esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Bed and Materials  
### Test Bed  
The test bed for the generation of the signals used in our analysis is described in Figure 2. This is a conducted test bed connected with RF cables. The entire set-up was properly calibrated, and the loss of the RF equipment (e.g., RF cables and adapters) was recorded and considered in the measurement and data collection phases. The testbed is composed of the following laboratory equipment:  
* Agilent E8267D PSG Vector Signal Generator. This signal generator was used for a weather radar test signal defined in [23] with a sampling frequency of 40.00 MHz and a pulse width of 1 microsecond. According to the technical specifications of [23], such weather signal is repeated in time with two different Pulse Repetitions Frequencies (PRF)s. The first PRF (called PRF1 in the rest of this paper) is set to a value of 800 Hz, and the second PRF (called PRF2 in the rest of this paper) is set to a value of 1200 Hz. The number of pulses for each PRF is 18. This configuration with PRF1 and PRF2 creates a sequence of pulses with one pulse lasting 1 microsecond, one pause lasting 1.25 ms, one pulse lasting 1 microsecond, and one pause lasting 0.833 ms. The carrier frequency for the train of radar pulses is set to 5650 MHz in the PSG Vector Signal Generator.
* The RF channel emulator based on the NI-VST (Vector Signal Transceiver by National Instruments) PXIe-5645R, which was extended with additional fading models (e.g., Nakagami-m). The channel emulator implements the TDL model as a modification (i.e., using the Nakagami-m fading model and with the first 6 taps) of the TDL fading models based on the standard 3 GPP TR 38.901 Release 14 standard (version 14.0.0, page 66 to 70) [20]. The Nakagami-m distribution was used because it has various advantages versus other fading models [24]: (a) it has greater flexibility and accuracy in matching some experimental data than the Rayleigh, lognormal, or Rice distributions, (b) it is a generalized distribution, which can model different fading environments, and (c) Rayleigh and one-sided Gaussian distribution are special cases of Nakagami-m model.
* Tektronix RSA 306A Real-Time Spectrum Analyzer with 40 MHz of bandwidth with SignalVU PC software, which is used to collect the signal output from the RF channel emulator. A sampling frequency of 28 MHz is used as an optimal balance between the need to properly sample the signals and to limit the amount of data to be processed by TFT algorithms and the CNN.  
The digital output from the Real-Time Spectrum Analyzer was collected and recorded in a Personal Computer (PC) equipped with SignalVu-PC Software for further data processing using MATLAB. The description of the computing environment is in Section 4.3.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 3 State of the Art
### Destination-Less Forwarding Protocols
#### 3.1.3 Randomised Rumor Spreading (RRS)  
RRS [11] is a simple protocol in which nodes broadcast a data packet randomly at regular intervals. The packets can be destination-less or destination-oriented. Our preliminary results showed that the performance of RRS is very low compared to Epidemic.
## 3 DMCAP System
### Channel Quality Distortions Due to Malicious Events  
Evaluation of the changes and distortions happens due to abnormal environmental properties, and the distortions initiated due to malicious attacks are the major risks of WUGSN. In this concern, the WUGSN channels are vulnerable to jamming attacks, wormhole attacks, packet dropping attacks, Distributed Denial of Service (DDoS) attacks, and other malicious injections. On the field, the channel security system needs to classify the attacks and other distortions regularly.  
The proposed system configures both channel assessment policies and security practices to monitor the wireless communication parameters [39]. Algorithm 4 presents VGAN engine to be active in sensor nodes to monitor the wireless transmissions. The VGAN-enabled IDS works against different types of attacks based on attack knowledge installed in the node's local storage [40]. VGAN-IDS initiates event generator functions and event discriminator functions to monitor the adversarial activities involved in the channels.  
VGAN-IDS performs actively over multi-channel neighbor interactions to classify channel distortions into malicious issues and environmental issues separately. Thus, the proposed DMCAP model monitors environmental distortions and malicious events independently to estimate the quality of wireless channels under uncertain conditions. The VGAN-IDS model and channel assessment policies proposed in the article safeguard the multi-channel data transmission against channel distortions and suspicious events. The proposed system was implemented as shown in Section 4. Section 4 provides an immense impact on the development of proposed DMCAP model and performance evaluations. Figure 3 illustrates the overall system model implemented inside each sensor node supports for reliable network communication. The sensor node has hardware internal components and software internal components. As shown in Figure 3, the software module of each WUGSN sensor node contains proposed DMCAP procedures, VGAN-IDS engine, reactive channel estimator, and DMCAP/IDS activator procedures on demand. In addition, Figure 4 illustrates the overall DMCAP system design and the integral phases used for managing reactive data transmission. The proposed internal learning system and channel assessment procedures was initiated for active channels and nodes [41; 42].  
