esa-sceva/satcom-chunk-collection huggingface dataset:
# Error-Correction Coding Using Polynomial Residue Number System
## 4 Conclusions  
The article discusses the principles of constructing redundant codes of polynomial residue number system. Several theorems concerning the properties of codes of PRNS were considered and proven. It was shown that the distortion of any residue transforms an allowed code combination into a prohibited one. In this case, the criterion for the presence of an error in the code combination of PRNS is a violation of Condition (32). Studies of the code of polynomial residue number system, which has minimal redundancy, have been carried out. We have shown that this code only allows for detection an error in the code combination of PRNS. It was proposed to introduce two control bases to expand error-correction abilities. We have shown that the use of two control bases makes it possible to correct any erroneous residue in the code combination of PRNS. The article presents the developed algorithm for calculating the positional characteristic (a polynomial interval) in which only modular operations are used. This algorithm makes it possible to detect and correct errors without performing a reverse conversion from a code of PRNS to a positional code or performing a division operation, which is confirmed by the examples given in the article.  
Conceptualization, I.A.K.; Data curation, K.T.T., A.A.O. and N.K.C.; Formal analysis, I.A.K. and V.P.P.; Investigation, I.A.K., V.P.P., K.T.T. and A.A.O.; Methodology, I.A.K.; Project administration, I.A.K.; Software, N.K.C.; Supervision, I.A.K.; Validation, V.P.P., K.T.T. and A.A.O.; Visualization, V.P.P., K.T.T. and A.A.O.; Writing--original draft, I.A.K.; Writing--review and editing, N.K.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the Russian Foundation for Basic Research, project No. 20-37-90009 and by the Russian Science Foundation (Moscow), project No. 22-21-00768.  
Not applicable.  
The authors declare no conflict of interest.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 1 Introduction
These problems suggest a pressing need to review the issues surrounding TD quality and how it impacts ML-generated maps, and to recommend a set of best practices and standards for minimizing and accounting for those errors, which are the primary aims of this paper. Although map error can also originate from other sources, such as the specific ML classifier selected or the parameterization approach used [31; 58; 59], we focus solely on issues of input data quality. As such, this paper complements existing work focused on assessing final map accuracy [37; 38; 39; 40; 41; 44; 45]. This paper is organized into four sections. In Section 1, we review current practices in the treatment of TD for categorical and continuous map creation. We also cover map accuracy procedures, given that the two processes are often intertwined and affected by many of the same issues [47], and accuracy assessment procedures are needed to assess the impacts of TD error. In Section 2, we identify the most common sources of TD error and inconsistency. In Section 3, we illustrate the impacts of uncertainty in TD generation with case studies that span a range of typical EO applications: building and road mapping, global surface flux estimates, and mapping agricultural systems. In Section 4, we propose guidelines for (1) best practices in collecting and using TD, (2) minimizing TD errors associated with training sample design error and collection, (3) characterizing and incorporating TD error in final map outputs, and (4) communicating TD error in scientific and public documentation.
## 4 Design Methodology for SOA-Enabled HONoC
### Minimum SOA Gain and Laser Source Power Allocation
#### 4.3.1 Minimum SOA Gain Allocation  
The SOA gain should be sufficiently large to compensate for the insertion loss between two adjacent SOAs along the signal path. Thus, the laser source power need not be increased even if the network size grows. Overall, the laser source power level is determined by the insertion loss of the longest signal path that does not pass through any SOAs.  
As shown in Figure 10, the top-leftmost sub region \(REG(1,1)\) occupies the largest area among the sub regions because uniform placement of SOAs is performed from the top-leftmost corner of the network according to the proposed SOA placement algorithm. Assuming that the reference laser source power level is calculated by considering the longest path formed within \(REG(1,1)\), we can compute the minimum SOA gain to compensate for the insertion loss without increasing the laser source power.  
