esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Approach
### Demodulation
#### 3.4.2 Taillight State Recognition  
The ROIs for the left and the right taillights are cropped from every frame and analyzed using a simple convolutional neural network. The first step is to reshape the input image to a size of 28 \(\times\) 28 pixels with three channels. Additionally, the input pixel values are normalized from the range of 0 to 255 to the range \(-1\) to \(+1\). This helps to train a neural network, as convergence is usually faster if the average of each input variable over the training set is close to zero [26]. The used network consists of three convolutional layers and two dense layers. A summary of the neural network layers is shown in Table 1. The output of the network shows if the taillight in the input image is ON or OFF. For the training of this neural network, a training set with more than 10,000 labeled sample images of the prototype using different lighting settings, car models and cameras was used. An excerpt of those sample images is shown in Figure 12. During the training process, a dropout of 50% was used to prevent overfitting. After 6 epochs of training, the network reached an accuracy of more than 98% in cross-validation. The final network scored an accuracy of 99.4% on the validation test set.  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Layer** & **Type** & **Output Size** & **Activation Function** & **Kernel Size** & **Stride** \\ \hline
1 & Input & 28 \(\times\) 28 \(\times\) 3 & & & \\ \hline
2 & Convolutional 2D & 28 \(\times\) 28 \(\times\) 6 & ReLU & 5 \(\times\) 5 & 1 \\
3 & Convolutional 2D & 14 \(\times\) 14 \(\times\) 12 & ReLU & 5 \(\times\) 5 & 2 \\
4 & Convolutional 2D & 7 \(\times\) 7 \(\times\) 24 & ReLU & 4 \(\times\) 4 & 2 \\
5 & Flatten & 1176 & & & \\
6 & Dense & 200 & ReLU & & \\
7 & Dense (Output) & 2 & Softmax & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of neural network layers for taillight state recognition.  
Figure 11: Taillight detection on example images of cars on a road [1].  
Figure 12: Excerpt of images used as training data.  
The proposed design of the taillight state recognition network is optimized for the usage with the prototype implementation. In future work, when real cars are used for the communication, this design might be changed, if it is not capable of reliable recognition of the taillight states. However, with this prototype implementation, the performance of the network is sufficient for the use case.  
For the demodulation of the signal, the taillight state is recognized in every recorded frame for both modulated taillights using the taillight ROIs. The recognized state is compared with the previously recognized taillight state. If the state is the same as before, the receiver can infer that no phase shift between the two frames happened and hence, a logic 0 is received. Otherwise, if the taillight state changed, due to an applied phase shift, i.e., the modulation signal was inverted, a logic 1 was transmitted. These single transmitted bits are collected into a received bit stream to interpret the transmitted message.
# Deep Learning of Diffuse Optical Tomography Based on Time-Domain Radiative Transfer Equation
## 2 Methodology
### Multi-Step Classification Method  
It is difficult to classify all 28 positions directly at once. Thus, we adopt a two-step classification. In the first step, all datasets are classified into 8 groups of domains, and each group is classified into domains in the second step. We find that this multi-classification method is more effective for absorber detection than a single-step method. In Figure 1,  
Figure 2: Temporal profiles of absorption measure \(A=I_{\text{abs}}(t)/I_{\text{noabs}}(t)-1\) on 8 detectors (D1–D8). The horizontal axis is time in units of nanoseconds (ns). The upper left panel shows the profiles for an absorber located at the #3 position, upper right panel for an absorber at #26 position, lower left panel for an absorber at #0 position, and lower right panel for an absorber at the #13 position.  
the grouping for the two-step classification is illustrated. The small red boxes are the 8 groups, and one group is composed of either 4 or 2 domains. Although such domain decomposition is not unique, we have found that this particular decomposition works for our deep learning scheme.  
The training and test data are the temporal profiles of absorption measure \(A(t)\) at the eight detector positions, which are obtained through radiative transfer simulations for an absorber located at a given domain. In addition, we add random noises on the simulation data. We assume a Gaussian type of noise which forms \(\exp(-x^{2}/2\sigma^{2})\) with a standard deviation of \(\sigma=0.001\) or 0.01. This is suitable for classification by LSTM deep learning. Temporal profiles of the absorption measure with noises are plotted in Figure 3. We generate 2500 datasets in total, which include 89 datasets for each absorber position.
