esa-sceva/satcom-chunk-collection huggingface dataset:
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 5 Backup Path Routing and Spectrum Allocation Algorithm
### Backup Path Spectrum Allocation Algorithm  
When the backup path \(\beta(s_{i},d_{i})\) is constructed, the channel on each link of the path is allocated. The backup path spectrum allocation algorithm (BPSR) is proposed in this section. The BPSR algorithm is seen in Line 20 of Algorithm 1.  
The fundamental idea of the BPSR algorithm is as follows: for all link (\(u\),\(v\)) of \(\beta(s_{i},d_{i})\), if the link (\(u\),\(v\)) is allocated a channel, then the link keeps the same allocated channel. If the link (\(u\),\(v\)) is not allocated a channel, then the following strategy is used to allocate the channel. The channel \(x_{2}^{*}(u,v)\) is selected according to Equation (18).  
If the link (\(u\),\(v\)) and the link (\(a\),\(b\)) (which is a primary link of the primary path for \(\gamma_{i}\),) are allocated the same channel, the two links interfere with each other, so then the channel \(x_{2}^{*}(u,v)\) is allocated to (\(u\),\(v\)), and the load on channel \(k\gets x_{2}^{*}(u,v)\) is set to 0, \(l_{(u,v)}^{k}=0\). If not, then the channel \(x_{2}^{*}(u,v)\) is allocated to (\(u\),\(v\)), and the load on channel \(k\gets x_{2}^{*}(u,v)\) is set to \(b_{i}\), \(l_{(u,v)}^{k}=b_{i}\).  
It is also possible that the bandwidth constraints cannot be satisfied as per Figure 1 in the BPSR algorithm. The same channel adjustment strategy can be used to solve the problem. If there is a link that cannot meet the QoS constraint, then the tag variable flag is set to 0, which means that the request could not be accepted.
```
Input: G = (\(V\),\(E\)), \(\gamma_{i}\) = (\(s_{i}\),\(d_{i}\),\(b_{i}\)), \(\alpha\) (\(s_{i}\), \(d_{i}\)), \(\beta\) (\(s_{i}\), \(d_{i}\)) Output: x(\(u\),\(v\)) (1) flag \(\gets 1\); (2)while (\(\forall(u,v)\in\beta(s_{i},d_{i})\)){ Calculate \(x_{2}^{*}(u,v)\) according to Equation (17); (4) if(x(\(u\),\(v\)) = 0){ while(\(\forall(u,b)\in\alpha(s_{i},d_{i})\)){ if(x_{2}^{*}(u,v)=x(u,b)){  x(u,v) \gets x_{2}^{*}(u,v)\) ; (8) \(k\gets x(u,v)\); (9) \(l_{(u,v)}^{k}=0\); (10) break; (11) }//end if (12) //end while (13) //end if (14) if(x(\(u\),\(v\)) = 0){  k \(\gets x_{2}^{*}(u,v)\) ; (16) Calculate \(\mathbb{B}_{\Psi}^{k}(u,v)\) according to Equation (6); if(\(b_{i}>\mathbb{B}_{\Psi}^{k}(u,v)\)){ sort ascending; (19) while(\(\forall k\in K(u,v)\)){ if(\(b_{i}<\mathbb{B}_{\Psi}^{k}(u,v)\)){  x(u,v) \gets k\); (22) \(l_{k}^{(u,v)}\gets b_{i}\); (23) break; (24) }//end if (25) //end while (26) if(x(\(u\),\(v\)) = 0){  flag 0; (28) break; (29) }//end if (30) }else{ \(x(u\),\(v)\) \(\leftarrow\)\(x_{2}^{*}(u,v)\) ; (32) \(k\gets x_{2}^{*}(u,v)\) ; (33) \(l_{(u,v)}^{k}\gets b_{i}\) ; (34) //end if (35) //end if (36) //end while (37) return flag
```  
