esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines
## 2 Related Works
### User Experience (UX)
The subjective collaborative quality, satisfaction, and performance are used for understanding user experience in the remote collaboration study by projecting a gesture on a remote user from a local user using a mixed reality headset [53]. Most of the remote collaboration studies evaluate user experience either using virtual reality, augmented reality, or mixed reality 3D technology, but a few studies involve 2D-basedremote collaboration. [PERSON] et al. [54] compared the user experiences of 2D-based video conferences, 3D video interfaces, and 3D with annotations. The results showed that 3D with annotation has a higher rating compared to just 2D-based interface. Currently, there is a lack of study on remote collaboration using virtual conference applications, especially during the COVID-19 pandemic, which is what motivated this study. Moreover, in order to understand the user experience, this research would like to focus more on the users' comments and feedback to evaluate the current virtual conference application for collaborative tasks. Finally, based on the previous study, several measurements have been identified to aid in the construction of a user study, particularly on remote collaboration on the current virtual conference applications. A guideline is also proposed based on the comments and feedback provided by the users.
## 2 Materials and Methods
### Expected Accuracy  
This chapter is dedicated to accuracy, which, according to the available material, can be expected. The manufacturer provides (Table 2), for ideal conditions, a laser head accuracy of 5 mm and GNSS of 20 mm in the horizontal direction and 30 mm in the vertical direction. However, these are not the only variables that affect the resulting accuracy. Others such as the INS and DMI system, the spacing of the insertion points, the type of alignment, the speed, and, above all, the fact that the measurement is not carried out under ideal conditions enter the calculation.  
Compared to the paper [25], which reports a 3D standard deviation of 1.7 cm, a worse accuracy can be expected. The authors used a significantly denser point field of control points with high accuracy, and the checkpoints were very well signalised and marked with a chequerboard target. It follows from this article that the accuracy of MLS under very good conditions using precisely defined points may be better than that claimed by the manufacturer.  
The option of a small spacing of the control points is not economical in real mapping in built-up areas, and the checkpoints in this project are chosen as objects of interest for mapping. Spatial points may be less easy to identify.  
Also, the testing conditions in this article are not ideal according to the manufacturer's datasheet, the view of the sky is often obscured, and we can expect worse GNSS accuracy. As the work evaluates several types of control point configurations, the resulting expected 3D standard deviation of the mobile scan can be assumed to be between 2 and 5 cm. In the cases where TS control points are used, an accuracy of around 2 cm can be expected. Using GNSS control points and for point clouds with larger control point spacing, worse accuracies can be expected.
## 3 Coverage Control Using Memetic Algorithm
### Wake-up Scheme
In addition, let \(s_{\psi_{i},k}\) represents the \(k\)-th possible subset of \(\psi_{i}\), and \(\chi\) represents the number of subsets of \(\psi_{i}\). In order to recover the lost network coverage using sleeping nodes, the wake-up scheme will seek the neighboring nodes of \(n_{i}\) and awaken some of them by the following procedure. _Procedure of the wake-up scheme_  
**variable**_best_comb_, _best_value_ = 0;  
\(map,res=\llbracket 0,\ 0,\...\,0\rrbracket\), \(length=M\);  
**If** node \(n_{i}\) has exhausted its energy and can not work correctly **then**  
**For**\(k=1\) to \(\chi\)**do**  
**For** every element \(d\) of \(s_{\psi_{i},k}\)**do**  
\(map=\pi_{d}\cup map\);  
**End**  
\(res=map\cap\pi_{\text{uncovered}}^{n_{i}}\);  
**If**\(\sum\limits_{\gamma\text{ }res}\gamma+\frac{1}{size(s_{\psi,k})+1}>\text{ }best\_value\)**then**  
\(best\_value=\sum\limits_{\phi\text{ }res}\phi+\frac{1}{size(s_{\psi,k})+1}\);_best_comb_=_s_\({}_{\psi,k}\) ;
