esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Data Exchange between WebAssembly Modules
## 4 Karmem Approach
### Communication
#### 4.3.4 Runtime  
Karmem introduces a specialized runtime that deviates from traditional WASM by managing module memory uniquely, focusing on shared memory regions. It leverages the existing Wazero virtual machine, which offers JIT/AOT compilation capabilities but allows for customization to suit various programming languages. This approach ensures optimized performance across different hosts. Karmem requires specific OS functionalities, such as mmap for shared memory management, which constrains its deployment based on available OS resources and needs custom implementation for each OS.  
Runtime prioritizes Linux and FreeBSD due to their popularity [64] and license, respectively. Windows, while being the most popular OS for desktops, has inconsistencies in its support for certain memory allocation techniques across different versions of Windows. These inconsistencies can complicate the implementation of the Karmem runtime and its tests, particularly when the process involves allocating memory addresses without the immediate commitment of physical memory. Some operating systems introduce significant limitations. iOS/iPadOS/tvOS are noticeable examples due to a lack of MAP_JIT support, which prevents WASM runtimes from creating executable memory. The feasibility of Karmem on mobile and non-traditional platforms such as gaming consoles and other BSD variants remains largely unexplored, but they potentially align with existing Linux and FreeBSD implementations. The integration challenges across different platforms emphasize the need for adaptable and flexible software solutions within diverse hardware environments.  
Communication between modules, whether guest-guest or guest-host, is facilitated by the host. This enables the calling of WASM modules that implement the interfaces defined in KarmemIDL.  
To allow communication and function calls, a common interface has been defined so that all WASM must export all functions described, as shown in Figure 19. This communication interface is similar to waPC as it allows for the dynamic invocation of other functions.  
When a WASM module is loaded and initialized via _start, the runtime calls the \"KarmemInit()\" function. This function must report the number of interfaces it exports and imports. Subsequently, the runtime calls the \"KarmemImport()\" and \"KarmemEx  
Figure 19: Functions exported or imported by the guest.  
port()\" functions to obtain information about the interfaces that are imported and exported. Memory allocations are handled by KarmemAlloc functions.
mobile warehouses [13] are under consideration. Ensuring proper cooling conditions requires designing cooling devices with optimal parameters, which will also be environmentally friendly [14; 15]. When constructing them, it is necessary to take into account national and international legal acts that impose actions aimed at limiting environmental threats, including the ozone hole [16] and the greenhouse effect [17]. The use of synthetic refrigerants (f-gases) is limited and, therefore, the natural refrigerant use is dedicated to the newly designed devices.  
Research work was carried out which enabled the construction of an innovative MainBox device, ensuring temperature conditions (+5 \({}^{\circ}\)C or -18 \({}^{\circ}\)C). This product is intended to store food under certain temperature conditions and enable the collection of ordered food products by customers without having to wait for the supplier. The device structure has been developed, taking into account the appropriate materials, type of sealing, chamber size, construction of the chamber door, necessary elements of the cooling and condensate drainage system, as well as automated control of temperature. The natural refrigerant propane is used in the cooling system [18]. Based on its properties, innovative solutions have been designed for, among others, cooling installation, temperature monitoring and regulation, security system, and ventilation of food storage chambers. There is an increased interest in the use of hydrocarbons in refrigeration, e.g., propane (R600a) in mixtures [19; 20; 21], isobutane (R290) in domestic refrigeration [22], and mixed R290/R600a in the compressors [23].  
In general, supply chains and logistics have been disrupted due to the pandemic situation [13]. The developed device is a response to the current society need. It is the right solution for providing food to consumers in the difficult reality of the sanitary regime, caused by the epidemiological threat of COVID-19 (coronavirus) [24]. Currently, the development potential of this form of food trade has increased enormously, and the crucial point is ensuring optimal storage conditions. On the other hand, it is the dissemination of pro-environmental solutions related to the natural refrigerant use and action aimed at climate protection by reducing the direct emission of greenhouse gases which translates into a minimalization of the carbon footprint (CF) of this device [25; 26].  
