esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and Data Description  
### Energy Intensity in I-O Frameworks  
For environmentally extended I-O modeling, the imports assumption and I-O model selection are important components of empirical analysis. Since most embodiment studies use the Leontief I-O model [26; 27; 28] and the non-competitive imports assumption, which is more suitable for embodiment studies [29], we consider only the Leontief I-O model with non-competitive imports assumption.  
I-O models can describe the relationship between final demand and energy consumption from the demand side. The basic single-area I-O model can be expressed by Equation (1):  
\[x=Ax+y \tag{1}\]  
where \(x\) is the vector of total output, \(y\) is the vector of final demand, and \(A\) is a direct consumption coefficient matrix that reflects the input amount of intermediate goods in each sector required for the production activities in that sector. Through a simple transformation, Equation (1) can be rewritten as a Leontief model:  
\[x=(I-A)^{-1}y=By \tag{2}\]  
where \(B\) is the Leontief inverse matrix whose elements reflect the complete influence of a sector's unit production activities on the output of various sectors. In Equation (2), the total output is pulled by final demand through the Leontief inverse matrix. Correspondingly, the value added and energy consumption formation process of a region can also be examined from the same perspective, as shown in Equations (3) and (4). \(E\) is defined as the total energy consumption of a country:  
\[GDP=v=\hat{d}\cdot B\cdot y=1^{\prime}\cdot H\cdot y \tag{3}\]  
\[E=f_{v}{}^{\prime}\cdot v=f_{v}{}^{\prime}\cdot H\cdot y \tag{4}\]  
where \(v\) is the value added vector; \(d\) is the vector of the value added coefficient, which is the value added created by unit production activities; \(H=\hat{d}B\) is the value added requirement coefficient matrix, whose elements reflect the total pulling effect of the unit production activities of a sector on the value added of each sector; and \(f_{v}\) is the sectoral energy intensity coefficient vector, which is the energy consumption generated by the value added by each sector.  
The EEI is defined as the ratio of embodied energy to the embodied value added. According to [15], in the single-region I-O model, the energy intensity on the production side can be expressed as the EEI of the final demand. Thus, the energy intensity (energy consumption per unit of GDP) of a country can be expressed as the weighted sum of the EEI of each sector  
\[EEI=\frac{E}{GDP}=\frac{f_{v}{}^{\prime}\cdot H\cdot y}{1^{\prime}\cdot H \cdot y} \tag{5}\]Industry weighted form:  
\[EEI=\sum_{j=1}^{n}w_{j}EEI_{j} \tag{6}\]  
where \(j\) is the industry number and \(w_{j}\) is the share of sector \(j\)'s value added embodiment in the final consumption to the total value added.
## 1 Introduction
In [17; 18], the current differential protection (CDP) with restraint characteristics is constructed using the positive-sequence current fault component (PSCFC), which effectively solves the weak feeding problem of the active distribution network. However, the protection sensitivity is reduced or even refuses to trip in the case of high resistance faults or those containing the unmeasurable load branch (ULB). Reference [19] proposed a CDP method based on sequence components, which overcomes the problem of the low sensitivity of traditional differential protection by introducing a differential coefficient but does not consider the influence of the ULB. In [20], the longitudinal protection is constructed using the amplitude and phase characteristics of the PSCFC on both sides of the line during internal and external faults. It has an adaptive braking threshold, but it requires strict data synchronization. Although the CDP proposed in [17; 18; 19; 20; 21; 22] improves the selectivity of protection, it is still restricted by one or more of the following factors: (1) high fault resistance will reduce the sensitivity of protection; (2) the ULB will affect the reliability of protection; (3) high data synchronization requirements; and (4) DG random output will lead to protection misjudging. It is necessary to propose a complete CDP scheme to address the shortcomings in the above research;
3. Since it is difficult to fully adapt to complex fault situations in the distribution network using only local information, centralized protection schemes using network-wide information have been proposed [23; 24; 25; 26]. The distribution terminals upload the multi-point measurement information, and the master station uses the multi-point measurement information to identify the fault. Due to its dependence on the master station for decision-making, the centralized protection scheme will fail when the communication or master station fails, which limits the application of this method in the existing distribution network. In conclusion, the CDP has absolute selectivity, which is one of the most effective methods to solve the new problems of active distribution network protection [27]. To break through the limitations of traditional CDP, this paper proposes a novel CDP based on an adaptive phase angle compensation coefficient. The remainder of this paper is organized as follows: Section 2 analyzes the amplitude and phase characteristics of the PSCFCs in active distribution networks. The action characteristics of the traditional CDP and the principle of the novel CDP based on the adaptive phase angle compensation coefficient are described in Section 3. Section 4 introduces the influence of unmeasured load branches on the proposed protection and corresponding countermeasures. Section 5 analyzes the error of the fault data self-synchronization algorithm and the ability of the protection against synchronization error. In Section 6, the effectiveness of the proposed protection scheme is verified by PSCAD/EMTDC. Section 7 summarizes the results and the contributions of this paper.
