esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication
## 3 Satellite-Aided NOMA with Cooperative D2D Communication
### Toy Network

Table 1: An example of the different possible pairs and the corresponding achievable system sum rate for the possible formations with \(N=4\) UEs.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 4 Results  
### Model Training and Classification  
The Cosine Annealing Learning Rate Scheduler is a commonly employed strategy for adjusting the learning rate in the training process of neural networks. In this study, the Cosine Annealing Learning Rate Scheduler was applied, and the learning rate was decreased from an initial value of 0.001 to a final value of 0.0001. The dataset was divided into non-overlapping training and test sets, with a ratio of 7:3, resulting in a training set of 65,408 and a test set of 28,033 entries.  
This study utilized the entire ADFECGDB dataset as the test set to validate the accuracy and reliability of the proposed algorithm. Each data entry consisted of 1 min recordings, with a total of 11,492 entries in the dataset.
# Applications of Nanomaterials in RFID Wireless Sensor Components
## 5 Applications
### Gas Sensing  
Invisible harmful gases, which affect not only human respiratory systems but also the overall metabolism, need to be detected swiftly, even in trace amounts. RFID sensor systems enabling low-cost wireless detection of hazardous substances utilize various nanomaterials to effectively detect gas molecules [142]. [PERSON] et al. implemented a wireless hydrogen gas detection system by introducing Pt-decorated reduced graphene oxide (Pt-rGO) nanocomposites into the antenna of a UHF-RFID tag (Figure 9a) [65]. When the sensor is exposed to gas, Pt particles adsorb hydrogen gas molecules, causing electrons to move to rGO, leading to a change in material resistance. The change in antenna resistance causes impedance mismatch, allowing the RFID system to identify the detection. Pt_rGO firmly fixed on a flexible plastic substrate exhibits significant mechanical durability and demonstrates high detection capabilities for hydrogen gas without an external power source.  
[PERSON] et al. used a nanocomposite composed of three components, ZnO, CuO and rGO, to effectively detect carbon dioxide (Figure 9b) [116]. The formation of rod-shaped ZnO and petal-shaped CuO composites increases adsorption sites for gas molecules, and the  
Figure 8: (**a**) Digital-to-digital transducers manufactured by AlN sputtering and their standardized resonant frequency shift [139]. (**b**) Manual RFID tag using Cu-doped IL as a sensitive material [122]. (**c**) Solution-processable copper electrodes made by inkjet printing of a Xerogel scaffold [132]. (**d**) Water-based MXene-based ink for screen printing [124]. (**e**) The manufacturing process of printing aerosolized nanocomposites consisting of Ag and MWCNT and its multi-strain sensor application method [125].  
introduction of rGO with excellent electrical conductivity enhances the sensitivity of the detection material. The three-layered structured composite material shows a sensitivity of 0.009 ppm/dB, a response time of 29.1 s, and exhibits repeatability and stability, effectively detecting carbon dioxide at room temperature.  
[PERSON] et al. created a detection pattern for nitrogen dioxide using multi-dimensional and porous Fe\({}_{2}\)O\({}_{3}\) nanoparticles produced through dual nozzle electrospinning (Figure 9c) [117]. A composite paste, produced by introducing PANI:PSS as a conductive matrix, is incorporated into the antenna pattern, demonstrating detection performance over a wide range of NO\({}_{2}\) gas concentrations from 0.5 ppm to 50 ppm. The selection of various nanomaterials, structural control, and composite formation enhance the sensitivity of the RFID system's detection material, enabling effective detection.
# Preventing the Cloud Networks through Semi-Supervised Clustering from Both Sides Attacks
## 4 Experiment
### Implementation of Semi-Supervised Clustering Results in the Confusion Matrix
#### 4.3.1 Evaluation  
After obtaining results, these results have been applied to the confusion matrix to obtain the instrument's performance in which the accuracy has been evaluated.  
