esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Their Outcomes
### Performance Measures  
The confusion matrix is utilized in this section to construct the model's learning requirements. False positive (FP), false negative (FN), true positive (TP), and true negative (TN) are the constituents of the confusion matrix (FN). We present the confusion matrix to demonstrate our model's classification efficiency. The confusion matrix highlights which predictions were right and which were incorrect. According to Table 6, our model has a detection rate of 0.95 for all attack and normal categories.  
Additionally, we assess our proposed model using the different metrics that are widely utilized in intrusion detection systems. The mathematical equations of precision, recall, and f-score are presented below (Equations (8)-(10)).  
Accuracy: Equation (8)'s accuracy reveals the model's accurate predictive performance. Accuracy is a measure that quantifies the total percent of detected and erroneous alarms generated by an IDS model; it represents the total rate of success of any IDS and is calculated as:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
\(TP=true\) _positive_, \(TN=true\) _negative_, \(FP=false\) _positive_, _and \(FN=false\) _negative_  
Precision: the false negative rate (FNR), sometimes referred to as precision, is the proportion of miscategorized attacks to the overall number of attack occurrences. The precision produced from Equation (9) indicates how many positive forecasts are predicted exactly:  
\[Precision(p)=\frac{TP}{FP+TP} \tag{9}\]  
\(p=precision\)_, \(TP=true\) _positive_, \(FP=false\) _positive_, _and \(FN=false\) _negative_  
Recall: the detection rate (DR), also known as the true positive rate (TPR) or recall, is the percentage of properly identified malicious occurrences in relation to the total number of malicious vectors. Equation (10), which calculates recall, reveals how many true positives are successfully forecasted:  
\[Recall(r)=\frac{TP}{FN+TP} \tag{10}\]  
\(r=recall\)_, \(TP=true\) _positive_, _and \(FN=false\) _negative_  
F-score: The F1 score is critical since it provides further information about the IDS's performance. It takes into account false positives and negatives. The F1 score is advantageous in particular when the distribution of class labels is unequal or unbalanced. The F-score, which can be calculated using Equation (11), demonstrates the consistency of recall and sensitivity:  
\[F_{score}=2x\frac{P\times R}{P+R} \tag{11}\]  
\(\text{R}=\text{Recall},\text{ P}=\text{Precision}\)  
Table 7 summarizes the accuracy, recall, and F1-score of the different CNN + BiL-STM models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predicted Actual** & **Attack** & **Normal** \\ \hline Attack & 0.95 & 0.05 \\ Normal & 0.05 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix based on our suggested technique for four unique occurrences (TP, FP, TN, and FN).  
Table 7 summarizes the accuracy, recall, and F1-score of the different CNN + BiLSTM models, with and without feature selection. The best accuracy of 94.52 percent was attained by our suggested model CNN-BiLSTM (10).
# Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
## 2 Materials and Methods
### Tissue/Non-Tissue Classifier for the TCGA Tissue Datasets  
Artifacts in tissue slides such as air-bubbles, blurring, compression artifacts, pen markings, and tissue folding could adversely affect the learning process of appropriate features for a given task and thus could limit the performance of a classifier. In few previous studies, specified algorithms to detect artifacts such as blur or tissue-folds were presented [18]. In the present study, we tried to classify the various artifacts into improper tissues all at once with a deep learning-based classifier. As it is impossible to analyze a WSI as a whole, WSIs are often sliced into small image patches for deep learning. Thus, we built a deep learning-based proper/improper tissue classifier for 360 \(\times\) 360-pixel image patches at 20\(\times\) magnification to remove all of these artifacts at once. From the TCGA WSI datasets, more than 100,000 proper and improper tissue patches each were collected. A simple convolutional neural network (CNN) was trained with the patches to discriminate between proper and improper tissue patches. We called it as a tissue/non-tissue classifier (Figure 1) and only tissue patches were used for normal/tumor classification. The CNN consisted of 12 [5 \(\times\) 5] filters, 24 [5 \(\times\) 5] filters and 24 [5 \(\times\) 5] filters, each followed by a [2 \(\times\) 2] max-pooling layer.
