esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 3 Proposed Method  
The proposed method is executed based on the supervised deep-learning pipeline for classifying images. There are five steps, starting from the printing process, where the QR code is first generated from digital files. Then, all the QR code documents are arranged to be printed in the same batch. Next, as its name suggests, all the printed QR code documents are scanned in the scanning process, which is also called the digitalization process. QR code extraction is where the scanned documents are thrown into a program, and the QR codes are automatically cropped and saved individually. In dataset preparation, all the cropped QR codes are split into 16 smaller image blocks (Figure 5) before being fed into CNN models during the Run Model process. Only 4 blocks are shown because of limited space (all 16 image blocks are used in the actual training).  
The diagram in Figure 5 illustrates the process for the deep learning printed source identification of the QR codes.  
1. Printing Process: * BW QR Codes are printed on 11 printer models (Table 1). * Color QR Codes are printed on 8 printer models (Table 2).
2. Scanning Process: The printed QR codes are digitalized using a dedicated office scanner, and all the documents are saved in TIF lossless image format. BW QR code uses 8-bit grayscale format, while color QR code is saved in 24-bit RGB format.
3. QR code extraction: Using MATLAB, the _regionprops_ and _incorop_ functions are combined to automatically crop and resize all 24 QR codes on one page. This process is looped 24 times from the top left to right.
4. Dataset preparation: An open-source image viewer, XnView MP, is implemented to divide all extracted QR codes into 4 \(\times\) 4 image blocks by batch. Moreover, all BW QR code images are converted from 8-bit format to 24-bit format, as required by the pretrained models. The cropping process does not affect the image quality because the image is neither reconstructed nor resized.
5. Run model: The CNN model is operated to put a new set of images into categories.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Brand** & **Model** \\ \hline
1 & Canon & LBP3310 \\ \hline
2 & Canon & LBP6300 \\ \hline
3 & Canon & M417 LIPSX \\ \hline
4 & Epson & L350 \\ \hline
5 & Epson & L3110 \\ \hline
6 & Fuji Xerox & P355d \\ \hline
7 & HP & M283 \\ \hline
8 & HP & M377 \\ \hline
9 & HP & M401 \\ \hline
10 & HP & M425 \\ \hline
11 & Konica Minolta & Bizhub C364 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Printer and Brand Model List (BW QR Code).  
Figure 5: Proposed method’s process flowchart.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 5 Type of Suspension Systems  
There are three types of suspensions used to minimize undesirable vibration in the driver-seat structure system, namely, passive, semi-active, and active. However, some types of these suspensions fail to reduce the vibration under certain frequency ranges due to the high cost and excessive energy consumption [39]. All the above-mentioned suspension types can be operated by either hydraulic, pneumatic or electrical actuators. Passive suspension system is a conventional vibration isolator, which can attenuate the vibration only at a certain frequency range specifically in the high frequency range [40]. However, it fails to reduce the vibration at the low frequency domain practically from 1 to 10 Hz. It mainly consists of spring and shock absorber, which is well known as a damper. The spring stiffness and damping coefficient should be optimized properly to provide ride comfort in the high frequency range. An actuator can be added to the spring and damper to convert the passive suspension into an active suspension system, or a variable damper such as MR damper with a feedback control system can replace the conventional damper to form a semi-active suspension system.  
Semi active suspensions comprise of a shock absorber that is essentially used to regulate and control the suspension damping by adjusting its parameters [41]. The required control force can be obtained according to the adjustable shock absorber position and its damping force. The MR and ER semi active suspensions are the most popular dampers being used recently because they work effectively to provide a quick response for energy dissipation level in the low-mid frequency range. However, a poor performance can be generated in the high frequency rang due to the specific dynamic range of the ER and MR fluid [42; 43].  
Active suspension system is an actuator-based system, which can reduce the seated human body vibration [44]. It is a computer-controlled suspension system that takes advantage from both mechanical and electronics aspects. Operationally, the sensors send the signals into the controller, which produces a control force signal fed into the active suspension system. The active suspension actuator then generates an opposite force that eliminates the spring deformation. In the active seat suspension, the actuators provide an energy which allows the seat to move vertically with respect to its base. It provides a good vibration attenuation regardless the road disturbances and noise variation. Figure 3 displays the type of suspension systems.
