esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 3 Performance Analysis
### Outage Probability
#### 3.1.2 End-to-End Downlink  
Similarly, by substituting (12) into (13), the outage probability of the end-to-end downlink is written as:  
\[P_{out\_down}=1-e^{-\frac{\gamma_{th}}{B\gamma_{1}}}\sum_{m=0}^{N-1}\frac{ \gamma_{th}^{m}}{(B\overline{\gamma_{1}})^{m}m!}\left(1+\frac{\xi^{2}}{\Gamma( \alpha_{3})\Gamma(\beta_{3})}\times G\begin{array}{c}3,1\\ 2,4\end{array}\left(\alpha_{3}\beta_{3}\sqrt{\frac{\gamma_{th}}{\tilde{\gamma}_{2 }}}\bigg{|}\begin{array}{c}\phi_{1}\\ \phi_{2}\end{array}\right)\right), \tag{15}\]  
where \(\phi_{1}=1,\xi^{2}+1\); \(\phi_{2}=\xi^{2},\alpha_{3},\beta_{3},0\).
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 2 Background and Foundations
### Characteristics of Disconnected Areas  
As Vehicle-to-Vehicle (V2V) communications enable direct interaction between nearby vehicles, in disconnected areas, the internet connectivity is limited or unavailable, which affects critical communications among vehicles. This factor makes the task of maintaining continuous connectivity between vehicles in such conditions a very difficult challenge. This is commonly the case in non-residential areas and outside urban locations: tunnels, mountains, or remote locations where RSUs are not deployed. To illustrate, while GSM communications technologies are certainly widespread, they are not universally accessible due to geographic limitations. For example, the GSMA website reveals that the coverage is not always guaranteed, and it is easy to find many areas in different countries where there is road infrastructure but a lack of 2G coverage (let alone coverage from 3G or higher networks). Meanwhile, although satellite communications can offer greater coverage, they do so with limited capacity, higher latency, and at a cost that may not be considered viable.  
According to [5, 6], about 20% of people live in rural areas in the United States and Canada, compared with 56% in the European Union (EU) and 60% in China. These low-signal areas have structural or geographical attributes that affect the wireless signals from propagating properly and thus cause weak or no connectivity between vehicles and infrastructures [7]. The lack of connectivity in these areas is not just a function of physical obstructions but is tied to ecosystem triggers and infrastructural determinants as well [8].  
As an example, this study focused on vehicular communication in the Peak District, Figure 3, a National Park in central England, aiming to measure the impact of infrastructures in rural regions with limited RSU density. The figure indicates various road routes and intersections but a lack of infrastructure. Moreover, the roads are often narrow and winding, increasing the potential for encountering hazards. For example, Monyash Road (A623) connects scattered landmarks like \"Inside Out Photography\" and nearby farms or fields situated far apart with no visible supporting infrastructure. This highlights the disconnection and challenges in linking isolated points in the region.  
This lack of connection extends beyond the technology portion as it has real-world ramifications for vehicle safety and operational efficiency. The real-time data transmission and reception of safety messages have been greatly affected because they can not make vehicles communicate to RSUs or trusted authorities on a regular basis. Impacts on vital functions from traffic management, emergency response coordination, and navigation updates. As such, the latency and packet loss that can result translates to longer message travel times and more potential for traffic accidents while on roadways. This is due to the connectivity limitations in these challenging scenarios [9].
## 1 Introduction and Motivation
### Neural-Network-Based Algorithms  
The motion prediction-based and graph-based algorithms provide suitable results, but their computational time may be high [9]. Therefore, neural-network-based solutions are presented, which aim to make the computation of the algorithms faster. As it is described above, the graph-based algorithm consists of two main layers. The upper layer is responsible for the motion prediction of the surrounding vehicles, and the lower layer, using this information, computes the collision-free reference points. There can be complex traffic situations, in which the prediction and computation of the graph can be time-consuming. The prediction and the computation of the collision-free points can be made by a neural network.  
As a first step, several simulations have been performed to get a large amount of data. The appropriate dataset is crucial for each machine-learning, otherwise, the overfitting of the model may occur. The results of the graph-based algorithm are saved. Using the saved data, a neural network is trained. The aim of this is to make the prediction, and the computation of the reference trajectory faster. Unfortunately, a disadvantage of the process is that guaranteeing the performances of the trained neural network is challenging. This can result in not acceptable outputs of the neural network, which can be dangerous in some situations. To solve this problem, [PERSON] et al. [10] proposed a design architecture to guarantee the performances during overtaking. [PERSON] et al. [11] presented a trajectory design method taking into account several performances. Using the potential field approach, the surrounding vehicles are incorporated. At the same time, other information is built in such as maximum lateral acceleration, longitudinal acceleration, width of the road. This leads to the multi-objective optimization problem, which cannot be solved in real-time. To make the algorithm implementable, a neural network is used. In this case, the output of the neural network is not just a reference point but a feasible trajectory. [PERSON] et al. [12] introduced an adaptive-neural-network-based lateral control for autonomous vehicles.
## Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation  
**[PERSON] 1,2, [PERSON] 1,*, [PERSON] 1, [PERSON] 3 and [PERSON] 3**  
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 114 Nanta Street, Shenyang 110016, China; E-Mail: [EMAIL_ADDRESS]  
Graduate School of Chinese Academy of Sciences, Beijing 100049, China  
LTO, The South China Sea Institute of Oceanology, Chinese Academic of Science, 164 Xingangxi Road, Guangzhou 510301, China; E-Mails: [EMAIL_ADDRESS] (L.Y.); [EMAIL_ADDRESS] (Y.S.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-24-2397-0731; Fax: +86-24-2397-0076.  
_Received: 12 December 2011; in revised form: 24 December 2011 / Accepted: 27 December 2011 / Published: 2 January 2012_  
**Abstract:** The micro-scale and meso-scale ocean dynamic processes which are nonlinear and have large variability, have a significant impact on the fisheries, natural resources, and marine climatology. A rapid, refined and sophisticated observation system is therefore needed in marine scientific research. The maneuverability and controllability of mobile sensor platforms make them a preferred choice to establish ocean observing networks, compared to the static sensor observing platform. In this study, marine vehicles are utilized as the nodes of mobile sensor networks for coverage sampling of a regional ocean area and ocean feature tracking. A synoptic analysis about marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, and ocean feature tracking and observing techniques is given. Combined with the observation plan in the South China Sea, we provide an overview of the mobile sensor networks established with marine vehicles, and the corresponding simulation results.  
**Keywords:** marine vehicles; observing system; sensor networks; path planning
## 5 Analysis and Discussion
### Impacts of Digitalization Improvement on EPC Projects' Cost Performance
#### 5.1.3 Discussion of Cost Performance  
The responses to the questions about the impact of building type on cost performance showed a high level of agreement (see Table 1); however, the building type of only one successful case was commercial; the building types of the four neutral cases, respectively, were commercial, religious, HOPSCA, and residential. According to the utilization of the latest digital technologies, there is no substantial evidence to indicate the impact of building type on cost saving.  
According to reports by Endeavour House and Festival Place, the estimated cost saving during the construction phase should be in the range of 8.5-9.5% [46]. The estimated mean cost saving during the construction phase was 3.24% and the final mean value in real practice was 1.36%.  
With a short expert group discussion, the leading potential causes of the variation in the cost performance when each latest digital technology was applied in the EPC projects were inferred as follows:  
1. the regional discrepancy in urban areas resulted in labor costs;
2. the regional discrepancy in the material market resulted in the price of the material;
3. the discrepancy in information transparency between constructors and owners resulted in expense claims;
4. the discrepancy in the technical level of engineers resulted in different estimated data;
5. the discrepancy in statistical methods resulted in different performance data.
# A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Turkiye
## 3 Ground-Motion Prediction Models (GMPMs)
\(d_{3}\) is period-dependent model coefficient for the anelastic attenuation. The functional expression for distance scaling is provided for \(R<80\) and \(R>80\); \(R\) is calculated as \(\sqrt{R_{JB}^{2}+h^{2}}\) where, \(R_{JB}\) indicates the Joyner-Boore distance. \(h\) is the pseudo-depth term (km) and is estimated by the regression. \[f_{R,AA}(R)= \begin{cases}0&for\ R\leq 80\ km\\ (d_{3})(R-80)&for\ R>80\ km\end{cases}  \tag{5b}\]  
The site function (\(f_{S}\)) is represented by the addition of linear (\(F_{lin}\)) and nonlinear (\(F_{nl}\)) site amplification functions:  
\[f_{S}(V_{S30},PSA_{r})=F_{lin}+F_{nl} \tag{6}\]  
In this study, we used linear scaling and soil nonlinearity to represent site amplification behavior. We primarily used the site amplification function from [PERSON] and [PERSON] [19]. The nonlinear site amplification model coefficients were also adapted, and the linear term was calculated. The functional form for the linear site amplification is:  
\[ln(F_{lin})= \begin{cases}(s_{1})ln\Big{(}\frac{V_{s0}}{V_{ref}}\Big{)},&V_{s3 0}<V_{c}\\ (s_{1})ln\Big{(}\frac{V_{c}}{V_{ref}}\Big{)},&V_{s30}\geq V_{c}\end{cases}  \tag{7}\]where \(s_{1}\) is the regression coefficient, \(V_{n\!f}\) is the reference shear-wave velocity at which site amplification reaches unity (\(V_{n\!f}\) = 760 m/s), and \(V_{C}\) is the shear-wave velocity at which we observe that ground-motion intensity no longer scales with \(V_{S30}\). The nonlinear site amplification function is:  
\[ln(F_{n\!l})=s_{2}\Big{[}e^{s_{3}(min(V_{S30},760)-360)}-e^{s_{3}(400)}\Big{]} ln\left(\frac{exp(ln(PSA_{r}))+s_{4}}{s_{4}}\right) \tag{8}\]  
where \(s_{2}\), \(s_{3}\) and \(s_{4}\) are regression coefficients and \(PSA_{r}\) is the PSA for a reference rock site (\(V_{n\!f}\)), obtained by evaluating Equation (1).
