esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
### Dataset  
We select four types of fraud events that are commonly seen in SMS and phone texts from the CCL2023 telecom network fraud dataset [30], such as impersonating customer service, impersonating leadership acquaintances, loans, and public security fraud. The dataset is available on GitHub ([[https://github.com/1361095044/Telecom_Fraud_Texts](https://github.com/1361095044/Telecom_Fraud_Texts)]([https://github.com/1361095044/Telecom_Fraud_Texts](https://github.com/1361095044/Telecom_Fraud_Texts))) (accessed on 26 June 2024). This dataset includes brief descriptions of the cases, that is, the victim's transcripts, exported from the anti-fraud big data platform of the public security department. These cannot be used directly for this model's detection because what we need are the text messages or phone calls from the criminal suspect to the victim. Keywords are extracted from the data; then, a model is generated and controlled by experts; and finally, the approximate original text information that was sent by humans is obtained. Then, we also add some data, which integrates recent fraud events. Our dataset has a better real-time performance, such as incorporating related fraud from the epidemic era. We can also add datasets based on recent fraud incidents, such as recent Facetime fraud incidents. These four types of fraud data are combined with normal text messages and phone calls to form a five-category dataset, with a total of 12,506 pieces of data. The specific category labels and their quantities are shown in Table 2.  
We also use a public dataset, the FBS dataset [9], which is divided into an Illegal Promotion class, an Advertisement class, an \"others\" class, and a Fraud class, with a total of 14,058 pieces of data. The specific classification labels and their quantities are shown  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **Number** & **Example** \\ \hline normal text & 8412 & \\ public security fraud & 987 & \\ loans & 1001 & \\ impersonating customer & 1106 & \\ service & 1000 & \\ acquaintances & & \\ \hline Total & 12,506 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of our dataset categories and examples.  
Figure 3: Structure of loss function.  
in Table 3. Illegal Promotion is a subcategory of Advertisement, but it is illegal in China's legal system, so we split the data into four main categories in total.  
These two datasets are actually imbalanced, as shown in Tables 1 and 2. However, we chose to proceed this way to align with real-world scenarios better, where common texts are more prevalent. Therefore, no balancing of the datasets was performed.  
To evaluate the model's effectiveness in text classification tasks, an additional public news dataset, THUCNews, was used. The details on the category labels and their frequencies are presented in Table 4.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Characteristics** \\ \hline Matrix algorithm & It has fast location speed but poor information fault tolerance. \\ \hline Intelligent optimization & It has high fault information tolerance, but its computational \\ algorithm & efficiency and convergence stability are poor. \\ \hline Linear integer & It has high fault information tolerance and strong convergence \\ programming algorithm & stability but poor computational efficiency. \\ \hline Hierarchical fault & Its convergence reliability and computational efficiency are \\ location method & better than the intelligent optimization algorithm, but it \\  & requires higher information reliability in port nodes. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of fault location methods. In view of these problems, this paper proposes a fault section location method for a distribution network based on divide-and-conquer (DAC). The following three aspects are the main features of this work:  
* The application of road vectors to establish causal links between fault information and faulty equipment, the equivalent transformation of logical operations in the mathematical model of traditional fault location, and the construction of a numerical computational model of fault location. * Defines \"compatible sets\" and \"approximation gains\" and validates its properties, and then proves that the mathematical model constructed in this paper conforms to the recursive structure of DAC. * Proposes a fault location method based on DAC, which uses the recursive model of \"Divide-Conquer-Combine\" in each level of recursion. The linear time \"approximation gain\" computations and constant time divide and combine operations are included in each layer of recursion to improve the computational efficiency while ensuring the accuracy of fault location. The rest of this paper is organized as follows. In Section 2, the numerical calculation model of fault location is proposed. Section 3 demonstrates the recursive structure of the proposed model. In Section 4, the DAC-based fault location method is proposed, and its computational efficiency is analyzed. In Section 5, the accuracy and computational efficiency of the method in this paper are verified by simulation tests. Section 6 summarizes the paper.
