esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Security and Privacy of Baby Monitor Apps
## 5 Mobile App Analysis
### Assessing the Confidentiality of Video Streams
#### 5.6.2 Security Issues Threatening the Confidentiality of Video Streams
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Security Issue** & **Dormi** & **BabyCam** & **Saby** & **Babyphone Mobile** \\ \hline Files may contain hard-coded sensitive information, such as usernames, passwords, keys, etc. & & & & \\ The app uses SQLite Database and executes raw SQL queries. & & & & \\ The app uses an insecure random number generator. & & & & \\ SHA-1 is a weak hash known to have hash collisions. & & & & \\ MDS is a weak hash known to have hash collisions. & & & & \\ Insecure implementation of SSL. Trusting all the certificates or accepting self-signed certificates is a critical Security Hole. & & & \\ The app can read/write to external storage. Any app & & & & \\ can read data written to external storage. & & & & \\ The app uses the encryption mode CRC with & & & & \\ PKCS5/PKCS7 padding. This configuration is & & & & \\ vulnerable to padding oracle attacks. & & & & \\ Insecure WebView implementation. Execution of & & & & \\ user-controlled code in WebView is a critical security & & & & \\ hole. & & & & \\ Remote WebView debugging is enabled. & & & & \\ IP address disclosure. & & & & \\ \hline \hline \end{tabular}  
* Security issue without impact on video confidentiality.
* False Positive.  
\end{table} 
Table 10: Security issues identified in baby monitor apps using MobSF. the code. Again, this holds the potential for unnoticed compromise of the key exchange protocol by performing a MitM attack. Furthermore, we found the Dormi app contacts a web server to obtain the relay server IP address; while the connection is TLS-secured, the web server's certificate is not correctly verified. This allows an attacker to perform an unnoticed MitM attack, which may result in the redirection of relay server connections. Security Issue #4--Weak Password Requirements (CWE-521): Although the Babyphone Mobile app offers optional end-to-end encryption of video streams, it does not mandate the use of end-to-end encryption. In case the user does not set up end-to-end encryption, anyone with access to the HTTPS server can view the monitoring stream. We elaborated before that password protection in the BabyCam application does not provide any security benefits in our threat model. However, we still consider waiving the use of passwords in the default settings to be a security issue, as potential adversaries not in a MitM position have to overcome the basic authentication mechanism to be able to access the video footage. Based on our results, we evaluated the confidentiality of video footage transmissions for each investigated baby monitoring app (see Table 11), categorizing the apps into three categories depending on the identified security issues: the confidentiality of video footage is fully ensured, the confidentiality may be compromised in certain circumstances, and the confidentiality is not ensured.
## 2 Materials and Methods
### Data
#### 2.2.1 Remote Sensing Imagery
Previous research estimating PP through correlation with satellite-derived VI suggested removing the influence of water pixels at Donana National Park by selecting a threshold corresponding to the baseline of the phenological curve [58]. [PERSON] et al. [59] suggested the use of the Modified Normalized Difference Vegetation Index (MNDVI) to identify vegetation in the flooded area of Donana National Park, which was preferred over the Normalized Difference Vegetation Index (NDVI) that created false positives in areas where vegetation was not expected. For this reason, we decided to filter the water pixels by defining a threshold of the MNDVI. A baseline of MNDVI = 0.1, corresponding to the minimum reflectance observed in the vegetation throughout the year was found. A threshold of 0.05 was then selected to identify and remove water pixels (MNDVI < 0.05) not contributing to the GPP of the ecosystem. Table 3 displays the number of images for each tile for different cloud coverage thresholds and months during the period of analysis. All available images from 1 October 2020 to 8 June 2021 with cloud coverage lower than 30% were used for the computation of the workflow. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Month** & \multicolumn{6}{c}{**Cloud Coverage**} \\ \hline  & \multicolumn{2}{c}{\textless{}100\%} & \multicolumn{2}{c}{\textless{}50\%} & \multicolumn{2}{c}{\textless{}30\%} \\ \hline  & T29 SQA & T29 SQB & T29 SQA & T29 SQB & T29 SQA & T29 SQB \\ \hline
