esa-sceva/satcom-chunk-collection huggingface dataset:
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 3 IANT Scheme Design
### Design of Reader Side Scheme  
The IANT scheme proposed in this paper can be summarized as follows:  
1. In the first sub-slot, \(K\) tags sequentially transmit \(N_{0}\) symbols to reader in the order of index, and the receiving end of reader calculates \(\Phi_{k}\) according to Formula (4).
2. The reader selects the \(i\)-th and \(j\)-th tags that satisfy Formula (8). It first sends \(K\) symbols to all tags, where the \(i\)-th symbol bit is the bit '1', and the rest are '0'.
3. The selected \(i\)-th tag responds to the reader with U symbols, and U represents the index of the \(i\)-th tag in binary form. After receiving the response symbol, reader converts the binary index to decimal \(\hat{i}\) and checks whether it is equal to the value of \(i\).
4. Reader sends \(K\) symbols to all tags again, the \(j\)-th symbol bit is bit '1', and the rest are '0'. Repeat step 3) for the \(j\)-th tag.
5. If \(\hat{i}=i,\hat{j}=j\), reader sends a sine wave, otherwise the reader remains silent.
6. If the \(i\)-th tag and the \(j\)-th tag detect the sine wave of the reader's response, they both send \(N_{T}\) symbols at the same time, corresponding to data and noise signals respectively. Next, we introduce in detail the operation of the IANT scheme in each sub-slot.
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### Hybrid Storage Approaches
#### 2.3.1 Blockchain and Off-Chain Integration Approaches  
This storage method enables the integration of smart contracts and transaction records on the blockchain with off-chain data storage mechanisms. While data integrity is guaranteed on the blockchain, physical storage is handled by external systems.  
_IPFS CIDs_: InterPlanetary File System (IPFS) Content Identifiers (CIDs) store only references to data on the blockchain, while the actual data are kept within the IPFS network. Since the references are stored on-chain, the data remains verifiable and secure. This method is a cost-effective solution for storing large datasets; however, as the data itself is not stored on-chain, the system is not fully decentralized [15; 92].  
Beyond blockchain-off-chain system integration, distributed and decentralized cloud solutions represent another key component of hybrid storage strategies.
# Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation
## 3 Datasets
### Characteristic of the Model  
A horseshoe-shaped tunnel was analysed in this study with a 4 m radius and 1.4 tunnel width/height ratio. There are a total of 2735 tunnels in South Korea. The widths of 85% of the total tunnels are distributed from 8 to 14 m according to Road, Bridge and Tunnel Statistics [21]. Additionally, the tunnel width/height ratios of 56% of the total tunnels are distributed from 1.2 to 1.6. It was expected that the 100,000 numerical analyses would require a lot of computational time, so an axisymmetric analysis was performed to shorten the time. Moreover, it was expected that the tunnel radius would greatly influence the total analysis time. Therefore, to confirm our expectations, ten preliminary numerical analyses were performed for tunnel radii of 4, 5, 6, and 7 m. These radii are typically half of the tunnel widths generally distributed in South Korea. The effect of tunnel width/height ratio on the numerical analysis time was not expected to be significant like width. Therefore, the tunnel width/height ratio applied was the median value of 1.4 to generalize the most distributed range of 1.2 to 1.6. The same values of the ground parameters were used for the different tunnel radii, and different values of the ground parameters were used when analysing the same tunnel radius. Based on the ten numerical analyses of the tunnel radii of 4, 5, 6, and 7 m, the performance times were obtained as 5, 13, 24, and 41 min, respectively. These results imply that about 35 days, 90 days, 167 days, and 285 days would be required to perform the 100,000 numerical analyses for the four radii. Thus, the radius was selected as 4 m for efficient analysis since the time difference between tunnel radii of 4 m and 7 m is 250 days; in addition, a difference of 55 days is expected even with the next highest radius of 5 m. The tunnel is located at the lower part of the surface at 40 m, and the lower and right boundaries are set at distances of 36 m each (=4.5 \(\times\) diameter) from the centre of the tunnel, as shown in Figure 3. The gravity was 9.81 m/s\({}^{2}\) in a downward direction, and the X displacements at the left and right boundaries were set to 0, while the Y displacement at the lower boundary was set to 0.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Number** & \(\gamma\) **[kg/m\({}^{3}\)]** & **UCS [MPa]** & **m\({}_{\rm i}\)** & **GSI** & **K** \\ \hline
