esa-sceva/satcom-chunk-collection huggingface dataset:
## Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
1 College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; E-Mails: [EMAIL_ADDRESS] (M.W.); [EMAIL_ADDRESS] (X.L.)  
2 Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-29-8385-6356.  
External Editor: [PERSON]  
_Received: 13 June 2014; in revised form: 3 September 2014 / Accepted: 15 September 2014 / Published: 26 September 2014_  
**Abstract:** In electric power systems, power cable operation under normal conditions is very important. Various cable faults will happen in practical applications. Recognizing the cable faults correctly and in a timely manner is crucial. In this paper we propose a method that an annealed chaotic competitive learning network recognizes power cable types. The result shows a good performance using the support vector machine (SVM) and improved Particle Swarm Optimization (IPSO)-SVM method. The experimental result shows that the fault recognition accuracy reached was 96.2%, using 54 data samples. The network training time is about 0.032 second. The method can achieve cable fault classification effectively.  
**Keywords:** power cable; cable faults; SVM; recognition; competitive learning network; annealed chaotic
# Energy-Water Management System Based on MPC for a Greenhouse in a Mapuche Indigenous Community
## 7 Simulation Results
### Short-Term Management: Window Controller for Climate Control
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Controller** & _Tsec\%_ & _Hsec\%_ & _Tnor\%_ & _Hnor\%_ & \#\(\Delta\)\(W_{s}\) \\ \hline \multirow{3}{*}{Summer} & MPC & 95.37 & 83.61 & 26.14 & 20.94 & 1937 \\  & MPC & 96.04 & 76.96 & 42.05 & 16.35 & 1895 \\  & Manual & 98.98 & 60.04 & 31.99 & 11.92 & 230 \\  & MPCF & 97.27 & 67.21 & 38.13 & 8.98 & 1216 \\ Winter & MPC & 98.35 & 66.09 & 44.81 & 7.76 & 640 \\  & Manual & 77.16 & 43.75 & 7.47 & 6.88 & 280 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Climate control results. A final observation that can be made is that in both cases, the window state changed more during the summer than the winter, and the MPCF resulted in more window changes than the MPC. From these differences and the performance of the controllers in terms of temperature and humidity, it can be concluded that the system had some difficulties when trying to control humidity using the proposed strategy. Moreover, Figure 7a indicates that the current criteria used to determine day and night conditions might be faulty, and full days might be considered nights if the weather is very cloudy.
# Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals
## 2 The Local Weighted Linear Prediction Algorithm
### NDFA Algorithm  
Now the weighted coefficient of point on evolutionary trajectory can be calculated. By utilizing the weighted coefficients, the NDFA algorithm can successfully accomplish the task of data reduction. The steps of the approach are listed as follows:  
(1) Choose the proper embedding dimension \(m_{\text{max}}\), delay time \(\tau_{\text{min}}\), initial condition \(X_{F}(0)\), and for each lead of 12-lead ECG signals, construct the vector \(X_{l}\) as:  
\[X_{l}=(x(T),x(T+\tau_{\text{min}}),\cdots,x(T+(m_{\text{max}}-1)\tau_{\text{ min}}))\in R^{m}, \tag{11}\]  
where \(T=1,2,\cdots,N-(m_{\text{max}}-1)\tau\) and \(X_{l}\) is the evolutionary trajectory of the \(l\)th lead ECG signal on reconstructed trajectory. \(X_{F}(0)\) is chosen as centroid of all the neighbor neighboring vectors \(X_{s}(0)=(x(0),x(0+\tau_{\text{min}}),\cdots,x(0+(m_{\text{max}}-1)\tau_{\text {min}}))\), which is the point of the reconstruction trajectory of the \(s\)th lead ECG signal at \(p=0\).  
