esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Security and Privacy of Baby Monitor Apps
## 5 Mobile App Analysis
### Device Pairing  
Protecting surveillance sessions from unauthorized access should be a key concern for video surveillance solutions. Therefore, the Dormi, Saby, and Babyphone Mobile apps use a  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Permission** & **Dormi** & **BabyCam** & **Saby** & **Babyphone Mobile** \\ \hline ACCESS\_COARSE\_LOCATION & & & \\ ACCESS\_FINE\_LOCATION & & & \\ CAMERA & & & \\ RECORD\_AUDIO & & & \\ READ\_EXTRNAL\_STORAGE & & & \\ READ\_PHONE\_STATE & & & \\ WRITE\_EXTERNAL\_STORAGE & & & \\ WRITE\_SETHINGS & & & \\ NEARBY\_WIT\_DEVICS & & & \\ POST\_NOITFCATIONS & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dangerous permissions listed in the Android Manifest of baby monitor apps.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**App** & **Permission** & **Requests** & **Purpose** & **Required** \\  & **Runtime** & **Code** & & **(Android 12)** \\ \hline Dormi & READ\_PHONE\_STATE & & & - & \\ Dormi & WRITE\_SETHINGS & & & \\ BabyCam & ACCESS\_COARSE\_LOCATION & & & \\ BabyCam & ACCESS\_FINE\_LOCATION & & & \\ BabyCam & WRITE\_EXTERNAL\_STORAGE & & & \\ BabyCam & NERARY\_WIT\_DEVICS & & & \\ BabyCam & POST\_NOITFCATIONS & & & \\ BabyCam & POST\_NOITFCATIONS & & & \\ Baby & WRITE\_EXTERNAL\_STORAGE & & & \\ Baby & READ\_EXTERNAL\_STORAGE & & & \\ Baby & POST\_NOITFCATIONS & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Linkage of suspicious, dangerous permissions, and app functionality.  
pairing workflow to authorize a parent device before accessing a surveillance session. This workflow consists of two to three steps, depending on the application's implementation (Table 6). In the first step, the monitoring device creates a pairing code. In the second step, this code must be entered on the parent device, which then connects back to the child device using the pairing code. By entering the correct pairing code, a monitoring session is established.  
Although the Babyphone Mobile app does not require additional confirmation of connection requests on the monitoring device, both the Saby and Dormi apps do. Separate confirmation of incoming connections is advantageous, as potential brute-force attacks on the pairing code and the corresponding monitor sessions are effectively mitigated. We propose further investigating how brute-force attacks on pairing codes can lead to session theft and suggest investigating potential attacks in future work. The Babycam app does not use a pairing workflow and grants access to anyone who knows the connection parameters.
## 6 Incremental Current Prediction Model
### Evaluation
In this experiment, the incremental model would have rejected around 250 (1.6%) welds, whereas the best average-based models rejected over 800 welds (5.2%). A process engineer confirmed the benefit of this approach, as it meant that the monitoring system would be less affected by unexpected behavioral shifts while still signaling these shifts. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**MAP Error (\%) Excluding First**} \\
**Model** & \multicolumn{3}{c}{**X Welds after a Maintenance Period Transition**} \\  & **0** & **50** & **100** & **250** & **500** \\ \hline Incr. Grey-box (\(\alpha=0.005\)) & 0.984 & 0.976 & 0.968 & 0.956 & 0.954 \\ Incr. Grey-box (\(\alpha=0.01\)) & 0.956 & 0.949 & 0.944 & 0.941 & 0.943 \\ Incr. Grey-box (\(\alpha=0.02\)) & 0.931 & 0.925 & 0.923 & 0.923 & 0.925 \\ Incr. Grey-box (\(\alpha=0.05\)) & 0.892 & 0.888 & 0.887 & 0.888 & 0.889 \\ Incr. Grey-box (\(\alpha=0.10\)) & 0.860 & 0.857 & 0.856 & 0.857 & 0.858 \\ Avg (\(N=25\)) & 1.165 & 1.160 & 1.156 & 1.126 & 1.094 \\ Avg (\(N=50\)) & 1.263 & 1.256 & 1.249 & 1.215 & 1.171 \\ Avg (\(N=100\)) & 1.394 & 1.388 & 1.379 & 1.342 & 1.293 \\ Avg ES (\(N=25\), \(\alpha=0.01\)) & 1.564 & 1.556 & 1.548 & 1.511 & 1.461 \\ Avg ES (\(N=25\), \(\alpha=0.02\)) & 1.344 & 1.338 & 1.330 & 1.295 & 1.249 \\ Avg ES (\(N=25\), \(\alpha=0.05\)) & 1.166 & 1.161 & 1.156 & 1.125 & 1.091 \\ Oracle (Non-Incr. Grey-box) & 1.028 & 1.027 & 1.026 & 1.025 & 1.022 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean absolute percentage error of welding current prediction for all models for all welds in maintenance periods 2 and 3, excluding a number of welds that occur just after either behavioral change. All average-based models perform best when ignoring up to 500 welds after each transition, indicating they require a long time to adjust. In contrast, the physics-based models perform optimally after 50 to 250 welds. Overall, the incremental model outperforms the average-based models and even the oracle model. This shows that welding current is also affected by local temporal changes captured by the incremental model.