1 & 2395.5 & 95.0 & 6 & 10 & 0.5 \\
2 & 2466.9 & 115.6 & 9 & 20 & 0.75 \\
3 & 2538.2 & 136.1 & 12 & 30 & 1 \\
4 & 2609.6 & 156.7 & 15 & 40 & 1.25 \\
5 & 2680.9 & 177.2 & 18 & 50 & 1.5 \\
6 & 2752.3 & 197.8 & 21 & 60 & 1.75 \\
7 & 2823.6 & 218.3 & 24 & 70 & 2 \\
8 & 2895 & 238.9 & 27 & 80 & 2.25 \\
9 & 2966.4 & 259.4 & 30 & 90 & 2.5 \\
0 & 3037.7 & 280.0 & 33 & 100 & 2.75 \\ \(\Delta\) & 71.3 & 20.6 & 3 & 10 & 0.25 \\ \hline \end{tabular}
\end{table}
Table 1: Selected values for the five parameters.
# Designing and Implementing Weather Generators as Web Services
## 2 Addressing Bottlenecks of Legacy Weather Generators for Service
### Climatological Data Preparation  
Climate variability impacts crop growth. Climate data are used for simulating crop growth, planning agricultural management and farm decisions [12]. Crop models use daily meteorological data as inputs for simulating crop growth [13]. The key variables that impact crop growth are temperature, solar exposure and precipitation [14]. Temperature substantially affects crop growth and phenological development, while solar exposure significantly affects crop photosynthesis [15]. Precipitation represents water availability in an area. These variables are often generated in a weather generator.  
In this section, we describe the general procedures in preparing climate data for running legacy weather generators. These steps must be performed often manually, as illustrated in Figure 1 in the part of climatological data preparation. From the retrieval of data (from a data source), one needs to learn the data accessing protocol and format. After retrieving the data, all missing data need to be filled, as weather generators cannot process a dataset with missing data above a threshold volume. A weather station captures weather data at various time-intervals. In the case of public weather station networks, they usually publish their daily data, but some stations capture data at every 5- or 10-min intervals and publish those data as they are. In such cases, the data must be aggregated at daily interval and their unit of measurement (UOM) must be changed accordingly. Matching the UOM of each climate variable is very important; otherwise, there could be some hidden errors in the final dataset. Lastly, the aggregated daily data must be transformed in formats compatible with weather generators.  
Most weather generators and crop models require daily weather data as inputs. ICASA standard format provides a basis for writing daily weather data (WTH) [16]. WTH format is written in plain text, with special syntax and formatting rules (Figure 3). The weather generators used here accept daily weather data in the same format as WTH file (yearly), and longer-term (multiple years) weather files (WTD or WTDE file). Weather file in WTH format is used in Decision Support System for Agro-technology Transfer (DSSAT), one of the most popular crop simulation platforms used today.
###### Abstract  
Liquid biofuels are of special interest due to the possibility of their application as a substitute for fossil liquid fuels. The necessary step is to investigate the possibility of bio-fuel application in terms of its properties and similarities to fossil liquid fuels (e.g., crude oil, heavy fuel oil, diesel). The properties and combustion performance of heavy fuel oil (HFO) and products of the fatty acids distillation residues (FADR) were analyzed in this study. The application of animal-fat-delivered fuels is fully suggested in the literature. Nevertheless, the investigations focused on the raw materials or their transformation into diesel. The novelty of this paper is the utilization of FADR as a substitute for HFO. The utilization of FADR allows the use of this material as a feedstock to obtain valuable products (fuel) and avoids generating waste after animal fat processing. The experimental investigations were carried out using a technical-scale 150 \(\mathrm{kW_{th}}\) combustion chamber. FADR can be recognized as a substitute for HFO due to its beneficial calorific properties and viscosity. Other beneficial effects are the significantly lower emission of \(\mathrm{SO_{2}}\) (lower than 1 ppm) and PAHs (i.e., 355 \(\mathrm{\SIUnitSymbolMicro m^{3}_{n}}\)) during the combustion of FADR. Finally, the application of FADR requires less energy demand for fuel heating and pressurization.  
