esa-sceva/satcom-chunk-collection huggingface dataset:
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
###### Abstract  
Given the problems of uneven distribution, strong time variability of ground service demands, and low utilization rate of on-board resources in Low-Earth-Orbit (LEO) satellite communication systems, how to efficiently utilize limited beam resources to flexibly and dynamically serve ground users has become a research hotspot. This paper studies the dynamic resource allocation and interference suppression strategies for beam hopping satellite communication systems. Specifically, in the full-frequency-reuse scenario, we adopt spatial isolation techniques to avoid co-channel interference between beams and construct a multi-objective optimization problem by introducing weight coefficients, aiming to maximize user satisfaction and minimize transmission delay simultaneously. We model this optimization problem as a Markov decision process and apply a value decomposition network (VDN) algorithm based on cooperative multi-agent reinforcement learning (MARL-VDN) to reduce computational complexity. In this algorithm framework, each beam acts as an agent, making independent decisions on hopping patterns and power allocation strategies, while achieving multi-agent cooperative optimization through sharing global states and joint reward mechanisms. Simulation results show that the applied algorithm can effectively enhance user satisfaction, reduce delay, and maintain high resource utilization in dynamic service demand scenarios. Additionally, the offline-trained MARL-VDN model can be deployed on LEO satellites in a distributed mode to achieve real-time on-board resource allocation on demand.  
LEO satellite communications; beam hopping; multi-agent reinforcement learning; VDN; resource allocation Article
## 3 Results and Discussion
### Characterization of Microclusters  
Static light scattering was carried out to determine the size of the obtained microclusters. Figure 2 shows the particle size distribution with a mean value of 100 \(\upmu\)m, whereas a fairly broad size distribution was observed. The SEM image (Figure 3) reveals the porous polymer scaffold of irregular shape exhibiting pore sizes in the micrometer range. It also shows that the primary nanoparticles clearly interpenetrated in each other's shell. This is the result of the particular primary particle architecture having a soft-shell allowing interpenetration but a hard, highly cross-linked core, that hampers coalescence on the other hand. The pore size distribution was further investigated by mercury intrusion porosimetry as shown in Figure 4. It can be seen that the obtained porous microclusters exhibit a broad pore size distribution where the majority of pore sizes is in the range between 5 and 10 \(\upmu\)m. Indeed, the shown specimen can be referred to as a macroporous material as virtually no pore sizes below 50 nm could be detected, indicating negligible presence of micro- or mesopores according to IUPAC nomenclature [33]. These findings are also in line with the results obtained from gas physisorption and the adsorption and desorption curves, which are shown in Figure 5. The obtained isotherm is a type-II isotherm, which is characteristic of macroporous materials according to the IUPAC classification [34]. This is due to unrestricted monolayer-multilayer adsorption, typical of physical adsorption on macroporous adsorbents [35]. The absolute surface was found to be only 11.4 m\({}^{2}\)/g as a consequence of the interpenetration of the primary nanoparticles, which clogs the majority of the smaller pores. However, there is a marginal hysteresis between the adsorption and desorption curves at higher relative pressures which is associated with the filling and emptying of mesopores by capillary condensation, confirming the minor relevance of mesopores. The asymptotic increase of the adsorbed nitrogen volume at relative pressure approaching one, indicates instead a significant population of macroores, though [31]. Since the adsorption in micropores occurs at lower relative pressures, the slope and height are indicators for micropores. In fact, the adsorbed volume at low relative pressures is very low (\(<\)5 cm\({}^{3}\)/g) which excludes the presence of micropores. An in-depth analysis of the tuning of product properties as cluster size, specific surface or pore-size distribution by alteration of the primary nanoparticle architecture is published elsewhere [25].  
Figure 2: Particle size distribution of the microclusters measured by static light scattering.  
Figure 4: Differential pore size distribution of the microclusters by mercury intrusion experiments. The majority of the pores is in the order of several microns, whereas pores below 50 nm are virtually inexistent.  
Figure 3: SEM image of the produced microclusters revealing pores of several microns. (**a**) 10,000\(\times\) magnification, (**b**) 20,000\(\times\) magnification, (**c**) 50,000\(\times\) magnification.
# An Anonymous Offline RFID Grouping-Proof Protocol
## 6 Description of EAGP
### Eagp\({}^{(2)}\)
#### 6.1.3 Verification Phase  
There are two steps in the verification phase: (1) Reader verification step, (2) Verifier verification step.  