10-2020 & 6 & 6 & 6 & 5 & 5 & 3 \\ \hline
11-2020 & 6 & 6 & 5 & 4 & 4 & 5 \\ \hline
12-2020 & 6 & 6 & 4 & 5 & 4 & 4 \\ \hline
01-2021 & 7 & 7 & 4 & 4 & 3 & 3 \\ \hline
02-2021 & 5 & 5 & 1 & 1 & 1 & 1 \\ \hline
03-2021 & 6 & 6 & 4 & 6 & 4 & 6 \\ \hline
04-2021 & 6 & 6 & 2 & 1 & 2 & 0 \\ \hline
05-2021 & 8 & 8 & 7 & 7 & 5 & 5 \\ \hline
06-2021 & 6 & 6 & 5 & 4 & 3 & 3 \\ \hline Total & 56 & 56 & 38 & 37 & 31 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Available Sentinel-2 images from October 2020 to June 2021 for different cloud coverage. Figure 2: Tiles of Sentine-2 products over the study area of Donana National Park. [Image retrieved from the Copernicus Open Access Hub]. Together with the Sentinel-2 images, MOD17 products were also retrieved from the GEE catalog for the same period (1 October 2020 to 8 June 2021). This data set was used to compare the Sentinel-2-based GPP results with the MOD17 products and analyze their performance in comparison with in situ measurements.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 2 The Navigation Filter
### Observation Model  
The measurements consist of the Doppler shifts from LEO SVs (converted to range rates) and the Earth's magnetic field from a triad of magnetometers.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 7 Experimental Results
### Energy Consumption  
In this experiment, we aim to compare the total energy consumed by each placement algorithm and verify whether our approach can generate energy-efficient placement plans.  
We observe in Figure 8 that our approach spends less energy than the other approaches, while it delivers more placed requests (see Figure 7). The VNF sharing mechanism helps to achieve such a result, since fewer edge nodes are demanded to meet a given number of requests, while greedy and random generally choose VNF instances spread in more edge nodes.  
Figure 8: Consumed energy in edge nodes.  
Figure 7: Percentage of placed SFC requests.  
As the resources of the environment become exhausted with more than 400 requests, all placement strategies spend the same amount of energy, which is the expected behavior for the proposed extreme scenario.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Effect of the Denoising AutoEncoder
However, in Figure 9, the graph was drawn using 2 years of data to show the effect of the denoising AutoEncoder clearly. In Figure 9, the result using only the ConvLSTM shows a significantly delayed predicted result, as in Figure 8. The average error of the test set is 0.998 K, and when converted at a ratio according to the maximum and minimum value, it has an error of 10.97%. For the model using an additional denoising AutoEncoder, the delay prediction is clearly resolved. The average error of the test set is 0.474 K, showing an error of 5.26%. This is a 53% reduction in error compared with learning using the ConvLSTM alone, and as mentioned, the delay prediction problem has been solved. Therefore, in this study, a model combining a denoising AutoEncoder and the ConvLSTM was used to predict ocean weather.
## 4 Results  
### Bulk Rock  
Regarding bulk rock composition, the analytical results are shown in Table 1. Specifically, the samples were characterized by a content of SiO\({}_{2}\) of 74.36 \(\pm\) 2.15 wt.%, Al\({}_{2}\)O\({}_{3}\) of 12.70 \(\pm\) 0.84 wt.%, Fe\({}_{2}\)O\({}_{3}\) of 1.91 \(\pm\) 0.52 wt.%, Na\({}_{2}\)O of 3.35 \(\pm\) 0.38 wt.%, and K\({}_{2}\)O of 4.47 \(\pm\) 0.47 wt.%.  
The analyzed samples, in accordance with the R1-R2 classification diagram [41], were predominantly classifiable as granites, with some samples falling within the field of granodiorites (Figure 3). According to the MALI/SiO\({}_{2}\) diagram (Figure 4), the analyzed samples were generally classified as calc-alkaline, while according to the ASI/SiO\({}_{2}\) diagram (Figure 4), they appeared to be slightly metaluminous.  