animal fats; fatty acids; heavy fuel oil; bio-liquids; combustion; NOx +
Footnote †: journal: Applied  
_Article_  
Application of Fatty Acids Distillation Products as a Substitute for Heavy Fuel Oil in Stationary Combustion Chambers
## 4 Proposed Algorithm
### BCRB Derivation  
The BCRB is computed as \(BCRB=Tr\{\mathbf{J}_{B}^{-1}\}\) with \(\mathbf{J}_{B}\in\mathbb{R}^{N\times N}\) being the Bayesian Fisher information matrix (FIM) [42].  
Figure 4: The flowchart of proposed algorithm.  
The \(i,j\)-th element of \(\mathbf{J}_{B}\), \(\mathbf{J}_{ij}\) is equal to \(E_{XY}[\
abla_{x_{i}}\log p(x,y)\
abla_{x_{i}}^{T}\log p(x,y)]\). Herein, \(\
abla_{v}\triangleq[\frac{\partial}{\partial_{v_{1}}},...,\frac{\partial}{ \partial_{v_{q}}}]\). Then \(\mathbf{J}_{ij}\) can be calculated as  
\[\mathbf{J}_{ij} \triangleq E_{XY}[\
abla_{x_{i}}\log p(x,y)\
abla_{x_{j}}^{T}\log p (x,y)]\] \[=-E_{XY}[\
abla_{x_{i}}\
abla_{x_{j}}^{T}\log p(x,y)]\] \[=E_{XY}[\
abla_{x_{i}}\log p(y|x)\
abla_{x_{j}}^{T}\log p(y|x)]\] \[+E_{X}[\
abla_{x_{i}}\log p(x)\
abla_{x_{j}}^{T}\log p(x)]\] \[=-E_{XY}[\
abla_{x_{i}}\
abla_{x_{j}}^{T}\log p(y|x)]-E_{X}[ \
abla_{x_{i}}\
abla_{x_{j}}^{T}\log p(x)].\]  
Therefore, the Bayesian FIM is given by  
\[\mathbf{J}_{B} =-E_{XY}[\
abla_{x}\
abla_{x}^{T}\log p(\mathbf{y}|\mathbf{x})] -E_{X}[\
abla_{x}\
abla_{x}^{T}\log p(\mathbf{x})]\] \[=-E_{(y,x)}\{\frac{\partial^{2}\mathcal{L}(\mathbf{y}|\mathbf{x} )}{\partial\mathbf{x}\partial\mathbf{x}^{T}}\}-E_{(x)}\{\frac{\partial^{2} \mathcal{L}(\mathbf{x})}{\partial\mathbf{x}\partial\mathbf{x}^{T}}\}. \tag{12}\]  
Firstly, we compute the second part of Equation (12). To solve \(\mathcal{L}(\mathbf{x})\), we must obtain the density function of signal \(\mathbf{x}\) in the first place. In our model, the sparse signal \(\mathbf{x}\) follows the Gaussian distribution with a mean vector \(\boldsymbol{\mu}\) and a covariance matrix \(\boldsymbol{\Sigma}\). \(\boldsymbol{\Sigma}=\text{diag}(\gamma_{1},...,\gamma_{N})\) is a diagonal matrix with \(\gamma_{i}\) denoting the variance of \(x_{i}\). As a result, the density of \(\mathbf{x}\) is  
\[p(\mathbf{x};\boldsymbol{\gamma})=\prod_{i=1}^{N}(2\pi\gamma_{i})^{-1/2}e^{- \frac{(x_{i}-p)^{2}}{2\gamma_{i}}}. \tag{13}\]  
Hence \(\mathcal{L}(\mathbf{x})=\sum_{i=1}^{N}-\frac{1}{2}\ln 2\pi\gamma_{i}-\frac{(x_{i}- \mu)^{2}}{2\gamma_{i}}\).  
