esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Data
### Statistical Analyses
Figure 2: Visual representation of the data transformation method in which fixing a time window \(t\), the impact of one time series into the other, represented by (\(X^{(t)}\)), is summarized into entropy index (\(Y^{(t)}\)). For each experiment period, the transfer entropy calculation was done per segmentation (time window), which transformed the exchanged information (causal direction) between one platform into a complexity measure value (entropy index). The entropy index calculated the causal effect from the right-side to the left-side of a time segment, as well the effect of the same time segment from the left-side to the right. Additionally, the Kruskal-Wallis test was adopted in order to check the statistical significance between Sham-Anodal versus Sham-Cathodal stimulation, to test the difference, in each time period, across their information flux between the force places (that is, observing the Left\(\rightarrow\)Right, and also the Right\(\rightarrow\)Left), further information [22; 23]. Moreover, given the open gap in the literature on the different biological effects concerning the sham condition [18], in this study we highlighted this comparison in each time period block. Furthermore, the discussion on these findings is related to the existence of a carryover effect between pre/post stimulation versus baseline [24]. For better understanding, Figure 3 visually represents the present methodology framework, from importing data to extracting its information.
# Business Impact Analysis of AMM Data: A Case Study
## 5 Results
### Risk Assessment by BIA Guidelines by Asset
#### 5.2.9 Measuring Point C2 (MPC2)  
In the case of continuous metering category C2 with remote communication, it is possible to remotely read and control the tariff control. In case of communication failure, a field worker is sent to the site. Of the Type C meters, this is one of the most numerous groups. _Legal and contractual obligations_: If the MPC2 fails, it will not be possible to remotely read the electricity at the OM; furthermore, the switching time will not be observed. If this happens at the wrong time, for many sites, there may be a violation of Decree No. 359/2020 Coll., No. 540/2005 Coll., Act No. 458/2000 Coll. and Decree No. 408/2015 Coll. If the distributor does not control the load at these customer sites, it could cause significant damage to the distribution network and thus violate all standards of safe supply. Therefore, the risk is set as high. _Management and operation of the organization_: A large-scale communication failure could have a major impact on the management and operation of the organization as tens of thousands of sites could be affected because the company would not be able to remedy the situation on the ground. Depending on the nature of the outage and the length, in extreme cases, there could be a substantial impact on operations. _Loss of credibility_: As far as this aspect is concerned, the potential failure of the network is due to its inability to be managed by the MP with blocked appliances. This could also have a large media impact because the security of electricity supply in some areas could be disrupted. For this reason, the risk is high. _Financial loss/Disruption of activities_: A large financial loss could only occur if there is a communication failure involving several thousand sites at a time when it is necessary to control loads by unplugging or switching on blocked appliances. Unfortunately, the amount cannot be precisely quantified, but could exceed several billion in the event of litigation. Therefore, the risk of impact is determined to be high. _Provision of essential services_: In terms of the number of points of consumption, this is the largest type of metering, so a significant impact on society can be expected in the event of a major communications outage in the form of unavailable services. A comprehensive assessment of the risks and parameters of data availability and maturity is presented in Table 21.
## 5 Conclusions  
In this paper, we have discussed the strategy of data collection with a mobile sink which was named GSDCS. We first proposed a virtual grid structure to assist data collection. Each grid cell was given \(RCN\) (row column number) and \(DN\) (direction number). Based on the structure, a way of data routing was proposed later. Combined with \(RCN\) and \(DN\), data packets could be routed to sink easily. Then we proposed the trajectory planning of mobile sink. In the sink trajectory planning, sink has a tendency to move to the event area where most source nodes are located. By doing so, the total length of routing paths is shortened. Finally, to make the network more balanced, we proposed a way to re-elect the head node in each virtual grid cell. Compared with VGDRA in the simulation, GSDCS has a better performance in specific application scenarios.  