## 2 Materials and Methods
### Climate Data  
AWS located in or nearby the selected vineyard blocks (Table 1) provided daily average (TG), minimum (TN) and maximum (TX) air temperatures, as well as daily precipitation totals for the period of 2011-2017. As climatic data before 2011 are not available from the AWS, these variables were obtained from a gridded climatic database (PTHRES), available throughout Portugal on ca. 1 km spatial resolution and from 1950 onwards D. (1950). The nearest grid point to each vineyard was retrieved from PTHRES. As the determination coefficients of the linear regression models between PTHRES and AWS temperatures were very high (>90% for TN and >98% for both TG and TX), linear equations were used to estimate TG, TN and TX before 2011. However, since the determination coefficients for precipitation were variable (51-92%), a multiplicative method was used instead. Firstly, the climate-means of AWS and PTHRES monthly precipitation totals were calculated for each calendar month for the baseline period of 2011-2015. The corresponding monthly ratios were then interpolated to the daily timescale by a second-order polynomial fit. The daily precipitation totals before 2011 were then reconstructed by multiplying the corresponding ratio by PTHRES precipitation.
## 4 Inference Rules for Matching Geographic Ontologies in Different Languages
### Conceptual Framework
#### 4.1.1 Homology Relations  
Between two objects, \(A\) and \(B\), a homology relation is a relation that is reflexive and symmetric. Let us denote \(\mathfrak{w}\) as this relation, so that \(A\)\(\mathfrak{w}\)\(B\). Therefore, both \(A\)\(\mathfrak{w}\)\(A\) and \(B\)\(\mathfrak{w}\)\(A\) hold. Remark that an equivalence relation (\(\equiv\)) is a homology relation, which is also transitive.  
When we want to compare two geographic objects, we need to ascertain whether the geometric shapes (taking measurement errors into account) are similar or not. For that purpose, the ideal will be to create an equivalence relation, that is to say, reflexive, symmetric and transitive. However, due to the previous remarks, one can mention that transitivity is not every time verified. Therefore, let us consider geometric homology relations (denoted as \(\mathfrak{w}_{G}\)).  
For crisp geographic objects (\(A\) and \(B\)), the boundaries are well known and agreed upon, but there are practically always measurement discrepancies. In this case, to match them, we can compare their geometric shapes (Figure 9) and their locations, for instance, by their centroids. By definition, two geographic objects, \(A\) and \(B\), are considered as geometrically homologous iff:  
\[\begin{split}\mathit{Geom}(A)\ \mathfrak{w}_{\mathsf{IG}}\ \mathit{Geom}(B)\\ \  
Regarding toponyms, equivalence relations can be created, for instance by writing \"U.S.A.\"\(\equiv\)\"United States of America\". By extension, an equivalence class can be defined. However, for the translation of toponyms (Venice, Venezia, _etc._), there is no systematic way to define them, and there is no authority to define them, except dictionaries. In other words, we are dealing with traditional translations agreed upon by many people. As a consequence, we can define a toponymic homology relation, such as \(\mathfrak{w}r\), so that we can write _Venezia_\(\mathfrak{w}\)_Venice_. A homology class can be made by regrouping all of the agreed upon translations of a toponym. See Figure 10a,b.  
For types, similar remarks can be made, and another homology relation can be defined to link types and their counterparts other languages.  
To alleviate the notation, we will use the symbol \(\mathfrak{w}\) without subscript throughout this text.
# Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
## 3 Results and Discussion
### Membrane Conductivity, Area Resistance and Ion Selectivity
It is noteworthy that the conductivity used in Equation (6) was converted from the membrane resistance values measured in sulfuric acid using an H-cell, since the ion transportation direction for membrane resistance measurement is in the same direction as ion transporting in VRFB operation. AMV shows the highest ion selectivity (2.2 \(\times\) 10\({}^{7}\) S-s-cm\({}^{-3}\)) due to its extremely low vanadium crossover and acceptable conductivity. Although the PFSA-based membranes exhibited higher vanadium permeability, they showed great ion selectivity (0.3 \(\times\) 10\({}^{6}\)-1.4 \(\times\) 10\({}^{6}\) S-s-cm\({}^{-3}\)) due to their relatively high proton conductivities. FAP-450 also displayed comparable ion selectivity (1.6 \(\times\) 10\({}^{6}\) S-s-cm\({}^{-3}\)) with PFSA-based membranes. In summary, the ion selectivity of each membrane is in the following order of magnitude: AMV \(>\) CMV\(\approx\)AHA \(>\) FAP-450 \(\approx\) N212 \(\approx\) DF \(\geq\) FS-930 \(>\) VAN. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Membranes**} & **Ionic Conductivity** & **Area Resistance** & **Ion Selectivity** \\  & **\(\sigma\)****(mS\(\cdot\)cm\({}^{-1}\))** & **(\(\Omega\)\(\cdot\)cm\({}^{2}\))** & **(S\(\cdot\)s\(\cdot\)cm\({}^{-3}\)\(\cdot\)10\({}^{6}\))** \\ \hline N212 & 70 \(\pm\) 6 & 0.47 & 1.0 \\ \hline FS-930 & 84 \(\pm\) 8 & 0.54 & 0.3 \\ \hline DF & 60 \(\pm\) 1 & 0.48 & 1.4 \\ \hline AMV & 3.9 \(\pm\) 0.0 & 2.08 & 21.8 \\ \hline CMV & 4.6 \(\pm\) 0.4 & 0.62 & 5.5 \\ \hline AHA & 2.4 \(\pm\) 0.2 & 2.39 & 2.8 \\ \hline FAP-450 & 38 \(\pm\) 3 & 0.59 & 1.6 \\ \hline VAN & 0.54 \(\pm\) 0.01 & 1.23 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ionic conductivity obtained in deionized (DI) water at RT and membrane area resistance measured in 2 M H\({}_{2}\)SO\({}_{4}\) as well as the calculated ion selectivity data for the selected membranes.