(1) Reader verification step:  
Reader calculates \(Y^{\prime}=yP,k^{\prime}_{a}=x(Y^{\prime})\bigoplus r_{a}\), \(k^{\prime}_{b}=x(Y^{\prime})\bigoplus r_{b}\) and validates the Equations (4) and (5):  
\[(y^{-1}T_{a}-m_{a}P)\times x(T_{b})^{-1}=k^{\prime}_{a}P \tag{4}\]  
\[(y^{-1}T_{b}-m_{b}P)\times m^{-1}_{a}=k^{\prime}_{b}P \tag{5}\]  
The utilization of group key \(y\) can prove that tag A and B belong to the same group and be scanned by the reader simultaneously.  
(2) Verifier verification step:  
The second verification stage is executed by the verifier to authenticate the tag's identity in grouping-proof. The procedure of tag A is described as follows, the verification of tag B is the same as it:  
Calculate the following equations  
\[w=m^{-1}_{a}\bmod n \tag{6}\]  
\[u_{1}=s\times w\bmod n \tag{7}\]  
\[u_{2}=r_{a}\times w\bmod n \tag{8}\]  
Figure 3: The EAGP.  
\[x_{a}=x(u_{1}P+u_{2}PK_{A}) \tag{9}\]
* If \(x_{a}=r_{a}\) is valid, the validation is successful, and the verifier stores the proof in the server as a record. Otherwise, the validation fails and the proof is abandoned.
## 2 Materials and Methods
### Calculation of Shortwave Infrared-Transformed Reflectance (STR)  
The concept of STR was proposed by [PERSON] et al. [25] in relation to soil moisture. The derivation of STR is based on a simple model of Kubelka-Munk theory [38], which describes radiative transfer with absorption (\(k\)) and scattering (\(s\)) in a soil layer, considering a downward and an upward light propagation flux perpendicular to the layer. By analytical solution of the equations for absorption and scattering coefficients, reflectance (\(R\)) is obtainable as the function of \(k\) and \(s\)[39]:  
\[R=1+\frac{k}{s}\sqrt{\left(\frac{k}{s}\right)^{2}+2\frac{k}{s}} \tag{3}\]  
The rearrangement of Equation (3) gives 'transformed reflectance' (\(r\)) denoted by \(r=k/s\)[25]:  
\[r=\frac{k}{s}=\frac{(1-R)^{2}}{2R} \tag{4}\]  
[PERSON] et al. [25] tested Equation (4) for soil moisture using multiple spectral frequencies in laboratory conditions for a range of soil types, and found that SWIR bands (wavelength between 1100 and 3000 nm) are optimal for soil moisture detection. Therefore, Equation (4) was qualified as 'SWIR-transformed reflectance' (STR) [40]:  
\[\text{STR}=\frac{(1-SWIR)^{2}}{2(SWIR)} \tag{5}\]  
In the calculation of STR, commonly used spectral bands include Landsat-8 Band 7, Sentinel-2 Band 12 and MODIS Band 7 [40; 41; 42; 43; 44]. For this study, \(SWIR\) in Equation (5) refers to Landsat-8 Band 7 (2200 nm). High STR values indicate high soil moisture and vice versa.
## 3 Results and Discussion  
### Cell Culture and pDNA Recovery  
Cell culture was inoculated from a previous culture at exponential growth phase. A cell culture OD of 5.47 was reached after eight hours of culture. pVAX1-NH36 recovery and purification was done according to kit protocol. pVAX1-NH36 was concentrated in the ultrafiltration system described above to obtain a plasmid stock solution with a concentration of 774.6 mg/L. A ratio of A260/A280 = 1.9 was obtained. This measurement is used to assess the purity of DNA. A ratio of \(\sim\)1.8 is generally accepted as \"pure\" for DNA. If the ratio is appreciably lower, it may indicate the presence of protein, phenol or other contaminants that absorb strongly at or near 280 nm.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 3 Materials and Methods
### Improved YOLOv5
#### 3.3.2 Improve BottleNeckCSP Module  
Because it is necessary not only for the UAV object detection algorithm to accurately identify animals in different environments in the grassland, but also to reduce the model as much as possible and increase the calculation speed in order to realize real-time detection using a UAV, the BottleNeckCSP structure in the backbone network of the YOLOv5s framework is optimized. This ensures that, while improving the detection speed, the accuracy of object detection does not change significantly, thereby resulting in a lightweight UAV object detection model.  