**Acknowledgments:** The work is by \"Qing Lan Project\" and \"the National Natural Science Foundation of China under Grant 61572172, 61602152 and 61601169\", supported by \"the Fundamental Research Funds for the Central Universities, No.2016B10714 and No.2016B03114\", supported by \"Science & Technology Pillar Program (Social development) of Changzhou Science and Technology Bureau, No.CE20165023\", and supported by \"Finep, with resources from Funtel, Grant No. 01.14.0231.00, under the Radiocommunication Reference Center (Centro de Referencia em Radiocommunicacoes - CRR) project of the National Institute of Telecommunications (_Instituto Nacional de Telecomunicacoes - Inatel), Brazil, by National Funding from the FCT - Fundacao para a Ciencia a Tecnologia through the UID/EEA/50008/2013 Project, by Government of Russian Federation, Grant 074-U01\".  
**Author Contributions:** [PERSON] researched the literature, conceived of the study concepts, and drafted the manuscript; [PERSON] and [PERSON] carried out the simulation and analyzed the simulation results; [PERSON] and [PERSON] provided useful suggestions and helped revise the manuscript. All authors read and approved the final manuscript.  
Figure 22: The comparison with VGDRA when only the updating energy is considered.
## 3 Results
### Crop Growth Patterns Estimated by Planet Dove Imagery  
Figure 4 shows the average NDVI time series of each crop type and practice in the field survey area created from the Planet Dove images. These series represent the crop growth patterns of each crop type and practice. Ratooning and spring planting of sugarcane had a similar crop growth pattern. In the summer planting of sugarcane, changes in the percentage of vegetation cover with time differed greatly from those in ratooning and spring planting. Pastures had high NDVI values throughout the year. Pineapple had a lower NDVI value throughout the year than did pastures. Agricultural facilities had lower NDVI values than any crop types and practices. The NDVI value of purple yam peaked in July, then decreased gradually, and was lowest in November. While the acquisition rates of the original NDVI values based on Planet Dove imagery were low from January to February and in June, spectra related to land cover information were usually obtained within 5 to 10 days for the rest of the periods.  
Figure 3: _Cont._  
Figure 4: Average normalized difference vegetation index (NDVI) time series created from the Planet Dove images taken between August 2017 and November 2019 for each class in the field survey area (lower), and the acquisition rates of original NDVI values for 5-day composition in all the target pixels analyzed in this study (upper).  
Figure 3: Positional deviation between Planet Dove bands by the zero-mean normalized cross-correlation method: Maximum displacement between red-green-blue (RGB) bands in the transverse direction (**a**) and vertical direction (**b**), and maximum displacement between RGB bands and the near-infrared (NIR) band in the transverse direction (**c**) and vertical direction (**d**). Since the validation was performed in the range of \(\pm 10\) pixels in the XY directions of the image, a shift of 10 pixels or more was rounded to 10.
# Protoporphyrin IX Based All-Solid-State Ion-Selective Electrodes for Choline Determination In Vitro
## 3 Results and Discussion  
### Dynamic Responses and Calibration Curve  
The potentiometric response of the Ch sensor was evaluated by diluting 0.1 M of ChCl solution with a continuous stir at 400 rpm in the background of acetate buffers at pH 7.4. The Ch ISE was calibrated from 10\({}^{-7}\) M to 3 \(\times\) 10\({}^{-3}\) M, and a typical stepwise trace is shown in Figure 3A. With the addition of ChCl stock solution each time, the sensor reaches a stable state in 5 s. The results demonstrated that the Ch ISE would be the perspective to be used in real-time Ch monitoring.  
Figure 2: (**A**) Potentiometric detection system in Ch solutions; and (**B**) structure of the Ch all-solid-state ISE; (**C**) photograph of the Ch ISE with a centimeter ruler as a reference.  
