esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 3 Methodology
### Feature Extraction with Pretrained CNN Models  
Convolutional neural networks (CNNs) have significantly advanced the field of computer vision, achieving exceptional performance in tasks such as image classification, object detection, and semantic segmentation. A key advantage of CNNs is their ability to learn robust feature representations from large-scale datasets, which can then be effectively transferred to a wide range of visual analysis tasks [37].  
Pretraining CNNs on extensive datasets, such as ImageNet, has become a widely adopted practice in deep learning. These pretrained models are capable of capturing rich and generalizable feature representations, which can be leveraged for tasks with smaller, domain-specific datasets [38]. Transfer learning, which involves fine-tuning a pretrained CNN model on a target dataset, has been successfully applied in various applications, including structural health monitoring and defect detection [39; 40].  
An alternative and commonly used technique is employing pretrained CNNs as feature extractors [41; 42; 43]. In this approach, the activations of intermediate layers serve as input features for subsequent classifiers or regression models. This method has demonstrated superior performance compared to traditional hand-crafted feature extraction techniques, which often require extensive domain expertise and manual feature engineering [44; 45]. The effectiveness of this technique stems from the hierarchical feature learning capabilities of CNNs-lower-level layers capture generic features such as edges and textures, while higher-level layers encode more complex, task-specific representations.  
For this study, a variety of state-of-the-art pretrained CNN models were selected, including Xception [46], VGG [47] (VGG16, VGG19), ResNet [48; 49] (ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2), MobileNet [50; 51] (MobileNet, MobileNetV2), DenseNet [52] (DenseNet121, DenseNet169, DenseNet201), NASNet [53] (NASNetMobile, NASNetLarge), EfficientNet [54; 55] (B0-B7), and ConvNeXt [56] (Tiny to XLarge). These models, originally trained on the ImageNet dataset, which contains over 14 million images across 1000 categories, have demonstrated outstanding performance in a wide range of computer vision applications. By utilizing these pretrained models, the study aims to leverage their feature extraction capabilities to enhance the detection of pile defects in low-strain pile integrity tests.
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### User Association (UA)  
Each user in HetNet is associated with a single or multiple BSs, and a UA scheme is used to determine which BS provides the best service. Users are associated with a BS according to their data rate and SINR, as shown in Figure 6. UA is critical for enhancing the total sum rate as well as the EE and SE. In this section, we discuss the recent work that has been conducted on user association schemes, and the research is summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multirow{2}{*}{**Issue**} & \multirow{2}{*}{**Methodologies**} & \multirow{2}{*}{**Advantages**} & \multicolumn{2}{c}{**Limitations/**} \\  & & & & **Future Work** & \\ \hline \multirow{8}{*}{\begin{tabular}{} \\ \end{tabular} } & Associate user devices with competing MBS and SBS & Cross-entropy algorithm & Maximizes data rate and sum rate & Power control and resource allocation need to be considered. & [100] \\ \cline{2-5}  & Associate user devices with appropriate BS. & Centralized and distributed approaches & It enhanced the performance based on different scenarios. & The network energy efficiency is not taken into consideration. & [101] \\ \cline{2-5}  & Associate user devices with appropriate BS. & Association schemes based on downlink uplink access is superior in terms of & EE is needed more attention and consideration. & [102] \\ \cline{2-5}  & & decoupled access & UA, data rate, and SE. & & \\ \cline{2-5}  & User association in HetNets & A distributed method based on deep reinforcement learning & Achieved a significant sum rate when considering the dynamic environment is not considered. & The mobility of the user within & [103] \\ \cline{2-5}  & & A novel approach by employing second-order & Effective in reducing traffic load while also increasing data rate, average EE, and coverage. & The impact of imperfect CSI needs to be analyzed. & [104] \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{} \\ \end{tabular} } & Determine optimal associations in D2D HetNet & Evolutionary Game theory & Boost the performance of the network. & A distributed Open Radio Access Network controller & [105] \\ \cline{2-5}  & & & &  needs to be considered. & \\ \hline \multirow{2}{*}{
\begin{tabular}{} \\ \end{tabular} } & Find a more feasible & Developed & The coverage probability of the backhaul-aware UA scheme is outperformed. & Limited to a single MBS and single SBS scenario. & [106] \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the related work for user association.  