## 4 Materials and Methods
### Lag Analysis  
Whereas part of the precipitation is absorbed by vegetation, the remaining precipitation either becomes surface runoff or is preserved in the soil after permeation, both of which still constantly contribute to vegetation growth. Therefore, only analyzing the effect of a fixed period of precipitation on vegetation productivity cannot accurately reflect the relationship between the two. To solve this issue, this study adopted lag analysis to quantify the lag in the response of vegetation growth to changes in precipitation.  
Previous studies generally found the time lag at the monthly scale of vegetation response to climate to be short [43; 44; 45]. Therefore, we selected the cumulative lag month and time lag to quantitatively measure the lag in the response of the vegetation NPP to precipitation change. Subsequently, we calculated the correlation coefficient between the two factors. Starting from the vegetation NPP in a certain month, the number of previous months when the cumulative precipitation value was recorded increased successively, which was defined as the cumulative lag month. In this study, as the water absorbed during vegetation growth not only included the precipitation from the current month but also the previous several months, the delay in the month was defined as the time lag (lagx).  
Vegetation growth in Northeast China mainly occurs from May to September (growth season), when the time lags were also the most significant. This study calculated the lagged correlation coefficient between the vegetation NPP and precipitation in the growing seasons. The month of July is used below as an example to describe the specific steps of the lag analysis:  
1. Calculate the correlation coefficient between the vegetation NPP and precipitation in July between 2003 and 2012, i.e., for a lag period of 0 months (lag0) at a cumulative number of lag months of 1.  
2. Calculate the correlation coefficient between the vegetation NPP in July and cumulative precipitation in June and July between 2003 and 2012, i.e., for a lag period of 0 months (lag0) and a cumulative number of lag months of 2.
3. On the same basis, calculate the correlation coefficients of the vegetation NPP for a lag period of 0 months and the cumulative precipitation of 3, 4, and 5 months.
4. Calculate the correlation coefficient between the vegetation NPP in July and precipitation in June, i.e., for a lag period of 1 month (lag 1) and a cumulative number of lag months of 1.
5. Calculate the correlation coefficient between the vegetation NPP in July and the cumulative precipitation in May and June, i.e., for a lag period of 1 month (lag 1) and a cumulative number of lag months of 2.
6. On the same basis, calculate the correlation coefficients between the vegetation NPP in July at a lag period of 1 month and the cumulative precipitation of 3, 4, and 5 months.
7. On the same basis, calculate the correlation coefficients of the vegetation NPP in July for different lag periods with different cumulative numbers of lag months.
# Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems
## 2 Preliminary  
### Problem Formulation  
In LSAPs [4], given a set of \(n\) jobs \(I=\{1,...,n\}\) and \(n\) agents \(J=\{1,...,n\}\), we wish to minimize the cost of assigning each job to each agent. Assigning job \(i\) to agent \(j\) results in a cost value of \(U_{ij}\), and the problem can be formulated as follows:  
\[\min\sum_{i=1}^{n}\sum_{j=1}^{n}u_{ij}x_{ij}, \tag{1}\]  
subject to:  
\[\sum_{i=1}^{n}x_{ij}=1,\quad j=1,...,n, \tag{2}\]  
\[\sum_{j=1}^{n}x_{ij}=1,\quad i=1,...,n, \tag{3}\]  
\[x_{ij}\in\{0,1\},\quad i,j=1,...,n, \tag{4}\]  
where \(x_{ij}\) is the decision indicator value. \(x_{ij}=1\) when job \(i\) is assigned to agent \(j\); otherwise, \(x_{ij}=0\). We can rewrite the cost values as an \(n\) by \(n\) matrix \(\mathbf{U}=[\mathbf{u}_{1},...,\mathbf{u}_{n}]^{T}\) and decision indicator values as an \(n\) by \(n\) permutation matrix \(\mathbf{X}=[\mathbf{x}_{1},...,\mathbf{x}_{n}]^{T}\), where \(\mathbf{u}_{i}=[u_{11},...,u_{in}]^{T}\) and \(\mathbf{x}_{i}=[x_{i1},...,x_{in}]^{T}\). In the communication field, many optimization problems are similar or can be formulated as LSAP [1; 2; 3].  
