esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Standard Normal Variant and Spectra  
The number of outliers detected by the MD method was less than 1% for the calibration dataset and less than 2% for the validation dataset, which was due to the image processing method setting thresholding limits that removed large clumps of cells. While [PERSON]'s thresholding method did improve the cell cluster separation, overlapping cells still existed. Figure 3 shows an example image of _Salmonella Heidelberg_ taken at 638 nm, with the raw image shown in Figure 3A, and the cell segmentation image shown in Figure 3B. Here, we can see that some cells are touching other cells and some are not.  
To increase the number of cells analyzed per image, an improved single cell separation method would need to be implemented. Figure 4 shows the mean spectra for the _Salmonella_ calibration data set (\(n=3315\)). In Figure 4A, it is noticeable that the raw TH spectra show a large variance in intensity values, ranging from around 1500 to 16,000 a.u. at a maximum peak of 638 nm. Applying the row-based SNV preprocessing step placed the spectra on a consistent scale, as shown in Figure 4B.  
Figure 4: Raw (**A**) and preprocessed (**B**) _Salmonella_ spectral profiles for the calibration dataset.  
Figure 3: Hyperspectral microscope image of _Salmonella_ Heidelberg at 638 nm: (**A**) Raw image and (**B**) cell segmentation image with extracted pixels shown in white.  
High collinearity between bacterial species is an issue that should be taken into consideration. Because PCA utilizes an orthogonal transformation of the spectra to calculate the PCs, this aids in negating the influence of collinearity in the classification model. An advantage of SIMCA is that it is sensitive to dissimilarities between objects [22], which is significant given the close spectral relationships between bacteria. Eliminating these false outliers is key in the prediction of _Salmonella_, as type II errors can result in a pathologically contaminated food product to be released to the consumer market. Careful consideration of outliers was performed in this application; determining too many bacterial cells to be outliers would result in underfitting the prediction model, thus being counterintuitive to the purpose of this SIMCA application, and potentially resulting in a high number of type II errors.
## 3 Materials and Methods  
### Design and Development of DAS  
The sun generates solar energy that is used by the PV array. The photovoltaic system incorporates solar array, meteorological and electrical parameters with different techniques for data transmissions. The CR1000 consists of sets of sensors for measuring and logging in meteorological parameters such as solar irradiance, ambient temperature, relative humidity and wind speed while DT80 consists of different sets of devices used to measure electrical parameters like current and voltage of PV, MPPT, battery and inverter. Temperatures between 17 to 45 \({}^{\circ}\)C with relative humidity conditions within a range from 15 to 90% (non-condensing) and solar irradiance of up to 1250 W/m\({}^{2}\) were recorded in the system. Besides, the designed DAS was exposed to dust and high wind speed in the range of 2.1 to 2.5 m/s. A refrigeration system and a stove of up to 2 kW were used to draw power from the PV array and battery system. Figure 4 shows the block diagram of DAS measuring indoor and outdoor parameters.  
Shunt resistors are aligned, each at the output of the PV array and MPPT while the input in each is connected to the battery bank and the inverter to measure the current flowing into and out of the respective components. The DC power output from the batteries is fed directly into the inverter for conversion into AC power at a frequency of 50 Hz and voltage of 230 V. The output current, voltage and power from the inverter are measured using the IEM 2150 energy meters. However, the two loggers (CR1000 and DT80) automatically record each reading taken from the respective sensor which is at that point stored in the internal memory of the corresponding loggers and can henceforth retrieve periodically through Wi-Fi to the personal computer for analysis.
## 4 Experiments and Analysis
### Evaluation Metrics  
To evaluate the performance of the proposed models, we adopt several key metrics: Precision, Recall, F1 Score, and mean Average Precision (mAP). Additionally, model complexity is assessed based on GFLOPs (Giga Floating Point Operations), Frames Per Second (FPS), the number of parameters (M) and the weight size.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Closed Eyes** & **Yawn** & **Playing Phone** & **Drinking** & **Single Hand** \\ \hline YawDD-E & 1177 & 1385 & 1269 & \(\sim\) & \(\sim\) \\ SfdDD & \(\sim\) & \(\sim\) & 1597 & 1700 & 3053 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The distribution of labeled categories in the study. “\(\sim\)” means this category is not present in the dataset.  
Figure 6: Sample display of the YawDD-E and SfdDD datasets.  
Precision is defined as the proportion of correctly predicted positive samples among all samples predicted as positive. It reflects the accuracy of the model's positive predictions and is calculated as:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{9}\]  
where \(TP\) (True Positives) denotes the number of correctly predicted positive samples and \(FP\) (False Positives) refers to the number of negative samples incorrectly predicted as positive.  
Recall measures the proportion of actual positive samples that are correctly identified by the model, indicating its ability to capture positive instances. It is defined as:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{10}\]  
where \(FN\) (False Negatives) denotes the number of positive samples incorrectly predicted as negative.  
