esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Method for the Detection of Events or Situations  
For a concise understanding of the proposed method, it has been divided into several fundamental stages, supported by block diagrams that are presented in Figure 1 and illustrate the processing sequence. Audio signals are converted to spectrograms in data processing using short-time Fourier transforms (STFTs). The resulting spectrograms are normalized to ensure they have a uniform range of values, making it easier to train the model. In the next stage, convolutional layers are applied to the set of normalized spectrograms to extract relevant features [28]. These convolutional layers act as local feature detectors for identifying spectrogram acoustic patterns. The data is divided into training and validation sets. The deep learning model is trained using the training set and tuned to minimize loss based on known labels. The categorical cross-entropy loss function is used, suitable for multiclass classification.  
Figure 1: Stages of the proposed method for the detection of events or situations through the identification of sounds.  
With the model trained, it is applied to new spectrograms to detect and classify sound events. Each spectrogram is subjected to the model, which assigns probabilities to different classes of events. The type with the highest chance is considered the prediction of the model. Model predictions undergo a post-processing process to improve accuracy. Thresholds can be applied to adjust the model's sensitivity and reduce false positives. Finally, the results show the detected events and their classification. This comprehensive method combines the power of deep learning and spectrogram rendering for accurate and efficient detection of sound events or situations [29]. The modular structure of the technique, supported by block diagrams, allows for a clear understanding of each stage and how it contributes to the overall goal of early event detection in diverse and dynamic environments.
## 2 Materials and Methods
### Preparation of Plant Growth-Promoting Bacteria (PGPB) Inoculums and Seed Treatments  
Four PGPB strains, _Acinetobacter pittii, Acinetobacter oleivorans, Acinetobacter calcoaceticus_, and _Comomonas testosterone_, previously isolated from a durum wheat field at Lavello (Basilicata region, Southern Italy), showed a great potential to solubilize the insoluble forms of phosphate, potassium, zinc, and to fix N\({}_{2}\) gas, respectively. Bacterial compatibility test, as fully described by [PERSON] et al. [15], was performed in order to use a mixture of 4 PGPB strains as inoculation treatment. Since colonies did not show any antagonistic symptom and there was no inhibition zone on the plates, a consortium of the four cultures for seed inoculation, by mixing equal volume of the grown bacterial cultures, was used. Briefly, the inoculums were prepared separatelyin flasks containing Nutrient Broth (NB, Sigma-Aldrich, Steinheim am Albuch, Germany) medium, starting from single colonies picked from Nutrient Agar (NA, Sigma-Aldrich, Steinheim am Albuch, Germany) plates, under agitation at 100 rpm for 24 h. For this reason, we used the optimizing growth conditions in NA medium for _A. pittii_ (0.78% NaCl and 28\({}^{\circ}\)C), _A. oleivorans_ (0.67% NaCl and 25 \({}^{\circ}\)C), _A. calcoaceticus_ (0.64% NaCl and 27 \({}^{\circ}\)C), and _C. testosterone_ (0.92% NaCl and 23 \({}^{\circ}\)C) and then an equal proportion of each strain was used to obtain the mixture of 4 PGPB. The bacterial culture was concentrated by centrifugation at 5000 rpm for 15 min. The pellet was washed 3 times in a sterile potassium chloride (CAS Number: 7447-40-7, Sigma-Aldrich, Steinheim am Albuch, Germany) solution (0.9%, _w/v_) and then re-suspended in the same saline solution. The density of the bacterial suspension to be inoculated was brought to 10\({}^{6}\) CFU mL\({}^{-1}\) corresponding to an optical density at 600 nm of 0.6-0.7.  
Durum wheat seeds (var. Furio Camillo, an improved cultivar with large ears, black awn, 1000-grain weight 45-50 g, good resistance to lodging and cold, tolerance in diseases, high gluten index, and high protein content, provided from the Department of Agricultural and Environmental Science (DiSAAT), University of Bari Aldo Moro) were previously sterilized in 1% sodium hypochlorite solution for 10 min and washed several times with sterilized distilled water. The bacterial suspension was used for seeds inoculation before planting in pots (overnight at room temperature), as well as for pots inoculation every 3 weeks. Seeds were also treated with sterile potassium chloride solution (0.9%, _w/v_) for chemical fertilizers (full dose, half dose) and control (no fertilizer) treatments. Then, 10 seeds were randomly selected and planted in each pot. After the germination, 6 healthy seedlings were kept until sampling time.
# Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
## 5 Conclusions  
* A half-period elimination method is innovatively proposed in this paper to accurately predict the zero-crossing point of short-circuit currents. The periodic and non-periodic components are separated, and the recursive least squares method combined with fast Fourier transform is applied to enable the real-time and precise prediction of short-circuit currents.
* Simulation and recording tests are carried out for the half-period elimination method, and the results show that the prediction error of the sixth zero-crossing point of the algorithm is less than 0.2 ms under the conditions of adding 40 dB noise, a sampling frequency of more than 10 kHz, and a sampling time of more than 15 ms, which is of practical value.
* In order to solve the time-wasting problem that may occur in the traditional controlled fault current interruption sequence, this paper further develops a double-sampling controlled fault current interruption sequence. The sequence optimizes the calculation process of the target over-zero point and improves the accuracy and timeliness of prediction through two-stage sampling and calculation. Experimental results show that the sequence can significantly improve the accuracy of over-zero point prediction and effectively reduce the waiting time of the system.  
Conceptualization: X.Y. and Q.L.; methodology: H.L. ([PERSON]); software: H.L. ([PERSON]); validation: D.H., S.X. and J.H.; formal analysis: X.D.; investigation: X.Y.; resources: H.L. ([PERSON]); data curation: S.X.; writing--original draft preparation: H.L. ([PERSON]); writing--review and editing: X.D.; visualization: S.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of China Southern Power Grid, grant number CGYKJXM20220346.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available for privacy and legal reasons.  
Authors [PERSON], [PERSON], [PERSON], [PERSON] was employed by the company Electric Power Research Institute of EHV Power Transmission Company China Southern Power Grid. Author [PERSON] was employed by the company EHV Power Transmission Company China Southern Power Grid Guangzhou. Author [PERSON] was employed by the company Kunming Bureau of UHV Transmission Company of China Southern Power Grid Co., Ltd. The  
Figure 10: Effect of half-cycle elimination method with double-sampling sequence calculation.  
remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Analysis on Free Vibration and Stability of Rotating Composite Milling Bar with Large Aspect Ratio
## 3 Approximate Solution  
Since the exact solutions of Equations (18) and (19) cannot be obtained, [PERSON] method is used to simplify the bending deformation expression as follows:  
\[\left\{\begin{array}{l}V=\sum\limits_{j=1}^{N}v_{j}(t)\xi_{j}(x)\\ W=\sum\limits_{j=1}^{N}w_{j}(t)\xi_{j}(x)\end{array}\right. \tag{21}\]  
where, \(\xi_{j}(x)\) denotes the mode shape of bending motion, which is the mode shapes of a uniform, non-rotating, isotropic, and fixed-free Euler-Bernoulli beam, namely  
\[\left\{\begin{array}{l}\cos(\beta_{j})\cosh(\beta_{j})=-1\;,\\ \lambda_{j}=-\frac{\cos\beta_{j}+\cosh\beta_{j}}{\sin\beta_{j}+\sinh\beta_{j}} \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \;\;\;\  