## 3 Results and Discussion
### Influence on Mounting Structure
\[P^{\prime}_{mp} \approx V_{mp}(I_{mp}+\Delta I^{\prime}_{mp})\rightarrow\Delta P^{ \prime}_{mp}=P^{\prime}_{mp}-P_{mp}\approx V_{mp}\Delta I_{mp} \tag{8}\] \[\rightarrow \frac{\Delta P^{\prime}_{mp}}{P_{mp}}=BPG\approx\frac{\Delta I^{ \prime}_{mp}}{I_{mp}} \tag{9}\]  
In Equation (9), if the ideal condition is analyzed, \(\Delta I^{\prime}_{mp}\) is approximately \(\Delta I^{\prime}_{sc}\), thereby facilitating the estimation of ideal BPG (refer to Table 5). To derive this estimation, \(\Delta I^{\prime}_{sc}\) was approximated as the median value for voltages below 9 V, considering that the irregularities of the curves preclude confident linear regression analysis for low voltages (before the deepest kink). These medians are depicted as dotted lines for each case in Figure 10a, with the corresponding values provided in Table 5 (\(\Delta I_{sc}\)). As can be seen in Table 5, the presence of light obstruction by the mounting structure can reduce to a third (tilt 0\({}^{\circ}\) or 15\({}^{\circ}\)) or half of the potential BPG (tilt 30\({}^{\circ}\) or 45\({}^{\circ}\)). In addition, as shown in Figure 9, the contribution of the secondary face could be practically wasted. It is crucial to solve, or at least fully understand, the problem of non-uniform illumination in order to take full advantage of bifacial PVs. Its presence can severely reduce bifacial contribution. This is particularly challenging since low non-uniformity (as the one required in IEC 60904-1-2) is hardly achievable under real outdoor conditions [10] or even in controlled indoor laboratory tests [42]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**bPV2-bPV1**} & \multicolumn{2}{c}{**bPV1**} & \multicolumn{2}{c}{**BPG**} & \multirow{2}{*}{\(\Delta\)**\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\)\(\Delta\The impact of non-uniform rear illumination could potentially be reduced by bPV modules of half-cut cells since, in the same sense of partial shading, the current reduction in the secondary face due to non-uniformity would be related to one-sixth, rather than one-third, of the module surface. However, some recent results suggest that half-cut bPV modules could produce a new hotspot phenomenon (\"hotspot mirroring\") [43]. We are currently conducting tests to investigate in this direction. An additional mitigation measure has been proposed when reinforced bars are placed in the mounting structure (similar to those in Figure 4) [21]. If the distance between the bars and the module is equal to the beam width, significant BPG recovery (more than 60%) could be achieved by a proper structure configuration (see [21] for more details).
## 2 Materials
### Data Source and Preparation of Influencing Factors
#### 2.2.2 Influencing Factors  
Considering the geological conditions, geographic environment, and characteristics of landslide disasters in Badong County, a 30 m \(\times\) 30 m raster was selected as the basic unit for spatial landslide prediction in accordance with the technical requirements of the China Geological Survey's geological disaster investigations (1:50,000). Based on prior research, this study utilized ArcGIS version 10.7 to extract 14 influencing factor layers (Figure 2) as indicators for modeling landslide susceptibility in the study area. These factors included four topographic-geomorphic variables (elevation, slope, aspect, and curvature), three geological condition variables (distance from fault, EGRG, and slope structure), five hydrological condition variables (distance to river, TWI, flow path length, flow width, and mean annual rainfall in 2020), and two surface cover variables (land use type and NDVI).  