Figure 3: Proposed DMCAP system in sensor node.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Real-World Outdoor Experiments
#### 5.2.2 Place Recognition
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Dataset** & **Loss** & **F1** & **Recall** &
\begin{tabular}{c} **TOP1** \\ **Accuracy** \\ \end{tabular} \\ \hline ResNeXt50 & \(D_{Scene}\) & 0.417 & 0.736 & 0.732 & 0.762 \\  & \(D_{Land}\) & 0.362 & 0.791 & 0.782 & 0.839 \\ Vgg19 & \(D_{Scene}\) & 0.389 & 0.768 & 0.753 & 0.776 \\  & \(D_{Land}\) & 0.332 & 0.811 & 0.792 & 0.832 \\ GoogleNetV4 & \(D_{Scene}\) & 0.468 & 0.681 & 0.668 & 0.756 \\  & \(D_{Land}\) & 0.402 & 0.769 & 0.706 & 0.844 \\ MobileNetV3 & \(D_{Scene}\) & 0.437 & 0.703 & 0.671 & 0.754 \\  & \(D_{Land}\) & 0.384 & 0.775 & 0.742 & 0.821 \\ ResNet18 & \(D_{Scene}\) & 0.521 & 0.621 & 0.548 & 0.703 \\  & \(D_{Land}\) & 0.469 & 0.714 & 0.625 & 0.769 \\ ResNet34 & \(D_{Scene}\) & 0.491 & 0.695 & 0.683 & 0.714 \\  & \(D_{Land}\) & 0.412 & 0.743 & 0.714 & 0.801 \\ ResNet50 & \(D_{Scene}\) & 0.441 & 0.686 & 0.674 & 0.737 \\  & \(D_{Land}\) & 0.356 & 0.813 & 0.808 & 0.829 \\ ResNet101 & \(D_{Scene}\) & 0.398 & 0.765 & 0.761 & 0.793 \\  & \(D_{Land}\) & **0.305** & 0.838 & 0.832 & 0.851 \\ ResNet50 & \(D_{Scene}\) & 0.402 & 0.767 & 0.758 & 0.786 \\  +CBAM & \(D_{Land}\) & 0.322 & **0.845** & **0.842** & **0.857** \\ \hline \hline \end{tabular} The bold values indicate the best performance among the compared methods.  
\end{table} 
Table 3: Results of different neural network models trained on \(D_{Scene}\) and \(D_{Land}\). In the evaluation, the CBAM-enhanced ResNet50 model demonstrated superior performance, achieving higher accuracy than the traditional ResNet101 model with the test dataset (right-most column of Table 4). These results confirmed the significant impact of CBAM on the decision-making process of the ResNet50 model and highlighted its exceptional ability for extracting important features and capturing key information. Notably, despite the addition of the CBAM module, the improved model required a minimal increase in ROM, and it even exhibited better performance in terms of memory usage. Particularly in terms of memory efficiency, the ResNet50\(+\)CBAM model performed better than other advanced models, such as ResNeXt50 and MobileNetV3, which is important for memory-constrained airborne environments. Furthermore, considering the running efficiency requirements for practical applications, the ResNet50+CBAM model's ability to process each image with a short running time further validated its potential applicability for real-time image processing tasks (fourth column of Table 4). These advantages make the CBAM-enhanced ResNet50 model an ideal choice for airborne image processing systems because it exhibits higher performance, while maintaining an efficient use of computational resources.