Here the embedding dimension and delay time should be selected as:  
\[m_{\text{max}}=\text{max}\{m_{s},s=1,2,\cdots,L_{n}\}, \tag{12}\]  
\[\tau_{\text{min}}=\text{min}\{\tau_{s},s=1,2,\cdots,L_{n}\}, \tag{13}\]  
(2) For the \(l\)th evolutionary trajectory \(X_{l}\), calculate the parameters \(D(p)\), \(D_{r}(p)\), \(\alpha(p)\) and \(\alpha_{r}(p)\) by Equations (3), (4), (6), and (7), respectively. With the two FISs, \(\omega_{d}(p)\) and \(\omega_{\alpha}(p)\) being properly estimated, the weighted coefficient \(\omega_{l}(p)\) of the state \(X_{l}(p)\) at the \(p\)-step can be computed by Equation (10).  
(3) Compute linear fitting parameters \(a\) and \(b\) by the least square equation:  
\[g(a,b)=\min_{a,b\in R}\{\sum_{l=1}^{L_{n}}\omega_{l}(p)[X_{l}(p+1)-ae-bX_{l}(p )]^{2}\}, \tag{14}\]  
where \(e=[1,\cdots,1]_{m_{\text{max}}}^{T}\).  
(4) Through the linear prediction equation, the final fused state \(X_{F}(p+1)\) is calculated as:  
\[X_{F}(p+1)=ae+bX_{F}(p). \tag{15}\]  
In this section, NDFA for 12-lead ECG signals is introduced. In the algorithm, the linear prediction equation is used to compute the fused state. With the qualitative characteristics of original signal preserved, the weighted coefficient of each reconstructed trajectory is estimated through FIS properly. In the next section, the algorithm will be applied to 12-lead ECG signals and the performance of this algorithm will be further illustrated.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 3 Results  
### Experimental Setup and Dataset Processing  
To assess FFN's stability under different settings of facet count \(K\) and SLIC/SPSLIC iterations, we conducted a sensitivity analysis on the same Ubuntu 20.04 workstation (16-core/32-thread AMD RYZEN 2950X CPU, 128 GB DDR4, three RTX 2080 Ti) with identical training hyperparameters (100 epochs, batch size = 2, LR = \(10^{-3}\), Adam optimizer). We varied \(K\) over [512, 1024, 2048] while keeping Differentiable SLIC/SPSLIC iterations at 10, and measured ASA, UE, and inference time on 50 k-point clouds. ASA remained within 0.3 pp of its baseline (98.8%) and UE within 0.2 pp, while inference time scaled linearly (from 620 ms at \(K\) = 512 to 2150 ms at \(K\) = 2048). Next, with \(K\) = 1024 fixed, we varied SLIC/SPSLIC iterations over [5, 10, 20]. The ASA improved by only 0.1 pp beyond 10 iterations, and the UE reduction was under 0.05 pp, whereas the runtime increased proportionally. These results demonstrate that FFN's performance is robust to reasonable variations in \(K\) and iteration count, and that our chosen defaults (\(K\) = 1024, iterations = 10) deliver a strong accuracy-efficiency trade-off.  
After training, inference was deployed on a Windows 10 small-form-factor PC (AMD Ryzen 7 3700X CPU, 32 GB DDR4 2133 RAM, and NVIDIA GTX 1080 Ti GPU) using PyTorch 1.10.2 and CUDA 10.2. During testing, 1024 facets were generated, and the SPSLIC was iterated 10 times. This setup simulates model training on a high-performance server and tests the feasibility of a resource-limited environment.  
The dataset comprised 531 point clouds from tobacco (93), tomato (310), and sorghum (128). Each point cloud contained 10,000-100,000+ points. To standardize the input size, Farthest Point Sampling (FPS) reduced each sample to 10,000 points. An unsupervised SLIC algorithm then produced coarse facets on downsampled point clouds. These coarse facet labels, combined with the instance labels, were used as the inputs for training. The data were split roughly 4:1 into a training set (431 point clouds) and a test set (100 point clouds: 17 tobacco, 60 tomato, and 23 sorghum).