The calibration curve was plotted in Figure 3B. The sensitivity of the sensor was in the linear range of \(10^{-6}\)-\(3\times 10^{-3}\) M is \(52.71\pm 0.95\) mV/decade. According to the IUPAC recommendation, the detection limit is 0.49 uM, where the linear response and nonresponsive segment in the calibration curve intersect [37]. Compared with the ionophore of octa amide cavitand, the cation-\(\pi\) bonding between Ch and tetrapyrrole of protoporphyrin IX, as well as hydrogen bonds formed between the O-H group in Ch and the porphyrin core enhanced the interaction, which improves the performance of Ch sensor [28,30].
# Welding Bead Inspection Using Image and Multi-Sensor Fusion
## 4 Experimental Results
### Experiment Setups  
In this experiment, sensor data and image data generated during the production of SRDs were analyzed. A total of 412 SRD welding beads were analyzed, consisting of 400 good samples and 12 bad samples. Regarding the sensor data, good samples exhibited outputs, as shown in Figure 3a, while in cases of defects, current, voltage, or mixed gas values were either entirely or partially unused, resulting in data values of zero or small values, as seen in Figure 3b,c. In the image data, good samples were represented as shown in Figure 5a,b, whereas bad samples exhibited characteristics where only a portion of the welding bead is present, as shown in Figure 5c-f or, in extreme cases, where no welding bead is present at all, as shown in Figure 5g,h. The input image dimensions were 256 by 256. The experiments were conducted under conditions of approximately 700 Lux illumination and an average brightness of about 200 for the welding bead. For setting the threshold values in the sensor data, experiments were conducted with variations in the \(\alpha\) and \(\beta\) values, and the results showed that the highest accuracy was achieved when \(\alpha\) and \(\beta\) were both set to 0.5 and 5, respectively, as shown in Table 1. Therefore, these values were chosen as the threshold values for analysis.  
The PC specifications were as follows: Windows 10 Pro, i9 with NVIDIA GeForce RTX 3080 (NVIDIA, Santa Clara, CA, USA) with GDDR6X 10 GB, 3.7 GHz per processor, and Python 3.8.  
Figure 5: The SRD welding bead products: (**a**) good-1; (**b**) good-2; (**c**) bad-1; (**d**) bad-2; (**e**) bad-3; (**f**) bad-4; (**g**) bad-5; (**h**) bad-6.
# Noninvasive Suspicious Liquid Detection Using Wireless Signals
## 3 Method Design
### Method Design  
The experimental scenario used to facilitate the detection and identification of suspicious liquids is shown in Figure 1. Our experiment was conducted in a conference room and used two sets of equipment placed on a desk to collect data. The transmitter and receiver were one meter apart. The liquid to be detected was placed statically between the transmitter and receiver at the same height. One set of equipment worked at 2.4 GHz, and the transmission and reception of signals were completed by the wireless network adapter of the computer and three omnidirectional antennas. The bandwidth was 20 MHz, and the output power of the transmitter was set at \(-\)5 dBm. The other worked at 4.8 GHz, which is consistent with the 5G standard in China, with the RF signal generator as the transmitter and spectrum analyzer as the receiver. The bandwidth was 100 MHz, and the output power of the transmitter was set at \(-\)5 dBm. The receiver collected the RF signal at the frequency of 4.8 GHz and 2.4 GHz, corresponding to the C-band (5G) and the S-band, respectively.  
To facilitate the liquid detection and identification leveraging Wi-Fi signal, we exploited wireless channel information (WCI), the fine-grained description of the wireless channel, to capture the minute differences of the channel state change introduced by different liquids. Our method employed the amplitude information of WCI. First, we extracted the WSI from a pair of transmitters and receivers. Second, we preprocessed the data to remove the environmental noise and eliminate the singular value. Then the data were feature selected by a Principal Components Analysis (PCA) algorithm and classified by a K-Nearest Neighbor (KNN) algorithm. The flow chart of the method is shown in Figure 2.  
