esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Model Training Results  
The training environment utilized was torch 1.13.1 + CPU for training acceleration. PyCharm served as a Python interactive interface. During the training phase, all models underwent training using the standard SGD optimizer, employing a decay and momentum value of 0.9. The weight decay is consistently set to \(4\times 10^{-5}\). The cosine learning schedule is implemented using an initial learning rate of 0.05.  
Upon setting appropriate model parameters, the training set was introduced into YOLOv5-BiFC for training. The model was continuously optimized using the stochastic gradient descent (SGD) algorithm until the training was completed. Throughout the model training process, the loss function curve consistently decreased, indicating continuous  
Figure 12: Comparison of results of Cyle GAN and improved GAN. (**a**) Results generated by Cycle GAN. (**b**) Results generated by improved Cycle GAN.  
Figure 13: Enhanced graph based on improved Cycle GAN in different epochs.  
optimization of the model. The variation in the loss function curve of our improved model is illustrated in Figure 14a, where the horizontal axis represents the number of training epochs and the vertical axis represents the loss value. The loss function curve can be divided into three parts: rapid descent, gradual descent, and stability. The loss value rapidly decreased within the first 20 epochs, and beyond 60 epochs, the loss value of the improved model gradually stabilized, with train_loss and val_loss approaching each other, indicating the absence of overfitting in the training results. Therefore, the improved model trained through 100 iterations was selected as the emission detection model for outlet pollution detection. A comparison of mAP curves before and after the improvement of the YOLOv5 model at an IoU of 0.7 is presented in Figure 14b. After 100 epochs of training, the improved YOLOv5 model exhibited a relatively significant increase in the mAP value, indicating a better detection performance for outlet pollution with the improved model.
## 4 Results and Discussion
### Comparing the RMR Value to the GSI Value  
The chart Geological Strength Index is plotted as a function of RMR\({}_{89}\) values in Figure 7. According to the calculated results, the linear regression is shown in Table 2.  
Figure 7: Correlation between Chart GSI and RMR values in Bñaapáti project (**a**), and in Telfer project (**b**).  
The studied correlations show distinct connections between the two rock mass characterization methods. Good correlations between a and b constants from Equation (3) were identified in different projects.  
The use of rock-type-based equations and constants from Table 1 did not identify a clear or discernible correlation between RMR89 and GSI. However, further examination of the relationship between 'a' and 'b' constants indicate a strong correlation for individual cases. The connection between a and b constants obtained from Equation (4) are illustrated in Figure 8.  
The calculated values from Hungary (Bataapati) and Australia (Telfer) from this paper are also shown in Figure 8. Constants a and b from Equation (4) can be correlated by:  
\[\text{b}=-50.13\text{a}+43.13\ (\text{R}^{2}=0.896) \tag{8}\]  
This value is considered as site- and rock-type dependent.  
In Figure 8, the various relationships appear to follow a linear relationship with the exception of published data by [PERSON] and [PERSON] [43]. When these data are omitted, an even higher correlation is attained:  
\[\text{B}=-53.77\text{a}+47\ (\text{R}^{2}=0.977) \tag{9}\]  
Using this latter Equation (9), a = 1, while b = \(-6.29\). These values are commensurate to the values suggested by [PERSON] and [PERSON] [33].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**a** & **b** & **R2** & **Rock Type** & **Ref.** \\ \hline
0.793 & 2.010 & 0.736 & granitic rocks & Bataapati \\
0.876 & 0.935 & 0.876 & siltstones, sandstones and quartzites & Telfer \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equations of the existing correlations between RMR89 and GSI in the studied projects.  
Figure 8: The correlation between different a and b constants regarding different correlations between GSI-RMR89 values.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 4 Generalized Design Approach
### Horizontal Flow  
From a designer's perspective, comprehensively understanding the design process not only requires the standard specifications (efficiency, bandwidth, and gain), but also additional considerations [56]. The process of analyzing and ultimately selecting the proper architecture through mathematical analysis is referred to as horizontal flow in this context.  
The alternative categorization presented in this work, which is depicted as a tree diagram in Figure 9, offers a new perspective on design principles. To include simplified design insights, any RFPA can be referred to by its time domain waveform, whether it is designed for a range of frequencies or for back-off efficiency levels referred to as space. The union between frequency and space indicates that it can represent either the frequency, the space, or a combination of both.  
