esa-sceva/satcom-chunk-collection huggingface dataset:
# A Flexible FPGA-Based Channel Emulator for Non-Stationary MIMO Fading Channels
## 3 Flexible Hardware Architecture and Implementation  
### System Architecture  
The flexible architecture of our proposed channel emulator is presented in Figure 1. It includes two primary units: the config unit and the signal processing unit. The config unit consists of user-defined scenario module and channel parameters calculation module. It provides an interactive interface for setting environment related parameters, and then calculates the channel parameters, i.e., the path number, delay, power, Doppler frequency, and phase. These channel parameters are passed through by the peripheral component interconnect express (PCIE) bus to the signal processing unit. Each signal processing unit has a four-channel structure with the analog-to-digital converters (ADC), digital-to-analog converters (DAC), and FPGA. Thus, a single signal processing unit can implement a \(4\times 4\) MIMO channel emulation. It should be noted that the proposed system architecture is flexible and theoretically supports arbitrary scaled MIMO channels within the limitation of transmission rate of PCIE.  
The signal processing unit in Figure 1 is the most important and difficult part and it generates and superposes the multiple channel fading in real-time. Due to the flexibility and parallelism, FPGA is adopted as the core operation chip in the signal processing unit. It includes three modules: delay module (DM), generation module (GM), and superposition module (SM). The first module realizes the predefined delay of each propagation path, the second module generates channel fading coefficients, and the last one carries out the superposition operation and outputs the signal. As we can see, the final output can be expressed as  
\[\left[\begin{array}{c}y_{1}\left(l\right)\\ y_{2}\left(l\right)\\ y_{3}\left(l\right)\\ y_{4}\left  
which is equivalent with the theoretical result obtained from (1)-(3).
## 3 Spatial Analysis  
### Land Cover Statistics  
The resulting thematic land cover types derived from the Planet SkySat imagery were converted in a vector data layer and used to summarize the total area and percentages of land cover classes for the city boundary of Chattanooga and approximate neighborhood boundaries within the Chattanooga city limits. The most dominant land cover type for Chattanooga is forested land cover, at 49% (Figure 6). The mean forested area for neighborhoods is 45%, with the Downtown, Highland Park, and Glenwood neighborhoods having the smallest amount of UTC in the city, at less than 30% forested land (Figure 7).  
A uniform percent canopy cover layer was also created by generating a hexagonal grid for the city, with hexagons totaling 100 acres in size, using the ArcGIS Pro geoprocessing tool \"Summarize Within\". The total area of forests was calculated for each grid and displayed as a percentage (Figure 8), giving an equal comparison of UTC density across the city [35].
## 3 Results
### Simulation of Combined Models  
As described in the sections above, a separately designed UAV model was combined and, after that, the designed machine was imported there with the system-level model. With the system-level model, the critical parameters for the UAV had been implemented, such as torque and power with desired speed and load torque. In this regard, the machine performance could be seen in the UAV model in a fast and accurate way. The results from motor implementation to the created UAV model were gathered according to the created flight profile.  
In Figure 13, the altitude command and the UAV's response is shown. The command was generated according to the flight profile, which was shared in the previous sections. The machine's ability to provide the required torque as a response to altitude commands can be seen.  
In Figure 14, the created thrust power that came from the designed motor is shown. The thrust power was calculated by considering both the propeller and the mechanical component efficiencies. In addition to the propeller, other aerodynamic parameters were considered with the related calculation.  
Figure 12: Thermal mapping of designed motor.  
Figure 13: Altitude command and response for simulated UAV model.  
Shaft torque and speed are critical parameters for creating the required thrust power to fly an aircraft. In Figure 15, the shaft power and speed changes are shown that were created by the designed motor.  
The SoC was another critical parameter for the flight profile, because it defined the flight duration in this case. As explained before, the simulation was stopped when it reached 20% of the SoC level. Also, it can be seen in Figure 16 that energy consumption changed according to the altitude commands, which required more or less thrust to keep to the selected path.