# HS-SPME Gas Chromatography Approach for Underivatized Acrylamide Determination in Biscuits
## 3 Results and Discussion
### Acrylamide Quantification in Biscuits  
The developed methodology for AA quantification was tested in a commercial wheat biscuit, treated with asparagine. The biscuits contained 203 \(\pm\) 45 ug/kg of AA, which was quantified by in an external certified laboratory using a conventional HPLC method. Using the developed methodology, the content of AA in these biscuits treated with asparagine was found to be 212 \(\pm\) 34 ug/kg, which is in accordance with the values provided by the certified laboratory (Figure 6).  
Biscuits prepared using original wheat recipe contained higher amount of AA, 472 ug AA per kg of biscuits compared to the biscuits prepared using mitigation methodologies (Figure 6). The mitigation effect of the addition of asparagine [41] is due to the conversion of free asparagine into aspartic acid, which is no longer able to form AA [42]. According to the obtained results, addition of asparagine to the wheat flour biscuits allowed to decrease AA content below the benchmark level recommended by EFSA for biscuits (350 ug/kg) [5], demonstrating the relevance of asparagine as a mitigation strategy in biscuits.  
Substitution of 50% of the wheat flour by the same amount of rice flour in biscuit recipe also resulted in the decrease of AA levels, to 279 ug per kg of biscuits, value below the recommended benchmark value [5]. Effect of rice flour addition is due to the lower contents of free asparagine in this fl  
Figure 5: Acrylamide external calibration curve (RSD < 8.9%).  
The most effective mitigation methodology was found to be addition of 2% of spectate to biscuit dough allowing AA content to decrease to 147 ug per kg of biscuits (Figure 6). These results are in accordance with AA mitigation strategy by pH lowering of the dough using acidic polysaccharides [29].
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Signal Multi-Feature Extraction and Combination  
When processing ECG signals from pregnant women, scholars typically employ various signal processing techniques to extract and analyze the signal features, primarily including the time domain, frequency domain, time-frequency domain, and nonlinear feature values [36; 37; 38]. Time domain analysis, one of the most direct signal analysis methods, uses the ECG signal as a model input to preserve complete feature information. This paper extracts 60-point signals as time domain features directly. Frequency domain analysis reveals periodic and frequency information contained within the signal. This study applies the Fourier transform method to convert the signal from the time domain to the frequency domain, obtaining 60-point frequency domain information. Time-frequency domain analysis provides joint distribution information of time and frequency domains, enhancing morphological features of the data and expanding feature diversity [21; 39]. This study calculates time-frequency domain signals using the wavelet transform method. Given the varying sizes of the time-frequency plots after wavelet transformation, to facilitate processing by CNNs, the dimensions of the time-frequency domain data are uniformly adjusted to 24 \(\times\) 60.  
The fetal heart rate is a high-dimensional chaotic signal that follows specific nonlinear rules; thus, in the analysis of fetal heart rate signals, research methods from nonlinear dynamics, such as approximate entropy and Lyapunov exponents, are often considered [40].  
Figure 3: Amplification of fetal ECG and sliding window segmentation.  
Nonlinear analysis can help us understand ECG signals' complex structures and dynamic behaviors. This paper primarily extracts seven types of feature values from the ECG signal.
