esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network's performance while neglecting the equally crucial aspect of inference speed. In this study, we propose a method for lightweight super-resolution reconstruction of remote sensing images, termed SRRepViT. This approach reduces model parameters and floating-point operations during inference through parameter equivalent transformation. Using the RSSOD remote sensing dataset as our benchmark dataset, we compared the reconstruction performance, inference time, and model size of SRRepViT with other classical methods. Compared to the lightweight model ECBSR, SRRepViT exhibits slightly improved reconstruction performance while reducing inference time by 16% and model parameters by 34%, respectively. Moreover, compared to other classical super-resolution reconstruction methods, the SRRepViT model achieves similar reconstruction performance while reducing model parameters by 98% and increasing inference speed by 90% for a single remote sensing image.  
remote sensing image; super resolution reconstruction; vision transformers; structural re-parameterization +
Footnote †: journal: Applied
# Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture
## 4 Current Applications and Future Perspectives
### Other Methods
Its internal microprocessor performs a consistent scan of the excitation (\(\lambda_{\text{ex}}\)) and emission (\(\lambda_{\text{em}}\)) wavelengths, providing the fluorescence spectrum as a 3D intensity plot (surface) in 2D spectral coordinates \(\lambda_{\text{ex}}\), \(\lambda_{\text{em}}\), whose discrete representation is known as the spectral fluorescence signature (SFS) [114; 115] or excitation-emission matrix (EEM) [116]. The latter term is mostly associated with high-resolution spectra obtained with costly stationary laboratory equipment for identification and characterization in mixture analysis, see, e.g., [117]. In contrast, contemporary SFS spectrometry is associated with low-cost, small-size and portable instrumentation capable of working for hours in the field using an internal battery, without any supporting infrastructure [49]. These features have shaped a trend in the future development of 3D spectral scanning, based on increasingly cheaper, autonomous, and user-friendly instruments. All of the above developments were made possible by recent impressive advances in microelectronics and engineering of light-sensitive materials and dispersive optical elements. These same advances have led to another remarkable breakthrough--in the field of passive fluorescence spectroscopy, which studies the fluorescence emission excited by sunlight and uses its spectrum as a data source for deriving numerous vegetation indicators related, among others, to the fluorescence-photosynthesis relationships and stress effects. Before the late 1990s, the solar-induced fluorescence (SIF) technology was limited in its ability to provide conclusive evidence that a very weak upwelling fluorescence emission induced by natural light could be reliably distinguished from the dominant reflected radiation in the same waveband [118]. Early conclusive measurements of SIF relied on the Fraunhofer line-depth principle (FLDP), which is based on the upwelling vegetation emission measurements in the spectral ranges corresponding to absorption lines (minimums) of solar radiation [119]. Remarkably, nowadays numerous efforts of scientists and engineers, stimulated by the revealed close relationship between SIF and the actual plant photosynthetic rate, have resulted in a plethora of reliable SIF assessment methods that bring together state-of-the-art spectrometers as well as innovative signal processing, calibration techniques and modeling [120]--to exclude the influence of numerous intervening processes in the vegetation and environment that affect quenching, light absorption, re-absorption and scattering of fluorescence signals [118]. The SIF methodologies have gone far beyond on-the-spot measurements, embracing dedicated spaceborne instruments [121, 122] and affordable airborne measurements using UAVs [122, 123].