The Positive Prediction (_PP_) indicates a positive probability of an outcome. This means how likely it is to detect _intrusion_ from _intrusive packets_:  
\[PP=\frac{TP}{TP+FP}PP=\frac{62}{62+2}=96.87\%\]  
The Negative Prediction (_NP_) indicates a negative probability of an outcome. This means the probability that incoming packets are _authentic_ and have _no intrusion_.  
\[NP=\frac{TN}{FN+TN}NP=\frac{33}{3+33}NP=91.67\%\]  
After finding the positive and negative predictors, it has been discussed that, when intrusive activities have been performed on a cloud server, the intrusion detection rate of the tool has been 96.87%, while when authentic packets are transmitted on a cloud server, the tool's authenticity rate has been 91.67%.  
Tool Accuracy  
The proportion of correctly predicted data points out of all data points is known as accuracy. Accuracy indicates how often the model was correct or how well it worked:  
\[TA=\frac{TP+TN}{TP+TN+FP+FN}\]  
\[TA=\frac{62+33}{62+33+2+3}\]  
\[TA=\frac{95}{100}\]  
\[TA=95\%\]  
The datasets have been implemented on the confusion matrix to check the tool's efficiency, and then different results have been obtained in which the tool accuracy was 95%. If different mechanisms for label clustering and unlabeled clustering are used in this tool, the attack rate in the cloud server can be further reduced.  
K-Fold Cross Validation  
In this article, 100 different samples of datasets have been taken, and these datasets have been applied to K-fold cross-validation in which the value of K is taken as 5, and then five times iterations have been done. After that, different results have been obtained by applying different formulas, and the performance of the tool has been identified, as shown in Table 3: Sensitivity (Se), Precision (Pr), Accuracy (Acc), Specificity (Sp), and Error Rate (ER).  
After obtaining the different results, it is discussed that data can be stored in a clustered form with the help of semi-supervised clustering. These clustered data can be divided into different categories by implementing some semi-supervised mechanisms. This paper uses semi-supervised clustering to protect data from internal and external side attacks. Different mechanisms can be implemented when someone tries to access a cloud server based on the user's behaviour. The attack rate can be completely determined if more algorithms are used. The most significant advantage of semi-supervised clustering is that each clustered value can be stored. Clustered means storing all user's records, whether valid or invalid and dividing them into different categories based on these records. A cloud server can be secured internally and externally if semi-supervised clustering is used efficiently, and different mechanisms can be applied to each user behaviour.
# Additive Manufacturing in Underwater Applications
## 5 Areas of Application
### Infrastructure  
The building, maintenance, and repair of underwater and offshore infrastructure seems to be one of the most important areas in the usage of underwater 3D printing. This technology can provide a reliable solution in many areas, including underwater pipeline repair, offshore wind power foundations, submarine tunnels, artificial islands, and other activities connected with ocean engineering [26; 28]. This technology also aids in modeling some marine infrastructure's behavior and its investigation under different conditions [81]. It also is a promising solution for coastal and nearshore construction infrastructure, where it overcomes the limitations connected with traditional building technologies [7]. However, further solutions still require research work, including the development of new materials with unique properties, suitable concrete pumping equipment, and proper construction parameters [26].  
In some cases, polymer parts are also used as a form of infrastructure; for example, a printed ABS material was developed as a floating antenna for coastal ocean monitoring [39]. Moreover, stainless steel was applied as a material for the 3D printing of models of marine infrastructure for crashworthiness verification [53].  
In the case of infrastructure building, some futuristic ideas have also been considered, such as using AM as an unconventional technology for offshore oil production on the Arctic shelf [82]. The authors consider the potential cost of subsea drilling units and all construction technology necessary for exploration and estimate it to be between 127 and 137 billion US dollars, in the case of using 3D printing as an alternative to traditional technology [82].
