esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamics Analysis of Space Netted Pocket System Capturing Non-Cooperative Target
## 3 Dynamics Modeling of Space Netted Pocket
### Contact Collision Force Model  
Contact and collision are important features for the space netted pocket system grabbing the target. In this work, the contact and collision characteristics are described by the contact force models, which include the normal collision force model and the tangential friction force model.  
Therefore, the contact and collision force at the collision point consists of the normal contact collision force \(F_{n}\) and the tangential friction force \(F_{t}\). Then, the contact and collision forces can be expressed as:  
\[\mathbf{F}=\mathbf{F}_{n}+\mathbf{F}_{t} \tag{19}\]  
For the normal collision force, a nonlinear spring damping model including the elastic force and damping force is used, which is expressed as [42]:  
\[\mathbf{F}_{n}=K_{n}\delta_{n}^{1.5}\mathbf{n}-D_{n}\mathbf{v}_{n} \tag{20}\]  
where \(\delta_{n}\) is the penetration depth, \(\mathbf{n}\) is the normal unit vector and \(\mathbf{v}_{n}\) is the normal relative velocity. The expressions of \(k_{n}\) and \(g_{n}\) are expressed as follows:  
\[\begin{array}{l}K_{n}=\frac{4}{3}E_{\mathrm{eff}}\sqrt{r_{\mathrm{eff}}}\\ D_{n}=\alpha\sqrt{K_{n}m_{\mathrm{eff}}}\delta^{\frac{1}{4}}\end{array} \tag{21}\]  
where \(r_{\mathrm{eff}}\) is the radius of curvature of the collision point, and it is expressed as \(r_{\mathrm{eff}}=\left(r_{1}^{-1}+r_{2}^{-1}\right)^{-1}\). \(r_{1}\) and \(r_{2}\) are the radii of the two collision bodies. \(\frac{1}{E_{\mathrm{eff}}}=\frac{1-v_{1}^{2}}{E_{1}}+\frac{1-v_{2}^{2}}{E_{2}}\), in which \(v_{1}\) and \(v_{2}\) are [PERSON]' s ration of the collision bodies. \(E_{1}\) and \(E_{2}\) are the elasticity modulus of the collision. \(\alpha\) and \(m_{\mathrm{eff}}\) are expressed as the following:  
\[\begin{array}{l}\alpha=\sqrt{\frac{5\ln^{2}c_{r}}{\ln^{2}c_{r}+\pi^{2}}}\\ m_{\mathrm{eff}}=\frac{m_{1}m_{2}}{m_{1}+m_{2}}\end{array} \tag{22}\]  
where \(c_{r}\) is the [PERSON]'s coefficient of restitution, and \(m_{1}\) and \(m_{2}\) are the mass of the collision bodies.  
The tangential friction is modeled using the Coulomb friction law to describe the contact and collision in tangential direction, which is presented as following:  
\[\textbf{{F}}_{t}=\mu(|v_{t}|)\textbf{{F}}_{n} \tag{23}\]  
where \(\mu\) is the coefficient of friction, which is determined by the tangential relative velocity \(v_{t}\).  
The cable element contacts and collides with the target in the process of capturing and towing the target. The contact force can be transformed to a generalized nodal force based on the principle of virtual work.
# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 4 Experiments
### Comparison with Other Models  
In addition, we also compare other models with PF-Informer, and the experimental results are shown in the following table. The data set used is multilayer target data set, and the comparison models are GRU-ODE-BAYES, GRU, LSTM, the autoregressive integrated moving average (ARIMA), a CNN, and N-BEATS. The results are shown in Table 3.  
We determine that PF-Informer performs best among the tested models, and its MSE and MAE are very low.
## 3 Experimental Results
### Results of IEEE 33-Bus System
#### 3.1.1 Voltage Profile  
The effects of the DGs and CBs allocation on the voltage profile with different scenarios are shown in Figure 5. Although the bus voltages are increased when only active power (with DGs installation) or only reactive power (with CBs installation) is supplied to the distribution system, the bus voltages are increased much more when both active and reactive power is supplied to the distribution system (with DGs and CBs installation, or with DGs installation at optimum p.f). The best result is obtained by determining the optimum p.f.  