## 3 Results and Discussion
### Comparison Results Summary  
The performance comparison of the proposed method with other existing methods for eight pest datasets is shown in Table 12. [PERSON] et al. [35] applied a CNN and MobileNetV2. They used the Adam optimizer for large-scale pest classification and achieved an accuracy of 82.95% for eight classes of agriculture pests. Their model was trained for large-scale pest classification. However, due to the CNN, the ideal effect was not achieved in the case of large-scale differences in pest images. [PERSON] et al. [36] used a novel Transformer auto-encoder to capture the features and benefits in the classification accuracy. In the case of eight pest images, as well as small samples, the method proposed by the authors reached 85.17% for mAP. We can see that models such as Vision in Transformer (ViT) models that require large datasets for training do not work well on datasets containing images of small targets such as pests. In this case, it is difficult for ViT to capture image features, resulting in inaccurate recognition. At the same time, the field environment is  
Figure 16: RS Transformer, Faster R-CNN, YOLOv8, and DETR output through the RGSDD system.  
Figure 15: (**a**) RS Transformer with RGSDD, (**b**) Faster R-CNN with RGSDD, (**c**) YOLOv5m with RGSDD, (**d**) YOLOv8 with RGSDD, and (**e**) DETR with RGSDD.  
complex, and the image quality is full of uncertainties due to the large influence of factors such as sunlight and region when taking pictures, which lead to reductions in accuracy. In order to improve the accuracy of other models, we mixed the pest pictures generated by the RGSDD into the total training dataset in a 30% proportion, and we found that [PERSON] et al. [35]'s method was significantly improved by 6.40% and [PERSON] et al.'s method was improved by 3.06%, which proved the universality and practicability of the RGSDD method. From the experimental results, our proposed method comprising RS Transformer and the RGSDD provides good performance in few-shot learning for pest classification.
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
## 1 Introduction
Therefore, some kind of new method needs to be explored to resolve some of these mentioned problems. Here, longitudinal steel bars were set as stiffeners in the tube of square thin-walled CFST columns and spot welding was used to fix the steel bars onto the tubes, which were proposed as a new structural measure in this paper. Compared with some existing steel-plate stiffeners (Figure 1), the steel-bar stiffeners are simpler and more convenient for construction. In addition, steel bars have certain longitudinal rigidity and can be connected to the steel-pipe wall by spot welding. Using the spot-welding method can simplify the construction process and reduce the workload of welding significantly as well. To test the effect of such structural measures and their feasibility in practical projects, an experimental study was conducted in this paper on the compression behaviors of square thin-walled CFST columns with steel-bar stiffeners inside the tube. Several setting methods of the steel-bar stiffeners were compared and analyzed on their improving effects on the mechanical performance of stub columns through static-loading experiments. Figure 1: Comparison of existing stiffeners and steel-bar stiffeners. (**a**) Binding bar; (**b**) tensile sheet; (**c**) angle brace; (**d**) steel bar.
## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Supervised Learning Methods
SVM was used by [PERSON] and [PERSON] [39] for automatic ECG classification by analyzing 12-lead ECG waveforms. The analysis was done using the PTB Diagnostic ECG Database containing various instances of QRS complex, P-wave and T-wave morphologies. Empirical tests on 209 patients yielded a good result with 94.16% recognition rate. [PERSON], [PERSON] [18] proposed a Support Vector Machine (SVM)-based arrhythmatic beat classification using ECG signal dataset from MIT-BIH to categorize patient heart status into normal and abnormal subjects. DWT along with DENLMS adaptive filter were used to extract HRV features from the ECG signals. The results from the experiments indicated that SVM-based classifier achieved a maximum accuracy of 96%. Linear SVM based on PCA Network (PCANet) feature extraction approach was also applied on MIT-BIH database ECG dataset for automatic recognition of five classes of arrhythmia by [PERSON], [PERSON] [17]. Their results indicated that the method has high recognition accuracy(up to 97.77%) in classifying noisy and skewed heartbeats. [PERSON], [PERSON] [86] presented a robust expert system platform for diagnosing arrhythmia from multivariate heterogeneous medical time series consisting of ECG, EEG and HRV using SVM, MLP and NEAT. The approach tested on the dataset from MIT-BIH database yielded the highest accuracy of up to 77% on NEAT. The peculiarity of the system is that it can use any of the time series to perform the diagnosis. In another direction, [PERSON], [PERSON] [65] applied One-Class SVM for building an ECG biometric authentication from features extracted from recordings using different dimensionality reduction. The work built an authentication layer to provide the security of ECG recordings in a diagnostic expert system. The findings from the experiments indicated higher recognition rates of Gaussian based SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA). **Findings:** There has been extensive research interest in the application of supervised learning to ECG interpretations especially for detecting specific heart problem or for identification. Such applications are based on binary classification and DBN, Jrip, DT, k-NN and SVM have been widely used. Nonetheless, researchers have used SVM more than any other supervised learning algorithms for ECG interpretations in the last decade due to its robust structure that could accommodate complex and numerous features of ECG. SVM was, however, found to perform poorly in detecting multiple heart diseases from ECG signals.
