esa-sceva/satcom-chunk-collection huggingface dataset:
# Offline Identification of a Laboratory Incubator
## 3 Experiments
### Experimental Results
#### 3.2.4 Results for the Box-Jenkins Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Validation Criteria**} & \multicolumn{2}{c}{**G**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**MAE**} \\ \cline{2-7}  & **Step** & **PRBS** & **Step** & **PRBS** & **Step** & **PRBS** \\ \hline \multicolumn{7}{c}{ARX[2 2]} \\ Prediction k: 2 & 95.6526 & 98.7377 & 5.47874 \(\times\) 10\({}^{-5}\) & 5.0382 \(\times\) 10\({}^{-6}\) & 0.0049216 & 0.0016 \\ Prediction k: 10 & 86.1103 & 94.1006 & 0.0005925 & 0.0011005 & 0.017526 & 0.0074 \\ Prediction k: 20 & 80.7099 & 88.3159 & 0.00107867 & 0.00043167 & 0.0244397 & 0.0145 \\ Simulation & 77.2656 & 85.1858 & 0.00149826 & 0.00069394 & 0.0289541 & 0.0211 \\ \hline \multicolumn{7}{c}{ARMAX [2 2 2]} \\ Prediction k: 2 & 94.7955 & 98.5564 & 7.3182 \(\times\) 10\({}^{-5}\) & 6.638 \(\times\) 10\({}^{-6}\) & 0.00454041 & 0.0020 \\ Prediction k: 10 & 87.8214 & 95.2388 & 0.000429946 & 7.2225 \(\times\) 10\({}^{-5}\) & 0.0167575 & 0.0065 \\ Prediction k: 20 & 86.247 & 92.2919 & 0.000548299 & 0.00018899 & 0.0189746 & 0.0105 \\ Simulation & 86.1957 & 90.5396 & 0.00055293 & 0.00028334 & 0.0191662 & 0.0128 \\ \hline \multicolumn{7}{c}{OE [2 2]} \\ Simulation & 83.0517 & 84.0369 & 0.000856901 & 0.00080505 & 0.0240122 & 0.0235 \\ \hline \multicolumn{7}{c}{BJ [2 2 2]} \\ Prediction k: 2 & **96.2799*** & **99.4335** & **4.01182 \(\times\) 10\({}^{-5}\)** & **9.1998 \(\times\) 10\({}^{-7}\)** & **0.0041233** & **0.0007** \\ Prediction k: 10 & **91.4721** & **98.1200** & **0.000210516** & **1.0131 \(\times\) 10\({}^{-5}\)** & **0.0108614** & **0.0021** \\ Prediction k: 20 & **90.4577** & **96.4682** & **0.000263956** & **3.5756 \(\times\) 10\({}^{-5}\)** & **0.0124288** & **0.0040** \\ Simulation & **90.3288** & **95.0771** & **0.000271132** & **7.0355 \(\times\) 10\({}^{-5}\)** & **0.0123917** & **0.0074** \\ \hline \hline \end{tabular}  
* The best values in terms of the three measures among the models are marked in bold font.  
\end{table} 
Table 5: Comparison of the models in the study. Figure 24: G vs. prediction horizon for the second scenario. Figure 23: G vs. prediction horizon for the first scenario.
