esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Empirical Results
### Oil prices  
We start our forecasting exercise in evaluating the ability of the GPR index in forecasting oil prices (Table 2). Given that the dependent variable for all models is the WTI oil prices, we consider the country specific GPR index of each economy and the first lag of the WTI dependent variable as regressors. As we observe, the OLS and the SVR models exhibit similar forecasting and they both fail to outperform the RW model. Their performance is worse in the longer than the shorter horizons in MAPE terms. In these cases, the markets appear efficient, since we cannot outperform the RW model. Thus, trying to forecast these time series is impossible and any comparison between the two forecasting models is redundant. Thus, the GPR index has limited ability in foreseeing future oil price fluctuations, since it fails to outperform a simple guess about the future value of oil prices. This finding is interesting, especially for oil exporting countries such as Saudi Arabia, where we would expect that conflicts and terrorist acts that affect oil inventories would influence oil prices. Thus, unlike [19], who argue that their GPR index adheres to oil price fluctuations closely, our out-of-sample forecasting exercise reveals that the \"true\" forecasting ability of the emerging economies' index is limited, regardless of the economy under examination, oil producing or not. This finding could be attributed to the way that the index is constructed, since most articles in leading global newspapers have limited coverage of peripheral incidents and especially for emerging economies. To visualise the forecasts of our models, in Figure 2 we depict the actual and the forecasted oil prices for Saudi Arabia in the one-month ahead and in Figure 3 in the 12-months ahead forecasting windows respectively, for the period Jan-2017 to May-2018. The selection of the Saudi Arabia over the other emerging economies is motivated by the fact that oil is the main exporting commodity of this economy. We graph only the 17 last forecasts so that the figures are readable.
## 4 The Carbon Emissions Assessment on the Life Cycle of Recycled Concrete Block
### Discussion and Interpretation of Global Warming Potential (GWP)  
The calculated GWP (kg CO\({}_{2}\)-eq) related to the production of per unit volume of concrete blocks are listed in Table 7. The total index consists of the carbon emissions of each mixed material during quarrying, transportation, and production processes that take place within the system boundaries.  
Figure 9: The cradle-to-gate concrete blocks production processes.  
It can be understood from Table 7 that the production process of aggregates and the transportation of materials were main factors leading to differences in carbon emissions between NAC and RAC blocks. The total GWP for RAC block (with recycled aggregate replacement ratio was 100%) was 7.2% lower than that of NAC block. This opinion was inspiring for the promotion and application of RAC blocks. In addition, the calculated total GWP and the allocation of GWP by the major concrete ingredients and production processes are illustrated in detail in Figures 10 and 11.  
It is well known that cement production makes the greatest contribution to GWP, and it accounts for about 89.7% of total carbon emissions. Moreover, the transportation of raw materials to the concrete block plant was the second highest source of emission, which was about 7.5% of the total carbon emissions. The percentage of other concrete mixtures in total GWP was as follows: 1.2% for NA, 1.7% for RA, and 0.73% for sand.  
According to the data of each stage, the transport of raw materials was the decisive reason that the carbon emission of concrete decreased with the increase of the replacement ratio of RA. The shorter transport distance was mainly due to the raw material of the RA being waste concrete, the source was less restricted by the region, and the processing plant can be located close to the mixing plant. However, from the life-cycle inventory, the carbon emission of RAC block will exceed that of the NAC block when the transported distance of RA exceeds a certain range. In addition, the carbonization of RA to absorb CO\({}_{2}\) was also of great significance for the total GWP. However, the measured mechanical performance of the concrete block decreased with the incorporation of RAs. It is necessary to limit the proportion of RAs to 50%. Therefore, it can be concluded that the RAC blocks prepared by RAs (replacement ratio was 50%) do have great significance towards improving the environmental quality.