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
###### Abstract  
Within a global Land Data Assimilation System (LDAS-Monde), satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) products are jointly assimilated with a focus on the Euro-Mediterranean region at 0.5\({}^{\circ}\) resolution between 2007 and 2015 to improve the monitoring quality of land surface variables. These products are assimilated in the CO\({}_{2}\) responsive version of ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model, which is able to represent the vegetation processes including the functional relationship between stomatal aperture and photosynthesis, plant growth and mortality (ISBA-A-gs). This study shows the positive impact on SSM and LAI simulations through assimilating their satellite-derived counterparts into the model. Using independent flux estimates related to vegetation dynamics (evapotranspiration, Sun-Induced Fluorescence (SIF) and Gross Primary Productivity (GPP)), it is also shown that simulated water and CO\({}_{2}\) fluxes are improved with the assimilation. These vegetation products tend to have higher root-mean-square deviations in summer when their values are also at their highest, representing 20-35% of their absolute values. Moreover, the connection between SIF and GPP is investigated, showing a linear relationship depending on the vegetation type with correlation coefficient values larger than 0.8, which is further improved by the assimilation.  
land surface model; land data assimilation system; accuracy; fluorescence 1
###### Abstract  
This retrospective study aimed to evaluate a possible correlation between the characteristics of the mandibular ramus and lower third molar impaction by comparing a group of subjects with an impacted lower third molar and a second group with normal eruption for an early prediction of this pathology. This comparison was made using linear and angular measurements, which were taken on digital panoramic radiographs. **Materials and methods:** A total of 726 orthopantomographs (OPT) were examined, and 81 were considered suitable for the present study. The results were divided into two groups: a control group and an experimental group. The control group comprised 38 cases in which patients had at least one lower third molar that had erupted, and the experimental group comprised 43 cases in which patients had at least one lower third molar that was impacted or partially impacted. In total, 16 variables (11 linear, 4 angular, and 1 ratio) were determined and measured by an experienced observer. **Results:** The control group had a larger retromolar space, a larger impaction angle and a higher ratio of retromolar area to the third molar, compared to the experimental group. In contrast, the experimental group showed a deeper sigmoid notch depth than the control group did. In the control group, moderate positive correlations were found between both the length of the coronoid and the width of the third molar, and the retromolar space. Furthermore, in the experimental group, moderate positive correlations were found between both the angular condyle-coronoid process and the inclination of the lower posterior teeth, and the retromolar space. **Conclusion:** this study showed that the angle of a lower third molar, in relation to mandibular pain, can be an index for predicting tooth inclusion.  
third molar impaction; mandibular ramus dimensions; molar inclusion +
Footnote †: journal: Applied sciences
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 3 Link-Level Model
### Link Performance Model
#### 3.3.1 Receiver Sensitivity  
Table 2 shows the SF sensitivities of GW (\(S_{g}\)) and ED (\(S_{e}\)) [44; 45; 46], respectively. For both the EDs and GW, the sensitivity decreases with an increasing SF. A packet can be detected by a device if its \(P_{rx}\) is above the sensitivity level. In our experiment, we assume that the \(P_{rx}\) of a packet is constant during the whole experimental period. If the power is sufficient to start the decoding process, a packet is assumed to be decodable till the end of the reception. Lastly, it is assumed that if two weak signals from the EDs arrive concurrently at the GW, they cannot be perceived as decodable even if the sum of their powers is above the sensitivity; this is owing to the fact that the signals interfere [5].  
In our experiment, destructive interference is assumed, and the packet would be lost even if the receiver is locked onto it. The receiver sensitivity is dependent on the choice of SF (i.e., 7 to 12), as given by [44]:  
\[\text{Sensitivity }(\mathrm{dBm})=-174+10\log_{10}(BW)+NF+SNR. \tag{7}\]  
where, in Equation (7), \(-174\) dBm is the thermal noise computed for 1 Hz of \(BW\); \(NF\) represents the noise margin at GW (6 dB) [44]; and \(SNR\) is the signal to noise ratio for a given SF, as shown in Table 3[45].