The article describes the experimental work performed to verify the correct operation of the cooling and freezing prototypes of devices (MainBox), which are the subject of the project. The purpose of this study was to investigate the operation of the innovative MainBox device, in real conditions, for food storage in optimal temperature.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.2 Cooperative Energy-Efficient Routing Protocol (CO-E2 RP)  
In [148] the author presented a cooperative and E2 RP (CO-E2 RP) for UWSNs. The repeater node is chosen from Tx anchor node to sensor Rx node, which is based on channel capacity and signal-to-noise ratio (SNR).
## 4 Results
### Comparative Experiment of Weight Setting in Cross-Entropy Loss Function  
As explained in Section 3.4.3, to alleviate the problem of sample imbalance, this method uses weighted cross-entropy as a loss function. In both the individual site experiments and the combining site experiment, the classification results with different weights are shown in Figure 7. As described in Section 4.4 above, because the freezing mode in the individual site experiment was slightly better than the fine-tuning mode, only the classification results of the freezing mode were listed in the individual site experiments, and the classification accuracies with different \(\alpha\) values are shown in Figure 7a. Similarly, the fine-tuning mode in the combining site experiment was slightly better than the freezing mode, so only the classification results of the fine-tuning mode were listed in the combining site experiment, and the classification accuracies with different \(\alpha\) values are shown in Figure 7b. Figure 7a,b show that the overall classification accuracy improved with the increasing weight \(\alpha\) of the ADHD samples in the cross-entropy loss function on the condition that \(\alpha>1\). Although the data imbalance at the different imaging sites was different, the overall performance of the classification in the individual site experiments was optimal for each test data when \(\alpha\) was equal to three. On the other hand, compared to \(\alpha=1\), \(\alpha=4\), and raw_svm, the classification accuracy in the combining site experiment remained the highest in most sites (three of five) with the same setting, \(\alpha=3\). However, setting \(\alpha=2\) seemed to be optimal for the overall performance of classification in the combining site experiment.  
Figure 7: Classification results of weight \(\alpha\) in cross-entropy loss function with different values: (**a**) accuracy of the individual site experiments; (**b**) accuracy of the combining site experiment. With the increase in weight \(\alpha\) of the ADHD samples in the cross-entropy loss function, for instance, when setting \(\alpha>1\), the overall classification of the individual site experiment accuracy could be improved. Owing to the heterogeneity of the fMRI data, the classification accuracy of the combining site experiment appeared to fluctuate with different \(\alpha\) values. For the overall performance of the classification, setting \(\alpha=3\) in the individual site experiments and \(\alpha=2\) in the combining site experiment seemed optimal.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
###### Abstract  
One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To address these three problems, this paper proposes a VPN-encrypted traffic identification method based on ensemble learning. Firstly, aiming at the problem of feature redundancy in VPN-encrypted traffic features, a method of selecting encrypted traffic features based on mRMR is proposed; secondly, aiming at the problem of data class imbalance, improving the Xgboost identification model by using the focal loss function for the data class imbalance problem; Finally, in order to improve the identification rate of VPN-encrypted traffic identification methods, an ensemble learning model parameter optimization method based on optimal Bayesian is proposed. Experiments revealed that our proposed VPN-encrypted traffic identification method produced more desirable VPN-encrypted traffic identification outcomes. Meanwhile, using two encrypted traffic datasets, eight common identification algorithms are compared, and the method appears to be more accurate in identifying encrypted traffic.  
