esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, it should be noted that there are interactions between RONS and organic materials in the real skin, generating secondary RONS [21]. The byproducts from organic materials can be considered as a source of increased diffusion of RONS through tissues. In order to investigate the impact of the SC layers on the diffusion properties of RONS from NTAPP, we used an agarose gel film with a SC layer that was artificially manufactured with lipid extracts from Hacet keratinocytes. The concentrations of long-lived species, H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\) and NO\({}_{3}\)\({}^{-}\), were quantified through a UVAS measurement method [23; 24]. The permeation of RONS increased with the NTAPP exposure time and decreased with the distance to the sample. The agarose gel barrier effect was different for the three RONS no matter whether the SC layer is present or not. Additionally, the outermost layer sectioned from a pig skin was investigated as another SC model. The effects of SC on the RONS diffusion and the applicability of the skin model are discussed. In the Appendix A, the accuracy of spectral analysis was validated with chemicals, and data on the effects of the thickness and the density of agarose gel films were added.
## 2 Components of High-Speed Data Transmission for Lightweight and Small UAV Platforms
### Modulation and Demodulation Units for Optical Communication Terminals  
Modulation and demodulation are critical aspects of high-speed data transmission optical communication systems. Their primary purpose is to convert information into optical signals suitable for transmission in optical fibers and to restore them to the original information at the receiving end. The modulation and demodulation units of light and small UAV visual communication are two of the core components of realizing visual communication, and the modulation and demodulation units in visual communication significantly impact the data transmission speed. By optimizing the performance of the modulator and demodulator and increasing the transmission rate of the optical communication system, faster and more efficient data transmission can be achieved.  
Modulation unit: The role of the modulation unit is to convert electrical signals into optical signals. It uses a laser as the light source, converts the electrical signal into an optical signal through a modulator, and transmits it using an optical antenna. The modulation unit adopts digital modulation technology, such as COFDM modulation and demodulation technology, which has the advantages of a strong anti-jamming ability, a long transmission distance, and resistance to multipath fading.  
Demodulation unit: The role of the demodulation unit is to restore the received optical signal to an electrical signal. It uses a photodetector as the receiver, and signals, through the demodulator, will be converted into electrical signals and output to the subsequent processing circuit. The demodulation unit employs a photodetector with high sensitivity and low noise performance and a demodulator with high-efficiency anti-interference capability and high demodulation accuracy to reduce the received optical signal. The small and lightweight UAV's visual communication modulation and demodulation units are characterized by being small and lightweight, having high performance, and having low power consumption. They can be widely used in UAVs, robots, and remote-controlled vehicles. At the same time, these units support high-speed mobile data transmission. They can maintain a stable communication connection in motion, which provides an essential technical guarantee for high-speed data transmission in UAVs.
## 4 Application to Etna  
We have quantitatively applied the PEW scheme to different Etna lava fountains that occurred between 2011 and 2021, for which data from the three systems were available. In Figure 5, we show the RGB frames recorded by the ENT camera in Nicolosi for: (a) 10 April 2011 at 10:32, (b) 12 April 2012 at 14:57, (c) 23 November 2013 at 10:05, (d) 3 December 2015 at 02:49, (e-f) 4 December 2015 at 09:24 and 20:33, respectively, (g) 5 December 2015 at 14:55, (h) 16 February 2021 at 16:33, and (i) 23 February 2021 at 23:48.  
Figure 4: The WR-ESPsR flowchart: relations among \(Q_{M}\) (kg/s) retrievals derived from the WR and corresponding \(Q_{M}\) levels (low values in orange and high values in red) are shown.  
Figure 5: The original RGB thermal images from the ENT camera for each Etna event (**a–i**) as specified in the black bar below: the date (dd-mm-yyyy) and UTC time (hh:mm:ss:00). The color bar on the right side of each picture shows the uncalibrated temperature ranging between \(-10/-20\)\({}^{\circ}\)C and \(50/80\)\({}^{\circ}\)C.  
It should be noted that this system has only been tested and applied to a limited number of events selected as test cases due to the availability of simultaneous sensor measurements (WR, TC, and IS). For each eruptive episode, we evaluated the correlation among the \(Q_{M}\) retrieval (solid blue line), the IP (dimensionless) signal (dashed magenta line), and the \(v_{ex}\) estimation (solid magenta line) to prove the sensitivity of possible real-time automatic system implementation. Each EW level, associated with a specific sensor, is plotted as dashed lines with different colors: WR-Q\({}_{\rm M}\)L (light blue dashed line), WR-Q\({}_{\rm M}\)H (blue dashed line), IS-EW1 (orange dashed line), IS-EW2 (red dashed line), TC-EW1 (green dashed line), and TC-EW2 (dark dashed line).
