esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Given the varying sensitivity of savanna physiognmies to rainfall seasonality, information on vegetation phenology, derived from different vegetation indices calculated from the satellite constellation, can be important for savanna classification [28; 29; 30]. It is plausible that the incorporation of phenological metrics may enhance the classification accuracy of pastures, particularly in distinguishing severely degraded areas from other classes. However, in our present work, the time series was utilized solely to identify the most favorable periods for discriminating pasture degradation classes. As another limitation of our work, we did not consider pasture species when defining the training and validation datasets, although the species were identified in the field. This decision was influenced by the predominant presence of _Brachiaria brizanthu_ in almost all surveyed pastures, making pasture species a less significant factor in introducing uncertainties into the classification results. Conversely, pasture management could potentially introduce spectral variability, especially in studies exploring interannual variations in vegetation indices associated with vegetation productivity or land degradation. In our future investigations of pasture degradation, it is imperative to explore and test alternative machine or deep-learning approaches. However, in the savanna environment, numerous studies have consistently demonstrated the efficacy of RF in discriminating savanna physiognomies, outperforming other classifiers such as Support Vector Machine and Spectral Angle Mapper, or producing comparable results with deep learning approaches [27; 29; 30; 31]. For instance, in a study aimed at differentiating pasture from non-pasture areas in Brazil, [PERSON] et al. [31] reported F1-scores of 95% for RF, whereas a deep learning approach based on U-Net achieved 97% using the validation dataset. Given the substantial spectral similarity among the five classes of pasture degradation tested for classification, it is essential to acknowledge that traditional approaches, such as Linear Discriminant Analysis (LDA), should not be overlooked in this context. Each method brings its own set of strengths and weaknesses. Exploring diverse techniques, metrics, and approaches ensures a comprehensive understanding of their applicability in addressing the specific challenges posed by pasture degradation classification in the savanna environment.
# Distributed Reliable and Efficient Transmission Task Assignment for WSNs
## 3 Description of Reliable and Efficient Task Allocation in WSNs
### Reliable and Efficient Task Allocation Problem Model  
WSNs can be represented by graph \(G(V,E)\). \(M(t)\) represents the set of tasks to be allocated at time \(t\), and \(m_{i}\in M(t)\) represents the \(i\)-th task in \(M(t)\). In a task allocation for WSNs, the two processes of task mapping and task scheduling are included. First, each task is mapped to the sensor node in the graph \(G(V,E)\) that is represented by the function \(\varphi(m_{i}):M\to V\). When a task is assigned to multiple nodes, communication task scheduling is performed between the nodes, and the process of path allocation is represented by the function \(\phi(m_{i}):L\to E\). Therefore, the reliable and efficient WSNs task allocation problem can be abstracted into the following constraint optimization problems:  
Input: \(TG(V,E)\), \(RT(S,\ Ps)\), \(M(t)\)  
Output: \(\varphi(m_{i}):M\to V\), \(\phi(m_{i}):L\to E\)  
Satisfying Equation (11):  
\[\begin{array}{rl}\min&Eng\varphi(m_{i})=\sum_{\tau\in V}e_{ij},&m_{i}\in M (t)\\ &\text{s.t}&R_{WSN}\geq R_{s},\forall\tau\in V\\ &C_{i}\geq P_{i}+S_{i}\end{array} \tag{11}\]  
where \(TG(V,E)\) represents the physical topology graph of the network, \(V\) represents the set of sensor nodes in the network, \(E\) represents the set of links between the nodes in the network, \(RT(S,Ps)\) represents the routing topology of the network,\(S\) represents the source node set and \(Ps\) represents the path set of all source nodes to the sink in the network. \(Eng(\varphi(m_{i}))\) is the energy consumption of task \(m_{i}\) that is equal to the sum of the energy consumption on all nodes in the network assigned to task \(m_{i}\). From this, Equation (11) indicates that the goal of the task allocation to WSNs is to minimize energy consumption under the constraints of reliability and deadline. Given that this is a nonlinear mixed-integer programming problem, it can be solved using a heuristic algorithm.