**End**
**End**  
_output_ = _best_comb_;
**End**  
The main objective of the wake-up scheme is to generate an optimal schedule to awaken some sensor nodes in a sleeping mode. Similar to MAs, the wake-up scheme also emphasizes on energy-efficient coverage. The proposed wake-up scheme requires the sink to transmit a wake-up signal to the neighboring nodes of the dying node from the sink. If these sensor nodes constantly receive the signals, energy consumption will increase significantly. To deal with this problem, some studies [36, 37] and corporations [30, 31] have proposed a radio wake-up circuit to transmit and receive wake-up signals through different radio channels. In general, transmitting or receiving a packet wastes more energy than performing a sensing task (see Tmote Sky [30] and Mica [31]). In this way, a considerable amount of energy on the sensor nodes can be saved by turning off the onboard communicating module. Once the wake-up circuit receives a wake-up signal, it will activate some of the sensor nodes. The wireless sensor node equipped with wake-up capability has become a commercial standard. As a result, we suppose that each sensor node in the applied CWSN has a built-in wake-up module. As mentioned above, the sink will awaken some sensor nodes according to the optimal schedule of nodes yielded by the wake-up scheme at the beginning of the next round. If any sensor nodes die again, the wake-up scheme will re-evaluate the coverage and decide to awaken some other sensor nodes to recover the uncovered POIs. Through this way, the coverage preservation of POIs can be achieved effectively.
## 6 Discussion and Future Directions
### Future Directions
#### 6.5.3 Exploration of Node Selection Mechanisms  
Frameworks that are use-specific are evolving in the IoV integration of BC and ML. For example, approaches such as hierarchical FL, asynchronous FL, Opportunistic FL, and synchronous FL have now been proposed to enhance performance and optimize the FL deployment [153; 157; 158]. A similar development is witnessed in the deployment of consensus algorithms. Some of the recently developed consensus algorithms include Proof of Federated Learning (PoFL) [151], BFT-DPoS [153], PoU [164], etc. These have all shown to be effective in their application. Future studies could develop novel and innovative algorithms and adaptable frameworks for optimal implementation. There is also a need to optimize the mechanism of node selection [153]. Solutions that consider factors such as energy consumption, scalability, communication cost, computational cost, and the overall system running time should be explored in future studies. This will lead to enhancements in system applicability and performance in ITS.
## Problem Statement  
The scenario considered in this paper is illustrated in Figure 1. A ground station serves \(N_{S}\) satellites simultaneously, on the same frequency band, using a CDM system. (The satellites may be in the same or different orbits and are at varying distances; the analysis is based on the \(E_{b}/N_{0}\) ratio of each individual satellite).
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
## 3 Materials and Methods
### Data Standardization  
After thoroughly cleaning the dataset, we proceeded to separate the data into features and labels in preparation for model training. The features represented by the variable X were isolated by excluding the \"Class\" column from the dataset. Concurrently, the labels, represented by the variable y, were specifically assigned to the \"Class\" column.  
To ensure a fair and balanced comparison across the different features, we normalized the data using the MinMaxScaler, an essential tool for scaling feature values within a specific range. This step ensures that the model can learn efficiently without being affected by variations in scale among the different input features. The normalization process implemented by the MinMaxScaler follows this formula:  
\[X_{scaled}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{1}\]  
where  
* \(\mathbf{X}\) represents the initial value of the feature;
* \(\mathbf{X}_{min}\) is the minimum value of the feature in the dataset;
* \(\mathbf{X}_{max}\) is the maximum value of the feature in the dataset;
* \(\mathbf{X}_{scaled}\) is the resulting normalized value of the feature.  