The F1 Score is the harmonic mean of precision and recall, providing a balanced evaluation metric, especially valuable in imbalanced datasets. It is calculated as:  
\[F1=\frac{2\times P\times R}{P+R} \tag{11}\]  
Average Precision (AP) is computed as the area under the precision-recall curve, derived from the IoU (Intersection over Union) score between the predicted and actual bounding boxes. It is defined as:  
\[\text{AP}=\sum_{i=1}^{n}P(i)\cdot\Delta R(i) \tag{12}\]  
where \(P(i)\) is the precision at the \(i\)-th threshold, and \(\Delta R(i)\) is the change in recall at that threshold.  
Mean Average Precision (mAP) is the average of APs across all classes. Specifically, [EMAIL_ADDRESS] refers to AP computed at an IoU threshold of 0.50, while [EMAIL_ADDRESS]:0.95 averages AP over multiple IoU thresholds from 0.50 to 0.95 with a step size of 0.05. The overall mAP is given by:  
\[\text{mAP}=\frac{1}{n}\sum_{c=1}^{n}AP_{c} \tag{13}\]  
where \(n\) is the total number of classes, and \(AP_{c}\) is the average precision for class \(c\).
# Hot Carrier Photocurrent through MOS Structure
## 3 Results and Discussion
In the case of forward bias, depletion of the semiconductor surface further followed by inversion (collection of electrons) takes place. Since the semiconductor is of p-type conductivity, density of electrons collected at the surface is much lower than that was for the holes, and the heated-electron-caused photocurrent is negligible (Figure 6b). Polarity of the photocurrent supports this model--negative potential with respect to the grounded terminal is induced across the load resistor in the case of reverse bias, and the sign changes to the opposite one when the forward bias is applied. Linear dependence of the photocurrent on the laser intensity supports the hot carrier model of the photoresponse formation (Figure 4c). Since the photocurrent has a capacitive nature, it can be expressed as  
\[I_{ph}=\frac{dQ_{S}}{dt}=\psi_{S}\frac{dC_{S}}{dt}, \tag{2}\]  
where \(Q_{S}\) is the charge accumulated at the semiconductor surface, and \(\psi_{S}\) stands for the semiconductor surface potential [25]. Some reasoning on this equation needs to be discussed. First, since more carriers are accumulated at higher applied voltage, then the  
Figure 5: Dependencies of semiconductor surface layer capacitance \(C_{S}\) (open orange dots, left axis) and laser induced photocurrent \(I_{ph}\) (solid blue dots, right axis) on reverse bias voltage across the MOS-1 sample. Figure 6: Energy band diagram of the structure and model of photoresponse formation under reverse (**a**) and forward (**b**) bias voltage. laser pulse-induced carrier heating causes bigger mass diffusion in the semiconductor bulk direction, and thus stronger capacitive current flows though the structure. Second, the photocurrent is proportional to the rate of change of capacitance. In this way, such photocurrent can find application in detection of very short laser pulses--the shorter the pulse, the stronger the photosignal. In general, carrier energy relaxation time is in the order of \(10^{-12}\) s in typical semiconductors; it means that picosecond-long laser pulses could be detected directly. Third, the laser radiation alters capacitance of the structure--this effect can be used to design an infrared semiconductor photovaractor operating on the basis of the hot carrier phenomenon. Similarity of the _I-V_ and _Iph-V_ dependences in Figures 3 and 4a, respectively, implies that photocurrent through the \"bad\" MOS-2 structure should be related to the leakage current. Polarity of the photocurrent pulse does not depend on the bias voltage and it is always negative with respect to the grounded terminal. The rise of this photocurrent can be explained in the following way. In the case of reverse bias, polarity of the photocurrent pulse corresponds to the reduction of the dc current under the action of the laser light. The dc current flows through the MOS structure in the direction from the p-type semiconductor to the metal. That is, the laser pulse causes de-accumulation of the holes in the opposite direction as indicated in Figure 6a. When forward bias is applied, illumination induces increase of the dc current.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 4 Multidimensional Projection Approaches and Domains
### Isometric Feature Mapping  
Another important variation of Classical Scaling is the Isometric Feature Mapping (Isomap) [55], a global nonlinear projection technique. Isomap transforms the distance relationships among multidimensional data instances before projecting them into the visual space [31]. Isomap aims to capture the data's topological (manifold) structure by computing geodesic distances by turning the dataset into a graph structure and considering distances based on the shortest path between vertices. Isomap is probably the first projection technique to resort to topological data analysis mechanisms to achieve dimensionality reduction [56].  