## 4 Material and Methods
### Gene Annotation and Data Analysis  
To ensure the accuracy of the data, the raw data is first filtered to obtain high-quality, clean data. Additionally, Bowtie2 ([[http://bowtie-bio.sourceforge.net/bowtie2/index.shtml](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)]([http://bowtie-bio.sourceforge.net/bowtie2/index.shtml](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)) accessed on 14 January 2021) was used to align the clean data of each sample with the reference genome ([[https://www.ncbi.nlm.nih.gov/genome/genomes/1041](https://www.ncbi.nlm.nih.gov/genome/genomes/1041)]([https://www.ncbi.nlm.nih.gov/genome/genomes/1041](https://www.ncbi.nlm.nih.gov/genome/genomes/1041)) accessed on 14 January 2021) and the gene expression level was measured by RSEM ([[http://deweylab.github.io/RSEM/](http://deweylab.github.io/RSEM/)]([http://deweylab.github.io/RSEM/](http://deweylab.github.io/RSEM/)) accessed on 14 January 2021), yielding transcripts per million reads (TPM). For each dataset, DESeq ([[http://www.bioconductor.org/packages/release/bioc/html/DESeq.html](http://www.bioconductor.org/packages/release/bioc/html/DESeq.html)]([http://www.bioconductor.org/packages/release/bioc/html/DESeq.html](http://www.bioconductor.org/packages/release/bioc/html/DESeq.html)) accessed on 14 January 2021)was used to analyze and identify the differences in genes among the samples. The significantly differentially expressed genes (DEGs) were identified with the inclusion criteria of \(\mid\)FC\(\mid\) > 2 and a \(p\)-adjust < 0.05. The transcriptome data were submitted to NCBI's sequence read archive (SRA) database, and the SRA accession number is PRJNA943099. For the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs, KOBAS 2.0 ([[http://kobas.cbi.pku.edu.cn](http://kobas.cbi.pku.edu.cn)]([http://kobas.cbi.pku.edu.cn](http://kobas.cbi.pku.edu.cn)) accessed on 14 January 2021) was used to identify a statistically significantly enriched pathway using [PERSON]'s exact test. To control calculated false positive rates, \(p\)-values were corrected using 4 multiple testing methods (Bonferroni, Holm, Sidak, and the false discovery rate), and the pathways with a \(p\)-adjust < 0.05 were chosen.
# Finite Element Approaches to Model Electromechanical, Periodic Beams
## 3 Numerical Analysis  
In order to examine whether the proposed numerical approach is appropriate, a series of numerical experiments were carried out to verify the impact of the resonance circuit parameters on the physical properties (the width and placement of band gaps) of the periodic beam. During calculations the periodic boundary conditions were assumed.  
The graphs shown in Figure 5 represent frequency response functions in the ranges from 0 to 250 kHz for the periodic beam with a resonant circuit being: open, closed or tuned to the specific frequency. The left column of Figure 5 shows the results obtained by the use of the FEM, the right hand side column of this Figure--by the TDSFEM [12] respectively. It may be noticed that there appeared two natural band gaps in given frequency ranges for the passive structure. Tuning the PZT circuits to the resonant frequency introduced an artificial band gap in the range of that frequency. However tuning the circuits to the frequency in the range of the natural periodic beam band gap, with the inactive PZT element, significantly widened the band gap. It should be also mentioned that for a lower range of the frequency spectra both the FEM and the TDSFEM results were quite similar, but in a higher frequency range the FEM results were distorted. The reason for that has been widely discussed in [14], where several features of the numerical models have been addressed.  
