esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of Previous Studies  
Various studies worldwide have focused on accident analysis, including both marine accidents and accidents in general. This study discusses methods for predicting marine accidents using marine spatial analysis and artificial intelligence (AI) ML against the backdrop of the relatively small number of systems such as MTSA. This section is divided into four sections. The first section analyzes research on accident identification from a spatial perspective. The second section examines systems related to risk assessment in maritime traffic safety. Next, studies on accident or risk prediction algorithm models are discussed in the context of various industries beyond maritime transportation. Finally, research that utilizes techniques for data sampling across diverse topics is analyzed.  
Previous studies on identifying accident risk using algorithms have several limitations. They often focus on the data classification methodology rather than final outcomes such as accident rates, or they derive results by comparing the importance factor values of models based on various machine learning techniques. However, they do not identify the variables that are important for predicting accident risks. In this study, we addressed the problem of imbalanced data sets containing accidents using the SMOTE technique mentioned in the fourth section. Subsequently, we derived improvement metrics for accident occurrence probabilities after applying the MTSA system and identified the variables that are important for predicting accident risks. Furthermore, considering the accuracy derived from the evaluation to represent the validity of the model, we used it as a correction factor to determine the reduction rate of accidents after MTSA from a conservative perspective. The technical novelty of our study lies in the application of these multifaceted methodologies to derive the final results.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Required Transmitter Power for Minimum Reliability
QoS scenarios Q2 and Q3 have almost the same minimum power requirements, although they have different QoS specifications. The main factor affecting power in Q2 is a high data rate, and in Q3, it is high reliability. Finally, the combination of high reliability, a high data rate and a long range makes the QoS scenario Q6 the most-power-consuming scenario. Regarding the coding schemes, from Figure 5 we can deduce the superiority in power of the BCJR-based algorithms. BCJR and logBCJR need less 0.2-0.3 dBm and 0.8-2.7 dBm power than maxlogBCJR and SOVA, respectively, for all QoS scenarios and system simulation scenarios. Moreover, polar needs more 1.1-2.2 dBm power than the BCJR-based algorithms. In terms of power, the worst algorithm is LDPC, which requires 1.5-3 more dBm than the BCJR-based algorithms.
# Internet of Things for Sensing: A Case Study in the Healthcare System
## 5 Experimental Setup  
We implemented the experiment in a large room (25 m \(\times\) 25 m) as shown in Figure 1. The experiment had two parts. In the first part, microwave-absorbing materials were deployed along the walls. Three tables, three chairs, and two persons at one time, one taking the measurements and a subject, were present inside the room, as shown in Figure 1a. The main reason for using microwave-absorbing material was to reduce multipath reflections that could affect the experimental results. In the second part, as indicated in Figure 1b, the experimental design was the same, but the microwave-absorbing material was removed, and it was observed that the percentage accuracy (described in Table 3) slightly decreased due to multipath propagation. S band antenna was deployed as a transmitter, and an omni-directional antenna served as a receiving antenna. The transmitter and receiver were placed 15 m apart at a 1-m height. The receiving antenna worked as the detection point. A channel information collection tool introduced by [PERSON] [33] was installed in the host computer would constantly receive 20 wireless CI packets per second. In order to differentiate the ND episodes from other daily activities, we tested five body motions: walking, squatting, push-ups, sitting on chair, and subject falling asleep due to narcolepsy. Each body motion disturbed the wireless medium; as a result, a unique CI signature was recorded, indicating a particular human activity. The experiments were performed for 16 ND patients.  
Figure 1: Experiment design for detection sleep attacks. (**a**) Experimental setup with the microwave absorbing material. (**b**) Experimental setup without microwave absorbing material.