Figure 6: User association in HetNet.
# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 4 Case Studies
### Results  
We evaluated our bio-inspired movement constraints against unweighted map and expert-designed solutions by measuring the total number of completed tasks and distinguishing between tasks completed with and without recovery actions due to conflicts such as near-collisions during one-hour simulations. The results are shown in Figure 12.  
Figure 11: Simulation in ROS2/Gazebo: (**a**) 3D view of the rooms’ layout, (**b**) top-down view, and (**c**) RViZ view.  
Figure 10: Analysis of punctuality for industrial scenarios: (**a**) scenario A and (**b**) scenario B.  
In the rooms scenario, our algorithmic solution improved the efficiency as fewer recovery actions were needed (18.7 \(\pm\) 2.21 vs. 20.9 \(\pm\) 2.02 for the unweighted map) while more tasks were completed overall (45.6 \(\pm\) 3.72 vs. 41.4 \(\pm\) 4.03). The higher proportion of tasks without recovery actions (26.9 \(\pm\) 4.12 vs. 20.5 \(\pm\) 4.50) indicates more effective traffic management.  
For industrial scenario A, our approach showed a significant improvement, with more tasks completed (123.4 \(\pm\) 5.72) than both the unweighted map (113.6 \(\pm\) 8.95) and expert solutions (119.0 \(\pm\) 6.32). Notably, fewer recovery interventions were required (10.1 \(\pm\) 2.96 vs. 14.2 \(\pm\) 2.94 for the unweighted map and 10.4 \(\pm\) 2.50 for the expert solution), indicating more efficient movement patterns.  
In the more complex industrial scenario B, which involves five AMRs, our solution achieved higher throughput (73.5 \(\pm\) 5.15 tasks) compared to the unweighted map (68.4 \(\pm\) 4.45) and expert solutions (72.4 \(\pm\) 4.99). The recovery interventions were comparable across all the approaches (21.7 \(\pm\) 2.95 for our algorithmic solution, 20.7 \(\pm\) 2.95 for the unweighted map, and 22.3 \(\pm\) 2.50 for the expert solution), suggesting that some baseline level of conflict resolution may be unavoidable in complex environments.  
These results show that our bio-inspired constraints consistently improve system performance, particularly in scenarios with clear traffic patterns. The method effectively reduces the number of recovery actions while maintaining or improving throughput across different environmental complexities.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs
### Query-Driven Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**References** & **Applications** & \begin{tabular}{c} **Data** \\ **Models** \\ \end{tabular} & \begin{tabular}{c} **Problem** \\ **Identify** \\ \end{tabular} & \begin{tabular}{c} **Network** \\ **Lifetime** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Wastages** \\ \end{tabular} & \begin{tabular}{c} **Data** \\ **Delivery** \\ \end{tabular} & \
\begin{tabular}{c} **Data** \\ **ergation** \\ \end{tabular} &
\begin{tabular}{c} **Methods** \\ \end{tabular} & **Limitations** \\ \hline
[93] & \begin{tabular}{c} Security \\ specifications \\ \end{tabular} & \begin{tabular}{c} Hybrid is \\ driven (event and \\ time monitoring) \\ \end{tabular} & \begin{tabular}{c} Non-time- \\ critical \\ monitoring \\ \end{tabular} & - & - & \begin{tabular}{c} Accuracy \\ for event or \\ time \\ monitoring \\ \end{tabular} & - & - & \begin{tabular}{c} Constraints \\ on \\ time \\ monitoring \\ \end{tabular} & \
\begin{tabular}{c} Authentication \\ on \\ sequences \\ of action \\ \end{tabular} &
\begin{tabular}{c} It is not actually \\ designed for secure \\ software \\ \end{tabular} \\ \hline