Figure 6 shows the influence of the PZT circuits resonance frequency on the width of beam band gaps. This example was calculated with the TDSFEM. In this case it has been demonstrated how changes in the resonant frequency of PZT circuits allows one to control the ranges of blocked frequencies in the case of forced vibrations. Red colour represents frequency ranges that will propagate freely in the structure, the other colours (blue, green and yellow) represent different levels of attenuation.  
The graphs presented in Figure 7 illustrate the effect of changes in the electrical resistance on the active periodic structure frequency band gaps. Here the TDSFEM has been used. The figures shown on the left hand side present the results calculated for 1 \(\Omega\) RLC circuit resistance, the right hand-side--5 \(\Omega\) respectively. As it can be noticed higher values of the resistance in the RLC circuit increased the energy dissipation and, as a result, widens the band gap. This effect is more significant in case, when the resonant frequency of the RLC circuit is equal to the passive structure frequency band gap. It it may be noticed in the bottom right graph from Figure 7.  
Figure 4: Construction of inertia and stiffness matrices in the case of the SFEM.  
Figure 5: Frequency response functions of a passive and active periodic structure with the resonant circuit tuned to the frequency 50 kHz or 100 kHz (marked with a red line) and resistance 1 \(\Omega\).  
Figure 6: Dependence of the vibration amplitude on the resonant frequency of the RLC circuits.
# IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals
## 2 Condition Detection Algorithm
### Neural Networks  
It is well known in the machine learning community that if the features are informative enough, the classifier does not need to be complex in order to reach right decision. If the features are power values of coefficients from Table 1, the main requirement the classifier needs to fulfill is to be computationally simple and easily support various levels of problem complexity. Therefore, NN are the ideal choice because they have been shown to work both on state estimation problems [26] and when implementation on inexpensive processors is required [27].  
In this paper, we used the multilayer neural network, whose structure is shown in Figure 2. The MNN consists of \(K-1\) hidden layers and one output layer. The DWT power coefficients are used as input into the MNN, while the MNN output represents the estimated state of the machine. The \(k\)th layer consists of \(N^{k}\) neurons and the output of each neuron is:  
\[a_{i}^{k}=\sigma^{k}\Bigg{(}\sum_{i=j}^{N^{k-1}}w_{ji}^{k}a_{j}^{k-1}+b_{i}^{k} \Bigg{)},i=1,\ldots,N^{k}, \tag{3}\]  
where \(w_{j,i}^{k}\), \(j=1,\ldots,N^{k-1}\) and \(b_{i}^{k}\) are the weighting coefficients and bias of \(i\)th neuron, whereas \(N^{k-1}\) is the number of neurons in \((k-1)\)th layer. The activation function is denoted by \(\sigma^{k}\), which is nonlinear if the neuron belongs to the hidden layer and linear if the neuron belongs to the output layer.  
The weighting coefficients and the biases of the neural network are \"trained\" in such a way as to minimize the following cost function:  
\[J=\sum(x_{i}-y_{i})^{2}, \tag{4}\]  
where \(x_{i}\) is the targeted output, and \(y_{i}\) is the output predicted by MNN. In this paper, the Levenberg-Marquardt based backpropagation algorithm is used for training, in which the information is propagated to the network in a backward manner in order to adjust the weights and minimize the cost function [28].  
In addition to the neural network coefficients (parameters) optimization, it is important to adequately choose the number of neural network layers \(K\) and the number of neurons in each layer \(N^{k},k=0,\ldots,K-1\) (hyperparameters).  
Figure 2: Multilayer neural network architecture (\(N^{0}\) inputs, \(K-1\) hidden layers and \(1\) output).
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 2 Introduction to Performance Assessment and Fault Warning Methods
### Fault Early Warning Method
#### 2.4.1 Cumulative Anomaly Curve Method  
The cumulative anomaly method calculates cumulative anomaly values for each temporal node, generating a time-indexed curve that visually represents trend variations. The curve's inflection points, determined by slope direction reversals (ascending-to-descending or vice versa), approximate the timing of abrupt changes in the target time series [25]. For a given time series \(X\), the cumulative anomaly at time \(t\) is defined as  
\[x_{t}=\sum_{i=1}^{t}\left(x_{i}-\overline{x}\right) \tag{6}\]  
In the equation, \(\overline{x}\) denotes the mean value of series \({}_{x}\).