## 3 Results  
### Identification, Classification and Properties of RcTPS Genes  
To identify _TPS_ genes in the _R. chinensis_ genome, we performed a BLASTP search using known _A. thaliana_ TPS protein sequences as queries. To further verify the identity of the detected rose TPS sequences, the hidden Markov model (HMM) profile of the conserved C-terminal domain (PF03936) and N-terminal (PF01397) domain of TPSs was downloaded from the Pfam database [58] and used as a query in the HMM search. All putative _TPS_ genes were further confirmed by SMART [59] and NCBI-CDD searches for the presence of the complete domain. We identified 49 _TPS_ gene candidates in the _R. chinensis_ 'Old Blush' genome (named _RcTPS01_ to _RcTPS49_; Table 1). The lengths of the proteins encoded by these _RcTPS_ genes varied from 455 (RcTPS07) to 857 (RcTPS49) amino acids. The predicted isoelectric points ranged from 5.06 (RcTPS43) to 8.91 (RcTPS47). The protein molecular weights varied between 52.89 kDa (RcTPS07) to 98.52 kDa (RcTPS49). According to two widely used subcellular localization predictors (Plant-mPLoc [60] and pLoc-mPlant [61]), 14 RcTPS proteins were predicted to be chloroplastic TPSs. The remaining 35 RcTPS proteins were predicted as chloroplastic or cytoplasmic TPSs.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
## 3 Experimental Validation
### Validation of Final UWB Pulse Expander  
Finally, the complete design of the pulse expander was tested. The fabricated design can be seen in Figure 22. The pulse expander was tested using the waveform shown in Figure 12. The pulse before and after expansion can be seen in Figure 23.  
It can be seen that the input pulse is expanded to a duration of about 455 ps, and some ripples can be observed in the tail of the pulse. When \(R_{\mathrm{iso}}=\infty\), the output pulse energy is 0.19 pJ. However, when \(R_{\mathrm{iso}}=100\)\(\Omega\), the output pulse energy is 0.11 pJ, and the duration is about 25 ps shorter. These results agree with the calculations and simulation results.  
Figure 21: Measured output waveforms from the pulse splitter with microstrip delay lines, showing relative time shifts between the pulses of 175 ps and 140 ps.  
Figure 23: Measured input (duration 210 ps) and output pulse waveforms of the pulse expander with \(R_{\mathrm{iso}}=\infty\) (pulse duration 455 ps) and \(R_{\mathrm{iso}}=100\Omega\) (pulse duration 430 ps). The actual delay between the input and output pulses is not shown to enable comprehensible pulse duration comparison.  
Figure 22: Fabricated UWB pulse expander.
## 3 Results
### Germany  
Data on greenhouse production in Germany is relatively scarce. [PERSON] (2011) suggest that only 3689 hectares are covered by greenhouses, of which an estimated 80% are glass greenhouses, 15% foil, and 5% stiff plastics, while 2500 hectares are heated [38]. The main crops cultivated in the German greenhouses are tomato, cucumber, certain plants and other crops. It is important to note that most of the facilities are relatively old; 43.1% of the total number of greenhouses, which corresponds to almost 1600 ha, were built before 1982. Even though some of these facilities were upgraded to comply with the modern-day standards, most of them are still outdated and only 10.6% of the total facilities were built after 2000. Furthermore, most of the production area under glass which is specialized for vegetable crops is owned by \"small\" farmers. Regarding holding size, 3800 facilities are 1000 m\({}^{2}\), almost 5600 facilities cover between 1000 m\({}^{2}\) and 5000 m\({}^{2}\) and the remainder of facilities are larger than 5000 m\({}^{2}\)[38].  
For Germany, [PERSON] et al. (2012) investigates the energy consumption in greenhouses for tomato and cucumber production. The biggest share of the energy inputs is attributed to heating purposes, whereas a small portion of the energy inputs account for fertilizers (Figure 7). The available data suggest that close to 13,000 GJ/ha are consumed in the greenhouse cultivation of both crops, of which 99.6% accounts for heating purposes. According to [PERSON] et al. (2020), black coal is the largest source of energy for heating purposes followed by natural gas and renewables (Figure 8 and Table A7) [39].  
Figure 6: Greenhouse energy consumption for selected crops in the Netherlands (GJ/ha) (based on [15; 37]).