## 3 Results
### Study of the \(ESV\) in Guizhou Province
#### 3.1.1 Time Series Changes in the ESV in Guizhou Province
The ESV increased the most between 2015 and 2020, with an increase of 2.03%, indicating that the overall trend in the ESV in Guizhou Province is improving. Forests contributed the most to the ESV in Guizhou Province, accounting for more than 80% in all eight periods, followed by cropland with a contribution rate of 7% to 9%, and finally shrubland and grassland. The trend in each ecosystem type was that the ESV in shrubland grassland decreased, while the ESV in cropland, forest, and water increased, and the contribution tended to increase continuously. Water and forest had the fastest average annual growth rates of 3.47% and 0.12%, respectively. However, because the area of water was much smaller than the forest area, the ESV growth contribution was lower than for the forest. Over the 35 year period, the area of forest increased by 4.23% and its ecosystem value increase was 3.93 times higher than the total ESV increase, indicating that the forest change was the main cause of the ecosystem value change in Guizhou. In terms of the value of the different ecosystem service types (Figure 3), the largest proportion of each period was accounted for by regulating and supporting services, which were above 66% and 22%. The value of the provisioning and regulating service types increased, while the value of the supporting and cultural service types decreased. The value of the provisioning services type increased by USD 43 million, with the highest increase of USD 21 million being attributed to water provision in particular. The value of the regulating service types increased by USD 402 million, especially the hydrological regulating service type, which had the largest increase at USD 497 million. The remaining three services decreased by USD 95 million. The supporting service and cultural service types decreased in value by USD 51 and USD 8 million, respectively, with the largest decrease occurring among the supporting services in the value of the biodiversity service type, with an average annual decrease of 5.5%. Figure 3: The value of each ecosystem service type.
## 3 Results
### Land Use and Land Cover Distribution
#### 3.2.1 Study Sites

Table 5: Accuracy assessment results for the LULC maps of Porto Velho (PA—Producer’s Accuracy, and UA—User’s Accuracy). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Porto Velho** & \multicolumn{3}{c}{**2018**} & \multicolumn{3}{c}{**2019**} & \multicolumn{3}{c}{**2020**} & \multicolumn{3}{c}{**2021**} & \multicolumn{3}{c}{**2022**} & \multicolumn{3}{c}{**2023**} \\
**Land Cover** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **UA\%** \\ \hline Terra-Firme Forest & 99.7 & 95.9 & 95.5 & 94.9 & 98.3 & 93.3 & 97.1 & 94.9 & 97.8 & 95.7 & 96.6 & 95.5 \\ Floodplain Forest & 81.8 & 100.0 & 85.7 & 90.6 & 84.6 & 96.5 & 89.7 & 88.1 & 78.6 & 91.7 & 83.3 & 100.0 \\ \hline Secondary Forest & 92.9 & 96.3 & 91.0 & 90.4 & 90.6 & 94.8 & 93.7 & 97.0 & 93.7 & 93.1 & 93.9 & 92.5 \\ Agriculture/Pastureland & 97.9 & 95.0 & 98.6 & 94.1 & 98.6 & 97.5 & 98.6 & 97.3 & 98.1 & 96.3 & 97.3 & 97.3 \\ Burned Areas & 91.7 & 95.7 & 86.2 & 96.2 & 93.3 & 96.6 & 100.0 & 100.0 & 89.7 & 92.9 & 100.0 & 89.5 \\ Barren Land & 98.3 & 96.7 & 97.8 & 98.5 & 96.7 & 95.9 & 96.9 & 98.3 & 99.2 & 99.2 & 98.4 \\ Development Areas & 98.4 & 98.4 & 97 The frequency of change analysis (Table 7) provided insights into the dynamic nature of LULC in both areas by quantifying the trajectories of the land cover changes within the study period. In Manaus, 60,268 ha experienced at least one change in LULC, with 4129 ha changing every year throughout the study period. Porto Velho demonstrated even greater dynamism, with approximately 119,616 ha changing at least twice and 6138 ha changing annually.