## 2 Related Work
### User Interface  
Various user interfaces for robot operation have already been developed and are still being developed [42; 43]. More specifically, voice [44; 45] interfaces and augmented/virtual reality(AR/VR) [46; 47] interfaces have been attempted in various fields. Brain-computer interfaces(BCIs) [48] using an electroencephalogram(EEG) [49; 50] or an electromyogram (EMG) [51; 52] are also being studied. However, the user interface types are inflexible if the scope is limited to commercialized MARs. Most adopt button or joystick interfaces because they are easy and intuitive [8; 9; 10; 12; 13; 14]. However, this may limit the range of available patients, since these interfaces require physical contact from the user. Nonetheless, the development a fully automated MAR that rarely requires user commands is also not a perfect solution; users prefer things that can be operated by themselves, rather than autonomous robots [53].  
This study adopted a face-recognition-based interface that does not demand physical contact, so that even severely handicapped users with spinal cord injuries can operate it alone. Some more closely related works are introduced as follows. [PERSON] et al. tracked the mouth's position by recognizing the mouth's shape [50], and other studies recognized mouth-opening [54] or eye-closing [55] as a user command. [PERSON] et al. developed an interface that uses deep learning to recognize the opening/closing of the mouth and eyes [56]. Some studies also utilized facial gesture [57] or head movement [58] recognition in the interface. [PERSON] et al. implemented an interface that selects the desired food by tracking the user's eye gaze [59].  
Most of the existing gaze recognition research has the drawback of requiring a calibration process and expensive sensors to accurately detect the user's gaze. Meanwhile, in a study using a universal webcam, it was verified that real-time recognition is possible by extracting a low-resolution (\(320\times 240\)) eye image from a user's facial video and recognizing the gaze point in \(200\) ms for each image [60]. However, in a study that recognized the user's gaze based on the user's geometric characteristics in the mobile environment, only the approximate gaze area was predicted, despite a calibration process longer than one minute [61; 62]. Studies were conducted to recognize the approximate direction of the gaze, not the exact gazing point, using a CNN structure and an RGB camera without a separate correction process. The proposed method was confirmed to be feasible by classifying the gaze direction of the user's pupil, estimated by two CNNs with 97% accuracy for the three classes of 'center,' 'left,' and 'right' [63; 64].
# Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
## 3 Effectiveness of ML and HPC Techniques in the COVID-19 Era
### Role of ML and HPC Techniques in Data Analytics in the COVID-19 Era  
Data have become a crucial part of knowledge-based applications. They unlock the knowledge enclosed in underlying data. The latest analytics techniques with hyperparameter tuning functionalities have proven successful in many applications. We present the role of ML and HPC in analytics in the COVID-19 era in Figure 5. ML techniques can assist in knowledge extraction. For instance, they can be widely used in identifying patients with and without COVID-19. ML techniques can also be used to rank the comorbidities that can lead to deaths, ICU admission, oxygen need, etc. They can also be used to separate people that can easily become targets of COVID-19 due to their work nature or hygiene practices. In addition, they can assist in trends analysis, privacy-preserving analytics, and data distribution across organizations. In contrast, HPC techniques have more utility from an administration point of view; for example, community clustering involves large-scale data processing. In this regard, HPC techniques are handy to load, process, hold intermediate results, and deliver results to interested parties. Therefore, both these techniques play a critical role in the COVID-19 era. In some cases, both techniques are jointly used to perform relevant tasks. The potential discussion for more useful features of both technologies can be determined from the latest studies [9, 10, 11, 12].  
Figure 4: Role of ML and HPC in the data life cycle to fight COVID-19 through technology.
## 4 Discussion  
### Multi-Domain Assessment  
#### 4.1.1 Kinematics  
When considering the active range of motion, all the patients could reach the mouth with their impaired limb both at T0 and T1 with exception made for Patient 3. More in detail, elbow flexion is the main biomarker characterizing the capability of performing the HTMM. Therefore, with respect to the reference trajectory, it can be observed that patients' free movement shows similar values in elbow flexion at the end of the movement, indicating that all five patients were able to achieve physiological angles at elbow level, with no noticeable difference between T0 and T1. On the contrary, the shoulder flexion angle is similar at T0 and T1 in Patients 1 and 2, while in Patients 3 and 5 it shows high variability since at T1 the shoulder flexion detaches more from the reference value in respect to T0. Finally, Patient 4 shows a slight recovery toward the physiological pattern. Such results may suggest that the effect of the therapy was, on average, not effective on the range of motion, since it did not promote the coordination of articular patterns towards the reference path performed with the robot. This conclusion could also be suggested by the emergence of a compensatory strategy adopted by Patient 3 to reach the mouth by over-flexing the shoulder. However, such findings can be related only to the reduced sample of patients enrolled in this study, and cannot be considered as representative of the post-stroke chronic population.  