# An Improved CNN Model for Within-Project Software Defect Prediction
## 5 Results and Discussion
### Performance of the Improved CNN Model
We compared our results with five machine learning models, FIX, and RANDOM baseline. We followed the within-version WPDP pattern for the experiment. We present our experimental results in terms of F-measure, G-measure, and MCC in Tables 6-8. From the three tables, we observed that FIX got 0 for each version in terms of G-measure and MCC, and RANDOM got results close to 0.5 and 0 in terms of G-measure and MCC. Both models hardly even got the best scores for any version of a project, which indicated that the improved CNN model and other five machine learning models performed well. We also observed that our improved CNN model ranked first for F-measure, G-measure, and MCC. Most significantly, our model improved the F-measure by 6% as compared with the DT model. As for G-measure and MCC, our model improved on the RF and NB models by 5% and 2%, respectively. The results indicated that our improved CNN model beat state-of-the-art machine learning models in terms of F-measure, G-measure, and MCC metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Traditional (LR)** & **DBN** & **[PERSON]’s CNN** & **Improved CNN** \\ \hline Camel & 0.329 & 0.335 & **0.505** & 0.487 \\ \hline JEdit & 0.573 & 0.480 & **0.631** & 0.590 \\ \hline Lucene & 0.618 & 0.758 & **0.761** & 0.701 \\ \hline Xalan & 0.627 & 0.681 & 0.676 & **0.780** \\ \hline Xerces & 0.273 & 0.261 & 0.311 & **0.667** \\ \hline Synapse & 0.500 & 0.503 & 0.512 & **0.655** \\ \hline Poi & 0.748 & **0.780** & 0.778 & 0.444 \\ \hline average & 0.524 & 0.543 & 0.596 & **0.618** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different models on the SPSC dataset. The decimal values represent the average F-measure values. The best F-measure values are highlighted in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Traditional (LR)** & **DBN** & **Li’s CNN** \\ \hline
**DBN** & 0.1098 & - & - \\ \hline
**Li’s CNN** & 0.0038 & 0.0790 & - \\ \hline
**Improved CNN** & 0.0079 & 0.1563 & 0.5339 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Holm–Bonferroni test of prediction on the SPSC dataset.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 3 Methods
### Training WFCNN  
#### 3.2.1 Loss Function  
WFCNN defines the loss function based on the cross entropy of the sample:  
\[H(p,q)=-\sum_{i=1}^{m}q_{i}\log(p_{i}) \tag{1}\]  
where \(m\) is the number of categories, \(p\) is the category probability vector with a length of \(m\) output by WFCNN, and \(q\) is the real probability distribution, generated according to the manual label. In \(q\), the components are 0 except for that corresponding to the pixel's category, where it is 1. In the WFCNN, each pixel is regarded as an independent sample, allowing the loss function to be defined as:  
\[loss=-\frac{1}{total}\sum\sum_{i=1}^{m}q_{i}\log(p_{i}) \tag{2}\]  
where _total_ represents the sample count.
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 3 Results
### Calibration of SAW Strain Gages
#### Calibration with Temperature  
The relative resonant frequencies of SSE E015 and SSE E017 gages measured at different temperatures and in the absence of applied torque are plotted in Figure 8a. The theoretical resonant frequencies are included for comparison (blue triangles). The difference between theoretical and experimental data is quite clear. The difference between the relative resonant frequencies of each gage with temperature is plotted in Figure 8b. Following the same procedure that was used for the calibration of the temperature sensor, the data was fit to a second order polynomial, allowing for the determination of coefficients \(C_{1}=2.31\times 10^{-6}/^{\circ}\)C and \(C_{2}=-1.38\times 10^{-8}/^{\circ}\)C\({}^{2}\) (Equation (11)). These coefficients will be used for the calculation of the torque (Equation (12)).
