esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Overview of Roadside Surveillance Systems  
With the advancement of technology, roadside surveillance systems have developed significantly in terms of object detection, tracking, classification and behaviour analysis, thus improving accuracy and reliability. Surveillance refers to the processes of focusing systematic and routine attention on certain human behaviours for influencing, managing, protecting or directing purposes [4]. This may entail observation from a distance by means of electronic equipment, such as closed-circuit television cameras (CCTV) or interception of electronically transmitted information, such as internet traffic or phone calls. Surveillance is frequently utilized by governments for intelligence gathering, crime prevention and investigation, or for the protection of a process, person, group or object. For example, traffic surveillance by video cameras is common, using cameras installed in many different locations, e.g., mounted along the highways to monitor real-time traffic situations, thus controlling traffic flow and facilitating traffic management by governments. According to [PERSON] and [PERSON] [5], roadside surveillance systems are utilized to observe the roadside activities and behaviours of vehicles and road users, and they include moving object detection, recognition, classification, tracking, behavioural analysis and retrieval. This enables real-time monitoring of persistent and transient objects within a specific environment. By applying advanced information and communication technologies, roadside surveillance systems are able to solve transport problems such as road hazards and traffic congestion and, ultimately, they can help to achieve transport efficiency [6]. Smooth traffic conditions are crucial to road users and the public, and traffic congestion can lead to a range of negative effects. According to [PERSON] [7], traffic congestion is very costly and has an obvious negative effect on productivity, fuel consumption, environmental quality and quality of human life. The effects of traffic congestion include, but are not limited to, delays in journeys, increasing air pollution and carbon dioxide emissions, blocking of emergency vehicles and spillover effects from congested roads to other roads. For instance, traffic congestion may delay the schedules of truck drivers' loading and unloading activities, thus affecting the management of logistics companies. Some vehicles even circle the road network to wait for available parking spaces. This wastes fuel and energy. Therefore, the ultimate objective of roadside surveillance systems is to facilitate traffic and fleet management, since traffic congestion, accidents and low traffic efficiency result in a waste of resources [8].
## 3 Results and Discussion
### Machine Learning Based Optimization in Designing the Model  
The manual hyperparameter designing in a model has a few limitations such as the dependency of knowledge and experience of the users, time consuming to find the optimal hyperparameters, and difficulty in handling huge data when the number of parameters and their range of values increase. The users require the capacity to identify the relations between the hyperparameters and the results obtained through visualization tools [37]. In order to overcome these limitations, machine learning based hyperparameter optimization has been used in recent researches. One among them is bayesian optimization [38]. This  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mini-Batch Size** & **Accuracy (\%)** \\ \hline
1 & 85.16 \\
16 & 85.95 \\
32 & 88.04 \\
64 & 90.73 \\
128 & 87.12 \\
256 & 86.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation of proposed model for various mini-batch sizes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Learning Rate** & **Accuracy (\%)** \\ \hline
0.000001 & 85.16 \\
0.00001 & 87.06 \\
0.0001 & 88.04 \\
0.001 & 90.73 \\
0.01 & 78.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation of proposed model for various initial learning rates.  
was used in our experiment and the initial learning rate and batch size were set to 0.0006 and 64, respectively. The number of epoch was set to 82.  
The performance evaluation of the model with manual selection and machine learning based optimization is given in Table 5. It is observed from Table 5 the performance of the model is increased with machine learning based optimization. Hence, this result is used to compare with the other models.
## 2 Height Factor Model for GNSS Tomography
### Construction of the Height Factor Model
#### 2.3.2 Anisotropic Component of SWD for Inside Signals
Similarly, the anisotropic gradient delay of inside signals can be written in the following equation:  
\[SWD_{anisotropic}=\lambda_{aniso}(h)\cdot\Delta L_{G}^{integral}, \tag{16}\]  
where \(SWD_{anisotropic}\) is the anisotropic delay component of inside signals on the same ray path with the observed signal, and \(\Delta L_{G}^{integral}\) represents the anisotropic delay component of the observed signal. As a consequence, the SWD of inside signals can be computed with  
\[ \begin{split} SWD_{inside}&=SWD_{isotropic}+SWD_{anisotropic }\\ &=\lambda_{iso}(h)mf_{w}(\varepsilon)ZWD_{total}+\lambda_{aniso}(h )\Delta L_{G}^{integral}(\varepsilon,\alpha).\end{split}  \tag{17}\]  
In this work, \(ZWD_{total}\) and \(\Delta L_{G}^{integral}(\varepsilon,\alpha)\). were calculated with the ZTD, \(G_{NS}\), and \(G_{EW}\), respectively, which were estimated by GAMIT/GBLOK(v.10.7) [41]. The SWD of inside signals could be converted into SWV using Equation (3) for the improved tomography model.