## 3 Method: Automated Target Detection
### Morphological Operations  
The basis for deriving the water masks is the GSW dataset introduced in Section 2.3. This is transformed into one binary land-water mask by defining a threshold value of 50%. This value will ensure the extraction of a mean surface area for permanent lakes/reservoirs with seasonal changing areas. Moreover, targets that are only temporally flooded will also be detected as well as reservoirs, which are created within the observation period of GSW; i.e., after 1984 but before about 2000. Later, masks for different water occurrences are derived (Section 3.2) and their impact is studied in Section 5.2.  
The binary input mask is subject to three different morphological operations from image processing: erosion, dilation and closing [28]. To define the effect of the operators on the image, quadratic kernels are used as structuring elements. First, an erosion is performed on the binary input mask. This is done to remove small-scale water bodies such as rivers, ponds and very small lakes. For these small-scale waters, it is difficult to infer satellite altimetry water level time series with sufficient data quality because of the influence of land (see Section 5.4). On the other hand, their removal speeds up the processing significantly. The kernel diameter is set to 21 pixels (approximately 630 m, depending on the geographic latitude), leading to the removal of all water bodies that are consistently narrower. The effect of this step is shown in Figure 2: Figure 2a displays the original binary mask and Figure 2b shows the resulting mask after the erosion was performed. Subsequently, the inverse operation, called dilation, is performed with a kernel size of approximately 4830 m (161 pixels), indicated by the grey area in Figure 2c. In order to preserve the original water body outlines, the result is overlain with the initial binary mask. Only pixels that are marked as water in both masks will be regarded as water in the resulting land water mask; i.e., the intersection between both is taken (called conjunction in Figure A1). This intersection is marked in white in Figure 2c. In the last step, a closing operation with a kernel size of about 390 m (13 pixel) is applied. This operation removes small islands and bridges. This allows two parts of a water body that are separated by bridges, for example, to be united (see Figure 2d.)
# Required Evidence for Clinical Applications of Liquid Biopsy Using Especially CTCs in Lung Cancer
## 3 Liquid Biopsies
### Circulating Cell-Free DNA  
Besides CTC, DNA also circulates in the bloodstream, either by active secretion or as a waste product from decaying or apoptotic cells [50]. In the bloodstream, circulating cell-free DNA (cfDNA) from healthy body cells is mixed with small amounts of circulating tumor DNA (ctDNA). Recently, sensitive next-generation sequencing methods have been developed that are able to accurately measure low mutant allele frequencies in plasma [51]. This development makes single-gene tests obsolete, though digital droplet PCR may still be usable for the detection of minimal residual disease in patients with known mutations. Mutations in DNA detected in the plasma show a strong correlation with the presence of mutations in the primary tumor [31, 51]. When e.g., EGFR, ALK, BRAF, ROS1, RET aberrations are detected in plasma, this information can be used for treatment decision without the need for a tumor biopsy [32, 52]. When the mutation is present in the plasma sample, the outcome is almost similar compared to those measured in biopsies. The role of ctDNA and immune-related mechanisms is evolving as a tumor mutational burden and genes related to immune resistance such as STK11 and KEAP1 which can be measured from plasma ctDNA.
## 7 System Performance and Evaluation
### Information Sharing Web Page  
In this system, Java 2 Enterprise Edition (J2 EE), support application platforms, e-Science platforms, and Web 2.0 applications were utilized for the system interface (described in Section 4.3). Moreover, java data base connectivity (JDBC) was used for the I/O data. Figure 10 shows a screen shot of standard monitoring at different SNs. Figure 11 shows how the Web interface switches to turn ON/OFF two actuators of ventilation fans remotely via a computer. The main server easily detects environmental conditions by comparing present readings with threshold limits values for the initial assessment of the serving state of the mine. The warning is in the form of emergency lights and alarms; therefore, some of the nodes were equipped with alarms and warning lights.  
Figure 9: (**a**) Changes in receive signal strength index according to distance and (**b**) the distance error for various nodes.  
Figure 11: Screen shot of the designed computer interface for ON/OFF switching of auxiliary fans.  
Figure 10: Screen shot of sensing data and information sharing based on IoT.