## 5 Predicted Results Using the Model
### Evaluation of Model Precision  
To evaluate the goodness of fit for the developed \(DMC\) models, the R-square (\(R^{2}\)) and the mean square error (\(MSE\)) were used in this paper, which are defined as follows  
\[R^{2}=1-\frac{SS_{res}}{SS_{tot}} \tag{7}\]  
where \(SS_{res}\) denotes the sum of squares for residuals, and \(SS_{tot}\) denotes the sum of squares for total.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}-\hat{Y}_{i}\right)^{2} \tag{8}\]  
where \(Y_{i}\) denotes the predicted value, \(\hat{Y}_{i}\) denotes the actual value, respectively.  
The fitting precision of the \(DMC\) models as to the total of the testing set and the three sub-testing sets at different levels of strain rate is tabulated in Table 8.  
It was found that the models provided accurate and reliable predictive results with the test data. Moreover, it should be mentioned that due to the fact that different failure modes of RC columns were not distinguished in the development of \(DMC\) models (i.e., all the data were mixed), this may have exerted a certain impact on the precision of the predictive models. Thus, further studies are needed to improve the applicability of the models.
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 4 Hybrid Sensor Fusion Algorithm Overview
### Testing Obstacle Detection and Tracking using Kalman Filtering
#### 4.2.3 Extended Kalman Filtering
We linearize these functions by finding their Jacobians, so the linearized approximated system becomes:  
\[\hat{x}_{k+1}=F\cdot\hat{x}_{k}+\hat{\xi} \tag{4}\]0  
\[\hat{x}_{k+1}=F\cdot\hat{x}_{k}+\hat{\xi} \tag{4}\]1  
where \(F\) and \(H\) are approximated as: \(F=\frac{\partial f}{\partial x}\) and \(H=\frac{\partial h}{\partial x}\). For our tracking implementation, we use two sensor measurements: LiDAR and radar. From LiDAR point cloud data, we acquire the object's timestamped position in \(x\) and \(y\) directions: \(P_{x}\), \(P_{y}\). From radar data, we acquire the objects timestamped position in \(x\) and \(y\) directions: \(P_{x}\), \(P_{y}\) as well as the objects timestamped velocity in \(x\) and \(y\) directions: \(V_{x}\), \(V_{y}\). LiDAR sensor measurements are in Cartesian coordinates therefore linear. Radar sensor measurements are typically in polar coordinates (unless a separate radar signal processing is done), i.e., timestamped \(\rho\), the radial distance from the sensor to the detected objects, \(\phi\), the angle between the radar beam and the x-axis as well as \(\dot{\rho}\), the radial velocity along the radar beam. For this nonlinear case we need to map our predicted state vector from Cartesian to polar coordinates using a nonlinear measurement function \(h(\hat{x}_{k+1})\); in order to linearize the measurement function, we take its Jacobian using Equation (15):\[H=\left[ \begin{array}{ccc}\frac{P_{x}}{\sqrt{(P_{x})^{2}+(P_{y})^{2}}}&\frac{P_{y }}{\sqrt{(P_{x})^{2}+(P_{y})^{2}}}&0&0\\ -\frac{P_{y}}{(P_{x})^{2}+(P_{y})^{2}}&\frac{P_{x}}{(P_{x})^{2}+(P_{y})^{2}}&0&0 \\ \frac{P_{y}\cdot(P_{x}\cdot P_{y}-P_{y}\cdot P_{x})}{((P_{x})^{2}+(P_{y})^{2})^ {15}}&\frac{P_{x}\cdot(P_{y}\cdot P_{x}-P_{x}\cdot P_{y})}{((P_{x})^{2}+(P_{y})^ {2})^{15}}&\frac{P_{x}}{\sqrt{(P_{x})^{2}+(P_{y})^{2}}}&\frac{P_{y}}{\sqrt{(P_ {x})^{2}+(P_{y})^{2}}}\end{array} \right] \tag{15}\]  
where \(P_{x}\) and \(P_{y}\) are the object's positions in x and y-direction; \(V_{x}\) and \(V_{y}\) are the object's velocities in x and y-direction. We use these measurement values in the Extended Kalman filter algorithm.