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
## 4 Discussion
Additionally, exploring the potential of hybrid models that combine the strengths of the neural network and traditional approaches could lead to the creation of more robust and reliable diagnostic systems capable of operating effectively under limited computational resources and changing external factors. Furthermore, future research prospects include conducting similar computational experiments with an expanded dataset size for generalizability and extending the on-board helicopter condition-monitoring experiment time to improve reliability.
## 4 Experimental Evaluation
### Experiment Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experimental Plan** & **Translation Accuracy/\%** & **Model Size** & **Inference Time/ms** & **Performance Degradation Rate** \\ \hline No Compression & 45.7 & 100\% & 150 & 0\% \\ Structured Pruning & 45.1 & 62.4\% & 120 & \(-1.3\%\) \\ knowledge distillation & 44.3 & 54.7\% & 110 & \(-3.1\%\) \\ Mixed Compression & 43.2 & 49.7\% & 85 & \(-5.7\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of the LightChatGLM model at different compression levels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experimental Plan** & **Translation Accuracy/\%** & **Model Size** & **Inference Time/ms** & **Training Time/h** \\ \hline Baseline Model & 45.7 & 100\% & 150 & 10 \\ Parallel Training Algorithm & 45.7 & 100\% & 150 & 6 \\ Mixed Compression & 43.2 & 49.7\% & 85 & 10 \\ Hybrid Data Fine-Tuning & 46.3 & 100\% & 150 & 10 \\ Parallel Training + Mixed Compression & 43.4 & 49.7\% & 85 & 6 \\ Parallel Training + Hybrid Fine-Tuning & 46.7 & 100\% & 150 & 6 \\ Mixed Compression + Hybrid Fine-Tuning & 44.8 & 49.7\% & 85 & 10 \\ LightChatGLM & 45.4 & 49.7\% & 85 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative results of LightChatGLM ablation experiments. The parallel training algorithm significantly reduced the training time from 10 h to 6 h, without compromising the translation accuracy compared with the Baseline model. This demonstrates its effectiveness in accelerating training while maintaining model performance. The mixed compression technique provided notable benefits by reducing the model size by 10% and decreasing inference time by approximately 43%. However, the application of mixed compression alone resulted in a slight degradation of model performance, indicating that while compression is effective in optimizing resource efficiency, it may adversely impact the model's accuracy. Hybrid data fine-tuning, on the other hand, improved the model's translation accuracy from 45.7% to 46.3%, highlighting its ability to enhance the model's understanding of complex linguistic structures, particularly in tasks related to computerized English comprehension. When combining parallel training, mixed compression, and hybrid data fine-tuning, LightChatGLM demonstrated superior performance across various metrics. This integrated approach yielded the best overall outcomes, particularly in balancing translation accuracy, reducing model size, accelerating inference time, and shortening training duration, thereby offering significant practical advantages.
# The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin:
###### Abstract  
Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01-d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.  