In [16], the LSAP problem was firstly decomposed into multiple sub-assignment problems to implement DNNs. However, the DNNs in [16] did not address the sequential information in the cost matrix, as multiple FNN and CNN models must be applied to solve all sub-assignment problems simultaneously. Here, a single neural network model is considered to extract the necessary features to solve LSAPs.
# Optical Wireless Communications Using Signal Space Diversity with Spatial Modulation
## 3 Results
### Analysis of the Theoretical and Numerical BER Results
At a small \(\varphi\) (e.g., \(\varphi=0\)), an increasing \(d_{1}\) and a decreasing \(d_{3}\) are obtained with the growing \(\frac{h_{2}}{\rm{h_{1}}}\), where \(d_{1}\) contributes more to the BER due to \(d_{3}>d_{2}>d_{1}\). When \(\varphi\) is increased (e.g., \(\varphi=25^{\circ}\)), \(d_{3}\) begins to increase with the growing \(\frac{h_{2}}{\rm{h_{1}}}\). Still, \(d_{1}\) is much smaller and contributes more to the BER. When \(\varphi\) is further increased (e.g., \(\varphi=45^{\circ}\)), \(d_{1}\) can achieve a larger value than \(d_{3}\) and, thus, \(d_{3}\) contributes more to the BER. As \(d_{1}\) begins to decrease while \(d_{3}\) is increased with the growing \(\frac{h_{2}}{\rm{h_{1}}}\), the turning point with a local optimum BER performance occurs when the condition \(d_{1}\cong d_{2}=d_{3}\) is met. When \(d_{1}\) is further decreased to achieve \(d_{3}>d_{1}\), the BER performance is degraded, as the smaller \(d_{1}\) contributes more to the BER again. On the other hand, when the \(\frac{h_{2}}{\rm{h_{1}}}\) value is fixed, the BER performance is similar to the first channel gain setting with an increasing rotation angle. Figure 6: Results of the 2 nd channel gain setting. (**a**) BER. (**b**) Constellation distance. In brief, for the second channel gain setting, the best achievable BER performance provided by the SSD scheme is not as good as in the first setting. The SSD scheme works well when \(\frac{\mathrm{h}_{2}}{\mathrm{h}_{1}}\) is reasonably high, and the upper limit is not only set by the channel gain imbalance required for SM, but also set by the condition \(d_{1}\cong d_{2}=d_{3}\). Besides, the BER performance of the SSD scheme can also be improved at a larger signal constellation rotation angle when \(d_{1}\leq d_{3}\) is met. The results show that the optimum BER improvement ranges from \(10^{-1.26}\) to \(10^{-3.0}\) with \(\frac{\mathrm{h}_{2}}{\mathrm{h}_{1}}=0.2\) and \(\varphi=30^{\circ}\). The average BER improvement ranges from \(10^{-x}\) to \(10^{-x-1.23}\) (\(1.26\leq x\leq 3.53\)), which is still over one order of magnitude.