# _remote sensing_
## 2 Methodology
### Clock Correction  
The clock offset values of precise and broadcast ephemerides cannot be compared directly. The time group delay of B3I was regarded as the reference time group delay (TGD), which was included in the broadcast clock correction parameter of BDS-3 [3; 4; 5; 32; 33]. However, the ionospheric-free combination of B1I and B3I was regarded as the reference time group delay, which was included in COD's precise clock correction parameter [8; 10; 16]. When working with other signal combinations, TGD must be employed [14; 34; 35; 36]. There are differences in the underlying realization of the GNSS-specific system's time scales. These differences are typically larger than the inherent precision of clock solutions, affect all satellites of a constellation in the same manner, and result in a systematic bias that may vary from epoch to epoch. To account for this bias, an ensemble clock difference was therefore computed at each epoch from the average broadcast-minus-precise clock values of satellites in a constellation. Subsequently, each clock offset difference was corrected for this ensemble's average [14; 16; 17]. Therefore, the formula for evaluating the accuracy of clock offsets is represented as  
\[\left\{\begin{aligned} \Delta t^{i}&=\mathrm{d}t^{i}- \mu\\ \mathrm{d}t^{i}&=i^{i}_{sp3}-t^{i}_{bnd(1,3)}\\ t^{i}_{nd(1,3)}&=t^{i}_{bnd}-\frac{\ell^{2}_{b} \mathrm{TGD}_{1}}{\ell^{2}_{1}-\ell^{2}_{3}}\end{aligned}\right. \tag{1}\]  
where \(\Delta t^{i}\) is the accuracy of clock offset of the \(i\) satellite; \(\mathrm{d}t^{i}\) is the one time difference between the precise ephemeris clock offset \(t^{i}_{sp3}\) and the broadcast ephemeris clock offset \(t^{i}_{brd(1,3)}\), which is corrected via the group delay correction of \(\mathrm{TGD}_{1}\); \(t^{i}_{bnd}\) is the satellite clock offset obtained from direct computation; \(f_{1}\) represents the value of B1I's frequency; \(f_{3}\) represents the value of B3I's frequency; and \(\mu=\sum_{i=1}^{n}\mathrm{d}t^{i}\) is the average value of \(\mathrm{d}t^{i}\), while \(i\) is from 1 to \(n\) (\(n\) is the number of the satellites).
## 1 Introduction  
The therapeutic properties of many mushrooms, known to indigenous cultures across the globe, have been traced to their underlying chemical compounds with the advent of chemistry and molecular biology. Although the most well-documented are their use in cancer treatment due to beneficial glucan and proteoglycan synthesis [1], mushrooms have other therapeutic properties including antioxidant, antihypertensive, hepatotoxicity, antifibrotic, anti-inflammatory, antidiabetic, antiviral, antimicrobial, and cholesterol-lowering properties [2].  
Great improvements in biomass yield and bioactive compound production have been achieved with the help of biotechnological cultivation technics and conditions applied to mycology [3, 4, 5, 6].  
_I. obliquus_, also known as Chaga, is a mushroom that lives in certain parts of Europe and Asia at latitudes of 45-50\({}^{\circ}\) N as a parasite of birch trees [7]. In this environment, the Chaga mushroom grows very slowly and is not a reliable source of pharmaceutical compounds for industrial applications [8].  
Recent studies have shown that the Chaga mushroom can produce polyphenols [9], flavonoids [10], melanins [11], and tannins [12]. It is also able to synthesize biologically active ergosterol peroxide, with a role in anticancer activity [13], as well as betulin, which has antiviral and anti-inflammatory properties [14].  
Optimization of the various parameters or products of _I. obliquus_ culture regularly employs a fractional factorial design, coupled with response surface methodology [15, 16].  
In this study, we attempted to increase the biomass of submerged cultures of Chaga mycelium by using a custom mix-process experimental design capable of predicting an optimum nutrient broth composition. Response surface methodology was used to evaluate the effects of different biotechnological parameters on biomass yield and played a role in the optimization process.