VPN-encrypted traffic identification; ensemble learning; Xgbooost; feature selection; Bayesian optimization +
Footnote †: journal: Applied  
Article
## 3 Results  
### Microbial Growth Rates in Simulated Kimchi Medium  
To investigate the adaptability of 17 LAB in kimchi containing cabbage, radish, salt, and several conditions, the cell growth rates were measured in the SKJ at their optimal temperatures (30 or 37 \({}^{\circ}\)C) for 24 h (Figure 1). The commercial starter, _Le. mesenteroides_ DRC 1506 was cultivated simultaneously as a reference strain. Among the tested strains, six, including DRC 1506, grew (OD\({}_{600}\) > 0.4) after 24 h and their growth rates were in the following order: _L. reuteri_, _L. fermentum_, _L. rhamnus_, _Le. mesenteroides_, _L. salivarius_, and _L. paracasei_. Along with their growth, pH values also decreased in a similar pattern. Among these strains, _L. reuteri_ and _L. fermentum_ showed superior growth rates compared with _Le. mesenteroides_, revealing their outstanding adaptability in nutrient environments in the SKJ. Meanwhile, the other strains, including four species of bifidobacteria, did not grow significantly in SKJ. Presumably, the medium compositions in SKJ may not have met the nutritional requirements of these strains.  
Besides, the adaptability of 17 LAB at low temperatures was tested by cultivating in SKJ at 15 \({}^{\circ}\)C for 5 days (Figure 2). Among the tested strains, _Le. mesenteroides_ showed the fastest growth rate to reach OD\({}_{600}\) 0.6 after 4 days, showing its excellent adaptability to low temperature conditions. Likewise, three strains, _L. fermentum_, _L. rhamnus_, and _L. paracasei_, grew at 15 \({}^{\circ}\)C in the SKJ. _Le. mesenteroides_-inoculated SKJ reached pH 4.4 after 1 day, which is the optimal acidity providing the preferred taste in kimchi [23]. Meanwhile, _L. plantarum_, _L. rhamnus_, and _L. paracasei_ took 2 days; _L. casei_ took 4 days; and _L. fermentum_ took 5 days (Figure 2b) to reach pH 4.4. Notably, _L. plantarum_, _L. casei_, _L. acidophilus_, and _L. bulgaricus_ did not grow well at low temperature, but the pH decreased in the cultures. Compared to the other strains, _Le. mesenteroides_ produced lactic acid more quickly. At the end of the fermentation at 15 \({}^{\circ}\)C, the pH of _L. rhamnus_ and _L. paracasei_ was approximately 3.7 and that of _L. fermentum_ was about 4.2, which did not show much difference from that of _Le. mesenteroides_ (pH 3.7), indicating that an appropriate amount of acid was produced during fermentation. Therefore, _L. fermentum_, _L. rhamnus_, and _L. paracasei_ showed similar growth patterns to those of _Le. mesenteroides_.  
Figure 1: Cell growth (**a**) and pH changes (**b**) of the simulated kimchi juice (SKJ) after inoculation of 17 probiotic lactic acid bacteria (LAB) at their optimal growth temperatures, 37 or 30 \({}^{\circ}\)C. Optical density (OD\({}_{600\text{hm}}\)) values and pH values were measured for 24 h. The dotted lines represent little microbial growth (<OD 0.3) and pH changes (<pH 5.0). Results are expressed as means \(\pm\) standard deviations (\(n\) = 3).
## 3 Results and Discussion
### Investigation of Return Loss During Ablation  
The power measurement system, consisting of a power meter and two power sensors, measured real-time power every second during ablation, which was then converted to return loss using Equation (3).  