## 3 Results
### Training and Validation Results  
Having defined the CNN topology, the focus is on the training process, which is set to run for 30 epochs. Every epoch comprises of a full cycle on the entire training set, consisting of 620 samples. Also, the option of shuffling the training data to the input layer is applied at the beginning of each epoch.  
Figure 4 presents a comparison of the validation graphs, each of which is derived from the training procedure with one of the analyzed optimizers. According to the diagram, the training process is set to run for a maximum of 270 iterations, in which the accuracy of the validation data is calculated. It is recalled that the validation set is not used to update the network weights, but to assess whether a model suffers from either overfitting or underfitting. Finally, a validation patience value of 3 is set to stop the training process, in case the same validation value is produced at least three times, indicating that the CNNs have learned sufficiently from the image data.  
Figure 4: Validation line graphs for the trained CNN models, with the SGDM optimizer producing a more optimal validation accuracy (96.5%) than Adam (91.7%). The accompanying lines below show that both graphs are produced in conjunction with the calculated loss.  
At first, the \(\text{CNN}_{\text{Adam}}\) validation graph (Figure 4) is monitored, which shows the convergence of the neural network's training process during the 162 nd training iteration. This was the result of the combination of overfitting with the production of three similar validation values equal to 91.7%. In contrast, the \(\text{CNN}_{\text{SGDM}}\) graph shows that the SGDM optimizer performed better as it did not overfit the training data. This led to better results, as the deep classifier not only completed the learning process by running in all 30 epochs but also produced a higher validation value of 96.7%.
# A Simple AMC Antenna for Liquid Monitoring in an Infusion Bag
## 3 Working Principle
The common materials used include polypropylene (PP), polyethylene (PE), polyvinyl chloride (PVC), and ethylene vinyl acetate (EVA). To evaluate the impact of infusion bag materials and thickness on the antenna's performance, we conducted simulation analyses. Figure 11 presents the simulated \(\left|S_{11}\right|\) results for different materials and thicknesses. As shown in Figure 11a-d, the material variations have a minimal influence on the antenna's resonant frequency, which remains stable at 2.4 GHz in the without-liquid state and at 2.0 GHz in the with-liquid state. However, while bag thickness influences the antenna's resonance depth, the return loss remains below \(-10\) dB in both states, ensuring effective impedance matching. Therefore, the proposed antenna is well suited for use with infusion bags composed of various materials and thicknesses, maintaining stable performance despite these variations. Figure 3:1 (a) Simulated \(\left|S_{11}\right|\) of the antenna sensor with different thicknesses of the liquid. (b) Linear fitting curve between the resonance frequency and thickness of the infusion bag.
## 2 Bearing Design and Principle of its Operation  
Consider a radial aerostatic bearing (Figure 1a), which consists of a shaft (1), a housing (2) and a movable inner sleeve (3), resting on an elastic suspension (6). In the housing (2), there are throttling feeders (4), and in the movable sleeve (3), there are damping feeders (5), whose resistance in a stable bearing should be about an order of magnitude lower than the resistance of feeders (4) [14]. The surfaces of the housing, bushing and elastic suspension form inter-throttle chambers (7), whose volume plays a significant role in ensuring an acceptable dynamic quality of the structure. The elastic suspension has the ability to resist deformation under the action of pressure forces on the surfaces of the sleeve (3). To reduce the negative effect of the circumferential air flow in the inter-throttle cavities for the load characteristics of the bearing, they are separated from each other by a sufficient number of longitudinal baffles, as shown in Figure 1b.  
Compressed air under supply pressure \(p_{s}\) = const (the pressure source is not shown in Figure 1), overcoming the resistance of throttling feeders (4), enters the inter-throttling cavities (7) of the volume \(v_{p}\) under pressure \(p_{p}<p_{s}\). Further, through damping feeders (5), air enters the bearing gap under pressure \(p_{k}<p_{p}\) and then flows out into the environment with pressure \(p_{a}\). An increase or decrease in the load \(f\) on the bearing, respectively, increases or decreases the pressures \(p_{k}\) and \(p_{p}\) in the loaded area and decreases or increases in the unloaded area of the bearing gap.  