[94] & \begin{tabular}{c} Healthcare \\ monitoring \\ \end{tabular} & \begin{tabular}{c} Query \\ driven \\ \end{tabular} & \begin{tabular}{c} High \\ reliability \\ \end{tabular} & Low & \begin{tabular}{c} Packet lost \\ collusion, \\ over \\ emitting \\ \end{tabular} & \
\begin{tabular}{c} Prime requirement \\ \end{tabular} & No need &
\begin{tabular}{c} Sleep and \\ wake up \\ \end{tabular} & High memory cost \\ \hline
[95] & \begin{tabular}{c} Advertising \\ mobile sink’s \\ location by \\ flooding \\ \end{tabular} & \begin{tabular}{c} Query \\ driven \\ \end{tabular} & \begin{tabular}{c} Injection \\ location \\ and data \\ collection \\ \end{tabular} & \begin{tabular}{c} Select \\ minimum \\ length \\ routing \\ \end{tabular} & \begin{tabular}{c} Queries \\ and data \\ routing \\ \end{tabular} & \begin{tabular}{c} Data \\ delivery \\ delay \\ \end{tabular} & \
\begin{tabular}{c} Vertexous \\ nodes \\ \end{tabular} &
\begin{tabular}{c} Virtual ring \\ infrastructure \\ \end{tabular} & Data loss if one sensor \\ node fails \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. with the help of an angle-based forwarding algorithm. The main purpose of the wheel node algorithm is to decrease the data delivery time and enhance the data delivery ratio. However, more wheel node energy is wasted compared to simple network nodes. In the same way, [PERSON] et al. [80] propose a query-driven virtual grid-based data dissemination QDVGDD protocol for wireless sensor networks consuming a single mobile sink to decrease the network overhead and control the data delivery ratio. Initially, WSNs constructed a virtual infrastructure that controls and spreads both query and response packets. A mobile sink surrounds the sensor field in a clockwise direction and then spreads query packets in the sensor field to show its interest.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Sliding Time Window  
The block corresponding to the sliding time window is detailed in Figure 2. In this block, the signals received by the SDR devices are conditioned, updating the perception every 100 ms. Each SDR device delivers a complex signal \(r_{i}(n)=x_{li}(n)+jx_{Qi}(n)\) for \(i=1,2,\ldots,N\) for the \(N\) devices sequentially connected.  
Basically, for each considered SDR device, three parameters are set: the sampling rate \(f_{si}\), the center frequency (of reception), and the gain. For the devices used in the proposed scenario, we have a sampling rate of 20 MHz for the HackRF One, 3.2 MHz for the RTL-SDR, and 30.72 MHz for the LimeSDR. The initial gain of each device is 0 dB, which means that the power of the signal perceived by each device is not increased. Nevertheless, this parameter can be modified if the \(PU\) transmission power is imperceptible by the SDR device (this value is chosen by the user).  
The central frequency \(F_{CT}\) of desired spectrum (multiband spectrum bandwidth) is chosen by the user according to the following steps (Figure 3):  
* The bandwidth of each connected SDR device is added to conform the complete bandwidth \(Bw_{T}\) to be observed.
* The total bandwidth \(Bw_{T}\) is centered in \(F_{CT}\).
* Lastly, the center frequency \(f_{Ci}\) of each connected device is assigned.
* Steps are repeated every time the value of \(F_{CT}\) is changed.  
Figure 2: Sensing stage: received signals for the SDR devices, updated every 100 ms.
# An Efficient Task Autonomous Planning Method for Small Satellites
## 3 Methods
### Algorithm Design  
In this paper, we propose an improved genetic algorithm. The improvements include the design of crossover and mutation operators for different individuals, and an improved roulette selection operator. The flowchart of the algorithm is shown in Figure 4.  