The tree diagram depicts that on an abstract level, the design of an RFPA is the result of time, coupled with frequency, and space. Here, frequency plays an important role in the bandwidth efficiency trade-off, whereas space plays an important role in the linearity efficiency trade-off. An overview of the cited works categorized using this viewpoint is given in Table 1.  
This flow serves as a valuable guide. The interplay of these three categories for the design of efficient RFPA begins with the selection of required waveforms--either overlapping or non-overlapping. Then, for a wide bandwidth, a step or continuous mode of operation can be used. Lastly, to optimize RFPA efficiency at the back-off level, the load modulation technique, or supply modulation, can be used. With the system requirements clearly defined, the necessary frequency and power specifications can be pinpointed.  
Figure 9: An alternative viewpoint of categorizing RFPA design.
# Potential of Room Acoustic Solver with Plane-Wave Enriched Finite Element Method
## 3 Numerical Experiments
### Sound Propagation Problem in a Coupled Room  
#### 3.3.1 Problem Description and Numerical Setup  
In the second example, a real-world acoustic application is considered again where the model is based on an existing office plan in Kobe University and the boundary conditions are based on actual sound absorber installed in the offices. The installed absorber is a porous type absorber, which is different from the previous example. Additionally, as a further demonstration of the effectiveness of PUFEM, we present computed SPL distributions inside rooms with a fine spatial resolution, comparing with the reference solution and the standard FEM. Figure 8 presents a coupled room (\(S_{\text{a}}=60.48\) m\({}^{2}\)) composed of four rooms where the largest room is the same as that used in the first numerical example and where the other three are soundproof rooms with highly absorbing boundaries. The room boundaries comprise reflecting impedance boundary \(\Gamma_{\text{z,1}}\) and highly absorbing impedance boundary \(\Gamma_{\text{z,p}}\) of a rigid-backed porous sound absorber. We gave a real valued surface impedance corresponding to \(\alpha_{0}=0.05\) to the reflecting boundary \(\Gamma_{\text{z,1}}\). An equivalent fluid model [51] with [PERSON]'s empirical equation [52] was used to calculate the surface impedance of the porous absorber. Regarding the porous material, glass wool of 32 kg/m\({}^{3}\) with 100 mm thickness was assumed. The flow resistivity was set as 13,900 Pa s/m\({}^{2}\). The absorption characteristics of \(z_{\text{n}}\) and \(\alpha_{0}\) are presented in Figure 8. The sound absorber has high absorption coefficient greater than 0.9 at frequencies higher than 500 Hz. Therefore, in this problem, sound fields have high SPL difference among the largest sized room and the three soundproof rooms. We applied \(v=1\) m/s for the vibration boundary \(\Gamma_{\text{v}}\). We placed 18 sound receivers in the room.  
Figure 9 shows a PUFEM mesh discretized using rectangular elements of various lengths in the range of 0.2-0.4 m. The mesh includes three times larger elements than wavelength at the upper-limit frequency. The constant \(C\) for plane wave enrichment varies from 5 to 14 with an interval of one. In addition, \(N_{\text{ele}}\) and \(N_{\text{node}}\) respectively denote 413 and 493. However, similarly to the earlier numerical example, we used two FE meshes respectively discretized with 0.01 m square elements and 0.005 m  
Figure 8: Coupled room model including a vibration boundary, with impedance boundaries of a reflecting surface and a rigid-backed porous absorber: S, R respectively represent the sound source and receivers.  
square elements to calculate the reference solutions. The 0.01 m mesh was used to calculate frequency responses at 20 Hz to 1500 Hz. For higher frequencies, we used 0.005 m mesh. The DOF of the meshes are, respectively, 612,000 and 2,448,000.