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 5 Bridge Health Monitoring
### Preliminary Results with a 2-Axle Half-Car Model
 \tag{24}\]  
The calculated damage indicator values are shown in Table 3. For this simple example, there is a clear strong correlation between the damage indicators and the true loss of flexural rigidity. For the high-speed case, _Damage Indicator_ 1 is less accurate because of dynamic effects, though it can show the damage state. The mean calculated MR-ILs are passed through a low pass filter to remove the oscillation at the bridge frequency, which makes results better. Other filters may be appropriate for other examples; the filters used here were effective for this case. For other cases, it may, for example, be necessary to remove records at particular speeds. _Damage Indicator_ 2 is an effective way to detect bridge damage, even with dynamic effects. In fact, bridge first natural frequency and other frequencies may influence the calculated MR-IL values. Nevertheless, this indicator can be used to detect the level of damage with reasonable accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Property** & **Type** & **Symbol** & **Unit** & **Value** & **Mean** & **Standard Deviation** \\ \hline Sprung mass & Local & \(m_{s}\) & kg & Varied & \(32\times 10^{3}\) & \(3.2\times 10^{3}\) \\ Sprung mass moment of inertia & Local & \(I_{s}\) & kg m\({}^{2}\) & & \(61.6\,m_{s}\) & \\ Spring stiffness & Global & \(K_{1}\), \(K_{2}\) & N/m & \(0.73\times 10^{6}\) & \\ Damping & Global & \(C_{1}\), \(C_{2}\) & N s/m & \(7.5\times 10^{3}\) & \\ Distance of axle to body centre of gravity & Global & \(D_{1}\), \(D_{2}\) & m & 8.5 & \\ \hline \end{tabular}
\end{table} 
Table 2: Vehicle properties used to calculate MR-IL.
# Research on Rollover Stability and an Anti-Rollover Warning System for the Vibroseis Truck
## 4 Working Principle of the Anti-Rollover Warning System for the Vibroseis Truck  
Based on the vibroseis truck rollover warning index \(A\), an anti-rollover warning system for the vibroseis truck was established. It consists of a microprocessor, a speed sensor, an angle displacement sensor, an inertial measurement unit (IMU), a slope meter, and an alarm device. Among them, the microprocessor is used to receive and analyze signals from the sensor. The angle displacement sensor mainly monitors the steering angle between the front body and the rear body. The inertial measurement unit is used to monitor parameters such as the roll angle and the lateral acceleration of the vehicle. The slope meter is used to monitor the slope angle.  
The microprocessor calculates the vehicle rollover warning index \(A\) and runs the rollover warning algorithm. The warning index \(A\) is compared with the multi-grade warning scope, and the system judges the degree of danger based on the vibroseis truck warning level and emits a sound-light alarm to remind the driver to take reasonable measures to prevent a rollover accident.
## 7 Experiment
### Task-Oriented Teleoperation  
In this subsection, we report the use of our system to manipulate the robot to place two pens into two different containers. The experimental setting is shown in Figure 17. The robot was placed at a location in the room where the demonstrator could not see it. A Kinect II RGB-D camera was employed to capture the motions of the human demonstrator. The first-person perspective of the robot was captured by the robot's head-mounted camera, and the third-person perspective of the overall task situation was monitored by an RGB camera; both video feeds were shown on the screen to provide views of the task in real time.  
The initial position of the robot dictated that the robot should seize the pens in its right hand. However, the containers were out of range of the robot's right arm when the robot was in the double-support mode, as depicted in Figure 18a,c. The left arm was not used to reach the black container because it was not convenient to transfer the pen from the right hand to the left hand. Thus, two approaches were employed to reach the containers out of range of the robot's arm. To reach the green container, the robot was required to lift its left foot and lean rightward to cover a longer distance, as shown in Figure 18b. To reach the black container, the robot was required to walk leftward, as shown in Figure 18d.  
Figure 16: Snapshots of some difficult single-support motions that the robot can perform by imitating human demonstrators with different body shapes.  
Figure 15: Left-tibia similarity index trajectories in the first experiment as calculated using the two evaluation methods.  
In the experiment, the robot imitated a series of motions performed by the human demonstrator and successfully placed the pens into the containers. A-H in Figure 19 show snapshots of the motions of the human and the robot during the process of placing the blue pen into the right green container. I-P in Figure 19 show snapshots of the motions of the human and the robot during the process of placing the pink pen into the left black container.  
This teleoperation experiment verifies that the proposed imitation system can be applied to execute real-time teleoperation tasks stably and effectively.  
Figure 17: The real-time imitation system used in the task-oriented teleoperation experiment.  
Figure 18: Description of the teleoperation task; (**a**) shows that the robot cannot reach the green container in the double-support mode; (**b**) shows that when the robot leans rightward in the single-support mode, it can easily reach the container; (**c**) shows that the robot cannot reach the black container in the double-support mode; (**d**) shows that when the robot walks leftward, it can easily reach the container.
# Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees
## 2 Materials and Methods  
### EMG Database  
For this investigation, we use the database collected by [PERSON] et al. [10]. They recruited nine trans-radial amputees with unilateral amputation to participate in their study where six movements including different grip and finger movements were recorded. The gestures available in this database are: thumb flexion, index flexion, fine pinch, tripod grip, hook grip and spherical grip (Figure 1).  
Each of these gestures was performed at 3 different force levels by the users: low, medium and high.
## Nomenclature  
\begin{tabular}{l l l l}
**Parameter** & **Explanation** & **Parameter** & **Explanation** \\ \(m\) & tractor mass & \(I_{s}\) & equivalent inertia of the sun gear \\ \(g\) & gravity acceleration & \(I_{c}\) & equivalent inertia of the carrier \\ \(f\) & rolling friction coefficient & \(I_{p}\) & equivalent inertia of pinion relative to \\  & & the center of mass \\ \(\beta\) & the angle between traction resistance & \(I_{ps}\) & equivalent inertia relative to the \\  & and the horizontal direction & center of sun gear \\ \(r_{q}\) & road wheel radius & \(I_{m1}\) & equivalent inertia of EM\_S \\ \(\eta_{t}\) & transmission efficiency & \(I_{m2}\) & equivalent inertia of EM\_R \\ \(\theta\) & incline angle of the road & \(T_{m1}\) & output torque of EM\_S \\ \(F_{T}\) & traction resistance & \(T_{m2}\) & output torque of EM\_R \\ \(F_{total}\) & total resistance on the tractor & \(T_{\max 1}\) & maximum output torque of EM\_S \\ \(\lambda\) & penalty factor & \(T_{\max 2}\) & maximum output torque of EM\_R \\ \(\eta_{a}\) & actual velocity & \(T_{L}\) & driving resistance torque \\ \(i_{s}\) & reduction gear of sun gear motor & \(T_{c}\) & output torque of the carrier \\ \(i_{r}\) & reduction gear of ring gear motor & \(T_{r}\) & output torque of the ring gear \\ \(i_{m}\) & main reduction gear & \(T_{s}\) & output torque of the sun gear \\ \(i_{f}\) & final reduction gear & \(\omega_{m1}\) & rotational speed of EM\_S \\ \(k\) & characteristic parameter of planetary & \(\omega_{m2}\) & rotational speed of EM\_R \\  & gear set & & \\ \(Z\) & number of plowshares & \(\omega_{\max 1}\) & maximum rotational speed of EM\_S \\ \(b_{1}\) & width of ploughshare & \(\omega_{\max 2}\) & maximum rotational speed of EM\_S \\ \(h_{s}\) & tillage depth & \(\omega_{c}\) & rotational speed of carrier \\ \(s_{r}\) & soil-specific resistance & \(P_{e}\) & instantaneous energy consumption \\  & & & of battery \\ \(I_{r}\) & equivalent inertia of the ring gear & \(P_{\max 1}\) & maximum output power of EM\_S \\ \(U_{dec}\) & battery open circuit voltage & \(P_{\max 2}\) & maximum output power of EM\_R \\ \(R_{bat}\) & internal resistance & \(P_{e\_current}\) & instantaneous energy consumption of \\  & & & battery in the current working mode \\ \(Q_{bat}\) & capacity of battery & \(P_{e\_other}\) & instantaneous energy consumption of \\  & & & battery in the non-working modes \\ \(P_{bat}\) & output power of the battery & \(\eta_{o}\) & overall efficiency \\ \end{tabular}
## 3 Results
### Evaluation Metrics and Datasets  
The evaluation of AI models for DFU management metrics assesses various aspects of model performance. Accuracy is the most critical metric for all models, offering a clear measure of the model's reliability by identifying the proportion of correct predictions among all predictions. Following this, the F1 score is important for its ability to balance precision and recall, making it particularly important in handling imbalanced datasets. For segmentation tasks, the dice score and IoU are crucial. The dice score evaluates the overlap between predicted and ground truth regions, while the IoU quantifies the precision of wound area identification by measuring the overlap between predicted and actual segmentations. For prediction tasks, researchers use the MAE and RMSE to assess the magnitude of errors in continuous predictions. While the MAE provides the average magnitude of errors, the RMSE gives greater weight to larger errors, making