esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Feature Selection and Model Development
#### 3.3.4 Statistical Power Considerations  
Based on established guidelines, our sample size (\(n=90\), with 34 CI and 56 CN cases) provides adequate power for the primary classification analysis. The AUC of 0.910 achieved with narrow confidence intervals (0.853-0.967) demonstrates robust discrimination. Our nested cross-validation approach and bootstrap-derived confidence intervals further confirm the model's stability despite the modest sample size.  
For the secondary correlation analyses, the power calculations indicate that detecting moderate correlations (\(r=0.3\)) with 80% power at \(\alpha=0.05\) requires approximately \(n=84\) participants [50]. Our sample of 90 participants therefore exceeds this threshold, though the detection of smaller effect sizes may be limited. However, the subgroup analyses (MS: \(n=45\); NMOSD: \(n=34\)) may be underpowered for detecting moderate correlations within disease groups. While our results indicate robust discrimination, a larger, multi-center cohort would strengthen the reliability of these findings further.  
Figure 11: The confusion matrix for the simplified 5-feature model, showing slightly lower specificity but comparable sensitivity versus those of the complete model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & \begin{tabular}{c} **Complete Model** \\ **(Eight Features)** \\ \end{tabular} & \
\begin{tabular}{c} **Simplified Model** \\ **(Five Features)** \\ \end{tabular} &
\begin{tabular}{c} **Baseline Model** \\ **(Four Features)** \\ \end{tabular} \\ \hline AUC & 0.910 (0.853–0.967) & 0.921 (0.853–0.989) & 0.884 (0.817–0.952) \\ Sensitivity & 0.765 (0.588–0.893) & 0.750 (0.566–0.885) & 0.735 (0.556–0.871) \\ Specificity & 0.911 (0.804–0.970) & 0.828 (0.706–0.914) & 0.821 (0.696–0.911) \\ F1-Score & 0.800 (0.679–0.895) & 0.727 (0.590–0.836) & 0.725 (0.592–0.831) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of key models with 95% confidence intervals.
## 3 Results
### Performance Analysis on Weather Data
The statistical performances were presented by means of BIAS and RMSE, as defined in Equations (2-3), where \(\mathrm{X}_{\mathrm{p}}^{i}\) are the bias-corrected daily reanalysis values for the variable of interest and \(\mathrm{X}_{\mathrm{b}}^{i}\) are the daily weather observations at the AWSs sensors. Figure 4 shows the differences between the computed performance indicators of each reanalysis dataset and the kriging estimator (as depicted in Figure 3), providing information about the convenience of using reanalysis datasets versus spatially interpolated ground sensor weather data. The benchmarks data for the computations of the BIAS and RMSE remain the daily weather observations at AWSs sensors. Figure 4a shows the differences between the corresponding absolute BIAS, while Figure 4b shows the differences between the corresponding RMSE. Except for RMSE computed for RS, the color scales are built to range from negative values when kriging is performing better than reanalysis (blue on the color bar), to positive values when kriging is performing worse than reanalysis (red color on the color bar). In case of RS, the color scale represents only positive increasing values from blue to red. Reanalysis and kriging show generally similar accuracies in temperature estimates (see T in Figure 4a), while kriging slightly outperforms reanalysis in term of RMSE (see T in Figure 4b). ERA5-Land shows generally more favorable RMSE values than UMS. AWS n.1 is a special case that deserves a note, since it is located on the ridge of the Sorrento's Peninsula, characterized by high elevation in proximity of the coastline. The effect of this complex terrain features cannot be represented by the coarse spatial resolution of the numerical grid. Moreover, as shown in Figure 2b, E5L does not provide valid data grid points located in proximity of this coastal ridge. Any estimation at AWS n.1 must rely on bilinear interpolation of grid points located in the inland plain. With respect to wind speed, both BIAS and RMSE show that kriging estimator outperforms reanalysis roughly in only half of the AWSs and that E5L outperforms UMS. With respect to vapor pressure deficit and solar radiation, differences in BIAS and in RMSE show that kriging estimator outperforms reanalysis and that E5L outperforms UMS, which presents limitations in predicting solar radiation, as also reported in the UERRA user guide [56]. Figure 4: Differences between statistical indicators of reanalysis data (UMS and E5L) and kriging (Figure 3): (**a**) absolute BIAS; (**b**) RMSE.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 3 Hardware Architecture
