imprinted Membrane
#### 2.4.3 Preparation of Imprinted Polymer  
In the current experiment, the nano-UO\({}_{2}\)\({}^{2+}\) imprinted polymer was fabricated by precipitation polymerization. In the first step, 2 mmol of methacrylic acid (MAA) and 2 mmol of pyridine-2,5-dicarboxylic acid (uranyl-binding ligand) were dissolved in 10 mL of DMSO and placed in a 100 mL round bottom flask. In the second step, 1 mmol UO\({}_{2}\)(NO\({}_{3}\))\({}_{2}\) of imprinted metal ion (template) was slowly added to the round bottom flask and stirred for 2 h at room temperature. In the third step, 100 \(\mu\)L EGDMA and 0.05g AIBN were added as crosslinking agent and initiator. In addition, 0.05 g Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) was also further augmented. The oxygen was removed from the sample solution by agitation of nitrogen in the sample for 10 min. The polymerization was conducted in an oil bath at 65 \({}^{\circ}\)C with magnetic stirring at 300 rpm for 12 h. To remove the unreacted material, the prepared polymer was washed with 1:4 (\(v/v\)) methanol/water for several times. Then, the imprinted metal ions (uranyl ions) were leached out with HCl (0.5 mol L\({}^{-1}\)). Until there was no uranyl ion in the washing solution. Comparing the potential value of the eluted electrode with the blank group, it can be assumed that there is no uranyl ion in the solution. Eventually, it was cleaned with double distilled water until the pH value is neutral, dried in vacuum at 60 \({}^{\circ}\)C for 8 h, and then set aside. Figure 1A presents the experimental process.  
Figure 1: Diagrammatic sketch of (**A**) the preparation of MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE sensor; (**B**) the MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE sensor template extraction-rebinding mechanism.  
The non-ionic imprinted polymer was prepared by the same way without adding template ion (UO\({}_{2}\)\({}^{2+}\)). By means of the effect of a magnetic field, the magnetically imprinted polymer can be firmly fixed on the electrode surface. The non-ionic imprinted polymer was arranged by the same way without template ion (UO\({}_{2}\)\({}^{2+}\)). Then, 1.0 mg of the above fabricated polymer was sonicated and dispersed in 1.0 mL of tetrahydrofuran, and 25 \(\mu\)L of the suspension was uniformly dropped onto the surface of the MCPE to obtain the sensor. The particular mechanism is demonstrated in Figure 1B.
# Detection of On-Chip Generated Weak Microwave Radiation Using Superconducting Normal-Metal SET
## 2 Experimental Section
### NISIN SET as a Microwave Photon Detector
(**a**) At \(n_{\rm g}=0\) and small bias voltages, the electron tunneling to the island is prohibited by the energy barrier \(E_{\rm t}\), while absorption of a photon with energy \(E_{\rm ph}\geq E_{\rm t}\) results in bringing one quasiparticle to the island (red dotted arrow); (**b**) When \(n_{\rm g}=1\), the height of the tunnel barrier is reduced and applying a small bias voltage results in the finite tunneling current \(I_{\rm NISIN}\). expression was used, e.g., by [PERSON] _et al._[3]), is relatively high as compared to that in the second step. The second step includes a quasiparticle escape out of the superconducting island via one of two different tunneling processes: either with or without a Cooper pair formation in the island. These two possible processes are only favorable if they involve the quasiparticle state excited in the first step and both have the same rate, \(\Gamma_{-}\sim 10^{5}\) s\({}^{-1}\ll\Gamma_{+}\). Rate \(\Gamma_{-}\) depends on the properties of the superconducting island and the tunneling resistance \(R_{T}\) while being independent of the biasing conditions as shown in [20]. The current \(I_{\rm INBIN}\) is thus determined by the lowest effective rate, _i.e._, \(I_{\rm INBIN}\approx 2e\Gamma_{-}\), where the factor 2 sums up the rates of both possible processes in the second step. When the device is irradiated with photons with energy \(E_{\rm ph}=hf\geq E_{t}\), an electron from the lead can tunnel into (Figure 2a, red dotted arrow) or out of the superconducting island (the diagram is not shown) and sum up with the induced charge \(n_{\rm g}e\), thus effectively changing the gate potential \(V_{\rm g}=n_{\rm g}e/C_{\rm g}\rightarrow(n_{\rm g}\pm 1)e/C_{\rm g}\). Starting from the even state, this change opens, for a short time, the single quasiparticle current channel similar to that in the odd state. The lifetime of the excited state depends on the properties of the NISIN device, in particular, on the rates of the quasiparticle escape or its recombination inside the superconducting island and greatly exceeds the inverse tunneling rate \((\Gamma_{-})^{-1}\). This enables sequential tunneling of many electrons and, therefore, operation of the NISIN SET as a current amplifier with the gain \(G\equiv I_{\rm INBIN}/e\Gamma_{\rm ph}\gg 1\), where \(\Gamma_{\rm ph}\) is the photon absorption rate. Table 1 presents the parameters for one of the NISIN transistors used in our experiment including the size of the superconducting island and tunnel junctions, the junctions' tunnel resistance \(R_{T}\), the island charging energy \(E_{\rm C}\) and the superconducting gap energy \(\Delta\).