Once the laser source power is determined by the insertion loss of Path A, the next step is to identify the larger insertion loss between Path B and Path C to compute the minimum SOA gain. Finally, the minimum SOA gain is chosen as the larger value between the insertion loss of Path X2 and that of Path Y2. To summarize, the minimum SOA gain can be expressed as follows:  
\[G_{min}=max(L_{X2},L_{Y2}), \tag{16}\]  
where \(L_{X2}\) and \(L_{Y2}\) denote the insertion loss of Paths X2 and Y2, respectively. Considering mesh size and SOA spacing, we propose the following algorithm to calculate the minimum SOA gain:  
As the optical signal passes through the router in a straight manner, Algorithm 2 checks the paths \((\mathrm{W}\rightarrow\mathrm{E})\) and \((\mathrm{E}\rightarrow\mathrm{W})\) in the \(X\)-axis and \((\mathrm{S}\rightarrow\mathrm{N})\) and \((\mathrm{N}\rightarrow\mathrm{S})\) in the \(Y\)-axis, respectively, to find the maximum power loss. Then, \(L_{X2}\) and \(L_{Y2}\) are independently calculated for each axis. If \(n_{x}\geq 2\), the  
Figure 10: Example of determining the minimum SOA gain considering traffic paths across the top-leftmost non-SOA region.  
maximum hop count is the SOA spacing along the \(X\)-axis (\(t_{x}\)) (lines 2-3). Otherwise, the hop count is the number of routers in the \(X\)-axis (\(n\)) excluding \(t_{x}\) (lines 4-6). The same process is performed on the \(Y\)-axis (lines 7-11) and the larger insertion loss between \(L_{X2}\) and \(L_{Y2}\) is chosen as the minimum SOA gain (line 12).
## 4 Applications of Photosynthetic Systems
### Biomimetic Applications  
Artificial photosynthesis is envisioned as a promising technique for harvesting solar energy through water splitting and CO\({}_{2}\) reduction to generate high-energy chemical fuels [186; 187; 188; 189; 190]. Although the field of artificial photosynthesis is still in its infancy phase of research, recent advances in synthetic biology have provided a significant boost to this interdisciplinary research field. The main goal of artificial photosynthesis is to assemble molecular systems into larger-scale constructs for replicating the natural processes of photosynthesis which is a quite challenging and complex task in itself [190]. Today, artificial photosynthesis is largely focused on understanding and mimicking the ultimate functionality of the natural photosynthetic phenomena for producing energy-rich fuel using cheap and environmentally friendly biomimetic compounds [188; 190]. Thus, the essential components of an artificial photosynthetic device would be: (i) a light harvester (e.g., semiconductor) for converting solar photons to excited states, generation of charge-separation and regulation of the flow of collected excitation energy to the reaction sites, (ii) a reduction active reaction site and an oxidation active reaction site, where conversion of excited states to redox potential occurs, (iii) molecular catalysts (i.e., transition metal complexes) to assist water splitting and CO\({}_{2}\) reduction system, and (iv) linkages of different molecular and nano- and macro-scale components of artificial photosynthetic elements [188; 189; 191; 144].  
The artificial photosynthesis is a good source of production of chemical energy that can be used to reduce the amount of carbon dioxide present in the environment [192; 193; 194; 195; 196; 197; 198; 199; 200; 201; 202]. The use of artificial fertilizers has been increased in the past few decades for boosting food productivity for satisfying the needs of the growing population [203; 204]. The increased consumption also brings production of the byproducts of the natural processes occurring on a daily basis that can be utilized in the production of sustainable and green energy utilizing artificial photosynthesis. Several computational techniques are also available in the literature for studying artificial photosynthesis [205; 206; 207; 208]. In addition, the use of the small molecules to trigger the photosynthesis reaction has been recently studied in [209; 210; 211; 212; 213; 214].
## 3 Result
### Band Combination  
Based on the single-band factor correlation analysis above, four bands (P2, P3, P4, P5) other than panchromatic band (P1) are combined flexibly to reveal their correlation with Chla [43]. Twenty band combinations with relatively high correlation with Chla concentration were applied for analysis in this study (as shown in Table 3).  
Figure 2: Correlation between band reflectance and sampled chlorophyll-a (Chla). (**a–d**): The linear relationship between the reflectance of band 2,3,4,5 and Chla concentration. The abscissa is the reflectance of a single band, and the ordinate is the natural logarithm of Chla (Ln(Chla)).  