## 2 Energy Management and Review of Surveys
### The Advantages of Microgrids  
The MG brings several advantages to the power industry. From a grid point of view, the main advantage of an MG is that it is treated as a controlled entity within the power grid, which can be operated as a single aggregated load [24]. Based on the literature, the following subjects can be addressed in this regard [24; 25; 26; 27]:  
* Operation or investment issues: reducing both electrical and physical distance between generating units and consumers may contribute to:
* Improving the reactive power support of the whole system, thus enhancing the voltage profile;
* Operating in both modes of connected to the main grid or islanded, which increases the supply reliability of consumers;
* Separating and isolating itself from the utility, during a grid disturbance, which helps in continuous operation of MG;
* Reduction in distribution and transmission feeder overloading;
* Reducing the power losses in distribution and transmission sub-system;
* Reducing/postponing the investment expansion for large-scale generation and transmission systems;
* Cost saving: utilization of waste heat in CHP units, which increases the energy efficiency and finally leads to a decrease in overall costs;
* Integrating RES low-voltage distribution grids;
* Increasing power system reliability by reducing customer outage and service restoration time.
* Power quality issues: improving the power quality and reliability, due to:
* Better matching of generation and demand;
* Reducing the impacts of large-scale transmission and generation outages;
* Minimizing the downtimes;
* Voltage profile improvement.
* Market issues:
* Significant reduction in market power exercised by established generation companies;
* Using MGs to provide ancillary services, such as power reserve capability, local voltage support, and load frequency control;
* Reduction in energy price due to widespread application of distributed generation units.
* Environmental issues:
* Physical proximity between DGs and consumers may increase consumer information on more rational use of energy;
* Reduce the GHG emissions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ESS Technology**} & \multirow{2}{*}{**Power Range (MW)**} & **Energy Density** & \multirow{2}{*}{**Power Density (W/L)**} & **Round Trip Efficiency** \\  & & **(Wh/L)** & & **(\%)** \\ \hline SMES & 0.1–10 & 0.2–25 & 1000–4000 & 95, 95–98 \\ FES & \textless{}0.25, 0–0.25, 0.01–0.25, 0.1–20 & 20–80 & 1000–2000 & 90–93, 93–95, 90–95 \\ PHS & 10–5000 & 0.5–1.5 & 0.5–1.5 & 75–85, 70–85, 65–87 \\ CAES & 5–300, 5–1000 & 3–6 & 0.5–2 & 50–89, 70–79, 70–89 \\  & \multicolumn{4}{c}{Different types of batteries} \\ Pb-A & 0–20, 0–40 & 50–80 & 10–400 & 75–80, 70–90 \\ Li-ion & 0–1, 0-100 & 200–500 & 500–2000 & 85–90, 90–97 \\ Na-S & 0.05–8, 0.05–34 & 150–250 & 150–230 & 80–90, 85–90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Technical characteristics of some energy storage technologies [23].
## 5 Discussion  
### Visual Effect Comparison  
From Figure 3, it can be seen that the contours of typical ground objects are enhanced, intra-class consistency is significantly enhanced and minor textures are eliminated in the pseudo-TCT image compared with the original image. Compared with the TCT image, not only is the contrast of the ground objects further improved but also the contours of the ground objects are clearer. Furthermore, the pseudo-TCT images are displayed in a more realistic pseudo-color. Vegetation is shown in green, shadows and water bodies in blue, buildings and roads in purple and orange, and bare soil in brown. Vegetation growth and water transparency can be indicated by the color shades, which is more beneficial to manual interpretation. From Figures 5 and 1, it can be found that different combinations of the pseudo-TCT images can enhance the visual effects of ground objects, and all images make buildings, roads, and vegetation more prominent. However, it is impossible to find out which combination image has the best effect from the images only because some  
Figure 9: Local images of intra-class consistency of different images. (**a**) local images of the original image; (**b**) local images of intra-class consistency image of the original image; (**c**) local images of intra-class consistency image of the TCT image; (**d**) local images of intra-class consistency image of the pseudo-TCT image.  
combination images have similar visual effects. With the continuous development of big data and machine learning, remote sensing images also need more and more labeled data, and the enhancement of visual effects is important to produce labeled data. Unfortunately, due to the limited data in this experiment, it is impossible to verify whether the visual effects on other images will be also enhanced by the pseudo-TCT.