Regarding the results obtained through the calculation of the CIPW norm, the analyzed samples were composed as follows in terms of quartz, alkali feldspars, and plagioclase (Table 2): quartz ranged between 26.4 and 40.6 wt.%, with an average abundance of 33.8 \(\pm\) 4.1 wt.%; orthoclase ranged between 21.3 and 31.9 wt.%, with an average abundance of 26.4 \(\pm\) 2.8 wt.%; ablite ranged between 25.6 and 40.2 wt.%, with an average abundance of 28.3 \(\pm\) 3.2 wt.%; anorthite ranged between 2.8 and 10.1 wt.%, with an average abundance of 6.1 \(\pm\) 2.3 wt.%. Based on these data and in accordance with the QAP classification diagram [43], these samples fell within the field of granites except for one sample falling within the field of granodiorites (Figure 5).  
Regarding the average composition of granite waste in terms of REEs, thorium, and uranium, the results are reported in Table 3. These samples showed an average \(\Sigma\)REE of 146 \(\pm\) 48 ppm and ranging between 70 and 223 ppm. Thorium concentration was on average 14 \(\pm\) 2 ppm with values ranging between 10 and 18 ppm. Uranium concentration was on average 1.7 \(\pm\) 0.3 ppm with values ranging between 1.2 and 2.5 ppm. The chondrite-normalized REEs patterns generally showed a moderate enrichment in light REEs (LREEs),  
\begin{table}
\begin{tabular}{c c} \hline
**wt.\%** & **Mean \(\pm\) std. dev.** \\ \hline SiO\({}_{2}\) & 74.36 \(\pm\) 2.15 \\ TiO\({}_{2}\) & 0.20 \(\pm\) 0.07 \\ Al\({}_{2}\)O\({}_{3}\) & 12.70 \(\pm\) 0.84 \\ Fe\({}_{2}\)O\({}_{3}\) & 1.91 \(\pm\) 0.52 \\ MnO & 0.04 \(\pm\) 0.02 \\ MgO & 0.59 \(\pm\) 0.37 \\ CaO & 1.77 \(\pm\) 0.56 \\ Na\({}_{2}\)O & 3.35 \(\pm\) 0.38 \\ K\({}_{2}\)O & 4.47 \(\pm\) 0.47 \\ P\({}_{2}\)O\({}_{5}\) & 0.07 \(\pm\) 0.03 \\ L.O.I. & 0.55 \(\pm\) 0.24 \\ \hline \end{tabular}
\end{table}
Table 1: Mean composition of the granite waste samples from the Buddusö quarry (18 analyzed samples).  
Figure 3: R1\(-\)R2 classification diagram in which the analyzed granite waste samples are plotted.
## 1 Introduction
### Notation  
In this paper, matrices are represented by boldface capital letters and vectors are denoted by boldface lowercase letters. Scalars are denoted by italic letters. Time-varying vectors and matrices show discrete-time index as a subscript index, while in time-varying scalar elements the time index is denoted in square brackets. A regression vector is represented as \(\mathbf{x}_{n}\in\mathbb{R}^{M\times 1}\ =\ \left[\begin{array}{cccc}x\left[n \right]&x\left[n-1\right]&\ldots&x\left[n-M+1\right]\end{array}\right]^{T}\), where \(M\) is the overall vector length and \(x\left[n-i\right]\) is individual scalar entry at the generic time instant \(n-i\). A generic coefficient vector, in which all the elements depend on the same time instant, is denoted as \(\mathbf{w}_{n}\in\mathbb{R}^{M\times 1}=\left[\begin{array}{cccc}w_{0}\left[n \right]&w_{1}\left[n\right]&\ldots&w_{M-1}\left[n\right]\end{array}\right]^{T}\), where \(w_{i}\left[n\right]\) is the generic \(i^{\text{th}}\) individual entry at the \(n^{\text{th}}\) time instant. All vectors are represented as column vectors. The index related to a generic \(j^{\text{th}}\) filter is denoted as superscript, e.g., \(\mathbf{w}_{n}^{\left(j\right)}\).
