esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning
## 1 Introduction
However, in this paper, another network, the Relation-Aware Global Attention Network (RGA-Net) [18], is based on relationship perception and pays more attention to global feature information. Compared to CBAM as the method that integrates channel and spatial attention, RGA has higher discrimination between foreground and background and can extract more effective information. In order to improve the model training effect and speed up the model training, most deep learning models currently use the Batch Normalization Layer (BN layer). However, in the training process, the BN layer depends on the batch size because it needs to calculate the intermediate statistics based on the batch, which can easily lead to inconsistency between training and testing results. So, in this paper, the BN layer changes into a Filter Response Normalization (FRN) layer [19]. The main contributions of this paper are listed below:  
1. We proposed a novel model based on deep learning to identify the graphite ore grade quickly and conveniently. 2. We proposed the image-cutting algorithm to build and enhance the datasets in the image database. 3. Utilized image cutting to expand the dataset and combined it using the Feature Pyramid Networks (FPN) method to extract features. 4. Proposed the Relation-Aware Global Attention (RGA) attention mechanism for the model pair that focuses more on the Region of Interest (ROI). 5. Proposed the Filter Response Normalization (FRN) layer to fit with a small image batch size. This article is divided into five different sections, each providing support for a comprehensive understanding of the model method we proposed. The Introduction section lays the foundation for subsequent sections by providing an overview of the topic under discussion. Section 2 describes the detailed feature extraction process of the Faster R-CNN model. Section 3 provides a detailed introduction to our approach and steps for improving the Faster R-CNN model. Section 4 critically reviews the experiments and results of relevant research, comprehensively outlining the experimental design and methods used to validate the effectiveness of the model. Finally, Section 5 provides a concise and persuasive conclusion that summarizes the main findings of the article and provides a glimpse into future research avenues.
# Shear-Wave Velocity Model from Site Amplification Using Microtremors on Jeju Island
## 4 Results
### SSP Site
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Lower Limit Depth of**} & \multirow{2}{*}{**Shear Velocity (m/s)**} & \multicolumn{4}{c}{**RC**} \\  & & & **B (1–2)** & **B (2–3)** & **B (3–4)** & **B (4–5)** & **B (5–6)** \\ \hline
1 & 40–60 & 900–1100 & \multirow{2}{*}{**0.10**} & \multirow{2}{*}{**0.26**} & \multirow{2}{*}{**0.30**} & \multirow{2}{*}{**0.10**} \\
2 & 100–120 & 1100–1300 & & & & \\
**3** & **320–340** & **600–800** & & & & \\
4 & 460–480 & 1200–1400 & & & & \\
5 & 590–610 & 1500–1700 & & & & \\
6 & Basement & 2400–2600 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Depth, shear velocity (Vs), and RC of SSP site. Figure 17: Estimated shear-wave velocity (Vs) profile from the inversion of SSP site; the thin lines indicate all the inversion results, and thick gray line indicates the optimal Vs profile. gravity, and low resistivity, likely related to the LVL owing to their similar sedimentary origins [28; 30; 31; 32]. However, other studies suggest that the area east of the Bukcheon-Pyoseon line does not contain a Seoguipo sedimentary layer [29; 46; 47]. Consequently, the LVL identified in this study at the SSP site was inferred to originate predominantly from the U-formation with a marine origin, excluding the Seoguipo sedimentary layer. As indicated in Table 4 and Figure 17, the SSP site exhibited the highest resonance frequency (0.43 Hz) and largest resonance amplitude (4.99 Hz) among the eight sites in the Jeju region, as well as the CJD site. Moreover, the LVL was located at the shallowest depth (320-340 m below the surface) and had the lowest Vs value (600-800 m/s). These characteristics, particularly the proximity of the LVL to the surface and its large resonance amplitude being significantly greater than unity, were likely directly related to the decision by the Korea Meteorological Administration (KMA) to close this site, aiming to minimize the distortion of the observed waveform. Most prior studies suggest that the characteristics of the U-formation and Seoguipo sedimentary layer, including the low velocity, gravity, and resistivity, are associated with the LVL, whereas certain researchers dispute the presence of the Seoguipo sedimentary layer in the eastern region. This is further elaborated in the Discussion section.
## 3 Results and Discussion
### Plugging Mechanism  
The microscopic morphology of poly (MMA-BA-ST) was observed by scanning electron microscopy, as shown in Figure 9.  
Poly (MMA-BA-ST) is a nanoparticle that has a hexagonal prism shape. This means that poly (MMA-BA-ST) can enter the nanopore and the prismatic shape is easier to accumulate into a stable plugging layer. Figure 10 shows the mud cake diagram before and after the addition of poly (MMA-BA-ST).  