**Algorithm 5**BPSR
## 3 Results and Discussion
### HR-TEM Analysis  
To find additional confirmation on the nano-scaled samples, an HR-TEM study was performed and is shown in Figure 6a,b. In order to investigate the particle size and shape and nanostructure, the HR-TEM analysis was executed for MnFe\({}_{2}\)O\({}_{4}\) NCs (Figure 6a) and MnCe\({}_{0.3}\)Fe\({}_{1.7}\)O\({}_{4}\) NCs (Figure 6b). HR-TEM photographs of the samples clearly show spherical-shaped particles in the manner of nanostructures. Crystallographic clarifications were performed by SAED patterns of MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) NCs (x = 0.3) as shown in Figure 6c. A regular of significant Debye rings agreeing to the planes _hkl_; 311, 400, 511, and 440 of the MnFe\({}_{2}\)O\({}_{4}\) cubic crystal structure indexed a test of the purity and crystallinity of the products. The Debye rings appeared incessant and diffused, and they also had an evidently visible agreement with good crystallinity, which show additional leaflets that demonstrate that the products are very much in the nano regime and well-developed nanoparticles. The images demonstrate that the spherical-shaped MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) NCs are aggregated and differences were observed among the surface of the samples.  
Figure 4: FT-IR spectra of spinel MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) (x = 0.0, 0.1, 0.3 and 0.5) NCs.  
Figure 5: HR-SEM images of (**a**) MnFe\({}_{2}\)O\({}_{4}\) and (**b**) MnCe\({}_{0.3}\)Fe\({}_{1.7}\)O\({}_{4}\). (**c.d**) Different magnifications of MnFe\({}_{2}\)O\({}_{4}\) NCs.
## 5 Experimental Results  
Experiments to measure the performance of SDR-based FM-ACSK prototype were carried out. In the experiments described in Sections 5.1-5.3 single Simulink model performed transmitter and receiver real-time simulations, running two or three SDRs connected to the same computer. Identical 27 cm long coil whip antennas were used for transmission and reception in these experiments. Antennas were located 4 m from one another. In the last experiment, described in Section 5.4, two completely independent computers with their own SDR units and antennas located 20 m apart from each other were used.
# Analysis and Design of Infrared Search and Track System with Afocal Zoom Telescope
## 4 Design of the IRST Optics
### Design Results  
Two different schematics of the resulting 6\(\times\) IRST optics with an FSM are demonstrated in Figure 4. The zoom system consists of a 7-singlet 6\(\times\) zoom afocal telescope and a 3-singlet objective with a focal length of 60 mm and uses only silicon, germanium, and zinc sulfide, three kinds of common infrared materials. An envelope within 250 mm \(\times\) 140 mm \(\times\) 95 mm from the outmost lens surface to the focal surface is realized with two folding mirrors. The largest clear aperture of 91 mm is located on the first component, and the larger movable element with a semi-diameter of 38 mm can be found to be the second movable singlet along the direction of light propagation. Also, a focus lens is set to adapt to different ranges and environments. Furthermore, more fold mirrors could be added to the optics to achieve a more compact envelope.  
Figure 4: Schematic of the resulting 6\(\times\) zoom IRST optical system.
## 4 Experimental Results
### Speech Distortion Index  
The speech distortion index (SDI) was defined to measure the deformed degree of a speech signal. It is given as [21]  
\[SDI(\$(\eta,v),\;\$(\eta,v))=\frac{E\{[s((\eta,v)-\$(\eta,v)]^{2}\}}{\sigma_{s}^{ 2}} \tag{41}\]  
where \(E\) denotes mathematical expectation. \(\sigma_{s}^{2}\) is the variance of speech.  