In accordance with the \"1:50,000 Engineering Geological Survey Specifications\" and considering the geological characteristics of the study area, such as rock type, structure, and strength, the region was classified into nine engineering geological rock groups (Table 1).  
The slope structure in this study was classified based on the relationship between stratigraphic inclination, dip slope gradient, and direction, with the specific classification criteria presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Types of Slope Structures** & **Definition** \\ \hline
1 & Downward dip slope & \(((\mid\alpha-\beta\mid\in(0,30^{\circ}])\) or \((\mid\alpha-\beta\mid\in[330^{\circ},360^{\circ})))\) \\  & & and \((\gamma>10^{\circ})\) and \((\delta>\gamma)\) \\
2 & Synclinal slope & \(((\mid\alpha-\beta\mid\in(0,30^{\circ}])\) or \((\mid\alpha-\beta\mid\in[330^{\circ},360^{\circ})))\) \\  & & and \((\gamma>10^{\circ})\) and \((\delta<\gamma)\) \\
3 & Parallel slope & \((\mid\alpha-\beta\mid\in(30^{\circ},60^{\circ})\) or \((\mid\alpha-\beta\mid\in[300^{\circ},330^{\circ}))\) \\
4 & Lateral slope & \((\mid\alpha-\beta\mid\in(60^{\circ},120^{\circ})\) or \((\mid\alpha-\beta\mid\in[240^{\circ},300^{\circ}))\) \\
5 & Anticlinal slope & \((\mid\alpha-\beta\mid\in(120^{\circ},150^{\circ})\) or \((\mid\alpha-\beta\mid\in[210^{\circ},240^{\circ}))\) \\
6 & Reverse slope & \((\mid\alpha-\beta\mid\in(150^{\circ},180^{\circ})\) or \((\mid\alpha-\beta\mid\in[180^{\circ},210^{\circ}))\) \\ \hline \hline \end{tabular} Where \(\alpha\) is the slope direction, \(\beta\) is the rock inclination, \(\gamma\) is the rock dip, and \(\delta\) is the slope gradient.  
\end{table}
Table 2: Slope structure classification criteria.  
Figure 2: Influencing factors used: (**a**) elevation; (**b**) slope; (**c**) aspect; (**d**) curvature; (**e**) distance to fault; (**f**) EGRG; (**g**) slope structure; (**h**) distance to river; (**i**) TWI; (**j**) flow path length; (**k**) flow width; (**l**) mean annual rainfall; (**m**) land use type; and (**n**) NDVI.
## 4 Results
### Correlation Analysis  
Assessing the relationship between a bridge's performance, deterioration, and maintenance, as they relate to various bridge surface types, can offer valuable insights into the maintenance trends for specific bridge decks. The results of the correlation analysis are shown in Table 8. If a strong correlation is found, this would suggest a linear relationship, indicating that most of the variability in how bridge decks are maintained is linked to the deterioration rate and overall performance of the bridge deck. In addition to the correlation between maintenance, performance, and deterioration, understanding the influence of each variable can shed light on their distinct role.  
Generally, the correlation between performance and deterioration ranged from non-existent to weak. When examining specific surface wearing types, we found that the impact of deterioration and performance on maintenance was mostly weak. The strongest positive correlations between performance and maintenance were observed in the cases of Bituminous (0.131) and Wood or Timber (0.080). Conversely, the strongest negative correlations between performance and deterioration were seen in Monolithic Concrete (\(-0.270\)), followed by Monolithic Concrete for performance and maintenance (\(-0.316\)). A strong correlation, if observed, would point to a linear relationship, suggesting that the changes in bridge maintenance were primarily driven by deterioration and overall performance. However, the unique relationships between bridge health metrics and surface-wearing types suggest a non-linear relationship and limited influence on the maintenance patterns of the bridge decks. Such an analysis offers valuable insights that could aid in creating predictive models for future maintenance and bridge performance across varied surface types.  