Figure 8: Variation of permeability and plugging rate for different doses of poly (MMA-BA-ST).  
Figure 9: Microscopic morphology of poly (MMA-BA-ST).  
From Figure 10, it can be seen that poly (MMA-BA-ST) formed a plugging layer on the surface part of the mud cake and effectively participated in the formation of the mud cake. Combined with the evaluation of mud cake plugging properties in 3.5., it can be concluded that poly (MMA-BA-ST) enhanced the plugging properties of the mud cake and made the mud cake denser, thus reducing the permeability of the mud cake. Figure 11 shows the plugging mechanism of poly (MMA-BA-ST) on the nanopore seams of shale formations.  
It can be seen from Figure 11 that poly (MMA-BA-ST) contains more ester groups and benzene rings, which makes poly (MMA-BA-ST) hydrophobic and temperature resistant [24; 25]. In the process of plugging pores and fractures, poly (MMA-BA-ST) effectively participated in the formation of the filter cake. It formed a plugging layer on the surface of the filter cake, preventing oil-based drilling fluids from invading deep shale. Part of the poly (MMA-BA-ST) enters the nanopores and cracks with the intrusion of the filtrate, and the polygonal shape is more favorable for the aggregation of poly (MMA-BA-ST) in the pores and cracks. The pressure action deforms the poly (MMA-BA-ST) and forms a tight plugging layer in the pores and slits, further preventing the filtrate's intrusion.
## 5 Discussion
Indeed, Figure 4 shows a clear lowering of the AO region in concurrence with the storm (lower panel) with respect to the quiet reference conditions (upper panel). Such results are in agreement with ultraviolet (UV) observations by DMSP/SSUSI [37; 53] for both hemispheres (Figure 8c,d). Indeed, UV images in the right column of Figure 8 display clear auroral precipitation at lower latitudes than during pre-storm conditions (left column) as a consequence of the high level of geomagnetic activity induced by south-oriented \(B_{z,IMF}\) (see Figure 3) and the expansion of the AO boundaries towards lower latitudes (red dashed lines in Figure 8). Peaks in the particle count rate during the recovery phase pinpoint a phenomenon of electron acceleration in the radiation belts, which could be ascribed to either adiabatic radial transport or nonadiabatic local heating by resonant interaction with very-low-frequency (VLF) waves [54]. Indeed, in order to make a discrimination between the two drivers, one can observe the radial phase space density (PSD) profiles of REPT electrons--directly monitored in the core of the belts--in the invariant (\(\mu\),\(K\),\(L^{*}\)) space at fixed \(\mu\) and \(K\). Radial diffusion moves (mostly 90\({}^{\circ}\) pitch angle, i.e., equatorially mirroring) electrons across different \(L^{*}\) values while the \(\mu\) and \(K\) invariants remain conserved, thus producing  
Figure 8: The auroral ultraviolet images by the SSUSI instrument on board the DMSP satellite. Panel (**a**) refers to the quiet Northern Hemisphere observations; Panel (**b**) refers to the quiet Southern Hemisphere observations; Panel (**c**) refers to the stormy Northern Hemisphere observations; Panel (**d**) refers to the stormy Southern Hemisphere observations; the red dashed curves refer to the upper and lower boundaries of the auroral oval. The red curve refers to CSES-01’s orbit. The selected reference is the Altitude Adjusted Corrected Geomagnetic (AACGM) coordinate system. PSDs with a monotonic decrease from the outer source. Conversely, local acceleration makes PSDs increase over a limited \(L^{*}\) range, with local peaks and negative radial gradients at higher \(L^{*}\) values. Here, we set \(K\) at 0.1 \(R_{E}G^{\frac{1}{2}}\) under T04 field modeling, which addressed electrons with pitch angles generally greater than 45\({}^{\circ}\) and measured nearly continuously by the REPT instrument over a still broad range of \(L^{*}\)[55]. On the other hand, electron energy corresponding to a particular \(\mu\) changes with \(L^{*}\), such that, fixing \(\mu\) at 4500 \(\frac{MeV}{G}\), the range between \(\sim\)3 MeV and \(\sim\)7 MeV can be monitored. Following the evolution of REPT PSDs from late 26 August to early 30 August (Figure 9), at higher \(L^{*}\) values, peak structures with negative gradients were recovered, which, along the previous revelation of persistent chorus waves during the August 2018 event [56], calls for a dominance of local heating.