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Application Layer
#### 4.1.5 Environment  
Air pollution is the product of human activities executed daily, such as the use of transport vehicles and of appliances in residential and industrial settings. These activities can generate a big amount of carbon monoxide that affects the air quality. If pollution is uncontrolled, it could cause cancer, respiratory illnesses, and cardio-vascular diseases [46]. The World Health Organization has identified tolerable pollution level thresholds and has defined the concept of \"atmospheric pollution\" [43], which refers to the presence of airborne pollutants such as fuels, carbon, bacteria, and other microscopic particles in the atmosphere. Six pollutants, i.e., ozone (O\({}_{3}\)), nitrogen dioxide (NO\({}_{2}\)), sulfur dioxide (SO\({}_{2}\)), carbon monoxide (CO), and two types of fine particulate matters (PM10, PM2.5) are detrimental to both environment and public health [46]. Table 4 shows the relation between pollutants and diseases. Air quality is directly related to people health and quality of life. Typically, people spend more than 90% of their time in indoor environments [47]. CO\({}_{2}\) levels over 1000 ppm indicate a potential air problem in indoor environments. In this sense, the continuous monitoring of CO\({}_{2}\) concentration is important to detect any anomaly in the quality of air.  
The use of IoT solutions in ambient assisted living (AAL) permits the design of smart objects with the capabilities of sensing the environment and executing specific actions for their users. IoT sensors installed in the urban environment also allows the detection of pollution concentration in real time [48]. With the collected information by the aforementioned solutions in conjunction with machine learning algorithms and data analytics, it is possible to identify and generate warnings in case of high levels of air pollution.
# Performance and Control Strategy of Real-Time Simulation of a Three-Phase Solid-State Transformer
## 4 Digital Real-Time Simulator
### eHS Circuit  
The main challenge to simulate the SST topology is the DAB switching frequency; trough performance tests for a VSC converter operating as an AFE the simulation time-step was fixed in \(T_{s}=50\) us; considering a switching frequency for the DAB \(f_{sDAB}=5\) kHz meaning that the circuit will compute four samples per-cycle for the modulation technique, which in fact meets the Nyquist criterion. Therefore, this strategy is not suitable for the simulating an SST generating overruns under transient conditions, thus a smaller time-step is needed to accurately represent electrical system dynamics.  
A solution to reduce the RTS time-step is the use of an electric Hardware Solver (eHS), which allows the parallel simulation of electric circuits using the FPGA on the OP5607 with a much smaller time-step. The eHS uses the Pejovic method to solve the electric circuit in parallel with the main process [20].  
This circuit is programmed in the eHSx64 block, which is located in the Opal-RT libraries and handles the writing and communications of the electric circuit between the FPGA and the main algorithm. Its simulation parameters are listed in Table 2.  
In addition, the eHS block can operate with the Loss Compensation Algorithm (LCA), which allows the simulation to compensate the power losses that might occur by the Pejovic method. Considering all these, the eHS circuit is made in Simulink based on [21]. An advantage in the use of eHS in this configuration is the simplicity when the model is created in comparison with other FPGA programming methods as in [16], since the SimPowerSystem electrical model is automatically deployed to the FPGA using the RT-LAB.  
The transformer characteristics are calculated using [22], and the fixed time-step for the eHS circuit \(T_{eHS}\) is optimized each time-step for the algorithm. Also an advantage on the eHS simulation is the use of only one OP5607 core in comparison with [14], in which the circuit is split into several subsystems to divide the computational load among all the available cores.
## 4 ACO Algorithm Path Planning and Improvement
### Comparison of Ant Colony Algorithm Before and After Improvement  
The improved ACO algorithm was compared with the traditional ACO algorithm, and the simulation experiments were conducted to verify the feasibility and superiority of the improved ACO algorithm. The simulation results of the picking sequence planning for multiple target picking points in three-dimensional space are shown in Figure 8, where the red solid indicates the starting point of the end effector of the robotic arm. Figure 7(a) indicates the picking sequence planned by the traditional ACO algorithm, Figure 7(b) indicates the optimal path length of each iteration of the traditional ACO algorithm, Figure 7(c) shows the picking order of the improved ACO algorithm, and Figure 7(d) shows the optimal path length of each iteration of the improved ACO algorithm. The test results are shown in Table 6.  
