esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Results
### Analysis of Subsidence Monitoring in the Area along the Great Wall
A profile analysis was conducted on the D3 segment, extracting surface subsidence data from four profiles, namley L1, L2, L3, and L4, as shown in Figure 11. It is evident from Figure 11 that significant subsidence is observed in the southern portions of L1, L2, and L4, as well as in the northern part of L3. By comparing historical satellite images with this subsidence data, it is apparent that all four subsidence locations coincide with active coal mining areas. Based on this observation, it can be inferred that surface deformation resulting from coal mining activities is the primary cause of subsidence and deformation in Segment D3 of the Great Wall. Figure 7: Time series ground deformation and precipitation at Luoquebao Beacon tower. Segment D4 is located in Yanggao County, Datong City. The wall mainly consists of highly pure yellow clay, and the terrain is characterized by alluvial plains and hills. The deformation rates of the D4 wall are shown in Figure 12. The exposed surface rocks in this area are fragmented, and adverse geological phenomena like interlocking and landslides provide ample solid material sources for debris flows [44; 45]. Reports from the Yanggao County Land and Resources Survey Bureau indicate that the county frequently experiences natural disasters such as debris flows and earthquakes due to its unique geological and ge  
Figure 8: Deformation rate map of Great Wall Segment D2. Figure 9: Photographs of unstable slope sections of Segment D2 of the Great Wall. omorphic conditions. Combining this information with the \"Assessment Map of Landslide Risk in China\" published by the China Geological Environment Monitoring Institute, it can be inferred that Yanggao County falls into the category of a low-risk landslide disaster and moderate landslide hazard zone, as shown in Figure 5:0 This suggests that geological hazards resulting from the region's special geological conditions are the primary factors contributing to the subsidence of Segment D4 of the Great Wall. Additionally, the opening of the Yanggao section of the Great Wall for tourism has introduced human-induced effects on the subsidence and deformation of Segment D4 due to tourism activities and potential anthropogenic damage. Figure 5:1 Cross-sectional profile of surface subsidence for Segment D3 of the Great Wall. Figure 5:2 Deformation rate map of Great Wall Segment D3.
## 1 Introduction
Different branches of engineering employ nonwoven ultrafine fiber mats: biomedical and industrial fields such as membrane technology, filtration media, energy harvesting, electrical power transmission and storage, tissue engineering, medical prostheses, drug delivery, and wound healing [5; 20; 21]. In this work, the constituting polymer chosen for the spinning dope solution was PAN. It is a synthetic colorless polymer with the linear formula (C\({}_{3}\)H\({}_{3}\)N)\({}_{n}\), obtained from the polymerization of acrylonitrile. It has a thermoplastic behavior and does not melt under usual operation conditions for the above-mentioned applications, as it has a high crystalline melting point at 317 \({}^{\circ}\)C and a glass transition temperature at 104 \({}^{\circ}\)C [22; 23]. The yearly worldwide amount of PAN produced is around 2.73 million tons [24]. PAN fibers are also the most used precursor in the production of high-quality carbon fibers, covering around 90% of the market [23]. The carbon content in PAN is 67.9%; therefore, it has, compared to other precursors, a high yield of carbonized fibers above 50%, combined with a high Young's modulus and good mechanical properties [25]. It is fundamental that the polymer precursor has an acrylonitrile content above the 85%, not being an homopolymer, otherwise the final product will not result in a satisfactory PAN precursor fiber [25]. In this work, different shares of biochar derived from biomass gasification were added to the spinning material. The aim of the experimental part was to define if biochar particles can be included in the fiber material despite differences in the diameter. Biochar, compared to PAN, is produced in an inexpensive way; indeed, it is an energy production byproduct. By reducing the amount of PAN in carbon fibers production, the cost of production and the use of nonrenewable resources could be reduced. The biochar share in the material will also contribute to the shift in the carbon footprint toward a partial carbon negativity. To further stress the importance of by-product-derived biochar, this work used the char produced through vineyard pruning gasification [26]. Unfortunately, by-products such as vine pruning material are usually characterized by higher inorganic contents compared to high-quality biomass fuels (i.e., wood pellets) [27]. For this reason, particular attention was paid to the role and the presence of inorganics through all the fiber production processes. Biochar must be properly cleaned and milled before using it in the solution. Despite the milling process, biochar particles strongly differed in size from the fibers, suggesting that more performing milling procedures should be used in the future. On the other hand, this study proved that the biochar ashes are removed during the fiber carbonization process.