As the previous part is irrelevant to \(\mathbf{x}\), we abbreviate it as \(\tilde{g}\). Then the formula in matrix form can be written as  
\[\mathcal{L}(\mathbf{x})=\tilde{g}-\frac{1}{2}\mathbf{x}^{T}\boldsymbol{\Sigma }^{-1}\mathbf{x}. \tag{14}\]  
As a result \(\frac{\partial^{2}\mathcal{L}(\mathbf{x})}{\partial\mathbf{x}\partial\mathbf{x }^{T}}=\boldsymbol{\Sigma}^{-1}\).  
Secondly, we turn to deal with the first part of FIM. To obtain \(-E_{(y,x)}\{\frac{\partial^{2}\mathcal{L}  
Finally, we put the two parts together as the description of FIM,  
\[\mathbf{J}_{B} =-E_{(y,x)}\{\frac{\partial^{2}\mathcal{L}(\mathbf{y}|\mathbf{x})}{ \partial\mathbf{x}\partial\mathbf{x}^{T}}\}-E_{(x)}\{\frac{\partial^{2} \mathcal{L}(\mathbf{x})}{\partial\mathbf{x}\partial\mathbf{x}^{T}}\}\] \[=\frac{1}{\sigma^{2}}\mathbf{\Psi}^{T}\mathbf{\Phi}^{T}\mathbf{ \Phi}\mathbf{\Psi}+\mathbf{\Gamma}^{-1}\]  
Therefore, in our model, BCRB is represented by \(Tr\{\frac{1}{\sigma^{2}}\mathbf{\Psi}^{T}\mathbf{\Phi}^{T}\mathbf{\Phi} \mathbf{\Psi}+\mathbf{\Gamma}^{-1}\}\).
## 2 Materials and Methods
### Hardware Setup  
The main aim of the research was to create inexpensive hardware that runs on open software. Ergo, we used an Arduino Uno with an ultrasonic sensor (in Figures 2 and 3) and a GPS. Table 1 shows the hardware specification and Figure 4 shows the components used in the setup. The architecture of the proposed system consisted of four parts: an ultrasonic sensor, Arduino, a server module, and a heads-up display module. The ultrasonic sensor and Arduino are used to gather information about potholes and speed bumps and their geographical locations, and this information is sent to the server. The server module receives information from the microcontroller module, processes, and stores in the database.  
Figure 1: Flow chart of the proposed novel system includes ultrasonic sensor with dynamic time warping (DTW).  
Figure 3: Timing response of the ultrasonic sensor.  
Figure 2: Working of ultrasonic sensor, (**a**) Plain road, (**b**) Speed bump, and (**c**) pothole.  
Ultrasonic sensors are utilized to determine the separation between the vehicle frame and the street surface numerically, and this value is passed to the microcontroller. Threshold distance is defined as the distance between the body of the vehicle and the smooth road surface, which depends upon the ground clearance of vehicles. If the value obtained by the ultrasonic sensor is more than the threshold distance, it recognizes it as a pothole, and if it is less, it is recognized as an unimportant irregularity, otherwise the street is smooth.  