Figure 5: Voltage profile of the IEEE 33-bus system in different scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Bus No.** & **DG Size (MW)** & **OPF** & **Power Loss (KW)** & **Loss Reduction (\%)** \\ \hline \multirow{3}{*}{BSOA [34]} & 13 & 0.698 & 0.86 & & \\  & 29 & 0.402 & 0.71 & 29.65 & 85.94 \\  & 31 & 0.658 & 0.70 & & \\ \hline \multirow{3}{*}{IA [35]} & 6 & 0.9004 & 0.82 & & \\  & 14 & 0.6298 & 0.82 & 22.29 & 89.45 \\  & 30 & 0.9004 & 0.82 & & \\ \hline \multirow{3}{*}{EA [36]} & 13 & 0.798 & 0.9 & & \\  & 24 & 1.099 & 0.9 & 11.80 & 94.41 \\  & 30 & 1.050 & 0.71 & & \\ \hline \multirow{3}{*}{AOA} & 14 & 0.7776 & 0.9081 & & \\  & 24 & 1.0535 & 0.9018 & 11.7752 & 94.42 \\ \cline{1-1}  & 30 & 1.0504 & 0.7136 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of optimum DGs allocation at optimal p.f for IEEE 33-bus system.  
The effect of DGs installed at different p.f values on the voltage profile is given in Figure 6. The mean bus voltage is increased to 0.9815 p.u with the installation of DGs at unity p.f, while, it is increased to 0.9967 p.u with the installation of DGs at optimum p.f. Consequently, bus voltages are approached 1 p.u and voltage deviation is minimized with the optimum p.f solution.
## 3 Results
### Coordination Pattern Results
#### 3.2.2 Stand-to-Sit  
The results of the UT/LT showed that there was no difference in LT/UT kinematic coordination pattern during stand-to-sit between people with and without CLBP (Figure 4a).  
There was no difference in LB/LT kinematic coordination pattern during stand-to-sit between people with and without CLBP (Figure 4b).  
Additionally, the results showed that the pattern of in-phase coordination in the LB extension movement and pelvic posterior tilt with LB dominancy in the healthy group was significantly (\(p=0.024\)) more dominant than in the low back pain group (Figure 4c).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Upper Trunk**} & \multicolumn{2}{c}{**Lower Trunk**} & \multicolumn{2}{c}{**Lower Back**} & \multicolumn{2}{c}{**Petivs**} \\ \hline  & _Sil-to-sid_ & _Sand-to-sit_ & _Sil-to-sid_ & _Sand-to-sit_ & _Sil-to-stand_ & _Stand-to-sit_ & _Sil-to-stand_ & _Stand-to-sit_ \\ Healthy group & 45.82 \(\pm\) 15.71 & 41.60 \(\pm\) 10.78 & 50.16 \(\pm\) 22.09 & 44.82 \(\pm\) 16.36 & 46.48 \(\pm\) 13.64 & 48.01 \(\pm\) 16.98 & 32.17 \(\pm\) 9.38 & 36.28 \(\pm\) 8.21 \\ CLBP group & 39.45 \(\pm\) 10.95 & 39.88 \(\pm\) 12.15 & 42.06 \(\pm\) 10.84 & 41.42 \(\pm\) 10.83 & 43.24 \(\pm\) 11.43 & 43.59 \(\pm\) 12.41 & 25.37 \(\pm\) 6.64 & 33.38 \(\pm\) 8.69 \\ \(p\) Value & 0.495 & 0.831 & 0.492 & 0.718 & 0.709 & 0.664 & 0.210 & 0.626 \\ \hline \end{tabular}
\end{table}
Table 2: The results of ROM data in degrees (Ave \(\pm\) SD).  