## 4 Numerical and Experimental Results
### From Periodic to Fibonacci Sequence and Vice Versa
One can observe the apparition of a transmission zero in Figure 2c around \(f=98\) MHz which is induced by the stub of length \(d_{2}\) of the \(A\) block, which plays the role of a defect. This transmission dip appears in the middle of the allowed transmission band corresponding to the periodic structure in Figure 2b, giving rise to a rejective filter in which all the waves propagate through the structure except those around the frequency 98 MHz. It is well known [67] that the frequencies of the transmission zeros induced by a short-circuited stub of length \(d_{2}\) grafted along a guide is given by [67]\(f_{n}=\frac{nc}{2d_{2}\sqrt{d^{2}}}=98n\) MHz (\(n\) being an integer). Therefore, the first transmission zero (i.e., \(n=1\)) falls at 98 MHz (Figure 2c). By substituting more \(B\) blocks by \(A\) blocks, the dip in the transmission becomes larger as shown in Figure 2d. When several \(B\) blocks are replaced by \(A\) blocks in order to achieve the 6 th Fibonacci structure (Figure 2e), one can see that the transmission zero around \(f=98\) MHz becomes a large gap, known as a primary gap, although the structure is non-periodic. Nevertheless, the appearance of transmission dips can also be observed in the bands around 40 MHz, 60 MHz, 130 MHz and 150 MHz. These gaps indicated with arrows in Figure 2e, become secondary gaps when the generation number increases (see Figure 3). If we continue randomly substituting \(B\) blocks by \(A\) blocks (Figure 2f,g), one can notice that the central gap around 98 MHz becomes larger and the transmission dips associated to the secondary gaps in the transmission curves of the FS disappear in order to create two separate allowed bands in the case of the system composed of only \(A\) blocks (Figure 2h). Figure 3: Theoretical (solid lines) and experimental (open circles) evolution of the transmission coefficients versus the frequency for different structures. The dashed curves in (**a**–**g**) are the theoretical curves when the absorption is not taken into account. The inset of (**a**) corresponds to the schematic illustration of blocks A and B constituting the systems. (**a**) The periodic structure, (**b**–**g**) generation \(S_{k}\) (\(k=4\)–\(9\)) of the Fibonacci structure. One can compare the scale of the frequency axis near the central frequency \(f_{c}\) in the insets of the figures associated with the generations \(S_{4}\), \(S_{5}\), \(S_{6}\) and those of \(S_{7}\), \(S_{8}\), \(S_{9}\) respectively.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 6 Security Analysis
### Resistance to Replay Attack  
In the proposed authentication scheme, the previous collected information cannot pass the current data transmission procedure. As shown in the aforementioned group key distribution phase, the D2D transmission on message \(m\) is performed as \(([H_{1}(\gamma,TS)]_{\ell_{m}}\oplus m)||[H_{1}(\gamma,TS)]_{(\ell-\ell_{m})}\), where the current time stamp is involved in each transmission process. In this way, the regular data transmission process is resistant to reply attack.  
As for the authentication process, the certificate authentication method is adopted, where two partial secret keys \(\langle X_{i},\theta_{i}\rangle\) are respectively generated by SN and UE itself. Accordingly, the utilized partial secret key \(\vartheta_{i}\in\mathbb{Z}_{\mathcal{P}}\) is randomly selected after the successful validation of \(Auth_{i}=H_{2}(\mathit{Tid}_{i},\mathcal{T}_{i},\eta_{i})\). Note that the partial secret key \(\vartheta_{i}\), as well as the group key \(\gamma\in\mathbb{Z}_{\mathcal{P}}\), is considered as the randomly generated value in each authenticating session. In this way, the delivered \(\langle\delta,\varphi,a_{0},\ldots,a_{t-1}\rangle\) from the previous session cannot pass the current authentication. The replay attack is prevented in this way.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods
### Comparison Indices  
The performance comparison between the BOSS algorithm and other algorithms will be detailed in the Results section. The compared algorithms will be tested with a set of gray scale images and relative masks; in order to assess the accuracy of the algorithm's predictions against test masks, the use of suitable indices is necessary. These indices are heavily used in the segmentation field to compare two image masks as the U-Net output and ground truth from the dataset actually are. These indices can also be used to compare predictions of any couple of segmentation algorithms. Before introducing them, the notion of true positive (TP), false positive (FP) and false negative (FN) must be given:  
* TP: A counter that increases by one unit every time both the predicted pixel and the reference one belong to the foreground set;
* FP: A counter that increases by one unit every time the predicted pixel belongs to the foreground set while the corresponding mask one is a background pixel;
* FN: A counter that increases by one unit every time the predicted pixel belongs to the background set while the corresponding mask one is a foreground pixel.  
