esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Step 3: Life Cycle Impact Assessment
#### 3.3.1 Step 3.1: Micro-Assessment  
The micro level includes the Environmental Life Cycle Assessment (E-LCA) and Social Life Cycle Assessment (S-LCA; [59]) used to quantitatively assess the midpoint impacts. Calculation of characterization (Ci) and normalization (Ni) [9] is necessary to obtain (in the next step) the integrated weighting using the LCA+C2C endpoint. In this case, the Simapro [82] software was used for E-LCA, and Green Delta [83] for S-LCA. The results may be found in Table 4, column LCA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**LCA**} & \multicolumn{4}{c}{**C2C**} \\ \hline
**Dimension** & \multicolumn{2}{c}{**Categories and Metrics**} & \multicolumn{2}{c}{**Category**} & \multicolumn{1}{c}{**Results**} & \multicolumn{1}{c}{**Certification**} \\ \hline \multirow{5}{*}{E-LCA} & **Impact category** & \(\mathbf{C_{i}}\) **[unit]** & \(\mathbf{N_{i}}\) **[year]** & **Ma. Health** & \begin{tabular}{c} **Result ABC-X** \\ M/U/PC/E * \\ \end{tabular} & **Level** \\ \cline{2-6}  & \begin{tabular}{c} Abiotic dep. (kg Sb eq) \\ \end{tabular} & 4.18 & \(2.64\times 10^{-11}\) & Al 6060 T6 & C/A/A/B \\ \cline{2-6}  & \begin{tabular}{c} Acidification (kg SO\({}_{2}\) eq) \\ \end{tabular} & 5.62 & \(1.74\times 10^{-11}\) & Al 5754 H111 & C/A/A/B \\ \cline{2-6}  & \begin{tabular}{c} Eutrophication (kg PO\({}_{4}\) eq) \\ \end{tabular} & 0.25 & \(1.95\times 10^{-12}\) & Steel 304 & C/A/A/B \\ \cline{2-6}  & \begin{tabular}{c} Global warm. (kg CO\({}_{2}\) eq) \\ \end{tabular} & 498.97 & \(1.36\times 10^{-11}\) & Cardboard & X/A/A/A/A \\ \cline{2-6}  & \begin{tabular}{c} Freshw. tox (kg 1.4-DB eq) \\ \end{tabular} & 38.19 & \begin{tabular}{c} 3.08 \(\times 10^{-14}\) \\ \end{tabular} & \begin{tabular}{c} LDPE \\ \end{tabular} & \
\begin{tabular}{c} X/A/A/A/A/A \\ \end{tabular} & Basic \\ \cline{2-6}  &
\begin{tabular}{c} \(\  
On the other hand, the product is evaluated with C2C, obtaining the qualitative level of the five categories (material health, material reutilization, renewable energy and carbon management, water stewardship and social fairness). The results may be found in Table 4, column C2C.  
At this stage, independently documenting the information from the analyses is recommended, as the reports can be used later to obtain the Ecolabel Type III (EPD) [75] and C2C certification [84].
# Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples
## 7 Results
### Rock and Ore Specim Test
(**b**) The figure illustrates the specific locations of the cracks within the specimen. Figure 12: Schematic diagram of the electrode arrangement. The system is capable of simultaneous acquisition of multiple electrical parameters, enabling the inversion processing of various parameters to fully reflect the electrical characteristics of the rock samples. Firstly, the three-dimensional resistivity inversion imaging was conducted based on the magnitude and current data. The results are shown in Figure 13. From the inversion results, it can be observed that there is a significant low resistivity zone along the \(Z\)-axis at 30-60 mm, which corresponds to the location of the crack on the surface of the specimen. This can be attributed to the decrease in resistivity caused by water-filled cracks. In other positions of the specimen, the resistivity values remain relatively constant and exhibit a uniform distribution, corresponding well with the specimen. After performing Fourier transform on the raw data, phase data were obtained. Figure 14 shows the inversion results of the phase. In Figure 14a, a three-dimensional segmentation algorithm was applied to the specimen, revealing the phase distribution of the specimen's cross-section. In Figure 14b, a threshold technique was employed to extract high phase values in the fracture zone, providing a better reflection of the fracture locations. Figure 15 illustrates the inversion results of the resistivity magnitude. Based on the impedance data, the chargeability [38] results can be calculated, as shown in Figure 16. It can be observed that both inversion results reflect the locations of cracks in the specimen. We also observe the presence of a red anomaly in Figure 16. Considering that the sample was not subjected to splitting procedures, we speculate that the presence of the red dots may be indicative of internal variations in density, porosity, moisture content, and other factors relative to the surrounding area. Figure 13: Inversion results of resistivity. Figure 14: Inversion results of phase. (**a**) Phase distribution of the specimen’s cross-section. (**b**) High-lighted positions of crack. Figure 15: Inversion results of magnitude.