The microcontroller module (Arduino) is the main controller of the entire system and is responsible for incorporating the ultrasonic sensors and sending and receiving signals between the GPS receiver and the sensors. The microcontroller module is merely the processing power of the whole system and is utilized to accumulate data about potholes and their topographical areas, and these data are passed onto the server. The server module gets this data from the microcontroller and uploads its respective location signatures to the cloud server. This location will appear on the GPS system whenever any vehicle passes through or near it. The Arduino contains several filters to smoothen out the output from the ultrasonic sensor. The basic variations from the ultrasonic sensor are bolstered to the filters, where the information is smoothed and standardized. The filters are to filter the vibration due to some irregularities from the road surface apart from speed bumps and potholes. These vibrations are removed from the ultrasonic sensor information to lessen the impact of other outside powers and for the system to be able to differentiate between a constant/normal variation and an actual pothole or a bump.
# An Approach for Selecting the Most Explanatory Features for Facial Expression Recognition
## 2 Related Work
In these works, the variability of facial changes was studied using regions or points of interest in the face images. The notion of SRs--finding sparse solutions to under-determined systems--has been applied in several scientific fields. [PERSON] et al. used sparse models that are similar in nature to the network of neurons in V1--the first layer of the visual cortex in the human brain--and, more generally, to the mammalian brain [24; 25]. Patterns of light are represented by a series of innate or learned basis functions, whereby sparse linear combinations form surrogate input stimuli to the brain. Similarly, for many input signals of interest, such as natural images, a small number of exemplars can form a surrogate representation of a new test image. In SR systems, new test images are efficiently represented by sparse linear coefficients on a dictionary \(D\) of over-complete basis functions. Specifically, SR systems comprise an input sample \(x\in R^{m}\) (where \(m\) is the number of features), along with a dictionary \(D\) of \(n\) samples, \(D\in R^{m\times n}\). SR solves the coefficients \(\alpha\in R^{n}\) that satisfy the \(l_{1}\) minimization problem \(x^{*}=D\). [PERSON] et al., [PERSON] et al., and [PERSON] et al. showed the advantages of exploiting sparsity in pattern classification, which has been demonstrated extensively for FER problems [26; 27; 28]. The experimental results of [26] showed that the magnitude of the representation error in the facial feature vectors obtained by SR provided a good metric with which to classify facial expressions. Different expression representation techniques have been created, and many more are expected to be created in the next few years. Representations may be features designed by experts (feature engineering methods), as was demonstrated by [PERSON] et al., [PERSON] et al., and [PERSON] et al. [21; 29; 30; 31], or embedded vectors obtained from training a deep neural network. The main objective of this work was to define a new facial expression recognition system that uses the representations obtained from different sources and combines them using a learned model. For this goal, we performed the following steps: (1) we first obtained several representations of facial expressions by applying different representation methods; (2) we then transformed each obtained representation into the same domain, the domain of representation errors of sparse representation (SR); finally, (3) in this new space, we trained a combination model to classify the signals. We used the extended Cohn-Kanade data set (CK+), the BU-3 DFE data set, and the JAFFE data set for validation. The major innovation of this work is the exploration of feature extractors to select those features that are the most explanatory for facial expression recognition, using a sparse representation-based classifier.
## 4 Discussion  
The results of the direct analysis of the bridge are summarized in Figure 13. This figure compares the values of Midas/Civil with those obtained using the proposed methodology when shear deformation effects were considered in terms of deflections (Figure 13a) and rotations (Figure 13c). The latter included the rotations numerically calculated by the derivation of the vertical deflections, including shear effects throughout the beam directrix (axis \(x\)), i.e., \(\mathrm{d}(v_{b}+v_{\mathrm{s}})/\mathrm{d}x\). The results of the shear diagram are also presented in Figure 13b. As the structure is isostatic, these diagrams were not affected by the effects of the shear deformations.  
Figure 11: Yunbao Bridge: (a) cross-section and (b) finite element model (FEM).  
Figure 12: Loading case: (a) loads from the formwork traveler and (b) modeled loading case.  
