esa-sceva/satcom-chunk-collection huggingface dataset:
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 5 Proposed Network Deployment
### Second Phase: Service
#### 5.2.1 Re-Configuration of Static Network  
In Algorithm 2, MS is the number of training scenarios (4), MT is the maximum simulation time steps (1500), CS is a candidate solution provided by the GA that encodes potential field coefficients and exponents, and h is the number of communication hops allowed between UAVs. At each simulation step (t), every UAV scans for users (AssociateUsers) within \(U_{gr}\) to find their positions and bandwidth requirements. The time complexity of this operation is \(\mathcal{O}(m)\). Similarly, FindNeighbors finds other UAVs within \(U_{ar}\) distance and records them as neighbors with the time complexity of \(\mathcal{O}(n)\). ActivePotentials finds a set of potential fields depending on the hop count (h), and ComputeDir computes the resultant potential field for each UAV by summing all potential fields acting on a UAV using Equation (8).  
\[\vec{P}_{k}=\vec{P}_{b}+\sum\limits_{i=1}^{h}(\vec{P}_{u_{i}}+\delta_{r}\vec{ P}_{r_{i}})+\delta_{c}\vec{P}_{c} \tag{8}\]  
Next, MoveAll(Dir) moves the \(k\)th UAV in the direction of \(\vec{P}_{k}\). These three previous functions take a constant time for execution. After \(MT\) time steps, the algorithm computes the fitness for each training scenario by summing the normalized bandwidth coverage and the normalized number of active AUs. Finally, once the evaluation of all four scenarios is over, the algorithm returns the average of these fitness values as the fitness of this candidate solution to the genetic algorithm.  
Note that, in Equation (8), \(h\) refers to the number of hops for communication. The vector sum of the potential fields for UAV \(k\) given by \(\vec{P}_{k}\) provides a desired heading for the \(k\)th UAV to turn toward. Each term in the equation describes a potential field whose parameters need to be optimized. To move the \(k\)th UAV toward users, an attractive potential field is generated based on users' bandwidth requirements (\(\vec{P}_{b}\)), and to reduce the per UAV bandwidth coverage and save energy, the \(k\)th UAV uses an attractive potential field toward neighboring UAVs whose magnitude depends on the neighbor's bandwidth load (\(\vec{P}_{u}\)), as shown by Figure 5. At the same time, to avoid collisions, a repulsive potential field, \(\vec{P}_{r}\), ensures that UAVs do not collide with each other. This potential field only applies to UAVs that are less than \(d^{*}\) distance away, and we used the Dirac delta function, \(\delta_{r}\), to model this, where \(\delta_{r}(\in\{0,1\})\). When UAV \(j\) is less than \(d^{*}\) distance away, then \(\delta_{r}=1\); otherwise, it is 0. Lastly, when a UAV has no users within \(U_{gr}\) and no UAVs within \(U_{ar}\)--that is, the UAV is inactive--it moves toward the command center as a default behavior. This is achieved with an attractive potential field (\(\vec{P}_{c}\)) based on the distance to the command center. Here, \(\delta_{c}\) is another Dirac delta function used to ensure that this potential field only acts on inactive UAVs.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 3 MC-BDP Reference Architecture
### MC-BDP Architectural Layers
#### 3.4.3 The Container Layer  
The container layer consists of containers running services based on container images. A container image can be understood as the blueprint of a service. It contains information about the dependencies needed to build the service such as runtime and middleware, as well as the addresses from where these can be fetched. Additionally, images contain instructions on how to build and deploy the service as a container, including the setting of environment variables and the mapping of ports. Images can be stored in public or private repositories, from which they can be easily retrieved and shared among developers. Since images contain all the necessary configuration that a service needs to run, nothing needs to be installed on the platform other than the container runtime, thus allowing consumers to compare offerings by different cloud providers more easily and to migrate to a different provider if needed.  