## Appendix A  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Seasonality** & \multicolumn{1}{c}{F} & \multicolumn{1}{c}{_df_} & \multicolumn{1}{c}{_p_} \\ \hline seasonality & 4.442 & 1, 513.268 & 0.043 \\ lobe & 9.663 & 1.962, Inf & \textless{}0.001 \\ seasonality \(\times\) lobe & 5.503 & 1.962, Inf & 0.003 \\ valence & 5.015 & 1.934, Inf & 0.011 \\ Seasonality \(\times\) valence & 3.31 & 1.934, Inf & 0.037 \\ lobe \(\times\) valence & 6.849 & 3.346, Inf & \textless{}0.001 \\ seasonality \(\times\) lobe \(\times\) valence & 0.452 & 3.346, Inf & 0.745 \\ frequency & 16.881 & 1, Inf & \textless{}0.001 \\ seasonality \(\times\) frequency & 2.745 & 1, Inf & 0.097 \\ lobe \(\times\) frequency & 153.738 & 1.767, Inf & \textless{}0.001 \\ seasonality \(\times\) lobe \(\times\) frequency & 2.726 & 1.767, Inf & 0.075 \\ valence \(\times\) frequency & 2.163 & 1.963, Inf & 0.106 \\ seasonality \(\times\) valence \(\times\) frequency & 0.692 & 1.963, Inf & 0.508 \\ lobe \(\times\) valence \(\times\) frequency & 11.751 & 2.735, Inf & \textless{}0.001 \\ seasonality \(\times\) lobe \(\times\) valence \(\times\) frequency & 0.587 & 2.735, Inf & 0.612 \\ hemisphere & 0.825 & 1, Inf & 0.362 \\ seasonality \(\times\) hemisphere & 0.698 & 1, Inf & 0.44 \\ lobe \(\times\) hemisphere & 2.28 & 1.428, Inf & 0.106 \\ seasonality \(\times\) lobe \(\times\) hemisphere & 0.112 & 1.428, Inf & 0.869 \\ valence \(\times\) hemisphere & 1.055 & 1.777, Inf & 0.331 \\ seasonality \(\times\) valence \(\times\) hemisphere & 1.714 & 1.777, Inf & 0.179 \\ lobe \(\times\) valence \(\times\) hemisphere & 2.845 & 3.683, Inf & 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the semi-parametric ANOVA-type statistics for the grouping factors seasonality (low vs. high), lobe (frontal, temporal, occipital), hemisphere (left, right), valence (negative, neutral, positive), frequency (alpha, gamma), and time-window (50–150 ms, 300–400 ms).
## 3 Experimental Settings  
### Datasets and Study Area  
In this study, reanalysis data from the Arctic Ocean Physics Reanalysis product of the Copernicus Marine Service was used as the training set. The data originate from the Arctic Ocean and sea ice reanalysis based on the Coupled Ensemble Data Assimilation System (TOPAZ4b). TOPAZ4 relies on version 2.2 of the HYCOM ocean model and employs an Ensemble Kalman Filter data assimilation with 100 dynamic members. Beginning in 1991, a 30-year reanalysis of the Arctic Ocean and sea ice was conducted and is provided as a multi-year physical product by the Arctic Ocean Forecasting Center (ARC MFC), which is part of the Copernicus Marine Environment Monitoring Service.  
The spatial resolution of the data is 12.5 \(\times\) 12.5 km, and the study focuses on the Arctic core area defined by a grid of 528 \(\times\) 512 cells (90 N, 50 N, 180 E, and 180 W), spanning 20 years from 2000 to 2020. The study area is depicted in Figure 3. Predicting sea ice concentration in the Arctic during the melt season is of significant importance for national entities and relevant enterprises in assessing future shipping potential. It also aids scientists in gaining a more comprehensive understanding and quantification of the pace and extent of climate change. Therefore, our focus will be on predicting the melting period from May to September each year. This period is when Arctic sea ice melting is most active, and studying the changes in sea ice concentration during this time will provide us with critical information and insights.  
Table 1 provides a detailed description of the specific parameters used as model inputs from the Copernicus Marine Service dataset, namely, sea water salinity, sea surface temperature, and sea ice thickness.  