Under the action of the force reactions of compressed air, elastic deformation of the suspension (6) occurs, causing displacement \(e_{p}\) of the sleeve (3) in the direction opposite to \(f\). In this case, the displacement of the shaft (1) relative to the body (2) will be of the total value \(e_{s}=e_{p}+e\), where \(e\) and \(e_{p}\) are the eccentricities of the shaft (1) and the sleeve (3). At \(e_{s}=0\), the bearing will have zero compliance (infinite stiffness) and, at \(e_{s}<0\), negative. With the correct determination of the elasticity of the suspension (6) and other bearing parameters, it is possible to provide any necessary compliance of the bearing gas gap, as shown for the thrust bearings in references [16; 20; 21].
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Patient Rights and Informed Consent in AI Healthcare
#### 5.5.6 Dynamic Consent and Security in the Age of AI-Enhanced Healthcare  
The continuous and dynamic nature of AI systems, which learn and adapt over time, necessitates an equally flexible and responsive consent process. Patients should receive regular updates on the capabilities and limitations of the AI systems involved in their care, ensuring that their consent is informed and reflective of the latest developments. Such a dynamic consent process is vital for preserving patient autonomy and fostering trust in healthcare systems integrating AI technologies. Concerns regarding equity and bias in AI systems are integral to the informed consent dialogue. Patients should be informed about the potential for AI systems to exhibit biases based on the data they are trained on, which could adversely affect patient care and outcomes. Patients need to understand the proactive steps to ensure that AI systems operate fairly and without prejudice, thereby safeguarding the quality of care for all individuals. Security measures are another critical aspect of AI in healthcare that must be transparently communicated to patients. They should be aware of the robust security protocols to protect their health data, including the potential risks of data breaches and the strategies to prevent them. This communication should extend to the specific security vulnerabilities associated with IoT devices and the measures implemented to mitigate such risks [2,22,38].  
The integration of AI in healthcare brings forth a need for adaptive regulatory frameworks and redefined patient consent processes. These changes are essential to address the ethical, legal, and practical challenges posed by the use of advanced AI technologies in healthcare settings, ensuring that patient rights are protected and informed consent processes are adequate in the era of AI. Incorporating these concepts into the discussion on patient rights and informed consent in AI healthcare will provide a more comprehensive and nuanced understanding of the challenges and requirements in this evolving field. It underscores the need for healthcare systems to develop adaptive regulatory frameworks and dynamic, informed consent processes that are responsive to the unique attributes of AI,such as algorithmic transparency, the ability of machine learning systems to evolve, and the continuous engagement and education of healthcare professionals and patients [1,2,22,38].
# Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model
## 4 Experiments
### Data Set  
As shown in Table 1, two well-recognized datasets, CIC-IDS2017 [23] and CIC-DDoS2019 [24], are adopted in our experiments. The datasets were collected by the Canadian Institute for Cybersecurity with Wireshark in emulated environments. They are generated using two types of usage profiles and multistage attacks, such as Heartbleed, and a variety of DoS and DDoS attacks. Collected traffic is then pre-processed with the CICFlowMeter [25]. It has 80 network traffic features, aiming to generate the varied DoS and DDoS traffic data. The resulting data set contains records of traffic features in CSV format. To fit in the numeric nature of the proposed framework, the non-numeric fields are transformed using One-Hot encoding. All fields are then normalized for the rescaling of their dynamic ranges.  