it useful when larger deviations are critical. Additionally, the RAE normalizes the MAE by comparing it against how a baseline model performs. The AUC is critical for classification models as it measures how well a model distinguishes between classes across different thresholds. These metrics provide a multidimensional framework for evaluating the effectiveness of AI models in improving DFU management. Table 7 presents all the equations for the metrics. Researchers benefit from the presentation of available datasets as it supports the development of AI-based solutions for DFUs and ensures study reproducibility. Various datasets advance machine learning and deep learning in DFU management. The DFU-Part (B) dataset [31] provides infection and ischemia foot images, featuring 9870 ischemia patches and 5892 bacterial infection patches. The DFUC2020 dataset [32] offers 4000 diverse images for training and testing, while the DFUC201 dataset [34] focuses on infection and ischemia detection with 15,683 image patches, which researchers classify into four classes: control (healthy skin), infection, ischemia, and a combination of both conditions (infection and ischemia). The DFUC2022 dataset [58] includes 2000 fully annotated images intended to support segmentation tasks. The Medetec dataset [51] offers 152 wound images with segmentation masks for DFU detection. The Kaggle repository [36] consists of four folders:
## 2 Radio Resource Management
### Power, Beamwidth and Bandwidth Flexibility
The second parameter minimizes the total power allocated to all beams (\(\sum_{b=1}^{B}P_{t}^{b}\), in W), where \(\beta_{2}\) (in 1/W) is the total power weight. The third parameter refers to the total bandwidth (\(\sum_{nc=1}^{N_{c}}\sum_{b=1}^{B_{c}}BW_{t}^{bc}\), in Hz) allocated to the beams of each color within the frequency plan, where \(B_{c}\) is the number of beams with the same frequency and polarization defined by color \(c\), \(N_{c}\) is the number of colors in the frequency plan and \(\beta_{3}\) (in 1/Hz or s) is the weight of the total bandwidth allocated in each color of the frequency plan. The cost function constraints are presented in (2)-(5). We show the minimum capacity constraint in (2) where \(C_{t}^{b}\geq R_{t}^{b}\) for each beam, provided if the power and bandwidth assigned to the b-th beam at time t are less than the maximum allowed for each beam (\(P_{max,b}\) and \(BW_{max,b}\) respectively) and the beamwidth is greater than the minimum allowed(\(\theta_{min,b}\)). In case the offered capacity cannot satisfy the beam requirement constraint, the offered capacity in the b-th beam will be the maximum possible value. In addition, the total power allocated to each beam should not be greater than the maximum total power of the system (\(P_{max,T}\)) (3), and the total bandwidth allocated in each color of the frequency plan should not be greater than the bandwidth per color (\(B_{max,c}\)) (4). Moreover, the beamwidth of the b-th beam must belong to the set of possible configurations previously established (5), where \(N\) represents the size of the set. The beamwidths must also meet the requirement of completely covering the entire service area.
## 3 Proposed Approach  
We present our results based on the novel Energy-Aware Framework where we first analyse the Machine Learning (ML) driven classification accuracies using six algorithms, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Discriminant Analysis (DA), Naive Bayes (NB), Decision Tree (DT) and Artificial Neural Network (ANN). ML-based algorithms use geographic BS locations, user cell association and the number of passengers in peak and off-peak times. Second, we estimate passenger's future locations using KNN algorithm with multiple distance metrics where _Mahalanobis_ distance metric plays a main role due to its best classification accuracy, and finally, we present energy saving coupled with \(CO_{2}\) emissions through \(Q\)-learning (QL)-based Reinforcement Learning (RL) while benchmarking against: (i) No-Switching (NS); (ii) Exhaustive Search (ES); and (iii) Greedy approach.