After normalizing the data, the next critical step was dividing the dataset into training and test sets. This division is essential to evaluate the model's performance on unseen data, ensuring its ability to generalize effectively. We used the train_test_split function from scikit-learn to accomplish this. For this task, 80% of the data was designated for the training set to train the model, while the remaining 20% was reserved for the test set to assess performance. This approach strikes a balance, providing enough data for solid model training and sufficient test data for a reliable performance evaluation.  
To rectify the problem of class imbalance in the training dataset, we utilized SMOTE (Synthetic Minority Over-sampling Technique). SMOTE creates synthetic cases for the minority class derived from existing examples, so successfully equilibrating the class distribution. This approach guarantees that the model is trained on a dataset with equal representation of all classes, hence augmenting its learning capacity and boosting overall accuracy. We utilized SMOTE using the fit_resample function from the imbalanced-learn package. This technique yielded a balanced training dataset, which was crucial for enhancing the model's capacity to learn from the data efficiently and generate precise predictions.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 2 Materials and Methods
### Detection Head  
The detection network uses three detection heads with GIOU-LOSS as the loss function and outputs three scales of feature maps with 20 \(\times\) 20, 40 \(\times\) 40, and 80 \(\times\) 80 grids for detecting small, medium, and large maize disease targets, respectively. Each grid contains three prediction boxes, each containing information about the confidence of the object and the position of the prediction box. Maize disease detection is accomplished at the detection head by non-maximal suppression (NMS) [40] as post-processing to eliminate duplicate redundant prediction frames and retain the prediction frame with the highest confidence.  
Intersection over union (IOU) is a metric for evaluating the accuracy and can be expressed as:  
\[\text{IOU}=\frac{|\text{A}\cap\text{B}|}{|\text{A}\cup\text{B}|} \tag{1}\]  
Figure 6: CSP1-X module.  
Figure 7: CSP2-X module.  
The MFF-CNN for border regression uses complete-IOU (CIOU) instead of IOU [41] for model training. CIOU is expressed as follows:  
\[\text{CIOU}=\text{IOU}-\frac{\rho^{2}\big{(}\text{b},\text{b}^{\text{gt}}\big{)}} {\text{c}^{2}}-\alpha\
u \tag{2}\]  
Among them, a denotes ground truth, B denotes the predicted frame, and \(\rho^{2}\big{(}\text{b},\text{b}^{\text{gt}}\big{)}\) represents the Euclidean distance between the centroids of the prediction box and the ground truth.  
The LOSS when CIOU is regressed can be calculated as:  
\[\text{LOSS}_{\text{CIOU}}=1-\text{CIOU}=1-\text{IOU}+\frac{\rho^{2}\big{(} \text{b},\text{b}^{\text{gt}}\big{)}}{\text{c}^{2}}+\alpha\
u \tag{3}\]  
where \(\alpha\) and \(\
u\) are denoted as follows:  
\[\alpha=\frac{\
u}{1-\text{IOU}+\
u} \tag{4}\]  
\[\
u=\frac{4}{\pi^{2}}\bigg{(}\arctan\frac{\text{w}^{\text{gt}}}{\text{h}^{ \text{gt}}}-\arctan\frac{\text{w}}{\text{h}}\bigg{)}^{2} \tag{5}\]