Figure 4: The flowchart of the improved genetic algorithm.
# Military Equipment Entity Extraction Based on Large Language Model
## 3 Method
### Data Augmentation with Chain of Thought  
Addressing the complexity and diversity of equipment entity names in military equipment text, this paper employs chain of thought prompts to guide the model in breaking down complex equipment entity extraction problems into step-by-step subproblems and solving them sequentially. The core steps involve constructing prompt templates for the data that need to be annotated and using GPT-3.5 to generate annotated data with chain of thought explanations.  
Figure 1: A framework diagram of the CoTNER method.  
Large-scale language models (LLMs) can capture more complex semantic and contextual information. In this paper, we utilize the GPT-3.5 model with 175 billion parameters. By incorporating chain of thought style explanations based on input text and entity labels into the prompts, interpretative explanations for the extracted results are provided. Below are some examples of annotated data generated by GPT-3.5 with a CoT-style explanation:  
**Text** The Russian Navy's Admiral [PERSON] frigate has arrived near the coast of the United States.  
**Answer:**  
\"entity\": \"[PERSON] frigate\",  
\"category\": \"Ship\",  
\"explanation\": \"The Admiral Gorshkov frigate is a type of naval vessel belonging to the Russian Navy, thus classified as a ship.\"  
\"entity\": \"United States\",  
\"category\": \"Country\",  
\"explanation\": \"The United States is a nation, thus classified as a country.\"  
**Text:** On the 15 th, India once again successfully test-fired an Agni-V ballistic missile from Wheeler Island in its eastern region.  
**Answer:**  
\"entity\": \"India\",  
\"category\": \"Country\",  
\"explanation\": \"India is a sovereign country, hence it falls under the Country category.\"  
\"entity\": \"Agni-V\",  
\"category\": \"Missile\",  
\"explanation\": \"Agni-V is a type of ballistic missile, hence it falls under the Missile category.\"  
\"entity\": \"Wheeler Island\",  
\"category\": \"Location\",  
\"explanation\": \"Wheeler Island is a location situated in the eastern part of India, hence it falls under the Location category.\"  
In practice, GPT-3.5 is initially provided with several complete examples as described above, allowing it to generate the explanation parts for other data based on these examples, as illustrated in Figure 2. This approach yields an enhanced dataset for military equipment. Subsequently, this enhanced dataset serves as the original data for further data filtering.
## 2 Related Works  
In [19], the authors introduce a new method for data hiding using multiple stego-images, which employs a modified least significant bit (LSB) matching technique to enhance both embedding capacity (EC) and image quality. The key advantage of this method is that each original pixel generates four new pixels, with secret data concealed in all four. The pixels are then readjusted to enhance the quality of the stego-images. Notably, four distinct stego-images are created from the different adjusted pixels, with each stego-image hiding one bit per pixel. This approach demonstrates resilience against RS-steganalysis. In [20], the authors propose an enhanced reversible data-hiding (RDH) technique that utilizes a shadow image, which is a replica of the cover image. By applying simple addition and subtraction to the shadow image pixels, they achieve greater capacity, improved stego-image quality, and increased ASA. During the embedding process, three shadow images of the CI are generated. In the next step, the pixels of the shadow image are adjusted according to the XOR features of the least significant bit planes. This technique shows remarkable resistance to various forms of steganalysis, including regular and singular analyses, pixel difference histogram analysis, and bit pair analysis. Furthermore, it effectively addresses the falling-off boundary issue. The steganographic solution proposed in [21] falls under key-generating techniques rather than traditional methods like bit injection or substitution. It creates a hidden message using the data carrier and a complex stego-key, while ensuring the data carrier maintains its original form without any alterations or additions. Additionally, in [22], the authors introduce a steganographic approach utilizing the decomposition of Catalan numbers. This method also keeps the data carrier unchanged, generating a hidden message solely from the key. As a result, the original file remains completely intact and cannot be compared with others due to the lack of modifications. Table 1 outlines the benefits of this new approach compared to earlier methods. Hermitian positive definite matrices (HPD) offer stability and optimality, while weighted Moore-Penrose (WMP) inverses provide flexibility and robustness. Our method uses a combination of HPD and WMP. When used together, they enhance the ability to handle complex and challenging problems in signal analysis and boundary issues. In RS-steganalysis, where signals might be obscured or altered, the combination of these mathematical tools allows for more effective detection and analysis by ensuring that the underlying matrix operations are well-behaved and the inverses are tailored to handle complex or noisy data effectively. In problems where the boundaries of solutions fall off or become problematic, the combination of HPD matrices and weighted Moore-Penrose inverses helps by ensuring