## 2 Overview of Supplementary Damping Systems
### Brief History and Development of Damping Technologies
#### 2.3.5 Key Milestones and Influential Projects  
1988: Completion of the Chiba Port Tower (Japan), featuring the first full-scale implementation of a hybrid mass damper system [37]. This investigation [37] focused on the management of structural vibrations through the application of a lever-type tuned mass damper (LTMD). The LTMD incorporates a constraint that confines motion at both ends of the lever. By combining the tuned mass damper (TMD) with this constraint, the LTMD regulates dynamic responses at two separate points. The parameters for the LTMD are initially estimated based on the TMD parameters and subsequently refined according to numerical results.  
1994: Retrofit of the Golden Gate Bridge (USA) with viscous dampers [38]. An extensive analysis of the structural systems of the Golden Gate Bridge was performed [38], assessing the impacts of dead, live, wind, and earthquake loads via finite element modeling techniques. The investigation employed Strand7 for modeling, with results validated through analytical calculations. The report [38] began with a detailed review of the bridge's structural elements, including a summary of the dimensions and sizes of its members and sections.  
1999: Construction of Taipei 101 (Taiwan), incorporating a tuned mass damper visible to the public [39]. Continuous data streams from translational and rotational seismometers installed at TAIPEI 101 allowed for the monitoring of natural frequencies over different time scales. By utilizing the seismic data recorded in 2014 on the 90 th floor of this high-rise and the meteorological data from a nearby weather station located just 1 km away, they examined [39] the characteristics and determinants of the ambient vibrations present in TAIPEI 101.  
2008: Completion of the Beijing National Stadium (China), utilizing energy dissipation dampers in its innovative structural system [40]. This study [40] primarily concentrated on ensuring adequate structural performance, fostering sustainable development, and achieving architectural beauty. It encompassed in-depth investigations into structural performance through the analysis of the composition and decomposition of elements, applicable to diverse structures such as high-rise buildings, long-span crossings, and spatial frameworks.
## 3 Results and Discussion
### Important Features
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline [PERSON] et al. [172] & Ethiopia, & SCA & SVM & Sentinel-1, & VV, VH, NDVL, elevation & - & SSM & SCA \\  & Regional & & MODIS, SRTM & & & & & \\ \hline [PERSON] et al. [173] & Ethiopia, Field & LR, ANN & Sentinel-1; & VV, surface heights (\(\mathrm{h}_{\mathrm{mm}}\)), surface correlation length (\(\mathrm{L}_{\mathrm{eff}}\)) & FDR & 5 & ANN \\  & & & Landsat 7 and 8 & & & & \\ \hline  & & & & VV, GLCM Contrast, GLCM Correlation, GLCM dissimilarity, & & & \\  & & & & GICEM Entropy, GLCM Homogeneity, GLCM Mean, GLCM & & & \\ [PERSON] et al. [174] & Kenya, Regional & SVR & Sentinel-2 & Standard Dev, GLCM Angular, SVM, NDVL, NSVM, MSAVL, & & & \\  & & & Sentinel-2 & MSAVL, 1/V, VH, NDVL, UNDV, CNDV+, GEMI & & & \\  & & & \({}^{*}\), ARVL, NDVL, MTSL, MCAL, REIE, SEIRE, BEICE, PS8 eA, IA, & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline [PERSON] and [PERSON] [175] & Australia, & S-ANFIS, MLP, & CosmO2 Cosmic & & & & & \\  & & & PNN, RBFN & Ray Sensor & & Neutron counts from cosmic ray probe, rainfall, T\({}_{\mathrm{e}}\), RH & - & \\ \hline [PERSON] et al. [67] & Global & LSTM & Sentinel-1, & VV, VH, SSM, soil textures *, elevation, slope, aspect, hill shade, & - & 10 & - \\  & & & Sentinel-, SMAP & T\({}_{\mathrm{e}}\), ET, precipitation * & & & & \\ \hline [PERSON] et al. [82] & Global & ANN & Sentinel-1, & VV *, VH *, the classical change detection SSM index ISSM, & - & 10 & - \\  & & & Sentinel-2 & incidence angle *, NDVI *, VH / V ratio * & & & \\ \hline [PERSON] et al. [176] & Global & XGBoost, ANN, & CVGNS, & & Reflectivity from CYGNSS, vegetation opacity, & - & SSM & XGBoost \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline [PERSON]- & & & & & & \\ [PERSON] et al. [177] & Global & Neural Network & SMOS & & & & \\  & & & & & & \\  & & & & & & \\ \hline [PERSON] et al. [178] & Global & RF & CYGNSS, & Reflective power, incident angle, trailing edge slope, NDVI, & & & \\  & & & & & vegetation water content, elevation, slope, soil properties & - & SSM & - \\ \hline [PERSON] et al. [59] & Global & RF, XGBoost & \
\begin{tabular}{} \end{tabular} & Landsat 8 (5 VNN bands, 2 SWIR bands, 2 TR bands, solar & & & \\  & Landsat & & elevation, angle), elevation *, slope, aspect *, and *, sil, clay *, & & SSM & XGBoost \\  & & & & precipitation, surface skin temperature, ERA 5 SM * & & & \\ \hline [PERSON] et al. [179] & Global &
\begin{tabular}{} \end{tabular} & Spectrometers & 1024 wavelengths into 20 components using Partial Least & & & 0,64, & \\  & & in MATLAB & & Square regression. & 1,92, & & Cubic \\ \hline [PERSON] et al. [180] & Global & RF & GNSS-R & Signal-to-noise ratio, elevation angle & - & SSM & RF \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 3 Materials and Methods
### Security Metrics for Satellite Networks
#### 3.5.1 Network Centrality Measure  
The network centrality measure evaluates the importance of the nodes within a network using a graph-based model [53]. This study utilizes the degree centrality and betweenness centrality to analyze the key nodes in the satellite network.  
The degree centrality measures the number of edges connected to a node, representing its connectivity. For instance, in the satellite network AG, the _GCS_ node has a degree centrality value of 0.667, as it is connected to four nodes, highlighting its critical role in network connectivity. Conversely, nodes such as the _GNSS_, which have fewer connections, exhibit lower degree centrality values, indicating comparatively less importance.  
The betweenness centrality assesses a node's role as a bridge along the shortest paths, signifying its impact on the network connectivity. For example, \(GW_{1}\) has the highest betweenness centrality value of 0.429, underscoring its importance as a data transmission intermediary and a potential target for attacks. In contrast, the _FW_ node has a lower betweenness centrality value, indicating a less significant role in network communication.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 3 Cotton Boll Disease Detection Algorithm and Improvement
### Model Training and Evaluation Metrics  
#### 3.4.1 Test Platform and Model Parameters  
The hardware configuration of the training model platform is an Intel Core [EMAIL_ADDRESS] GHz processor (Intel Corporation, Santa Clara, CA, USA), 32 GB of RAM, and an Nvidia GeForce RTX2080 Ti GPU (NVIDIA Corporation, Santa Clara, CA, USA). The software environment consists of Ubuntu 18.04 LTS 64-bit system, Python 3.8, the PyTorch 1.11.0 framework, and CUDA 11.4. During training, the model processes input images at a resolution of 640 \(\times\) 640 pixels, with a total of 250 epochs and a batch size of 16. Pretrained model weights are used for initialization. The Adam optimizer is employed, which dynamically adjusts the learning rate for each parameter by calculating the mean and variance of the gradients, thus accelerating convergence and improving training stability. The initial learning rate is set to 0.001, with a weight decay factor of 0.0005.