In the data preprocessing stage, we set the anomalous sea ice data (grid point data with sea ice concentration less than 0 or greater than 1) to 0. Then, the Arctic sea ice concentration and other ocean factor data were initialized to fit the input dimensions (number of samples, time steps, widths, heights, and number of channels) of the PDED-ConvLSTM model. Finally, sea ice concentration and other ocean factor features were fused into the channel  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Source** & **Units** \\ \hline Sea Ice Concentration & Copernicus Marine Service & Dimensionless \\ Sea surface temperature & Copernicus Marine Service & \({}^{\circ}\)C \\ Sea surface salinity & Copernicus Marine Service & PSU \\ Sea ice thickness & Copernicus Marine Service & M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on sea ice concentrations and influencing factors.  
dimension. The model uses the rolling window method to input the training data Ii-2 to Ii, and generates the predicted values of Arctic sea ice concentration Pi + 1 to Pi + 2 in the prediction layer.
## 2 Improved Gene Expression Programming for Determining a Creep Model
### Validation of Determining Creep Models Based on Improved Gene Expression Programming
#### 2.3.2 The Establishment of the Creep Model of Mine Rock
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Stress Level (MPa)** & \(k_{1}\) **(MPa)** & \(k_{2}\) **(MPa)** & \(\eta_{2}\) **(MPa-h)** & \(\sigma_{\text{s}}\) **(MPa)** & \(\epsilon_{\text{c}}\) & \(\eta_{\text{c}}\) **(MPa)** & \(n\) & \(\mathbf{R^{2}}\) \\ \hline
8.089 & 1821.744 & 24,471.412 & 289,834.709 & - & - & - & - & 0.990 \\
12.782 & 2449.737 & 30,585.493 & 439,248.118 & - & - & - & - & 0.916 \\
17.473 & 2868.332 & 26,152.558 & 1,872,245.355 & - & - & - & - & 0.972 \\
23.565 & 3361.528 & 25,121.742 & 1,384,693.139 & - & - & - & - & 0.963 \\
33.807 & 4389.741 & 65,796.573 & 347,331.434 & 30.751 & 0.010 & 3,305,240.379 & 2.987 & 0.955 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fitting parameters of the obtained creep model. Figure 12: Creep model tree of mine rock. Figure 13: The obtained creep model. From the analysis of Table 3 and Figure 14, it can be concluded that improved gene expression programming can be applied to establish a creep model by using creep bodies. The creep model is in good agreement with the experimental data, with the above correlation coefficient R\({}^{2}\) above 0.91. The results indicate that the proposed technique is reliable and can be applied in practice.
## 2 New Technologies and Applications
IoT combines various IoT framework technologies to transmit, receive, and manage real-time data from sensors. Sensors can be used to evaluate and control variables, such as temperature, humidity, vibration, or shock. Therefore, the application of IoT in various fields, especially in the field of manufacturing execution systems, can affect resource efficiency and remarkably improve production capacity. However, processing all collected data directly to a central server is inefficient and impractical due to limited computing, communication, and storage resources, overall energy and cost, and unreliable latency. Therefore, in another paper [11], the authors designed and implemented an IoT cloud platform using Pub/Sub technology to develop a high-performance IoT platform. To this end, as the size and frequency of data acquired from IoT nodes increased, performance was improved through MQTT and Kafka protocols and a multi-server architecture. Additionally, MQTT was applied for the fast processing of small-scale data, Kafka was applied for the stable processing of large-scale data, and various sensors and actuators were installed to measure the growth data of each device using protocols. The experimental results show that the MQTT Kafka platform is effective for use in environments where network bandwidth is limited or large amounts of data are continuously transmitted and received. In addition, the response time to user requests was measured to be within 100 ms on average, and the data transmission order for more than 13 million requests was confirmed to have an average of 113,134.89 records per second and 64,313 data processing performance per second. IoT is expected to reach 30.9 billion connected devices by 2025, driving exponential data growth due to diverse application source classification, mission criticality, and privacy constraints. This growth poses challenges in real-world scenarios related to data privacy, automation of big data management, and latency-efficient connectivity. Building intelligent model learning on this improved existing architecture requires sending local data to cloud servers, resulting in high backhaul congestion, personalization leakage, and the underutilization of network resources. Federated learning (FL) was introduced to solve the problem, but the problem has still not been solved. Therefore, another paper [12] presents a state-of-the-art solution to optimize the orchestration of FL communication by querying a deep reinforcement learning (DRL)-based autonomy approach. In this paper, the authors summarize application areas and contributing perspectives of five major application areas to provide insightful system architectures, processing flows, and critical parameters proposed in recent research using DRL/FL-based approaches. Additionally, DRL-based eFL or incomplete DRL/FL support systems present potential challenges and directions for becoming a practical approach in real-world deployments.