WRF-ARW model; Upper Indus Basin; Karakoram region; Pakistan; climate change +
Footnote †: journal: Applied Sciences  
1
## 2 System and Channel Model
### SIMO MRR TOW Links with Spatial Diversity and OC  
The total ABER metric for the investigated TOW MRR system with spatial diversity and OC signal reception is given as [4, 12, 22]:  
\[P_{e,av,M}^{\text{OC}}=\int_{h}f_{h}^{\rightharpoonup}\left(\overset{ \rightharpoonup}{h}\right)\,Q\Bigg{(}\frac{1}{\sqrt{2 MN_{0}}}\sqrt{\sum_{m=1}^{ M}(\eta_{m}h_{m})^{2}}\Bigg{)}d\overset{\rightarrow}{h}, \tag{17}\]  
where \(\overset{\rightharpoonup}{h}=[h_{1},h_{2},\ldots,h_{M}]\) is the vector of the total channel state for each one of the \(M\) copies of the propagating light signal.  
By substituting the accurate approximation of [35] for the \(Q\)-function, i.e., \(Q\approx\frac{1}{12}\Big{[}exp\Big{(}\frac{-x^{2}}{2}\Big{)}+3 exp\Big{(} \frac{-2x^{2}}{3}\Big{)}\Big{]}\), into Equation (17), the latter expression gives [36]:  
\[P_{e,av,M}^{OC}\approx\frac{1}{12}\prod_{m=1}^{M}\int\limits_{0}^{\infty}\exp \Big{(}-\frac{\eta_{m}^{2}h_{m}^{2}}{4 MN_{0}}\Big{)}f_{h_{m}}(h_{m})dh_{m}+ \frac{1}{4}\prod_{m=1}^{M}\int\limits_{0}^{\infty}\exp\Big{(}-\frac{\eta_{m}^ {2}h_{m}^{2}}{3 MN_{0}}\Big{)}f_{h_{m}}(h_{m})dh_{m}. \tag{18}\]  
By utilizing for each integral above \(\int_{0}^{\infty}x^{n}exp\big{(}-zx^{2}\big{)}dx=\frac{\Gamma\big{(}\frac{n+1 }{2}\big{)}}{2z},n>-1,\ Re(z)>0\) ([34] Equation (06.05.02.0001.01)), and by then using Equation (13), we conclude with the following analytical total ABER expression for the examined IM/DD OOK MRR TOW system with spatial diversity, NZB stochastic pointing errors and OC reception technique for the signal:  
\[\begin{split} P_{e,av,M}^{OC}&\approx\frac{1}{12} \prod_{m=1}^{M}\frac{\xi_{m}\big{[}\rho_{m}\big{(}\xi_{m}^{2}+2\big{)}\big{]} ^{-\xi_{m}^{2}/2}\Gamma\big{(}\xi_{m}^{2}/2\big{)}}{2(4M)^{-\xi_{m}^{2}/2}}\\ &+\frac{1}{4}\prod_{m=1}^{M}\frac{\xi_{m}\big{[}\rho_{m}\big{(} \xi_{m}^{2}+2\big{)}\big{]}^{-\xi_{m}^{2}/2}\Gamma\big{(}\xi_{m}^{2}/2\big{)} }{2(3M)^{-\xi_{m}^{2}/2}}\end{split} \tag{19}\]
###### Abstract
For the optimization of food supply chain purchasers' trading portfolios, the Practical Byzantine Fault Tolerance (PBFT) algorithm was laid out and validated using a consortium blockchain [9; 10]. However, major concerns in agriculture related to seed distribution have not received enough attention. There is a requirement of the seed distribution framework that allows farmers to keep track of seeds from breeders throughout the chain and to identify the sources of seeds. In addition, the supply chain framework allows transparent and fair bidding and tender mechanisms for logistics. Blockchain is an immutable technology consisting of interconnected blocks of data, each containing a list of transactions and a unique reference to the previous blocks [2; 11; 12; 13]. By assigning unique digital identifiers, blockchain enables the easy traceability of food products across the supply chain, incorporating essential information such as batch numbers and expiry dates [3; 14]. This ensures transparency and facilitates the efficient tracking of food products from their origin to the end consumer. Implementing a food ledger and transaction register through blockchain technology has the potential to prevent fraud and enhance traceability, enabling the identification of the sources of foodborne illnesses [15]. _Our Contribution:_ We propose a blockchain-based solution that not only significantly reduces corruption but also optimizes the transaction of seeds for farmers and government officials. The proposed approach is a \"decentralized application\" that keeps an immutable record of every transaction between farmers and the Food Corporation of India (FCI). It allows farmers to keep track of seeds throughout the supply chain across all stakeholders and to identify the original sources of seeds. In addition, the system grants non-fungible tokens corresponding