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 4 Method
### Handling Binary Document Images  
The processing of binary document images has four main phases: training, pseudo-RGB image generation, thresholding, and pixel label extraction (Figure 2). In the first phase, a CNN is trained to determine the similarity between a pair of patches. After training, the CNN is used as an encoder that embeds the patches into feature vectors. The feature vectors can be viewed as semantic features extracted by the CNN describing the text lines, however, they are unable to be read by humans since they are encoded in a learned feature space. In the second phase, the feature space of the patches extracted in the first phase is analysed using PCA, and a pseudo-RGB image is generated from the first three principal components of the features. By thresholding the pseudo-RGB image, we obtain the binary blob lines that pass through the main body of the text lines. The image of the blob lines is inputted to the extraction phase, where the text lines' pixels are labeled using an energy minimization function, which is assisted by the blob lines obtained from the previous step.
## 4 High-Speed PMSG Characteristics with Inconel 718 Shaft Material  
### Electromagnetic Characteristics  
Previously, PMSG using the STS420-J2 shaft material was confirmed to have a problem in that the requirements were not satisfied because of the inductance leaking into the shaft as it was transformed into ferromagnetic characteristics during the heat treatment process of the rotating body. Therefore, a design that satisfies the requirements was performed using Inconel 718, which has a non-magnetic characteristic for the shaft material. The magnetic flux leakage through the shaft after the heat treatment of the rotor was confirmed through FEM. This is shown in Figure 6, and it was confirmed that magnetic flux leakage through the shaft hardly occurred.
## 4 Conclusions  
The obtained results show that significant changes occurred during the 21 days of dry-aging of beef for both groups. The pH value increased from 5.67 to 5.80, the protein content changed significantly, from 17.57% to 22.37%, the fat content from 17.92% to 21.55%, and the collagen content from 1.12% to 1.54%. The significant changes in protein, fat, and collagen content were due to moisture loss during aging. The results also showed that the beef dry-aged for 21 days with _M. flavus_ biostarter were characterized by lower shear force  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Medium** & **Survival in Control** &
\begin{tabular}{c} **Survival after Biosarter** \\ **Usage** \\ \end{tabular} \\ \hline Chromogenic Listeria & 100\% & 100\% \\ PALCAM & 90\% & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Survival of _Listeria monocytogenes_ ATCC13932 on Chromogenic Listeria and PALCAM media (in % of plates on which the growth was observed).  
Figure 3: Mean number of bacterial colony-forming units (CFUs) developed on the surface of dry-aged beef over time, during the 21 days of the dry-aging process, when seasoned with _M. flavus_ biostarter (_Mf_) and without it (control).  
after grilling, with significant differences. The samples with biostarter were characterized by significantly higher flavor liking and better overall acceptability.  
Based on the analyzes carried out regarding the profile of volatile compounds occurring in meat samples, several compounds were present only in the meat samples aged with the addition of _M. flavus_, e.g., 1-tetradenol, 2-nonenal, trans-2 undecenoic acid, and the following esters: formic acid, hexyl ester, 10-Undecenoic acid, methyl ester, 4-methyl-pentanoic acid, and methyl ester. This may indicate that they are a specific marker of dry-aged meat with the addition of _M. flavus_. However, further research on the dry-aging of other types of meat is needed to confirm this observation.  
Finally, the presence of the biostarter had no significant effect on the number of bacteria or the survivability of _L.monocytogenes_ on the meat's surface in laboratory conditions.