## Nomenclature  
\begin{tabular}{l l} \(c\) & constant \\ \(C_{P}\) & specific heat (J/(kg\(\cdot\)K)) \\ \(D\) & diffusion coefficient (m\({}^{2}\)/s) \\ \(E\) & solution enthalpy (KJ/mol) \\ \(F_{,}f\) & dimensionless function of \(\eta\) and \(\zeta\) \\ \(g\) & gravity (m/s\({}^{2}\)) \\ \(\overline{h}\) & average heat transfer coefficient (W/(m\({}^{2}\cdot\)K)) \\ \(h_{fg}\) & latent heat of condensation (J/kg) \\ \(h_{so}\) & solution enthalpy of CO\({}_{2}\) (kJ/kg) \\ \(h_{x}\) & local heat transfer coefficient (W/(m\({}^{2}\cdot\)K)) \\ \(j\) & diffusive mass flux (kg/(m\({}^{2}\cdot\)s)) \\ \(L\) & length of the plate (m) \\ \(m\) & condensation mass flux (kg/(m\({}^{2}\cdot\)s)) \\ \(M\) & molar mass (g/mol or kg/mol) \\ \(P\) & pressure (Pa) \\ \(Par\) & parameter depending on \(T\) and \(P\) \\ \(Pr\) & Prandtl number \\ \(P_{si}\) & steam partial pressure (Pa) \\ \(q\) & heat flux (W/m\({}^{2}\)) \\ \(R\) & fugacity coefficient of CO\({}_{2}\) \\ \(S\) & solubility of CO\({}_{2}\) (kg/kg water or g/kg water) \\ \(Sc\) & Schmidt number \\ \(T\) & temperature (K) \\ \(u\), \(v\) & velocity (m/s) \\ \(U\) & inlet velocity (m/s) \\ \(x\), \(y\) & direction or coordinate (m) \\
**Greek symbols** \\ \(\alpha\) & mass fraction of CO\({}_{2}\) \\ \(\delta\) & film thickness (mm or m) \\ \(\eta\) & dimensionless liquid coordinate \\ \(\theta\) & dimensionless temperature \\ \(\mu\) & dynamic viscosity (Pa-s) \\ \(\zeta\) & dimensionless gas coordinate \\ \(\
u\) & kinematic viscosity (m/s\({}^{2}\)) \\ \(\rho\) & density (kg/m\({}^{3}\)) \\ \(\varphi\) & concentration function \\ \(\psi\) & stream function \\
**Acronyms** \\ CG & condensable gas model \\ \(\Delta HTC\) & average HTC deviation \\ \(\Delta NDT_{i}\) & deviation in non-dimensional \(T_{i}\) \\ \(\Delta NDW_{i}\) & deviation in non-dimensional \(W_{i}\) \\ NCG & non-condensable gas \\ NG & non-condensable gas model \\
**Subscripts** \\ cal & iterative calculation value \\ \(i\) & liquid and gas interface \\ \(l\) & liquid phase \\ \(w\) & wall \\ \(v\) & gas phase \\ \(x\) & local parameter \\ \(\infty\) & infinity \\ \(0\) & pure steam \\ \end{tabular}
## 3 Results
### mRNA Expressions  
The mRNA expressions with prepared NPs were analyzed with p53, Bax, and caspase-3 in HepG2 at a low concentration (25 \(\upmu\)g/mL) of GNPs-1, GNPs-2, and GNPs-3 (Figure 8), with the control gene scrutinized via qPCR at a 24 h incubation period. The recovered data disclose that the p53, Bax, and caspase-3 with GNPs-1 results were 3.8, 3.1, and 3.8, respectively (Figure 8). The obtained data authenticate that GNPs-1 exhibits the possibility of interacting with cells and their marker gene (caspase 3) and causes an up-regulation in mRNA expression. Similar observations were found in p53 and Bax, when introduced with GNPs-2 at 24 h incubation times [7,8]. Other acquired data, related to p53, Bax, and caspase-3 in HepG2 cells, at a 25 \(\upmu\)g/mL concentration of GNPs-2, disclose that the results were 3.2, 2.6, and 3.5, respectively (Figure 8). The larger-sized nanoparticles exhibit a special organization in their geometry. For instance, some are in polygonal, ellipsoidal, or circular shapes, and due to this, the surface area of the particles is reduced, as compared with the lower surface area of GNPs-1; this may cause a low up-regulation in gene expression.  
In the case of GNPs-3 (Figure 8), after reacting with marker genes p53, Bax, and caspase-3 in HepG2 cells in a 24 h incubation period, the normalization fold change was 2.9, 2.4, and 3.1, respectively, at 25 \(\upmu\)g/mL.  