## 2 Materials and Methods
### Antimicrobial Activity  
The antimicrobial activities of the natural extracts prepared according to the optimal BBE parameters were tested on ATCC (American Type Culture Collection) strains as follows: Gram-positive (_Bacillus subtilis_ ATCC 6633, _Enterococcus faecalis_ ATCC 29212, _Enterococcus faecalis_ VRE ATCC 51299, _Staphylococcus aureus_ ATCC 25923, _Staphylococcus aureus_ clinical isolate, _Staphylococcus aureus_ food isolate, _Streptococcus pyogenes_ ATCC19615, _Streptococcus anginosus_ clinical isolate, _Streptococcus anginosus_ group F clinical isolate, _Streptococcus mutans_ clinical isolate), Gram-negative (_Acinetobacter baumannii_ ATCC 19606, _Citrobacter freundii_ ATCC13316, _Escherichia coli_ ATCC 25922, _Klebsiella aerogenes_ ATCC 13048, _Klebsiella pneumoniae_ ATCC 13883, _Pseudomonas aeruginosa_ ATCC 27853, _Salmonella enterica_ ATCC 13076, _Salmonella_ group D clinical isolate), and fungi (_Candida albicans_ ATCC01231, _Saccharomyces cerevisiae_ ATCC09763) using the standardized disk diffusion Kirby-Bauer method [47]. Briefly, extracts obtained using BBE at 1/20 \(w/v\) solid/solvent ratio were concentrated up to half of their initial volume; 10 \(\mu\)L of extract was added to a sterilized paper disk (Macherey-Nagel 615, Dueren, Germany) of 6 mm diameter. The disks were added to the culture medium (Mueller-Hinton agar, pH 7.2-7.4). The incubation time was 20 h at 37 \({}^{\circ}\)C. Antifungal activity against _Candida albicans_ and _Saccharomyces cerevisiae_ was tested on the following culture medium: Mueller-Hinton agar, 2% glucose, methylene blue. The bacteria inoculum was standardized to 0.5 [PERSON] turbidity, corresponding to 1.5 \(\times\) 10\({}^{8}\) CFU/mL. The results were expressed as the diameter (mm) of the inhibition zone around the disks.
## 4 Results
### A Revised Algorithm Based on Machine Learning
#### 4.3.1 Implementation of Machine Learning Algorithms  
Firstly, BP-NN is implemented based on the Python keras module. To prevent overfitting, the model is set to three layers: input layer, hidden layer, and output layer. The number of neurons is 128, 64, and 1, respectively. The activation function of the input layer and the hidden layer is \"relu\", and the optimizer is \"Adam\". Secondly, from the model trained 10-100 times, the GridsearchCV optimization is used to find the optimal model. In addition, the structure of the BP-NN model, the selection of the optimizer, and the activation function can be further optimized. Here, we only optimized the training times, although other parameters can also be optimized using GridsearchCV optimization.  
The KNN, DT, SVM, RF, and BR algorithms are also implemented based on the Python sklearn module. The parameters of KNN include the weights of \"uniform\" and \"distance\", and the number of neighbors ranges from 1 to 10. The GridsearchCV optimization is used to find the optimal KNN model. To facilitate the comparison of these methods, the DT, SVM, RF, and BR algorithms all maintain the default parameters in the sklearn module and seek the optimal parameter settings by GridsearchCV optimization.  
Figure 4: Two-dimensional histogram of collocations between HY-2B/C/D and SMAP in the range of 0–50 m/s.  
Figure 5: RMSE and bias in wind speed from HY-2B/C/D scatterometers compared to SMAP. The dotted line is marked with triangles to represent RMSE, and the dotted line is marked with circles to represent bias (HY-2–SMAP).
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 1 Introduction
[13] explored the relationship between the crop and grassland Gross Primary Production (GPP) and VOD data taken from SMOS, ASCAT, and AMSR-E. They showed that changes in VOD are generally consistent with changes in GPP, especially for the C- and X-bands. Even though VOD data from AMSR-E, SMOS, SMAP, and ASCAT allow crop monitoring, the low spatial resolution of these products (25-km for SMOS, 9-km for SMAP, 12.5-km for ASCAT) is a limitation for crop monitoring and irrigation management in agriculture areas. Up to date, there is no VOD product available at the plot scale. The Sentinel-1A (S-1A) and Sentinel-1B (S-1B) sensors operating in the C-band have observation capacity allowing us to derive VOD at plot scale as these instruments provide images with a good revisit time (6 days over Europe) and a high spatial resolution (\(10\) m \(\times 10\) m). The aim of this study is to present a new approach for cropland VOD mapping at plot scale from S-1A and S-1B SAR data. The derivation of VOD is based on the use of the Water Cloud Model (WCM) [19]. The study was conducted over an area of \(50\) km \(\times 50\) km located in Catalonia, Spain, which contains a large variety of crop types. For this area, plot VOD values were derived from S1A and S1B SAR data for two crop growth cycles. The first crop growth cycle is between September 2017 and September 2018 and the second one is between September 2018 and September 2019. The NDVI derived from Sentinel-2 images corrected for atmospheric effects was used to evaluate the robustness of the derived VOD values. Section 2 presents the study area and the data used. Section 3 gives an overview of the methodology. The results are described in Section 4. Section 5 provides a discussion of the results. Finally, Section 6 presents the main conclusions.