A good prediction has background pixels and foreground ones almost in the same position inside an array as the associated ground truth mask. The goodness of the prediction can be assessed through the evaluation of Precision, Recall and F1 index.  
\[Precision=\frac{TP}{TP+FP}\times 100 \tag{8}\]  
It represents the percentage of the TP with respect to the sum of TP and FP. The lower the FP is, the higher the Precision will be (with maximum value equal to 100% under ideal conditions).  
\[Recall=\frac{TP}{TP+FN}\times 100 \tag{9}\]  
It represents the percentage of the TP with respect to the sum of TP and FN. The lower FN is, the higher the Recall is (with maximum value equal to 100% under ideal conditions). Recall and Precision are similar but with a slight difference. This is due to the distinction of FP and FN. Every time an FP or FN occurs, there is a discrepancy between the prediction and the reference mask, but the difference between FP and FN helps us better understand the behaviour of the trained network.  
Another index to be defined is F1:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{10}\]  
This index combines the Precision and Recall and lets us understand when the best compromise between FP and FN occurs. It reaches its maximum value when the discrepancy between the prediction and the mask is minimized. Ideally, the maximum value of F1 is 100%, but the best prediction will be the one with the highest value of the F1 index.
## 2 System and Signal Characterization
Moreover, the interference cancellation matrix \(\mathbf{C}_{k}\) can be computed as [15]:  
\[\mathbf{C}_{k}=\mathbf{A}_{k}^{H}\mathbf{H}_{k}-\mathbf{I} \tag{5}\]  
where \(\mathbf{I}\) is an \(R\times R\) identity matrix.
## 2 Materials and Methods
### Methods
#### 2.3.5 Accuracy Analysis of the Classification  
The use of CE and OE metrics is essential for evaluating model errors in RS systems, allowing for a more detailed and precise analysis of the model's ability to correctly identify positive cases and avoid incorrectly classifying positives. The CE metric is calculated by the proportion of false positives relative to the total number of events classified as positive, while the OE metric is calculated by the proportion of false negatives relative to the total number of actual positive events. OE can be calculated by Equation (6):  
\[\text{OE}=\frac{\text{False Negatives(FN)}}{\text{True Positives(TP)}+\text{ False Negatives(FN)}} \tag{6}\]  
Subsequently, CE can be calculated by Equation (7):  
\[\text{CE}=\frac{\text{False Positives(FP)}}{\text{True Positives(TP)}+\text{ False Positives(FP)}} \tag{7}\]  
These parameters allow monitoring of prediction accuracy and are widely used in RS literature, as highlighted by [PERSON] et al. [69] and [PERSON] et al. [70].  
The Overall Accuracy (OA) and the Kappa Index were also used as parameters for analyzing the thematic accuracy of the mapping.  
OA is used to measure the model's prediction accuracy and is the ratio of correctly classified samples to the total number of samples [71]. The OA can be calculated using Equation (8):  
\[\text{OA}=\frac{\sum_{i=1}^{n}x_{ii}}{N} \tag{8}\]  
where \(\text{X}_{ii}\) represents the number of correctly classified samples along the diagonal, and N is the total number of samples. The higher the OA value, the better the overall prediction accuracy of the model.  
The Kappa Coefficient is used to measure classification accuracy and is calculated according to Equation (9).  
\[\text{Kappa}=\frac{\text{P}_{0}-\text{P}_{\text{C}}}{\text{P}_{\text{P}}- \text{P}_{\text{C}}} \tag{9}\]  
where P\({}_{0}\) is the proportion of correctly simulated pixels, P\({}_{\text{P}}\) is the proportion of correctly predicted pixels in an ideal situation, and P\({}_{\text{C}}\) is the proportion of correctly predicted pixels in a random situation. The closer the kappa coefficient is to 1, the better the classification result matches the actual situation [71].