In all the cases of GNPs-1, GNPs-2 and GNPs-3 with the genes p53, Bax, and caspase-3, an apoptosis in the cells was created, leading to up-regulation in gene expression. All the data related to gene expression indicate that p53, Bax, and caspase-3 activity is directly related to the affected or apoptotic cells present in a population of grown cells. Based on the literature and obtained data, we conclude that the nanostructured materials act as an inhibitor on the growth/population of cancer cells [7,8]. The apoptosis in cell influences with various parameters, such as an increase in doses of nanostructures, higher incubation periods, and the sizes of the nanostructures, can be seen as an up-regulation in mRNA expression [7,8]. In the entire experiment of gene expression with GNPs-1, a higher expression of apoptotic genes was shown in cells, compared with the other nanostructures (GNPs-2 and GNPs-3), which may be due to their higher surface areas, low density, and spherical shape morphology, facilitating easy entry into the cells and easy reaction to their organelles [7,8].  
Figure 7: Bar diagram showing percentage change in ROS generation in HepG2 cells exposed to 25–100 \(\upmu\)g/mL of GNPs-1, GNP-2, and GNPs-3.
## 3 Performance Evaluation Methods
### System Utilization  
In practical applications, when the coverage region increases, there will always be regions of recurrent detection, which wastes the hardware resources. The utilization  
Figure 1: System Coverage Rate Schematic Diagram.  
is introduced here to evaluate the redundancy of sensor networks. Increasing system coverage while maintaining a high utilization rate is still very important.  
Due to the uniqueness of the algorithm, we can observe that \(R_{S}\), the key parameter determining coverage rate, does not participate in the calculation. It is obvious that the larger \(R_{S}\) is, the larger the coverage value is, but at the same time, the area repeatedly covered by each node is also increasing. Therefore, utilization is used to evaluate the situation of repeated coverage between nodes.  
\[U=\frac{V_{a}C}{Nv} \tag{21}\]  
where \(v\) represents the range that each node can cover, \(n\) represents the number of nodes, \(C\) represents coverage rate, and \(V_{a}\) represents the maximum range covered by node distribution  
\[V_{a}=\frac{3}{4}\pi R_{a}^{3} \tag{22}\]  
\(R_{a}\) is defined in the same way as in the above subsection.  
According to the Equation (21), when the whole 3D sensor network is deployed and reaches the equilibrium state, the change of the node sensing radius \(R_{S}\) will dramatically change the size of \(U\) since \(V_{a}\) and \(v\) are proportional to \(R_{S}^{3}\). Therefore, by combining \(C\) and \(U\), we can more comprehensively evaluate the quality of the distribution. We believe that a suitable \(R_{S}\) may improve the network deployment, in which both \(U\) and \(C\) achieve a high value.
## 4 Radar System Design and Construction Details
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Component** & **Part \#** & \
\begin{tabular}{c} **Operating** \\ **Frequency** \\ \end{tabular} & **Gain** &
\begin{tabular}{c} **Noise** \\ **Figure** \\ \end{tabular} & **Power** & **VDC** & **Current** & **Supplier** \\ \hline VCO & V2555C & 9.95–10.66 GHz & – & – & +12 dBm & 12 V & 150 mA & GMI \\ Power Splitter & ZX10-2-126-S+ & 7.4–12.6 GHz & – & – & – & – & – & Mini-Circuits \\ Attenuator & FW-12+ & DC–12 GHz & – & – & – & – & – & Mini-Circuits \\ Power Amplifier & ZX60-183-S+ & 6–18 GHz & 23 dB & – & +18.5 dBm & 5 V & 260 mA & Mini-Circuits \\ Horn Antenna & WRS90 & 8.2–12.4 GHz & 10 dBi & – & – & – & – & A-InfoMW \\ Low Noise & APTMP5- & 8–12 GHz & 40 dB & 3 dB & +25 dBm 1 & 15 V & 450 mA & AmpliTech \\ Amplifier 1 & 08001200-3025-D6 & & & & & & & \\ Low Noise & ZX60-0513 LN+ & 5–11 GHz & 19 dB & 1.8 dB & +12.2 dBm 1 & 5 V & 42 mA & Mini-Circuits \\ Amplifier 2 & ZVBP-1085G-S+ & DC–40 GHz & – & – & – & – & – & Mini-Circuits \\ Frequency Mixer & IQB0618 LK & 6–18 GHz & – & – & – & – & – & Marki \\ Low Pass Filter 1 & VLF-3400+ & DC–3.4 GHz & – & – & – & – & Mini-Circuits \\ Low Pass Filter 2 & EF502 & DC–100 kHz & – & – & – & – & – & Thorlabs \\ \hline \hline \end{tabular}  
* Power output at 1-dB gain compression.  