The Open Container Initiative (OCI) was launched by the Linux Foundation with the purpose of standardising container image formats and container runtime [73]. It has received widespread support in the community, including large donations of code by Docker [74], and is therefore proposed as MC-BDP's containerisation technology compliance requirement. Due to Docker's status as a paradigm in the industry today, this technology is suggested as a good fit for default implementations, since it has a strong community of adopters as well as an abundance of documentation. It is worth highlighting,however, that other container technologies such as Linux Containers (LXC), OpenVZ, and Linux-VServer exist, and could be used in place of Docker provided that they were supported by the orchestration technology selected. One clear advantage of using Docker is its popularity, as reported by Sysdig in their 2019 Container Usage Report [75]. According to this report, based on over two million real-world containers monitored by their technology, alternative runtimes such as Mesos or Linux Containers suffered a significant drop in usage and are nearly non-existent in recent commercial deployments. Docker, on the other hand, had an adoption of 79%, while a newcomer, CRI-O, amassed an impressive 4% of the market. The Sysdig report foresees an increase in the popularity of new container technologies as consequence of the standardisation promoted by the Open Container Initiative (OCI) [75]. This research is aware of this predicted trend and therefore adopts a flexible stance with regards to the container technology adopted.
# Review of Weed Detection Methods Based on Computer Vision
## 6 Discussion
### Multiple Complex Factors Affect Weed Detection  
The natural properties of weeds are complex, with a wide variety of species, wide distribution, numerous leaf shapes and sizes, and random growth, forming various texture features. In the bud stage of weeds, most plants are small in size, vary in appearance, and have high germination density. As a result, accurate statistics is difficult to perform. The main factors affecting the performance of weed detection are as follows:  
1. The influence of different growth stages. Most plants change their leaf morphology, texture, and spectral characteristics in different seasons or growth and development stages.
2. The influence of changing light conditions. When light conditions are different, the shade of the plant canopy and the angle of the sun will affect the color of the vegetation. Some scholars have used the ultra-green index and the Otsu algorithm to solve the problems caused by ambient light. In particular, [PERSON] et al. [156] solved the problems by using camera filters and different types of cameras. HIS color model was also applied, and grayscale images with H component were generated to reduce the impact of uneven lighting on color images [157].
3. Influence of overlapping leaves and occlusion. The accurate segmentation of plants is a challenging task. In complex actual field images, overlapping leaves, occlusions, leaf shadows, dead leaves, and damaged leaves will make it impossible to segment the leaves effectively when processing the images.
4. Bottleneck of weed detection. Factors, such as hardware, algorithm complexity, and plant density, limit the actual detection speed or accuracy. Hence, fast image processing and accurate weed identification remains extremely important challenges.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 2 Materials and Methods
### Hyperspectral Imaging Acquisition  
Spectral images were acquired in reflectance mode using a laboratory-based push-broom NIR system. The system was made up of a charge-coupled device (CCD) camera with a spatial resolution of 320 \(\times\) 256 pixels (Xeva-FPA-1.7-320, Xenics, Leuven, Belgium), a C-mount objective lens (F1.4 25-mm compact lens, Schneider Optics, Hauppauge, NY, USA), a line scan imaging spectrograph (Specim ImSpector V10E, Oulu, Finland) which covers the range of 946.6 to 1648.0 nm with 3.3 nm spectral resolution. The illumination was provided by two lamps of 250 W located at a 45\({}^{\circ}\) angle to the sample; and the line-by-lineimages were collected by a conveyor belt system (Velmex, Inc., Bloomfield, NY, USA) that moved the sample across the line field of view of the camera. To obtain the imaging of each sample, about 100 g of almonds were uniformly distributed on a black plastic plate of dimensions 12.5 \(\times\) 17.5 cm. The number of lines was set at 450, with a distance between lines of 0.39 mm; a hypercube image with dimensions of 450 \(\times\) 320 \(\times\) 212 was obtained for each sample. However, due to the presence of noise, only the spectral range between 1006 and 1594 nm (179 spectral bands) was considered for data modelling.  
Reflectance calibration was performed each hour by taking dark and white reference images. The dark reference was collected by covering the camera lens with a black cap and immediately after that, the white reference was collected using a 99% reflectance standard (SpectralonTM, SRS-99-10, Labsphere, Inc., North Sutton, NH, USA).