Despite the fact that the observed changes in execution time are very slight and not statistically significant, the kinematics of the movement at T1 is characterized by a noteworthy increase in the movement smoothness, which instead is statistically significant. This result is relevant since smooth movement execution is a typical feature of skilled motor control. Thus, even if the cohort of patients is too small to provide generalizable outcomes, the kinematic assessment suggests that relevant processes of motor learning may take place during the robot therapy. Such processes may not be oriented towards the recovery of a reference trajectory, but, rather, more likely to adapting motor learning strategies to compensate for motor impairments, including smoother activations, as already suggested by previous works [42].
# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
###### Abstract  
Vision-based 1D barcode reading has been the subject of extensive research in recent years due to the high demand for automation in various industrial settings. With the aim of detecting the image region of 1D barcodes, existing approaches are both slow and imprecise. Deep-learning-based methods can locate the 1D barcode region fast but lack an adequate and accurate segmentation process; while simple geometric-based techniques perform weakly in terms of localization and take unnecessary computational cost when processing high-resolution images. We propose integrating the deep-learning and geometric approaches with the objective of tackling robust barcode localization in the presence of complicated backgrounds and accurately detecting the barcode within the localized region. Our integrated real-time solution combines the advantages of the two methods. Furthermore, there is no need to manually tune parameters in our approach. Through extensive experimentation on standard benchmarks, we show that our integrated approach outperforms the state-of-the-art methods by at least 5%.  
1D barcode; barcode detection; deep learning; object detection; line segment detection Article
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### WSN Challenges and Utility in Hydrology
#### 3.5.3 Network Routing and Scaling  
In multi-hop large-scale WSN networking, the routing protocol plays an essential role for reliably collecting sensor data in real time. While WSN deployments appear promising due to the limitations of traditional data logging methods [98], the WSN scalability has proven to be a bottleneck in early studies. An increased network size introduces more data traffic, collisions and congestion in the network, resulting in network performance degradation. To mitigate this problem, starting from the summer of 2014, the ASWP network has adopted CTP + EER routing, which significantly reduces the workload of modes along efficient routes and thus extends the WSN lifetime.  
During the network expansion, from 52 nodes in 2014, to 88 nodes in 2015, and finally to 104 nodes in 2016, the network performance has not been noticeably influenced while operating CTP + EER. The packet reception rate (PRR) of the network remains above 96%. The average packet path length is 3.95 hops during the 52-node network, 4.76 hops during the 88-node network, and 4.73 hops during the 104-node network. In the summer of 2015, both the width and length of the WSN deployment was expanded, which caused the increase of the average path length. In the summer of 2016, the major network change was its density, which caused a slight reduction in the average path length and PRR. This result demonstrates that with the proper routing protocol, the network is able to maintain high levels of performance over various deployment scales.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 3 Smart Meter Technological Solutions in Europe and Projects for Their Implementation  
Smart metering contributes to the EU 2020 strategy for the reduction of 20% of the 1990's carbon emission levels by 2020. Most of the EU countries planned or already deployed nation- wide or selective smart-metering roll-outs, whereas only four countries reached the conclusion that a smart-metering deployment will not have a positive effect in a national level. Only Sweden and Italy accomplished their national rollout. Overall, it is expected that until 2020 around 200 million smart meters will be deployed with an estimated investment of 35 billion \(\epsilon\)[6]. In this work, we are interested in the technological solutions used for this scope. In general, different technologies have been preferred depending on the country and the application environment. In the following we present some of the smart metering technological solutions selected by different countries in Europe.
## 2 Related Works
### LEO Opportunity Navigation  
The burgeoning development of LEO satellites provides new opportunities for opportunity navigation. [PERSON] [41] pioneered the feasibility verification of Starlink positioning using carrier-phase observations, establishing a framework for LEO-based opportunity navigation. Building upon this, an efficient integer ambiguity resolution algorithm was developed via LEO differential carrier-phase processing [69]. While Doppler frequency can offer alternative navigation solutions, its tracking accuracy remains constrained [40; 42; 43; 44]. Signal cognitivie methodology is proposed to identify configurations of Starlink's OFDM signal [30; 38]. Moreover, [PERSON] [70] achieved positioning through blind estimation of OneWeb constellation beacons, whereas [PERSON] [71] enhanced navigation robustness by fusing Doppler measurements from Iridium NEXT and Orbcomm constellations. Fundamental research investigates the impact of LEO satellite oscillator stability on navigation performance [72]. The differential Doppler positioning method is proposed to improve navigation accuracy for orbit-unknown LEO satellites [73].  
However, the existing works rarely quantitatively evaluate the impact of different error sources, such as clock bias, ionosphere, and tropospheric delay, on navigation performance. This paper assumes that the LEO constellation transmits NR signals to quantitatively analyze the error source's impact on opportunity navigation performance. The experimental results provide critical insights to future LEO PNT services.
