esa-sceva/satcom-chunk-collection huggingface dataset:
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
###### Abstract  
The majority of the Wireless Sensor Network (WSN) localization methods utilize a large number of nodes to achieve high localization accuracy. However, there are many unnecessary data redundancies that contributes to high computation, communication, and energy cost between these nodes. Therefore, we propose the Intersection and Complement Set (IACS) method to reduce these redundant data by selecting the most significant neighbor nodes for the localization process. Through duplication cleaning and average filtering steps, the proposed IACS selects the normal nodes with unique intersection and complement sets in the first and second hop neighbors to localize the unknown node. If the intersection or complement sets of the normal nodes are duplicated, IACS only selects the node with the shortest distance to the blind node and nodes that have total elements larger than the average of the intersection or complement sets. The proposed IACS is tested in various simulation settings and compared with MSL* and LCC. The performance of all methods is investigated using the default settings and a different number of degree of irregularity, normal node density, maximum velocity of sensor node and number of samples. From the simulation, IACS successfully reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC.  
caputation cost; communication cost; energy; localization; Monte Carlo; WSN 2018182344; doi:10.3390/s18072344
## 3 Results and Discussion
### Shelf-Life of Wheat Breads Fortified with SWAP
#### 3.2.2 Mold Growth in Breads  
Mold surface coverage in breads during storage at 25 \({}^{\circ}\)C is presented in Figures 4 and 5. The appearance of mold growth on CON, ROS1%, ROS2%, and LEB1% was observed after 5 days of storage, while all other samples showed visible mold growth on the 6 th day of storage. Although SWAP is rich in phenolic compounds, which could potentially extend the shelf life of the fortified breads by eliminating mold growth, their incorporation into wheat breads at low levels of up to 2% did not prove to be sufficient for preventing mold spoilage and increasing the storage stability of the fortified breads since mold spoilage  
Figure 3: Textural properties (**a–d**) and moisture content (**e,f**) of wheat breads fortified with 1 and 2% oregano (ORE), rosemary (ROS), lemon balm (LEB) and spearmint (SPE) post-distillation solid waste residues stored for 0 (solid columns) and 4 (hatched columns) days at 25 \({}^{\circ}\)C; mean values for the same parameter with a different lowercase letter for the fresh (0 day) breads and different uppercase letters for the stored (4 days) breads are significantly different (Tukey’s test, \(p<0.05\)).  
was only retarded by one day, compared to control product. Additionally, higher moisture contents in the crumbs found on the 4 th day of storage for most of the SWAP breads (Figure 3f), compared to control, which can favor mold growth, might overshadow the potential anti-fungal activity of the aromatic plant components.  
Similar findings were also obtained when lemon balm solid waste at 2.5 and 5.0% levels were included in wheat bread formulations, showing the same results as a control product or extending the bread's shelf life by one day. On the other hand, the inclusion of lavender up to 10% wheat flour substitution resulted in delayed fungal spoilage, with mold appearing three days later than in control breads [17]. Delayed fungal growth has also been observed by other authors for breads fortified with _Cyperus rotundus rhizome_[37] and hop [38] extracts. Therefore, it can be concluded that mold spoilage can be inhibited depending on the type and the level of MAPs incorporated into the bread formulations.  
Figure 4: Surface coverage by mold in the crumb of wheat breads fortified with 1 and 2% oregano (ORE), rosemary (ROS), lemon balm (LEB), and spearmint (SPE) post-distillation solid waste residues during 10 days of storage at 25 \({}^{\circ}\)C.  
Figure 5: Representative images depicting the extent of mold coverage on the crumb surface of wheat bread without (control) and with the inclusion of 1% oregano (ORE) post-distillation solid waste residues in the bread formulation during storage at 25 \({}^{\circ}\)C.
## 3 Results and Analysis
#### Analysis  
The presented results compare the non-optimized case with respect to the optimized case. It is important to note that, in contrast to the existing methods in the literature [6,7], this work is built on a simulation that randomly introduces temporal network impairments. This means that network issues are not stationary and there is no indication when they can occur. Moreover, it demonstrates the ability to adapt to the problems of the proposed DRL-based optimizer.  