## 3 Static/Dynamic Response Characteristics of IEPCM
### Modal Analysis of IEPCM  
In fact, the vibration characteristics of a structure, such as the vibration shape, and the modal frequency can be extracted through modal analysis, and when its natural frequency has no repeated phenomenon, it indicates that its principal mode is orthogonal. Here, the lower surface of the IEPCM is fixed, the applied voltage between the upper and lower surface is defined to be 0 V, and then its modal analysis with first 40 order frequencies is analyzed by the modal frequency ranging from 10 kHz to 300 kHz. Figure 4 shows the fourth-order vibration mode of the IEPCM.  
It is not difficult to observe from Figure 4 that the modal frequency of the IEPCM appears starting from 200 kHz, and the resonance frequency is 210.3 kHz. The frequency distribution is crowded together, but there is no repeated frequency phenomenon, which implies that the principal mode of the IEPCM is orthogonal [19].  
Figure 3: Static strain distributions of IEPCM under applied 10 V voltage: (**a**) \(x\)-direction; (**b**) \(y\)-direction.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 3 Instruments for TD-NIRS
### Other New TD-NIRS Systems
#### 3.3.2 TD-DOT and TD-NIRS Systems for Optical Mammography  
In 2011, Hamamatsu Photonics developed a 48-channel three-wavelength optical mammography as shown in Figure 11a [52]. Breats were immersed in a hemispherical gantry filled with a matching fluid, and DOT images were reconstructed by an algorithm based on the microscopic Beer-Lambert law [9; 50]. Furthermore, a 12-channel TD-NIRS system for optical mammography with a hand-held probe shown in Figure 11b was developed to be used at a bed side [53]. Another TD-NIRS system for optical mammography, as shown in Figure 11c, was developed by a group from Italy to obtain TR transmittance images of compressed breasts [54]. The results of these systems are briefly described later in Sections 5.2.1 and 5.3.5.  
Figure 11: Photos of (**a**) the 48-channel TD-DOT for optical mammography [52]; (**b**) the 12-channel TD-NIRS system for optical mammography with a hand-held probe [53], and (**c**) the transmittance TD-NIRS imaging system for optical mammography with breast compression [54]. (**b**) Reproduced from Reference [53]. (**c**) Reproduced from Reference [54].  
Figure 10: Photo of the 32-channel TD-NIRS system, “MOSTIR”, developed by a group in the UK [49]. Reproduced from Reference [49].
# An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
## 2 Materials and Methods
### The Building Method of Simple Stable Feature Database
#### 2.2.1 Initial Feature Database Building  
Adaptive histogram equalization is first used to pre-process the reference image \(R\), and then the chosen feature algorithm is used to extract the feature points. The _Longitude_, _Latitude_, _Octave_, _Response_, _Angle_, _Size_, number of matches (\(M\)), number of unmatched matches (\(UM\)), number of consecutive matches (\(CM\)), and number of consecutive unmatched matches (\(CUM\)) are recorded for each feature, along with the feature descriptor _Descriptor_. Each feature in the initial feature database has an \(Index\), which is used to group similar features to form feature classes. Then, all the collected features are assembled as \(F_{0}=\{f_{1}^{0},f_{2}^{0},\ldots,f_{n_{0}}^{0}\}\) written in the initial feature database where \(f_{i}^{k}\) represents the feature class whose \(Index\) is \(i\) in the feature database, \(k\) is the number of images input and \(n_{0}\) is the total number of feature classes in the feature set. At first, \(n_{0}=N_{0}\), the matching parameters of each feature in the initial feature database are \(M_{i}=CM_{i}=UM_{i}=CUM_{i}=0\), \(i=0,1,2,\ldots,n_{0}\), and \(N_{0}\) is the total number of all feature points in the feature set \(F_{0}\).