\[\left\{\begin{array}{l}M_{11}{}^{ij}=M_{22}{}^{ij}=\int_{0}^{L}I_{m}\xi_{i}(x) \xi_{j}(x)\mathrm{d}x\\ G_{12}{}^{ij}=-G_{21}{}^{ij}=\int_{0}^{L}-2\Omega I_{m}\xi_{i}(x)\xi_{j}(x) \mathrm{d}x\\ G_{11}{}^{ij}=-G_{22}{}^{ij}=\int_{0}^{L}\left(c_{e}+c_{i}\right)\xi_{i}\xi_{ j}\mathrm{d}x\\ K_{12}{}^{ij}=-K_{21}{}^{ij}=\int_{0}^{L}c_{i}\Omega\xi_{i}\xi_{j}\mathrm{d}x \\ K_{11}{}^{ij}=K_{22}{}^{ij}=\int_{0}^{L}[D_{11}\xi_{i}{}^{\prime\prime\prime \prime}(x)\xi_{j}(x)+I_{m}\Omega^{2}\xi_{i}(x)\xi_{j}(x)]\mathrm{d}x\\ \end{array}\right. \tag{25}\]  
\[\left\{\begin{array}{l}f_{oj}=\int_{0}^{L}f_{o}\xi_{j}\mathrm{d}x\\ f_{wj}=\int_{0}^{L}f_{w}\xi_{j}\mathrm{d}x\\ \end{array}\right.\ \where  
\[\left\{\begin{array}{l}\mathbf{A}_{i}=\left(\begin{array}{cccc}\mathbf{0}&\mathbf{0}&\mathbf{I }&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{I}\\ -\frac{K_{11}}{M_{11}}-\frac{K_{21}id}{M_{11}}&-\frac{K_{12}+K_{21}zd}{M_{22}}& -\frac{G_{11}}{M_{11}}&-\frac{G_{12}}{M_{22}}\\ -\frac{K_{21}+K_{21}id}{M_{11}}&-\frac{K_{22}}{M_{22}}&-\frac{K_{22}zd}{M_{22}} &-\frac{G_{21}}{M_{11}}&-\frac{G_{22}}{M_{22}}\end{array}\right)\\ \mathbf{B}_{i}=\left(\begin{array}{cccc}\mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}\\ \frac{K_{21}id}{M_{11}}&\frac{K_{21}zd}{M_{22}}&\mathbf{0}&\mathbf{0}\\ \frac{K_{21}zd}{M_{11}}&\frac{K_{22}zd}{M_{22}}&\mathbf{0}&\mathbf{0}\end{array}\right) \\ \mathbf{q}(t)=\left(\begin{array}{cccc}\mathbf{V}(
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 2 Preliminaries
### Construction of Air Traffic Network Model
 \tag{2}\]  
Conducting a consistency test and calculating the maximum eigenvalue \(\lambda_{\text{max}}\):  
\[\lambda_{\text{max}}=\frac{1}{n}\sum_{i=1}^{n}\frac{\sum_{j=1}^{n}a_{ij}W_{j}}{ W_{i}}=\;4.1169 \tag{3}\]  
The consistency index \(CI\) is:  
\[CI=\frac{\lambda_{\text{max}}-n}{n-1}=0.0389 \tag{4}\]  
The consistency proportion \(CR\) is:  
\[CR=\frac{(\lambda_{\text{max}}-n)/(n-1)}{RI}=0.0438 \tag{5}\]  
where \(RI\) is the random consistency index. When \(n=4\), \(RI=0.9\). The judgment matrix satisfies the consistency test. The weight of each index is:  
\[W_{RS}=0.5650\;W_{FDR}=0.2622\]  
\[W_{MC}=0.1175\;W_{MF}=0.0553\]  
In order to eliminate the difference of indexes' magnitude, the maximum and minimum method was used to normalize. \[RS_{j}=\frac{RS_{j}-\text{min}RS(e)}{\text{max}\;RS(e)-\text{min}\;RS(e)} \tag{6}\]  
The remaining three indicators were treated in the same way. Finally, the comprehensive edge weight \(w_{i}\) of the aviation network is:  
\[ \begin{array}{c}w_{i}=0.5650\times RS_{i}+0.2622\times FDR_{i}\\ +0.1175\times MC_{i}+0.0553\times MF_{i}\end{array}  \tag{7}\]The larger the \(w_{i}\), the more complex the operation of the edge, and the higher the operation risk degree. In this paper, we assess the air traffic situation in East China. The air traffic network in East China can be constructed. In Figure 1, \(G_{2}(V_{2},E_{2},\omega_{2})\) consists of 104 nodes and 195 edges. Routes segments (edges) are connected by a series of airports and waypoints (nodes). The waypoints contain the navigation stations, crossing points, and reporting points. With the waypoints' help, pilots can keep on the right track by receiving the exact position signal from the ground navavoids. Each node coordinate is the actual geographic coordinate.