Figure 3: (a) LT/UT, (b) LB/LT, and (c) pelvis/LB segments angular displacement diagram in the sagittal plane, and the results of the coupling angle frequency during sit-to-stand.  
Figure 4: (a) LT/UT, (b) LB/LT, and (c) pelvis/LB segments angular displacement diagram in the sagittal plane, and the results of the coupling angle frequency during stand-to-sit.
## 4 Intelligent Building Control
### Safety Monitoring System  
Due to the decline in their physiology, the elderly often experience a decrease in mobility and ability to physically recover from injuries. Their bodies also become more vulnerable to other problems like sudden illnesses [50]. Thus, in this design, attention needed to be paid to the inhabitants' daily life and safety. Therefore, the i-Yard 2.0 team developed an intelligent app system that integrates a series of safety monitoring functions such as maintenance of occupants' physical condition and means of dealing with emergency situations. Information collected is transmitted to medical supervision, adult children, and outside peers by alarms, SMS messages, etc. The app includes systems for monitoring bodily condition and home positioning, as well as sounding emergency alarms [51]. All three systems acquire and transmit data through bracelets worn by the elderly occupants (see Figure 14).
# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
### Methodology  
The body of knowledge used in this paper is collected using various online available databases namely, Wiley Online Library, ScienceDirect, BioMedical Engineering OnLine, SpringerLink, IOS Press Content Library, MAG Online Library, Sciendo, IEEE Xplore, Taylor&Francis Online, National Library of Medicine, IGI Global, etc. For assembling the bibliography of papers which are dealing with any aspect of wound assessment, certain search terms were used. The used search terms and their combinations were \"image\", \"analysis\", \"assessment\", \"management\", \"wound\", \"chronic\", \"injury\", \"segmentation\", \"detection\", \"diabetic\", \"RGB-D camera\" \"computer\", \"neural network\", \"evaluation\", \"tissue\", \"classification\", \"3D reconstruction\", \"measurement\", \"healing\", \"evaluation\", \"imaging\", \"processing\", \"automatic\", and \"ulcer\".  
A total of 201 papers were obtained dealing with some aspect of chronic wound analysis, where the keywords, abstract, or conclusion satisfied the aforementioned search terms. Considering only papers from 2013 until 2020, a total of 79 papers were evaluated in this overview. Of these papers, 39 papers dealt with wound segmentation, 31 papers with tissue classification, 19 papers with measurement and healing and only 8 papers with 3D reconstruction. Certain papers dealt with more than one stage of wound analysis, so these papers were used in multiple sections of this review.  
Papers mentioned in segmentation section are strictly papers who delineate the wound boundary while the classification section includes papers which separate the wound tissues found inside the wound area. Measurement, 3D reconstruction and healing evaluation sections considered papers that work together with segmentation and tissue classification as obligatory stages. Measurement papers emphasize the main topic of measuring wounds, while 3D reconstruction papers taken into consideration focus on making the 3D wound model. Healing evaluation papers which are considered are extensions of tissue classification and/or wound measurement.  
Certain authors alongside with their co-authors published more than one research paper in the field, so their papers are summarized in separate paragraphs in appropriate sections where the most contribution has been recognized. Summary tables present most recent papers of such authors, but not all of them. Entries in summary tables are ordered by year of the paper publication as it is the most important inclusion criteria along with the topic of their research.