The index in (41) is between zero and unity for a denoised speech. A denoised speech signal is highly distorted when the SDI is close to unity. Conversely, denoised speech is lowly distorted when the SDI is near zero. Table 4 presents the SDI comparisons for each noise estimator. In the condition of car noise corruption with input SNR equaling 0 dB, the performances of the MCRA-FB and proposed methods are comparable and are superior to the MCRA method. In the other noise corruptions, the proposed method outperforms the other two methods. Accordingly, the proposed noise estimator can improve the quality of denoised speech by more preservation on speech components.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Noise Type**} & \multicolumn{2}{c}{**SNR**} & \multicolumn{3}{c}{**Speech Distortion Index**} \\ \cline{2-6}  & **(dB)** & **Noisy** & **MCRA** & **FB\_MCRA** & **Proposed** \\ \hline \multirow{3}{*}{White} & 0 & 0.2972 & 0.0945 & 0.0915 & 0.0898 \\  & 5 & 0.0939 & 0.0537 & 0.0499 & 0.0384 \\  & 10 & 0.0297 & 0.0341 & 0.0281 & 0.0168 \\ \hline \multirow{3}{*}{F16} & 0 & 0.3042 & 0.1287 & 0.1319 & 0.1202 \\  & 5 & 0.0961 & 0.0656 & 0.0636 & 0.0504 \\  & 10 & 0.0304 & 0.0365 & 0.0314 & 0.0218 \\ \hline \multirow{3}{*}{Factory} & 0 & 0.3175 & 0.1426 & 0.1443 & 0.1327 \\  & 5 & 0.1004 & 0.0698 & 0.0683 & 0.0540 \\  & 10 & 0.0317 & 0.0384 & 0.0332 & 0.0237 \\ \hline \multirow{3}{*}{Helicopter} & 0 & 0.3062 & 0.1193 & 0.1187 & 0.1126 \\  & 5 & 0.0968 & 0.0623 & 0.0591 & 0.0463 \\ \cline{1-1}  & 10 & 0.0306 & 0.0350 & 0.0295 & 0.0211 \\ \hline \multirow{3}{*}{Car} & 0 & 0.3758 & 0.0968 & 0.0642 & 0.0673 \\  & 5 & 0.1188 & 0.0467 & 0.0309 & 0.0255 \\ \cline{1-1}  & 10 & 0.375 & 0.0278 & 0.0164 & 0.0130 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of speech distortion index (SDI) for the denoised speech in various noise corruptions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Noise Type**} & \multicolumn{2}{c}{**SNR**} & \multicolumn{2}{c}{**LSD**} \\ \cline{2-5}  & **(dB)** & **MCRA** & **FB\_MCRA** & **Proposed** \\ \hline \multirow{3}{*}{Factory} & 0 & 2.86 & 2.96 & 2.79 \\  & 5 & 3.37 & 3.40 & 2.89 \\  & 10 & 4.26 & 4.10 & 3.22 \\ \hline \multirow{3}{*}{Helicopter} & 0 & 3.11 & 3.16 & 3.29 \\  & 5 & 4.08 & 3.84 & 3.09 \\  & 10 & 5.72 & 4.89 & 3.72 \\ \hline \multirow{3}{*}{Car} & 0 & 14.40 & 10.60 & 15.83 \\  & 5 & 20.68 & 14.88 & 14.52 \\  & 10 & 28.71 & 20.88 & 18.46 \\ \hline \multirow{3}{*}{Babble} & 0 & 3.65 & 3.95 & 3.20 \\  & 5 & 4.30 & 4.55 & 3.19 \\ \cline{1-1}  & 10 & 5.46 & 5.39 & 3.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 3 Mission Operations
### Flight Predictions
#### 3.2.1 Modeling of the Ascent  
In modeling the flight dynamics of the balloon, the forces of interest are gravity, buoyancy and drag. The buoyant force is equal to the weight of the air displaced by the balloon. Since lifting gases are by definition less dense than air, the buoyant force exceeds weight and propels the balloon upward. The buoyant force, which acts in the \(z\) direction, is:  
\[\overset{\rightarrow}{F}_{b}(z)=V(z)\;\rho_{air}(z)\;g(z)\;\hat{z} \tag{1}\]  
where \(V(z)\) is the balloon volume. Latex balloons can expand to a multiple of their launch site diameter at apogee, and the density \(\rho_{air}\) of the atmosphere at typical burst altitudes can  
Figure 8: Mission operation sequence.  