## 2 Materials and Methods
### Evaluation of the Antioxidant Capacity
#### 2.7.1 ABTS Assay  
Determination of ABTS radical scavenging activity of extracts was performed as described elsewhere [30] with minor modifications. ABTS radical cation (ABTS\({}^{\bullet+}\)) was generated by the oxidation of ABTS with potassium persulphate (K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)). The ABTS\({}^{\bullet+}\) was produced by reacting to a 7.0 mmol/L stock solution of ABTS with 2.45 mmol/L potassium persulphate (final concentration). The ABTS\({}^{\bullet+}\) solution was diluted (with distilled water) to an absorbance of 0.700 at 734 nm. A volume of 1.0 mL of ABTS\({}^{\bullet+}\) was added to aliquots of fortified tahini extracts, or appropriate standard solution of Trolox, and were vigorously stirred. Samples were incubated in the dark for 15.0 min at ambient temperature, and the absorbance was measured at 734 nm using a photometer (Rayleigh, VIS-7220G, Shenzhen, China). Results were expressed as \(\mu\)mol Trolox/g final product using the standard Troloxsolutions assayed at conditions similar to the sample extracts. Every sample was tested in triplicate.
## 4 Results
### Results of Expert Interviews
#### Human-Machine Connections, DIGITAL Ecosystems  
As mentioned earlier, the development of interfaces represents significant work and investment, but quite simple equipment can be used as a platform.  
The V3 firm employs a variety of simple platforms to connect machines and people. Workstations performing the final assembly of the end product after welding are equipped with tablets where the work of the workstation and workstation can be traced. This is where the worker logs in, gets the job done, and indicates when it is ready or has a problem (V3 interview).  
The other day-to-day device is the mobile phone that the sends an SMS to the production line when it detects a problem. Here, there is an escalation pyramid, so first the operator, then the shift manager, the production manager, and the operating leader will be notified if there is no confirmation of the problem at each level within a given time. In addition to the SMS, an application developed for the company can be used to display individual machines, production lines, and daily production performance data (V2, V3 interviews).  
The question of digital ecosystems divided the respondents. Three of the surveyed companies consider it a very distant future, but one feels it is feasible within five years, as the group of companies is in effect already working within it. Of the four companies, the latter is the largest and has a basic operational model for joint product design and development with customers. Its suppliers are members of the same group, so sharing information in this direction is less risky. Basically, those who believe it will come in the future, argued that this kind of deep, real-time data sharing would require a degree of trust between organizations and people that does not exist at present--and not just in Hungary: \"I can imagine this if the company can derive a clear business value from it that can be expressed in cash\" (V3 interview). Digital trust [28] is a critical factor in corporate cross-border data sharing and collaboration, and is created when there is a high level of security in terms of cyber security, data reliability and intellectual property (V1, V2, V3, V4 interviews).
## 2 Related Work
The energy hole problem occurs when certain SNs become overloaded because of their proximity to sinks, resulting in hastening energy loss and shortening network lifespan. [PERSON] et al. [36] apply a unique BS mobility model and SN delivery technique to avoid coverage problems and energy hole problems, accordingly. For preventing coverage holes, the delivery approach based on hexagonal node deployment is the best choice. When it comes to network longevity and energy left, the BS mobility model outperforms grid-based, random, and spiral (logarithmic) mobility models. Furthermore, to reduce the network's energy usage, [PERSON] et al. presented a novel method for choosing relay nodes in WSNs. It serves as a virtual backbone for connecting to the base station and selects relays based on channel awareness through game theory optimization. To determine the ideal number of relays needed for the small, medium, and high number of nodes distributed in the network, the relay nodes are varied. Network Simulator NS-2.35 is used to conduct simulations and assess networks in a wide range of scenarios. The results demonstrate that the suggested relay node selection algorithm enhances the network's lifetime, throughput, and energy usage [37]. The authors in [38] explored the optimization of relay node placement in WSNs using the A\({}^{\star}\) algorithm in dual home of multi-tier architecture, which will handle data transmission problems so that information will always be sent without being influenced by dead relays, and multi-tiered architecture will solve challenges over a large area, as data will be passed from SNs to close by RNs to sinks. Simulations evaluate the distribution patterns of random, rectangular, and triangular RNs over a range of area sizes.
## 4 Results and Compilation
### Results from Data Streaming Retrieving Technique
#### 4.1.2 Data Retrieving Using Multi-Reference Points  
For the entire study area, 65 reference points (RPs) were obtained based on the location of the ward office in each subdistrict: Cipocokjaya--8 points; Curug--10 points, Kasemen--10 points, Serang--11 points, Taktakan--12 points; and Walantaka--14 points. The results of streaming data retrieving using multi-references with a capturing distance 3 km, for example, that of Cipocokjaya, is shown in Table 2.  