## 3 Results and Discussion
### Mineral Analysis
Concerning Ca concentration, the results showed significant differences (\(p<0.05\)), ranging from 8452 mg/kg for AL to 28,465 mg/kg for AC. Our results are within the global range of Ca content (14,600 to 31,500 mg/kg) reported by [PERSON] et al. (2014) for six species of _Acacia_ (_A. angustissima, A. Hernpolobium, A. nilotica, A. polyacantha, A. senegal_, and _A. tortilis_ in the region of Tanzania, although [PERSON] et al. (2017) recorded higher values for _A. reticul_ (879,000 mg/kg) and _A. senegal_ (878,000 mg/kg). Significant differences (\(p<0.05\)) were observed for K among the species, ranging from 3510 to 11,470 mg/kg, for AD and AC, respectively. [PERSON] et al. (2010) obtained K content values of 1400 mg/kg for _A. harro_ in Africa, and [PERSON] et al. (2011) recorded values between 8300 and 18,000 mg/kg in _A. tortilis_, _A.nilotica, A. agustissima, A. rubusta, A. xanthophlolea_, and _A. nigrescens_. Regarding sodium content, which ranged from 150 to 2802 mg/kg for AD and AMs, respectively, significant differences were found among the species. To the best of our knowledge, no research or study regarding Na content specifically in acacia plants is available. Determining sodium (Na) content in leaves is essential for assessing their suitability for animal feed and soil amendments. This study is the first to measure Na levels in acacia leaves, establishing a valuable baseline for evaluating their potential effects. For animal feed, an adequate concentration of Na is crucial for regulating blood pressure and volume, ensuring proper cell function, and maintaining the correct pH in the gastrointestinal tract, which aids in diges
# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
## 1 Introduction
applied ArcGIS in terms of the \"Calculate Distance Band from Neighbor Count\", \"Incremental Spatial Autocorrelations\", and the \"Getis-Ord GI\" function to make the clusters of pig farms [21]. [PERSON] and [PERSON] grouped cattle farms according to their sizes in terms of large, medium, and minor scales using GIS [22]. Globally, to achieve the goal of sustainable renewable energy development, most countries worldwide have worked hard at a regional and global level by constructing biomass treatment facilities using spatial analysis [23]. Therefore, geospatial consideration and location modeling are required for a suitable location of treatment facilities' site selection and analysis. Another strand looks at land suitability analysis and site selection for urban and rural agriculture using a variety of methodologies, such as multicriteria decision-making (MCDM) [24]. MCDM approaches are utilized in site selection applications to determine the weights of defined main/sub-criteria [25]. [PERSON] and [PERSON] considered the thirty-three constraints and seven criteria factors for GIS suitability analysis [26]. [PERSON] et al. studied the suitability map for biogas power plants using the constraints of rural and urban areas, park and recreational areas, water bodies, wetlands, roads, transmission lines, and slopes [23]. [PERSON] et al. considered the criteria factor in terms of vegetation cover, crop cover, slope, road proximity, transmission line proximity, workforce potential, energy demand potential, population exposure, and viewshed to locate the biomass energy plant [27]. [PERSON], [PERSON] et al. use the analytical hierarchy process (AHP) of multicriteria decision-making to evaluate the weight of the factors [28]. Currently, there is no integrated assessment of the GIS cluster and site suitability analysis for biomass energy potential. Moreover, to support the strategy of bioenergyformulation with the circular economy concept for agriculture residues in Myanmar, this study aims to assess the energy generation potential of crop residue in terms of the township based spatial high/low clusters and to find suitable sites for treatment facilities for energy generation.
## 3 Results
### Distribution of Equivalent Stresses  
The simulation results are shown in Figures 2, 3, 5 and 7, and they demonstrate the location of the maximum equivalent stress values on the skin's upper surface when exposed to the laser in all scenarios, regardless of whether the maximum temperature is situated on the surface or within the tissue. Figure 8 shows the equivalent stress variations (at z = 0 and r = 0) with time and a wavelength of 800 nm, laser intensities of 1 and 2 W/mm\({}^{2}\), and a beam size of 1 mm. An exponential increase in stress was observed with time, with a difference of not less than 50% between the two studied strengths. Moreover, it is noticed that equivalent stresses reach a stable state after a time of about 400 s, which confirms the dependence of the mechanical effect on the thermal effect associated with laser radiation.  
Figure 8: Variations of equivalent stresses (at z = 0 and r = 0) with time at 800 nm wavelength, 1 and 2 W/mm\({}^{2}\) laser intensities, and 1 mm beam width.  
Figure 7: Distribution of the equivalent temperatures (\({}^{\circ}\)C) and equivalent stresses (MPa) across the model layers at a time of 600 s, intensities of 1 and 2 W/mm\({}^{2}\), and wavelength of 800 nm.  