### Modular Arithmetic-Logic Unit
#### 3.1.2 Processing Element  
The processing element (PE) includes an addition/subtraction module and a controller. The controller drives the addition/subtraction module to perform modular additions, subtractions, or multiplications. In our design, the addition/subtraction module naturally performs the modular reduction in each operation; there is no need for an additional modular reduction module. Each PE contains three registers. In the case of addition and subtraction, two registers are used to store the operands (registers A and B), and the sum/product (SP) register stores the sum or difference. In the case of multiplication, the multiplier and multiplicand are stored in registers A and B; the product is put into register SP. During the multiplication, the multiplier is shifted for each cycle. Depending on the width of the operands, the multiplication could be performed in multiple cycles. The final result is valid when the finish signal is activated. Figure 3 illustrates the architecture of the processing element. The register \(p\) is used to store the prime number for the modular operations. It is shared among different PEs in the system.  
Figure 4: Three-operands carry save adder.  
Figure 5: Three-operands conventional adder.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 5 General Design Principles and Features of Small Satellites for High-Resolution Fire Monitoring  
By exploring new technologies and methods, initial projects in the university environment showed that small satellites can prepare new experiments and technologies for prospectively larger missions or provide data for complementary scientific questions. Small satellite missions offer the following specific features: implementation with a small budget, shorter development and manufacturing times, smaller but focused user communities, and implementation of new technologies associated with higher technical risks [10].  
With respect to the cost drivers of satellite missions, small satellites appeared to be the most suitable solution to achieve challenging mission objectives under low-budget constraints, in particular due to the:  
* possibility of so-called \"piggy-back launches\" together with other satellites, because usually the cost of a piggy-back launch is much lower than that of a dedicated launch;
* accomodation of compact payloads with a high peak power (up to 100 W) and reasonable mean power consumption (<30 W); and
* technical feasibility of small satellite buses that provide sufficient electrical power and volume for these compact payloads with a high peak power and reasonable mean power consumption.  
Particularly with regard to the last two points and the associated technical challenges, innovative solutions had to be found for the BIRD demonstrator mission: (a) for the development of a bi-spectral IR camera with cooling machines as a compact sensor system for use in space; and (b) for the development of a small satellite as a complete system with:  
* a single-fault tolerance system design;
* a three-axis stabilization of the satellite's orientation;
* an electrical power supply of 200 W at peak; and
* a total mass of the satellite bus <100 kg.  
The BIROS satellite bus, like the nearly identical TET-1 satellite bus, is based on the same design principles as the BIRD satellite. The BIRD satellite bus segment structure shown in Figure 10[11] was new compared with the classic skin-frame structure (or panel structure) as used, for instance, in the French Myriad (see Figure 11) and Portuguese small satellite platforms.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 3 Methodology  
This section introduces the proposed time delay estimation algorithm called non-line of sight cancellation multiple signal classification (NC-MUSIC). The structure diagram of the proposed NC-MUSIC algorithm is shown in Figure 3. First of all, NLOS/LOS is recognized by cross-correlation results of received signal, if the received signal is NLOS propagation, the non-direct paths will be cancelled according to the cross-correlation results. Then, MUSIC peak search is conducted on the NLOS cancelled signal with the multipath number estimated by unsupervised multipath estimation. And finally, the accurate estimation of direct signal propagation time delay is outputted. The following is a detailed description of the NC-MUSIC algorithm.  
Figure 3: The structure diagram of the proposed NC-MUSIC algorithm.