# The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map
## 2 Preliminaries  
### Fuzzy c-Means Clustering  
Fuzzy c-means [34], a fuzzy clustering method, allows one piece of data to belong to two or more clusters. The objective function of fuzzy c-means is as follows:  
\[\arg min\}=\sum_{i=1}^{N}\sum_{j=1}^{n}u_{ij}^{m}\|x_{i}-c_{j}^{2}\| \tag{1}\]  
where \(N\) is the number of data points. \(n\) is the number of clusters. \(m\) is a fuzzification coefficient, which is commonly set to 2. \(x_{i}\) is the \(i\)th data point. \(c_{j}\) is the center of the \(j\)th cluster. \(u_{ij}\) is the degree of membership of \(x_{i}\) in the \(j\)th cluster, \(u_{ij}\in[0,1]\), \(\sum_{j=1}^{n}u_{ij}=1\).  
Fuzzy c-means performs through an iterative optimization of the objective function with the update of membership \(u_{ij}\) and the cluster centers \(c_{j}\).  
\[u_{ij}=\frac{1}{\sum_{k=1}^{n}\left(\frac{\|x_{i}-c_{j}\|}{\|x_{i}-c_{k}\|} \right)^{\frac{2}{n-1}}} \tag{2}\]  
\[c_{j}=\frac{\sum_{i=1}^{N}u_{ij}^{m}x_{i}}{\sum_{i=1}^{N}u_{ij}^{m}} \tag{3}\]  
This iteration will stop when it reaches a termination criterion, or until after a specified maximum number of iterations.
## 3 Strength Test
### Indirect Tensile Strength  
Before performing indirect tensile strength tests, the formed specimens were cured in a standard curing room for 1, 3, 7, 14, 28, 60, and 90 days. According to the Chinese Test Methods of Materials Stabilized with Inorganic Binders for Highway Engineering (JTG E51-2009) [36], strength tests at different curing times and with different cement content were also carried out on the MTS at a loading  
Figure 2: Variation patterns for the unconfined compressive strength with the curing time and cement content.  
rate of 1 mm/min. Five parallel tests were carried out at each curing time. In addition, the variation pattern of the indirect tensile strength was described by a logarithmic equation, and the strength test results carried out at different curing times were fitted by Equation (1). Figure 3 shows the fitting results of the indirect tensile strength tests, where \(R^{2}\) is the correlation coefficient of fitting; \(S_{c}\) is the indirect tensile strength; \(t\) is the curing time; and \(a\), \(b\), and \(c\) are the fitting parameters.  
From the fitting results, it can be noted that  
1. The discreteness of indirect tensile strength was small, and basic control was within 15%. The discreteness of indirect tensile strength value was greater than 10% compared to the unconfined compressive strength. The indirect tensile strength showed good agreement with the increase in curing time between different cement contents, which was consistent with the unconfined compressive strength growth trend.
2. The indirect tensile strength of cement-treated aggregate base materials monotonously increased with the curing time and cement content. The cement content considerably affected the indirect tensile strength. The indirect tensile strength at 3% cement content and 28 days is \(\sim\)1 MPa, the indirect tensile strength at 5% cement content and 28 days is \(\sim\)2.2 MPa, i.e., the strength value increased by 2.2 times. At 1-14 days, the unconfined compressive strength of cement-treated aggregate base materials rapidly increased, with a growth rate of \(\sim\)2 (from 0.8 to 1.6 MPa). At 14-90 days, the growth of indirect tensile strength decreased, but a certain growth trend was still observed (from 1.6 to 2.25 MPa). The Chinese Specification for Design of Highway Asphalt Pavement (JTG D50-2017) [37] has stipulated that the indirect tensile strength of the cement-treated aggregate base materials is about 10% of the unconfined compressive strength. From the test results, it can be observed that the indirect tensile strength at 3% cement content and 90 days is 1.1 MPa which is about 10% of the unconfined compressive strength at 3% cement content and 90 days.  
Figure 3: Variation patterns of the indirect tensile strength with the curing time and cement content.
## 8 Implementation and Results
### Countermeasure Results  
In the NDNtube simulation, the brute-force-based side-channel scenarios were implemented. To mitigate these attacks, traditional and this work's countermeasures are implemented. These are based on the following: _i._ Static probabilistic caching and freshness time, which are stated as traditional countermeasure methods. _ii._ Dynamic detection and defense configuration (DaD), which detects possible adversary detected nodes and applies the countermeasures only to these interfaces.  