## 3 Simulation Result
### Improving the Recovery from Noise by Analytic Extrapolation
When SNR \(<\) 10 dB, \(e\), \(e_{rec}\) have the same value, which means in this condition of SNR, the analytic extrapolation has little ability to expand the SBP. However, when SNR \(>\) 10, the \(e_{rec}\) decreases with respect to \(e\), which means that the resolution can be improved further increasing. However, even with the regulation, the reconstruction error still be higher than 0.1. To further reduce the error, it is beneficial to decrease the extrapolation range. Reducing the extrapolation range increases the number of equations relative to the number of unknowns in Equation (12), which reduces the ill-posedness of matrix \(A^{\prime}\), leading to more accurate reconstruction with lower error. Figure 7a shows the maximum extrapolation range (ERR) achievable when \(e_{rec}\) is less than 0.1. The maximum extrapolation range is roughly linearly proportional to the signal-to-noise ratio (SNR). At high SNR, regularization can increase the spatial bandwidth product by up to 1.8 times, corresponding to a resolution improvement of about 1.34 times. However, at low SNR, the analytic extrapolation only achieves a 1.3-fold increase in the spatial bandwidth product, resulting in a resolution improvement of approximately 1.15 times. Figure 6: (**a**) Illustration of the magnitude of eigenvalues of \(A^{\prime}\) versus k. (**b**) Illustration of the logarithm of eigenvalues of (**a**) versus k.  
To verify the improvement of the resolution optical field, the real part of the recovered optical field, diffraction-limited optical field, and ground truth are given in Figure 8(a1-c1), respectively, at the SNR of 80 dB and ERR = 1.8. Figure 8(a2-c2) shows the magnified area in the red rectangle of Figure 8(a1-c1), respectively. The improvement of the resolution is obvious in the dashed line in Figure 8(a2,b2). However, this structure becomes blurred with the SNR decreasing, as shown in Figure 8(a3-c3) with the SNR of 20, 40, and 60 dB, respectively. Figure 8: The improvement of resolution by analytic extrapolation with different SNR. (**a1–c1**). The real part of the optical field by analytic extrapolation, diffraction-limited, and ground truth. (**a2–c2**) The magnified red rectangle area of (**a1–c1**). (**a3–c3**) The magnified red rectangle area of (**a1**) with SNR of 20 dB, 40 dB, and 60 dB. Figure 7: **(a) Illustration of the \(e\) and \(e_{rec}\) with different SNRs at ERR = 2. **(b) Illustration of the extrapolation range (ERR) changed with the SNR. **
# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 4 Experiments Analysis and Discussion
### Performance Evaluation and Analysis
Therefore, UC-MPRP has unbalanced energy consumption and an unstable system, which will consume more energy in order to maintain the normal operation of the system. Compared with the above two, TTCF uses compression sensing technology to effectively compress the data volume, which can greatly reduce the network transmission volume. Moreover, the improved Dijkstra algorithm based on the effective multi-attribute decision value reduces the time to find the reliable path between the source and target nodes, and consumes less network energy while improving the transmission efficiency. Figure 7: Comparison of residual energy of each sensor node after applying TTCF strategy. Figure 9 exhibits the throughput change of each sensor node after using the TTCF strategy. Seen from Figure 9, although the sensors closest to the sink node have the shortest life cycle, its throughput is the largest, indicating that it undertakes a large number of forwarding tasks. The difference in the lifetime and throughput of the sensors farthest from sink node is not obvious, which indicates that these sensors do not undertake any forwarding tasks, while the throughput of the sensors between the above two types of nodes reflects that they undertake some forwarding tasks. Figure 10 describes the comparison of the networking throughput between this TTCF strategy and CRPBA and UC-MPRP. Seen from it, the line trend of TTCF is higher than that of CRPBA and UC-MPRP. CRPBA uniformly clusters the nodes in the network without considering the difference of the forwarding capabilities between the network center and the network edge nodes, resulting in the low utilization of some capable nodes, low data processing efficiency and poor network throughput. UC-MPRP adopts the multi-path routing transmission method, which enables more nodes in the network to participate in the data forwarding task and improve the forwarding efficiency. However, because some important nodes participate in multiple routing calculations and queuing waiting, it is very easy to cause network congestion and a low system throughput under the flow of big data. The higher throughput achieved by TTCF is because data compression makes the network capacity increase, and the forwarding mechanism based on multiple important attributes of nodes makes the number of data packets successfully delivered to the sink nodes increase greatly, the network processing ability enhance, and the networking throughput improve. Figure 8: Comparison of residual energy of the networking between TTCF, UC-MPRP and CRPBA. Figure 9: Throughput performance of each sensor node after applying TTCF strategy.