## 3 Results and Discussion
### Fourier-Transform Infrared Spectroscopy of Synthesized AgNPs
[10] have reported the extensive phytochemical varied found in the leaf extracts of _B. prionitis_, which included phenols, ascorbic acid, citric acid, and reducing sugars, among other compounds that could potentially contribute significantly to the reduction and stabilization of AgNPs. The majority of absorbance bands observed in the synthesized extracts of _B. albostellata_ displayed distinctive functional groups such as phenols, alcohols, terpenes, alkynes, aldehydes, primary, and secondary amines. Hence, the phyto-constituents existing in the _B. albostellata_ leaves and stems evidently displayed an important role in the bioreduction process of the synthesized AgNPs. The FTIR frequencies observed showed the occurrence of several functional groups (Table 4). Phenolic compounds were detected in the stem methanol, fresh leaves, fresh stems, and powdered leaves. The above results suggest that the observed AgNP capping may contain terpenoids and phenolic compounds, with several functional groups alcohols, carboxylic acids, and esters etc. These compounds could have been impacted by the various treatments used (ratios of leaf and stem material and different solvents), which may have reduced AgNO\({}_{3}\) to AgNPs by means of several bioactive compounds [102]. Furthermore, the presence of impurities may be linked to the presence of other organic substances in the different plant extracts [103]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Plant Extract**} & **Absorption** & **Types of** & **Functional** & **Compound** \\  & **Frequency** & **Absorption/** & **Group** & **Class** \\ \hline \multirow{8}{*}{Leaf methanol} & 3273.31 & Stretch & O-H & Alcohol \\ \cline{2-5}  & 2927.50 & Stretch & C–H & Alkane \\ \cline{2-5}  & 2346.67 & Stretch & O=C=O & Carbon dioxide \\ \cline{2-5}  & 2119.29 & Symmetrical & C=C & alkynes \\ \cline{2-5}  & 1622.25 & Stretch & C=C & Conjugated alkene \\ \cline{2-5}  & 1393.67 & Bending & C–H & Aldehyde \\ \cline{2-5}  & 1333.55 & Bending & O-H & Alcohol \\ \cline{2-5}  & 1287.63 & Stretch & N-O & Nitro \\ \cline{2-5}  & 1034.01 & Stretch & S=O & sulfoxide \\ \cline{2-5}  & 897.23 & Bending & C=C & Alkene \\ \cline{2-5}  & 813.23 & Bending & C=C & Alkene \\ \hline \hline \end{tabular}
\end{table} 
Table 4: FTIR spectral frequencies of synthesized AgNPs using extracts of _B. albostellata_.
## 4 Performance Analysis and Evaluation
### Optimality of EESA-RLC Algorithm  
The EESA-RLC algorithm allows SUs to learn and adapt to the dynamic environment to achieve an optimal solution through an optimal policy. The optimal solution means the optimal clusterhead \(CH_{j}^{*}\in H_{n}\) selected by the cluster member node \(MN_{i}\) through the optimal policy \(\pi^{*}\) that maximizes cumulative reward \(r_{k}^{\star}\). The necessary conditions required to achieve the optimal solution is presented in the following proposition and proof.  
**Proposition 1**.: _Consider a given set of neighboring clusterheads \(H_{n}=\{CH_{j}|j=1,2,3,...\,.\,.\,n_{hg}\}\) located at different distances \(dis_{n}=\{d_{ij}|j=1,2,3,...\,.\,n_{hg}\}\) from a member node \(MN_{i}\). The corresponding set of energy consumptions \(E_{TC}^{set}(B_{dt},d)=\{E_{TC,j}|j=1,2,3,...\,,n_{hg}\}\) for the member node \(MN_{i}\) to transmit its reading data to each of the clusterhead \(CH_{j}|j=1,2,3,...\,n_{hg}\) is a function of distance \(d_{ij}\) to the clusterhead. Each clusterhead \(CH_{j}\) and  
\(E_{TC,j}(B_{dt},d)\in E_{TC}^{set}(B_{dt},d)\) for transmitting data to the clusterhead over distance \(d_{ij}\) through the vacant channels. The selected clusterhead \(CH_{j}\) with minimum distance \(\underset{d_{ij}\in dt\,is_{n}}{\min}\,d_{ij}\) must minimize energy consumption \(E_{TC,j}\in E_{TC}^{set}(B_{dt},d)\), maximize vacant channels detection \(\underset{M_{V,i}\,H_{V,j}\,i\in Sch}{\max}\,(M_{V,j}\,\cap\,H_{V,j})\) and satisfy the pairwise constraint condition \(P_{w,k+1}^{t}\) such that \(M_{V,i}\,\cap\,H_{V,j}\
eq\emptyset\) and obtain the highest reward value for the energy consumption reward \(rw_{E,k+1}^{t}\) and highest reward value for the local decision accuracy reward \(rw_{D,k+1}^{t}\) to achieve the maximum cumulative reward value \(R_{k}=\underset{s_{k}^{t}\in s,a_{k}^{t}\in d}{\max}\,\left|\sum_{t=1}^{E_{ps }}r_{k}^{t}\,\left(s_{k}^{t},a_{k}^{t}\right)\right|\). If \(j\) denotes the index of the selected clusterhead \(CH_{j}\in H_{n}\) in states \(s_{1}to\,s_{k}\) that maximizes the cumulative reward \(R_{k}\), then for each state \(s_{k},j=\underset{a\,\in\,d}{\max}\,r_{k}(s_{k},a_{k})\) which denotes the optimal action selection policy \(\pi^{*}=a_{k}^{*}\) and the optimal clusterhead \(CH_{j}^{*}\in H_{n}\).