According to the architecture of the YOLOv5s network model, the backbone network contains three BottleNeckCSP modules, and there are more convolutional layers in this module. Although the convolutional layer can be used to effectively extract the features of a picture, there are also more parameters in the convolutional layer, which means that there are more parameters in the model, which leads to a decrease in calculation speed. In response to this problem, the BottleNeckCSP module is optimized in the backbone network. The convolutional layer of branch two is deleted, and the input of the BottleNeckCSP module is merged directly with the result of the branch one processing. This will lead to the increase of feature map channels after concat, so that the parameters of convolution will increase in output, and the number of parameters will remain unchanged after calculation. Considering the redundancy of the feature graph, a layer was deleted every four channels in the input channel of branch 2 to make the input channel 3/4 of the original, so as to reduce the number of parameters of the model under the condition of ensuring accuracy. The structure is shown in Figure 5a,b.
## 3 Localisation of a City Bus with GNSS
### Performance Evaluation of the GNSS-Based Localisation System
This means that it is possible to provide precise positioning of the bus during the entire docking manoeuvre based only  
Figure 5: Exemplary parts of the trajectories, where the RTK FIXED mode of GNSS was not available because of the poor sky visibility caused by buildings and trees. Image (**1**) presents the part of the trajectory from Figure 4B, while image (**2**) shows an example scene from Figure 4C. Figure 6: Absolute Trajectory Error calculated between the _moving_base_ (reference) and _rover_ positions. The error was calculated for the part of the Garbary PKM–Os. Debina route where the RTK FIXED mode of GNSS was partially not available due to high buildings and trees located near the road. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence (Bus Stops)** & **ATE\({}_{RMS}\)** & **ATE\({}_{max}\)** & **ATE\({}_{mean}\)** & **ATE\({}_{std}\)** \\ \hline part of Garbary PKM–Os. Debina & 0.777 m & 2.0943 m & 0.640 m & 0.439 m \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Errors between the _moving_base_ and _rover_ positions, calculated for the part of Garbary PKM–Os. Debina route, where the RTK FIXED mode of GNSS was partially not available. Columns contain the sequence name, and statistics (RMS, max, mean and standard deviation of the error, respectively). on satellite navigation and without the need to use any external sensors. The main reason for that is the open space around the chargers and the lack of nearby high-rise buildings that could interfere with the satellite signals.
## 5 Experiments and Results
### Discussion
Finally, the relatively small performance drop (\(\sim\)10%) when transitioning from the clean UrbanSound8K dataset to the noisy, real-world UN15 dataset demonstrates the strong generalization capability of the ResNet-TF model. This suggests that the proposed TF-attention design is not only effective in academic benchmarks but also practical for real-world urban sensing applications, such as smart city noise monitoring, public safety, and acoustic anomaly detection.
# Last-Mile Optimization Using Neural Networks
## 2 Related Work
### Online Game Classification  
[PERSON] et al. (2022) ([PERSON] et al., 2022) investigated detecting game bots in a mobile online game by using MLP neural networks. The results demonstrated that the proposed approach achieves a higher accuracy of 99.894% compared to the widely used RF algorithm.