## 4 Case Study
as the one that is compromised. Instead, a region where the attack might be at could be inferred. Hence, the superiority of the proposed framework is that it can identify and correct the attack in a single process. In addition, it can be used as a pre-processing step prior executing SE routine. For stealthy attack, a line is selected and its parameters, i.e., \(g\), \(b\) and \(b_{slt}\) are attacked gradually from 0 to 20% of their values. The performance index \(J\) as well as the \(CME\) in its normalized form (\(CME^{N}\)) are recorded. The results are shown in Figures 3 and 4. In Figure 3, the performance index \(J(x)\) (colored in blue) increased with the increase size of the attack in the line's parameters under attack. In this case, even though the performance index \(J(x)\) increased, the \(\chi^{2}\) test still did not detect the error. For identification, the \(CME^{N}\) is obtained for every attack and the absolute error is calculated and presented in Figure 4. As shown, due to the increase size of the attack in a single line, the error is spread into multiple estimation of measurements. After  
The same scenario of the previous stealth attack is simulated for multiple lines in this case. The results are shown in Figures 6 and 7. As seen from the figures, a similar trend has occurred. However, the errors in measurement estimation are increased. The  
Figure 4: \(CME^{N}\) absolute error single line case. Figure 5: Absolute error of line parameter correction single line case. bi-level model is performed and lines are corrected. The observed error in correction for the stealthy attacks is presented in Figure 8.  
Figure 6: Performance Index (J) multiple lines case. Figure 7: \(CME^{N}\) absolute error multiple lines case.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches
### FFD-Based Early Fault Diagnosis  
The FFD-based early fault diagnosis system often consists of three steps, including data acquisition, signal denoising and demodulation analysis. Figure 3 shows the flowchart of the FFD-based early fault diagnosis. Among these steps, signal denosing plays a vitally important role in extracting the weak fault characteristics. Many researchers are committed to developing denosing approaches to complete the early fault diagnosis, such as WT, adaptive decomposition methods, and sparse decomposition.
# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 1 Introduction
Systems have been developed that integrate AI to assist doctors in choosing the most effective cancer treatments based on a vast database of the medical literature, clinical guidelines, and patient records; these systems have been used in various clinical settings to personalize oncology therapies [11]. ChatGPT can serve as an auxiliary resource for diagnosis and decision-making across various disciplines, such as cardiology, radiology, and urology, or the psychiatric field [12,13]. These examples demonstrate how AI is already a fundamental component in modern medical practice, improving diagnostic accuracy, optimizing treatments, and saving lives through quicker identification of critical conditions. The integration of these advanced technologies in the healthcare sector represents one of the most promising frontiers for the medicine of the future [7]. Similarly, like all fields of medicine, dentistry is also undergoing a digital revolution driven by continuous technological progress and the use of AI among various new tools. Oral and maxillofacial radiology benefits from AI integration in automatic tooth and bone segmentation, identification of dental implant systems, artifact reduction, and pathology detection [14, 15, 16, 17, 18]. It has also been shown to be helpful in improving digital impression precision, in assisting in clinical photographic diagnosis, and, generally speaking, in the fields of 3D digital dentistry (i.e., CAD/CAM, 3D printing, 3D scanning) [19, 20, 21, 22, 23, 24]. Regarding GPTs, while real-world applications are under scrutiny, there are several initiatives aimed at evaluating their precision, practicality, and potential uses, taking into account their versatility. Therefore, this comprehensive narrative review aims to explore how ChatGPT can assist the entire dental sector, analyzing the potential of this technology and the ongoing studies on its use for clinical, research, and educational purposes.
## 4 Performance Evaluation
### Result of Solutions
#### 4.2.2 Linear Instances  
In a single knapsack, the profit of the selected item can be up to 0.0488\(\sim\)0.3911 as shown in Table 2. In terms of closeness to the optimal solution, as the size of capacity becomes larger, the closeness also becomes larger. The proposed algorithm has 98.7% in 10 capacity and 99.5% in 100 capacity. The proposed algorithm outperforms other algorithms in 40, 80, 100 capacity. In the three-knapsack case, except for capacity 80, the proposed algorithm shows outstanding results. Except for capacity 20, the proposed algorithm and the greedy method fall short of the random solution in the five-knapsack case. In capacity 20, the proposed algorithm outperforms the other two algorithms. The average profit of the proposed algorithm has the highest value in the overall capacity as shown in Table 11.