# Roman Urdu Sentiment Analysis Using Transfer Learning
## 2 Literature Review
### Deep Learning-Based Sentiment Analysis  
CNN, RNNs, RAEs, and DBNs are only a few of the DNNs that have been frequently employed for sentiment categorization [26] as a response to the limitations of standard machine learning methods. After applying the GRU to the recursive model and the tree structure of the LSTM, [PERSON] et al. [27] proposed a tree structure gated RNN. In addition, [PERSON] et al. [28] performed sentiment analysis using an LSTM network with several complex units. They conducted more tests on a bidirectional LSTM with two layers and achieved encouraging results. [PERSON] et al. [29] pursued a related line of questioning by evaluating a single-layer CNN extensively. They used Word2 Vec vectors to train the models.  
Multi-channel representation and variable-sized filters both produced the same outcomes. [PERSON] et al. [30] introduced an improved CNN at the character level, which was used to classify texts. Using word embedding, [PERSON] and [PERSON] [31] presented a multi-channel variable-size convolutional neural network. [PERSON] et al. [32] developed a dynamic CNN with k-max pooling. Their model correctly processed input phrases with varying durations, and it accurately captured both short-term and long-term dependencies. Words and phrases can be represented in a matrix and vector in a tree structure, as proposed by [PERSON] et al. [33]. [PERSON] et al. [34] proposed Recursive Neural Tensor Network (RNTN), and it employs tensors as compositional matrices for all tree nodes. Using convolutional and RNN, [PERSON] et al. [35] proposed a method for sentiment classification. They employed multi-view learning to categorize the intermediate features generated by convolutional and recursive neural networks [36]. [PERSON] et al. [37] combined Long-Short-Term Memory with a conditional random field layer to improve sentence-level sentiment analysis (BiLSTM-CRF). The simulation results on the benchmark datasets showed that their system improved phrase-level sentiment analysis. [PERSON] et al. [38] recommended using CNN and LSTM to infer polarity from IMDB movie reviews. This method's CNN classifier had two convolutional and two pooling layers. [PERSON] et al. [25] used CNN and bidirectional Long-Short-Term Memory to determine movie review polarity (BLSTM). The CNN-LSTM model beat CNN and LSTM classifiers. [PERSON] et al. [39] present a CNN-LSTM attention-based model (named ACL-SA). First, CNN's max-pooling is used to extract contextual features and minimize feature dimensionality. LSTM is used to capture long-term dependencies. [PERSON] et al. [40] used two deep learning algorithms, CNN and LSTM, with SVM for Persian movie reviews. [PERSON] et al. [41] collected COVID-19-related comments in the Albanian language. They incorporated the attention mechanism with the DL model to capture the semantic and global context of words for SA.  
DNN has produced significant results in SA; however, there is always room for improvement.
# Multipath Interference Impact Due to Fiber Mode Coupling in C+L+S Multiband Transmission Reach
## 4 Transmission Reach Assessment
### Transmission Reach Assessment Without MPI
It can be observed that, as expected, the GSNR for all three bands decreases with the number of spans, which is in accordance with the behavior found in [30]. Also, the L-band has a superior GSNR in comparison with the C- and S-bands, mainly due to the ISRS power transfer [5]. The C- and the S-bands have a GSNR with, respectively, less than 1.1 dB and 5.7 dB in comparison with the L-band's GSNR. In [5], similar results are obtained for a single span. Considering QPSK and 16-QAM signals and the fact that the practical required optical signal-to-noise ratio (ROSNR) for these two modulation formats is given in Table 3, for a BER equal to \(2\times 10^{-2}\)[31], the maximum transmission reach can be assessed as described in the fourth step of our procedure. As observed in Table 3, there is a 7.1 dB difference between the ROSNR in the reference bandwidth and the RSNR in the signal bandwidth, which comes from \(10\log_{10}(64\text{GBaud}/12.5\text{GHz})\). Note that in our computations, the GSNR in Equation (4) is computed in the signal bandwidth. The maximum transmission reaches for  
Figure 5: GSNR as a function of the channel frequency. Figure 6: GSNR per band as a function of the number of spans. QPSK signals are, respectively, 33, 25, and 8 spans, for the L-, C-, and S-bands. Likewise, for 16-QAM signals, the maximum transmission reaches are, respectively, 5, 4, and 1 spans for the L-, C-, and S bands. The C-band maximum reach is in line with the literature results, e.g., [32].