## 6 Discussion
### Conclusions  
In this work, we first classified MSIMP according to the time dimension, space dimension, and task planning and management; introduced the related disciplines and application areas regarding MSIMP in the pre-processing phase, task planning phase, execution phase, and feedback re-planning phase; and analyzed the constraints of MSIMP. Second, the mathematical model of MSIMP was introduced from four aspects--namely, the target mission set, satellite resource set, time window set, and evaluation set--and the application scenarios and characteristics of traditional optimization algorithms for MSIMP were analyzed in detail. Then, the MSIMP process was discussed to provide a comprehensive overview of the classical EMOEAs. Three types of commonly used surrogate models--namely, statistical models, function approximation models, and machine-learning-based models--were summarized and analyzed to explore surrogate-model-based algorithms for EMOEAs. Finally, improved SM-EMOEAs for MSIMP were discussed in terms of improved surrogate models, improved adaptive strategies, and solution diversity maintenance and quality assessment. Moreover, we summarized the improvement methods, such as the global surrogate model, local surrogate model, two-layer surrogate model, and aggregated surrogate model. We also summarized the adaptive improvement strategies, including the adaptive sampling strategy, multiple-population co-evolutionary strategy, and domain knowledge strategy, as well as the diversity maintenance and quality assessment methods, to enable broad comprehension.  
Methodology, X.Y. and M.H.; supervision, M.H. and G.H.; validation, P.L. and Y.W.; writing--original draft, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61403416.  
Not applicable.  
Thanks to [PERSON] for his important technical help.  
The authors declare no conflicts of interest.
## 6 Software Architecture of the Multi-Sensor Platform
### Application Phase
#### 6.2.3 Reconfiguration Task  
The reconfiguration task enables the sensor platform configuration to be dynamically modified using the LWM2M protocol. This unique protocol enables configuration management as objects and resources centrally managed from the cloud. This protocol uses a small portion of the memory (around 50 Kb RAM, 150 Kb FLASH) and is effectively managed by the RTOS of the MSP. The LWM2M is built over the LWIP TCP/IP stack, using the UDP protocol with CoAP overlay. The Wakaama implementation of the LWM2M stack in the POSIX environment is ported to the sensor platform and scaled-down to use minimum stack and memory while working alongside the LWIP UDP protocol in non-blocking mode to have seamless data traffic between various socket ports. The sensor platform works as a client, registering the configuration parameters and receiving requests for a change of parameters. Parameters such as sensor configuration, data manipulation, enabling/disabling of specific sensors, and battery charge requests are handled by the client, as listed in Table 1  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Peripheral** & **Parameters** \\ \hline \multirow{6}{*}{Mainboard} & Enable/disable sensor \\  & Modify destination IP/port \\  & Static/DHCP configuration \\  & Prioritization of sensor \\  & Send raw/processed data \\  & Request configuration detail \\ \hline Vibration sensor/ & Sampling frequency increase/decrease \\ Microphone sensor & Optional parameters (FFT, filters, sensitivity) \\ \hline \multirow{2}{*}{Strain gauge/torque/force sensor} & Sampling frequency increase/decrease \\  & Sensitivity configuration \\  & Calibration request \\ \hline \multirow{2}{*}{Temperature sensor} & Sampling frequency \\  & Calibration request \\ \hline SARA-R5 module & Periodicity of synchronization \\  & Cell time parameter configuration \\ \hline \multirow{2}{*}{Internal sensor} & Threshold to trigger an alarm \\  & Sampling frequency \\ \hline \hline \end{tabular}
\end{table}
Table 1: LWM2M. Client configuration.  