In addition, in this dynamic environment, network conditions can also change with the availability of more resources. This fact is leveraged by the optimizer by increasing the profile, leading to a higher QoE. After activation of the optimizer, not only is evident the reduction of very poor quality video segments (from 25% to 10%), but also the fact that 90% of video segments have a very good QoE (more than 3.5).
# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 3 Methodology
### Bi-LSTM-CRF Model Architecture, Hyperparameter Tuning, and Optimization
To ensure optimal performance, key hyperparameters, such as the learning rate, batch size, and number of epochs, were tuned based on empirical testing. A learning rate of 0.001 is selected to ensure stable convergence, while a batch size of 32 allowed for efficient processing without overloading the system's memory. The number of epochs is set to 50, providing sufficient training time for the model to learn complex patterns in the data without overfitting. By executing the model over multiple epochs with carefully chosen hyperparameters, sufficient computational resources are allocated to improve its predictive accuracy, thereby enhancing its capacity to identify patterns and generate more reliable predictions. To better understand the dataset, the model improves its \"knowledge base\" after each cycle. The iterative approach helps the model to get to know the subtleties of the dataset and make more accurate predictions based on the knowledge it has gained. The model performance is evaluated through three critical metrics: accuracy, recall, and the F1-score. However, relying only on accuracy is insufficient in evaluating an imbalanced dataset (where certain kinds of data may be disproportionately represented). As a result, we also include recall, which assesses the model's ability to recognize important data, and the F1-score, which combines recall and accuracy to produce a more complex evaluation of the model's performance. \begin{table}
\begin{tabular}{l l} \hline
**Component** & **Details** \\ \hline Input Representation & Concatenation of word embeddings and PoS vectors \\ \hline Embedding Layer & Embedding dimensionality ranges from 50 to 300 \\ \hline Bi-LSTM Layers & Multi-layer bidirectional LSTM with hidden dimensions \\  & ranging from 50 to 512 \\ \hline CRF Layer & Transition matrix with constraints for valid tag transitions \\ \hline Hyperparameters & Learning rate: 0.001 to 0.05 \\  & Weight decay: 1 \(\times 10^{-4}\) to 1 \(\times 10^{-2}\) \\  & Batch size: 8 to 64 \\ \hline Epochs & 300 \\ \hline Optimizer & Stochastic Gradient Descent (SGD) with gradient clipping \\ \hline Learning Rate & Tuned between 0.001 to 0.05 for stability and convergence \\ \hline Regularization & Weight decay: \(1\times 10^{-5}\) to \(1\times 10^{-2}\) to control overfitting \\ \hline Sequence Constraints & CRF ensures valid tag transitions between output labels \\ \hline Objective & Enhances sequential tagging by capturing contextual dependencies and transition constraints \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of the Bi-LSTM-CRF model architecture. \begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Train** & **Test** \\ \hline
**Records** & 790 & 514 \\ \hline
**Total** & 1304 \\ \hline \end{tabular}
\end{table} 
Table 3: Dataset split into training and testing records.
# Beyond Spectrograms: Rethinking Audio Classification from EnCodec's Latent Space
## 3 Experiments
### Results  
Figure 4 shows the result of the training process of the audio classification models based on EnCodec, in its versions of 24 kHz and 48 kHz, compared to those obtained using the mel spectrogram of the signal. The blue line represents the mel spectrogram, the orange line corresponds to the 24 kHz EnCodec version, and the green line represents the 48 kHz EnCodec version. All lines are smoothed with a 0.8 factor for better visualization.  
Additionally, the original unsmoothed lines are displayed in the same colors with reduced opacity for reference. Figure 3(a),d show the evolution of accuracy and loss values using the music/speech dataset, respectively. On the other hand, the evolution of accuracy and loss using the music genres dataset is shown in Figure 3(b) and Figure 3(e), respectively. Finally, Figure 3(c),f show the evolution of accuracy and loss using the environmental sound dataset.  