# A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras
## 4 Experimental Results
### Experimental Settings  
Our experiments applied the proposed model using ResNet50 plus ConvLSTM, which are included in Keras library. We utilized several hyperparameters for tuning the model to achieve the best result of our experiments. Table 3 compares the results of our experiments with different values for using data augmentation or not as well as the type of initial weight and optimizer. Consequently, we applied data augmentation on our dataset, and utilized glorot_uniform for initial weights and RMSprop as an optimizer in our experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Binary** & **No. Videos** & **Ucfcrimes** & **No. Videos** \\ \hline Abuse & 50 & Abuse & 50 \\ Arrest & 50 & Arrest & 50 \\ Arson & 50 & Arson & 50 \\ Assault & 50 & Assault & 50 \\ Burglary & 100 & Burglary & 50 \\ Explosion & 50 & Explosion & 50 \\ Fighting & 50 & Fighting & 50 \\ RoadAccident & 150 & RoadAccident & 50 \\ Robbery & 150 & Robbery & 50 \\ Shooting & 50 & Shooting & 50 \\ Shoplifting & 50 & Shoplifting & 50 \\ Stealing & 100 & Stealing & 50 \\ Vandalism & 50 & Vandalism & 50 \\ Normal & 950 & Normal & 50 \\ \hline Total & 1900 & Total & 700 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of videos for Binary and Ucfcrimes datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**4 MajCat** & **No. Videos** & **NREF** & **No. Videos** \\ \hline Theft & 150 & RoadAccident & 30 \\ (Burglary, Robbery, Shoplifting, Stealing) & & & \\ Vandalism & 150 & Explosion & 50 \\ (Arson, Explosion, RoadAccident, Vandalism) & & & \\ Violence behaviours & 150 & Fighting & 70 \\ (Abuse, Arrest,Assault, Fighting, Shooting) & & & \\ Normal & 150 & Normal & 150 \\ \hline Total & 600 & Total & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of videos for 4 MajCat and NREF datasets.  
Furthermore, we used glorot_uniform for initial weights and RMSprop as an optimizer in our experiments. Moreover, the learning rate of our model is set to \(10^{-4}\). The epochs are also set to 50, but the code stops once the loss function convergences and does not make more sense when it continues. It means, after each epoch, it calculates loss function, so if the difference of loss functions of two subsequent epochs is less than the tolerance value, the process stops, and the last accuracy will be the result. We also need to define the number of frames we take from each video file. We set this sequence length to 20 in our experiments. Regardless of its size, each video file is divided into 20 frames by considering the same interval of skipped frames.
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 3 Importance of Carbon Nanomaterials for the Corrosion Mitigation
### Carbon Fibres for Space Applications  
Carbon fibres and nano-fibres represent one more class of carbonaceous materials that is currently under consideration for the application in space technology and, particularly, for spacecraft protection. Figure 14a,b illustrate the use of carbon nanofiber-reinforced composites for aerospace applications, where fibres are added to enhance mechanical properties of the material and reduce the likelihood of their fracture as a result of collision impact or significant temperature fluctuations. The microstructure of the material after carbon coating followed by the reduction (Figure 14a) does not clearly confirm the presence of nanofibers, while the photograph of the surface of the fracture clearly shows carbon nanofibers reinforcing the top-most layer of the structure. It is worth noting that carbon nanofibers used for the reinforcement are not very resistant to atomic oxygen, and so, additional protection could be needed for the nanofiber-reinforced materials, as was recently demonstrated by [PERSON] et al. (2019). In their study, a carbon fibre-reinforced polymer-based moisture and outgassing barrier material system was designed, and when uninterrupted process of fabrication was implemented, the coating without any delaminations and holes was obtained (Figure 14c,d).  
Figure 14: Top panel: Carbon nanofiber-reinforced composites for aerospace applications. (**a**) Illustrates the microstructure of the material after carbon coating followed with the reduction. (**b**) Surface of the fracture with the carbon nanofibers clearly visible. Reprinted with permission from [PERSON] et al., 2010 (2019) & [PERSON]. Bottom panel: Atomic oxygen and UV protection for carbon fibre composite materials. (**c**) The focused ion beam was used to prepare the cross-section of the carbon fibre-reinforced polymer-moisture and outgassing barrier material system. The top protective layers delaminated due to interruption of the process in vacuum (shown in the blue ring on the photo). The red circle shows the pinholes by the atomic oxygen exposure. (**d**) same view of the focused ion beam cross-section made in the uninterrupted process, without any delaminations and holes. Reprinted with permission from [PERSON] et al., 2021 (2021) & 2021 ACS.