## 3 Results and Discussion  
### Calibration and Validation of SDSM  
Figure 5 depicts the simulated results for average monthly temperature. It can be seen that these results are in good agreement with field observations. This substantiates the efficacy of the proposed model for temperature simulation and climate projection in the context of temperature change. However, the simulation and projection of precipitation were found slightly challenging for the proposed model. The reason is that during the conditional setup of SDSM for precipitation, the intensity of local precipitation depends on the wet-dry period, which in turn relies on regional predictors like atmospheric pressure and air humidity [48]. This also explains why previous studies [54] show low-performance simulation outputs for precipitation of different regions. Besides the exception of some peaks, the monthly and seasonal mean and maximum simulated precipitation are consistent with the trends in observed data (Figure 6).  
Figure 4: Schematic diagram for the HEC-HMS model setup.  
Figure 5: SDSM calibration and validation using NCEP and GCMs predictors, respectively, for monthly mean temperature: (**a**) calibration and (**b**) validation.  
Figure 6: SDSM calibration and validation using NCEP and GCMs; For mean and maximum precipitation.
## 4 Results
Fourier series and integral Fourier transform are the basis of harmonic signal analysis. However, when analyzing ECG signals, these transformations do not provide the possibility of analyzing peaks, understanding the local properties of the signal and its frequency characteristics. Therefore, for these purposes, we used the characteristics of the Fourier spectrum. Instantaneous frequency calculates a spectrogram using short-term Fourier transforms versus window time. Spectral entropy estimates entropy based on a power spectrogram. The time output of the function corresponds to the center of the time windows. The selected ECG signals from the CinC Challenge database were divided into signals for training and signals for testing in a percentage ratio of 90:10. For training, the architecture of the LSTM neural network was assembled. The network consisted of two input layers, to which preprocessed signals were applied, and one hundred hidden recurrent layers. Preprocessing of signals using a symlet and subsequent application of spectral analysis functions made it possible to reduce the length of ECG signals to 255 HC. Table 3 presents the results of modeling various methods for detecting atrial fibrillation. The matrix of inaccuracies as a result of training the proposed system of neural network determination of atrial fibrillation from ECG signals is presented in Figures 10 and 11. The best indicator of the accuracy of ECG signal recognition was obtained using the method proposed in the work and amounted to 87.5%. This accuracy was obtained in the first simulation using 976 ECG signals. The indicator of the accuracy of ECG signal recognition during the second simulation was 87.4%. This result is identical to that obtained in the first simulation. The smallest indicator of recognition accuracy was \begin{table}
\begin{tabular}{c c} \hline
**Wavelet** & **Learning Outcome, \%** \\ \hline symlet 2 & 66.1 \\ symlet 3 & 71.0 \\ symlet 4 & 78.0 \\
**symlet 5** & **87.5** \\ symlet 6 & 82.1 \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation of the proposed method using a wavelet symlet with different coefficients. Figure 9: An example of a five-level decomposition of an ECG signal based on a symlet.
# Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites
###### Abstract  
Electric propulsion is of great significance to the development of high-efficiency and long-life satellites, and digital twins have gradually become a powerful tool for satellite engineering. Being affected by uncertainty factors such as the complexity and variability of the space environment and the satellite system, the digital twin model cannot accurately reflect the real physical properties. Therefore, it is crucial to update the satellite model to improve prediction accuracy. However, the complex structure and multi-physics process coupling of vector electric-propulsion satellites bring great challenges to model updating. According to the characteristics of the vector electric-propulsion satellite, this paper establishes mathematical models of the whole satellite. Additionally, a hierarchical model updating method is proposed and applied to the model updating case of a satellite with multiple subsystems. The simulation results show that the method is suitable for the model updating of the vector electric-propulsion satellite. Through multiple iterations of closed-loop cycles, the residual errors between the simulation values and the telemetry values can be decreased, and the errors between the estimated values and the true values of state variables can also be decreased by an order of magnitude.  
vector electric-propulsion satellite; digital twin; uncertainty quantification; hierarchical model updating +
Footnote †: journal: Applied  
Article
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 4 The Node Placement Algorithm
### Direct Node Placement Algorithm  
The second algorithm proposed is the Direct Node Placement algorithm, referred to as Direct MobileCC. Similar to the Dynamic MobileCC algorithm, it does not replace any existing topology control, congestion control, or routing algorithms but runs alongside them. The difference between the two algorithms relies on the use of the mobile nodes. While the Dynamic MobileCC uses a mobile node for each problem occurrence, the solution is local; the Direct MobileCC creates a completely new and direct (disjoint) alternative path of mobile nodes towards the sink. This solution provides a faster establishment of the connection to the sink node. As our experimental evaluation shows (cf., Section 6.3.1), this helps to reduce the number of dropped packages, trading, however, use of resources (and hence, energy).  
