esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Model
### Parameter Tuning Using JSSO Algorithm  
Finally, the JSSO algorithm is used to optimize the MFs. The traditional SSO technique is found to be effective in resolving real-time optimization challenges. The presented method gives the productive means of changing parameters [28]. It gives the best exploration capability for the primary searching method. However, it has the limitation that it suffers from lower convergence speed and requires higher convergence time. This challenge minimizes the population range to escape from the local optima. The abovementioned problems are considered to develop the new method with the integration of JA. The traditional java algorithm (JA) has different features; it can be simple for the developer, and only one phase has fewer variables. The traditional JA is exploited for multi-objective optimized problems. It is capable of finding the optimal value in a lesser period. This feature is taken into account to perform the new technique named _J_-SSO. Rather than rotational movement, JA updating is used. In this work, the new equation is expressed for the gradient of the main function of SSO represented by \(\
abla(of)\), as provided in Equation (21):  
\[\
abla(of)=\frac{abs\Big{(}of_{gbest}-of(j)\Big{)}}{of_{gbest}} \tag{21}\]  
The term \(of_{gbet}\) represents a better solution's fitness function, and the existing solution's fitness function is denoted as 0. Therefore, if the attained solution reaches the values of \(\
abla(of)<0.5\), then the location updating can be performed by SSO forward movement. Otherwise, the location updating can be performed using _JA_. The pseudocode of _J_-SSO is demonstrated in Algorithm 1. The hybrid optimized technique is stimulated by integratingdifferent optimized techniques or rules. It is proved that the best outcomes are for the fixed search problem. The hybrid optimized algorithm is demonstrated to attain the best convergence using different optimization principles.  
The JSSO technique derives a fitness function to attain improved classification performance. It determines a positive integer to represent the better performance of the candidate solutions. In this study, the minimization of the classification error rate is considered the fitness function, as given in Equation (22):  
\[fitness(x_{i})=\frac{number\ of\ misclassified\ samples}{Total\ number\ of\ samples}*100 \tag{22}\]
###### Abstract
BC plays a key role in the Earth's climate system because it absorbs solar radiation, influences cloud processes, and alters the snowmelt and ice cover. In contrast, OC has an overall cooling effect due to its scattering optical properties, although brown carbon (BrC) from organic matter (OM) could have the opposite effect. BC concentrations respond quickly to reductions in emissions because BC is rapidly removed from the atmosphere by deposition. Thus, BC emission reductions represent a potential mitigation strategy that can reduce global climate forcing from anthropogenic activities in the short term and slow the associated rate of climate change even if in the last Intergovernmental Panel on Climate Change (IPCC) suggest that the effect of BC could be lower [10]. Carbonaceous emissions also have an impact on meteorology at the local scale, particularly in mountainous regions, through a complex suite of processes involving the atmospheric water column [11]. Road traffic is the major air pollution source in urban areas [12; 13; 14; 15] due to fuel combustion (exhaust-traffic-related emissions), and brake/tyre wear (non-exhaust-traffic-related emissions). This urban contribution on PM concentrations varies greatly from region to region, with the current highest contribution from Africa and India and the lowest from Europe and North America due to the application of stringent standards for vehicle emissions [16]. The restrictions implemented during the COVID-19 pandemic provided an \"opportunity\" to show the major role of traffic emissions on PM and UFP concentrations in highly urbanised areas [17]. However, it has been demonstrated that diesel vehicles are responsible for approximately 75% of the total costs of air pollution related to road transport in Europe [18]. Road traffic is also recognised as the second-largest contributor of BC emissions (26%) in the European Union (EU), with major importance in cities [19]. Recent work by [PERSON] et al. [20] in the US showed the high spatial variability of BC concentrations in Oakland (California) using a dense network of low-cost sensors, highlighting the exposure of the population to vehicle emission. Stringent regulations on vehicle emissions was shown to reduce BC and OC concentrations in a study on urban sites in Canada in the period 2003-2019 [21], although the decrease in OC is less pronounced and probably counterbalanced by secondary organic aerosol (SOA) formation. The goal of this review is to compile and analyse the recent literature on emissions of carbonaceous PM and its precursors assigned to the road traffic sector. Particle number and polycyclic aromatic hydrocarbons (as OM) are species closely linked to carbon issues and will be part of this review. Some key conclusions and recommendations are proposed to enlighten decision makers in view of the next regulations on vehicle emissions in Europe and worldwide through the synergistic contexts of air quality and climate change.
# Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor
## 2 Related Work
### Scene Text Spotting  
Recently, several end-to-end scene text spotting methods have been introduced and have shown a remarkable result compared to independent scene text detection and recognition approaches. For instance, [PERSON] et al. [10] introduced an end-to-end text spotting technique from natural images using RPN as a text detector and attention Long Short Term Memory (LSTM) as a text recognizer. [PERSON] et al. [8] presented an end-to-end scene text-reading method using Single Shot Detector (SSD) [33] and convolutional recurrent neural network (CRNN) for scene text detection and recognition, respectively. [PERSON] et al. [34] introduced a unified network to detect and recognize multi-oriented scene texts from natural images. [PERSON] et al. [35] introduced an octave-based fully convolutional neural network with fewer layers and parameters to precisely detect multilingual scene text. The most recently proposed scene text-reading models are summarized in Table 1.  
Improving the feature extraction and recognition network will improve scene text detection, recognition, and text spotting problems. In [21], an OctConv feature extraction method has been proposed for object detection and improves its performance. Octave convolution addresses spatial redundancy, which was not addressed in the previously proposed methods. The OctConv does not change the connectivity between feature maps and it is different from inception multi-path designs [36; 37]. In our proposed bilingual text-reading method, we replace the ResNet-50 vanilla  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Model** & **Detection** & **Recognition** & **Year** \\ \hline [PERSON] et al. [11] & TextBoxes & SSD-based framework & CRNN & 2017 \\ [PERSON] et al. [19] & Deep TextSpotter & Yolo v2 & CTC & 2017 \\ [PERSON] et al. [34] & FOTS & EAST with RoI Rotate & CTC & 2018 \\ [PERSON] et al. [8] & TextBoxes++ & SSD-based framework & CRNN & 2018 \\ [PERSON] et al. [9] & Mask TextSpotter & Mask R-CNN & Character segmentation + Spatial attention module & 2019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of recently proposed end-to-end scene text recognition models.  
convolution with OctConv, which can operate quickly and produce accurate results in the extraction of features. As stated in [38], the limitation of Connectionist Temporal Classification (CTC), attention encoder-decoder, and hybrid (CTC and attention) method is improved using a time-restricted self-attention method for an automatic speech recognition system. In our proposed method, we integrate a time-restricted self-attention encoder-decoder module for recognition with feature extraction and bounding box detection layers.
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 3 Interference Evaluation Model
### Effective Carrier-to-Noise Ratio Degradation  
Considering the impact of receiver components on the signals, the \(C/N_{0}\) was defined as the ratio of the carrier signal power to the noise power at the correlator output of the receiver's carrier tracking loop [25; 26]. The degradation of the effective carrier-to-noise ratio, denoted as \((C/N_{0})_{\text{deg}}\), was defined as the difference between \(C/N_{01}\), which only represents the carrier-to-noise ratio when the desired signal is present at the receiver antenna port, and \(C/N_{02}\), which corresponds to the carrier-to-noise ratio when a mixture of the desired signal and RFI is present (with pulse blanking enabled). This parameter is crucial for characterizing the impact of interference on navigation signals. The \((C/N_{0})_{\text{deg}}\) caused by DME interference is derived as [13]  
\[\left(C/N_{0}\right)_{\text{deg}}=-10\log_{10}\left(\frac{1-PDC}{1+\frac{I_{0, WB}}{N_{0}}+Ri}\right), \tag{4}\]  
where \(I_{0,WB}\) represents the total continuous aeronautical RFI power spectral density (PSD), and \(N_{0}\) is the thermal noise power spectral density of the GNSS receiver. The key parameters governing DME interference are the pulse blanker duty cycle (\(PDC\)) and the interference-to-noise power spectral density ratio below the blanking threshold (\(Ri\)), both of which are determined by the receiver's pulse blanker characteristics. Assuming that the received peak power of the DME pulse signal is \(P_{k}\) and that it is converted into an equivalent rectangular pulse, with the PB threshold set to \(P_{th}\), the modeling of \(PDC\) and \(Ri\) is as follows.