## 5 Conclusions  
In this paper, we compared the results of supervised contrastive learning (SCL) and supervised learning (SL) with different learning rates and data augmentation methods  
Figure 7: ROC curves with different data augmentation methods for the supervised learning (SL) and supervised contrastive learning (SCL). The ROC curve max and ROC curve min stand for the ROC curves with the maximum AUC value and minimum AUC value, respectively.  
in diabetic retinopathy image classification. Large span learning rates, from 0.0001 to 0.1, were fully tested. Four kinds of data augmentation methods, including random horizontal flip, random vertical flip, random rotation, and color jitter, were selected. We validated the influences of different combinations and values of the above methods. The experimental results showed that the SCL had a higher mean accuracy than the SL with different hyperparameters. More importantly, the SCL was more robust than the SL. In medical classification, a robust system is more practical and valuable. Our work shows that the SCL is suitable for diabetic retinopathy binary classification. The proposed automatic classification model for diabetic retinopathy has the potential to be applied within the clinical decision support systems. In fact, some systems [33; 34] have been successfully applied to aid in diagnosing diabetic retinopathy.  
We validated the availability of the SCL for diabetic retinopathy binary classification on ResNet-18. More research on multiple classification and other kinds of deep models is expected. In the future, more experiments should be focused on multi-classification, which is closer to the real diagnosis. Furthermore, supervised contrastive learning also has the potential to increase the robustness of models in the medical image segmentation task.  
Conceptualization, X.F. and X.H.; methodology, S.Z. and X.Q.; writing--original draft preparation, S.Z.; writing--review and editing, X.H. and L.X.; supervision, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2020-I2M-2-006).  
We thank Nankai University for the fundus image dataset named DDR. This paper has benefited from the comments of the anonymous reviewers. [PERSON] thanks [PERSON] at the Department of Cardiology, Da Qing First Hospital, for the help with the project administration.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
## 4 Case Study
### Discussion  
The case study results can answer RQ1-RQ2 as follows.  
4.1 Does Our Categorization Method Estimate the Number of Ticket Categories and Categorize Tickets Accurately?  
In the case study, the product owner originally labeled and categorized tickets into eight categories. Then, we confirmed that the median value of the automatic estimation was 8.5, which is similar to the actual number of features categorized by the product owner. Furthermore, in terms of the categorization accuracy, the value of \(F_{P-IP}\) was 0.533 when the number of categories was eight, which is the same as the actual number of features. Furthermore, removing less descriptive tickets should improve the accuracy of our method.  
[] RQ1. Does our categorization method estimate the number of ticket categories and categorize tickets accurately? **Our method can precisely estimate the number of categories. In the project, the number of categories estimated by our method is similar to the actual number. The purity, inverse purity, and harmonic mean were 0.557, 0.513, and 0.533, respectively, when the number of categories was eight, which is the same as the actual number of features. These values indicate that there is room for further improvement. Since less descriptive tickets negatively influence the ticket categorization accuracy, enhancing the ability to handle less descriptive tickets should improve our method.**  
.4.2 Can Our Visualization Method Help New Members Understand the Implemented Features of a Project?  
During the interviews, most experts indicated that our method helps developers roughly grasp and prioritize the features.  
[] RQ2. Can our visualization method help new members understand the implemented features of a project? **Our method can help developers roughly understand the features of a software system and prioritize the features for the next development.**
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 3 System Model and Problem Description  
### System Model  
A typical architecture for satellite edge computing is first outlined, as shown in Figure 1. In this configuration, the LEO satellites are equipped with edge computing servers, to which computational tasks from ground users (e.g., ships, aircraft, monitoring devices, and mobile communication devices) are uploaded. It enables the direct processing of received tasks on the satellite nodes, without the need for traditional task transfer back to the ground cloud center for computing. The satellites can communicate with the surrounding satellites through ISLs. Meanwhile, in each time slot, information on the use of satellite resources and the received tasks information is transmitted to the ground station through the backhaul link. The ground station updates the offload decision in real time based on this information and sends it back to the satellite, taking into consideration the computing capabilities and task load status of each satellite in the constellation.  