## 2 Methodology
### ArcGIS Model Builder  
The model builder module in ArcGIS Pro 3.0 was utilized to automate the leak detection workflow process. Model builder is a tool used to create and modify geoprocessing models. It displays the selected functions in a diagram format, chaining separate tools together in a manner that enables the output of one function to be the input of the subsequent one [43]. The flowchart in Figure 3 illustrates the methodology used in this study. The steps of the proposed method are shown below:  
* Read the WDN sensor recordings of the three leakage predictors: pressure, flow, and chlorine.
* Apply the threshold values of the three readings.
* Use the buffer around the sensors' locations and intersect operators to assess the variability in the three predictors throughout the WDN.
* Use IDW to create raster layers for pressure, flow, and chlorine in ArcGIS Pro for the WDN.
* Use GWR to seamlessly integrate the three predictors and, at the same time, automate the leak detection workflow.
* Identify potential leakage locations.  
A drone can then be deployed to the areas of the potential leakage locations determined by our proposed method. The IR images taken for the potential leakage locations would be processed to accurately identify the leak locations. The IR drone images were processed using our method in [44], which is based on multitemporal infrared thermography.  
Figure 3: Proposed methodology flowchart.  
The pressure and flow data were inserted into the GIS model and compared with the thresholds. Subsequently, an IDW was created for each parameter to provide values to unmonitored locations. Following this procedure, a fishnet mimic of the area was created to select certain portions of the network using the query table tool (values below zero for pressure and above zero for flow). The vector fishnet was narrowed down, using the spatial join tool, to the locations suspicious of a leak, identified using the query table in the previous step. Each of the mentioned steps was applied to the pressure and flow individually. Next, the suspicious locations were merged using the intersect tool to identify the areas with drops in both pressure and flow. The same tool was later employed to select the parts of the network experiencing those drops. Finally, the chlorine values in the vicinity were identified by the intersect tool and were further compared with the minimum acceptable value using the query table to further narrow down the leak locations. The roles of each of the mentioned functions are discussed below.
## 3 Proposed BC-UAC Method
### The Reception Scheme Based on Segmented TFC
#### 3.4.1 Channel Estimation and Equalization  
Due to the large delay spread and obvious Doppler effect of the underwater communication channel, channel estimation and equalization technology should be adopted to improve the transmission performance. If the Doppler frequency offset is eliminated after variable sampling, the impulse response of the time-invariant underwater acoustic multipath channel can be expressed as:  
\[h(t)=\sum_{l=1}^{L}\alpha_{l}\delta(t-\tau_{l}) \tag{7}\]  
where \(L\) is the number of multipaths, and \(\alpha_{l}\) and \(\tau_{l}\) are the amplitude decline and time-delay of the \(l\)-th path, respectively.  
At the receiver, the multipath signals with different attenuations and delays are superimposed, which will introduce inter-symbol interference. In order to alleviate the influence of the multipath effect, the VTRM technique [34] combined with an IOMP algorithm is adopted for the channel equalization. The workflow of the channel estimation and equalization in the proposed BC-UAC method is illustrated in Figure 9, where the IOMP algorithm is used for the channel estimation, and the estimated results are used for the channel equalization.  
Figure 8: The frequency-shift in BC-UAC-FS.  
The IOMP algorithm is an improvement of OMP. Considering that the sparsity of the actual underwater channel is unknown, the number of iterations of the OMP algorithm depends on the effective length of the channel, which results in over-calculation. In the IOMP algorithm, a weak selection factor is introduced to obtain several matching vectors in each iteration. The residual threshold can be set as the ending condition of iteration. Hence, the IOMP algorithm speeds up the iteration speed, but the result of each iteration is suboptimal, and the channel estimation is slightly less accurate than that of the OMP algorithm.  
Let \(\hat{h}[n]\) denote the channel impulse response (CIR), estimated by the IOMP algorithm at sampling time \(n\), \(\hat{h}[-n]\) be the CIR after time-reversal, and \(r[n]\) be the received bionic modulated signal. After the convolution of \(r[n]\) and \(\hat{h}[-n]\), the virtual received bionic modulated signal \(r^{\prime}[n]\) is obtained. Hence, the process of the VTRM can be expressed as:  
\[\begin{array}{ll}r^{\prime}[n]&=r[n]*\hat{h}[-n]\\ &=(s[n]*h[n]+\varsigma[n])*\hat{h}[-n]\\ &=s[n]*h^{\prime}[n]+\varsigma[n]*\hat{h}[-n]\end{array} \tag{8}\]  
where \(h[n]\) is the real underwater channel, \(h^{\prime}[n]=h[n]*\hat{h}[-n]\) is the virtual time-reversal channel, \(\varsigma[n]\) is the additive noise in the underwater channel, and \(s[n]\) is \(s_{\text{TD}}[n]\) in (3) or \(s_{\text{TS}}[n]\) in (6). \(h^{\prime}[n]\) can be regarded as the equivalent channel through which the bionic communication signals pass. When \(\hat{h}[n]\) is close to \(h[n]\), the correlation peak amplitude is much higher than the side lobe. It is equivalent to the multipath signal energy superimposed on the main path signal. Hence, the VTRM can suppress multipath interference and achieve equalization.
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 5 Analysis of Results  
In this section, we present the analysis of papers that were included in our study following the methodology described in Section 4. Our search in Scopus on 25 May 2021 returned 217 papers of which 38 were selected as the most relevant to our research. Those are papers that use blockchain and at the same time try to ensure privacy in various areas of IoV. In Figure 2, we show the total number of papers that the query had returned as well as the number of papers that have been finally included and excluded in this study. The included papers are scattered between 2018 and 2021 showing a steady increase. More specifically, three papers were found in 2017, none of which correspond to our field of research, while for 2018, the search returned 17 papers, of which three were selected as the most relevant to our research questions. Moreover, in 2019 the query results were 49 papers and 11 were selected, while for the year 2020, 17 papers were selected as the most relevant. Finally, in 2021 the most relevant papers were only seven from a total of 35. Note that the small number of papers in 2021 compared to the previous years was due to the fact that the query was submitted in May, hence less than half of the year's papers were returned. As mentioned above, we chose only papers that used blockchain and tried to solve privacy issues on IoV. The IoV service areas that utilise privacy-preserving blockchain-based solutions (RQ1), as these have been identified in the analysed papers, are: _Authentication_, _Traffic Conditions_, _Data Exchange_, _Dynamic Navigation_, _Reputation Management_, _General Services_ and _Advertising_. Based on this categorisation, a comparative analysis of those papers is presented in Table 1 answering our research questions in this paper and also an overview of this analysis is shown in Figure 3. In this table, many of the proposed schemes have been named by the authors. For those that are not referred to by any name, we chose to name them by the initial letter of the distinguished words found on the paper's title to facilitate distinguishability. Figure 2: Number of papers included and excluded in our review.
## 3 Proactive Geospatial Information Services (PGIS)  
### Concept of PGIS  
Most of the traditional geospatial information services only provide simple data dissemination functions, which cannot meet the requirements in modern geospatial applications as discussed in this paper. For example, there are gaps between GIS systems and remote sensing platforms and gaps among different GIS systems, _i.e._, users have to spend lots of effort to transform data from one system to another. Facing the increasing explosion of data, information, and knowledge nowadays, it is desirable to develop a new GI paradigm with a simple interface but powerful capability to achieve complex tasks. One way to provide complicated GI services is to aggregate various types of GI service resources from small granularities based on user's specific demands. This on-demand aggregating service is defined as proactive geospatial information service (PGIS).  
The fundamental characteristics of PGIS include autonomy, self-adaptation, and aggregation and cooperation. Autonomy describes the capability of reaction to user's demands and achievement of the demands automatically or semi-automatically. For instance, an ideal PGIS system should be able to find appropriate sensor, data, and processing services by itself with little or no human interaction. Self-adaptation represents the capability of adjusting the system structure and behavior according to the changes in user's demands or system running environments. Aggregation and cooperation require to comprehensively share, aggregate and cooperate with various geospatial services to satisfy proactive and complex user demands.