## 5 Conclusions  
This study addresses the FJSP in multi-product, variable batch production environments by proposing an intelligent scheduling method that integrates HEDGNN with DRL. By constructing an HEDG model, the scheduling states are dynamically represented, leveraging the graph neural network's capability to deeply extract operation-machine interaction features. Additionally, the PPO algorithm's policy iteration mechanism facilitates the joint optimization of operation selection and machine assignment decisions. Experimental results, validated in the context of an aerospace structural component processing workshop and the well-established Brandimarte benchmark dataset, demonstrate that the proposed method significantly outperforms traditional dispatching rules and existing DRL approaches. The proposed method balances scheduling optimization and response efficiency, proving its superiority in various scheduling scenarios. Additionally, it consistently achieved competitive results across instances of different sizes, showing improvements over state-of-the-art DRL methods and rule-based heuristics.  
This research focuses on scheduling demands within static production environments, primarily addressing resource allocation issues arising from concurrent tasks. However, it does not account for dynamic disturbances such as equipment failures and urgent order insertions, which can impact scheduling stability. Future work should concentrate on dynamic event perception and the design of online rescheduling strategies. By integrating virtual and physical digital technologies, adaptive scheduling systems can be developed to enhance the robustness and practical applicability of scheduling systems in real-world engineering scenarios.  
Conceptualization, Y.P. and Y.L.; data curation, Y.P. and Y.C.; funding acquisition, J.Z. and Y.L.; methodology, Y.P.; writing--original draft, Y.P. and Y.C.; writing--review and editing, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key Research and Development Program of China (No.2022 YFB3302700) and National Natural Science Foundation of China (No. 52375486).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{3}{c}{**DRL-Based Methods**} & \multicolumn{3}{c}{**Rule-Based Heuristic Methods**} \\ \cline{2-7}  & **Proposed Method** & [46] & [47] & **SPT + NINQ** & **MWKR + NINQ** & **EDD + NINQ** \\ \hline MK01 & 52 & 48 & **45** & 49 & 57 & 55 \\ MK02 & 34 & 34 & **31** & 43 & 41 & 39 \\ MK03 & 212 & 235 & 220 & **205** & 234 & 230 \\ MK04 & 73 & 77 & 72 & 78 & 99 & 78 \\ MK05 & **182** & 192 & 185 & 184 & 202 & 184 \\ MK06 & **90** & 121 & 97 & 92 & 114 & 92 \\ MK07 & **199** & 216 & 210 & 214 & 220 & 214 \\ MK08 & 528 & **523** & 534 & 541 & 579 & 541 \\ MK09 & **340** & 375 & 356 & 350 & 397 & 350 \\ MK10 & **263** & 317 & 283 & 278 & 294 & 278 \\ Average & **197.1** & 213.8 & 203.3 & 203.4 & 223.7 & 206.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison on the Brandimarte dataset.
## 3 Thread Protocol Overview
### Device Types and Topology  
Thread network supports a connectivity model where the end devices are only connected to their parent nodes while all the routers are connected together, forming a mesh network. The devices in Thread play different roles based on their functionalities [14; 24; 25], as shown in Figure 2. In IEEE 802.15.4 networks, the commonly used terminologies of coordinator, router, and end device are prevalent. However, the Thread network architecture introduces specific roles and functionalities that bring added clarity and facilitate customized network design for IoT applications.  
* **End Device** These devices have reduced functionalities and are energy-constrained, similar to their counterparts in other wireless protocols like ZigBee and Bluetooth. However, Thread provides specific features to optimize energy consumption, such as allowing end devices to enter sleep mode to conserve energy. Unlike some other protocols, Thread end devices are always connected to their parent node, which has routing capabilities and remains continuously active. Thread also introduces the concept of Router Eligible End Devices (REEDs) that can be promoted to routers for improved performance. The dynamic up-gradation and down-gradation of devices is managed both explicitly by the network manager and automatically by the network based on prevailing conditions.
* **Router** Thread routers share similarities with routers in other protocols, such as ZigBee and Bluetooth, in terms of their routing capabilities. These devices require power throughout and are also called Full Thread Devices (FTD). All routers in the network are connected together, forming a mesh, and route the data packets from source to destination nodes. Additionally, Thread routers also provide joining and commissioning services to the end nodes. These nodes are also capable of becoming border routers or leader routers, and if a leader goes down due to some reason, these nodes can be automatically elected as leaders by the network.
