esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectroscopic and Microscopic Characterization of Flashed Glasses from Stained Glass Windows
###### Abstract  
Flashed glasses are composed of a base glass and a thin colored layer and have been used since medieval times in stained glass windows. Their study can be challenging because of their complex composition and multilayer structure. In the present work, a set of optical and spectroscopic techniques have been used for the characterization of a representative set of flashed glasses commonly used in the manufacture of stained glass windows. The structural and chemical composition of the pieces were investigated by optical microscopy, field emission scanning electron microscopy-energy dispersive X-ray spectrometry (FESEM-EDS), UV-Vis-IR spectroscopy, laser-induced breakdown spectroscopy (LIBS), and laser-induced fluorescence (LIF). Optical microscopy and FESEM-EDS allowed the determination of the thicknesses of the colored layers, while LIBS, EDS, UV-Vis-IR, and LIF spectroscopies served for elemental, molecular, and chromophores characterization of the base glasses and colored layers. Results obtained using the micro-invasive LIBS technique were compared with those retrieved by the cross-sectional technique FESEM-EDS, which requires sample taking, and showed significant consistency and agreement. In addition, LIBS results revealed the presence of additional elements in the composition of flashed glasses that could not be detected by FESEM-EDS. The combination of UV-Vis-IR and LIF results allowed precise chemical identification of chromophores responsible for the flashed glass coloration.  
flashed glass; multianalytical characterization; chemical composition; chromophores; laser-induced breakdown spectroscopy; thickness measurements +
Footnote †: journal: Applied Sciences
# Visual Static Hand Gesture Recognition Using Convolutional Neural Network
###### Abstract  
Hand gestures are an essential part of human-to-human communication and interaction and, therefore, of technical applications. The aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example, by means of natural language or hand gestures. In the context of human-machine interaction research, these methods are consequently being explored more and more. However, the realization of natural communication between humans and computers is a major challenge. In the field of hand gesture recognition, research approaches are being pursued that use additional hardware, such as special gloves, to classify gestures with high accuracy. Recently, deep learning techniques using artificial neural networks have been increasingly proposed for the problem of gesture recognition without using such tools. In this context, we explore the approach of convolutional neural network (_CNN_) in detail for the task of hand gesture recognition. _CNN_ is a deep neural network that can be used in the fields of visual object processing and classification. The goal of this work is to recognize ten types of static hand gestures in front of complex backgrounds and different hand sizes based on raw images without the use of extra hardware. We achieved good results with a _CNN_ network architecture consisting of seven layers. Through data augmentation and skin segmentation, a significant increase in the model's accuracy was achieved. On public benchmarks, two challenging datasets have been classified almost perfectly, with testing accuracies of 96.5% and 96.57%.  
static gesture recognition; _CNN_; color model transform; skin color segmentation; preprocessing; data augmentation; adam optimizer; cross-entropy loss
## 3 Experiment Analysis  
In this subsection, the experimental data of CETIM (Centre des Etudes Techniques des Industries Mecaniques de Senlis) [35] is used to extract the side-band frequency features with the proposed method. The experimental device includes a motor, brake, gearbox, and bearing. The single-stage reducer for the gearbox includes a pinion with 20 teeth and a gear with 21 teeth. The input rotation speed is 1000 rpm, and the sampling frequency is 20 kHz. The spectral subtraction denoising method is applied to obtain the processed signal and compare it to the original signal. Figure 3 shows that the impulse response of the fault information is much clearer.  
The frequency spectrum results are compared in Figure 4. The gear meshing frequency is clearer than the side-band frequency in the frequency spectrum of the original signal, as shown in Figure 4a. However, the side-band frequency features are enhanced by the spectral subtraction method, as shown in Figure 4b. The results indicate that the noise can be effectively removed by the spectral subtraction method to obtain the processed signal, which is beneficial for extracting the fault features by the EWT method.  