## 4 Applications of AI in Modern Communication Networks
### Security and Privacy
#### 4.3.2 Encryption and Privacy-Preserving Techniques
Machine learning techniques can enhance SMPC protocols by identifying which computations can be performed more efficiently and which require more secure handling. By leveraging AI, these protocols can ensure that data remains confidential during collaborative processing without compromising performance or accuracy. * Privacy-Preserving Data Analytics: Another key application of AI in privacy-preserving techniques is in privacy-preserving data analytics [108]. AI enables the analysis of large datasets without directly accessing sensitive or private information. Techniques such as homomorphic encryption, which allows computations to be performed on encrypted data, combined with machine learning, can be used to extract useful insights from encrypted datasets without decrypting the data itself [118]. This allows organizations to perform advanced analytics while respecting users' privacy. For example, in healthcare or finance, where sensitive data are often involved, AI-based privacy-preserving data analytics can help analyze trends or make predictions without ever exposing individual user data. This has significant implications for industries that must comply with privacy regulations such as the General Data Protection Regulation (GDPR) in the European Union. As shown in Table 6, various AI-based methods such as federated learning, homomorphic encryption, and differential privacy are utilized to preserve privacy while ensuring effective data analysis and computation in various application areas.
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
###### Abstract  
A Global Navigation Satellite System (GNSS) is a high-precision method for comparing clocks and time transfer. The GNSS carrier phase can provide more precise observable information than pseudorange. However, the carrier phase is ambiguous, and only pseudorange can provide the absolute time difference between two clocks. In our study, by taking full advantage of GNSS pseudorange and carrier-phase observables, a differential precise time transfer (DPT) method with a clustering constraint was employed to estimate the time difference between two clocks, aiming to achieve accurate and precise time and frequency transfer. Using this method, several time transfer results were analyzed for different baselines. For the common clock experiment, the time transfer results showed good consistency across different days, with an intra-day accuracy of within 20 ps. Furthermore, we evaluated the self-consistency of DPT using closure among three stations. DPT closure of the three stations had a peak-to-peak value of closure of about 25 ps. The closure did not change over time, indicating the self-consistency of the DPT processing in time transfer. Moreover, our results were compared to station clock solutions provided by the International GNSS Service (IGS), and the standard deviations (STDs) of the four baselines were all less than 100 ps within one month, confirming the time and frequency stability of the DPT method. In addition, we found that the time stability of DPT was less than 20 ps within one week. As for frequency stability, DPT achieved a \(10^{-16}\) level of modified Allan deviation (MDEV) at an averaging time of about 1 day and a sub-\(10^{-17}\) level at an averaging time of one week.  
GPS; DPT; differential carrier phase; single-difference ambiguity; time and frequency transfer 2023130694. [[https://doi.org/10.3390/app131910694](https://doi.org/10.3390/app131910694)]([https://doi.org/10.3390/app131910694](https://doi.org/10.3390/app131910694))  
Acticle 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract  
The purpose of this study was to systematically review the literature on the effect of exercise programs on the Repeated Sprint Ability (RSA) performance of soccer players. PubMed, Scopus and Google Scholar databases were searched for original research articles. The inclusion criteria for a study were to include different groups of intervention in soccer players and present the protocol's characteristics and the study's results. Twenty-one (21) studies met the criteria. For each study, the effect size (ES) and 95% confidential interval (CI) were calculated and evaluated as trivial, small, moderate or large. The results showed that in adults, the various programs showed from trivial (e.g., ES 0.185; 95% CI \(\pm\) 0.089) to large beneficial effects (ES 2.92; 95% CI \(\pm\) 0.29). At developmental ages, the beneficial effect was from trivial to moderate (ES 0.163; 95% CI \(\pm\) 0.174, ES 0.787; 95% CI \(\pm\) 0.074). Concerning the interventional programs, studies carried out by sprint and Small-Sided Games (SSG) interventions presented a large ES, while studies that carried out RSA exercises presented moderate ES. A variety of stimuli can improve the RSA performance of soccer players by causing trivial-to-large improvements. However, more effective are interventional exercises that include stimuli related to repeated sprint ability.  
RSA; football; strength; sprint; sport performance +
Footnote †: journal: Applied Sciences  
This study aimed to investigate and summarize the existing literature on the effects of different training methods/protocols on the ability of RSA in soccer. In this review, the studies were categorized following the age of participants and into those that included appropriate training methods (e.g., sprints) and those using nonrelevant training methods (e.g., pyometrics, strength training).