The aforementioned scenario involves a constellation of \(N\) satellites orbiting the Earth in a periodic manner, with each period being divided into multiple time slots of equal duration \(\tau\). At every time slot, each satellite can receive new tasks, which are defined by \(A_{i}(t)=\{D_{i}(t)\}\), where \(D_{i}(t)(bit)\) represents the data amount of the task. Notably, the amount of user demand may vary depending on the geographic region covered by each satellite. For instance, in a given time slot, one satellite may be serving urban users, whereas another satellite remains idle over the ocean. In this scenario, the urban satellite could offload tasks to the idle ocean satellite for processing, effectively utilizing the constellation's computing resources to their full potential. To illustrate this, not all satellite \(k(k\in N)\) could receive a task \(A_{k}(t)\) at each time slot. The number of satellites that will receive a new task at each time slot, denoted by \(M(M\leq N)\), and \(D_{i}(t)\) follows a random distribution with a certain range \([\lambda_{min},\lambda_{max}]\) and is independent and identically distributed. The tasks received by each satellite can be offloaded to other satellite nodes via ISLs for assistance in processing. A simple task offload schematic is employed to depict the scheduling process, as shown in Figure 2.  
Figure 1: Satellites edge computing architecture.  
Figure 2: Task Offload Schematic.
## 4 Simulation Results and Diversity Analysis
Thus, the variable \(\widetilde{\beta}_{r,S}^{2}\) can be simplified as  
\[\widetilde{\beta}_{r,S}^{2}=\left\|\widetilde{\mathbf{h}}_{r}\right\|^{2}= \sum_{i=1}^{N_{T}}\lvert\widetilde{h}_{r}^{(i)}\rvert^{2} \tag{22}\]where \(\widetilde{h}_{r}^{(i)}\) is the \(i\)-th element of the vector \(\widetilde{\mathbf{h}}_{r}\). It means that the achievable transmit diversity order is evaluated as \(N_{T}\). On the other hand, by taking the steps used in [18; 29; 30], the optimal RAS selection scheme of selecting \(N_{T}\) antennas among \(N_{R}\) receive antennas in the over-determined PSM system with \(N_{T}\) transmit antennas and \(N_{R}\) receive antennas can obtain the receive diversity gain of \(N_{R}-N_{T}+1\). Then, by exploiting the analysis approach employed in [18], an achievable total diversity order of \(N_{T}(N_{R}-N_{T}+1)\) can be obtained for the opt-LR-RAS-LR-ZF system. It is seen that the optimal LR-RAS selection algorithm of (11) can provide a significant improvement of BER performance compared to no antenna selection and thus the opt-LR-RAS-LR-ZF system outperforms the opt-RAS-ZF system. It is observed that the optimal exhaustive search RAS based on LR offers an extra receive diversity gain of 3, which is multiplied to the transmit diversity order \(N_{T}=2\) of LR-ZF without RAS selection. Meanwhile, the optimal RAS selection without LR has only 2 more diversity gains than the ZF system without RAS selection. Furthermore, the BER results of the subopt-LR-RAS-LR-ZF are close to those of the opt-LR-RAS-LR-ZF system. In Figure 3, the BER performance of antenna diversity systems of (b), (d), and (e) with \(N_{T}=4\) and \(N_{R}=5\) is compared to that of (a) and (c) with \(N_{T}=4\) and \(N_{R}=4\). Figure 4 employs the same simulation setup parameters as Figure 3 except for \(N_{R}=6\). Thus, the spectral efficiency is given as 4 bps/Hz. In the BER reference curves of Figure 3, the constants used for the diversity gains, \(G=1\), 2, 4, and 8, are \(c=1.2\), 2.3, 21, and \(3.35\times 10^{6}\), respectively. In Figure 4, the diversity orders of \(G=1\), 3, 4, and 12, use the constants of \(c=1.2\), 5, 21, and \(9.2\times 10^{11}\), respectively. It is seen that the LR-ZF-PSM without RAS selection outperforms the ZF-PSM without RAS selection, which is due to different diversity gain. That is, the LR-ZF-PSM without RAS selection can achieve the full transmit diversity of \(G=N_{T}=4\) while the diversity gain of the ZF-PSM is only \(G=N_{T}-N_{R}+1=1\). Recall that no RAS selection implies that the scenario of \(N_{T}=4\) and \(N_{R}=4\). It is shown that the opt-LR-RAS-LR-ZF PSM system outperforms the LR-ZF-PSM without RAS selection and also has much better BER performance than the opt-RAS-ZF PSM system. Simulation results also confirm the analysis of diversity gains. It is also observed that the opt-LR-RAS-LR-ZF can achieve 4 and 8 more diversity gains, which are associated with \(N_{R}=5\) and \(N_{R}=6\), respectively, compared with the LR-ZF with no RAS selection, while the  
Figure 2: BER of the proposed algorithms for the LR-PSM system with \(N_{T}=2\), \(N_{R}=4\), and QPSK.