## 3 Methodology and Data Sources
### Reconstruction of Hourly Transport Pathways and Wind-Field Grids
#### 3.3.1 Pre-Calculating Backward Trajectories  
The calculations of TrajStat run in units of a month, and the output trajectory data are stored in monthly files. Therefore, we pre-calculated the backward trajectories for November 2020 and April 2021, in which hourly backward trajectories with running hours from \(-1\) h to \(-24\) h were calculated once, and the arrival time (starting hours) of the backward trajectories were hourly intervals of a day from 0:00 to 23:00 local standard time. We used the center point of state-controlled air sampling sites in Changchun as the receptor site (start location) with an air mass arriving height of 100 m above ground level. The archived meteorological data used to calculate trajectories were GDAS with a resolution of \(1^{\circ}\times 1^{\circ}\). According to the pre-research on the temporal distribution of province-wide SOB fire spots before and during each PE, the duration of continuous SOB is generally not more than 24 h before the beginning of a PE. Some studies have estimated the lifetime of atmospheric particulate matter [41; 42]. Therefore, a \(-24\) h backward trajectory will cover the potential external source areas that are likely to affect the pollution level of a specific PE.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 4 Conventional Image Watermarking Schemes
### Transform Domain
#### 4.2.1 Discrete Cosine Transform (DCT)
Furthermore, the proposed scheme includes a novel color complexity weight from the \"Cb\" channel for high robustness. After evaluating the watermarking system under different types of attacks, such as JPEG compression, Gaussian noise, and geometric attacks, the superiority in terms of invisibility and robustness is evident. However, it should be highlighted that the time complexity analysis deserves further investigation, especially since JND has been used in the scheme. For the same purpose, [PERSON] et al. [86] recently designed a perceptual image watermarking scheme based on a tri-directional correlation of DCT coefficients. After generating these coefficients, the difference between two DCT blocks is determined based on three directions in the neighborhood. Specifically, the watermark embedding is performed using dependencies between the middle-frequency coefficients of adjacent blocks based on the JND model. Although, the results demonstrated high robustness to common image-processing attacks, the robustness tests were insufficient. Using the DCT transform in image watermarking techniques has various advantages, some of which have already been mentioned. However, geometric transformations can significantly alter the values of the DCT parameters, hence the interest in using a field with invariant properties against geometric transformations, such as rotation and translation. The different approaches dealing with desynchronization attacks reported in the literature are shown below.
# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
## 3 Results
### Cardiothoracic Ratio  
The means and SDs of CTRs were slightly higher when calculated from either manual or automated segmentations than when manually read (0.51 \(\pm\) 0.08, CV = 14.9 for the automatic segmentation model; 0.50 \(\pm\) 0.08, CV = 15.5 for the manual segmentation model; and 0.48 \(\pm\) 0.07, CV = 15.2 for the manual reading measurements; Figure 1). The results of the Wilcoxon test showed that the measurements of CTR from manual reading were significantly different when compared to the measurements from manual and automatic segmentation (statistic = 270.0, \(p\)-value \(<\) 0.001; statistic = 157.0, \(p\)-value \(<\) 0.001, respectively). There were no statistically significant differences between the measurements from manual and automatic segmentations (statistic = 489.0, \(p\)-value = 0.15). Measurements of CTR calculated from manual reading, manual segmentations, and automatic segmentations are shown in Appendix A Table A1. A stronger correlation of CTR measurements was observed for the values calculated from manual and automatic segmentation when compared to the values gained from manual or automatic segmentation and direct manual measurements (R = 0.913 vs. R = 0.813 vs. R = 0.804, respectively; Appendix A Figure A4). When analyzing the correlation between CTR measurements calculated from segmentations and direct manual measurements based on positive and negative cardiomegaly diagnosis using various CMR definitions, the strongest correlations for positive cardiomegaly diagnosis were observed for manual segmentation vs. automatic segmentation. On the other hand, in cases where the definition of cardiomegaly was based on LVM or LVM/BSA and the diagnosis was negative, the strongest correlations were observed for direct manual measurements vs. manual segmentations (Appendix A Table A2). The relationship between the CTR measurements based on the manual and automatic segmentations and manual reading reports was also examined using Bland-Altman plots (Figure 2). Figure 1: The cardiothoracic ratio from manual reading measurement, manual segmentation, and automatic segmentation. Box plots show the medians and first and third quartiles in boxes, as well as minimum and maximum values of the cardiothoracic ratio from manual measurement, manual segmentation, and automatic segmentation. The McNemar test revealed that the direct manual measurements were in agreement with various cardiomegaly definitions based on CMR (Appendix A Table A3). In addition, when LVEDV or LVEDV/BSA were used as the definition of cardiomegaly, the CTR measurements calculated from the manual segmentation were in agreement with these CMR definitions. Specificity reached 100% for manual reading measurements; however, the sensitivity was much lower when compared to the CTR measurements calculated from segmentations (see Table 1).