## 1 Introduction  
Despite advanced signal processing techniques used nowadays, the existing automatic speech recognition (ASR) system still cannot meet the performance of the human auditory system. This motivated many researchers to develop several robust feature extraction techniques. These techniques are based on modeling the physiological attitude of human audition to recognize speech in the presence of environmental noise and background interference.  
In the literature, there are numerous approaches have been proposed to address these problems. They are mainly categorized into two significant approaches [1]. These approaches are the feature-spaceapproach and the model-space approach. The feature-space approach depends on modifying the auditory features that suppress noises. It applies different adaptation techniques to the test features in order to match the training features. However, in the model-space approach, the acoustic model parameters are adjusted to decrease the noise effects. Although the model-space approach achieves higher accuracy in comparison to the feature-space approach, it still requires a higher computational time. In both the feature-space approach and model-space approach, the hidden Markov model (HMM) [2; 3] is mostly used as a statistical machine learning technique.  
In the literature, there are numerous feature extraction techniques, which have recently been applied in ASR systems. For instance, mel-frequency cepstral coefficient (MFCC) [4] and the perceptual linear predictive (PLP) [5] technique are considered the most widely used techniques in speech recognition and speaker identification systems.  
In the PLP technique, relative spectral (RASTA) [6; 7] filtering is combined with this feature extraction method to remove channel noises that vary slowly compared to speech waveforms. Cepstral mean and variance normalization (CMVN) [8] is another feature extraction technique that can be used to remove channel instability. There are many other advanced noise robust feature extraction techniques, such as gammatone frequency cepstral coefficient (GFCC) [9], zero crossing peak amplitude (ZCPA) [10], average localized synchrony detection (ALSD) [11], perceptual minimum variance distortionless response (PMVDR) [12], sparse auditory reproducing kernel (SPARK) [13] and Gabor filter bank features [14], and power-normalized cepstral coefficient (PNCC) [15].  
In the literature, the PNCC system has several advantages [15; 16]. The PNCC system reported in [15] demonstrates effective robustness against different sources of environmental disturbances such as background additive noise, linear channel distortion, and reverberation in comparison to MFCC and PLP methods. In this paper, the PNCC feature extraction method [15; 17] was modified to obtain acoustic features that can work against the noise at low SNRs without affecting the system performance. This paper is organized as follows: Section 2 discusses the proposed system in detail; Section 3 shows the experimental work and results; and Section 4 summarizes the outcomes of the paper and future works.
# Diffusion Model as a Base for Cold Item Recommendation
## 6 Conclusions  
This paper presented a novel diffusion model framework as an add-on solution for improving cold item recommendation performance in collaborative filtering (CF) systems. Our diffusion model-based generator generates collaborative embeddings for cold items by effectively leveraging items' side information. The second-phase refiner further enhances recommendation accuracy by utilizing simple neural network components and dropout regularization, ensuring effective refinement of embeddings with minimal system overhead.  
Through extensive experiments on real-world datasets, we demonstrated that our approach outperforms state-of-the-art methods in cold item scenarios, particularly at the start of item introduction. The model's performance and insights from hyperparameter sensitivity and ablation studies highlight the practical value and flexibility of integrating diffusion models into recommender systems. Our findings suggest that the diffusion models are a promising direction for addressing long-standing challenges in cold item recommendation.  
Future work will explore four main directions: (1) Developing more effective predictor architectures for the diffusion model to enhance the accuracy of generated collaborative embeddings. (2) Designing an end-to-end architecture that tightly integrates the diffusion generation module and the refiner module to improve overall efficiency and performance. (3) Conducting a thorough study for a more comprehensive analysis of the noise scheduler's stability in the diffusion model in the context of cold item recommendations. (4) Investigating structured comparison techniques to improve the reliability of item representations in cold-start recommendation settings.  
Conceptualization, [PERSON]; methodology, [PERSON] and S.C.; software, J.H.; validation, J.H. and S.C.; data curation, J.H. and S.C.; writing--original draft preparation, J.H.; writing--review and editing, J.H. and S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Dong-A University research fund.  