## 2 Derivation of Appropriate Mixing Ratio for SFRM
### Compressive and Tensile Tests  
The compressive and tensile strength for SFRM with diverse mixing ratios given in Table 2 were tested in accordance with KS L 5105 [30] and KS L5104 [31], as shown in Figure 4. The specimens for compressive tests were made in cubes with a \(50\times 50\times 50\) mm dimension in accordance with KS L 5105 [30]. The specimens for tensile tests were made in briquettes shown in Figure 4b, in accordance with KS L 5104 [31]. All the specimens were cured in water at room temperature for 28 days. For each mixing ratio, both compressive and tensile strengths were measured and averaged for three specimens, respectively. A universal testing machine (UTM) was used for compression tests, and a hydraulic oil pump was used for tension tests. The compressive strengths for different SFRM mixing ratios are plotted with respect to the volume fraction of amorphous steel fiber in Figure 5. It is observed that the fiber volume fraction does not affect the compressive strength of the SFRM significantly. The highest compressive strength 67 MPa was obtained for S/C10-W/C32, followed by S/C15-W/C35, and S/C20-W/C40 in a descending order. Based on the test result, the compressive strength of SFRM is more influenced by increased cement volume and reduced water cement ratio rather than the fiber volume fraction.  
The tensile test results are plotted in Figure 6. The five mixing ratios except S/C20-W/C25-AD01 show an increasing trend with respect to the fiber volume fraction. In more detail, it is observed that the tensile strength of SFRM increases little with 0.7% or less steel fiber volume fraction. However, the tensile strength of SFRM with 1.0% or higher steel fiber volume fraction increases compared to mortar without fibers by about 30%. S/C10-W/C32 with a relatively low sand-cement ratio and low water-cement ratio achieves a tensile strength of 6.6 MPa at with 1.3% steel fiber volume fraction resulting in a strength enhancement of 50%. From this observation, it is supposed that the increased amount of cement in paste enhances bond of steel fibers and makes a corresponding contribution to tensile strength.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & **Fiber Volume Fraction** & **Flow Value** & **Overlayable Thickness** \\  & **(\%)** & **(mm)** & **(mm)** \\ \hline \multirow{6}{*}{S/C20-W/C45} & 0 & 206 & 12.5 \\  & 0.15 & 207 & 10 \\  & 0.3 & 206 & 12.5 \\  & 0.5 & 196 & 12.5 \\  & 0.7 & 192 & 10 \\ \hline \multirow{6}{*}{S/C20-W/C50} & 0 & 236 & 7.5 \\  & 0.15 & 247 & 7.5 \\  & 0.3 & 228 & 7.5 \\  & 0.5 & 230 & 7.5 \\  & 0.7 & 231 & 7.5 \\ \hline \multirow{6}{*}{S/C15-W/C35} & 0 & 188 & 12.5 \\  & 0.15 & 185 & 10 \\  & 0.3 & 183 & 12.5 \\  & 0.5 & 174 & 12.5 \\  & 0.7 & 177 & 10 \\  & 1.0 & 180 & 7.5 \\  & 1.3 & 171 & 5 \\ \hline \multirow{6}{*}{S/C10-W/C32} & 0 & 198 & 10 \\  & 0.15 & 192 & 15 \\  & 0.3 & 182 & 12.5 \\ \cline{1-1}  & 0.5 & 178 & 12.5 \\ \cline{1-1}  & 0.7 & 173 & 12.5 \\ \cline{1-1}  & 1.0 & 182 & 10 \\ \cline{1-1}  & 1.3 & 171 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._
# Applying Infinite Petri Nets to the Cybersecurity of Intelligent Networks, Grids and Clouds
## 6 Specifying and Analysing Infinite Petri Nets
### Complex Deadlocks within Computing Grid Models  
**Special graphs have been introduced and studied to prove other properties of grids' models.** A graph of packet transmissions has been introduced to prove t-invariance via explicit composition of stationary repeated sequences of transitions' firings [45]. A graph of mutual blockings have been introduced to classify complex deadlocks within grid structures, a three causes of deadlocks have been revealed: (i) a circle of blockings; (ii) a chain of blockings ending at an already blocked cell; (iii) isolation by blocked nodes. Afterwards, it has been proven that complex deadlocks can be induced by ill-intentionedtraffic and appear in avalanche-like way imposing a serious thread to the grid security [49]. For this purpose colored Petri nets have been applied which allow hierarchical composition of a model and specification of timed parameters. Guns of traffic have been attached to the grid borders, the following concise and the most dangerous configurations involving two guns have been revealed: (i) a traffic duel; (ii) crossfire, and (iii) side shot. An example of a complete deadlock of an 8 \(\times\) 8 grid is shown in Figure 5. Inscriptions on arcs specify the number of packets in the internal buffer, forwarded to the corresponding device, and the number of packets in the port buffer, respectively; the internal buffer size is 100 packets. Within a real-life network, a deadlock is overcome by drop packet and timeout techniques but repeated deadlicks decrease the network performance and QoS considerably.  