## 3 Results and Discussion
### Characterization of Flax Yarns After Enzymatic Treatment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Fax Yarn**} & \multicolumn{2}{c}{**F\({}_{\text{max}}\)**} & **Diameter** & \(\sigma_{\text{f}}\) & \(\varepsilon_{\text{f}}\) & **Characteristic** & **Weibull** \\  & **[N]** & **[um]** & **[MPa]** & **[\%]** & **Strength \(\sigma_{0}\) [MPa]** & **Modulus \(m\)** \\ \hline Neat & \(19.8\pm 4.8\) & \(327\pm 95\) & \(236\pm 57\) & \(3.4\pm 0.42\) & 257.4 & 5 \\ Pelyve 2.5 wt \%-4.5 h & \(20.2\pm 3.5\) & \(376\pm 69\) & \(181\pm 31\) & \(3.55\pm 0.49\) & 194 & 6.8 \\ Pelyve 5 wt \%-4.5 h & \(16.9\pm 3.6\) & \(353\pm 59\) & \(172\pm 36\) & \(2.99\pm 0.47\) & 186 & 5.5 \\ Pelyve 7.5 wt \%-4.5 h & \(16.2\pm 4\) & \(348\pm 77\) & \(170\pm 41\) & \(2.95\pm 0.41\) & 186 & 4.8 \\ Pelyve 10 wt \%-4.5 h & \(14.8\pm 3.5\) & \(390\pm 78\) & \(123\pm 29\) & \(3.04\pm 0.47\) & 134 & 5 \\ Pelyve 2.5 wt \%-6 h & \(14.1\pm 3.4\) & \(382\pm 61\) & \(123\pm 29\) & \(3.03\pm 0.59\) & 134 & 4.6 \\ Pelyve 5 wt \%-6 h & \(13.7\pm 2.1\) & \(379\pm 63\) & \(121\pm 19\) & \(3.19\pm 0.55\) & 129 & 7.6 \\ Pelyve 7.5 wt \%-6 h & \(11.7\pm 3.6\) & \(370\pm 74\) & \(108\pm 33\) & \(2.93\pm 0.6\) & 120 & 3.5 \\ Pelyve 10 wt \%-6 h & \(11.1\pm 2.8\) & \(362\pm 63\) & \(107\pm 27\) & \(3.02\pm 0.54\) & 118 & 4.3 \\ \hline \end{tabular}
\end{table} 
Table 5: Tensile properties of neat and enzymatically modified flax yarns. The table shows also the relevant parameters of the Weibull distribution for the tensile strength.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
###### Abstract  
In the past decade, guided-wave testing has attracted the attention of the non-destructive testing industry for pipeline inspections. This technology enables the long-range assessment of pipelines' integrity, which significantly reduces the expenditure of testing in terms of cost and time. Guided-wave testing collars consist of several linearly placed arrays of transducers around the circumference of the pipe, which are called rings, and can generate unidirectional axisymmetric elastic waves. The current propagation routine of the device generates a single time-domain signal by doing a phase-delayed summation of each array element. The segments where the energy of the signal is above the local noise region are reported as anomalies by the inspectors. Nonetheless, the main goal of guided-wave inspection is the detection of axisymmetric waves generated by the features within the pipes. In this paper, instead of processing a single signal obtained from the general propagation routine, we propose to process signals that are directly obtained from all of the array elements. We designed an axisymmetric wave detection algorithm, which is validated by laboratory trials on real-pipe data with two defects on different locations with varying cross-sectional area (CSA) sizes of 2% and 3% for the first defect, and 4% and 5% for the second defect. The results enabled the detection of defects with low signal-to-noise ratios (SNR), which were almost buried in the noise level. These results are reported with regard to the three different developed methods with varying excitation frequencies of 30 kHz, 34 kHz, and 37 kHz. The tests demonstrated the advantage of using the information received from all of the elements rather than a single signal.  
Keywords:signal processing; defect detection; spatial domain; array analysis; pipeline inspection; ultrasonic guided waves (UGWs) +
Footnote †: journal: Applied Physics Letters
## 4 Case Study
### Background of the Case Study  
This article takes the GD section of the GS-HSR (Figure 2) as an example to conduct research on the site selection of the spoil site. The GD section of GS-HSR spans from the border of Jiangxi and Guangdong provinces in China. The total length of this section is 297.031 km, which includes 39.612 km of railway line, 207 bridges spanning 130.892 km, and 89 tunnels totaling 126.527 km in length. This extensive project traverses twenty critical environmentally sensitive areas, encompassing eleven forest parks (including two provincial, five municipal, and four county parks), and passes through four sections designated as strict land ecological control areas by Guangdong Province. It also impacts five drinking water source protection zones, highlighting the environmental sensitivity and the need for careful ecological preservation measures in the construction and operation of this case.  
The GD section of GS-HSR includes a total of 69 SDAs, covering a spoil area of 1.5844 km2 and a spoil volume of 13,680,000 m3. The three largest contributors to the spoil volume are tunnels, stations, and embankments, with respective spoil volumes of  
Figure 2: Schematic map of the GD section of GS-HSR.  
8,513,800 m\({}^{3}\), 2,618,000 m\({}^{3}\), and 2,017,600 m\({}^{3}\), accounting for 62.23%, 19.14%, and 14.75% of the total spoil volume, as shown in Table 2.