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 5 Algorithm Development: GNSS/Vision Integration
### Tightly-Coupled GNSS/Vision
#### 5.4.1 GNSS Observables  
Because tight coupling is used, it is necessary to estimate the receiver clock offset and clock drift in addition to the position and velocity states. Having this in mind, the GNSS measurements that fit the Kalman filter are the error in the range and the error in the range rate (to each satellite), expanded at the current estimate of the states. This is expressed as:  
\[\delta\mathbf{z}_{\textit{Gtns}}=\begin{bmatrix}\rho_{i}-(\hat{p}_{i}+cdt)\\ \dot{\rho}_{i}-(\hat{p}_{i}+cdt)\\ \vdots\end{bmatrix} \tag{35}\]  
where the parameters are defined as in Section 5.1.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 3 System Model  
In this section, we first describe the architecture of an underwater network in this paper. Then, this paper reviews the underwater channel model and derives Packet Error Rate (PER) that is used in the protocol analysis and in the simulation.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 3 Modeling the Arrival Processes
### Marginal Distribution of the Arrivals
#### 3.1.3 Pareto Distribution  
The density function of the Pareto random variable \(X\) with parameters \(m\) and \(\alpha\) is  
\[f(x)=\frac{\alpha m^{\alpha}}{x^{\alpha+1}},\qquad x\in(m,+\infty). \tag{6}\]  
If \(\alpha>1\) the expectation is finite, with value \(\mu=\frac{\alpha m}{\alpha-1}\), and if \(\alpha>2\) the variance is finite, with value \(\sigma^{2}=\frac{\alpha m^{2}}{(\alpha-2)(\alpha-1)^{2}}\). More generally, the \(k\)-th moment of \(X\) is given by  
\[\mathbb{E}[X^{k}]=\begin{cases}+\infty,&\text{ if }\alpha\leq k,\\ \frac{\alpha m^{k}}{\alpha-k},&\text{ if }\alpha>k,\end{cases} \tag{7}\]  
so the \(k\)-th moment is finite only if \(\alpha>k\). The related Pareto distribution shifted to zero is \(X-m\), and has, in the same ranges of values of \(\alpha\), expected value \(\frac{m}{\alpha-1}\) and variance \(\frac{am^{2}}{\alpha-2}-(\mu+m)^{2}\). The conditions for finiteness of the \(k\)-th moments are obviously equal for the shifted and non-shifted Pareto random variables. We will use for our numerical evaluations the quantization \(\lfloor X\rfloor\) as the point arrival process in each slot.
# A Review on Methods and Systems for Remote Collaboration
## 4 Visual Display Technologies
### 3D View  
Studies have been performed to obtain a 3D view that is presented to both operators. In one of these studies, [PERSON] [61] presented a prototype that uses an array of cameras to obtain a three-dimensional view for a remote environment.  
OmniKinect [62] also tries to obtain a 3D view, but instead of using multiple cameras, it uses Kinects, which have depth sensors, to acquire real-time video.  
The technology 3D Helping Hands [63] uses special cameras that are capable of 3D video capture in real time on both the worker's and the helper's side. The 3D videos acquired from the worker and the helper are then fused in real time to form a single, common workspace with an augmented view.  
A new prototype that uses depth cameras at the worker's space [64] can capture in 3D the worker's position and environment. The cameras are connected to a fusion PC that renders the video stream with the helper's visual instructions.  
[PERSON] [65] presented a new model of a remote collaboration system, where the local user has a smart device (tablet or smartphone) that captures the video information and sends it to the distant user. The remote user uses AR and adds annotations, which are overlaid on the source video and displayed on the local user's device.  
[PERSON] et al. [66] presented a mobile-based collaboration system in which the local user has a mobile phone equipped with a depth sensor in order to capture the local environment and display it in virtual reality (VR) as a 3D environment for the remote user. The system allows the expert to move independently in the virtual environment and to switch the view of the local user's smartphone to show the local changes in real time in the form of a 2D video.