## 4 Results and Discussion
### Hard Brake Scenario  
The performance of the NN over the INS in the hard-brake scenario is evaluated over 17 test sequences averaging 188 m of travel with a 259 m maximum journey length. From Table 12, we observe that much to our expectations, the hard brake scenario proves to be more of a challenge for the accelerometer than the gyroscope as the INS struggles to accurately estimate the displacement and orientation rate of the vehicle within the simulated GPS outage period. As further emphasized on Figures 5 and 11, Figures 6 and 7, the NN significantly outperforms the INS DR across all performance metrics employed by max and average CRSE of 15.80 m, 0.25 rad/s and 6.82 m, 0.09 rad/s for the NN compared to 133.12 m, 1.89 rad/s and 41.07 m and 0.37 rad/s, respectively, of the INS DR. The reliability of the NN in consistently correcting the INSs estimations to such accuracy is further established by its \(\sigma\) value of 4.23 and 0.08.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **IDNN (m)** & **INS DR (m)** & **IDNN (Rad/s)** & **INS DR (Rad/s)** \\ \hline \multirow{4}{*}{AEPS (/s)} & max & 0.15 & 0.91 & 0.01 & 0.02 \\ \cline{2-6}  & min & 0.01 & 0.02 & 0.00 & 0.00 \\ \cline{2-6}  & \(\mu\) & 0.05 & 0.43 & 0.00 & 0.00 \\ \cline{2-6}  & \(\sigma\) & 0.03 & 0.25 & 0.00 & 0.00 \\ \hline \multirow{2}{*}{Total Distance Covered} & max & 220.08 & & & \\ \cline{2-6}  & min & 137.72 & & & \\ \cline{2-6}  & \(\mu\) & 168.62 & & & \\ \hline \multicolumn{6}{c}{Number of Sequences evaluated} & 13 & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **IDNN (m)** & **INS DR (m)** & **IDNN (Rad/s)** & **INS DR (Rad/s)** \\ \hline \multirow{4}{*}{CRSE} & max & 15.80 & 133.12 & 0.25 & 1.89 \\ \cline{2-6}  & min & 1.15 & 5.74 & 0.03 & 0.06 \\ \cline{2-6}  & \(\mu\) & 6.82 & 41.07 & 0.09 & 0.37 \\ \cline{2-6}  & \(\sigma\) & 4.23 & 33.75 & 0.08 & 0.48 \\ \hline \multirow{4}{*}{CAE} & max & 14.75 & 133.12 & 0.05 & 1.17 \\ \cline{2-6}  & min & 0.08 & 1.37 & 0.00 & 0.01 \\ \cline{2-6}  & \(\mu\) & 0.44 & 26.50 & 0.02 & 0.21 \\ \cline{2-6}  & \(\sigma\) & 3.89 & 34.70 & 0.01 & 0.33 \\ \hline \multirow{4}{*}{AEPS (/s)} & max & 0.21 & 1.97 & 0.00 & 0.01 \\ \cline{2-6}  & min & 0.01 & 0.01 & 0.00 & 0.00 \\ \cline{2-6}  & \(\mu\) & 0.06 & 0.47 & 0.00 & 0.00 \\ \cline{2-6}  & \(\sigma\) & 0.06 & 0.43 & 0.00 & 0.00 \\ \hline \multirow{2}{*}{Total Distance Covered} & max & 258.79 & & & \\ \cline{2-6}  & min & 73.39 & & & \\ \cline{2-6}  & min & 188.48 & & & \\ \hline \multicolumn{6}{c}{Number of Sequences evaluated} & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Showing the performance of the IDNN and INS DR on the hard brake scenario.
## 3 Results and Discussion
### Textural Parameters  
Actually, textural amendment is one of the most commonly used modes in the evaluation and treatment of swallowing difficulties (dysphagia). It is an essential criterion used to define the organoleptic quality of food products. Table 3 shows the effect of adding 1% of _Spirulina_ on the textural properties of fish burgers.  
Compared to control burgers made from carp and barbel, the addition of 1% _Spirulina_ led to an increase in hardness from 8.35 to 13.53 N (\(p<0.001\)) and from 6.69 to 7.39 N (\(p<0.05\)), respectively. This could presumably be attributed to the composition differences between unfortified and _Spirulina_-fortified burgers, resulting in different protein/fat/water ratios, which are determining elements in the resulting gel consistency [34]. These results are in accordance with reports by [PERSON] et al. [36] and [PERSON] et al. [37] in burger patties with added seaweeds. However, [PERSON] et al. [38] suggested that the hardness was not modified by the addition of thyme and oregano essential oils in salmon and seaweed burgers. Furthermore, fibers and polysaccharides of _Spirulina_ can also play an important role in modifying the hardness of these processed products.  
Depending on the amount and type of fibers, controversial results have been reported on the hardness of food products. Thus, both hardening and softening have been observed when fibers were added to various cooked food products [39; 40]. Chewiness was increased significantly (\(p<0.001\)) when the microalga was added (Table 3). These results are in good agreement with the experimental data obtained by [PERSON] et al. [41]. Cohesiveness values increased when _Spirulina_ was added and statistically significant differences were found between the control and 1% _Spirulina_-fortified samples for the two types of fish burger. Cohesiveness results of carp and barbel burgers formulated with 1% _Spirulina_ were comparable with the values reported by [PERSON] et al. [42] for cattle burgers formulated with 0.5% (_w/w_) _Ulva lactuca_. In industry, the mentioned texture results might be considered satisfactory because _Spirulina_ did not mischaracterize the textural characteristics that were already known and accepted by the panelists.