According to the correlation analysis in Table 3, six combinations with strong correlation, and large correlation coefficient, are selected for detailed analysis and interpretation. Figure 3 is the grayscale map corresponding to the combination of six bands. Different band combinations provide different information, and the degree of correlation with Chla concentration is also different.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Band Combination (X)** & **Correlation Coefficient (R2)** \\ \hline (P5 – P4)/(P5 + P4) & 0.68 \\ log(P2/P3) & 0.82 \\ (P3 + P4)/(P2 + P5) & 0.91 \\ (P3 – P4)/(P3 + P4) & 0.81 \\ (P2 – P4)/(P2 + P4) & 0.97 \\ (P5 – P4)/(P2 + P3) & 0.81 \\ P5/(P3 + P5) & 0.11 \\ P5/(P4 + P5) & 0.67 \\ PS/(P2 + P4) & 0.01 \\ P5/(P3 + P4) & 0.29 \\ P5/(P2 + P3) & 0.09 \\ P5/(P2 + P5) & 0.34 \\ P4/(P3 + P5) & 0.88 \\ P4/(P2 + P3) & 0.77 \\ P4/(P4 + P5) & 0.67 \\ P4/(P3 + P4) & 0.55 \\ P4/(P2 –P4) & 0.66 \\ P4/(P2 –P5) & 0.74 \\ (P3 – P5)/(P3 + P5) & 0.11 \\ (P2 – P5)/(P2 + P5) & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Band combination.  
Figure 3: \(Cont.\)
## 3 Materials and Methods
### Experiment Test Method
#### 3.2.3 Pore Size Testing  
1. Multiple SEM images of the same porous material were selected for pore size measurement. The pore sizes were determined by measuring the diameters of inscribed circles formed by fiber overlaps in the images, using ImageJ 1.54f software. The pore sizes were then statistically analyzed to calculate their distribution, and a pore size distribution chart was generated. The median pore size was calculated based on the measurements.
2. The median pore size and pore size distribution of the fiber porous materials were tested using a CFP-1500 AE capillary pore size analyzer (Porous Materials Inc., Ithaca, NY, USA), following the ASTM F316-03 (2019) standard [42].
## 5 Conclusions  
Firstly, the forecasting models of wind power, photovoltaic power and load are introduced. By adopting the stacking ensemble learning model, the results of multiple base learners are integrated and outputted, which effectively improves the forecasting accuracy. In addition, MIC is used for feature selection to select the optimal set of input variables. Secondly, the forecasting power data is used to predict the probability of ALSC. Specifically, the RPF method considering multi-slack buses is used to calculate the deterministic ALSC problem, and the LHS-MCS method is used to generate probabilistic results. The distributions of wind power, photovoltaic power and load power are determined by the forecasting distributions generated by point forecasting value and error statistics. Consideration of the multi-slack buses makes the RPF calculation more in line with the actual situation, and hence can obtain more accurate and reasonable ALSC evaluation results. Finally, the modified IEEE 39-bused system is used to verify the effectiveness of the proposed method. The results demonstrate that both the stacking ensemble learning model and the MIC feature selection method can improve the forecasting accuracy. Besides the influence from the prediction of renewable energy and load, the multi-slack buses and the power imbalance allocation principles of multi-slack buses both have an impact on the calculation results of ALSC. Finally, the forecasting results of ALSC probabilistic evaluation can provide a reference for the load power supply capacity of power system in the future and can also provide an early warning for the risk of ALSC threshold overlimit. There are also some limitations in the research of this paper. For example, the accuracy of ALSC probabilistic forecasting significantly relies on the accuracy of wind power, photovoltaic power and load power forecasting. Therefore, some more advanced algorithms can be adopted in power forecasting to obtain more accurate power predictions. Conceptualization, S.L. and J.T. ; methodology, Q.S., S.L., Y.X. and Y.Z. ; software, Q.S., Y.Z. and J.W. ; validation, Q.S., S.L., X.Z. and Y.Z. ; formal analysis, S.L., Y.X. and J.W. ; investigation, Q.S., S.L. and Y.Z. ; resources, Q.S., S.L., Y.X. and X.Z. ; data curation, Y.Z. and J.W. ; writing--original draft preparation, Q.S. and Y.Z. ; writing--review and editing, Q.S., S.L., Y.Z. and J.T. ; visualization, Q.S., S.L. and Y.X. ; supervision, Q.S., S.L. and J.T. ; project administration, Q.S., S.L. and X.Z. ; funding acquisition, Q.S., S.L., Y.X. and X.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Program of China Southern Power Grid Co., Ltd., grant number YNKJXM20222174 and the Reserve Talents Program for Middle-aged and Young Leaders of Disciplines in Science and Technology of Yunnan Province, China, grant number 202105 AC160014. Not applicable. Data Availability Statement: Data are available on reasonable request. The authors declare no conflict of interest.