## 5 Limitations  
There are some limitations to this study. We did not exclude patients based on the height of lesion, age of injury, or degree of impairment, as narrow inclusion criteria would have reduced the sample size in this single-centered study and, thus, reduced statistical power. Moreover, brain physiology, and therefore, cortical connectivity, could potentially have been affected by patient age, years and regularity of practiced physical exercise, and type of sport. The sportive patients included in this study practiced wheelchair dancing, as well as hand-biking and marathon driving. We can assume that the latter groups were less familiar with complex movements as compared to dancers who have more practical experience in imagining movements and choreographies. A further limitation is the missing control for menstrual-cycle-dependent changes in the female participants. Several studies demonstrated a correlation between EEG signals and hormonal state in alpha frequency (\(\alpha\) peak frequency, \(\alpha\) band width, power in the \(\alpha\)2 frequency band, and the maximum power in the low-frequency \(\alpha\)1 band), as well as in the beta frequency [62; 63]. [PERSON] and colleagues found positive correlations between progesterone level and beta 1, while beta2 showed negative correlations [63]. The original plan of the study was to compare wheelchair dancers to other wheelchair athletes, where we wanted to investigate the specific response of dancers to music as a rhythmic cue. We assumed wheelchair dancers, as compared to other wheelchair athletes, would benefit more from music as a rhythmic cue over simple beep sounds. As the sample size of wheelchair dancers was too small to conduct this type of analysis, we had to limit ourselves to an overall comparison of the effect of music vs. beep sounds as conditions. Indeed, there were some differential effects that pointed to different networks activated by music during imagination of movement in the beta range, where sportive controls and non-sorptive patients showed higher left-to-right connectivity. Since analysis of sub-groups was not possible, the further interpretation of the effect of music with respect to sports type was limited. Generally, the sample size for the patient groups was small, and effects due to individual outliers cannot be excluded. The observed effects should be tested in further studies with a larger sample size. Another relevant method for recording connectivity during movement imagination is fMRI. With that method, the accuracy in localization of the regions of interest could be improved, and the reaction to imagining different sport activities could be compared. Finally, recording HD-EEG during movement represents a challenge due to strong muscle and movement artefacts, even if no head movements are included in the selected movement patterns. Although we subtracted the connectivity values of the control (resting) condition from the test condition, it is not guaranteed that all activation due to visual and auditory stimulation was excluded.
## 4 Standard Test Data Analysis Waterproofing Performance Gradation
### Test Result Values Grade Section Setting
#### 4.4.3 Temperature Dependency Performance Grade  
(1) Room temperature 60 \({}^{\circ}\)C tensile strength  
According to Table 7, room temperature 60 \({}^{\circ}\)C tensile strength section setting of each grade of standard test data values is as in the following Figure 4. When you look at the set grade sections, they were derived as grade 1 with 10.8 N/mm or more, grade 2 with 6.8 N/mm or more and less than 10.8 N/mm, grade 3 with 4.1 N/mm or more and less than 6.8N/mm, grade 4 with 3.3 N/mm or more and less than 4.1 N/mm and grade 5 with 2.0 N/mm or more and less than 3.3 N/mm.  
(2) 60 \({}^{\circ}\)C elongation ratio  
According to Table 7, to set grade section for elongation ratio between binding parts in case of fracture at room temperature 60 \({}^{\circ}\)C, they were derived as grade 1 with 598% or more, grade 2 with 423% or more and less than 598%, grade 3 with 429% or more and less than 249%, grade 4 with 208% or more and less than 429% and grade 5 with 150% or more and less than 208%. To show these in graph, they were set as in the following Figure 5.  
Figure 4: Section setting for each grade of room temperature 60 \({}^{\circ}\)C tensile strength.  
Figure 3: Section setting for each grade of tearing performance.  
(3). \(-\)20 \({}^{\circ}\)C tensile strength  
According to Table 7, to set grade section for standard test data value of low temperature \(-\)20 \({}^{\circ}\)C tensile strength, they were derived as grade 1 with 23.4 N/mm or more, grade 2 with 15.8 N/mm or more and less than 23.4 N/mm, grade 3 with 7.8 N/mm or more and less than 15.8 N/mm, grade 4 with 7.0 N/mm or more and less than 7.8 N/mm and grade 5 with 5.0 N/mm or more and less than 7.0 N/mm. To show these in graph, they were set as in the following Figure 6.  