Using this method, we carried out three experiments. Experiment 1 was to distinguish between suspicious and nonsuspicious liquids, Experiment 2 was to identify the type of suspicious liquids, and Experiment 3 was to distinguish the three different concentrations of alcohol.
## 3 RPM Path Planning Method Based on Probabilistic Roadmap and Heuristic Rule
### NLP Problem Solving and Initial Value Generation
#### 3.2.1 Probabilistic Roadmap Method  
When solving the path planning problem, the first element to focus on is whether the path is passable or not, and how to build a collision-free passable space in a complex environment is an effective means to improve the quality of the initial solution. The Probabilistic Roadmap (PRM) method is a probabilistic sampling-based algorithm, which is probabilistic complete, practical, and a very effective passable path planning algorithm [51].  
The PRM algorithm can be divided into two phases: the offline learning phase and the online query phase. In the learning phase, the roadmap of the passable space is constructed based on probabilities and can be represented as an undirected graph \(G=\{V,E\}\), where the set of nodes \(V\) represents the set of collision-free nodes. The connected set \(E\) represents the collision-free paths connecting neighboring nodes, and the sets \(V\) and \(E\) are both empty sets in the initial state. The PRM randomly constructs multiple collision-free nodes and adds them to the node set \(V\). It also defines a distance \(\rho\). For a node in the node-set \(V\), if the distance of the node from a node is less than \(\rho\), it is considered a neighbor node. Connect all neighbor nodes and determine whether the connection \(\tau\) collides with the obstacle; if there is no collision, then \(\tau\) is added to the connection set \(E\). The pseudo-code of the Roadmap Construction Algorithm is shown in Table 1.  
After completing the roadmap construction and entering the query phase, the PRM algorithm starts to solve the path planning problem, which uses the Dijkstra algorithm to search a passable path from the starting node to the target node from the undirected graph \(G=\{V,E\}\) according to the starting node, target node, and environment information of the problem.
# The BCL Method for DSSC: Basis and Applications +
## 4 A Big Challenge: Understanding the Absorption Band of Ruthenium Dyes  
It has been proposed [13] that one of the requirements for a good yield in DSSC is the directionality of the charge distribution in the excited state, produced by dye light absorption. For the electron injection process in the solar cell, metal-to-ligand charge transfer bands (MLCT) appear to be more effective, due to their directionality, than intraligand (IL) or \(\pi\)-\(\pi^{*}\) bands. When tested in solar cells, ruthenium polypyridinic dyes with extended \(\pi\)  
Figure 4: (**a**) Simulated ESR spectrum of the free radicals of the ligands, prepared “in situ” by electrochemical reduction in the cavity of an EPR instrument. (**b**) Nucleus which interacts with the unpaired electron according the EPR data. (**c**) The Fukui function, Equation (2), for the different fragment components.  
systems show an enhanced light-harvesting capacity that may or may not be reflected in a high collected electrons/absorbed photons ratio.  
In published articles concerning ruthenium complexes with chromophoric polypyridinc ligands, the assignment of the nature of the visible absorption band is not conclusive, given that the intraligand band could experience a bathochromic shift and approach, or even surpass, the MLCT band position.  
Focusing on the assignment of the absorption bands and the nature of the emitting state, the aim of this study [7] is to elucidate if all the absorbed photons of these dyes are potentially useful in the generation of electric current. To achieve this goal, a series of four ruthenium complexes with 4,40-distyryl-2,20-dipyridine (LH) and 4,40-bis[p-(dimethylamino)-R-styryl]-2,20-bipyridine (LN) \"chromophoric\" ligands (Figure 5) was studied.  
The observed experimental results were complemented with TDDFT calculations to elucidate the nature of the absorption bands, and a theoretical model was proposed to predict the available energy that could be injected from their singlet or triplet excited states.  
Spectroscopic and electrochemical experimental studies were made for the complexes shown in Figure 5, and are described in what follows. Their results were used as a base for the theoretical calculations.