## 5 The Concept of IoV
### Security Issues in IoV Communication  
A middle ground exists between the smart vehicle and the cloud server. This middle ground is commonly referred to as the fog cloud-based IoV. This important component enhances the vehicle's capabilities by enabling the vehicle to collect and process information locally and to make instant decisions during an accident rather than relying on a cloud server. For instance, several vehicles in the same vicinity can respond to a request from a vehicle within the same area instead of sending a request to the cloud, which may result in a delayed response. IoV highly depends on wireless communication for traffic flow management. For this reason, IoV is susceptible to various attacks such as data disclosure and session key leakage. To address these security issues, the following measures can be employed [75; 93; 94].  
Figure 4: IoV architecture.
## 2 Materials and Methods  
### Study Region  
The Xinjiang Uyghur Autonomous Region is located in the northwest of China (34 deg25'N-49 deg10'N, 73 deg40'E-96 deg23'E) at the heart of the Eurasian continent. It borders the Tibet, Qinghai, and Gansu provinces, as well as eight countries, including Mongolia and Central Asian nations. Xinjiang is the largest province in China, with an area of 1.6649 million km2. The region experiences a typical temperate continental arid climate characterized by fluctuating spring temperatures, hot summers, cool autumns, and long, cold winters. Xinjiang can be classified into northern and southern regions based on the Tianshan Mountains. The northern region is bordered by the Altai Mountains, whereas the southern region is bordered by the Kunlun Mountains. These ranges are in the Junggar and Tarim basins. Precipitation and snowmelt in Xinjiang are primarily sourced from the surrounding mountain ranges, which provide major water resources for the region. The plains receive minimal precipitation and have high evaporation rates, exhibiting a distribution pattern of more precipitation in the west, less in the east, more in the north, and less in the south. Mountainous areas receive more precipitation than the plains. The annual precipitation is approximately 150 mm [29]. Due to water scarcity, Xinjiang contains extensive deserts and bare land, resulting in an extremely fragile ecological environment. [30,31]. However, the unique natural geography and climatic conditions foster diverse vegetation types. According to the ecological zoning of China (Geographic Data Sharing Infrastructure, College of Urban and Environmental Science, Peking University [[http://geodata.pku.edu.cn](http://geodata.pku.edu.cn)]([http://geodata.pku.edu.cn](http://geodata.pku.edu.cn)), accessed on 20 December 2024), Xinjiang is divided into five ecological regions: R1, Altai Mountains-Western Junggar Mountains forest-steppe ecological region; R2, Junggar Basin Desert ecological region; R3, Tianshan Mountain forest-steppe ecological region; R4, Tarim Basin-Eastern Xinjiang Desert ecological region;and R5, Pamir-Kunlun Mountains-Altyn Tagh high-altitude desert grassland ecological region (Figure 1).
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis
### Hold Capability Comparison  
The HSNM represents the ability of an SRAM cell to retain data reliably when all access transistors are turned off and the BL and BLB are precharged. It serves as a key indicator of voltage stability during hold mode. To improve radiation hardness, the proposed EWS-16T cell incorporates a complex node configuration that includes both storage nodes (Q, QB) and internal nodes (S0, S1). However, this increased complexity also introduces stronger inter-node coupling, which can slightly degrade the voltage stability of the storage nodes during hold conditions. In general, radiation-hardened SRAM cells must balance competing design requirements. Enhancing soft-error resilience often comes at the cost of larger cell area, higher power consumption, increased circuit complexity, and a reduction in HSNM. As illustrated in Figure 10, the EWS-16T cell shows a lower HSNM than other hardened designs such as QCCS, SCCS, SAW16T, HP16T, RHSCC16T, and S8P8N. This reflects a conscious design trade-off that prioritizes soft-error tolerance and operational robustness in radiation-intensive environments, even at the expense of some hold stability.