# Area-Efficient Universal Code Generator for GPS L1C and BDS B1C Signals
## 6 Conclusions  
This paper proposed an area-efficient universal code generator for GPS L1C signals and BDS B1C signals. Previously, MB UCG [14; 15] stored all the PRN codes by signal, channel, and satellite in the ROM, leading to the largest hardware complexity. To mitigate the huge hardware complexity, LG UCG [16] generates the Legendre sequences on the fly and stores the generated Legendre sequences in the RAM. Based on the stored Legendre sequences in the RAM, the PRN codes are generated. Compared to MB UCG [14; 15], LG UCG [16] saves hardware by generating the Legendre sequence on the fly. However, LG UCG [16] demands significant hardware complexity due to the use of RAM. Recently, WG UCG [17] has generated PRN codes with Legendre sequences of ROM instead of RAM. By utilizing the characteristics of ROM, the hardware complexity and initialization time for the RAM are reduced. Finally, the main idea of the proposed universal code generator is to apply a time-multiplexing technique to the previous universal code generator in order to save the common hardware resources. The proposed structure shares the common hardware according to the scheduling of a time unit instead of using the same hardware at the same time by duplicating the same hardware. Although time multiplexing can lower the hardware complexity at the cost of increasing the latency, the proposed code generator is carefully designed in order not to degrade the system performance. As a result of the synthesis using the CMOS 65 nm process, the proposed code generator has an area reduced by 98%, 93%, and 60% compared to the memory-based universal code generator [14; 15], the Legendre-generation universal code generator [16], and the Weil-generation universal code generator [17], respectively. Among the current GNSS signals, as shown in Table 1, the proposed universal code generator can support only GPS L1C and BDS B1C signals since the two GNSS signals are based on the Legendre sequence. However, it can be seamlessly extended if Legendre sequence-based codes are added to the GNSS in the future regardless of the RF band.  
Conceptualization, H.Y.; methodology, J.P. and H.Y.; software, J.P.; validation, J.P. and H.Y.; formal analysis, J.P. and H.Y.; investigation, J.P., M.K. and G.J.; resources, J.P., M.K. and G.J.; data curation, J.P., M.K. and G.J.; writing--original draft preparation, J.P.; writing--review and editing, J.P. and H.Y.; visualization, [PERSON]; supervision, H.Y.; project administration, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Navcours Co., Ltd (Daejeon, Korea).  
Not applicable.  
This work was supported by Navcours Co., Ltd. and National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2020M3H2A1078119).  
The authors declare no conflict of interest.
## 3 Conventional SFO Estimation Scheme
### Conventional Scheme C  
In previous work [22], the frequency error \(\phi(k_{i})\) can be get by using the \(g\)-lag correlation. Denote \(N_{o}\) as the number of observed OFDM blocks. Based on \(N_{o}\) blocks, the \(g\)-lag correlation at subcarrier \(k_{i}\) takes the form  
\[\Lambda_{g}(k_{i})=\sum_{l=g+1}^{N_{o}}\hat{R}_{l}(k_{i})\hat{R}_{l-g}^{*}(k_{ i}),\ \ 1\leq g<N_{o}. \tag{8}\]  
Its application to the PLC system with \(N_{o}\) observed OFDM blocks leads to \[\hat{\phi}(k_{i})=\frac{N}{2\pi N_{u}}\sum_{g=1}^{N_{g}/2}p(g)\arg\left\{\Lambda_{ g}(k_{i})\Lambda_{g-1}^{*}(k_{i})\right\} \tag{9}\]  
where \(\{p(g)\}\) represents the weighting coefficients. By using \(\hat{\phi}(k_{i})\), an estimate of \(\Delta_{s}\) is computed as  
\[\hat{\Delta}_{s}=\frac{J(1)B(2)-J(2)B(1)}{J(1)J(3)-J^{2}(2)} \tag{10}\]  
where  
\[J(n)=\frac{1}{N_{c}}\sum_{i=1}^{N_{c}}k_{i}^{n-1}|H_{l}(k_{i})|^{2},\;n=1,2,3 \tag{11}\]  
and  
\[B(n)=\frac{1}{N_{c}}\sum_{i=1}^{N_{c}}k_{i}^{n-1}|H_{l}(k_{i})|^{2}\hat{\phi}( k_{i}),\;n=1,2. \tag{12}\]  
Note that this scheme depends on some information about the channel statistics such as the quantities \(J(n)\) and \(B(n)\), and still demands heavy computational complexity.
## 3 Results  
There were seven males and nine females in the EG, with an average age of 23.56 \(\pm\) 2.42 years and average weight, height, and BMI of 65.44 \(\pm\) 13.14 kg, 170.13 \(\pm\) 8.35 cm, and 22.46 \(\pm\) 3.31 kg/m\({}^{2}\), respectively. There were seven males and nine females in the CG, with an average age of 24.50 \(\pm\) 3.06 years and average weight, height, and BMI of 62.00 \(\pm\) 12.43 kg, 167.63 \(\pm\) 7.31 cm, and 21.96 \(\pm\) 3.36 kg/m\({}^{2}\), respectively (Table 1).  
According to statistics from the paired \(t\)-test, after the exercises, the CVA and SSA showed significant differences for the EG and CG (\(p<0.05\)). The CVA and SSA effect sizes before and after the EG and CG tests were large. According to statistics from the independent \(t\)-test, after the exercises, there were no significant differences in the CVA and SSA change values between the two groups (\(p>0.05\)), and the CVA and SSA effect size between the EG and CG tests were below small (Table 2).  
According to statistics from the paired \(t\)-test, after the exercises, the UT, MT, LT, and SA muscle activity were significantly different in the EG and CG (\(p<0.05\)). According to statistics from the independent \(t\)-test, after the exercises, there were no significant differences in the UT, MT, LT, and SA change values between the two groups (\(p>0.05\)). Only the effect size of MT before and after the EG was large, and the other effect sizes were medium (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **EG** & **CG** & **\(p\)** \\ \hline Age (year) & 23.56 \(\pm\) 2.42 1 & 24.50 \(\pm\) 3.06 & 0.344 \\ Height (cm) & 170.13 \(\pm\) 8.35 & 167.63 \(\pm\) 7.31 & 0.375 \\ Weight (Kg) & 65.44 \(\pm\) 13.14 & 62.00 \(\pm\) 12.43 & 0.453 \\ BMI (kg/m\({}^{2}\)) & 22.46 \(\pm\) 3.31 & 21.96 \(\pm\) 3.36 & 0.671 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General characteristics of subjects.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Pre** & **Post** & **t (\(p\))** & **Change** & **Cohen’s d 1** \\ \hline \multirow{4}{*}{CVA} & EG & 48.42 \(\pm\) 3.65 & 53.90 \(\pm\) 2.46 & \(-\)7.465 (0.000 \({}^{*}\)) & 5.49 \(\pm\) 2.94 & 1.87 \\  & CG & 48.86 \(\pm\) 2.86 & 54.18 \(\pm\) 3.20 & \(-\)7.086 (0.000 \({}^{*}\)) & 5.32 \(\pm\) 3.00 & 1.77 \\  & t (\(p\)) & \(-\)0.382 (0.705) & & & 0.164 (0.871) & \\  & **Cohen’s d** & & & & 0.06 \\ \hline \multirow{4}{*}{SSA} & EG & 46.05 \(\pm\) 3.48 & 53.25 \(\pm\) 2.32 & \(-\)8.847 (0.000 \({}^{*}\)) & 7.21 \(\pm\) 3.26 & 2.21 \\  & CG & 45.99 \(\pm\) 3.66 & 53.19 \(\pm\) 2.17 & \(-\)9.846 (0.000 \({}^{*}\)) & 7.60 \(\pm\) 3.09 & 2.46 \\ \cline{1-1}  & t (\(p\)) & 3.65 (0.718) & & & \(-\)0.352 (0.728) & \\ \cline{1-1}  & **Cohen’s d** & & & & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of CVA and SSA in two groups.
## 3 Proposed Method
### Energy Saving-Based Super-Clustering  
To increase communication scalability and reliability, clustering is conducted after network creation. The number of cars grouped within the grid increases as the number of vehicles in this network increases. The fog nodes cluster the vehicles using the modified density peak clustering method (MDPC). Clustering increases network connectivity, energy efficiency, effective topology management, and minimizes latency. According to the suggested study, the vehicle is grouped according to its distance, node positions, energy, stability, velocity, degree, and dependability.
## 3 Results
### Rnss + Four-Gnss
TDOP, which has same characteristics as PDOP, ranges from 0.22 to 0.64 (average: 0.44). Figure 14: Satellite visibility and DOPs of the four-GNSS-NG+7-QZSS+11-NavIC: (**a**) visible satellites; (**b**) PDOP; (**c**) HDOP; (**d**) VDOP; (**e**) NDOP/EDOP; (**f**) TDOP. To conclude, adding 7-QZSS and 11-NavIC to the four-GNSS-NG creates a further increase of the number of visible satellites and a decrease of DOP values in the Asia-Pacific region, where the great majority of the world's population and industrial activity are concentrated. This benefits high-precision positioning and navigation applications in urban environments, such as intelligent transportation and automated driving.
## 3 Results
### Real Time (RT) Analysis
#### 3.2.1 Distance  
There were significant differences between conditions at RT1 (F(2, 18) = 6.144, \(p\) = 0.009), RT2 (F(2, 18) = 6.149, \(p\) = 0.009), and RT6 (F(2, 18) = 589.535, \(p\) < 0.001). The NC (distance = 1.79 \(\pm\) 0.18 km) was greater than the FC (distance = 1.72 \(\pm\) 0.16 km, \(p\) = 0.026, d = 0.41) and the SC (distance = 1.71 \(\pm\) 0.14 km, \(p\) = 0.046, d = 0.50) for RT1. In RT2, NC values (distance = 1.86 \(\pm\) 0.19 km) were higher than the SC (distance = 1.80 \(\pm\) 0.19 km, \(p\) = 0.032, d = 0.32). In RT6, significant differences were found between all three conditions (FC = 0.97 \(\pm\) 0.10 km, NC = 2.02 \(\pm\) 0.23 km, SC = 3.24 \(\pm\) 0.35 km, \(p\) < 0.001, d \(>\) 1.0) for all comparisons (see Figure 2B).