* **Border Router** This router connects the Thread network with the outside world and enables data flow in and out of the network. It can also be termed the gateway of the Thread network to the global internet or cloud. It can support both WiFi and Ethernet for connection to an external network. Unlike other networks having a single gateway, in Thread multiple border routers can also exist in a network, thus avoiding a single point of failure.
* **Leader** Any router or border router that manages the Thread network is called a leader. It has all the functionalities of the router but also has additional responsibilities like assigning IP, upgrading or downgrading the router status of REEDs, and distributing the network configuration information to the routers in the network. Unlike other wireless protocols, such as Bluetooth and ZigBee, which may have central devices or  
Figure 2: Basic topology and device types of Thread network [14].  
coordinators, if the Thread leader goes down, a router is elected as a leader by other thread routers to perform network management tasks.
## 4 Results and Discussion
As shown in Figure 5, the performance of (2,4) is better than that of (4,2), which is explained by the fact that multiple antennas at the receiver give a better performance improvement than that of multiple antennas at the transmitter, when the number of total antennas is equal. Thirdly, Figures 6 and 7 depict the ASER versus the SNR with various HAP and user antenna configurations for BPSK and QPSK respectively. The Monte-Carlo simulation results validate the effectiveness of the analysis. As shown in Figures 6 and 7, the ASER decreases when the average SNR increases. We also observe that ASER is effectively decreased when the number of HAPs and user antennas increases. For example, when BPSK modulation is used, \(\gamma=10\:\mathrm{dB}\), and the network configurations are (4,4) and (2,2), the ASER performances are 16.8 dB and 3.5 dB better than that of a single antenna configuration, _i.e._, (1,1). When QPSK modulation is used, \(\gamma=15\:\mathrm{dB}\), and the network configurations are (4,4) and (2,2), the ASER performances are 10.1 dB and 3.1 dB better than that of a single antenna configuration. Finally, Figure 8 shows the ergodic capacity changes along with the SNR variation for different network configurations. From Figure 8, we can observe that the ergodic capacity, which is obtained by the perfect CSI and power allocation using the water-filling algorithm, is superior to that which is  
Figure 8: Ergodic capacity changes along with variation of SNR for different network configurations (perfect CSI and unknown CSI). obtained by the unknown CSI and equal power allocation scheme, for the case of multiple HAP and user antenna network configurations. However, the ergodic capacity is the same for the case of single HAP and single user antenna configuration. As the SNR increases, the performance of the equal power allocation scheme gradually tends towards that of the water-filling scheme. Figure 9 shows the ergodic capacity changes along with the Rician factor \(\kappa\) under different SNR \((N_{H}=4,N_{r}=4)\). We can discover that the ergodic capacity obtained by perfect CSI and power allocation using the water-filling algorithm is superior to that which is obtained using unknown CSI and equal power allocation scheme, for various SNR cases. Furthermore, we can see whether it is the perfect CSI or the unknown CSI, the Rician factor \(\kappa\) has the detrimental effect on the system capacity. If \(\gamma\) is \(-10\) dB, \(-5\) dB, 0 dB, 5 dB, and 10 dB, the \(\kappa\) has little effect on the system capacity when \(\kappa\) at small values. However, when \(\kappa\) is larger, e.g., \(\kappa=20\:\mathrm{dB}\), it seriously affects the system capacity. Additionally, as \(\gamma\) is increased, the extent of the damage increases. Therefore, we can observe that the LOS signal has a negative effect on the system capacity in HAPs network utilizing V-MIMO techniques.