## 3 Experiments
### Experimental Results and Analysis
#### 3.3.3 Dynamic Obstacle Avoidance Success Rate (OASR)  
In the simulation environment, dynamic obstacles (randomly moving vehicles and people) were set, and the obstacle avoidance performance of the BBCN model and other models was tested. The results are shown in Table 4.  
The BBCN model achieved a 95% success rate in dynamic obstacle avoidance, significantly outperforming the other models. The GNSS model, which relies entirely on global path planning and lacks local perception, achieved only a 40% success rate in dynamic obstacle scenarios. The Vision-SLAM model, while capable of local perception, was unstable in handling fast-moving obstacles, achieving 80% success. The GNSS-Vision model, which fuses Beidou and vision data using the EKF, showed an improvement, but still had a lower success rate than the BBCN model. This demonstrates that the BBCN model, with its dynamic path optimization and efficient collaboration mechanism, can more reliably handle dynamic obstacle scenarios.
## 4 Warm Core Structure Retrieval Experiment
### Retrieval Results
Hurricane Beryl gradually intensified at these three time points. The warm core structures retrieved from the three satellites reflect this characteristic, with the maximum temperature anomaly also gradually increasing. Therefore, utilizing multiple satellites to retrieve the warm core structure of the same TC can reflect their variation characteristics, which is of great significance for TC research.
## 4 Heuristic Algorithms for UiL Content Delivery Systems
### Capacity Allocation with Multiple Users  
A commonly adopted heuristic for radio resource allocation is to maximize the sum capacity. This, however, is not suitable for our problem setting because different users have different delay requirements. To this end, we propose a refined heuristic by letting each user's capacity be inversely proportional to his/her delay constraint. In this case, the problem of radio resource allocation can be formulated as  
\[\begin{array}{ll}\max&\sum\limits_{u=1}^{U}\sum\limits_{k=1}^{K}c_{uk}^{t} \rho_{uk}^{t},\\ \text{s.t.}&\forall t,B\times\sum\limits_{u=1}^{U}\sum\limits_{k=1}^{K}\frac{f( c_{uk}^{t})}{\alpha_{uk}^{2}}\times\rho_{uk}^{t}\leq P_{T},\\ &\sum\limits_{u=1}^{U}\rho_{uk}^{t}\leq 1,\\ & R_{1}^{t}:R_{2}^{t}:\dots:R_{U}^{t}=\frac{1}{\tau_{1}}:\frac{1}{\tau_{2}}: \dots:\frac{1}{\tau_{U}},\\ &\rho_{uk}^{t}\in\{0,1\},\end{array} \tag{21}\]  
where \(R_{u}^{t}=\sum_{k=1}^{K}c_{uk}^{t}\rho_{uk}^{t}*B\), which denotes the data rate of user \(u\) at time slot \(t\), and \(\tau_{u}\) is the required transmission delay for user \(u\). This problem is an integer programming problem, which is itself NP-hard. To solve this problem effectively, a greedy algorithm is proposed. The rationale of the algorithm is similar to the water-filling algorithm. The algorithm runs iteratively. In each iteration, a single bit is allocated to a user in a subcarrier. The allocation that demands the least power is chosen. The same process is repeated until the BS power constraint is reached.  
Figure 3: Theoretical bounds and simulation results of the total user interest as a function of the transmission delay with varying outage probability \(\xi\).  
The flowchart of the algorithm is shown in Figure 4. We note that our proposed algorithm is computationally efficient with a polynomial complexity. More specifically, the time complexity is \(O(K^{2})\) in the carrier allocation step and \(O(P_{T}UK)\) in the bit allocation step.
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 3 Tile-Type Transmitter Module Based on HTCC Technology
### Key Electrical Structure
#### 3.2.2 BGA Transition  
The BGA serves as the interconnection structure between the transmitter module and the PCB network. Similar to the quasi-coaxialstructure in the multi-layer substrate, the BGA structure is composed of a signal solder ball in the center surrounded by the ground solder ball. It can be divided into two parts: the transition from the inside of the HTCC module to the BGA pad on the back of the module, and the transmission from the BGA solder ball to the internal transmission lines of the multilayer PCB. A grounding plane is created around the solder ball. The capacitive effect between the grounding plane and the signal solder ball can be adjusted to match the inductance of the BGA solder ball, thereby achieving a good transmission performance in a wide bandwidth. Another key aspect of this design is ensuring that the diameter of the gap between the grounding plane and the solder ball is less than half the wavelength. If it exceeds half the wavelength, this structure can be equivalent to a loop slot antenna that radiates electromagnetic waves, significantly weakening the RF transmission performance of the transition structure. In essence, the BGA transition structure can be modeled as a coaxial system. By optimizing the size of the ground plane aperture, the spacing between the grounded solder balls, and the dimensions of the solder pads, an effective 50 ohm impedance matching can be achieved.  
In the proposed design, the BGA ball with a diameter of 450 um is used. The simulation model is shown in Figure 8, where the transmitter HTCC module is connected to the PCB via BGA. The inset shows the detail of the BGA transition where the signal solder ball is surrounded by the grounding solder ball, forming a coaxial-like structure. The BGA transition shows a return loss of better than 20 dB, and an insertion loss of less than 0.6 dB in a wide working bandwidth from 20 GHz to 30 GHz.  
To ensure assembly accuracy, the tolerance of the shape, cavity, and dimension of the BGA solder pad is strictly controlled. To assess the impact of the fabrication error in the BGA structure, a structure parameter analysis is carried out. The deterioration of return loss with respect to the offset of the solder ball position is simulated, as illustrated in Figure 9. The results show that the return loss is less than 20 dB, even with an offset of 100 um, which exhibits a good tolerance in fabrication and assembling.  
Figure 8: (a) The simulation model of BGA transition. (b) Simulation results of BGA interconnection.
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 3 Methodology
### Architecture Overview
#### 3.1.1 DCNv3_LKA_C2f Module  
While the YOLOv8s architecture employs C2f (CSP Bottleneck with 2 convolutions and faster) modules for multi-scale feature aggregation, their conventional convolutions have locality constraints that can limit modeling long-range spatial dependencies and handling irregularly shaped or occluded targets due to fixed sampling locations. Deformable Convolution v3 (DCNv3) [33] addresses fixed sampling by learning input-dependent offsets [34], allowing adaptive receptive fields crucial for object shape variations. This improves feature representation for complex objects and enhances computational efficiency through sparse sampling [33]. The mathematical description of DCNv3 is as follows:  
\[\mathbf{y}(p_{0})=\sum_{g=1}^{G}\sum_{k=1}^{K}\mathbf{w}_{g}\mathbf{m}_{gk} \mathbf{x}_{g}(p_{0}+p_{k}+\Delta p_{gk}), \tag{1}\]  
where  
* \(G\) denotes the total number of aggregation groups used to enrich the feature representation by partitioning the spatial aggregation process into multiple subspaces.
* \(\mathbf{w}_{g}\) represents the position-independent projection weights, which are shared within each group to reduce the number of parameters and computational complexity.
* \(\mathbf{m}_{gk}\) is the modulation scalar of the \(k\)-th sampling point in the \(g\)-th group, normalized by the Softmax function to ensure gradient stability during the training process.
* \(\mathbf{x}_{g}\) is a sliced input feature map corresponding to a specific aggregation group.
* \(\Delta p_{gk}\) refers to the offset of the sampling position of the \(k\)-th grid in the \(g\)-th group, dynamically adjusting the position of the convolution kernel to adapt to the complex target shape.  
To further enhance global feature capture, which is limited by traditional small receptive fields, we incorporate Large Kernel Attention (LKA) [36]. LKA efficiently achieves a large receptive field by decomposing a large kernel operation (e.g., into depth-wise, depth-wise dilated, and point-wise convolutions [36]), capturing long-range spatial dependencies with linear complexity. The synergy of DCNv3's dynamic sampling and LKA's large kernel advantage, termed DCNv3_LKA [52], improves detection performance in complex scenes. We fuse this DCNv3_LKA module into the bottleneck of C2f, as shown in Figure 2. The resulting DCNv3_LKA_C2f module alleviates the misdetection of complex and non-rigid targets and enhances global feature relationship modeling, especially for occluded objects, thereby reducing false negatives.