# Improved Calculation Method for Siphon Drainage with Extended Horizontal Sections
## 6 Conclusions  
The existing classical equations for siphon flow velocity are not applicable to siphon systems with extended horizontal sections because the dominant flow form in siphons is slug flow. Therefore, this study used theoretical analysis, numerical simulation, and experimental validation to explore the final performance of siphon flow velocity for different pipe diameters and lengths of long-distance siphon drainage. As a result, the following insights were obtained: (1) in the gas-liquid two-phase flow formed by the siphon, two subcategories of segmental slug flow with dry and wet force characteristics were generated due to the influence of pipe diameter and flow velocity; (2) the flow process of dry slug flow is closely related to the dynamic contact angle and the formation of the gas-liquid-solid three-phase moving contact line that causes a significant pressure drop; (3) in the long-distance siphon flow calculation process, the calculated length of the horizontal section was reduced to help improve the accuracy of the calculation. By distinguishing and calculating different flow types in the siphon drainage process, an improved method for long pipeline siphon flow velocity based on MATLAB iterative calculation was finally obtained, and its calculation results agreed well with experimental measurements. Furthermore, the error of the calculation results did not exceed 5%, which verifies the reasonableness and effectiveness of the method. Based on the abovementioned understanding, the application of siphon technology in future projects can be flexible in the construction plan by using the improved calculation method to select the pipe length, pipe diameter, and the number of boreholes according to the project drainage needs and topographic constraints. In addition, the following preliminary experience can be gained: the improved calculation method for siphon drainage technology provides a guiding direction for the selection of siphon pipe diameter, i.e., to form a fully developed wet slug flow to ensure no air accumulation in the pipe, and to avoid forming a dry slug flow as much as possible to prevent excessive pressure drop.  
Conceptualization, H.S. and Y.S.; methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China Key Project (42230/02) & National Natural Science Foundation of China Project (41772276).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Explainable Artificial Intelligence: Advancements and Limitations
## 4 Visual Explanations
### Pixel Attribution  
Pixel attribution techniques aim to find out image parts that contribute to their class predictions the most by creating heatmaps. These maps are used to mask input images to highlight specific parts crucial for a specific prediction. A heatmap is obtained by assigning an importance score to each pixel or a group of pixels. The intuition behind pixel attribution draws from the principles of linear models. In the case of a simple linear model where an input \(\mathbf{x}=(x^{(1)},\cdots,x^{(P)})^{\top}\) with \(P\) features is classified by, say, a single neuron without any non-linear activation, the class score for a particular class \(c\) is computed as \(S_{c}=\sum_{i}w_{c}^{(i)}x^{(i)}\), where \(\mathbf{w}_{c}=(w_{c}^{(1)},\cdots,w_{c}^{(P)})^{\top}\) is a weight vector. This intuitively means that each feature in \(\mathbf{x}\) contributes to the overall score based on its corresponding weight in \(\mathbf{w}_{c}\). A feature with a higher score, i.e., \(w_{c}^{(i)}*x^{(i)}\), has a greater impact on the class score, as \(S_{c}\) is a weighted sum of the feature values. Thus, features with higher values after being multiplied by their corresponding weights are considered more important in determining the class prediction. This simple idea is the foundation of pixel attribution methodologies which assign a score to each pixel in an input image based on its contribution to the class prediction.  
We can adapt the linear example above to a more complex image classification scenario achieved by CNNs. We let \(\mathbf{x}\in\mathbb{R}^{P}\) denote an input image with \(P\) pixels to be classified as one of the \(C\) classes. A trained model defines a mapping function \(f:\mathbb{R}^{P}\rightarrow\mathbb{R}^{C}\), which generates a probability vector expressed as \(g(\mathbf{x})=[S_{1},\cdots,S_{C}]\), where \(S_{c}\) represents the probability score for class \(c\). Attribution methods assign a relevance score to each pixel in \(\mathbf{x}\), denoted as \(\mathbf{r}_{c}=[r_{c}^{(1)},\cdots,r_{c}^{(P)}]\), where \(r_{c}^{(i)}\) represents the relevance score of pixel \(i\) for class \(c\). Obtaining these relevance scores helps us mask the input image accordingly and derive a saliency map [59]. As CNNs are complex architectures and include multiple layers with nonlinearities, the linear approach cannot be directly applied, i.e., obtaining \(\mathbf{r}_{c}\) is not as straightforward as \(\mathbf{w}_{c}\). However, various approximations have been proposed as detailed below.