The configuration data are stored in the EEPROM. When the controller starts up, the configuration data is first read from the EEPROM and stored in the configuration variables used to set up the sensors. When the LWM2M server requests a configuration update, these variables are updated, triggering the sensor task to send the configuration and save it in the EEPROM for the next startup.
# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 2 Materials and Methods
### Evaluation Metrics and Performance of Improved Model  
The evaluation metrics used in the experiment are \(P\) (Precision), \(R\) (Recall), \(mAP\) (mean Average Precision), and \(F_{1}\) (\(F_{1}\) score), and the formula for calculating \(P\), \(R\), \(mAP\), \(F_{1}\), is shown in Equation (10):  
\[P=\frac{TP}{TP+FP},\ R=\frac{TP}{TP+FN},\ mAP=\frac{\frac{\sum\limits_{1}^{N}AP }{N}}{N},\ F_{1}=\frac{2P\cdot R}{P+R} \tag{10}\]  
where \(TP\) (True Positive) is the number of positive samples with correct prediction; \(FP\) (False Positive) is the number of positive samples with incorrect prediction; \(FN\) (False Negative) is the number of negative samples with incorrect prediction; \(AP\) is calculated as the area under the \(P\)-\(R\) curve; \(N\) is the number of labeling categories; and \(N=16\). In this paper, \([EMAIL_ADDRESS]\) denotes the \(mAP\) for the IOU (intersection over union) threshold of 0.5 for the predicted boxes and ground truth boxes, and \([EMAIL_ADDRESS]\):0.95 denotes the average mAP of ten different IOU thresholds ranging from 0.5 to 0.95 with a step size of 0.05.  
The use of a confusion matrix provides a visual representation of the detection performance of the proposed method on the dataset. The classification confusion matrix for model validation, with an input size of 640 \(\times\) 640, is illustrated in Figure 7. The diagonal of the confusion matrix represents the prediction accuracy for each class, while the off-diagonal elements indicate the probability of a particular class being predicted as another class. In the confusion matrix, the background region serves as negative samples, while the positive samples, including breakage, self-explosion, flashover, and insulator strings, are included in the evaluation of model performance.  
From the confusion matrix analysis, it can be inferred that the insulation state detection method proposed in this paper demonstrates an overall high recognition accuracy. However, there are still relatively few instances of missed detections, which may be attributed to severe target occlusion or difficulty in capturing extremely small targets in the dataset. Specifically, the recognition rates for insulator strings, self-explosion, breakage, and flashover are 98%, 91%, 96%, and 96%, respectively. Meanwhile, the recognition rate  
Figure 7: Classification confusion matrix.  
for background is relatively high, indicating that complex backgrounds can indeed interfere with detection results.  
To further validate the detection performance of the proposed method, detection analysis was conducted using a test set. A class confidence threshold of 0.3 and an intersection over union (IOU) threshold of 0.75 were set. The visualized detection results are depicted in Figure 8. From Figure 8, it can be observed that the method proposed in this paper accurately detects insulator self-explosion, breakage, and flashover, even in scenarios involving target occlusion, dense targets, small target scales, and complex background interference.