(4) \(-\)20 \({}^{\circ}\)C elongation ratio  
According to Table 7, to set grade section for standard test data value for elongation ratio between binding parts in case of fracture at low temperature \(-\)20 \({}^{\circ}\)C, they are as in the following Figure 7. Set sections were derived as grade 1 with 376% or more, grade 2 with 287% or more and less than 376%, grade 3 with 172% or more and less than 287%, grade 4 with 93% or more and less than 172% and grade 5 with 50% or more and less than 93%.  
Figure 5: Section setting for each grade of room temperature 60 \({}^{\circ}\)C elongation ratio.  
Figure 6: Section setting for each grade of low temperature \(-\)20 \({}^{\circ}\)C tensile strength.
## 2 Stated Antenna Design Analysis  
This section presents a small-size wideband bear-shaped design with circular and rectangular slots inside the radiator and the slots inside the ground plane, fed through a coaxial probe of 50 \(\Omega\). As demonstrated in Figure 2, the provided antenna has discrete layers, including a resonator, a ground plane, and a substrate Roger RT5880 ( \(\epsilon_{r}\) = 2.2 and tan \(\delta\) = 0.0009), with a 0.254-mm thickness. The antenna has an overall volume of 7 \(\times\) 7 \(\times\) 0.254 mm\({}^{3}\) (0.027\(\lambda_{g}\)\(\times\) 0.027\(\lambda_{g}\)\(\times\) 0.0011\(\lambda_{g}\)). The ANSYS high frequency selective simulator (HFSS) software was used to simulate the proposed antenna. The antenna's return loss at the operational frequency of 915 MHz was determined to be \(-\)20 dB. The working bandwidth is 230 MHz. Table 1 summarizes the antenna's optimized parameters.
## 1 Introduction  
During the last few decades, the forest industry has progressed from manual to machine monitoring and harvesting to reduce hazards to the worker and increase productivity. Thanks to the recent advances in robot technology, remote control, vision, etc., autonomous machines now are being employed in agriculture, construction, medicine, and manufacturing. As a consequence, harvesters nowadays are controlled with a control area network (CAN)-based distributed control system and information system, with GPS localization to log data [1]. However, forstry is still a demanding area for robot technology, where reliability, precision, and adaptability to the dynamic changes are required to develop more enhanced autonomous or tele-operated operations. So far, mobile robots have been introduced as fundamental data-gathering tools for taking and processing high-resolution samples [1]. However, it is still hard to penetrate the forest autonomously due to the rough terrain and wheel slip. More recently, the introduction of unmanned aerial vehicles (UAVs) has provided a more efficient means of gathering environmental data where the operator can choose the spatial and time resolution of the data to be acquired while covering large areas [2]. Even though different kinds of UAVs have been proposed for automatic environmental monitoring, there are still technical challenges in terms of perception, control, and locomotion capabilities [3].  
In particular, the automatic environmental monitoring in indoor farming (e.g., in greenhouse agriculture) is still limited [4]. By means of micro-aerial vehicles (MAV), it may be possible to measure climate features, monitor the plant growth, etc. However, there is still a range of limitations (e.g., power computation, autonomy, payload capacity, battery, etc.). Therefore, in this paper we proposed a vision-based control system to achieve autonomous navigation and landing in indoor environments without requiring advanced calibration procedures or additional external sensors.  
Our main contribution is to propose a distributed off-board method capable of detecting a predefined fiducial marker and inferring information from a single defined marker in order to allow the MAV to localize itself relative to the target location even in the existence of disturbances in indoor environments (e.g., occlusion). The main reason why we have selected a distributed off-board method was due to the computation power and battery constrains of the MAV. Moreover, we have analyzed and compared different experimental methods for tuning the control parameters in order to select the most proper method that not only reduce the time required for tuning when the payload is changed (by simulating different payloads). Finally, the selected control parameters were also analyzed in order to verify the effect on the effective flight time, which has scarcely been investigated in the literature.