\begin{table}
\begin{tabular}{l l r r} \hline \hline
**Data Set** & **Traffic Type** & **\# of Instances** & **Ratio** & **Total \# of Instances** \\ \hline \multirow{6}{*}{CIC-IDS2017/Wednesday} & BENIGN & 440,031 & 0.63 & \\  & DoS GoldenEye & 10,293 & 0.014 & \\  & DoS Hulk & 231,073 & 0.333 & \\  & DoS Slowhtptest & 5499 & 0.008 & \\  & DoS Slowloris & 5796 & 0.008 & \\  & Heartbleed & 11 & \(1.5\times 10^{-5}\) & \\ \hline \multirow{2}{*}{CIC-IDS2017/Friday} & BENIGN & 97,718 & 0.432 & \\  & DDoS & 128,027 & 0.567 & \\ \hline \multirow{2}{*}{CIC-DDoS2019/NTP} & BENIGN & 14,365 & 0.0118 & \\  & DDoS/NTP & 1,202,642 & 0.9881 & \\ \hline \multirow{2}{*}{CIC-DDoS2019/LDAP} & BENIGN & 1612 & 0.0007 & \\  & DDoS/LDAP & 2,179,930 & 0.9992 & \\ \hline \multirow{2}{*}{CIC-DDoS2019/SSDP} & BENIGN & 763 & 0.0002 & \\  & DDoS/SSDP & 2,610,611 & 0.9997 & \\ \hline \multirow{2}{*}{CIC-DDoS2019/ Syn} & BENIGN & 392 & 0.0002 & \\  & Syn & 1,582,289 & 0.9997 & \\ \hline \multirow{2}{*}{CIC-DDoS2019/ NetBIOS} & BENIGN & 1707 & 0.0004 & \\  & DDoS/ NetBIOS & 4,093,279 & 0.9995 & 4,094,986 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CIC-IDS2017 and CIC-DDoS2019.  
Figure 6: The control flow of the incremental learning procedure.  
Performance indices include the confusion matrix, as shown in Table 2, and the Accuracy, Precision, and Recall, as defined in (5). Precision attempts to answer the question as to what proportion of positive identifications are actually correct. Recall concerns what proportion of actual positives are identified correctly. Precision measures the percentage of identified instances that are correctly classified.  
\[\left\{\begin{array}{l}\text{Accuracy}\triangleq\frac{TP+TN}{TP+FP+FN+TN}\\ \text{Precision}\triangleq\frac{TP}{TP+FP}\\ \text{Recall}\triangleq\frac{TP}{TP+FN}\\ \text{F1 Score}\triangleq\frac{2*TP}{2*TP+FP+FN}\end{array}\right. \tag{5}\]
# Wireless-Channel Key Distribution Based on Laser Synchronization
## 5 Discussion  
In this work, we have successfully demonstrated laser synchronization induced by a common wireless signal, achieving a cross-correlation of 0.95. This finding supports the feasibility of common-signal-induced laser synchronization when there is no direct optical connection between the drive source and response laser. It enables us to achieve high-speed key distribution over wireless links utilizing the wideband synchronized output of the laser. Moreover, it is worth noting that the key rate can be further improved by using a wider bandwidth drive signal, thanks to the positive correlation between the laser output and the drive signal's bandwidth. Nevertheless, the channel bandwidth remains the primary limiting factor in determining the achievable key rate, as it dictates the maximum bandwidth of the drive signal that can be transmitted.  
While we utilized a frequency band centered around 6.7 GHz for the drive signal in our experiment, it is not a strict requirement. We observed that when the center frequency of the drive signal is close to the laser's relaxation oscillation frequency, it is easier to achieve synchronization and obtain a stronger response intensity. Typically, the relaxation oscillation frequency of a laser can be adjusted within the range of 1 to 10 GHz by varying the bias current, allowing for the usability of frequency bands within this range [39].  
Our results demonstrate the feasibility of wireless key distribution at high speeds using common-signal-induced laser synchronization. When operating in a free space environment, the method of generating keys based on channel measurements is ineffective. In such cases, our scheme could serve as a valuable alternative, offering a high-speed key distribution rate. Within complex environments, such as multipath fading channels, severe changes in the receiving signal may significantly impair the efficacy of laser synchronization, ultimately leading to a reduction in key rate. By utilizing a digital signal as the drive signal, this difficulty may be resolved in an innovative manner. In combination with the robustness of digital wireless communication and the privacy of laser synchronization, high-speed security key distribution is expected in complex environments, and related work is currently under investigation.