## 1 Introduction
To consider that heterogeneity, the authors recovered the bitumen and carried out tests over it, whose results were used to predict stiffness through the depth of the specimen, concluding that the TEAGE method addresses in a more consistent way the ageing mechanisms that could be seen in the field. The ageing effect has been addressed in several studies, e.g., [37; 38; 39; 40]. [PERSON], et al. [41] studied, for the first time, ageing (long-term and short-term) effects using ITS tests, and found that for long-term oven-aged specimens, ITS increases with ageing, while for short-term oven-aged loose samples, ITS increases with the conditioning period until reaching a peak and then decreases. On these grounds and taking into account that there is a need to use new materials to obtain both sustainable and high-performance pavements (to support an increase of traffic intensity, the presence of large and heavier trucks, and all the climatic agents), nanomaterials have attracted increasing attention in bitumen modification. They tend to improve AC mixture performance [42; 43; 44; 45; 46]. Regarding moisture, nanomaterials can work as antistrip additives, improving aggregates coating, and reducing thus moisture susceptibility [47; 48; 49; 50; 51]. Nanoclay has been used successfully [43], besides other properties, to improve moisture resistance as well as ageing resistance [52; 50]. There are in the literature, studies encompassing moisture, ageing, polymers, and nanomaterials [26; 50; 52]. However, to date, few or no studies consider moisture, ageing, nanomaterials, and plastic waste simultaneously, i.e., a set of issues that can interact between themselves and may change AC mixture behaviour. This work attempted to contribute to that gap in literature by evaluating the moisture susceptibility of a nanoclay modified-AC mixture with flakes of plastic film collected as urban waste with and without ageing. The moisture susceptibility is evaluated by the ITSR value and the TEAGE method was used to simulate long-term ageing in a laboratory. For a better interpretation of the results, two additional AC mixtures are tested in parallel for comparison purposes, via a control AC mixture and a mixture with only plastic flakes. In addition, aged and unaged specimens are considered.
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 2 Materials and Methods
### Pedestrian Dead-reckoning (PDR) Module
#### 2.1.1. Heading Angle Estimation
\begin{array}{l}q_{0}(t+T)=q_{0}(t)-\frac{T}{2}\Big{[}\omega_{x}q_{1}(t)+ \omega_{y}q_{2}(t)+\omega_{z}q_{3}(t)\Big{]}\\ q_{1}(t+T)=q_{1}(t)+\frac{T}{2}\Big{[}\omega_{x}q_{0}(t)+\omega_{z}q_{2}(t)- \omega_{y}q_{3}(t)\Big{]}\\ q_{2}(t+T)=q_{2}(t)+\frac{T}{2}\Big{[}\omega_{y}q_{0}(t)-\omega_{z}q_{1}(t)+ \omega_{x}q_{3}(t)\Big{]}\\ q_{3}(t+T)=q_{3}(t)+\frac{T}{2}\Big{[}\omega_{z}q_{0}(t)+\omega_{y}q_{1}(t)- \omega_{z}q_{2}(t)\Big{]}\end{array} \right. \tag{6}\]  
where \(q_{i}(t)\) and \(q_{i}(t+T)\) are the quaternion values at time \(t\) and \((t+T)\), \(i=1,2,3,\cdots\), \(T\) is the sampling time interval. Then, normalizing the updated quaternion values and substituting them into Equation (7), the heading angle can be obtained. In our research, the first-order low-pass filter [33] and the first-order high-pass filter [34] are applied to process the observation noise of the acceleration raw data and the gyroscope raw data, respectively. Because there are serval heading angles in one second, the median filter [35] is used to reduce the measurement error and obtain more accurate results. \[\left\{ \begin{array}{l}\psi{=}\text{tg}^{-1}\frac{2(q_{1}q_{2}+q_{0}q_{3})} {1-2(q_{2}^{2}+q_{3}^{2})},\psi\in(0,2\pi)\\ \theta=-\sin^{-1}2(q_{1}q_{3}-q_{0}q_{2}),\theta\in(-\frac{\pi}{2},\frac{\pi}{ 2})\\ \gamma{=}\text{tg}^{-1}\frac{2(q_{2}q_{3}+q_{0}q_{1})}{1-2(q_{1}^{2}+q_{2}^{2 })},\gamma\in(-\pi,\pi)\end{array} \right. \tag{7}\]  
where \(\psi\), \(\theta\), and \(\gamma\) are the heading angle, pitch, and roll, respectively, \(\psi\) is also called yaw.