The improved ACO algorithm and its predecessor were planned in a 100 \(\times\) 100 \(\times\) 1200 three-dimensional space, with the starting coordinates at (0, 50, 600) (a red solid point), to map out the sequence of multiple crabapple picking paths for the end-effector of a robotic arm, starting and ending at the same point. As shown in Figure 7(a),c, both the planning algorithms can successfully plan the sequence of picking multiple points without traversing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Path Length/mm** & \
\begin{tabular}{c} **Number of** \\ **Iterations/Times** \\ \end{tabular} &
\begin{tabular}{c} **Convergence** \\ **Time/s** \\ \end{tabular} \\ \hline Traditional ACO & 1314.82 & 48 & 0.98 \\ Improved ACO & 1279.95 & 30 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of data before and after improvement.  
Figure 8: Comparison and simulation of the ACO algorithm.  
the same picking point multiple times, thus validating the effectiveness and feasibility of the proposed algorithm. By comparing Figure 8b,d and Table 4, it can be seen that the improved ACO algorithm has shorter paths, fewer iterations, and faster convergence speed. Therefore, the improved ACO algorithm in this paper outperforms the traditional ACO algorithm in planning the sequence of picking multiple points.
## 4 Discussion
#### Limitations and Strengths  
The main limitation of the study is that it used only three cases and a single session and the test and retest in one session, which does not allow us to consider sustained effects of the interventions. However, given the novelty of robotic devices in this population, the data of the present study may be a starting point regarding this intervention for children with CP levels IV and V. Therefore, although it is a valuable case series, broader conclusions require further investigation. Other limitations are that there was no blinding and there was no control group to compare. Moreover, two of the three participants did not change their knee flexion throughout the interventions, as it was always 0, which limits the results of the study. For strengths, our findings, although limited by the small sample size, provide a basis for future larger and more detailed studies that could expand on these results. Also, it may help professionals choose the most effective intervention, either individually or as a complementary treatment to others, to reduce spasticity, improve range of motion and impact the cardiovascular system in children with CP with levels IV-V according to the GMFCS.
## 4 Discussions
### Regulatory and Security Evaluation of Blockchain-Based Data Storage Solutions  
The adoption of blockchain-based data storage solutions must be evaluated within regulatory compliance frameworks. Regulations like the GDPR raise concerns as blockchain's  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criteria** & **On-Chain** & **Off-Chain** & **Hybrid** & **References** \\ \hline \multirow{3}{*}{Transaction Speed} & Low—Full node validation slows transactions & High—Off-chain solutions (Lightning Network, Rollups) improve speed & Medium–High—Hybrid balances both & [17; 19; 22] \\ \hline \multirow{3}{*}{Scalability} & Low—Block size and network congestion limit scalability & High—Off-chain solutions significantly escalability & Medium—Hybrid solutions mitigate scalability issues & [18; 19; 97] \\ \hline \multirow{3}{*}{Cost} & Very High—Transaction fees depend on network demand & Low—Off-chain solutions lower transaction fees & Medium—Hybrid solutions balance cost factors & [12; 15; 22] \\ \hline \multirow{3}{*}{Security} & Very High—Data integrity and security ensured by consensus mechanisms & Low—Relies on third-party trust & Medium—Hybrid maintains some on-chain security & [36; 47; 109] \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance comparison of blockchain-based data storage methods in terms of transaction speed, scalability, cost, and security.  
immutability conflicts with the right to data deletion. Security vulnerabilities in smart contracts and the absence of clear regulatory frameworks pose significant challenges to large-scale adoption. To enhance data security and privacy, integrating advanced cryptographic techniques such as Zero-Knowledge Proofs, Multi-Party Computation, and Post-Quantum Cryptography can significantly strengthen the security and resilience of blockchain-based storage systems.