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 2 Materials and Methods
### Coil Calibration and Resolution Enhancement  
The different sensitivity of each Hall element affects the measurement of the magnetic field. A Helmholtz coil is utilized to calibrate the sensitivity of Hall elements, and the equation for sensitivity calibration is as follows:  
\[S_{1}=\frac{V_{1}}{B}, \tag{1}\]  
\[\alpha_{n}S_{n}=\frac{\alpha_{n}V_{n}}{B}=S_{1}, \tag{2}\]  
where \(B\) is the value of the magnetic field generated by the Helmholtz coil, and \(V_{1}\) and \(S_{1}\) are the Hall voltage and sensitivity of the first Hall element in the array. \(V_{n}\), \(S_{n}\), and \(\alpha_{n}\) are the Hall voltage, sensitivity, and scaling factor of the \(n\) th Hall element, respectively. A software algorithm is used to calculate these scaling factors to achieve the sensitivity of the other Hall elements to be consistent with the sensitivity of the first Hall element.  
The special moving route of the X-Y axis motorized rail further improves the detection resolution of the system. Figure 7 describes the movement of the X-Y axis motorized rail. Unpackaged Hall linear array in the initial position indicated by solid black lines. The slide table drives the array in the way shown in Figure 7 to increase the number of points to be measured. The data are then rearranged and processed to plot higher-resolution images. This movement makes the spacing between adjacent points to be detected about 127.5 \(\upmu\)m. The spatial resolution is increased from 99.61 ppi to 199.22 ppi.  
Figure 6: Experimental setup. It contains the components of the magnetic field imaging system.  
The interpolation algorithm is used to increase the number of pixels and colors in these images, enabling a resolution of 398.43 ppi. The software flowchart and settings page are shown in Figure 8. The auto-zero function is included in the software to cancel out the zero voltage of each Hall element. As the unpackaged Hall linear array is driven by the slide to disrupt the data arrangement order, we have written a function to restore the order of data arrangement using MATLAB 2023a, which automatically arranges the data into a 2D matrix according to the number of movements of the array in different directions. This function also handles the data generated after the above special moves to draw higher-resolution images. Based on the magnetic field range to be measured, the interface provides a choice of the number of scanning elements whose detected data are processed.  
Figure 8: _Cont_.  
Figure 7: A moving method to further improve spatial resolution.
###### Abstract  
Regardless of their biogeographic origins or degree of artificialization, the world's forests are a source of a wide range of ecosystem services (ES). However, the quality and quantity of these services depend on the type of forest studied and its phytogeographic context. Our objective is to transpose the concept of ES, in particular, the assessment of forest ES, to the specific Mediterranean context of the North African mountains, where this issue is still in its infancy and where access to the data needed for assessment remains difficult. Our work presents an introductory approach, allowing us to set up methodological and scientific milestones based on open-access remote sensing data and already tested geospatial processing associated with phytoecological surveys to assess the ES provided by forests in an Algerian study area. Specifically, several indicators used to assess (both qualitatively and quantitatively) the potential ES of the Ouled Hanneche forest, a forest located in the Hodna Mountains, are derived from LANDSAT 8 OLI images from 2017 and an ALOS AW3D30 DSM. The qualitative ES typology is jointly based on an SVM classification of topographically corrected LANDSAT images and a geomorphic-type classification using the geomorphon method. NDVI is a quantitative estimator of many plant ecosystem functions related to ES. It highlights the variations in the provision of ES according to the types of vegetation formations present. It serves as a support for estimating spectral heterogeneity through Rao's quadratic entropy, which is considered a relative indicator of biodiversity at the landscape scale. The two previous variables (the multitemporal NDVI and Rao's Q), completed by the Shannon entropy method applied to the geomorphon classes as a proxy for topo-morphological heterogeneity, constitute the input variables of a quantitative map of the potential supply of ES in the forest determined by Spatial Multicriteria Analysis (SMCA). Ultimately, our results serve as a useful basis for land-use planning and biodiversity conservation.  
Mediterranean forest; LANDSAT; service-providing unit; GIS; landscape biodiversity; ecosystem functions; multi-criteria spatial analysis +
Footnote †: journal: _forests_