to approved seeds that explicitly state their date of cultivation and their owner to make the system more transparent. Furthermore, it enables a fair auction-driven transportation system for logistics. The proposed solution is implemented and deployed in a P2P (peer-to-peer) network and is executed on a two-phase verification system that primarily comprises nodes (FCI, farmer, warehouse, transport) in the chain. Farmers looking to buy seeds and fertilizers can check prices and availability at various FCI warehouses within a pre-specified location. Our proposed solution offers an effective mechanism for farmers to conveniently purchase seed products from warehouses and carry out transactions using Ethers \(Eth\) on the Ethereum mainnet private blockchain. The rest of this work is organized in the following manner. Section 2 describes the existing seed distribution system and its limitations. Section 3 describes the tool known as blockchain, along with its types and smart contracts. The proposed \"SeedChain\" framework is discussed in Section 4, while Section 5 discusses its implementation along with its features and security analysis. Finally, Section 6 discusses the conclusion and future work.
## 3 Results
The performance results for the LM and MM ROI areas are presented in Table 6, while the results for the healthy and torn meniscus cases are presented in Table 7.  
Table 6 shows that Method 3 is the most successful MM and LM segmentation method. For MM, 0.9006 (\(\pm\)0.0084) DSC and 0.9435 (\(\pm\)0.0104) Sensitivity values were obtained in the test set, and 0.9055 (\(\pm\)0.0069) DSC and 0.9008 (\(\pm\)0.0151) Sensitivity values were obtained in the external set. For LM, it showed superior performance with 0.8923 (\(\pm\)0.0134) DSC and 0.9522 (\(\pm\)0.0111) Sensitivity in the test set and 0.8916 (\(\pm\)0.0128) DSC and 0.9534 (\(\pm\)0.0129) Sensitivity in the external set. Table 7 shows the performance of the masks for healthy and torn meniscus segmentation. In particular, Method 3 achieves the best healthy and torn meniscus segmentation results in both datasets. Model 1 stands out as the second most successful method for healthy meniscus after Method 3. In the external set, Model 1 stood out with DSC values of 0.8705 (\(\pm\)0.0202) and Sensitivity values of 0.9591 (\(\pm\)0.0120). However, Method 3 gave more consistent results with lower uncertainty than Model 1. Method 2 ranked second in the segmentation of torn meniscus. It performed close to Method 3 with DSC values of 0.8882 (\(\pm\)0.0093) and Sensitivity values of 0.8826 (\(\pm\)0.0205) in the external set. Considering the quantitative results obtained by the proposed methods on the test and external sets, Method 3 performed the best. Cohen's Kappa, Jaccard Index, and Matthews Correlation Coefficient (MCC) metrics were used to evaluate the overall consistency of the method and its balance between classes. These metrics aim to demonstrate the method's success beyond chance-based classification and its Sensitivity to class imbalances. In the test set, these metrics were calculated as 0.8972 (\(\pm\)0.0071), 0.8158 (\(\pm\)0.0117), and 0.8991 (\(\pm\)0.0068), respectively. In the external set, 0.8999 (\(\pm\)0.0065), 0.8999 (\(\pm\)0.0065), and 0.9016 (\(\pm\)0.0061) values were obtained, respectively. The DSC and Sensitivity differences between the test set and the external set of Method 3 were compared to evaluate the model's overall segmentation success and generalization ability. According to the \(t\)-test analysis, the \(p\)-value for DSC was calculated as 0.364. Since this value was more significant than 0.05, it was determined that there was no statistically significant difference between the DSC metrics in the test set and the external set. On the other hand, the \(p\)-value for Sensitivity is calculated as 0.0000087, which is much smaller than 0.05, so there is a statistically significant difference between the Sensitivity metrics between the two datasets.