The proposed method can extract a greater amplitude of the side-band frequency for the gear fault feature. Figure 5 and Table 1 show the detailed information. The amplitude values of the first and second-order frequency are better for the proposed method than the others. The amplitude values of the first-order frequency are improved by 36.26%, 45.52%, and 43.89% with EMD, LMD, and DWT, respectively. The amplitude value is not improved by more than 50%, because the external noise is very low. Furthermore, the calculated frequency is 16.17 Hz, which approximately equals the theoretical value (15.87 Hz). Hence, we can conclude that the first-order frequency in the side-band frequency features equals the rotation frequency of the input shaft. This demonstrates that the proposed method is effective for gearbox fault diagnosis.  
Figure 4: Frequency spectrum results with spectral subtraction denoising: (**a**) frequency spectrum of the original signal; and (**b**) frequency spectrum of the processed signal.  
Figure 5: The fault features with square envelope analysis.  
Figure 3: The compared results with spectral subtraction denoising.
###### Abstract  
This paper presents an application to detect and track obstacles using a multilayer laser scanner. The goal of the detection system is to develop collision avoidance for the Light Rail Vehicle (LRV). The laser scanner, which is mounted in front of the tram, collects information in a four-scan plane. The object recognition and tracking module, which is composed of a three sub-modules segmentation, classification, and Kalman Filter tracking, was carried out on the raw data. Thus, data were provided for collision avoidance module. The proposed system was applied to a tram named \"Silkworm\" which is manufactured by Durmazlar Machine Inc. (Bursa, Turkey) and initial experimental tests have been conducted at the facilities of Durmazlar Machine Inc. in the city of Bursa, Turkey. This study aims to illustrate parts of the possible tests that can be carried out and to share with the scientific community an important application of multilayer laser scanners, although in the initial implementation phase, in urban rail transportation.  
Keywords:collision avoidance system; light rail vehicle; multilayer laser scanner; obstacle detection +
Footnote †: journal: Applied Sciences
## 4 Conclusions and Future Work  
In this research, 32 mix proportions were manufactured to study the effect of the UFS replacement and the water-cement ratio (W/C) on the workability, physical and mechanical properties, durability, and microstructural characteristics of mortars. After analysing the results, the following conclusions can be drawn:  
* Increasing the UFS replacement reduces the workability of the mixes, making it impractical to manufacture mortars with high UFS replacement and low W/C ratios.
* Increasing the UFS replacement decreases the compactness of the mortars, so it is necessary to increase the W/C ratio, reducing the specific gravity, dry density and bulk density and increasing the porosity and absorption.
* Low quantities of UFS replacement can lead to a slight improvement in mechanical properties, while high UFS replacement requires high W/C ratios and, consequently, it is not possible to achieve high values of mechanical properties.
* For the same workability, increasing the UFS replacement increases the wear value obtained. Even with a UFS replacement of 100%, the values obtained remain below the most restrictive value, which is 20 mm.
* From microstructural characterisation, it can be concluded that in mortars with low UFS replacement, the paste and the aggregate are uniformly distributed without the presence of pores and with good adhesion between the paste and the aggregate. When the UFS replacement increases, pores begin to appear in the mortar.
* From a practical point of view, the authors recommend using up to 25% replacement of UFS because, with a minimum amount of extra water, a workability equal to that of a conventional mortar is obtained; it has slightly higher mechanical strengths, and the mechanical durability is very similar to that of a control mortar.
* Future studies should be carried out in the field of mortars with UFS to increase the replacement percentage without increasing the W/C ratio, maintaining the same workability.  
Conceptualisation, G.G.D.A., P.T. and C.T.; methodology, G.G.D.A., J.A.S.-A., P.T., A.C., R.C., L.R.P. and C.T.; validation, G.G.D.A., J.A.S.-A., P.T., A.C. and C.T.; formal analysis, G.G.D.A., J.A.S.-A., P.T., L.R.P., A.C. and C.T.; investigation, G.G.D.A., J.A.S.-A., P.T. and A.C.; resources, R.C. and C.T.; data curation, G.G.D.A., J.A.S.-A. and P.T.; writing--original draft preparation, G.G.D.A., J.A.S.-A. and P.T.; writing--review and editing, G.G.D.A., J.A.S.-A. and C.T.; visualisation, R.C., L.R.P., A.C. and C.T.; supervision, R.C. and C.T.; project administration, R.C. and C.T.; funding acquisition, R.C. and C.T. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by SODERCAN, Cantabria, Spain.  