The analysis of Figure 13a shows that the shear effects played an important role in the deformation of the structure. In fact, the deformation at the edge of the cantilevers increased from \(-0.6\) mm to \(-3.1\) mm when these effects were considered. Figure 13c shows that the rotations obtained using the numerical derivation corresponded exactly with those obtained using the proposed method, where the shear rotations (\(w\)(Emid's)) were added to the bending rotations (\(w_{b}\)) calculated using the computer software. This figure also shows the effect of the shear diagrams in Figure 13b in the total rotations throughout the bridge deck.  
A comparison of the magnitude of the obtained bending (\(w_{b}\)) and the shear rotations (\(w_{s}\)) on the left-hand side of the node are summarized in Table 4. This table also includes the percentage of shear rotations in terms of the total rotations (\(w_{b}+w_{s}\)) in the different nodes in the cantilever.  
The results in Table 4 show that the shear rotations should not be neglected in the analyzed deck segments. Obviously, the errors in rotations were related to the shear diagram. In fact, the higher the shear forces, the higher the shear rotations. For example, at the edge of the cantilever (nodes 1 and 7), the errors of neglecting the shear rotations could be as high as 77.7%. As the precamber given to the next se  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rotation** & \(w_{b}\) **(rad)** & \(w_{s}\) **(rad)** & \(w_{b}\)/(\(w_{b}+w_{s}\))** \\ \hline \(w1\),1 & \(-6.6\times 10^{-5}\) & \(-2.3\times 10^{-4}\) & 77.7\% \\ \(w2\),2 & \(-5.6\times 10^{-5}\) & \(-1.7\times 10^{-4}\) & 74.8\% \\ \(w3\),3 & \(-3.5\times 10^{-5}\) & \(-1.7\times 10^{-4}\) & 82.7\% \\ \(w4\),4 & \(-0.8\times 10^{-5}\) & \(-1.5\times 10^{-4}\) & 94.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the rotations in the FEM.  
Figure 13: Yunbao bridge analysis: (**a**) vertical deflections, (**b**) shear diagram, and (**c**) rotations with and without shear effects.  
with the value of this rotation, a proper calculation can help to provide a proper geometry, especially with segments that have such deformable webs under shear.
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 4 Experimental Studies  
Extensive tests have been conducted to evaluate the performance of the proposed infrastructure. In each test, the standalone and DGNSS positions from smartphones are compared with the ground truth from a surveying-grade receiver. The Trimble GNSS receiver in Figure 3 receives RTK corrections from reference stations, and then solves for integer ambiguities to determine positions up to a few centimeters. Because smartphones offer the meter-level accuracy, this Trimble receiver is sufficient for evaluating the performance of the DGNSS based on NMEA messages.  
As shown in Figure 3, the DGNSS App was installed in two smartphones, a Unistrong smartphone from Beijing, China, and an HTC smartphone from Daejeon, Korea. The model of the Unistrong smartphone is A3S, and it integrates a NEO-7M GNSS module manufactured by the U-Blox company in Thalwil, Switzerland. This NEO-7M supports GPS and Beidou satellite systems. The version of the operating system of the Unistrong smartphone is Android 7.0.1. We also use the HTC One smartphone in the test, and it uses the Android operating system version 4.1. The CPU model of the HTC smartphone is Qualcomm Adreno 320. The satellite systems supported by HTC One include GPS and Glonass. The DGNSS App running in both smartphones is responsible for data communication and displaying the real-time positions on a map. The app also records the standalone and DGNSS positions in the log files for further analysis.  
The devices (the Trimble receiver and two smartphones) record positions in log files at the sampling rate of 1 Hz. For each test, the log files contain three types of positions: standalone and DGNSS solutions from smartphones, and RTK solutions from the Trimble receiver. For each epoch, the positions of the standalone GNSS and DGNSS from smartphones are compared with the position from the Trimble receiver, and the positioning errors are calculated.  
Some representative tests are presented in the following three subsections. The walking test is shown in the next subsection. Next, the walking tests in the second subsection show the effects of the  
Figure 3: Experimental system.  
ionosphere on the smartphone positioning in low-latitude areas and show how the infrastructure can mitigate them. Finally, the performance of the infrastructure in urban areas is illustrated.