# An Improved VGG16 Model for Pneumonia Image Classification
## 5 Conclusions and Prospects  
### Conclusions  
Since LeNet's emergence, CNNs have continued progressing with many breakthroughs and developments, and they provide great benefits for image recognition. Computational hardware and capacity have also improved significantly, supporting DNN requirements and extending their applicability. Therefore, developing deeper NNs to extract features can effectively and continuously improve recognition accuracy and, hence, modern CNN architectures commonly include many layers. First of all, we discovered the prediction result of using the VGG16 model on failed pneumonia X-ray images. Thus, this paper proposes IVGG13 (Improved Visual Geometry Group-13), a modified VGG16 model for the classification pneumonia X-rays images. Therefore, this paper proposed IVGG13, a modified CNN for medical image recognition by using open-source thoracic X-ray images from the Kaggle platform for training. The iVGG13 recognition rate was compared with the best current practice CNNs, which confirmed IVGG13's superior performance in medical image recognition and also highlighted VGG16 problems.  
Data augmentation was employed to effectively increase data volume and balance before training. Recognition accuracy without data augmentation ranged from 74% to 77%, increasing to >85% after data augmentation, which confirmed that data augmentation can effectively improve recognition accuracy.  
The proposed IVGG13 model required less training time and resources compared with the other considered CNNs. IVGG13 reduced layer depth with smaller convolutional kernels and, hence, used significantly less network parameters; this greatly reduced hardware requirements while achieving comparable or superior recognition accuracy compared with the other models considered. An accuracy of \(\approx\)89% was achieved after training with the augmented dataset, which is significantly superior to the other current best practice models.
# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 4 Analysis and Discussion
Therefore, a hybrid-hybrid approach (i.e., hybridising filter and wrapper criteria while mixing evolutionary techniques) for FS is a challenging research domain. * Feature selection plays a vital role in the biomedical area due to the high dimensionality of the data. However, very few works (22%) explored their techniques in this field. Therefore, the application of hybrid FS techniques to biomedical data is a very good research area for the future.
# Semantic-Aligned Cross-Modal Visual Grounding Network with Transformers
## 4 Experiments
### Experimental Results
#### 4.3.3 Comparison with the State-of-the-Art Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Resolution** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP** \\ \hline DETR [19] & & 0.850 & 0.847 & 0.831 \\ YOLOv5 [41] & & 0.862 & 0.854 & 0.836 \\ Deformable DETR [35] & 640\(\times\) & 0.859 & 0.857 & 0.838 \\ Sparse R-CNN [36] & 640 & 0.873 & 0.865 & 0.842 \\ YOLOv7 [24] & & 0.881 & 0.877 & 0.851 \\ SAC-VGNet & & **0.926** & **0.922** & **0.885** \\ \hline DETR [19] & & 0.875 & 0.869 & 0.841 \\ YOLOv5 [41] & & 0.898 & 0.882 & 0.856 \\ Deformable DETR [35] & 1280\(\times\) & 0.889 & 0.880 & 0.849 \\ Sparse R-CNN [36] & 1280 & 0.903 & 0.892 & 0.870 \\ YOLOv7 [24] & & 0.911 & 0.903 & 0.875 \\ SAC-VGNet & & **0.935** & **0.927** & **0.902** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance comparison between SAC-VGNet and the advanced approaches on FAD. Table 6 lists the resource requirement results. Since our SAC-VGNet is built on YOLOv7, by adding the multi-modal fusion module, it requires more resources and achieves a lower testing speed as compared to YOLOv7 with 21 FPS. Compared to Deformable DETR/DERT, our approach consumes fewer computations and achieves a faster speed to yield better detection accuracy for fine-grained visual grounding. Figure 7 shows the detection results of 12 testing images from both datasets. We can see that our method can correctly detect the aircraft types, while YOLOv7 generates some detection errors. For instance, \"Su34\" is misclassified as \"Mig31\" in the first example. Even in the case of small targets and complex surroundings (see the fourth example), our method could correctly recognize the category, and YOLOv7 may generate detection errors. We also discovered the detection error in our approach in the bottom left example of the third row, with several small, dense objects in different categories in an image. In such a case, the corresponding text descriptions are mixed, which would confuse the model in discriminating between these different objects. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **\#Param.** & **FLOPs** & **Size** & **FPS** \\ \hline Sparse R-CNN & 77.8 M & 23.3 G & 1333 & 23 \\ YOLOv5 & 86.7 M & 205.7 G & 1280 & 15 \\ YOLOv7 & 36.9 M & 104.7 G & 1280 & 26 \\ DETR & 41 M & 187 G & 1333 & 12 \\ Deformable DETR & 40 M & 173 G & 1333 & 19 \\ \hline SAC-VGNet & 45 M & 113 G & 1280 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Resource requirements as compared with the state-of-the-art approaches.