\[R\ (\mathrm{dB})= P_{\mathrm{ref}}(\mathrm{dBm})-P_{\mathrm{fwd}}(\mathrm{dBm}), \tag{3}\]  
where \(P_{\mathrm{fwd}}\) is forward power, \(P_{\mathrm{ref}}\) is reflected power, and \(R\) is return loss. The sum of the coupling factor error, the power sensor accuracy error, and the directional coupler insertion loss were compensated for by subtracting 1.98 dB from the return loss. This value was achieved from other characterisation experiments at 2.45 GHz. Although the power meter is a broadband device, the frequency was restricted to 2.45 GHz via the signal generator, and therefore the measured power was only that at the frequency of interest. This was validated on a spectrum analyser, where the measured quality factor of both the signal generator and the amplifier output when energised was 1.06 \(\times\) 10\({}^{9}\). The mean return loss (\(\overline{\mathrm{X}}_{\mathrm{RS}}\)) and standard deviation (\(\sigma_{\mathrm{RS}}\)) were calculated for each time sample (1 s). The results obtained are shown in Figure 8 and summarised in Table 1. At the start of the experiment, the antenna return loss was relatively the same for both scenarios. However, within the first 60 s of ablation, the annealed antennas outperformed the non-annealed antennas, notably around 21 s when the maximum \(\overline{\mathrm{X}}_{\mathrm{RS}}\) difference of 2.54 dB was observed. Despite this, the annealed antennas experienced higher \(\sigma_{\mathrm{RS}}\), which, at worst, was almost similar to that of the non-annealed antennas. Between 60 s and 120 s, changes in the tissue properties caused an increased mismatch for both antenna sets. However, the annealed antennas maintained a better match until the final 60 s, when both antenna sets showed similar behaviour.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & \multicolumn{2}{c}{**Annealed**} & \multicolumn{2}{c}{**Non-Annealed**} \\ \hline  & \(\overline{\mathrm{X}}_{\mathrm{RS}}\) & \(\sigma_{\mathrm{RS}}\) & \(\overline{\mathrm{X}}_{\mathrm{RS}}\) & \(\sigma_{\mathrm{RS}}\) \\ \hline
0 s & 11.65 dB & 1.6 dB & 10.82 dB & 0.09 dB \\
21 s & 18.30 dB & 5.25 dB & 15.76 dB & 1.78 dB \\
60 s & 9.41 dB & 0.83 dB & 8.73 dB & 0.96 dB \\
120 s & 7.84 dB & 0.90 dB & 7.64 dB & 0.75 dB \\
180 s & 7.41 dB & 1.08 dB & 7.33 dB & 0.55 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Return loss mean and standard deviation of key points in Figure 8.  
Figure 7: Measured return loss with a VNA at 2.45 GHz while the antenna was immersed in tap water before/after an ablation procedure.
# An Automatic Dry Sieving Mechanism for a Lunar Brick Payload
## 3 Results
### Fine Fraction Percentages for Different Sieving Angles  
Figure 2 shows the mesh size of the sieve stack on the \(x\)-axis and the cumulative fine fraction percentage of the total mass on the \(y\)-axis. The three curves on the figure indicate the measured fractions as the functions of the angle of the lunar regolith sieve unit with LHS-1 simulant. The fine fraction shows minimal mass for particles larger than 500 \(\upmu\)m, with no particles greater than the 710 \(\upmu\)m threshold. Ideally, particles greater than 500 \(\upmu\)m would be absent, but a small fraction appears in the measured data due to possible discrepancies between the mesh tolerances of the automated sieve and the measuring sieve stack. Despite this variance, the difference is deemed negligible, as reflected in the exceptionally low standard deviation (0.0095-0.1137%) observed in the measurements.  
The cumulative percentage of fine fraction was measured for LHS-1 across three distinct angles (35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\)). These experiments consistently yielded a fine fraction mass close to the 95% mark of the total sieved mass. Figure 2 illustrates a decrease in  
Figure 2: Cumulative fine fraction percentage at 35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\) using LHS-1 simulant.  
the fine fraction mass when higher angle configurations are considered. This reduction is attributed to higher regolith velocities at steeper angles, leading to fewer particle-sieve interactions and consequently lower fine fraction mass. Intuitively, one might propose minimizing the angle to enhance sieve efficiency. However, such a strategy increases the risk of introducing sieve blocking, something to be avoided for all automatic aperture-based separation techniques for lunar application.
# Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
## 3 Experiments and Results
### Baseline Model Adversarial Resistant Evaluation  
The adversarial robustness of baseline models is presented in Table 7. Four baseline models have been tested against three adversarial attacks: DeepFool, PGD, and FGSM. In Table 7, column, Normal, indicates ROC-AUC classification results using unaffected data, while DeepFool, FGAM, and PGD columns are ROC-AUC classification results under respective adversarial attacks. The general results indicate that our baseline models' performance is significantly affected by adversarial attacks, even for the baseline MPL model, which was used to generate the adversarial samples. There was a 31.03% reduction in the baseline MPL model's ROC-AUC under FGSM attack, with 29.89% and 28.74% under PGD and DeepFool, respectively. These results confirm that although deep learning models  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Features** \\ \hline DeepFool & ‘srv_count’, ‘count’, ‘dst_host_srv_count’, ‘is_guest_login’, ‘dst_host_same_src_port_rate’, ‘root_shell’, ‘dst_host_diff_srv_rate’, ‘diff_srv_rate’, ‘dst_host_same_srv_rate’, ‘service_eco_i’ \\ FGSM & ‘land’, ‘dst_host_same_srv_rate’, ‘dst_host_count_srv_reror_rate’, ‘root_shell’, ‘dst_host_same_src_port_rate’, ‘protocol_icmp’, ‘dst_host_srv_ser_reror_rate’, ‘service_ecr_i’,’num_outbound_cmds’ \\ PGD & ‘srv_count’, ‘count’, ‘dst_host_count’, ‘src_bytes’, ‘dst_host_srv_count’, ‘dst_host_same_srv_rate’, ‘same_srv_rate’, ‘service_ssh’, ‘service_domain’ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Feature participation top ten in adversarial example generation.  
Figure 8: Feature participation in PGD adversarial examples.  
increase classification performance, they are vulnerable to adversarial attacks, which can undermine their performance and adoption. The results also showed that the PGD attack performed better on classical machine learning multi-class classifiers. It managed to reduce the performance of the decision trees classifier by 65.82%, Random Forest by 70.51%, and Linear SVM by 70.51%, while DeepFool only managed to reduce classical machine learning multi-class classifiers by 17.71%, 32.05%, and 34.62%, respectively. FGSM was also inferior in attacking classical machine learning multi-class classifiers; its percentage reduction of decision trees classifier's ROC-AUC was 3.80% less than PGD and 3.85% less for both Random Forest and Linear SVM to PGD. These results align with [25], who confirmed that the FGSM attack's purpose is to be fast and to not have optimal attacks.
# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
###### Abstract  
The Kingdom of Saudi Arabia is considered to be one of the world leaders in olive production accounting for about 6% of the global olive production. Given the fact that 94% of the olive groves are mainly rain-fed using traditional methods of production, the annual olive production is witnessing a noticeable fluctuation which is worse due to infectious diseases and climate change. Thus, early and effective detection of plant diseases is both required and urgent. Most farmers use traditional methods, for example, visual inspection or laboratory examination, to identify plant diseases. Currently, deep learning (DL) techniques have been shown to be useful methods for diagnosing olive leaf diseases and many other fields. In this work, we use a deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pretrained CNN models, i.e., ResNet50 and MobileNet. Hence, we propose MobiRes-Net: A neural network that is a concatenation of the ResNet50 and MobileNet models for overall improvement of prediction capability. To build the dataset used in the study, 5400 olive leaf images were collected from an olive grove using a remote-controlled agricultural unmanned aerial vehicle (UAV) equipped with a camera. The overall performance of the MobiRes-Net model achieved a classification accuracy of 97.08% which showed its superiority over ResNet50 and MobileNet that achieved classification accuracies of 94.86% and 95.63%, respectively.  
plant disease identification; deep learning; ResNet50; unmanned aerial vehicle; MobileNet; smart farming; MobiRes-Net; classification Article  
MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases