esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[26] adopted the machine learning algorithm with a proposed traffic model to derive the stress spectra of welding joints in OSDs. [PERSON] et al. [27] applied the standard truck model with the measured weight in solving stress spectra, in which the Gaussian process surrogate model was used to improve efficiency. In this paper, for the first time the fatigue life of rib-to-deck joints in OSDs with TEUs are investigated, considering the novel engineering application and stochastic traffic model. Through the fatigue tests, the effectiveness of TEUs has been validated in enhancing the fatigue strength of rib-to-deck joints. A typical OSD bridge in China has been selected as the prototype for further investigation based on the test results. In the analysis, a stochastic traffic model has been employed in deriving the vehicle-induced stress spectra to simulate the fatigue action comprehensively. The framework of the stochastic model is established considering the randomness in both the vehicle model and their lateral distribution. The traffic model is first instantiated using the standard truck models in the design codes including the Chinese Code for Design called 'JTG' (China) [20], Eurocode 1 (Europe) [21] and AASHTO (USA) [22]. After that, the observed traffic data is also used to construct the model. The multi-scale finite element (FE) model is established for the prototype bridge to transform the traffic into stress, and the influence surface method is applied to improve computational efficiency. Monte Carlo simulations are carried out with a large sample size (i.e., \(10^{7}\) in this study) to derive the stress spectra for RD joints at different critical locations. Finally, comparative assessment is conducted on the fatigue life of RD joints in OSDs with TEUs and those with conventional U-ribs (CUs). The method applied in this study could be referred to as a general approach in the fatigue design of OSDs. Meanwhile, the result demonstrates the effects of position of RD joints on fatigue life. Moreover, this study also highlights the advantage of OSDs with TEUs in improving the fatigue life of RD joints.
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 4 Parameters Selection and Empirical Design  
### Selection of Variables and Source of Data  
In terms of variable selection, when forecasting stock index price and return, the opening prices, closing prices, lowest prices, highest prices, trading volume, and turnover are often considered. According to the extant literature, these six indicators of the stock index during four days or nine days before trading day are always used as input variables to predict future stock prices, for these indices would reflect adequate information of corresponding stock index [21; 22; 23; 24]. Investors will get as much information about the stock index as possible and reflect all the information obtained by themselves in these six characteristics of the stock index. This information may not only include information related to the stock index but also include other information that may affect the closing price of the stock index, such as macroeconomic development and industrial policy. Therefore, the opening price, closing price, lowest price, highest price, trading volume and turnover during nine days before the trading day are used as multi-dimensional input variables in this paper, and the closing price on the 10 th day is used as the predicted output variable for empirical prediction. The data used in this paper comes from the China Stock Market and Accounting Research (CSMAR) database.  
In terms of empirical time selection, considering that the Chinese capital market was affected by the stock market crash in 2015, the time period of the total sample space is selected in this paper as 15 January 2016 (the 10 th trading day in 2016) to 31 December 2020. That is, there are 21,762 pieces of data for each index of 1209 trading days. This paper uses the daily frequency data of the six characteristic variables of the 18 stock indexes in Table 1. In addition, this paper refers to the literature [24; 25; 26], the last 20 days, 60 days, and 250 days of the selected sample time are used as the test sets for short-term prediction, mid-term prediction, and long-term prediction, respectively. The other samples outside the forecast are the corresponding training sets.
###### Abstract  
Target-based point cloud registration methods are still widely used by many laser scanning professionals due to their direct and manipulable nature. However, placing and moving multiple targets such as spheres for registration is a time-consuming and tactical process. When the number of scans gets large, the time and labor costs will accumulate to a high level. In this paper, we propose a flexible registration method that requires the installation of only a low-cost cubical target: a die-like object. The method includes virtual coordinate system construction and two error compensation techniques, in which the non-orthogonality of the scanned facets, along with the unknown sizes of the dice are estimated based on projection geometry and cubical constraints so that three pairs of conjugate points can be accurately identified along the axes of the constructed coordinate systems for the registration. No scan overlap of the facet is needed. Two different low-cost dice (with a volume of 0.125 m\({}^{3}\) and 0.027 m\({}^{3}\)) were used for verifying the proposed method, which shows that the proposed method delivers registration accuracy (with an RMSE discrepancy of less than 0.5 mm for check planes) comparable to the traditional sphere- based method using four to six spherical targets spanning the scene. Therefore, the proposed method is particularly useful for registering point clouds in harsh scanning environments with limited target-setting space and high chances of target interruption.  
laser scanning; cubical target; dice; point cloud; registration; calibration; accuracy
## 3 Results
### Technical Analysis
#### 3.2.4 Power Quality Issues
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Power Quality Issues** & **Description** & **Efficacy** & **Efficiency** \\ \hline \multirow{4}{*}{[100]} & _Voltage (sag/swell, imbalance), frequency, real/reactive power, and harmonics_ & The inverter is controlled using the fuzzy space vector pulse-width modulation (FSV-PWM) technique & Simulations & The proposed method compared with the conventional ST-PWM control \\ \hline \multirow{4}{*}{[101]} & _Voltage (sag/swell, imbalance), frequency, real/reactive power, and harmonics_ & The inverter is controlled using the fuzzy space vector pulse-width modulation (FSV-PWM) technique & The proposed method’s results compared with PI—SLMMC control and without filter \\ \cline{1-1}  & _Harmonics reduction_ & (SLMMC)-based shunt active filter controlled by type-1 fuzzy & Simulations & The proposed method’s results compared with PI—SLMMC control and without filter \\ \hline \end{tabular}
\end{table} 
Table 7: Relevant papers considering power quality issues. Figure 6: Article distribution considering the approached PQ issue. 1—voltage sag/swell; 2—harmonics/THD; 3—voltage unbalance; 4—frequency variations; 5—low-power factor, reactive power; 6—voltage variation; 7—load frequency control; 8—frequency, harmonics/THD, and reactive power; 9—harmonics/THD and voltage sag; 10—harmonics/THD and reactive power; 11—frequency, voltage variations, harmonics/THD and reactive power; 12—voltage sag and flicker; 13—frequency variations and flicker; 14—reactive power and flicker; 15—voltage unbalance and flicker.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 4 Simulation Results
### Simulation Results

Table 4: Direct path identification rate of NC-MUSIC in UMa scenario with 20 MHz, 50 MHz and 100 MHz bandwidth. Figure 8: (**a**) CDF of the NC-MUSIC ranging error in UMi scenario with the cancellation time in the set {1,2,3}. (**b**) CDF of the NC-MUSIC ranging error in UMa scenario with the cancellation time in the set {1,2,3}. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bandwidth** & **Direct Path Identification Rate** & **False Positive Rate** & **False Negative Rate** \\ \hline
20 MHz & 87.22\% & 0.69\% & 12.08\% \\
50 MHz & 89.44\% & 0.00\% & 10.56\% \\
100 MHz & 85.00\% & 0.14\% & 14.86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Direct path identification rate of NC-MUSIC in UMi scenario with 20 MHz, 50 MHz and 100 MHz bandwidth. Figure 9a,b show, respectively, a comparison of cumulative distribution functions (CDF) between the NC-MUSIC and conventional MUSIC ranging error in UMi and UMa scenarios. Tables 7 and 8 show, respectively, the comparison of direct path identification rate between NC-MUSIC and conventional MUSIC in UMi and UMa scenarios. In the simulation experiment, the signal bandwidth is 100 MHz and the time of cancellation is 1. It can be seen from the figures and tables that the algorithm proposed in this paper is superior to the conventional MUSIC algorithm in ranging error and direct path identification rate both in UMi and Uma scenarios, and has good anti-NLOS ability and ranging performance in urban environment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cancellations** & **Direct Path Identification Rate** & **False Positive Rate** & **False Negative Rate** \\ \hline
1 & 72.22\% & 0.56\% & 27.22\% \\
2 & 77.92\% & 0.28\% & 21.81\% \\
3 & 71.39\% & 0.42\% & 28.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Direct path identification rate of NC-MUSIC in UMa scenario. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Direct Path Identification Rate** \\ \hline Conventional MUSIC & 69.86\% \\ NC-MUSIC & 85.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Direct path identification rate of NC-MUSIC and conventional MUSIC in UMi scenario. Figure 9: (**a**) CDF of the NC-MUSIC and conventional MUSIC ranging error in UMi scenario. (**b**) CDF of the NC-MUSIC and conventional MUSIC ranging error in UMa scenario. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cancellations** & **Direct Path Identification Rate** & **False Positive Rate** & **False Negative Rate** \\ \hline
1 & 85.00\% & 0.14\% & 14.86\% \\
2 & 85.83\% & 0.69\% & 13.47\% \\
3 & 80.00\% & 0.42\% & 19.58\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Direct path identification rate of NC-MUSIC in UMi scenario.
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 4 Platooning in Cellular V2X
### CAMs Exchange in the Platoon  
CAMs exchange in the platoon occurs cyclically. Each iteration is called an _update cycle_, and is triggered by the PL. Similarly to [24], it includes two steps:  
* PL-to-PMs communications. Initially, a PL transmits a CAM to all the PMs.
* PM-to-PM communications. Once the CAM from the PL is received by the platoon, each PM transmits its own CAM to update the one just behind.  
At the end of the update cycle, after all vehicles have obtained the data, they compute their next acceleration, based on the measured distance to the respective precedent vehicle (as provided by on-board sensors), and the data received from the preceding vehicle and the PL. Then, all vehicles simultaneously instruct their actuators according to the obtained data.  
The first transmission step (PL-to-PMs) is a broadcast transmission. The second one (i.e., the PM-to-PM) entails _several_ unicast transmissions. Two different schemes to schedule them can be envisaged.  
**Sequential transmission**. Each PM \(i\) transmits its data _in sequence_, after receiving the CAM from PM \(i-1\). In particular, at update cycle \(T_{c}\), PM \(i\) transmits to the respective follower, PM \(i+1\), the actual acceleration, \(a_{i}(T_{c})\), as computed at the end of the previous cycle \(T_{c}-1\). This scheme resembles the one described in [7] for IEEE 802.11p and is graphically sketched in Figure 2a. Sequential transmissions by adjacent PMs are typically enforced in the literature, when considering TDMA schemes on top of IEEE 802.11p [3; 6; 8].  
**Simultaneous transmission**. Since the information sent by each PM in its CAM at update cycle \(T_{c}\) is the acceleration _computed in the previous cycle_, \(T_{c}-1\), there is no need to impose a sequence on the  
Figure 2: Exemplary CAMs exchange within a platoon at a given update cycle. (**a**) Sequential CAM transmission mode; (**b**) Simultaneous CAM transmission mode.  
transmission of CAMs by PMs within the same update cycle. In other words, the new acceleration computation by each vehicle does not rely on information conveyed in the CAM from the preceding vehicle in the same cycle. A CAM exchange adhering to this scheme is exemplified in Figure 2b, where multiple PMs, sufficiently spaced apart, simultaneously transmit their CAMs using the same RBs under tight control of the eNodeB. Despite the behaviour of the controller algorithm does not demand for sequential transmissions, the viability of the alternative simultaneous approach has not been investigated so far, either for IEEE 802.11p or for C-V2X. This is mainly because the sequential scheme was deemed to better fit the _distributed_ IEEE 802.11p protocol. When considering, on the contrary, the _centralized_ architecture of C-V2X targeted in our work, we can argue in favour of simultaneous intra-platoon CAM transmissions, as long as they are scheduled to be interference-free by the eNodeB, as clarified in the following.
## 3 Results and Discussion
### Time Series Features--Transferability Analysis
Compared to other time series features, Z-Score VV is the shortest regarding the distance between the optimum and the time series feature for pixel-based classification (Figure 13). Thus, this time series feature seems to be the most relevant for the operational derivation of the class TFV, when using pixels as a foundation. The relevance of Z-Score VV is due to the fact that VV polarization is more influenced by the double-bounce effect indicating the presence of TFV. In comparison, VH is more influenced by different environment conditions and may change for different TFV types and study areas (Section 3.1). The distances of the Z-Score VV/VH to the optimal point differ from the distance between the Z-Score VV and optimal point only slightly. This shows that VH polarization also has an influence on the classification results and can be important in combination with VV polarization provided that the environmental conditions in the analyzed study area are known and characterized. This relevance is also shown in Figure 14, where the relevant feature based on objects is the Z-Score VV/VH, which contains both polarizations. Based on the findings in Section 3.1, which show that the increase in the VV time series at the date of the flood for TFV is more significant compared to the VH time series because the VH signal is influenced more by environmental conditions than by TFV, the Z-Score VV feature based on pixel elements is recommended for operational use. In addition, the mean value of UA and PA for the pixel-based classification for the Z-Score VV time series feature is highest (77.54%) compared to all other mean values of UA and PA. The combination of Z-Score VV + VH and Z-Score VV/VH based on pixel elements is recommended for operative use for extraction of the TOW and TFV, respectively. When no feature importance has to be calculated during the classification and the pre-processing of the S-1 data [8] has already taken place, the classification process takes between 1 and 5 minutes, depending on the available computing performance and the extent of the study area. The maximum extent that was analyzed is one-third of S-1 GRD images. By specifying the time series features the user, interaction is omitted and apart from the initialization of the algorithm the classification can be performed automatically. For the optimization and extension of the approach regarding different TFV types, further research in other study areas will be beneficial. Figure 13: Comparison of pixel-based time series features as a function of mean value and coefficient of variation. The statistical values (mean value and coefficient of variance) were calculated on the basis of the PA and UA accuracy values.
# Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses
## 3 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using R (version 4.3.2) and RStudio. We first ensured that the data were complete and addressed any missing values or outliers to maintain data integrity. Prior to correlation analysis, we tested the normality of the data using the Shapiro-Wilk test and inspected the scatter plots to confirm linearity. To address the first hypothesis, the trainees' pupil size metrics were correlated with their NASA TLX scores using [PERSON]'s correlation coefficient and simple linear regression. The relationships were analyzed and visualized using scatter plots generated with the GGally package ([PERSON] et al., Ann Arbor, MI, USA). For the second and third hypotheses, we assessed the equality of variances using [PERSON]'s test. Descriptive analyses were first performed on the original mean pupil size, adjusted pupil size, and NASA TLX scores across the task types and repetitions. Box plots and histograms were used to visualize the distributions. Subsequently, \(t\)-tests were performed to compare the outcome variables between the two tasks. Paired \(t\)-tests were used to compare outcome variables between two repeated trials. Data were reported as the means and standard deviations for each measure. All the statistical tests were two-tailed with a statistical significance level of \(p<0.05\).
## 4 Discussion  
Guaranteeing food quality and authenticity is a challenging task with common interest for all students, as they are also consumers of food, making this topic suitable for context-based learning. At these times, while the COVID19 pandemic exerts impact on production and transportation of food worldwide, citizen understanding of food production process is of particular importance.  
The first research question was: How can a complex food production process be modeled and evaluated? Modeling these processes requires system thinking which is developed while learning conceptual modeling with Object Process Methodology (OPM) using OPCloud. A trans-disciplinary team is needed to build such a model. Once built, the model can serve as part of learning materials for modeling context-based narratives focusing on environmental and food-related issues [7; 44; 52].  
The second research question was: What is the effect, if any, of an OPM-based conceptual modeling of food-related systems course on graduate students' systems thinking, modeling, and scientific understanding skills? We found a significant difference between students with modeling experience and those who were novices. As found before, gradual long-term processes are needed in order to develop these skills [26; 27]. This indicates that practicing the modeling skill might improve systems thinking. More research is needed to determine the long-term effect of the link between modeling and systems thinking skills. We recommend using these skills in studies of complex processes and sustainability issues, in line with some of the NGSS recommendations [2].  
The third research question was: What are the challenges and contributions that participants report during the learning processes? The students' feedback indicated that understanding food processes and modeling have similar trends. These similarities provides an indication that food and sustainability can be effectively used to develop systems thinking and modeling skills as they enable learning in context which contributes to the learning process [50; 51; 52].  
Overall, we found that the course contributed to the students, who are prospective teachers, in both the systems thinking and modeling skill perspectives, as well as to their scientific understanding and awareness to nutritional values and sustainability issues.
# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 5 Performance Evaluation  
### Simulation Setup  
In this section, we present simulation results to validate the effectiveness of the proposed optimization scheme, comparing it to five benchmarks.  
1. Benchmark 1--All local [31; 32; 33]: All MAR clients compute MAR tasks locally, without MEC offloading and SR.
2. Benchmark 2--All offload only [31; 32; 33]: All MAR clients offload MAR tasks to the MEC server without the SR process.
3. Benchmark 3--All offload and SR: All MAR clients request not only task offloading but also the SR process.
4. Benchmark 4--Minimum SR: Offloading decisions are optimum, but only the clients that have the lowest resolution data request SR tasks. It is one of the rule-based approaches for SR control.
5. Benchmark 5--Random [31; 32; 33]: The number of clients that request task offloading and SR is randomly decided.  
In our simulation, the total number of mobile devices is set to [5; 10; 15; 20; 25][34] to analyze the tendency from the number of clients (i.e., MDs). Each MD is assigned a single MEC server with the wireless channel bandwidth \(B=75\) MHz [13] and the computation resource \(f_{MEC}=15\) GHz. Also, each MD is allocated bandwidth resources and computation resources. The transmission power ranges from \(p=50\) mW to \(100\) mW randomly, and background noise power \(\sigma=-100\) dBm [34]. The workload density of computation tasks \(\omega_{n}\) is randomly distributed between \(500\) and \(1000\) cycles per bit [13], and the data size of video frame size \(d_{n}\) is randomly distributed between \(500\) and \(3000\) KB [20]. Each local device used by the MAR client has its own computing resource \(f_{n}=1\) to \(1.8\) GHz and a delay threshold of AR application \(L_{n}=20\) to \(25\) ms. The data size of offloaded tasks is \(80\%\) of the original tasks, although the proposed algorithm adopts the binary offloading decision in this paper. This is because the MAR tasks consist of a sequence of subtasks, and some subtasks cannot be offloaded to the MEC server [13].