Data retrieving using both techniques was carried out on the same day. Comparing the results of using single reference points with the results of multi-reference points, it was found that more unique data were obtained, as well as more locations and a greater number of users.  
The location points of cluster data always exceed the capturing distance set in the script program. The use of multiple reference points, as in the example above where there are eight points, makes it possible to obtain data for 7 (seven) unique cluster data location points. These include:  
\(-6.172153\), \(106.127458\); \(-6.148666\),\(106.127825\);  
\(-6.176018\), \(106.196267\); \(-6.20948\),\(106.129581\);  
\(-6.173101\), \(106.071077\); \(-6.134538\),\(106.167998\);  
\(-6.137688\), \(106.13405\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \# & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** \\ \hline
1 & 1 & kota\_2036170820.csv & 15 & 11 & 1 & \(-6.148666^{\circ}\), \(106.127825^{\circ}\) (6.00) \\
2 & 2 & kota\_2041170820.csv & 15 & 11 & 1 & \(-6.148666^{\circ}\), \(106.127825^{\circ}\) (6.00) \\
3 & 3 & kota\_2031170820.csv & 15 & 12 & 3 & \(-6.173101^{\circ}\), \(106.071077^{\circ}\) (11.59) \\
4 & 4 & kota\_2044170820.csv & 15 & 12 & 3 & \(-6.173101^{\circ}\), \(106.071077^{\circ}\) (11.59) \\
5 & 5 & kota\_2045170820.csv & 15 & 12 & 3 & \(-6.173101^{\circ}\), \(106.071077^{\circ}\) (11.59) \\ \hline \hline \end{tabular} Notes: (1) Capturing distance (km); (2) Source of file name; (3) Number of unique data; (4) Number of user(s); (5) Number of location(s); (6) Farthest point and distance (km).  
\end{table}
Table 1: Results of Twitter data retrieving using single reference point.  
In capturing data streaming, it can be seen that the use of multiple reference points produces more spatial data as opposed to the use of one reference point. A list of the usernames within the study area was also obtained, along with a number of his/her geotagged data during the data collection time. For trip production modeling purposes, a basic rule can be made regarding the number of geotagged data figuring his/her origin and the trip to work for at least two different locations, where two geotagged data in the same zone of the study area and two other data from elsewhere. Each datum must have a different date.  
By using data streaming retrieval with multi-reference points, we retrieved data from 2491 users. The data show that 416 (54%) had only one location, 249 (32%) had two locations, 70 (9%) had three locations, and 47 (5%) had four or more locations.
## 2 Materials and Methods
### Architecture Design and Implementation
#### 2.1.3 Construction of Shelf-Life Prediction Model
Therefore, this paper constructed a neural network with the structure of one single hidden layer, referring to the models of excellent performance from scholars at home and abroad [19,20]. 2. Determine the number of nodes in the input, output, and hidden layers. According to the environmental parameters received from the data acquisition unit of CCT-QPS, the number of input layer nodes was four. The number of output layer nodes was one since the output signal was determined to be shelf life. The number of hidden layer nodes was 4-12 according to the empirical Equation (1). \[\mathrm{p}=\mathrm{sqrt}(\mathrm{m}+\mathrm{n})+\mathrm{a}\] (1) where, m--the number of nodes in input layer; n--the number of nodes in output layer; a--a constant between 1-10;  
It could be seen from Table 1 that when the number of hidden layer nodes was different, the epoch and mean squared error (MSE) would change accordingly. When the number of hidden layer nodes was 10, the MSE was the smallest, and the prediction accuracy was the highest. Therefore, the number of hidden layer nodes of the BP neural network in this paper was determined to be 10. The structural framework of a shelf-life prediction model based on the BP neural network is shown Figure 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hidden Layer Nodes** & **Epoch** & **Mean Squared Error (MSE)** \\ \hline
4 & 164 & 0.0151 \\
5 & 591 & 0.0129 \\
6 & 279 & 0.0142 \\
7 & 237 & 0.0119 \\
8 & 397 & 0.0112 \\
9 & 223 & 0.0105 \\
10 & 343 & 0.00843 \\
11 & 335 & 0.00933 \\
12 & 351 & 0.00951 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of neural network training results with different numbers of hidden layer nodes. 3. Select the network function and set the relevant parameters for training. The network functions and parameter configuration results are shown in Tables 2 and 3.