\end{table} 
Table 4: Component specifications. Figure 11: Photograph of field portable cart-mounted radar system. Figure 10: Photograph of X-Band 10-GHz breadboard radar system.
## 2 Materials and Methods
### Earthquake Nowcasting  
Using the concept of natural time, [PERSON] et al. [58] introduced EQ nowcasting to estimate seismic hazards [29, 32, 59, 65, 66, 122, 123, 124, 125, 126, 127, 128, 129, 130]. Many applications of EQ nowcasting have been published in various fields of Earth Sciences including induced seismicity [60, 62], the temporal clustering of EQs [61], and theoretical EQ models [131], while recently the method has been applied [132] for nowcasting volcanic eruptions.  
Our group in a series of publications [124, 125, 129] has employed EQ nowcasting to obtain epicentral information for a future EQ. To this end, the EQ potential score (EPS) of EQ nowcasting is calculated [58] using the cumulative distribution function \(F(n_{s})\) for the number \(n_{s}\) of'small' EQs (of magnitude \(M\geq M_{\sigma}\)) that take place in the time interval between two 'large' EQs (of magnitude \(M\geq M_{\lambda}\)) within a large region, e.g., \(\text{N}_{23}^{\text{S2}}\text{E}_{5}^{\text{S0}}\) for the eastern Mediterranean [124]. Using a self-consistency radius \(R\), [PERSON] et al. [124] introduced average EPS maps by calculating \(n_{s}\) within a circle of radius \(R\) centered at a point \((x_{ij},y_{ij})\) of a square lattice--denoted by \((n_{s})_{ij}\)-- and averaged the resulting EPS values \(F[(n_{s})_{ij}]\) within the same radius \(R\) to obtain at a point \((x_{i_{0}j_{0}},y_{i_{0}j_{0}})\) the value  
\[\langle\text{EPS}\rangle\equiv\frac{1}{N}\sum_{i,j}^{d(x_{i_{0}j_{0}}x_{ij}x_{ ij})\leq R}\hskip 14.226378 ptF[(n_{s})_{ij}], \tag{15}\]  
where the summation is restricted to the lattice points whose distance \(d(x_{i_{0}j_{0}},y_{i_{0}j_{0}};x_{ij},y_{ij})\) from the observation point \((x_{i_{0}j_{0}},y_{i_{0}j_{0}})\) is smaller than or equal to \(R\), and \(N\) stands for the number of these points. The study of the eastern Mediterranean which is relevant to our case of the Kahramanmaras, Turkey, EQ doublet [3] resulted in an optimum value of \(R=250\) km when using \(M_{\sigma}=4.0\), \(M_{\lambda}=6.0\) leading to  
\[F(n_{s})=1-\exp\left[-\left(\frac{n_{s}}{134.9}\right)^{0.973}\right], \tag{16}\]  
when using the United States National Earthquake Information Center Preliminary Determination of Epicenter (US-NEIC PDE) catalog available from the United States Geological Survey [[https://earthquake.usgs.gov/earthquakes/search/](https://earthquake.usgs.gov/earthquakes/search/)]([https://earthquake.usgs.gov/earthquakes/search/](https://earthquake.usgs.gov/earthquakes/search/)) (last accessed on 19 May 2023); for more details, see [124].