## 4 Experimental Evaluation
### Experimental Results and Analysis
#### 4.2.1 Performance Metric Evaluation Based on Four Datasets
Regarding adaptability to heterogeneous environments, the PPFL-SAGIN effectively handled data distribution differences among different devices through dynamic weighting factors, maintaining a high rate of recall improvement across all datasets. This advantage was particularly evident in the more complex EuroSAT and UC Merced Land Use datasets. In terms of knowledge transfer efficiency, especially in the initial rounds (1-3), the PPFL-SAGIN quickly established a high recall baseline, verifying that its knowledge sharing mechanism effectively integrates knowledge from different devices in the early stages of training, avoiding the problem of traditional methods requiring a longer time to form effective representations. Figures 3(d), 3(d), 3(d) and 3(d) illustrate the F1-score curves for the PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL on the MNIST, EuroSAT, UC Merced Land Use, and RSI-CB datasets. The PPFL-SAGIN method performed best on all four datasets, maintaining a leading advantage from the first epoch and consistently maintaining the highest F1-score throughout the training process. The PPFL-SAGIN not only achieved the highest final performance but also converged significantly faster than other methods. This advantage was particularly pronounced in the more complex EuroSAT and UC Merced Land Use datasets. The two-tier reinforcement learning device selection strategy adopted by the PPFL-SAGIN, combined with meta-learning and hierarchical attention mechanisms, significantly improved the convergence efficiency of the model. Figures 3(e), 3(e), 3(e) and 3(e) illustrate the average loss curves for the PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL on the MNIST, EuroSAT, UC Merced Land Use, and RSI-CB datasets. In terms of convergence performance, the SAGIN method exhibited the optimal loss convergence curve across all three datasets, with the lowest final loss value. Notably, on all four datasets, the initial loss (at the first epoch) of the PPFL-SAGIN was lower than that of the other three methods, indicating that this method can effectively utilize the adaptive knowledge sharing mechanism of transfer learning to quickly adapt to data distribution from the early stages of training. The PPFL-SAGIN also demonstrated a faster rate of loss decline, especially in the first 5 epochs of training, with a slope of decline significantly greater than other methods, reflecting the efficiency of its optimization strategy.
## 3 Results and Discussion
### Emission Characteristics
#### 3.5.3 NOx Emissions  
Figure 9 shows NOx formation for all the tested fuels as a function of engine load. The increment in combustion temperature sets the appropriate conditions to promote nitrogenates oxidation, thus producing NOx. Therefore, it can be stated that NOx formation depends significantly on the in-cylinder temperature, the concentration of oxygen in the fuel, and the residence time of the reaction.  
Based on the results, the maximum NOx emissions are reached at a full engine load. Specifically, ABE implementation facilitates reducing the overall emissions of NOx, but HHO upgrades this share. The latter implies that the magnification of the in-cylinder temperature provided by the hydroxy doping suppresses the positive effect of the ABE. The enhanced behavior of ABE standalone blends can be attributed to the higher vaporization heat that limits the air-fuel mixture temperature in the intake stage, which reflects on reduced combustion temperature, thus reducing the potential of NOx formation. This pattern is consistent with the experimental outcome of a similar investigation that directly measures the temperature at the intake valve closing [12; 46].  
The highest NOx emission levels were achieved by the ABE 5 + HHO, followed by pure gasoline, ABE 5, ABE 10 + HHO, and ABE 10. Incorporating the oxygenated compounds in the air/fuel mixture reduces NOx emissions between 0.36 to 1.87 g \(\bullet\) kWh\({}^{-1}\) compared to gasoline. In contrast, the integration of hydroxy doping in the intake air for the ABE 5 case maximizes the emissions levels between 0.16 to 0.27 g \(\bullet\) kWh\({}^{-1}\) but ABE 10 + HHO remains behind the baseline fuel during all the engine loads. The higher oxygen content in the air/fuel mixture can directly contribute to the rise in NOx emissions by incorporating HHO in the fuel blends.  
It is essential to mention the trade-off between the effect of HHO doping based on fuel economy and emission levels. Based on the fuel metrics (Figure 6), the integration of hydroxy gas minimizes fuel consumption, which ratifies prospective benefits in terms of fossil-fuel depletion and economic viewpoints. Moreover, since ABE is replacing up to 10% of fossil fuel, the net fuel saving escalates based on a global perspective. In the counterpart, hydroxy doping stimulates NOx formation, as corroborated in this section. A potential solution to mitigate the impact of NOx emissions magnification could be integrating waste  
Figure 9: NOx emissions for fuels tested.  
heat recovery (WHR) technologies that further enable a higher fuel utilization ratio. The integration of exhaust gas recirculation systems can be another feasible opportunity to promote the sustainable operation of ICEs.