approach 1% of sea level density, while variations of the gravity acceleration \(g(z)\) are less than 1-2%. The buoyant force is opposed by the downward force of gravity on the balloon:  
\[\overrightarrow{F_{g}}(z)=-m_{tot}\ g(z)\ \hat{z} \tag{2}\]  
where \(m_{tot}=m_{balloon}+m_{payload}+m_{Helium}\) is the mass of the whole system. Finally, the drag force can be computed as follows:  
\[\overrightarrow{F_{d}}(z)=-\frac{1}{2}\rho_{air}(z)\ C_{D}\ A(z)\ |V_{rel}| \overrightarrow{V}_{rel} \tag{3}\]  
where:  
\[\overrightarrow{V}_{rel}=\overrightarrow{V}-\overrightarrow{V}_{W} \tag{4}\]  
is the balloon's velocity relative to the surrounding air, \(C_{D}\) is the drag coefficient and \(A(z)\) is the cross-sectional area of the balloon in the direction of \(\overrightarrow{V}_{rel}\). The total force acting on the balloon is then:  
\[\overrightarrow{F_{t}}(z)=\left[V(z)\ \rho_{air}(z)-m_{tot}\right]\ g(z)\ \hat{z}-\frac{1}{2}\ \rho_{air}(z)\ C_{D}\ A(z)\ |V_{rel}| \overrightarrow{V}_{rel} \tag{5}\]  
The vertical motion is then derived by integrating the total force using [PERSON]'s law and imposing the initial conditions and the burst conditions. Two burst conditions can be set, i.e., either the burst altitude, or the burst diameter as provided by the manufacturer of the balloon.
## 1 Introduction  
A new method to evaluate seismic risk in regional areas is proposed as an attempt to preserve Italian historical and architectural cultural heritage. To this purpose, the spatial and temporal correlations between the acoustic emission (AE) data obtained from the monitoring sites and the earthquakes that have occurred in specific ranges of time and space are examined.  
The predictive power and the non-invasive procedure of the AE technique (no external excitation is provided, since the source of energy is the damage process itself) can be exploited to preserve Italian cultural heritage, as historic buildings and monuments are exposed to seismic risk and, in general, to severe, long-term, cyclic loading conditions or harsh environmental conditions.  
Buildings and structures exposed to the action of earthquakes with moderate magnitude, which are rather frequent in the central and southern regions of Italy, may undergo accelerated aging and deterioration. Such damage processes, often inaccessible to visual inspection, eventually lead to an increased vulnerability to the actions of major earthquakes, with catastrophic results [1].  
The two regions considered in this paper surround the Italian cities of Varallo and Bologna, where the authors have recently carried out AE monitoring of important historical monuments for structural stability assessment. In particular, in the Italian Renaissance Architectural Complex of \"The Sacred Mountain of Varallo\" the structure of the Chapel XVII was analyzed, while in the City of Bologna the stability of the \"Asinelli Tower\", known as the highest leaning tower in Italy, was evaluated [2, 3].  
According to seismic analysis, several studies investigated spatial and temporal correlations of earthquake epicenters, involving, for example, the concepts of Omori's Law and fractal dimensions [4, 5, 6]. Other authors tried to study the complex phenomenon of seismicity using an approach that is able to analyze the spatial location and time occurrence in a combined way, without subjective a priori choices [7]. The present approach leads to a self-consistent analysis and visualization of both spatial and temporal correlations based on the definition of correlation integral [8].  