###### Abstract  
This research proposed the design, fabrication, and experiments of a surface acoustic wave resonator (SAWR)-based multi-sized particles monitor. A wide range selection and monitoring of large coarse particles (LCP), inhalable particles (PM\({}_{\text{10}}\)), and fine inhalable particles (PM\({}_{\text{2.5}}\)) were achieved by combining high-performance 311 MHz SAWRs and a specially designed cascade impactor. This paper calculated the normalized sensitivity distribution of the chip to the mass loading effect, extracted the optimal response area for particle attachment, analyzed the influence of the distance between nozzle and chip surface on the particle distribution, and evaluated the collection efficiency of the specially designed 2 LPM (L/min) impactor through computational fluid dynamics simulation software. An experimental platform was built to conduct the response experiment of the sensor to particle-containing gas generated by the combustion of leaf fragments and repeatability test. We verified the results of the particle diameter captured at each stage. This research suggests that the sensor's response had good linearity and repeatability, while the particles collected on the surface of the SAWR in each impactor stage met the desired diameter, observed through a microscope.  
surface acoustic wave resonator; cascade impactor; particle detection Article
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 2 Literature Review  
### Facility Location Problems  
Facility location problems (FLPs) involve selecting a subset of locations from a set of candidates and determining which demand points will be served by each facility [16]. FLPs have long been a classic subject in various disciplines, such as geography, operations research, and management science. The study of facility location problems dates back to the early 20 th century, with [PERSON]'s work on the warehouse location problem, which aimed to minimize the total travel distance between warehouses and customers [17]. In the 1960s to 1980s, several key models were introduced by researchers such as [PERSON] [18, 19], [PERSON], [PERSON], [PERSON], and [PERSON] [20], and [PERSON] [21], including the P-Median model, P-Center model, Set Covering Location Problem (SCLP), and Maximal Covering Location Problem (MCLP). Many of the classical models in facility location research have been developed based on these foundational models. For example, by combining several models and incorporating multi-objective optimization, researchers developed the multi-objective facility location problem [1, 22]. These models often seek compromise solutions using Pareto optimality or other multi-objective techniques, such as weighted methods and goal programming. In the 1990s, researchers began to include uncertain parameters in these models, addressing issues such as demand fluctuations and cost uncertainties through methods like robust optimization and the fuzzy set theory [23, 24]. Similarly, building upon these classic static models, researchers have introduced time-varying constraints to account for demand fluctuations, population changes, or shifts in transportation networks, thereby developing dynamic location models [12, 25].  
Overall, after several decades of research, facility location problems have produced a wealth of findings. For more detailed information on FLPs, readers may refer to the reviews by [PERSON] and [PERSON] [2], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [26], [PERSON], [PERSON] and [PERSON] [27], [PERSON], [PERSON] and [PERSON] [28], [PERSON] and [PERSON] [29], and [PERSON] and [PERSON] [30], which provide comprehensive overviews of the topic. However, conducting a comprehensive review of all these facilities is beyond the scope of this paper. We have instead focused on reviewing community-facility location problems (CFLPs) and dynamic facility location problems, which are closely related to our work.
## 3 Results
### Testing Results
In addition, from scenario g, which used the cGAN (pix2 pix) method [8], we conclude that the TV term contributed to better visual results than that of the L\({}_{1}\) term only (cGAN). The cGAN-generated image  
Figure 3: Visual result of each scenario. (**a**–**k**) Visual result of scenario a–k, respectively; (**g**) is the result of pix2 pix (baseline method) [8]. The ground truth images are from generated photos [17]. contained artifacts around the mouth and uneven skin tone. This shows that our proposed method successfully improved the baseline method. From the eleven scenarios that were carried out, scenario e produced the best evaluation value, with the \(\lambda_{1}=100\), and \(\lambda_{2}=0.00001\). Figure 4 shows the resulting photo images of the best scenario of cGAN-TV. Table 4 shows the SSIM and FID evaluation values from the validation and testing datasets. It can be observed that the SSIM value generated from the testing dataset was 0.06 lower than that of the validation dataset. The obtained FID value was also higher than that of the validation dataset. This is because the photo images in the testing dataset came from real data and had never been seen before by the model. Meanwhile, the validation data was collected from the same source as the training dataset. The validation and training datasets had the same face size, head position, and lighting. While the test dataset had a slightly different configuration. Figure 5 shows three examples of the results of the testing phase. Visually, the generator could create a similar image, but the result was not as good as the validation dataset. This is because the character of the images in the training dataset was quite different from those in the testing dataset, which were collected from real persons. In addition, this was caused by the sketch dataset used, which was not perfectly generated. However, our GAN was able to create images that were quite similar to the corresponding original images, with SSIM values of 0.83, 0.74, and 0.71, with an FID of 93.02. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **SSIM** & **FID** \\ \hline Validation & 0.83 & 94.705 \\ \hline Testing & 0.73 & 93.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result of cGAN-TV on validation and testing data. Figure 4: Result of cGAN-TV on validation dataset (ground truth images by generated photos [17]) with the best scenario. The values below the predicted images represent SSIM/FID from the cGAN-TV results.