Here we would like to note that it is not a kind of DDoS attack. We use comparably low intensity traffic that can be easily disguised say under some multimedia traffic. We call it a technique of induced (programmable) deadlocks. Arranged sources of ill-intentioned traffic can possess more sophisticated variable structure that allows disguising them even better.
# Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs
### CEJ Selection  
After the bounding box was selected, the algorithm extracted every point in the foreground region and detected the edge corresponding to the upper border of the enamel-cementum region. Since enamel and cementum are strong ultrasound reflectors, their intensities are very large in comparison with those of gingiva [17]. The contour of the tooth has a small V-shaped valley at the neck of the tooth where the enamel and cementum meet [31]. The bottom of the valley is the location of the CEJ. Based on these characteristics, the detection algorithm calculated the absolute value of the vertical change, \(\Delta j\), of the two consecutive pixels along the detected enamel-cementum border, and then compared the value with that of the previous location. The position of the CEJ was determined by the location with the greatest absolute change \(\left|\Delta j\right|_{max}\). Finally, remapping the coordinate of the CEJ back to its original coordinate, the algorithm marked the CEJ in the original image.  
The algorithm was implemented on a 64-bit Windows 10 operating system with an Intel Core i5-2500 Quad-Core (3.30 GHz) CPU and 8 GB random access memory (RAM). The image processing and CEJ detection were realized through MATLAB version R2020b (MathWorks, Natick, MA, USA). Our user interface allows the user to select the images directly from the monitor's screen with the option to manually select the CEJ location or to detect it using the proposed detection method.  
Figure 2: Homomorphic filtering. The input \(d(i,j)\) and the output \(\hat{d}(i,j)\) are the unprocessed and filtered images, respectively. The components include logarithmic transformation (ln), 2D discrete Fourier transform (DFT), Gaussian high-pass filtering [\(H(\cdot)\)], 2D inverse discrete Fourier transform (IDFT), and exponential transformation (exp).
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 4 Methodology
### Path Loss Models  
The 3 GPP TR 36.777 draft [38] outlines urban and rural environments for UAV implementations over LTE release 15 networks. The standard proposes system level parameters and channel models, which are included in our proposed scenario. For the 3.5 GHz band, we used the model in [38] based on 3 GPP TR 38.901 [39], which accounts for 3D scenarios and is optimal for our simulation tool requirements. [PERSON] et al. in [26] use this model in the 3.5 GHz band. Therefore, we proposed to use the urban macro (UMa) environment path loss model, which is described in Equations (1) and (2) for the line-of-sight (LoS) and NLoS respectively; where f\({}_{\text{c}}\) is in GHz, d\({}_{\text{3D}}\) and h\({}_{\text{drone}}\) in meters. The shadow fading standard deviation \(\sigma_{\text{SF}}\) was 4 dB and 6 dB for LoS and NLoS, respectively. This model is valid for distances up to 4.5 km, and increases as the h\({}_{\text{drone}}\) increases [26]. One of the advantages of using this model is its applicability to frequencies up to 100 GHz; hence, it will also be used for the 60 GHz band simulations. The path loss \(PL_{\text{LIMa-LOS}}\) in LoS and \(PL_{\text{LIMa-NLOS}}\) in NLoS conditions (in dB) is calculated as follows [34; 35]:  
\[PL_{\text{LIMa-LOS}}=28+22\log_{10}(d_{3D})+20\log_{10}(f_{c}) \tag{1}\]  
\[PL_{\text{LIMa-NLOS}}=13.54+39.08\log_{10}(d_{3D})+20\log_{10}(f_{c})-0.6(h_{ drone}-1.5) \tag{2}\]