## 2 State-of-the-Art: IAR Applications for Training and Assistance
### Shipbuilding IAR Systems  
Some of the previously analyzed works share constraints with the shipbuilding sector, like construction [45; 46]. Nevertheless, there are not many assistance or training systems in the literature specially devoted to shipbuilding. Most of the literature found is focused on pipes, one of the key parts that is manufactured in a shipyard. For instance, the work detailed in [47] focused on pipe management through markers. Another pipe-related work was presented in [48], where the authors visualized the design dimensions of pipes to adjust them to the actual installation dimensions. A very recent implementation was presented in [49]. The authors proposed a Microsoft HoloLens AR-assisted guidance system for an assembly task. The application acts as a data-visualization interface for files obtained from 3D-modeling and process-planning software. The feasibility of the system was evaluated through a test carried out by six students. The obtained results showed an increase of the assembly efficiency of 25.6%. Nevertheless, the results of an evaluation questionnaire indicated that although the system is user-friendly, Microsoft HoloLens devices still have room for improvement.  
Regarding the different available commercial solutions, recent works have already reviewed the most relevant IAR commercial hardware and software tools and evaluated the performance of some of them in a shipyard workshop and inside a ship under construction [50]. With respect to specific training and technical assistance applications, there are also several software solutions that come either from companies like PTC (Vuforia Enterprise AR Suite), TeamViewer (TeamViewer), Microsoft (Dynamics 365), Atos (Air Assist [51]), Cappemini (Andy 3D [52]), Sonovision (Reflekt [53]), Librestream [54], Wideum (Remote Eye [55]), and Innovae (ATR [56]). The analyzed solutions have similar characteristics, but they lack flexibility and do not fully fulfill the requirements of a shipyard. This is due to the fact that they are closed proprietary solutions that depend on the developing company when adding new functionality, so they incur additional costs for paying for licensing or subscription fees. Moreover, for some requirements, it may be necessary to create an additional ad-hoc application that may not be integrated directly into the original IAR solution. In addition, the ideal IAR solution should be compatible with different devices smartphones, tablets, AR smartglasses (e.g., ODG, Vuzix, Google, Epson Moverio, Realwear), and different collaborative platforms with different operative systems. Furthermore, it must be considered that the data must be owned, controlled, and managed by the shipbuilder, since they are usually considered as critical information, especially for military vessels. Finally, the solution should be designed to be fully integrated and operative with other products and services of the shipbuilding company (e.g., digital twin, Enterprise Resource Planning (ERP), Product Lifecycle Management (PLM), Manufacturing Execution System (MES), cloud services, IoT services).