When studying the equivalent stresses according to the radial direction, it is noticed that the maximum value appears in the central area and a decrease in the values with decreasing distance from the surface, while the maximum values fade in the longitudinal direction when the depth increases beyond the distance of 3 mm. In addition, the differences between 1 and 2 W/mm\({}^{2}\) intensities are more pronounced according to the diagonal direction than according to the longitudinal or axial direction.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### CubeSat Missions Dedicated to Astronomical and Heliophysics Research  
Traditionally, astronomical research relied on large and complex space observatories based on the ground and large and costly space-based platforms. However, CubeSats have once again proven themselves as suitable and cost-effective platforms for in-space astrophysical studies.  
By utilizing IR/VIS/UV telescopes, they have showcased their capabilities on detecting exoplanets transits and stellar radiation environments, providing valuable insight on planetes' atmospheres and also evaluating star-planet interactions. Apart from optical telescopes, CubeSats have been using X-ray and gamma-ray detectors to study high-energy phenomena, related to solar activity and present in galactic structures. In addition, CubeSats have been utilized in radio astronomy applications and heliophysics studies. By studying and monitoring solar energetic particles, as well as monitoring the dynamics present in the radiation belts, CubeSats aid the prediction of space weather that can impact satellite operation and even astronaut safety. Finally, missions incorporating constellations have demonstrated the feasibility of a small fleet of CubeSats functioning as a unified large-scale observatory.  
Despite the observed challenges in several missions, such as the high pointing accuracy need and the limited aperture size based on the platforms' size, CubeSats are, can, and will revolutionize in-space astronomical research. By incorporating advanced optics, novel AI driven automations and distributed sensor networks in constellations, they can function as high-precision and scalable astronomical observatories.
## 3 Results and Discussion
### Optimization of DSPE Conditions
#### 3.2.1 Effect of the Amount of Zeolite H-Beta  
In our study, five dosages of the zeolite H-Beta sorbent (25, 50, 75, 100, and 125 mg) were evaluated on the extraction of the analytes. The results indicated an upward trend for the extraction efficiency of the eight neonicotinoids with the amount of zeolite H-Beta increasing from 25 to 50 mg (Figure 3A). Except for acetamiprid and thiazolprid, the recoveries of the other six neonicotinoids decreased by 6.5-10.7% when 75 mg of zeolite H-Beta was used. The extraction efficiencies remained stable with a further increase in the zeolite H-Beta dosage. When the amount of zeolite H-Beta increased to 125 mg, the recovery of acetamiprid dropped by approximately 7%. Obviously, as the amount increased, the elution efficiency decreased under the limited solvent. Therefore, 50 mg of zeolite H-Beta was selected for further use. Previous literature reported UiO-66 of 40 mg as dSPE material for the preconcentration of neonicotinoid insecticides in water [6]. Sorbents made of pure SDS (175 mg surfactant/g alumina) and mixed SDS-TBA (80 mg SDS and 0.05 mg TBA/g alumina) were selected for the extraction of pesticides from water [28]. The use of 50 mg of zeolite H-Beta was comparably less and could result in acceptable recoveries.
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 4 Trustworthy, Security and Privacy in Crowdsourcing Internet of Things  
In crowdsourcing, current research for privacy protection can be grouped based on two different phases found in crowdsourcing: (1) Task allocation; and (2) Data aggregation. Task allocation is an essential component and indispensable service for the response time and ensuring task quality. To have efficient task allocation and recruitments, the center or server needs to utilize data-related participant and worker profiles. This poses a threat to privacy leaks. There are privacy protection techniques for the requestors/owners or participants/workers during the phase of task allocation. There are few research works for privacy-preserving task allocating mechanisms to preserve the privacy of the locations for the end-users. The authors in [130] proposed a helpful task assignment to protect task and worker privacy protection based on cryptosystems. Although there are many approaches to preserving privacy in crowdsourcing, the focus of this section is on studies that protect privacy in crowdsourcing IoT and Fog based crowdsourcing.  