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 2 Related Theories and Methods
### Ensemble Patch Transformation (EPT)
#### 2.1.1 Patch Process  
A \"patch process\" is a dependent patch of data at a given time \(t\), designed to identify local features of the data. Two types of patches are considered in EPT, rectangular and oval patches. The differences between the two types of patches are not very obvious. Using an oval patch allows us to obtain the central tendency of the data, as the generated curves are smoother. However, there is a need to capture abrupt changes in the data in highly non-smooth load sequences, and in this case, a rectangular patch is more suitable than an oval patch. Therefore, a rectangular patch will be used in this paper for our proposed short-term load prediction model. Only the rectangular patch is described below.  
Given a period \(\tau\), the patch \(P_{t}^{\tau}(X_{t})\) of the load sequence \(X_{t}\) at time \(t\) is centered at the point \((t,X_{t})\). The patch at this location \((t,X_{t})\) is a closed rectangle formed by the points \(\left(t+k,\min_{k\in[-\tau/2,\tau/2]}\{X_{t+k}\}-0.5\gamma\tau\right)\) and \(\left(t+k,\max_{k\in[-\tau/2,\tau/2]}\{X_{t+k}\}+0.5\gamma\tau\right)\), where \(k\in[-\tau/2,\tau/2]\), the width of the patch is \(\tau\) and the height is \(h_{t}^{\tau}\).  
\[h_{t}^{\tau}=\max_{k\in[-\tau/2,\tau/2]}\{X_{t+k}\}-\min_{k\in[-\tau/2,\tau/2] }\{X_{t+k}\}+\lambda\tau \tag{1}\]  
where \(\gamma\) is the scale factor.  
In order to make the upper and lower envelopes of the extracted load series more homogeneous and thus extract a more stable load trend, the value of \(\gamma\) is taken as 1 in this paper.  
The upper envelope of the rectangular patch is \(U_{t}^{\tau}(X_{t})\), the lower envelope is \(L_{t}^{\tau}(X_{t})\), and the average envelope is \(M_{t}^{\tau}(X_{t})\):  
\[U_{t}^{\tau}(X_{t})=\max_{k\in[-\tau/2,\tau/2]}\{X_{t+k}\}+0.5\gamma\tau \tag{2}\]  
\[L_{t}^{\tau}(X_{t})=\max_{k\in[-\tau/2,\tau/2]}\{X_{t+k}\}-0.5\gamma\tau \tag{3}\]  
\[M_{t}^{\tau}(X_{t})=\frac{1}{2}(U_{t}^{\tau}(X_{t})+L_{t}^{\tau}(X_{t})) \tag{4}\]
# Effect of Characteristics of Ground Motion on Seismically Induced Sliding Surface of Slopes
## 4 Results  
### Failure Surfaces Determined from Pseudo-Static Analyses  
For comparison purposes, the critical slip surfaces were calculated from pseudo-static analyses. They were calculated with the Morgenstern-Price method using the commercial software Slope/w [30]. The surfaces were calculated for zero horizontal load and equivalent loads that result from PGA = 0.25, 0.5, 0.75 and 1.0 g.  
A comparison of the effect of soil properties on the failure surface for static condition for the uniform slope inclined at an angle of 45\({}^{\circ}\) is depicted in Figures 4 and 5. Figure 4 shows the friction angle effect on the failure surface for a fixed cohesion value of 6 kPa. The results show that the failure surfaces shape is not highly affected by the soil friction angle. Similar trends are observed for other values of cohesions and slope angles. Figure 5 presents the change in the shape of failure surface with a variation of soil cohesion value for \(\phi^{\prime}\) = 30\({}^{\circ}\). As the cohesion increases, the static failure surface becomes deeper.  
a static analysis up to an amplitude of 0.50 g for the slope inclined at an angle of 45\({}^{\circ}\). The failure surface is revealed to widen at higher PGAs. For other slope angles and soil properties, similar trends were observed. The pseudo-static analysis outputs demonstrate that the sliding surface may be influenced by the applied load at higher values of input static equivalent load. The failure surfaces were determined from pseudo-static analyses by applying equivalent lateral loads. They are compared with those derived from parallel dynamic analyses, the details of which are presented in the following section.