# Shear-Wave Velocity Model from Site Amplification Using Microtremors on Jeju Island
## 4 Results
### UDO Site  
Figure 18 presents the observed and synthetic HVSR curves for the optimal Vs model at the UDO site, a small island located at the eastern end of the Jeju region, as illustrated in Figure 1. The observed HVSR curve featured a prominent peak at a frequency of 0.33 Hz (Table 4) with a peak amplitude of 3.20. For the variance bounds ranging from 0.25 to 2.5, the misfit sizes of the inversion using the MC simulations ranged from 349.54 to 2.29, whereas the SA algorithm yielded misfit sizes for the best-fit inversion model between 254.19 and 2.45 within the same variance bounds. Overall, the matching between the observed and synthetic curves was satisfactory, with minor discrepancies near the first peak of the double peak and in the frequency band above 1.5 Hz.  
Figure 19 displays the Vs profile at the UDO site, showing all the inverted and optimal Vs models. The profile revealed a layer up to depths of approximately 140-160 m with velocities ranging from 400 to 600 m/s. By employing the site classification method on the outcrop shear-wave velocity, as indicated in Table 5, the outcrops at the UDO sites (1100-1300 m/s) were categorized as \(\mathbb{S}_{\mathrm{C}}\), ranking it as the softest site class among the nine  
Figure 18: Observed and synthetic HVSRs of UDO site; the red line indicates the observed curve, and the black dotted line indicates the synthetic curve.  
sites investigated. The second layer, extending to depths of 240-260 m, exhibited velocities of 900-1100 m/s. The third layer, characterized by velocities of 1200-1400 m/s, extended to depths of 390-410 m. The fourth layer, with velocities of 1500-1700 m/s, extended to depths of 590-610 m. Subsequently, the fifth layer, identified as the LVL with velocities of 900-1100 m/s, extended to depths of 790-810 m. This LVL displayed a significant velocity contrast of around 600 m/s on average compared to the overlying layer. Finally, the velocity increased remarkably, connecting to the basement at depths of 2100-2300 m/s [45].  
The RC values for the UDO site, as listed in Table 14, encompassed four prominent positive peaks (0.33, 0.13, 0.10, and 0.38) and one substantial negative peak (\(-\)0.23). The negative RC value of \(-\)0.23 signals the presence of an LVL, extending from depths of 590-610 m to 790-810 m, approaching the basement.  
The UDO site was distinguished by its unique stratigraphic characteristics, which deviated from prior expectations of the absence of the Seoguipo sedimentary layer. These findings suggest the possibility of an LVL at the UDO site, potentially formed by recent local magmatic intrusion events rather than being solely attributed to the U-formation of marine origin. This hypothesis and its implications are further explored in the Discussion section.
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 4 Experiments and Results
### Results and Discussions
#### 4.2.4 Warm-Up Round Number Comparison  
This section examines the influence of various warm-up rounds \(R_{W}\) on performance. The ResNet20 model was employed to conduct experiments on the NSL-KDD dataset, with 10 uniformly distributed Rw values within the range of [0, 900]. Figure 10 illustrates the outcomes. Inaccurate global data distribution information is the consequence of the global model's insufficient training when \(R_{W}\) is assigned to 0. The quality of virtual data generated by the data generator is adversely affected by this condition, which in turn impacts the training of local models and the overall performance of the global model. The data generator generates higher-quality simulated data as \(R_{W}\) increases from 0 to 700, leading to the acquisition of more precise data distribution information from the global model. This improvement enhances the efficacy of the global model by improving the training quality of local models. The global model's optimal performance is observed when \(R_{W}\) reaches 700. The model's efficacy is minimally affected by further increases to 800 and 900. This section explores the impact of varying warm-up rounds (\(R_{W}\)) on model performance. Experiments were carried out on the NSL-KDD dataset using the ResNet20 model, with 10 evenly spaced \(R_{W}\) values selected from the range [0, 900]. The results are depicted in Figure 10. When \(R_{W}\) is set to 0, insufficient training of the global model results in inaccurate global data distribution information. Consequently, this affects the quality of the virtual data produced by the data generator, impairing both local model training and the overall global model performance. As \(R_{W}\) increases from 0 to 700, and the data generator produces higher-quality simulated data, allowing the global model to acquire more accurate data distribution insights. This enhancement improves local model training, thereby boosting the global model's effectiveness. The global model achieves its best performance when \(R_{W}\) is set to 700, while further increases to 800 and 900 have minimal additional impact on its efficacy.