## 3 Results and Discussion
### Analysis of Hardness and Elastic Modulus Results
#### 3.3.2 Relationship Between Hardness and Elastic Modulus  
According to Formulas (1) through (7) and the dimensional analysis, and assuming that the compliance of the diamond indenter does not affect the elastic modulus, the relationship between the hardness and elastic modulus can be analyzed. By combining Formulas (5), (6), and (8), it can be derived that the hardness and elastic modulus satisfy the relationship expressed in Formula (9).  
\[E_{\text{IT}}/(1-v^{2}_{\text{IT}})=E_{r} \tag{8}\]  
\[H_{\text{IT}}=\lambda E_{\text{IT}} \tag{9}\]  
where \(\lambda\) is the slope of the linear relationship between the hardness and elastic modulus.  
Formula (9) is employed to model the relationship between the hardness and elastic modulus for coal samples subjected to various treatment conditions, with the fitting results presented in Figure 13 and the corresponding parameters listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Coal Sample Status** & **Slope (\(\lambda\))** & **Goodness of Fit (\(R^{2}\))** \\ \hline initial & 0.07571 & 0.9837 \\ humidification & 0.06488 & 0.9720 \\ heating & 0.07291 & 0.9877 \\ Humidification–heating & 0.06971 & 0.9914 \\ summary & 0.07140 & 0.9820 \\ \hline \end{tabular}
\end{table}
Table 3: Hardness and elastic modulus fitting parameters.  
Figure 13a illustrates the fitted relationships between the hardness and elastic modulus of coal samples under different treatment conditions. Combined with the fitting parameters listed in Table 3, the experimental results demonstrate a strong linear correlation between the two mechanical properties. The goodness-of-fit (R\({}^{2}\)) values under the initial, humidification, heating, and hygrothermal coupled treatment conditions were 0.9837, 0.9720, 0.9877, and 0.9914, respectively, indicating that the linear model constrained to pass through the origin provides an excellent fit across all cases. Figure 13b further integrates the test data from all conditions, yielding an overall R\({}^{2}\) of 0.9820. According to Equation (9), the hardness and elastic modulus are positively correlated under all treatment conditions, indicating that this relationship is independent of the coal's physical state.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 3 Surface Correlation-based Fingerprinting Method
### Domain Conversion  
RSS measurement is distributed in the time domain since it is measured at a certain time interval on a terminal device. Thus, it can be expressed as:  
\[\text{Meas}=\left[\begin{array}{cccc}t_{1}&RSS_{t_{1}}^{1}&\cdots&RSS_{t_{1}}^{ n}\\ \vdots&\vdots&\ddots&\vdots\\ t_{m}&RSS_{t_{m}}^{1}&\cdots&RSS_{t_{m}}^{n}\end{array}\right] \tag{2}\]  
where, \(RSS_{t_{m}}^{n}\) is the received signal strength from \(n\)th BS at \(t_{m}\). The conventional fingerprinting database consists of the RP coordinate and the RSS values at that point as Equation (1). The conventional fingerprinting methods compare the instantaneous RSS data (\(RSS_{t_{m}}^{1:n}\)) at time \(t_{m}\) with the RSS vector on each RP, so it is possible to compute a location. Given that the RSS pattern measured on time domain cannot be compared with the RSS pattern on the database distributed in the spatial domain, the measured RSS data should be converted into a spatial domain. To do this, the proposed fingerprinting method utilizes the pedestrian step length estimation algorithm already proposed in an earlier study [24], where step length is modeled as a linear combination of the acceleration amplitude, walking frequency, and sway angle at every step. The SC method estimates the walking distance \(d\) by accumulating the step length and accumulates the measured RSS according to the walking distance as given in Figure 3. Thus, the _Surface_ can be expressed as:  
\[S=\left[\begin{array}{c}S_{1}\\ \vdots\\ S_{m}\end{array}\right]=\left[\begin{array}{cccc}d_{1}&RSS_{d_{1}}^{1}& \cdots&RSS_{d_{1}}^{1}\\ \vdots&\vdots&\ddots&\vdots\\ d_{m}&RSS_{d_{m}}^{n}&\cdots&RSS_{d_{m}}^{n}\end{array}\right] \tag{3}\]  
where, \(m\) is the number of steps, \(S\) is the _Surface_ at walking distance \(d_{m}\) and \(RSS_{d_{m}}^{n}\) is the received signal strength from \(n\)th BS at walking distance \(d_{m}\). The proposed fingerprinting method estimates location by correlating the RSS pattern on the reference trajectory with the measured _Surface_. The reference RSS pattern generated from the fingerprinting database also forms a _Surface_ as in Equation (3) by calculating the Euclidean distance between the adjacent RPs.  