## 2 Materials and Methods
### Methods  
#### 2.3.1 BPH trap Catches and Weather Data Preprocessing  
To predict BPH abundances, weather variables (including Tmin, Tmax, RH, and rainfall) and host-plant variables such as NDVI derived from satellite remote sensed data for the prediction months and one-month prior were used as input variables. In addition, the monthly BPH light trap catches were natural log-transformed prior to analysis in order to meet the assumptions of the regression [53; 54; 55; 56], which were considered as an output variable in all models. The dataset for all variables during December-March from crop season 2006/2007 to 2014/2015 at the CHN site, crop season 2011/2012 at the SPB site, crop season 2010/2011 at the NTB site, and crop season 2010/2011 at the KKL site was used as a training dataset; the training dataset consisted of a total of 48 samples. The remaining observations from crop season 2015/2016, 2012/2013, 2011/2012, and 2011/2012 at the CHN, SPB, NTB, and KKL sites, respectively, were used as the testing dataset to verify the prediction models. The duration time of the  
Figure 2: A light trap at the Chai Nat Rice Research Centre was used to collect brown planthoppers.  
data between December to March was chosen to develop the model because this period is usually used for planning pest monitoring and surveillance. All details are summarized in Table 1.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 4 Seismic Capability Index System of Highway Tunnels
### Analysis of the Evaluation Index
#### 4.1.3 Index Analysis of the Operation Management Factors
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tunnel Structural** & **Relevant Specifications and** & **Relevant Regulations or Experience** & **Specific Indicators** \\ \hline
**Factor Index** & **Engineering Experience** & **Relevant Regulations or Experience** & **Specific Indicators** \\ \hline
**Technical Specifications for** & (1) & The specification gives the recommended types of \\ Construction of Highway Tunnel” & & & \\ (JTG/T 3660-2020): & (2) & The section size directly affects the size of \\ \(\text{J}\text{G}\text{T}\) & (3)  
By consulting the relevant specifications and engineering experience (relevant regulations or experience are shown in Table 3), the technical condition indicators of civil engineering structures can be expressed by the technical conditions of the tunnel portal section structure, portal structure, lining structure and pavement structure. Emergency management level indicators include the use of an emergency management system and the use of emergency plan drills. Maintenance management level indicators can be evaluated by establishing and improving the maintenance management system and inspecting maintenance. The emergency management system is embodied in having a complete set of emergency organizations and emergency plans and responding to sudden earthquake disasters through coordination and cooperation among multiple departments. The emergency plan drill included the drill method and the number of drills. The emergency plan drill can provide the corresponding experience for the tunnel custody unit and improve the emergency response capacity. The maintenance management system refers to the implementation of a hierarchical responsibility system for tunnel management and should regularly assess the quality of tunnel maintenance and train technical personnel; inspection and maintenance conditions refer to whether the daily inspection, regular inspection, regular inspection, daily maintenance and maintenance work are comprehensive and timely. \begin{table}
\begin{tabular}{c c c} \hline
**Tunnel Structural** & **Relevant Specifications and** & **Relevant Regulations or Experience** & **Specific** \\
**Facter Index** & **Engineering Experience** & **Indicators** \\ \hline \multirow{7}{*}{ \begin{tabular}{c} Seismic constructions \\ \end{tabular}  } & \multirow{7}{*}{(1)} & The seismic measures of lining are required in the specification. For example, the seismic fortification of the tunnel, the change section of soft and hard strata and the change section of structural form should be equipped with seismic joints. For the earthquake-prone area, a certain number of seismic joints should be set up in the tunnel passing through this area. \\
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Technical Benefits to IoT Devices
#### 6.1.1 Improved Spectral Utilization  
Basically, CR-based technologies are designed to detect the presence/absence of PU signals in a licensed band. The absence of PU signals in a licensed band provides for opportunistic communication, whereas the presence of PU signals signifies the need for immediate rescission from the band. Such a novel communication paradigm aims to improve the use of underutilized bands leading to improved QoS experiences for end users. An example of a CR-LPWAN that provides for improved spectral utilization is the sensor network over white spaces (SNOW) solution, which takes advantage of white spaces in the TV band for opportunistic transmission [87]. Thus, the use of CR is promising as it will guarantee less constraints on the channel bandwidth size of LPWAN technologies, which will ultimately improve their capacity for higher data rates [85].
## 2 Materials and Methods
### Pre-Processing and Processing EO Data  
#### 2.4.1 Pre-Processing  
PRISMA L2D images were co-registered, with an Automated and Robust Open-Source Image Co-Registration Software (AROSICS v.1.11.0) algorithm using the Sentinel-2 image acquired at the closest date, to assure the co-registration (of about 0.5 pixel of RMS), and then smoothed by a Savvitzky-Golay filter (frame size of 7, 3 rd degree polynomial). A total of 55 bands, including overlapping bands (band numbers 63-68), atmospheric absorption bands, and bands with SNR below 100, were excluded from processing. The image dimensions (composed by 173 bands after band removal) were reduced by the principal component analysis (PCA) approach and then PCs were normalized by computing the standard deviation of the image. An optimum of 15 and 5 PCs were selected for the PRISMA and Sentinel-2 image dimension reduction, respectively. To reduce the effects of the spectral mixing of the plants cultivated by farmers around fields, a negative 30 m buffer (1 pixel) at the edge of the fields was considered.