## 3 Results  
In the first two experiments, whether the proposed DCNN can distinguish bacilli from splendid negatives and faint negatives is shown in Table 2. According to the gold standard, the DCNN model generated the likelihood of each image block being a bacillus as a probability. A probability higher than 0.5 was regarded as a bacillus. As a result, the DCNN model yielded an accuracy of 96.6%, a sensitivity of 94.3%, and a specificity of 98.0% for the splendid group and an accuracy of 95.3%, a sensitivity of 91.1%, and a specificity of 97.1% for the faint group, respectively. By mixing the two groups into the color group as shown in Table 3, the DCNN achieved an accuracy of 95.3%, a sensitivity of 93.5%, and a specificity of 96.3%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Splendid group (1022 negative blocks) & 96.6\% & 94.3\% & 98.0\% \\ \hline Faint group (1383 negative blocks) & 95.3\% & 91.1\% & 97.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance from incorporating 613 positive blocks and two negative groups, showing the differentiating ability of the proposed deep convolutional neural network (DCNN).  
Figure 5: The computer-aided diagnosis workflow. (**A**) A whole-slide pathology image. (**B**) Cropped samples fitting to mycobacteria bacilli (20 \(\times\) 20 pixels) (**C**) Positive and negative sample selection. (**D**) Transferred deep convolutional neural network (DCNN) model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Color group & 95.3\% & 93.5\% & 96.3\% \\ \hline Grayscale group & 73.8\% & 70.7\% & 75.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance from incorporating 613 positive blocks and 1202 negative blocks with and without color information using the proposed DCNN.  
In the last experiment, grayscale images were used to emphasize the importance of color information. The stain initially used for the slides is composed of two main colors, red and blue, which are the two primary colors for distinction. After the color information was removed, the DCNN achieved an accuracy of 73.8%, with a sensitivity of 70.7% and a specificity of 75.4%. The result implies that morphological features can be used to obtain at least 70% accuracy without color features.
## Data Presentation
### Calculation of Geomagnetic Perturbations  
The geomagnetic perturbation is closely related to the horizon value variation of the local magnetic field and this can be further calculated by  
\[\Delta H=\langle S_{R}(H)\rangle+H_{SYM}\cos(\lambda)+D_{iono} \tag{12}\]  
where \(\langle S_{R}(H)\rangle\) denotes the solar daily variation of the earth's magnetic field related to the regular ionospheric dynamo. \(\lambda\) denotes the magnetic dip angle of the given location, \(D_{iono}\) represents the magnetic disturbance due to the disturbed ionosphere electric currents, \(H_{SYM}\) represents SYM-H, the contributions of the electric currents in the magnetosphere (refer to Reference [1]).
###### Abstract  
With the rapid development of heterogeneous satellite networks integrating geostationary earth orbit (GEO) and low earth orbit (LEO) satellite systems, along with the significant growth in the number of satellite users, it is essential to consider frequency compatibility and coexistence between GEO and LEO systems, as well as to design effective system resource allocation strategies to achieve efficient utilization of system resources. However, existing beam-hopping (BH) resource allocation algorithms in LEO systems primarily focus on beam scheduling within a single time slot, lacking unified beam management across the entire BH cycle, resulting in low beam-resource utilization. Moreover, existing algorithms often employ iterative optimization across multiple resource dimensions, leading to high computational complexity and imposing stringent requirements on satellite on-board processing capabilities. In this paper, we propose a BH-based beam scheduling and resource allocation framework. The proposed framework first employs geographic isolation to protect the GEO system from the interference of the LEO system and subsequently optimizes beam partitioning over the entire BH cycle, time-slot beam scheduling, and frequency and power resource allocation for users within the LEO system. The proposed scheme achieves frequency coexistence between the GEO and LEO satellite systems and performs joint optimization of system resources across four dimensions--time, space, frequency, and power--with reduced complexity and a progressive optimization framework. Simulation results demonstrate that the proposed framework achieves effective suppression of both intra-system and inter-system interference via geographic isolation, while enabling globally efficient and dynamic beam scheduling across the entire BH cycle. Furthermore, by integrating the user-level frequency and power allocation algorithm, the scheme significantly enhances the total system throughput. The proposed progressive optimization framework offers a promising direction for achieving globally optimal and computationally tractable resource management in future satellite networks.  
keywords: beam-hopping; heterogeneous satellite networks; resource allocation; spectrum sharing +
Footnote †: journal: Electronics  
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