## 3 Results and Discussion
### Experimental Test Results and Simulation Results  
As shown in Figure 11, the tensile test results of flat-molded specimens in different flow directions reveal distinct stress-strain responses. These differences clearly demonstrate the anisotropic mechanical behavior of the material, which is strongly influenced by the fiber orientation resulting from the injection molding process.  
Figure 12 presents the comparison between experimental tensile test results and simulation predictions for specimens oriented at 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\) relative to the injection flow direction. The simulation demonstrates high reliability in capturing the anisotropic mechanical behavior of short fiber-reinforced polymer (SFRP) composites. The flat specimen geometry used in the simulation matches that of the actual test specimens, allowing for direct validation of the fiber direction-dependent mechanical response.  
Figure 13 illustrates the injection molding simulation results, highlighting the influence of flow patterns on fiber orientation distribution. The simulation provides detailed outputs including fiber orientation tensors, shrinkage, warpage, residual stresses, and the principal direction ratio (SIGMAsoft Max. Eigenvalue). These parameters are visualized across the molded part and are critical for understanding the internal structure and anisotropic behavior of the composite.  
Figure 11: Result of tensile testing according to fiber orientation.  
Figure 10: RVE model for generating the material properties of PA66/GF50 (wt%).  
The fiber orientation is shown to align strongly with the injection flow, particularly in regions of high shear near the gate. This directional alignment significantly affects local mechanical properties. The integration of these simulation results into the finite element method (FEM) framework enables accurate mapping of material properties, improving the predictive capability for structural performance under real-world loading conditions.  
The simulation results for the product, as shown in Figure 14, can be observed to closely match the test results in terms of fracture location. The strength analysis results show tensile strength of 23.8 kN, and compressive strength of 26.5 kN, with a reliability level of approximately 93%.  
To further validate the dynamic stiffness characteristics, hammering tests and FRF (frequency response function) analysis were conducted, as illustrated in Figure 15.  
Figure 14: Motor mount test results and simulation results.  
Figure 12: Result of tensile testing and simulation result.  
Figure 13: Injection molding simulation results.  
In mode analysis, accurate representation of anisotropic stiffness can enhance the reliability of predicting dynamic stiffness since it is predominantly influenced by the M (mass) and K (structure stiffness) characteristics.
# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
## 5 Conclusions  
Summarizing the characteristics of the proposed method, firstly, the frequency domain features and time-frequency domain features are extracted using FFT and GST, respectively, which increases the dimensional of the extracted features, diversifies the fault features, and complements the fault information. Secondly, a two-channel model that combines 1D-CNN and 2D-CNN is proposed, with the frequency spectrum and time-frequency image as the input. Then, the features of individual channels are fused and incorporated into the training and learning process to achieve the fusion of feature layers. The experimental results show that the method can accurately identify different fault types and better fault diagnosis performance than many deep learning methods. In addition, the method shows good robustness and stability on highly unbalanced datasets. The limitation of the proposed method is that: the model input considered in this paper is a vibration signal. Using FFT and GST for feature extraction is very appropriate, which can achieve better fault diagnosis results. However, FFT and GST may not have good feature extraction effects for other types of features, such as current or sound signals. Using suitable feature extraction methods for current or sound signals is necessary. Therefore, the main work in the future is to research more effective feature extraction methods and further improve the fault diagnosis effect of the TC-CNN model under unbalanced datasets by fusing multiple types of signal features. Meanwhile, GAN can be combined with the proposed method in future work to achieve better diagnosis results.  
Conceptualization, Y.Q. and X.S.; methodology, Y.Q. and X.S.; validation, Y.Q. and X.S.; data curation, Y.Q.; writing--original draft preparation, Y.Q.; writing--review and editing, X.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This paper uses the rolling bearing fault diagnosis datasets of CWRU, which can be obtained from [[https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)]([https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)). Last accessed on 21 August 2022. More detailed data used to support the results of this study are available from the corresponding authors upon request.  
The authors declare no conflict of interest.