Not applicable.  
The authors of this research would like to thank Reinosa Forgings & Castings for the foundry sand as well as the Mexico board, Consejo Nacional de Ciencia y Tecnologia (CONACYT), for support during this project.  
The authors declare no conflict of interest.
# Multiscale Dynamic Attention and Hierarchical Spatial Aggregation for Few-Shot Object Detection
## 2 Related Work
### Multiscale Feature Extraction and SPP  
In recent years, multiscale feature fusion techniques have played a pivotal role in advancing FSOD, particularly in addressing the challenges of limited training samples and varying object scales [25]. These techniques leverage the hierarchical nature of convolutional neural networks to integrate features from different layers, capturing both fine-grained details and broader contextual information. Notable advancements include MPSR, which enhances feature extraction for under-represented object scales using attention mechanisms, and Meta-DETR [26], which combines feature pyramids with meta-learning frameworks to improve adaptability to novel classes. Additionally, feature aggregation in feature pyramid networks (FPNs [27]) and adaptive feature fusion in AF-SSD [28] dynamically adjust feature weights, effectively optimizing model performance in few-shot scenarios. Among these advances, SPP module stands out as a core tool for multiscale feature extraction. By applying pooling operations with varying kernel sizes to input feature maps, SPP module generates fixed-size feature representations, thereby enhancing the model's robustness and flexibility in handling objects of different scales.  
In summary, transfer learning methods combined with two-stage detection frameworks provide a powerful solution for FSOD by ensuring scalability, efficiency, and high detection accuracy. Thus, MDA-HAPP adopts a transfer-learning-based architecture with Faster-RCNN as the experimental baseline, leveraging its strong candidate region generation and accurate detection capabilities to address FSOD challenges effectively. However, despite these advancements, there remains a need to enhance the sensitivity to multiscale features and improve the model's ability to focus on crucial spatial information. To address these limitations, we introduce a novel MultiScale-DynaAttention mechanism, which enhances the network's sensitivity to varying scales of features and strengthens its focus on critical spatial information, thus improving the robustness and accuracy of FSOD models. Additionally, we propose a spatially adaptive multiscale object detection framework that incorporates a hierarchical adaptive-pyramid pooling (HAPP) module. This module dynamically adjusts pooling strategies and kernel sizes to extract multiscale features from intermediate network layers, which are then fed into multiple detection heads to produce comprehensive results. By effectively leveraging intermediate features, this framework captures diverse object characteristics across scales and further enhances the performance of FSOD models.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 5 Simulation Results
### Results and Analysis
#### 5.3.6 Deviation Factor  
Figure 12 presents the evolving trends in the path deviation factor for the two protocols as the number of nodes increases. The path deviation factor is defined as the ratio of the discrepancy between the actual transmission distance of a data packet from source to sink and the theoretical straight-line distance to the theoretical straight-line distance between the source and sink nodes. Consequently, when the actual transmission distance of a data packet closely approximates the true distance, the path deviation factor is minimized, leading to reduced supplementary overhead.  
As observed from Figure 12 an augmentation in node count yields a lower path deviation factor for WLQRP, signifying a closer alignment with the actual path and diminished supplementary overhead. In contrast, the path deviation rate of VBF and HH-VBF rise as the number of nodes increases. This phenomenon arises from the higher likelihood of nodes aligning with the straight-line path between the source and sink nodes as the node count escalates. When selecting relays, WLQRP comprehensively considers the link quality of both next-hop and subsequent nodes, enabling it to opt for the most suitable relay based on factors such as position, success rate, and energy, effectively reducing supplementary overhead. Conversely, the increased node count under VBF and HH-VBF introduces interference in selecting the optimal relay, resulting in an elevated path deviation factor.