The authors declare no conflicts of interest.  
Figure 9: T-SNE visualization of item embeddings.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 4 Challenge: Data Secrecy
### Cryptographic Approaches  
One direct approach to protect the data secrecy is to transfer the encrypted data between the machines. Encryption can be applied to protect data secrecy for both raw data and model training and inference. We introduce the two most common and representative encryption approaches here, i.e., homomorphic encryption and functional encryption. However, it has to be emphasized that data encryption approaches are time-consuming, and the communication cost is high.
## 4 Simulation Experiments
### Performance Evaluation for the Random Mobility Scenario  
From the previous results, it can be concluded that DoA estimation is crucial for beamforming performance. When all other conditions remain the same, the beamformer's SINR output is inversely proportional to the DoA error. However, the routes of UAVs are more complex than those of most traditional ground vehicles that are served in cellular networks. The varying trajectories and flexible headings of UAVs lead to constant variation in the DoA information and therefore may cause turbulent fluctuations in the output of DoA estimation algorithms.  
In this subsection, we use a UAV random walk model to further verify the suitability of the proposed algorithm for UAVs with high mobility. In the model, the initial distance between the UAV and the BS is 490 m. The amplitude of the UAV velocity is constant at 160 km/h, whereas its direction varies every 300 ms. The azimuth and polar angles of the velocity are chosen randomly between \(0^{\circ}\) and \(360^{\circ}\) and between \(-60^{\circ}\) and \(60^{\circ}\), respectively. The DoA estimation interval is 200 ms. The random walk process lasts 6000 ms and is executed 20 times at each point, and the average absolute errors are presented in Figure 8.  
Figure 8: DoA estimation errors of the proposed and the improved MUSIC algorithms for UAV’s random walk flight mode.  
It can be seen that the proposed algorithm maintains accurate DoA estimations throughout the whole process. The overall stability is also remarkable: the average absolute errors remain between 0.08\({}^{\circ}\) and 0.36\({}^{\circ}\). Compared with the errors reported in the previous subsections, the output is more accurate here. This is because of the longer distance between the UAV and the BS, which causes the same position errors to correspond to smaller DoA estimation errors. Thus, the proposed DoA estimation algorithm maintains more accurate DoA estimations over longer distances.
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 5 Application and Evaluation
### Application and Results
In this way, the data of several participants can be compared with each other in order to check whether they provide a coherent overall picture. For example, the vessel's track recorded by the LiDAR sensor could be compared with the AIS recordings of the vessel itself. Even in the case of real-time manipulation during a critical traffic event, data recordings that do not fit into the overall picture could be detected in this way so that they can still be identified as being manipulated. Since it is not always foreseeable when a critical traffic situation will occur, it is also essential to evaluate the required time from the initiation of the documentation process to the final signed data. Therefore, as part of the practical application of the approach, we collected data for the duration of creating the communication channel, deriving the common TSA basis, and signing and storing a data chunk (c.f. Table 2). In addition, we also ran the scenario 1000 times simulatively to see if the durations also matched the values from the field test. The simulation was executed on a computer with an Intel Core i9-9900K processor and 32 GB of RAM. As can be seen, the presented approach requires a total of 1067 milliseconds under real conditions from initiating the communication channel to storing the first signed chunk in the database. This time is composed of the three phases of establishing the communication channel with 636 ms, the TSA negotiation of the participants with 313 ms, the signing of the data with a TSA, and subsequent storage in the local database with a duration of 118 ms. Even faster results were obtained within our 1000 simulative runs. On average, the total duration was 236 ms. The creation of the communication channel with 30 ms was significantly faster than in the testbed. This can be explained by the faster and more stable internet connection in the simulation environment. Furthermore, the derivation of a TSA set with 100 ms and signing and saving of the chunks with 106 ms was also faster than in the testbed.