## 2 Materials and Methods
### Carotenoid Profile and Quantification by HPLC-Dad  
Aliquots of 5 g from each oil were transferred to brown round bottom flasks and dissolved in 20 mL diethyl ether. Saponification was performed by stirring with 25 mL solution of KOH (30%) for 5 h, in the dark, at room temperature, under inert gas. The reaction mixture was transferred into a separation funnel containing diethyl ether and water. The organic phase was washed repeatedly with NaCl 5% solution, until complete removal of alkali (neutral pH of the water phase). The organic phase was filtered and dried over sodium sulphate and the solvent was removed completely using a rotatory evaporator, at 35 \({}^{\circ}\)C under reduced pressure. The residues containing carotenoids were dissolved in ethyl acetate and filtered through 0.2 \(\upmu\)m PTFE filters prior to injection.  
HPLC separation of carotenoids was performed using a Shimadzu LC20 AT HPLC system (Shimadzu Corporation, Kyoto, Japan) with a SPDM20A diode array detector and a YMC C30 reversed phase column (250 \(\times\) 4.6 mm i.d., 5 \(\upmu\)m particle size). The mobile phase consisted of solvent A, methanol/methyl tert-butyl ether/water (83:15.2, \(v/v/v\)), and solvent B, methyl tert-butyl ether/methanol/water (80:7.2, \(v/v/v\)). A gradient system was used, as follows: 0 min 0% solvent B, 20 min 0% B, 130 min 82% B, 132 min 0% B, followed by equilibration of column for 10 min. The flow rate was 0.8 mL/min and the chromatograms were recorded at 450 nm. The DAD operated in the range of 300-600 nm for the acquisition of UV-VIS spectra. Carotenoid standards \(\beta\)-carotene, lutein and zeaxanthin (purity \(\geq\) 98%, \(\geq\)95% and \(\geq\)98%, respectively) were purchased from Extrasynthese (Lyon, France). The identification of individual carotenoids was based on the comparison of their retention time, elution order on C30 column and UV-VIS spectra (Amax, spectral fine structure (%III/II)) with those of the available standards and with literature data. The quantification was performed using external ten-points calibration curves constructed in the range 1-100 \(\upmu\)g/mL for the three major carotenoids. The correlation coefficients were R\({}^{2}\) = 0.9912 (\(\beta\)-carotene), R\({}^{2}\) = 0.9991 (lutein) and R\({}^{2}\) = 0.9996 (zeaxanthin) [30].
# Effects of the October 2024 Storm over the Global Ionosphere
## 5 Discussion
Subsequent shielding and overshielding responses can alter or reverse these effects at mid- and low latitudes. In addition to the direct electric field effect, disturbance dynamo processes driven by storm-time neutral wind introduce the anti-Sq current systems. These include westward and poleward electric fields during the day and the eastward field at night, especially at midlatitudes [30]. Collectively, these electrodynamic and thermospheric interactions create favourable conditions for LSTID excitation and amplification during intense geomagnetic storms. The ROTI maps over the Southeast Asian equatorial ionosphere reveal clear signatures of storm-induced EPBs during the geomagnetic storm of 10-11 October 2024. During the storm's initial phase (15:00-17:00 UT), corresponding to local nighttime, intense ionospheric irregularities (>1 TECU/min) appeared in the Southern Hemisphere around 15:15 UT,peaked near 15:40 UT, and dissipated by 16:45 UT, with a noticeable southwestward movement. These EPBs are consistent with previous studies [26; 36] showing that super EPBs can form during geomagnetic storms due to the combined effects of PRE and PPEFs, leading to rapid vertical rise and latitudinal expansion. Similar post-sunset EPB activity was also reported by [PERSON] et al. (2025) [26] over Southern Australia during the 2024 Mother's Day storm. In the recovery phase, weaker perturbations (\(\sim\)0.4 TECU/min) were observed near the equator around 01:50 UT and 03:05-03:25 UT, likely driven by DDEFs, which are known to suppress or weakly trigger EPBs during post-midnight hours.