Initially, the Direct MobileCC algorithm calculates the position of the first mobile node placed in the network with the use of the Dynamic MobileCC algorithm. Then, a direct line is created that starts from the first placed mobile node and ends at the sink. This line is filled with additional mobile nodes until a direct connection with the sink is achieved (see Algorithm 2 and Appendix A.2).  
Its operation is based on the following functions:  
* Calculation of the position of the first mobile node using the Dynamic MobileCC algorithm.
* Creation of a path consisting of mobile nodes, starting from the first mobile node that was placed from the previous function and ending at the sink.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 3 Methods
### Random Waypoint UAV Mobility Model  
The random waypoint mobility model is widely adopted in simulation environments due to its simple approach and easy accessibility. This model employs two main parameters, pause time and velocity; both are inversely proportional to each other.  
Let us suppose that the starting \(I_{th}-UAV\) has a constant velocity chosen value from \(\left[0,~{}V_{max}\right]\), and the velocity and direction of the UAV vary until it travels from location \(L_{0}\) to \(L_{1}\). After reaching \(L_{1}\), the UAV has some pause time, \(T_{p=1}\); as mentioned above, the velocity and pause time have an inverse relationship. Therefore, different mobility scenarios are generated by changing the velocity of UAVs accordingly [43]. The waypoints or stop locations can be drawn from the uniform density function as below:  
\[UAV(I_{th})=\left\{\begin{array}{ll}\frac{1}{\lambda},&\quad a\in A\\ 0&\quad otherwise\end{array}\right. \tag{5}\]  
Here, \(A\) is area of the network, and \(a\) is an area of location where the UAV exists; hence, \(a\) is the location vector drawn from the uniform distribution belonging to \(=U(A)\). The location vector is evaluated as:  
\[a=\Delta x+\Delta y+\Delta z \tag{6}\]  
Here, \(\Delta x\), \(\Delta y\), and \(\Delta z\) represent the change in each coordinate that occurs in the position of the UAV. Moreover, the movement of UAVs consists of range \(\left[V_{min},~{}V_{max}\right]\) and can be calculated from a discrete distribution as:  
\[f(V)=\left[V_{min},~{}V_{max}\right] \tag{7}\]  
The UAVs are moving with different velocities; therefore, it is necessary to calculate velocity variation, which is described as a change in distance per unit of time:  
\[V_{x}=\frac{\Delta x}{\Delta t},~{}~{}V_{y}=\frac{\Delta y}{\Delta t},~{}~{}~ {}V_{z}=\frac{\Delta z}{\Delta t} \tag{8}\]Based on the above equation, total velocity change can be calculated by simply adopting the Euclidean formula:  
\[\Delta V=\sqrt{{V_{x}}^{2}+{V_{y}}^{2}+{V_{z}}^{2}} \tag{9}\]  
The relative speed between two UAVs can be captured by utilizing the basic equation of random waypoint mobility [44]:  
\[RS(UAV_{A},UAV_{B})=|V(t)_{A}-V(t)_{B}| \tag{10}\]  
Here, \(V(t)_{A}\) and \(V(t)_{B}\) are relative velocities of both UAVs with respect to time. Finally, the relative speed of all UAVs present in the network is evaluated in the mobility metric, which describes the speeds of UAVs as follows:  
\[\overline{MM}=\frac{1}{|UAV_{A},\quad UAV_{B}|}\sum_{A=1}^{N}\sum_{B=A+1}^{N} \frac{1}{T}\int_{0}^{T}RS(UAV_{A},UAV_{B})dt \tag{11}\]  
Figure 2 shows the mobility movement of a UAV in a 3D network at different locations at different time intervals. After generating the mobility model in the UAV ad-hoc network, the next step is the selection of PHC-UAVs, explained in the following section. PHC-UAV