## 4 Proposed Scheme
### Session Key Exchange Phase
The server registered the clients and saved the identity and password of the clients \(HID_{C}\) and \(HPSW_{C}\)
1: Client Sends \(HID_{C}\) to Server : \(HID_{C}\to Server\)
2:if\(HID_{C}=HID_{C}\)then
3: Server generates \(N_{1}\), \(N_{2}\) and sends to client secretly \(N_{1}^{*}\) and \(N_{2}^{*}\to Client\)
4: Client generates a larger random number as \(N_{C}:N_{C}\gets rand()\)
5:if\(N_{C}=0\)then
6: Go to Step 4
7:endif
8: Set \(C=(N_{C}\times N_{1})+N_{1}\)
9: Set \(R_{C}=(C\times N_{2})+N_{1}+N_{2}\)
10: Client sends \(R_{C}\) to Server : \(R_{C}\to Server\)
11: Set \(r_{C}=R_{C}-(N_{1}+N_{2})\)
12: Set \(N_{C}=(r_{C}/(N_{1}\times N_{2}))-1\)
13: Server generates a larger random number as \(N_{S}:N_{S}\gets rand()\)
14:if\(N_{S}=0\)then
15: Go to Step 13
16:endif
17: Set \(S=(N_{S}\times N_{1})+N_{1}\)
18: Set \(R_{S}=(S\times N_{2})+N_{1}+N_{2}\)
19: Server sends \(R_{S}\) to client : \(R_{S}\to Client\)
20: [Client performs the same process from 11 to 12]
21: /* Client and server compute the same key as */
22: Set \(K_{S}=(N_{C}\oplus N_{S})\ mod\ M\)
23:if\(K_{S}=0\)then
24: Go to Step 4
25:endif
26:else
27: Connection Terminate
28:endif
```  
**Algorithm 1** Proposed Key Exchange Algorithm
# A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model
###### Abstract  
In this paper, a novel MRGP-SS method is proposed to deal with the reliability analysis problems under multiple failure modes. First, a random moving quadrilateral grid sampling (RMQGS) method is proposed to improve the randomness and uniformity of initial samples. Second, an adaptive procedure, which combines the multiple response Gaussian process (MRGP) model and the novel active learning functions, is proposed to efficiently and accurately produce surrogate models for failure surfaces. In this regard, two novel learning functions are introduced to adapt to different iterative cycles, one is employed to correct the quality of samples, and the other is used to search for the samples closest to the limit state surface. Third, the subset simulation (SS) is integrated into the adaptive MRGP model to estimate the failure probability under multiple failure modes with fewer function calls and time consumption. Numerical and engineering case studies are finally provided to demonstrate the effectiveness of the proposed method.  
multiple response Gaussian process; subset simulation; reliability analysis; active learning function +
Footnote †: journal: Applied  
Acticle  
A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Z. A Novel Reliability Analysis Approach Under Multiple Failure Modes Using an Adaptive MRGP Model. _Appl. Sci._**2022**, 12, 8961. [[https://doi.org/10.3390/app12188961](https://doi.org/10.3390/app12188961)]([https://doi.org/10.3390/app12188961](https://doi.org/10.3390/app12188961))
# Recommendation System for a Delivery Food Application Based on Number of Orders
## 4 Proposed Recommendation System
### Proposal's Algorithm  
Algorithm 1 shows the pseudocode of our proposed recommendation system based on KNN. Our method has two parameters: the number of neighbors, \(k\); and the minimum percentage of orders to consider a restaurant among the preferred ones, \(pmin\). We recommend using \(k=5\) and \(pmin=0.1\). In the next section, we experiment with different values of these parameters.  