Figure 3: Domain conversion: (**a**) Received Signal Strength (RSS) pattern on time domain; (**b**) RSS pattern on the spatial domain.
# A Flexible and Simple Lossless DWT Filter Bank Using a MAXFLAT FIR Half-Band Filter
## 4 Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Image**} & \multirow{2}{*}{**bpp**} & \multicolumn{5}{c}{**Filter Bank 1 (Irreversible)**} \\ \cline{3-6}  & & **JPEG2000** & \multicolumn{5}{c}{**s-Filter Bank**} \\ \cline{3-6}  & & **9/7** & **3-Tap** & **5-Tap** & **7-Tap** \\  & & **(K = 1)** & **(K = 2)** & **(K = 3)** \\ \hline \multirow{3}{*}{Lena} & 0.6 & 51.73 & 52.59 & 52.64 & 52.81 \\  & 0.5 & 50.11 & 49.55 & 51.01 & 50.94 \\  & 0.4 & 46.20 & 45.68 & 45.89 & 46.26 \\ \hline \multirow{3}{*}{Couple} & 0.6 & 51.99 & 52.50 & 52.57 & 52.83 \\  & 0.5 & 48.80 & 49.39 & 49.30 & 49.55 \\  & 0.4 & 45.72 & 45.63 & 45.69 & 46.10 \\ \hline \multirow{3}{*}{Tank} & 0.6 & 52.10 & 52.42 & 52.51 & 52.89 \\  & 0.5 & 50.44 & 50.04 & 50.52 & 51.00 \\  & 0.4 & 45.68 & 45.69 & 45.80 & 46.13 \\ \hline \multirow{3}{*}{Cameraman} & 0.6 & 53.25 & 53.35 & 53.59 & 53.68 \\  & 0.5 & 52.05 & 52.11 & 52.23 & 52.57 \\ \cline{1-1}  & 0.4 & 48.18 & 48.67 & 48.19 & 48.30 \\ \hline \hline \end{tabular}  
* The simulations were performed in the lossy (irreversible) JPEG2000 standard.  
\end{table} 
Table 3: Coding performance comparison at 0.6–0.4 bpp.
# Structured Alumina Substrates for Environmental Catalysis Produced by Stereolithography
## 2 Materials and Methods
### Catalytic Activity Tests  
All washcoated monolithic cores were tested for NO selective catalytic reduction by NH\({}_{3}\) in a heated quartz glass reactor [27] in a feed of 10 vol% O\({}_{2}\), 5 vol% H\({}_{2}\)O, 500 ppm NO, 0-600 ppm NH\({}_{3}\) with balance N\({}_{2}\) at a gas hourly space velocity (GHSV = volumetric gas flow/volume of reactor occupied by the coated monolith, STP) of 50,000 h\({}^{-1}\). The NOx conversion was calculated as X\({}_{\rm NO}\) = 100(-[NO\({}_{\rm x}\)]\({}_{\rm lin}\) - [NO\({}_{\rm X}\)]\({}_{\rm out}\))/[NO\({}_{\rm x}\)]\({}_{\rm lin}\), where [NO\({}_{\rm x}\)]\({}_{\rm in}\) and [NO\({}_{\rm x}\)]\({}_{\rm out}\) are the NOx concentrations (NO+NO\({}_{2}\)) upstream and downstream of the catalyst ([NO\({}_{\rm x}\)]\({}_{\rm lin}\) = 0). Online gas analysis of the reactor exhaust gas was carried out using a Fourier transform infrared spectrometer (Nexus, Thermo Fisher, Waltham, MA, USA) equipped with a 2 m heated transmission gas cell. The maximum NOx conversion was measured by increasing NH\({}_{3}\) concentration in the feed from up to 600 ppm (NH\({}_{3}\)/NO\({}_{\rm x}\) = 0-1.2) at each temperature at constant NO concentration in the feed. From the same set of measurements, the NOx conversion at 10 ppm NH\({}_{3}\) slip was obtained, as well. The NOx conversion at 10 ppm NH\({}_{3}\) slip can be significantly lower than the maximum NOx conversion, depending on the type of catalyst mass, catalyst geometry, and GHSV. To avoid unnecessarily high NH\({}_{3}\) slip, NOx conversions at 10 ppm NH\({}_{3}\) slip are often targeted for real applications [26; 28].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entry** & \multicolumn{2}{c}{**Catalyst Mass**} & **Specific Surface** & **Washcoat Thickness c**  \\  & **g\(\cdot\)L\({}^{-1}\)a** & **mg\(\cdot\)cm\({}^{-2}\)b** & **cm\({}^{2}\cdot\)cm\({}^{-3}\)** & **\(\mu\)m** \\ \hline M1 & 71 & 2.8 & 25.1 & 22 \\ M2 & 124 & 5.0 & 25.1 & 38 \\
3D-printed & 70 & 5.3 & 13.3 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the three monolith cores studied for NH\({}_{3}\)-SCR.