## 4 Conclusions  
The MW-HTCO\({}_{2}\) method was successfully applied for pectin extraction from grape-fruit peel. According to the Box-Behnken design of RSM, among the extraction parameters, temperature and extraction time had a significantly effect on pectin yield; however, liquid/solid ratio was not effective. Pectin was extracted rapidly and easily without any toxic chemicals or time-consuming extraction process. The MW-HTCO\({}_{2}\) method is also capable of extracting more pectin than the conventional extraction method. Pectin extracted from GP using CE and MW-HTCO\({}_{2}\) methods had different characteristics. Pectin obtained by the MW-HTCO\({}_{2}\) method was better in terms of DE, GalA, Eq.W, methoxyl content, color, and apparent viscosity. Using grapefruit pectin obtained by the MW-HTCO\({}_{2}\) method resulted in good textural characteristics in apricot jam. This result suggests that GP pectin may be an alternative to be used as a thickener in the jam industry. The results of this study also confirm that MW-HTCO\({}_{2}\) is an environmentally friendly, efficient, and time-saving method  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Apricot Jam Samples** & **Firmness** & **Consistency** & **Cohesiveness** \\ \hline Control & 398.72 \(\pm\) 2.54 \({}^{\text{a}}\) & 883.30 \(\pm\) 4.77 \({}^{\text{a}}\) & 321.51 \(\pm\) 0.78 \({}^{\text{a}}\) \\ MW-HTCO\({}_{2}\)-GPP & 459.47 \(\pm\) 8.37 \({}^{\text{b}}\) & 1009.29 \(\pm\) 13.13 \({}^{\text{b}}\) & 393.23 \(\pm\) 1.44 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Control: jam without pectin; MW-HTCO\({}_{2}\)-GPP: jam with MW-HTCO\({}_{2}\)-GPP. Data are presented as mean \(\pm\) standard deviation. Different superscript letters in the same column differ significantly (\(p\) < 0.05).  
\end{table}
Table 4: Results of texture properties of apricot jam samples.  
Figure 3: FTIR spectra of CE and MW-HTCO\({}_{2}\) extracted GPP.  
compared to the traditional method. However, further studies should be performed to reveal the energy efficiency of the MW-HPCO\({}_{2}\) system in the extraction process.  
T.O.: Methodology, software, validation, formal analysis, investigation, visualization, writing, original draft preparation; H.N.O.: methodology, formal analysis, data curation, investigation, visualization, writing--review and editing; D.K.Y.: Conceptualization, methodology, validation, software, formal analysis, resources, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 4 Probabilistic Model (PM) for the Distributed Approach
### Similarity and Flexibility Measures  
Considering the angle \(\theta\) between \(v_{u}\) and \(v_{w}\), as depicted in Figure 3 (left), the information of moving direction may be further used to weight the possibility for being a communication group member. In order to weight the importance of each neighboring vehicle, the forward cone is divided into two regions, A and B. As depicted in Figure 9, let Region A and Region B be the cone with the opening angle \(2\theta_{A}\) (\(\theta_{A}\leq\theta\)) and the transition transient area, respectively. Note that the parameter \(\theta_{A}\) (_i.e._, half of the opening angle of Region A) can be regarded as a measure for the \"flexibility\" of neighboring vehicles with respect to the source vehicle \(u\). Thus, under the circumstance of a high moving speed, a small value of \(\theta_{A}\) may be applied. Accordingly, for the vehicles inside Region A, the probability for being a group member is given by:  
\[p_{w}=1,\ \forall\ w\in A \tag{10}\]  
For the vehicles inside Region B, the concept of the \"similarity\" measure and the weighting model of moving direction between vehicles \(w\) and \(u\) are used to determine the probability for being a group member, which yields:  
\[p_{w}=\left\{\begin{array}{ll}(v_{w},v_{u}),&\mathrm{if}\ (v_{w},v_{u})>0\\ \gamma\cdot|(v_{w},v_{u})|,&\mathrm{otherwise}\end{array}\right. \tag{11}\]  
where \(0<\gamma<1\) and \((v_{w},v_{u})\) is the ordinary inner product for the moving vectors of vehicles \(w\) and \(u\).  