\begin{table}
\begin{tabular}{c|c c c c c} \hline  & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{C}\) & \(\mathbf{d}\) & **Difference with \(\mathbf{x}\)** \\ \hline \(x\) & 0.19 & 0.01 & 0.80 & 0.00 & \\ \hline \(o\) & 0.03 & 0.03 & 0.91 & 0.03 & \(\left(\left|0.19-0.03\right|+\left|0.80-0.91\right|\right)/2=0.135\) \\ \(p\) & 0.06 & 0.09 & 0.50 & 0.35 & \(\left(\left|0.19-0.06\right|+\left|0.80-0.50\right|\right)/2=0.215\) \\ \(q\) & 0.29 & 0.05 & 0.21 & 0.45 & \(\left(\left|0.19-0.29\right|+\left|0.80-0.21\right|\right)/2=0.345\) \\ \(r\) & 0.30 & 0.15 & 0.15 & 0.40 & \(\left(\left|0.19-0.30\right|+\left|0.80-0.15\right|\right)/2=0.380\) \\ \(s\) & 0.00 & 0.92 & 0.03 & 0.05 & \(\left(\left|0.19-0.00\right|+\left|0.80-0.03\right|\right)/2=0.480\) \\ \hline \end{tabular}
\end{table}
Table 2: Example of the proposed recommendation system. Rows represent the clients’ normalized vectors (\(x\), \(o\), \(p\), \(q\), \(r\), and \(s\)). Columns represent restaurants (\(a\), \(b\), \(c\), and \(d\)). Blue columns represent the preferred restaurants of client \(x\). Green cells represent the recommended restaurant based on our recommendation system.  
```
Input: Historic number of orders from clients to restaurants, represented as clients' vectors, \(c^{i}\), and the vector of the client who wants a recommendation, \(c^{x}\). Output: List of recommended restaurants. Hyperparameters: \(k\) and \(pmin\).
```  
**Algorithm 1** Pseudocode of the proposed recommendation system.
# Design and Dynamic Analysis of the Wire-Line Coring Robot for Deep Lunar Rocks
## 3 Dynamic Analysis of the WSC Robot
### Maximum Support Force Analysis of the Anchor Module
Therefore, the shape of the anchor module after unfolding is equated to an incomplete cylinder, and the missing part is represented by the equivalent rounded angle \(\theta_{m}\) in the calculation, as follows:  
\[\theta_{m}=2\pi\frac{r}{R} \tag{6}\]  
where \(r\) is the radius of the anchor module when it is not unfolded, and \(R\) is the length of the anchor plate unfolded from the center of the WSC robot. Combining Equations (5) and (6) yields the maximum support force that the lunar regolith can withstand which is:  
\[\frac{M_{u}}{R}+F_{u}\leq\mu_{s}RH\theta_{m}p_{u} \tag{7}\]  
Figure 8: Schematic diagram of ultimate bearing capacity of drilling hole wall. where \(\mu_{s}\) is the lunar regolith friction coefficient;  
\(H\) is the length of the anchor plate;  
\(M_{u}\) is the drilling torque;  
\(F_{u}\) is the drilling pressure. If \(H\) is determined, the relationship between the theoretical maximum drilling pressure and torque with depth can be obtained separately according to Equation (7), as shown in Figure 9. According to Figure 9, the drilling pressure and torque that can clearly be provided by the current depth support. Since the pore ratio of the shallow lunar regolith increases rapidly with the increase in lunar regolith depth, the anchoring effect in shallow lunar regolith above 0\(\sim\)4 m still varies drastically with the depth. The pore ratio of deep lunar regolith is close to the limit value, and the anchoring effect of the anchor module tends to be stable. Therefore, the drilling power needs to be changed in real-time when the WSC robot is working on shallow lunar regolith, and the full drilling power can be exerted when the WSC robot is working on a deep lunar regolith. According to the design target, the operation at full power is available in the lunar soil layer below 4.7 m depth. However, since the specific dynamic environment of the lunar soil layer is unknown, operation at full power should be performed again in deeper, more stable lunar soil below 7 m. When the anchor module was first anchored in the lunar soil, the bit module had already been drilled to a depth of more than 1 m. At this moment, the support force is hardly able to support the WSC robot to continue drilling. The solidifying cylinder could be added to assist with drilling.