The developed system learns to classify speech and music, music genres, and environmental sound better (i.e., with higher accuracy and lower loss) using EnCodec latent representation, compared with the mel spectrogram. Moreover, it reaches values close to the maximum accuracy and minimum loss much earlier (in fewer epochs) than the mel spectrogram-based model. All experiments run for 1000 epochs.  
Let us look at speech and music classification at epochs 81/109. The proposed system obtains better results with EnCodec's latent representation than the mel spectrogram-based model at the end of the run (94.27%/94.40% vs. 94.14%). When classifying music genres, at epochs 154/122, the results are already better with EnCodec's latent representation than with the final mel spectrogram-based (71.73%/71.41% vs. 71.30%). Finally, when classifying environmental sound at epochs 110/111, EnCodec's latent representation results are also better than mel spectrogram-based (35.90%/34.25% vs. 34.20%).  
The evolution of the loss values obtained throughout the training of the different model versions is similar to that of the accuracy. With all datasets, the system proposed in this work takes much less time to reach better loss values than the mel spectrogram-based model ever gets, even after 1000 epochs. Specifically, these reach values reach their maximum at around 100 epochs in the speech and music dataset, 200 epochs in the music genre dataset, and 50 epochs in the environmental sound datasets, compared to 400, 800, and 400 epochs for the mel spectrogram-based model, respectively.  
Table 1 shows the average inference accuracy obtained with the model proposed in this work: 96.88% in music and speech classification, 80.84% in genre classification,  
Figure 4: Training results for both datasets.  
and 58.85% in environmental sound classification. The best results are obtained with the EnCodec-based models in all the instances.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 2 Related Work
### The Framework of GAN and ResNet
#### 2.2.2 Advantages of Residual Network  
In 2015, ResNet [63] has led to a series of breakthroughs in image classification and detection-whenever emergence, and became a backbone network in many computer vision tasks for feature extraction. At that time, researchers wanted to improve the accuracy of networks by deepening the layers, but the unexpected degradation or gradient vanishing appeared. Then [PERSON] et al. [63] came up with the solution that constructing the new layers by identity mapping which just the core idea of ResNet. Figure 2 shows the residual block with shortcut connections.  
Each residual block contains a few stacked nonlinear layers. The input of the former layer jumps over some middle layers and is added together with the layer behind to learn an identity mapping. As we all know, if the residual \(F(x)=0\), the stacked layers exactly achieve the identity mapping and the network performance will not degrade at least. Actually, the residual cannot be 0, so the stacked layers can learn new features anyhow based on input features to make the network achieve better performance. Another benefit is that the loss can transmit to former layers quickly to update  
Figure 1: The schematic diagram of the generative adversarial network (GAN).  
Figure 2: The residual block of a residual network (ResNet).  
the weights. All those advantages will help to promote the accuracy of networks. Some trained networks like ResNet-50, ResNet-101, ResNet-152, and so on, have been widely applied in image processing tasks.
## 4 Results
### Comparison and Verification of Typical Landslides
#### 4.3.3 Sanxingwan Landslide  
The Sanxingwan landslide is located in Sanxingwan Village, Minzhu Town, Langao County, and the elevation is 296 m. It is a large landslide, and the vegetation coverage is moderate, with small, sparse shrubs growing. This landslide was detected only by Sentinel\(-\)1A. The landslide is difficult to reach and there are no field verification data; however, validation can still be carried out via optical-image interpretation. Four largeensive cracks can be clearly seen in the optical image. Additionally, the flank of the slope body is relatively deep and the crown on the slope body is obviously observed (Figure 11c). Although Sentinel\(-\)1A is mostly decoherent in this area, a concentrated deformation area was detected on the left back of the slope (Figure 11a). The coherence of ALOS/PALSAR\(-\)2 is relatively good in this area, but the monitoring results show that there is no obvious deformation (Figure 11b), which indicates a missing detection in this area.  