## 3 Simulations and Discussions  
To study the effectiveness of the proposed method, the following simulations are accomplished with respect to symmetrical and asymmetrical cantilever sandwich beams. When the beam test technique is employed, the thickness of the base beam is usually chosen as 1 mm-3 mm and the metal is often chosen to be steel or aluminium. As illustrated in Figure 2, here we consider three kinds of sandwich beams: beam 1 with 1-mm-thick and 1.2-mm-thick steel base beams; beam 2 with one 1-mm-thick steel base beam and one 1-mm-thick aluminium beam; beam 3 with two symmetrical 2 mm steel base beams. The sandwich beams are all of 250 mm length and has one identical 0.1-mm-thick damping layer.  
Firstly, the geometrical parameter \(Y\) can be obtained according to its definition formulas Equation (4). When the effective bending stiffness, uncoupled bending stiffness and system loss factor are achieved by the Kerwin-Ungar theory [10; 11], the modal characteristics (modal frequencies, bending stiffness  
Figure 2: Three sandwich beams with one identical damping layer.  
ratios and structure loss factors) can be achieved. Thus the modal loss factor and shear modulus of the viscoelastic material can be obtained according to Equations (21) and (22), respectively.  
In the following sections, two kinds of damping material setting conditions are studied.
## 4 Results
### Results Maps from Preprocessing  
A total of 12 neurons were prepared by Sentinel-2 optical satellite images of spring and fall season, 6 input data were generated each: NDVI, NDWI1, NDWI2, NDre1, NDre2, and PCA texture. Figures 6-8 show the NDVI, NDWI, and NDre maps of Sentinel-2 represented by using ArcMap 10.4 (ESRI, Redlands, CA, USA). Preprocessed Sentinel-2 images with a Statistical-empirical model shows very little topographic effect even in mountain slope area (see Figures 6-8).  
Figure 6: Input neurons of NDVI created from Sentinel-2 image: (**a**) spring and (**b**) fall season.  
Figure 7: Input neurons of NDWI1 and NDWI2 created from Sentinel-2 image: (**a**) NDWI1 in spring and (**b**) fall season, (**c**) NDWI2 in spring and (**d**) fall season.  
In addition, PCA texture maps were generated using Equations (4) and (5) from seasonal Sentinel-2 optic satellite images, respectively. The smoother the surface, the higher the texture values as shown in Figure 9a,b.  
Figure 8: Input neurons of NDr1 and NDr2 created from Sentinel-2 image: (**a**) NDr1 images in spring and (**b**) fall season, (**c**) NDr2 images in spring and (**d**) fall season.  
Figure 9: Input neurons of PCA texture created from Sentinel-2 image: (**a**) spring and (**b**) fall season.  
The other two input neurons were generated by differentiating the DTM obtained from NGII and the DSM obtained from WorldDEM as described above. From the canopy height map obtained from this process, two data were generated by a moving window technique with the kernel of \(5\times 5\). The first kind of canopy height map is an average canopy height map that could be used to estimate the average height of the canopies in the forest as shown in Figure 10a. Most average values range from 7 m to 15 m, with average values up to 25 m in some regions. Another canopy height map is a standard deviation canopy height map that reflects the varying heights of adjacent trees (Figure 10b). The varying vertical structure of forest means that there is a difference in the canopy height, so the standard deviation of the height data was obtained to recognize the difference in height of adjacent trees. In other words, the higher the standard deviation in forest region, the larger the difference in the tree height of the region, which leads to complicated vertical structure of the forest.