Based on these considerations, we have tried not only to analyze the seismic activity in the considered regions, but also to correlate it with the AE activity detected during the structural monitoring. By adopting a modified Grassberger-Procaccia algorithm, we give the cumulative probability of the events' occurrence in a specific area, considering the AE records and the seismic events during the same period of time. In the modified integral the cumulative probabilities \(C\) (\(r\), \(\tau\)) are the function of the regional radius of interest, \(r\), and of the time interval, \(\tau\), both considering the peak of AEs as earthquake \"precursors\" or \"aftershocks\" [9].
## 4 Results and Analysis
### Pupil Area  
#### 4.1.1 Data Preprocessing  
Before performing data analysis, original pupil data was collected by the eye tracker (Figure 3) and preprocessed to reduce the interference of abnormal data. For example, in Figure 3, pupil data in a green rectangle reflects abnormally low amplitude estimates of brief duration. This phenomenon can be caused by several factors, such as eye blink, sharp changes in luminance, and abnormal reflection in the cornea. In addition, pupil data in the red rectangle is another type of abnormal data, mainly due to the color similarity of iris and pupil dilation.  
Before analyzing the dynamics of the pupil area, the pupil blink data and unlikely pupil area were eliminated. Figure 4 illustrates an example of processed data along with original data. As observed in Figure 4, preprocessed pupil data is messy, possibly due to the driver's iris tremors and their mental activity. The noisy data is smoothed by Wavelet Transform through a MATLAB program in this study. It was noted that there were significant individual differences in eye movement parameters among the six drivers. It should be noted all experimental figures after Figure 4 used processed data only.  
Figure 4: Processed data.  
Figure 3: Original data of pupil area.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 7 Characteristics of the Developed System
### Link Budget  
The critical parameter in our application is the energy, in particular the energy required for the cluster heads to transmit their beacon and useful information to the UAV. The relationship among energy, distance and frequency is given in Equation (14) [18]:  
\[Path\ loss=32.45\ dB+20\ Log_{10}\ (frequency\ in\ MHz)+20\ Log_{10}\ (distance\ in\ Km) \tag{14}\]  
We realize that at a large distance from the UAV, it is preferable to use a lower frequency to minimize the power consumption of the cluster head.  
In our developed system, the UAV and sensor nodes are performing two major types of communication: one is a long range communication for identification/localization/navigation and other is a short range for the data exchange. For the long range communication, we don't need a high bandwidth. We are using a bit lower frequency that can penetrate to larger distances with very low energy. High frequency (the required one is some standard such as WiFi) is only used for data exchange, as the data size may be large enough so we need high bandwidth. Short-range communication is more energy demanding and increases exponentially with increasing distance. In our developed system, we care about this transmitter receive distance. Link budgets for both the frequencies are defined in the next section.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 5 Sota Defense Mechanisms
### Activation Clustering  
Activation clustering [14] utilizes the activation of the last layer to determine whether a model is benign or backdoored. Typically, a model relies on the activation function of its last layers to make a decision. This method employs clustering-based techniques to ascertain if a model has been compromised. Each class sample is fed into the model to collect activation data. The activations for each class are then segregated using a K-means clustering algorithm, following dimensionality reduction with principal component analysis (PCA) [56]. The K-means clustering algorithm separates the activation data into groups to discern whether the model is backdoored or benign. The clustering effectiveness is measured by the Silhouette score. A high Silhouette score indicates that the activation functions are well-clustered, suggesting the presence of poisoned data. Conversely, the activation functions of benign data typically yield a low Silhouette score.
# Mdip1
## 5 Results
### Utility Measurement  
To assess the utility of the transformed data, we conducted IoT classification and anomaly detection on both the original and transformed datasets. Firstly, we evaluated the IoT classification results by classifying seven IoT devices using both the original and transformed data. We trained a model using the original data as the baseline measurement and achieved a classification accuracy that was not 100% due to the use of a single feature. However, we found that the perturbation method had a negative effect on the classification accuracy, especially when using a large \(\lambda\) value, as shown in Figure 11b,c. In contrast, the transformation method had a minimal impact on accuracy, as seen in Figure 11d, where we were able to achieve almost the same classification results using the transformed data. Following this, we visualized the transformed anomaly and presented the anomaly detection results.  