Figure 10: Lianfeng landslide. (**a**,**b**) show annual deformation-rate results from ALOS/PALSAR\(-\)2 and Sentinel\(-\)1A, respectively. (**c**) is the optical image on Google Earth; (**d**–**f**) are the field-survey pictures showing the areas in white boxes A, B and C in (**c**), respectively. The P1 and P2 points marked on the figure are the points for time series deformation analysis in Chapter 5.4.2
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 3 Results
### 1D-Var Response to Single-Level Perturbations of the Observation
(**a**) Impulse perturbations (red horizontal lines, marked b–c) added to an observed COSMIC profile of the bending angle (heavy black line) at the impact heights of 3, 5, and 8 km. (**b**) The 1D-Var response to the 8 km perturbation in temperature (red line; \({}^{\circ}\)C), relative humidity (blue dashed; 0.2 by fraction), and pressure (green; 0.2 hPa). (**c,d**) are same as in (**b**) but for the perturbations at the impact heights of 5 and 3 km, respectively. Note that the responses are shown as functions of the height.
## 5 Conclusions  
This paper proposed a new WBC segmentation method by using color-space-based k-means clustering. A novel color adjustment method was applied before segmentation, thus improving the segmentation accuracy. The color components of RGB, HSI, and CMYK color spaces were applied to form the feature vectors of the k-means cluster. The k-means clustering results provide information that can be used for WBC segmentation. The experimental results show that the proposed method achieves precision rates of 94.6% and 95.1% for evaluation parameters A1 and A2, respectively, and higher segmentation accuracy for each type of WBCs. The experimental results indicate that the proposed method achieves high overall accuracy in cytoplasm segmentation and small leak and false detection ratios. The traced boundaries of the proposed method are more accurate than those of other methods. Moreover, the proposed method is immune to light conditions to some extent and is robust. All of the abovementioned characteristics demonstrate that the proposed method is accurate and effective for WBC segmentation, and outperforms traditional methods.
## 2 Overview of IoV
### Challenges in the IoV System
#### 2.3.5 Security-Related Issues  
The IoV system is ladened with numerous security vulnerabilities; this is owing to its heterogeneous nature and in combination with the ever-increasing number to vehicles joining the network [45]. The traditional IoV was developed based on PKI technology [50]; this approach results in huge communication costs [50]. In this system, authentication nodes are centralized. These nodes have overworked, as they cannot be proxied, thereby making it easily compromised [27; 50]. Aside from authentication concerns, other security requirements of a typical IoV system include data integrity, data confidentiality, access control, data nonrepudiation's, availability, and anti-jamming requirements [45]. Addressing the security and other concerns on IoV is critical as the threats are real. Any security breaches or misap could compromise the entire system. This has forced researchers into finding different ways in tackling these problems. One such approach is incorporating the blockchain technology into the IoV system. The proposed blockchain technology boast promising features that should ensure authentication, data exchange and trading, transparency, and confidentiality to be guaranteed [56; 57]. Participating nodes in the network can now utilize blockchain's decentralized, tamper-proof, secure, and traceable characteristics, to promote the overall system sustainability [57].
## 6 Discussion
### Open Research Issues  
Some basic assumptions were made when we discussed the system model, such as the fact the sensors were fixed after deployment, synchronized, symmetric, and location-aware. However, in real-world implementations, these assumptions are very difficult to realize. It will consume a lot of energy to synchronize and ensure the location awareness of the sensors. Hence, network lifetime cannot be as long as proposed in a theoretical environment. Besides, in a hazardous environment, such as a battle or regions which often attacked by typhoons, sensors may suffer from failures, displacement, and unsteadiness. Therefore, a new protocol with less assumptions and practical issue considerations may be needed.