Isomap builds an undirected weighted graph \(G=(V,E)\), such that \(v_{i}\in V\) represents a data instance \(x_{i}\) and \(w_{ij}=\delta(x_{i},x_{j})\) is the weight function. The graph edges can be created with the _k-nearest neighbor_ (k-NN) approach, which considers a data distance to define each \(e_{i}\in E\). Therefore, the \(k\) closest vertices to a specific vertex \(v\) are connected. With this process, the \(\delta\) function information is surrogated by the shortest path between points calculated by some path algorithm, such as Dijkstra [57]. Moreover, the graph's adjacent matrix \(D\) is considered as the \"distance\" matrix of data, with cells related to data with no edge filled up by so value.  
Finally, the visual space is created by applying some projection technique, such as MDS, in the \(D\) matrix. Sadly, the calculation of distance and the spectral decomposition affect computational performance that attains complexity \(O(n^{3})\). The topological stability of Isomap depends on the correct choice of \(k\)[58]. Moreover, isometric mapping of high dimensionality into low one is possible only in particular conditions, resulting in mistakes and distortions [7].  
[PERSON] and [PERSON] [59] created a more computationally efficient version of Isomap, named L-Isomap. First, it selects \(s\) landmarks to generate the graph representing relationships among the landmarks and the other data instances. After that, the LMDS technique is applied to project the landmarks and approximate the positions of the remaining points. It reduces the Isomap complexity to \(O(sn\log n)\).
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 4 Results
### Descriptive Statistics for Video Features and Engagement Scores
#### 4.1.2 Consumer Engagement Scores  
The consumer engagement scores were collected in the observational study using UES-SF. The scores were calculated for the 30 videos and all four UES-SF dimensions (see Section 3). The results for 150 participants are shown in Figure 2.  
Figure 1: Basic statistics for low-level video features derived from the ads. The table columns represent the minimum, maximum, mean, and standard deviation for each video feature, respectively. The last two columns show the randomized bar graph of raw consecutive feature values for each ad and their histograms.  
The reliability coefficient hierarchical \(\omega\) of the overall model is \(\omega=0.81\). Measurement models of all four dimensions of USE-SF are tau-equivalent [33] and the pertained reliability coefficient is Tau-equivalent \(\rho_{T}\). For comparability, we also report commonly used reliability coefficients Cronbach's \(\alpha\), see Table 3. The reliability of the dimensions aesthetic Appeal (AE) and Reward (RW) is very high, and the estimated reliability of the dimensions Focused Attention (FA) and Perceived Usability (PU) is moderately high. The obtained values are comparable to the reliability measures reported in the original article presenting UES-SF [5].  
Figure 2 shows the statistics for the UES-SF scores. The scores for the UES-SF dimensions are given on a scale from 1 to 5. The minimum, maximum, mean, and standard deviation are summarized in the first four columns. The randomized bar graphs in the fifth column represent the individual dimension values of engagement bound to each consecutive observation. The last column contains the histograms. The values of Focused Attention (FA) are distributed over the entire score range in a near-normal shape. Perceived Usability (PU) scores tend to express a high number of lowest rates (1), with other values distributed uniformly. The distributions of Aesthetic Appeal (AE) and Reward (RW) tend to dominate in high values between 3 and 5. Overall engagement score (UES) expresses appearance of a normal distribution with a mean value of 3 but of a relatively small deviation.
## 4 Experimental Results and Discussion
### Performance Analysis of Clustering  
Clustering is an essential method for ensuring efficient data transmission. The proposed work used the K-medoids approach to cluster similar data into one group based on the average energy consumption of each node [25; 26].  
Figure 3 and Table 2 represent the comparison graph for the average energy consumption by the proposed approach and the existing methods, K-means and Fuzzy C-means clustering. Therefore, the hybrid approach consumed 0.0196 J on average for cluster size 2, whereas the K-means method consumed 0.0245 J and the Fuzzy C-means logic approach consumed 0.0294 J.  
Figure 3: Performance analysis of the energy consumption of the proposed ASFO algorithm.  
Figure 2: Data path from the source node CH1 to the destination node CH5.  
Table 3 and Figure 4 show the comparison of CH selection by the proposed ASFO method with two conventional methods, the krill herd algorithm and the WOA. The CH selection in 250 rounds by the proposed method based on the average residual energy is 39.2337 J, whereas the existing methods, the krill herd algorithm and the WOA, have 34.3295 J and 29.4253 J, respectively.  
Figures 4 and 5 show that the proposed ASFO algorithm is more effective than the conventional algorithm for clustering and CH selection. Thus, the proposed method efficiently formed the cluster and chose the CH for the corresponding cluster.
## 2 Related Works  
As sensors adopted in WSNs have limited computation and energy resources, they are naturally vulnerable to resource depletion attacks, such as denial of service (DoS) attacks and forced authentication attacks [15]. DoS attacks threatening WSNs and the corresponding countermeasures have been well studied [16, 17, 18]. A downgraded version of distributed DoS attacks includes a reduction in quality (RoQ) attacks while trying to bring down the quality of service (QoS) of the network rather than completely denying service [19]. Even though several attempts have been made to protect against RoQ attacks [20, 21, 22], most of them can only be applied at the transport layer and not in the routing layer.  