## 4 Experimental Results and Analysis
### Comparison with Existing Methods
#### 4.3.1 Classification Results on the IP Dataset  
Using the IP dataset, 20 samples of each type of ground object were randomly used as the training set, and the remaining samples were used as the test set. The classification results obtained via the nine dimensionality reduction algorithms are shown in Table 3. Figure 4 visualizes the classification maps obtained via these nine dimensionality reduction algorithms.  
In Table 3, it can be seen that when using the IP dataset, the dimensionality reduction effect of the method based on multi-manifold embedding is better than that of the traditional single-manifold algorithm, which proves the correctness of the multi-manifold assumption. Additionally, considering the spatial features of HSIs, the classification performance of the manifold learning algorithm based on the spatial-spectral features is better than that of the manifold learning algorithm that only considers the spectral feature. Among these algorithms, the proposed method achieves the highest classification accuracy, and the OA, AA, and Kappa are 92.87%, 90.26%, and 91.48%, respectively. Compared to SSMRPE, which also considers the spatial-spectral features, the proposed method is improved by4.12%, 3.89%, and 3.88% in OA, AA, and Kappa. Since the proposed method adaptively fuses the multi-manifold maps of spatial and spectral dimensions, the proposed method more adequately extracts the spatial-spectral features of each class of HSI, thus achieving the best effect. As can be seen in Figure 4, compared to other dimensionality reduction algorithms, the classification result map obtained via the proposed SSAMMA is richer in detail and has the least number of misclassified samples. In contrast, the classification maps obtained via the two-dimensionality reduction algorithms, i.e., NSPE and MLE, have the most misclassified samples and a large amount of noise in the classification result maps. In addition, the classification result map obtained via the algorithm that considers the spatial-spectral features has clearer classification boundaries and better visual effects than the single-manifold learning algorithm and the multi-manifold algorithm that only uses spectral features.
## 4 Proposed Approaches
### Threshold for Gap of Normalized Eigenvalues
#### 4.2.1 Datasets Generation  
In the first step, datasets of NN gaps and NS gaps for learning and computing optimal threshold are generated. In order to keep consistency with the datasets, the eigenvalues are normalized before generating NN gaps and NS gaps. Note that the diagonal elements of covariance matrix are the received signals powers with noise power, and the trace--the sum of diagonal elements--of the covariance matrix is equal to the sum of the eigenvalues of the covariance matrix [32]; this fact means that the eigenvalues are greatly changed by signal power and noise power, which makes it difficult to determine the threshold. Thus, the eigenvalues are normalized at first in the T-GANE procedures. By this preliminary process, T-GANE can be applied regardless of the signal power and noise power.  
The normalized eigenvalues \(e_{i}\) are defined as  
\[e_{i}\triangleq\frac{\lambda_{i}}{\sum_{k=1}^{M}\lambda_{k}}, \tag{18}\]  
where \(i=1,\ldots,M\). Then, the gaps of normalized eigenvalues \(\Delta e_{i}\) are defined as  
\[\Delta e_{i}\triangleq e_{i+1}-e_{i}, \tag{19}\]  
where \(i=1,\ldots,M-1\). Moreover, two sets named \"NN gaps set\" and \"NS gap set\" are defined as  
\[E_{\text{NN}}=\{\Delta e_{i}\mid i=1,\ldots,M-D-1\}, \tag{20}\]  
\[E_{\text{NS}}=\{\Delta e_{i}\mid i=M-D\}, \tag{21}\]  
respectively.  
To generate the datasets for learning, NN gaps and NS gaps in various situations, i.e., different arriving angle, source number, and SNR, should be collected. Two datasets named \"NN gaps dataset\" and \"NS gaps dataset\" are defined as  
\[E_{\text{NN}}^{\text{data}}=\bigcup_{q=1}^{Q}E_{\text{NN}}^{q}, \tag{22}\]  
\[E_{\text{NS}}^{\text{data}}=\bigcup_{q=1}^{Q}E_{\text{NS}}^{q}, \tag{23}\]  
respectively, where \(E_{\text{NN}}^{q}\) and \(E_{\text{NS}}^{q}\) are \(E_{\text{NN}}\) and \(E_{\text{NS}}\) of the \(q\)th situation, respectively. Note that \(Q\) denotes the number of situations for generating the datasets.