# Secure Information Transmission with Self Jamming SWIPT
###### Abstract  
This article is focused on implementing simultaneous wireless information and power transmission as a physical layer security measure by using artificial noise. A series of high energy precoded symbols is simultaneously transmitted along with the information symbols over a Rayleigh frequency selective fading channel. The high energy precoded symbols act as an artificial noise for the eavesdroppers. The energy symbols are precoded on the basis of a legitimate user's channel matrix to form a null space vector, which eliminates the interference of energy symbols at the information symbol receiver antennas, while allowing the rectenna to harvest energy from the superimposed information and energy symbols. We analyze the secrecy rate and error rate performance at the receiver under different circumstances, and we show that the performance of the legitimate user can be improved by using the iterative block decision feedback equalization method at the receiver.  
Keywords:simultaneous wireless information and power transmission (SWIPT); physical layer security; wiretap channel model; iterative block decision feedback equalization (IB-DFE) +
Footnote †: journal:
# An Overview of Fog Data Analytics for IoT Applications
## 5 Research Challenges
### Quality of Service  
The sheer amount of requests and network traffic from connected devices jeopardize the network performance. This results in significant delays, broken data streams, dropped calls, and glitches in video calls. Moreover, only some of the services in the network hold equal priority; some time-sensitive application [66] needs to be serviced quickly and continuously with required resources; otherwise, it would result in troublesome delays and fragmented data packets. On the other hand, applications such as a file transfer could bear some delay in the data transfer and still have intact packets. Therefore, ensuring the QoS is essential for managing the network effectively and delivering the values demanded by different services.  
The need for QoS parameters such as reliability, the continuous availability of bandwidth and low-latency servicing, resource provisioning, high-priority data transfer, and mobility poses a great system design challenge. Real-time data application needs a continuous bandwidth and low-latency servicing for the best user experience. A revolutionary black-box multi-algorithm described in [67] shows how end-to-end latency might be reduced by 60%-70% while largely utilizing the temporal locality. The processing and networking time are the two parameters used to measure the end-to-end latency. Similarly, the fog node must be able to prioritize data processing needs and serve required resources so that the execution does not introduce any further delay. Authors in [68] proposed a method for estimating resource allocation, based on how many resources will be utilized according to historical data and customer usage patterns, named media fog resource estimation (MeFoRE). Table 2 compares various works by authors in their research and shows which QoS aspects were considered by them.  
With traditional networking, fog computing is refrained from its true potential for QoS and flexibility with a lower latency by constantly learning solutions from modern developments. SDN gracefully decouples the control plane from the data plane and emulates centralized control over the data plane. A fog layer implementing SDN networking would benefit from flexibility and data forwarding control offered by SDN. A fog node can work as an SDN controller that guides the data plane according to network requirements, which will ultimately help to improve the QoS and efficiency of data analytics tasks, eliminating latency issues.  
Fog-based systems can deliver a high-quality user experience to end users. First, the preference of services is decided based on the behavioral usage patterns, geographical location, and mobility context, and then the user experience is optimized from gained values of parameters. The work of [69] describes delivering the QoS to users in IoT ecosystems that leverage a virtual infrastructure proposed as the self-organizing fog of things (SOFT-IoT). The fog of things gateway has been set up to work as a smart device based on fog computing in the environment.