As a subcategory under the umbrella of resource depletion attacks, power-draining attacks have been widely discussed in previous studies [23, 24, 25, 26]. In power-draining attacks, energy storage (usually battery) is naturally considered the primary target. Unlike DoS attacks that disable the immediate availability of the network, power-draining attacks intend to deplete the network's power over a long time perspective. Several simple attempts following this attack pattern have already been evaluated [27, 28, 29]. An instance of the power-draining attacks, vampire attacks, target routing protocols that are adopted in WSNs [13]. Vampire attacks are not protocol-specific; in other words, even the protocols that are designed to be secure cannot be immune from them. Instead, routing protocols could be exploited. In worse cases, harmful but protocol-compliant messages can be generated by adversaries. Consequently, it is difficult to trace back the origin of these attacks and prevent them.  
Very few countermeasures have been proposed to prevent vampire attacks. In [13], an upgraded version of clean-slate secure sensor network routing protocol (PLGP) [27], known as PLGPa, is presented. PLGPa relies heavily on cryptographic methods and may incur extra costs in computation and transmission. Considering the limited computingpower and battery capacity of wireless sensors, solutions with better energy efficiency and less hardware overhead are worth investigating.  
A summary of these aforementioned existing works are listed in Table 1.
# Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research
## 3 The Cybersecurity Aspects of Microgrid
### Vulnerabilities and Threats in Microgrid
#### 3.2.1 Common Vulnerabilities in Microgrid  
Although a combination of the cyber-system and the critical physical infrastructure can be beneficial, it creates several vulnerabilities that can lead to threats. Such vulnerabilities can expose a microgrid to physical system damage if they are not adequately addressed.  
Cyber-physical vulnerabilities in a microgrid are inherited from the distributed power system. These vulnerabilities are developed by the following:  
* **Wireless communication.** Such communication uses radio frequency, which makes it challenge to prevent physical access to users, especially in case of public access to the network. Although it has several advantages, it faces the risk of attacks, including interception and intrusion, that can be larger than in a wired network.
* **Heterogeneous communication technology.** Modern power systems are deployed through the use of various technologies. These technologies, which are either wired or wireless, create challenges for a robust and uniformed cybersecurity policy due to the need to protect the communication infrastructure.
* **Increased communication to external networks.** This occurs in a microgrid because communication to the external network helps maintain its performance and safety through continuous data exchange with the main operator. However, it exposes the created communication line to outsider threats.
* **Internet Exposure.** The exchange of data through the Internet plays an important role in providing ancillary services for the microgrid, including data on fuel prices and weather forecasts. This environment exposes the system to attacks through the Internet.
* **Increased system automation.** System automation improves the effectiveness and flexibility of operation by preventing the likelihood of human error. However, this creates new vulnerabilities, where the system has more access points, thus increasing the possibility of attacks.
* **Increased use of automation device and distributed control.** The possibility for a security breach is created through the heightened penetration of monitoring and control capabilities of the system. The boundaries of a microgrid have been extended and stretched in the digital era.
* **Cohabitation between legacy and new systems.** The sharing of a common infrastructure during contact between the microgrid controller and the operators of different distributions can introduce new vulnerabilities to the system.
* **Multiple independent systems.** Because the microgrid consists of such essential systems as computers, actuators, sensors, and emergency systems, it faces difficulty in guaranteeing uninterrupted communication, interfacing, and security between heterogeneous and independent systems.  
All these vulnerabilities are considered weaknesses that can be exploited by one or more threats.
## 4 Layered Models  
The lack of standardization in the developing areas in interconnected transportation systems has led to numerous models being proposed. Systems concerned with groundvehicles often separate functionality among various layers as illustrated in the Figure 1, with each performing a particular function or a set of functions [41]. However, systems designed for UAVs frequently lack this distinct separation [42]. Therefore, this section identifies specific layers in systems designed for ground vehicles and delineates common points with UAV networks to facilitate structuring them into layers. This is vital because even though both ground vehicle networks and UAV networks are undergoing rapid evolution, advances in ground vehicle networks are rarely reflected in UAV networks and vice versa [37]. An example is Cognitive IoV, which introduces cognition to the networks for the more efficient management of infrastructure and resources; this approach is not clearly defined in UAV networks despite its use in certain applications. In addition, such unification may advance the cooperation of both transport modes.  
While designs may vary, discernible patterns emerge in the layers and functionality of different models. Four fundamental layers can be identified, namely, sensory, communication, cognition, and application. The layers of some existing models and their characteristics are summarized in Table 2.