In NDNtube, the video segments can be also cached by the freshness period of the segment. Additionally to probabilistic caching (countermeasure to mitigate the attacks), the segments were configured by the freshness period (\(\approx\)100 ms). On the other hand, the DaD threshold (CHR) is configured as 5% to detect the adversary, and the detection period is configured as 0.5 s and applies each attack phase (minor, moderate, and severe) for 3 s.  
The DaD dynamically detected the attack and took countermeasure actions instead of statically configured routers to mitigate the attack. Figure 16 illustrates how DaD has dynamically mitigated the attacks in the attack period with its three phases (no-cache included). The CHR results illustrated that the nfd:DaD applied all countermeasures to adversarial faces because the attacks were continuous and they are considered as severe. Additionally, the statically applied (for all faces) countermeasures CHR results were obtained by the following: _i._ nfd:probabilistic and _ii._ nfd:freshness scenarios.  
Figure 16: NDNtube attack mitigation results.  
Figure 15: Determination of the target locations in AT&T topology.  
In no-countermeasure applied (Section 8.2) nfd:LRU policy scenario, the average CHR is obtained \(\approx\)16.4%. In this work, we attempted to decrease the no-countermeasure CHR value with implemented countermeasure scenarios (probabilistic, freshness, and DaD).  
Firstly, the statically configured countermeasures (nfd:probabilistic \(\approx\)4.1%) and (nfd:freshness \(\approx\)3.7%) are applied and CHR is decreased compared to the no-countermeasure scenario (\(\approx\)16.4%). These countermeasure results show that the attacks are mitigated. However, since all faces (including legitimate consumers) are affected by countermeasures, the content distribution efficiency is also decreased.  
The nfd:DaD is using multiple countermeasures depending on attack severity. In ndn:DaD configuration, the CHR is decreased to \(\approx\)0.7% from \(\approx\)16.4%. This result showed that attack mitigation is significantly improved with DaD compared to traditional countermeasure methods. Additionally, because DaD only applies countermeasures (depending on attack severity) to possible adversary nodes, the NDN content distribution is preserved compared to traditional methods.
## 3 Experiments and Results
### AMNBP Results of Multiple Events  
After the signal enhancement, we obtained reconstructed signals with better characteristics. Then, the collected noise was encoded and used as a reference. The MNBP values of various events are shown in Table 1. The first row is the average value. It can be seen that the noise average value was  
Figure 9: The enhancement and reconstruction of three types of typical signals: (**a**) standard knock, (**b**) reconstructed standard knock, (**c**) walk, (**d**) reconstructed walk, (**e**) manual mining, and (**f**) reconstructed manual mining.  
slightly greater than the MNBP values of the other three types of typical events, and the degree of event discrimination was not large. Equation (14) was used to achieve the AMNBP values after the adaptive adjustment, as shown in Table 2. The average gain was 30 times that of the AMNBP values. Thus, the four types of events could be easily distinguished by increasing the AMNBP values.  
The comparison graphs of the experimental results of the four types of signals are shown in Figure 10. The ordinate represents the AMNBP values of different events, and each point on the abscissa represents 40 ms, which corresponds to 400 points in the original data. In Figure 10, beige is the AMNBP value of the noise, light blue is the AMNBP value of the standard knocker, blue is the AMNBP value of walking, and dark blue is the AMNBP value of artificial mining.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Knocking** & **Walking** & **Mining** & **Noise** \\ \hline Means & 7.32 & 7.87 & 7.41 & 7.95 \\
1 & 6 & 6 & 7 & 6 \\
2 & 9 & 9 & 5 & 13 \\
3 & 2 & 14 & 12 & 3 \\
4 & 3 & 6 & 3 & 3 \\
5 & 7 & 3 & 8 & 3 \\
6 & 12 & 12 & 6 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: AMNBP values of the four types of events without adaptive adjustment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Knocking** & **Walking** & **Mining** & **Noise** \\ \hline Means & 105.01 & 252.04 & 381.82 & 7.95 \\
1 & 140.24 & 268.85 & 165.50 & 6 \\
2 & 263.25 & 676.27 & 831.59 & 13 \\
3 & 43.07 & 591.95 & 1022.47 & 3 \\  
From the graphs, four different kinds of events could be distinguished. After the self-adaptive adjustment, AMNBP increased by an average of 30 times. At the same time, the AMNBP images clearly reflected the frequency information of the event. This shows that this method could enhance the performance of the location and recognition of a \(\Phi\)-OTDR system.