Figure 6: Comparison of the baseline characteristics and performance of various bridge wearing surfaces including Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Slump Concrete, and Other.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute** & **P-M** & **P-D** & **D-M** \\ \hline All & \(-0.155\) & \(0.172\) & \(-0.259\) \\ Monolithic Concrete & \(-0.316\) & \(-0.270\) & \(-0.260\) \\ Gravel & \(-0.075\) & \(0.023\) & \(-0.239\) \\ Wood or Timber & \(0.080\) & \(-0.068\) & \(-0.321\) \\ Bituminous & \(0.131\) & \(0.050\) & \(0.171\) \\ Other & \(0.008\) & \(0.128\) & \(-0.203\) \\ Low Slump Concrete & \(-0.034\) & \(0.371\) & \(0.010\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Correlation analysis between Performance (P), Deterioration (D), and Maintenance (M) by Surface Wearing Type of Bridge Deck in the state of Nebraska.
### Major Contributions  
This work offers the following original contributions:  
1. A New Algebraic Framework: We develop a generalized framework for constructing alternant codes over residue class rings of Gaussian integers, \(\mathbb{Z}_{n}[i]\), extending classical BCH principles.
2. Factorization of \(x^{n}-1\): The factorization of \(x^{n}-1\) over \(\mathbb{Z}_{n}[i]\) is analyzed to form the theoretical basis for parity-check matrix construction.
3. Efficient Encoding and Decoding Algorithms: We provide procedures for encoding and syndrome-based decoding suited for Gaussian-integer based alternant codes, optimized for computational efficiency.  
Performance Evaluation:  
The proposed codes are evaluated in terms of redundancy, error correction capability, and complexity, particularly for medium-length codewords relevant to the IoT and segments of 5G.  
5. Application Potential:  
We demonstrate that the flexibility of \(\mathbb{Z}_{n}[i]\)-based codes opens opportunities for future use in quantum-safe and noise-resilient communication systems.
## 5 Prototype and Potential Applications  
Based on the previous method, we developed a prototype system called the adverse-weather-affected traffic incidents perception and warning system to visualize the collected data, extract features, statistics results, and alerting incidents, as shown in Figure 6.  
The prototype system has been initially deployed in CMA. The proposed method demonstrated advantages of feasibility, timeliness, and high accuracy compared to the traditional physical methods. Both the model and the system are still iteratively improving and upgrading according to CMA's management and decision requirements. In addition, the prototype system provides a consistent service for CMA, including adverse-weather-affected city-level traffic situation alerting and adverse weather impacts on traffic situations. In particular, the prototype system successfully forecasted the serious traffic situation caused by typhoons Hato and Pakhar in the south of China in mid-July 2017. Our proposed social approach will also be replicated to support the long-term plan, is the so-called \"Meteorology Plus Plan\" of the CMA. The plan includes \"Meteorology + Agriculture,\" \"Meteorology + Emergency Management,\" \"Meteorology + Tourism,\" \"Meteorology + Disease Prevention,\" \"Meteorology + urban planning,\" etc.  
Figure 6: Weather-related traffic incidents perception and warning system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Actual Incidents**} \\ \cline{3-4}  & & \begin{tabular}{c} **Hot Traffic Incident** \\ **(Warning Level 1)** \\ \end{tabular} & \begin{tabular}{c} **Non-Hot Traffic** \\ **Incident** \\ \end{tabular} \\ \hline \multirow{3}{*}{Forecasting incidents} & \
\begin{tabular}{c} Hot traffic incident \\ (warning level 1) \\ \end{tabular} & 7 & 0 \\ \cline{2-4}  &
\begin{tabular}{c} Non-hot traffic incident \\ \end{tabular} & 4 & 335 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrix of proposed alerting model in Beijing.  
In addition to the meteorological administration departments, the potential applications of our proposed method have also attracted attention from transportation administration departments like Qingdao municipal commission of transport, and the preliminary intention of cooperation has been reached. Weather is closely related to people's productiveness and activities. The potential value of our proposed method still needs to be explored. We believe that, with further development and improvement, the prototype could be widely applied by governments, businesses, and individuals in fields that are highly correlated with meteorology like agriculture, tourism, and health care.