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 2 Related Work
### Audio Datasets in CA  
From the previous research, we found that many studies have used a variety of datasets in developing CA audio processing systems. However, many of such studies are limited to Arabic alphabets and some basic words. Although, the challenging task in learning Arabic pronunciation is the correct use of Arabic short vowels. ASR is a way of automatically transcribing the speech into text [26]. One of the major challenges for ASR for Arabic is the predominance of non diacritized text material where diacritics [27] is the use of vowels (e.g., short vowels Fatha, Damma, and Khasra) for both acoustic and language modeling that can change the meaning of the sentence. However, the majority of the acoustic features for ASR are available without diacritized form [26]. It means that there is no vowel information, which results in the loss of variations in the pronunciation of the speech. In this scenario, it becomes difficult to train a reliable acoustic model without knowing short vowels. Authors report encouraging results for classifying Arabic phonemes, but they do not consider vowels in their study. The research on the Arabic speech for vowels focuses that the MSA [28] used the formants and consonant-vowels-consonant (CVC) utterances to identify vowel similarities and differences. The HMM technique was applied to classify vowels, and the resulting performance of phonetic features of vowels was analyzed. [PERSON] and [PERSON] [28] highlighted that different researchers had considered different phonemes for the Arabic language; for instance, some take 34 phonemes (28 consonants and six vowels)while others take 112 phonemes by considering four diacritics for each alphabet. So, indeed, Arabic short vowels are crucial in CA; however, the research is limited in this area.  
It has been emphasized that autonomous Arabic speech recognition poses challenges due to the vast lexical forms of vowels, which are semantically associated with a word in a sentence [2; 29]. Thus, the correct classification of Arabic alphabets pronunciation with vowels is another important research challenge. None of the above research work has considered the correct pronunciation of the CA alphabet with vowels. Furthermore, the research is also limited in preprocessing performance due to features identification and extraction tasks. Therefore, it is required to improve the existing methods and algorithms for Arabic speech classification.
## 4 Discussion  
In this section, the performances of high-voltage capacitors produced by 3D printing method are discussed comparatively in line with the results obtained from the study. The critical findings obtained from the study are presented in Table 6.  
When the data in Table 5 are examined, it is clearly seen that the performance analysis of the capacitors reveals quite complex findings. It has been determined that capacitors designed with identical structures have different capacitance values depending on the insulation materials. This is a normal and expected result as a result of the material difference. However, a noteworthy element here is that the 5% difference between the calculated capacitance values and the capacitance values obtained in the simulation study is independent of the material. Thus, it has been revealed that the capacitance value can be determined with a \(\pm\)5% error with the simulation study. In addition, there is a quite acceptable difference between the calculated capacitance value and the capacitor measurement results. This value varies between 1% and 6%.  
When comparing the performance of the capacitors in terms of the dissipation factor, the PETG capacitor is observed to have the lowest dissipation factor. However, when PDIV values are included as an additional evaluation criterion, it becomes evident that the ASA capacitor demonstrates the best performance. In contrast, the ASA capacitor shows poor performance in terms of breakdown voltage, failing to withstand 12.34 kV. When breakdown voltage is used as the primary criterion, the ABS capacitor also performs poorly, making it a less favorable choice.