For privacy-aware data aggregation, the current research can be grouped into two approaches: (1) Collection-oriented; and (2) Statistic-oriented. The former aims to collect high-quality sensed data, while the latter targets to perform different statistical analysis on collected or sensed data. Privacy concerns can be mitigated by increasing the perceived trustworthiness and information security in crowd systems. Trust and reputation management systems are commonly used widely in crowdsensing to assess participants' trustworthiness. This section presents some of these key works by classifying them into three main categories: (1) Trustworthy in crowdsourcing IoT, (2) Privacy in crowdsourcing IoT, and (3) Security in crowdsourcing IoT. Some authors and works may consider more than one aspect in their research.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 3 Simulation Scenarios
### SVS Indices Implementation
#### 3.2.4 \(Tvi\)  
As mentioned in Section 2.4, \(TVI\) is an index that calculates voltage variation from determined boundaries. In Equation (14), parameters are set to their standard amounts in [16]. Thus, parameters \(B\) and \(v_{st}\) are taken to be equal to 0.05 and 0.9, respectively. Then, \(TVI_{tot}\) is calculated for six defined scenarios and results are presented in Figure 7.  
It is obvious from Figure 7 that, in stable status, \(TVI\) is close to zero and, in unstable conditions, \(TVI\) take amounts more than zero. Because \(TVI\)s take different numbers according to system type, using \(TVI\) in SVS monitoring needs to determine corresponding thresholds. In our study cases, in a time interval of 2 s, \(TVI\) more than 10 is related to voltage instability, and we can say definitely that \(TVI\) would be increased with passing time in unstable conditions.  
Figure 6: \(PI\) calculated for defined scenarios: (**a**) Case a, (**b**) Case b, (**c**) Case c, (**d**) Case d, (**e**) Case e, (**f**) Case f.
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 3 LTE Schedulers
### Exponential PF (EXP/PF) Scheduler  
The EXP/PF technique was designed to serve multimedia use cases with regards to systems employing time multiplexing [4]. The aim here is to improve real-time data flow priority when compared with non-real-time streams. At every scheduling interval, multiplication of the user number occurs due to various available sub-carrier sets for all network users to share concurrently [22]. For the overall delay pertaining to the sent packet, EXP/PF applies the exponential function as well as the PF characteristics. For best-effort data flow, EXP/PF is employed as PF, while processing of real-time data flow is done as EXP/PF in the following manner:  
\[M_{I,K}^{EXP/PF}=\exp\biggl{(}\frac{\alpha_{i}D_{HOL,i}-x}{1+\sqrt{x}}\biggr{)} \cdot\frac{d_{k}^{i}(t)}{R^{i}(t-1)}, \tag{3}\]  
\[x=\frac{1}{N_{rt}}\ \sum_{i=1}^{N_{rt}}\alpha_{i}D_{HOL,i}, \tag{4}\]  
where \(N_{rt}\) denotes the number of active real-time DL flows.
## 2 Laser-Driven Very-High-Energy Electron/Photon Beam Radiation Therapy System  
The proposed radiation therapy system providing VHE electron/photon beams is composed of a drive laser system, laser plasma accelerator, electron beam focusing system, target system for the photon beam therapy, rotary gantry, and laser beam stabilizing system, as shown in Figure 2.  
The drive laser system, comprising a front end and amplifier chain delivering energy-amplified pulses, is followed by an optical pulse compressor in order to form a drive laser pulse. The drive laser  
Figure 2: A conceptual sketch (not scaled) of the proposed radiation therapy system comprising **(a)** a drive laser system; **(b)** a laser plasma accelerator; **(c)** an electron beam focusing system; and **(d)** a laser-guiding vacuum transport embedded in a rotary gantry. Alignment of mirrors M1–M4 are corrected by a laser beam stabilizing system consisting of an alignment laser S and beam position monitor cameras C1–C4, to keep a laser pulse focus on the entrance of a laser plasma accelerator, O.  
pulse is guided through vacuum transport optics and focused onto the laser plasma accelerator, both of which are installed in the rotary gantry. In the laser plasma accelerator, drive laser pulses generate VHE electron beams. The beam focusing system transports the electron beams from the laser plasma accelerator to a target in a patient for VHE electron beam treatment, while the VHE electron beams are converted to photon beams in the target system for VHE photon beam treatment. The laser beam stabilizing system monitors beam positions in the laser transport optics and corrects their alignment. Major components of the laser-driven radiation therapy system are described in detail as follows.