As shown in Figure 12, the anomalies in the Samsung SmartCam original dataset are transformed into the new data, which are still noticeable. Two malicious attacks in UNSW data impact a Belkin switch and a motion sensor, as shown in Figure 13. After transforming the real data, we find the new data preserve the anomalies occurring in the original time series data. We use the transformed data, which contain anomalies, and the original data to train a new model to detect the anomaly records. It is important to note that we did  
Figure 11: Confusion matrix of classification results of seven IoT devices based on packet size feature, with dark purple representing correctly classified instances and light blue representing incorrectly classified instances. (**a**) Original data; (**b**) Perturbed data (\(\lambda=6\)); (**c**) Perturbed data (\(\lambda=46\)); (**d**) Transformed data.  
not differentiate each attack. A binary classifier is trained to detect normal or attack traffic. We compare the accuracy of anomaly detection based on perturbed and transformed data. In Figure 14a, a base-line anomaly detection result shows that using the packet size feature cannot detect all anomalies. The model trained by the perturbed data cannot detect most of the anomalies. On the contrary, the model trained by the transformed data has a little higher accuracy than using the original data.  
To summarize, the transformation method does not impact data utility. The proposed transformation method not only transforms traffic patterns, but also keeps feature characteristics of the original data. As a result, the transformed data are deemed to have high utility. The synthetic data can still be used to understand data characteristics and main variables needed for data mining.  
Figure 12: Reconstructed anomalies of transformed Samsung SmartCam data with an Insteon-like packet size feature. The anomalies are highlighted.  
Figure 13: Reconstructed anomalies of Belkin data with an Insteon-like packet size feature. The anomalies are highlighted. (**a**) Belkin Switch; (**b**) Belkin wemo motion.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Coarse-to-Fine Keypoint Tracking
#### 3.2.1 Semi-Direct Coarse Keypoint Tracking  
Taking inspiration from the semi-direct visual odometry method [2], we introduce a coarse tracking method based on photometric constraints. We assume that the light intensity and texture around matching keypoints are similar in adjacent image frames. Map points from the last frame can be projected onto the current frame using the relative pose. If the relative pose is accurate, the photometric loss of the projected keypoint's patch will be minimal. As depicted in Equations (5) and (6), the pixels of the keypoint's patch, the image points in the last frame coordinate \(\mathbf{p}_{i}^{l}\), and the relative pose of adjacent frames \(\mathbf{g}_{lc}^{\text{coarse}}\) collectively form the photometric loss \(L_{p}\). Given the known map points and patch  
Figure 2: Diagram of our proposed coarse-to-fine two-stage keypoint tracking strategy. This process begins with relative pose estimation through patch photometric loss optimization, followed by refinement using the 3D–2D keypoint relationship for enhanced accuracy.  
pixel values of the last frame, we adjust the relative pose \(\xi_{lc}^{\text{coarse}}\) to minimize the photometric loss \(L_{p}\).  
\[\xi_{lc}^{\text{coarse}}=\arg\min_{\xi_{lc}^{\text{coarse}}}\frac{1}{2}\sum_{i \in\overline{\chi}}\
orm{L_{p}\left(\xi_{lc}^{\text{coarse}},\vec{p}_{i}^{l} \right)}^{2} \tag{5}\]  
\[L_{p}\left(\xi_{lc}^{\text{coarse}},\vec{p}_{i}^{l}\right)=\mathbf{I}_{c}\left( \pi\left(\xi_{lc}^{\text{coarse}}\cdot\vec{p}_{i}^{l}\right)\right)-\mathbf{I}_{l }\left(\pi\left(\vec{p}_{i}^{l}\right)\right) \tag{6}\]  
Here, \(\pi\) denotes the projection of the 3D map point from camera coordinates to pixel coordinates. \(\overline{\chi}\) represents the set of keypoint indices. The photometric loss is constructed using the patches around the projected points.