# Sport Performance Analysis with a Focus on Racket Sports: A Review
## 3 Results
### SR2: A Systematic Review of Publications on Racket Sports
#### 3.2.3 Analysis of Publication Year  
Figure 5 depicts the distribution of the selected publications over time. The distribution of publications is influenced by a major sporting event. The peak was recently recognized for the Olympic games. It could be for a variety of reasons, including:  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Sources** & **Articles** \\ \hline International Journal of Sports Science and Coaching & 9 \\ International Journal of Performance Analysis In Sport & 7 \\ Frontiers In Psychology & 5 \\ Journal of Sports Sciences & 5 \\ International Journal of Environmental Research And Public Health & 3 \\ German Journal of Exercise And Sport Research & 2 \\ Journal of Human Kinetics & 2 \\ Ricyde-Revista Internacional De Ciencias Del Deporte & 2 \\
2013 IEEE Conference on Computer Vision And Pattern Recognition Workshops & 1 \\
2016 IEEE 13 th International Conference on Wearable And Implantable Body & 1 \\ Sensor Networks & 2017 International Conference on Robotics Automation And Sciences & 1 \\
2017 International Electrical Engineering Congress & 1 \\
2018 19 th IEEE Mediterranean Electrotechnical Conference & 1 \\
2018 25 th International Conference on Systems Signals And Image Processing & 1 \\ Applied Sciences-Basel & 1 \\ Big Data & 1 \\ Computational Intelligence In Information Systems & 1 \\ Computer Vision And Image Understanding & 1 \\ Chaos Solitons and Fractals & 1 \\ Children-Basel & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The 20 most relevant sources.  
Figure 4: Sources of selected publications, average citations and average number of pages.  
* data are easily accessible to large groups;
* data are collected in a more advanced manner;
* more funding is available for performance analysis to achieve a gold medal;
* widespread public interest.  
Indeed, it depends on the Olympic priority for each sport, but in general, the Olympics are the most important event. A similar relationship could be found between football and the World Championship event. The first publication, in 1997, examined the competitiveness of elite professional tennis athletes. After a long 14-year wait, the first publication for another racket sport was published in 2011 for table tennis, followed by squash in 2014 and padel tennis in 2015. In 2016, badminton was the most recent racket sport to be included in publications.  
**Summary:** Except for 2020, the analyses of the published years confirm that the R1 and R2 research problems have been answered. When considering how to organize the research to have participants collaborate and have sufficient data for the study, this tendency could be an indicator.  
**Result.**_R1 and R2 research hypotheses were confirmed, with the exception of data from 2020. This anomaly occurred as a result of the pandemic, which caused the majority of sporting events, including the Summer Olympic Games, to be canceled._
###### Abstract  
Nondestructive testing (NDT) of composite materials is of paramount importance to the aerospace industry. Several NDT methods have been adopted for the inspection of components during production and all through the aircraft service life, with infrared thermography (IRT) techniques, such as line scan thermography (LST) and pulsed thermography (PT), gaining popularity thanks to their rapidity and versatility. On one hand, LST is an attractive solution for the fast inspection of large and complex geometry composite parts during production. On the other hand, PT can be employed for the characterization of composite materials, e.g., the determination of thermal diffusivity and defect depth estimation. In this study, the use of LST with an uncooled microbolometer camera is explored for the identification of artificially produced porosity and barely visible impact damage (BVID) on academic samples. The performance of LST is quantitatively assessed with respect to PT (considered the gold standard in this case) using a high-definition cooled camera through the contrast-to-noise ratio (CNR) criterium. It is concluded that, although in most cases the measured CNR values were higher for PT than for LST (as expected since a high-definition camera and longer acquisition times were used), the majority of the defects were clearly detected (CNR \(\geq\) 2.5) by LST without the need of advanced signal processing, proving the suitability of LST for the inspection of aerospace composite components. Furthermore, the deepest defect investigated herein (\(z\approx\) 3 mm) was detected solely by LST combined with signal processing and spatial filtering (CNR = 3.6) and not by PT (since pulse heating was not long enough for this depth). In addition, PT was used for the determination of the thermal diffusivity of all samples and the subsequent depth estimation of porosity and damaged areas by pulsed phase thermography (PPT).  
inferred thermography; nondestructive testing; pulsed thermography; line scan thermography; porosity; BVID; contrast-to-noise ratio; thermal diffusivity; depth estimation; pulsed phase thermography +
Footnote †: journal: Applied Sciences