Note that the above design logic is attributed to the fact that Region A is considered as the major area for determining the group members. That is, the vehicles in Region A may have a larger chance to be selected as a communication group member. In contrast, Region B is the area, where the frequency of the link up/down of the neighboring vehicles with respect to vehicle \(u\) may be high. Thus, in Region B, the probability for being a group member \(p_{w}\) is adjusted by a \"similarity\" measure. Therefore, the probability assignment of the neighboring vehicles for being the communication group members of the source vehicle \(u\) is complete.  
Figure 9: The probabilistic model of the proposed HDTC scheme.
# Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
## 2 Materials and Methods  
The Cancer Genome Atlas (TCGA) program provides frozen and FFPE tissue WSIs of various cancer types. We built deep learning-based normal/tumor classifiers, separately for the frozen and FFPE tissues of bladder (TCGA-BLCA), lung (TCGA-LUAD/LUSC), colon and rectum (TCGA-COAD/READ), stomach (TCGA-STAD), bile duct (TCGA-CHOL), and liver (TCGA-LIHC).
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 3 Computational Platforms

Table 7: _Cont._The microcontrollers, though ubiquitous and programable, are limited by the initial design configuration and do not allow reconfiguration at the hardware level. Programmable hardware architectures such as field-programmable gate arrays (FPGA) and system on chip (SoC) provide a solution to this bottleneck. Xilinix, Altera, Infineon, Lattice, and Actel are some of the leading solution providers in this domain. FPGAs are essentially blank devices that need codes to implement functionality, resulting in application-specific hardware that speeds up execution and improves efficiency manyfold at the cost of the increasing complexity of the hardware design, which has spurred libraries of design by all vendors to aid speedy development. The essential characteristics of a modern FPGA are programmable, computational, storage, and interface units of different kinds such as
# A Survey of Deep Learning for Electronic Health Records
## 4 Deep Learning Models
### Recurrent Neural Networks (RNN)  
CNN is frequently used to process time series or text language, and it is usually utilized to process input data or time series with a clear spatial structure. Although CNN can process time series, it is substantially weaker than RNN when compared to the feature information retrieved by RNN, which means it is better at processing data with long-term time dependence. To put it another way, RNN is more concerned with the overall feature representation of data.  
The actual frame of the RNN is presented on the left, and its expanded version is given on the right, as seen in Figure 6. The network can be seen to be a sequence structure, which has the advantage of preprocessing data of sequence types but also exposes the network to the risk of gradient explosion and disappearance.  
The RNN network has numerous difficulties in the practical application of EHR, such as gradient explosion or gradient disappearance. As a result, Long Short-Term Memory (LSTM) [27] and Gated Recurrent Neural Networks (GRNNs) [28] are commonly employed in practice. RNN is used in the development of both of them. The common thread is that while updating on \([t-1,t]\), the addition operation is used to reduce the occurrence of gradient difficulties.  
Figure 5: Two-channel Text-CNN network intent. After the convolution layer, it also traverses the linear layer. The input to the network is a representation matrix \(X\in\mathbb{R}^{N\times K}\). Where N is the number of characters in a word and K is the number of characters in a word and contains the most characters.  
The gradient problem is solved by translating the gradient descent product into addition in both LSTM and Gated Recurrent Neural Networks. When comparing the network models of the Gated Recurrent Neural network with LSTM, the Gated Recurrent Neural network has fewer parameters and is easier to converge, but LSTM still outperforms the Gated Recurrent Neural network when dealing with huge data sets. Users must make a reasonable decision based on their own requirements.