## 4 Experiment and Analysis
### Experimental Setting  
In this paper, we abstract the forgery image detection task as a binary classification problem and train a binary classifier for each dataset. The one-dimensional binary vector of the backbone output is converted into the interval (0, 1) using the Softmax. We set the classification threshold to 0.5 and use the accuracy (Acc) and area under the ROC curve (AUC) as model evaluation indexes. The AUC is calculated from the true positive rate (TPR) and true negative rate (TNR), where TPR is also called sensitivity, and TNR is called specificity. The formulas for these indicators are as shown in Equations (17)-(20).  
\[\text{Acc}=\frac{(TP+TN)}{(TP+FP+FN+TN)} \tag{17}\]  
\[TPR=\frac{TP}{TP+FN} \tag{18}\]  
\[TNR=\frac{TN}{TN+FP} \tag{19}\]  
\[\text{AUC}=\frac{1}{2}\sum_{i=1}^{n-1}\Big{(}(1-TNR)^{(i+1)}-(1-TNR)^{(i)} \Big{)}\times\Big{(}(TPR)^{(i+1)}+(TPR)^{(i)}\Big{)} \tag{20}\]  
The Acc is used to describe the classification accuracy of positive and negative image samples, and the more significant the Acc value, the better the classification effect of the model. The AUC is the value that the probability of actual image samples is greater than that of forged samples and can evaluate the performance index of detection models. The larger the AUC value, the more likely detection models will rank the positive samples ahead of the negative ones, where \(TP\) represents the positive samples predicted to be accurate, \(TN\) represents the negative samples predicted to be false, and \(FN\) represents the positive samples predicted to be false, \(FP\) represents the negative samples predicted to be accurate. The symbol \(n\) is the total number of positive and negative samples. The cosine annealing algorithm is introduced in model training to adjust the learning rate continuously, as shown in Equation (21).  
\[\eta=\eta^{i}+\frac{1}{2}(\eta^{i}_{\text{max}}-\eta^{i}_{\text{min}})(1+\cos( \frac{T_{cur}}{T_{i}}\pi)) \tag{21}\]  
where \(i\) represents the times of the model has run, \(\eta^{i}_{\text{max}}\) and \(\eta^{i}_{\text{min}}\) represent the maximum and minimum values of the learning rate, and define the learning rate range. The symbol \(T_{cur}\) represents the number of epochs executed, and \(T_{i}\) represents the total number of epochs during the \(i\)-th run. After executing \(T_{i}\) epochs, it will simulate a restart by increasing the learning rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **DeepFakes** & **FaceSwap** & **FaceShifter** \\ \hline Training numbers & 44784 & 44648 & 44784 \\ Testing numbers & 18215 & 18162 & 18216 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Introduction of the datasets used in this paper.
## 4 Voltage Vector Distribution Method
### Low Switching Frequency Method
Then the present candidate vector is assigned to inverter1's desired voltage vector, denoted by \(u^{*\rightarrow}_{s1\_LF}\), where the subscript LF means Low Frequency. The difference between inverter1's output power corresponding to \(u^{*\rightarrow}_{s1\_LF}\) and the desired value \(P^{*}_{inv1}\) can be obtained from the corresponding element in \(M_{us1}(4,:)\), recorded as \(D_{Pinv1\_LF}\), which is necessary in the selection strategy of voltage vector distribution methods. \(D_{Pinv1\_LF}\) indicates inverter1's power following deviation of the low switching frequency method. If all the candidate vectors are verified beyond the feasible region, the flag of low switching frequency method \(F_{LF}\) will be set to 0, meaning the low switching frequency method is unavailable in current situation; otherwise \(F_{LF}\) will be set to 1, indicating the low switching frequency method is valid. There are two examples shown in Figure 6. The meanings of lines, points, and vectors are the same with Figure 3. The numbers next to vertexes of the hexagon centered on \(O_{1}\) indicate the rankings of the corresponding candidate vectors in the sorted sequence, respectively. In the first situation, the first candidate vector in the sequence, whose endpoint is closest to line \(l_{1}\), is within the feasible region, so it is chosen to be \(u^{*\rightarrow}_{s1\_LF}\). However, in the second situation, the first two candidate vectors indicated by dashed lines are both invalid, thus the third candidate vector in the sequence is chosen to be \(u^{*\rightarrow}_{s1\_LF}\), making inverter1's power following deviation bigger than the first situation.