## 7 Discussion of the Filtering Impact on Security and Privacy
### Influence of Filtering on STRIDE Impacts  
Based on the previous assessments, influences on the threat impacts are aggregated conservatively, i.e., they are aggregated to show the least possible impact by always taking the minimum influence that was assessed for any of the attack vectors that lead to that impact. The results are presented in the Tables 11-14. \begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline  & **Spoofing** & **Tampering** & **Repudiation** & **ID** & **DoS** & **EoP** \\ \hline Smart Home Device (SHD) & \multirow{2}{*}{DWA} & MFU & SDS & \multirow{2}{*}{EPI} & DNA & \\  & & RCD & SDR & & DLF & \\ \hline Essential Server (ES) & SWA & & & SCD(ES) & SNA & IDC \\ \hline \begin{tabular}{l} Unessential \\ Server \\ \end{tabular} & & & SCD(US) & & NRO \\ \hline \
\begin{tabular}{l} SHD \(\rightarrow\) ES \\ \end{tabular} & & & SCD(SHD\(\rightarrow\)ES) & \\ \hline
\begin{tabular}{l} SHD \(\rightarrow\) US \\ \end{tabular} & & & SCD(SHD\(\rightarrow\)US) & \\ \hline \end{tabular}
\end{table} 
Table 11: Influence of _Bark_ on STRIDE impacts for \(S_{\delta}\). Rating the possible improvement through the usage of filtering: no/only low (grey), medium (yellow) and high (green). There are only a few threat impacts for which _some_ influence was found. These are the threats of wrong actions performed by a _Smart Home Device (DWA)_, as well as the loss of functionality (_DLF_) of such appliances in \(S_{\gamma}\), for which _no or only low_ influence was found in the other scenarios (_DWA_, _DLF_). This discrepancy can be explained by considering that for \(S_{\gamma}\), no data flows from _Smart Home Devices_ to _External Servers_ are modelled. Consequently, the vectors with _no or only low_ influence ratings (_XNE_, _DOS_), which this communication or the servers themselves introduce in the other scenarios, are therefore omitted in this one. A _medium_ influence was found for extracting private information (_EPI_) from a _Smart Home Device_ or running commands to damage it or turn it into a bot (_RCD_). For control devices, even a _high_ influence on those vectors was found.
## 3 Numerical Simulation of Tunnel Face Reinforcement Scheme
### Analysis of Simulation Results for Tunnel Face Reinforcement Schemes
#### 3.2.2 Longitudinal Ground Deformation Characteristics at the Shield Tunnel Launching Section  
To investigate the longitudinal ground deformation pattern during shield tunnel excavation at the launching section and assess potential construction risks, preventive measures against surface collapse were formulated in advance to reduce economic loss and the risk of casualties. Based on section 1-1 shown in Figure 2b, the longitudinal ground deformation patterns at different excavation stages were obtained, as illustrated in Figure 4.  
From Figure 4a,b, it is evident that when tunneling reaches the reinforced zone, ground settlement around the tunnel is relatively small. For both Scheme 1 and Scheme 2, the maximum ground settlement occurs at the crown of the tunnel in front of the shield machine, with maximum settlements of \(-\)29.73 mm and \(-\)15.80 mm, respectively. The maximum ground heave is observed at the tunnel invert, with peak values of 18.73 mm and 9.52 mm, respectively.  
Figure 4c,d illustrate that when excavation reaches the interface between the reinforced and unreinforced zones, the maximum settlement still occurs at the crown of the tunnel in front of the shield, measuring \(-\)59.53 mm and \(-\)26.94 mm for Scheme 1 and Scheme 2, respectively. The maximum heave in both cases is observed at the invert near segment ring 4, with values of 35.49 mm and 26.55 mm, respectively.  
From Figure 4e,f, when the excavation advances into the unreinforced zone, a settlement zone appears near the crown of segment ring 9 in Scheme 1, with a maximum settlement of \(-\)31.62 mm, exceeding the warning threshold. A heave zone is also seen at the invert of segment ring 4, with a maximum heave of 37.28 mm, also exceeding the warning value. In Scheme 2, while the settlement area is broader than in Scheme 1, the maximum settlement is smaller at \(-\)8.24 mm. The maximum heave location remains the same as in Scheme 1, with a value of 29.60 mm.  
During excavation in the reinforced zone and the interface with the unreinforced zone, the soil reinforced by jet grouting remains relatively dense and the settlement zones appear mainly ahead of the shield machine. Once tunneling progresses into the unreinforced zone, the shield no longer cuts through the grouted soil, and the maximum settlement remains near the crown of segment ring 9, while the maximum heave remains near the invert of segment ring 4. The results indicate that Scheme 1 exceeds the 30 mm deformation control threshold for both settlement and heave, while Scheme 2 remains within acceptable limits.  
Figure 4: Ground deformation in a longitudinal section of shield tunnel under different reinforcement schemes.