# Image-Based Crack Detection Using Total Variation Strain DVC Regularization
## 2 Method  
Image registration is the basis of DVC, and it consists of aligning features in the moving image to the reference image by using a displacement field. The estimation of the D-dimensional displacement field \(\mathbf{u}\colon\Omega\to\mathbf{R}^{D}\), that maps the moving image \(I_{m}\colon\Omega\to\mathbf{R}\) to the reference image \(I_{f}\colon\Omega\to\mathbf{R}\), requires the minimization of a cost function  
\[\hat{\mathbf{u}}=\arg\min_{\mathbf{u}}(C_{s}(\mathbf{u};I_{f},I_{m})+\lambda C _{R}(\mathbf{u})) \tag{1}\]  
where \(\Omega\) is the image domain, \(C_{S}\) is the image similarity metric, \(C_{R}\) is the regularization term, and \(\lambda>0\) is the weight of the regularization in the cost function. The image similarity metric measures how well the moving image matches the reference image. The metric should be chosen based on the images to be registered and the kind of misalignment, there is no clear rule for which metric should be chosen.  
The regularization term (\(C_{R}\)) constitutes the second component of the cost function and is essential due to the ill-posed nature of image registration. The available information from the image data, represented by gray intensity scalar values, is insufficient to guarantee a unique solution for the displacement field, which consists of a vector with three unknowns. To address this, constraints must be imposed on the displacement field to establish a well-posed problem. The introduction of constraints, known as regularization, restricts the degrees of freedom in the solution, ensuring a mechanically admissible DVC solution. The similarity metric together with the regularization term constitutes the objective function to be optimized over the search space that comprises the parameters of the transformation. The transformation serves as the spatial mapping of points from the moving image to the fixed image. Various types of transformations are available for this purpose, which are discussed in more detail in Section 2.3 of this paper. After obtaining the optimal values for the transformation parameters, the interpolator is employed to determine the intensities of the moving volume at positions where the parameters are not defined.
## 4 Case Study
### Result of Noise Generation  
To compare the performance with the Gaussian noise-based denoising approach presented in the preliminary study, the same dataset and experimental process were used.  
In alignment with the objectives of this study, high-frequency noise components from actual vibration signals were extracted by applying a high-pass filter to the raw data during the DAE training process. Figure 10 shows the extracted high-frequency components (above 1000 Hz) for both normal and fault signals. Figure 11 presents the corresponding FFT results of the extracted high-frequency noise components.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Gaussian Noise**} & \multicolumn{4}{c}{**Actual Answer**} & \multirow{2}{*}{**Running Rate**} & \multirow{2}{*}{**Failure Rate**} \\ \cline{3-5} \multicolumn{2}{c}{} & & **Normal** & & **Failure** & \\ \hline \multirow{5}{*}{Classification results by feature} & \multirow{2}{*}{Mean} & Normal & 7303 & 497 & \\ \cline{3-5}  & & Failure & 0 & 7800 & \\ \cline{3-5}  & & Normal & 7292 & 508 & \\ \cline{3-5}  & & Failure & 0 & 7800 & \\ \cline{3-5}  & & Failure & 0 & 7800 & \\ \cline{3-5}  & & Failure & 0 & 7800 & \\ \cline{3-5}  & & Standard & Normal & 7300 & 500 & \\ \cline{3-5}  & & Failure & 102 & 7698 & \\ \cline{3-5}  & & Normal & 7313 & 487 & \\ \cline{3-5}  & & Failure & 330 & 7470 & \\ \cline{3-5}  & & Normal & 7254 & 546 & \\ \cline{3-5}  & & Failure & 5755 & 2045 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of classification (Gaussian noise).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Wavelet Transform**} & \multicolumn{4}{c}{**Actual Answer (Unit: Case)**} & \multirow{2}{*}{**Running Rate**} & \multirow{2}{*}{**Failure Rate**} \\ \cline{3-5} \multicolumn{2}{c}{} & & **Normal** & & **Failure** & \\ \hline \multirow{5}{*}{Classification results by feature} & \multirow{2}{*}{Mean} & Normal & 7565 & 235 & \\ \cline{3-5}  & & Failure & 7690 & 110 & \\ \cline{3-5}  & & Normal & 7066 & 733 & \\ \cline{3-5}  & & Failure & 7049 & 751 & \\ \cline{3-5}  & & Normal & 7177 & 683 & \\ \cline{3-5}  & & Failure & 3755 & 5045 & \\ \cline{3-5}  & & Normal & 7307 & 493 & \\ \cline{3-5}  & & Failure & 6948 & 852 & \\ \cline{3-5}  & & Normal & 7323 & 477 & \\ \cline{3-5}  & & Failure & 6893 & 907 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of classification (Wavelet).
# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
## 3 Materials and Methods  
### Acronym  
The IHEPC dataset provided by [PERSON] and [PERSON] [30] on UCI Machine Learning Repository was used to validate several electric energy consumption prediction models [31] recently. This dataset contains 2,075,259 measurements collected from a house in Sceaux, France in five years between December 2006 and November 2010. A total of 25,979 missing values on 28 April 2007 were removed in preprocessing step. Table 1 presents all the variables in the dataset. There are nine variables (day, month, year, hour, minute, global active power, global reactive power, voltage and global intensity) and three variables collected from energy consumption sensors (sub metering 1, sub metering 2, and sub metering 3) attached with the meaning of each variable.  
From the IHEPC dataset, four datasets including minutely, hourly, daily, and weekly datasets, which indicate for real-time, short-term, medium-term and long-term predictions respectively, will be created. Figure 1 shows the samples of minutely, hourly, daily, and weekly datasets which originated from the IHEPC dataset. For each global active power in the time series shown in Figure 1, there are six variables including global reactive power, voltage, global intensity, sub metering 1, sub metering 2 and sub metering 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \# & **Variable** & **Description** \\ \hline
1 & Day & A value from 1 to 31 \\
2 & Month & A value from 1 to 12 \\
3 & Year & A value from 2006 to 2010 \\
4 & Hour & A value from 0 to 23 \\
5 & Minute & A value from 1 to 60 \\
6 & Global active power & The household global minute-averaged active power (in kilowatt) \\
7 & Global reactive power & The household global minute-averaged reactive power (in kilowatt) \\
8 & Voltage & The minute-averaged voltage (in volt) \\
9 & Global intensity & The household global minute-averaged current intensity (in ampere) \\  & & This variable corresponds to the kitchen, containing mainly a dishwasher, \\
10 & Sub metering 1 & an oven and a microwave, hot plates being not electric, but gas powered \\  & & (in watt-hour of active energy) \\
11 & Sub metering 2 & This variable corresponds to the laundry room, containing a washing machine, \\  & & a tumble-drier, a refrigerator and a light (in watt-hour of active energy) \\
12 & Sub metering 3 & This variable corresponds to an electric water heater and an air conditioner (in watt-hour of active energy) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The features of the IHEPC dataset.
## 5 Analysis and Results
### Case Study  
To check the efficiency of the proposed algorithm, we assume a case study in which the attacker can compromise two validating nodes out of five and try to manipulate or insert his data. Although, it is challenging for the attacker due to its randomness, but we are taking it as a worst-case scenario. This is clearly shown in Figure 9 where the attacker takes control of two validating nodes.  
Then, two blocks will be produced:  
* Block produced by node 2 with two verifications;
* Block produced by malicious node 4 with one verification.  
After the block is produced by node 2, it is valid due to the more significant number of verifications. So, the master increased its rating by 0.25 and decreased the ratings of malicious nodes 4 and 5 by one due to wrong validation and verification.  
In the previous section, Equation (3) describes the criteria for master node selection. In our case study, the rating is decreased by \(-1\). So, their chances of becoming masters are less than other nodes because the rating is multiplied by the number of votes, and the whole value becomes negative. These two malicious nodes can become masters only if they do four or more validations right by retaining first place in every validation. Their chances of becoming validating nodes are also affected because the master will not select more than one validating node with negative ratings. If one of them becomes a validating node again and does something wrong, its rating will be furthermore decreased in the same way.  
Figure 9: Case study for blockchain IoT.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 14 Cost and Economic Feasibility
### Operational and Maintenance Costs  
While the use of photocatalytic materials in air purifiers, paints, and building materials has the potential to reduce energy consumption and maintenance requirements compared to traditional filtration systems, there are still operational costs to consider. For instance, photocatalytic oxidation processes require the presence of light to activate the photocatalyst. This means that in dark or low-light environments, supplementary lighting systems may be necessary to ensure that the photocatalysts remain active, adding to the operational energy cost. The energy efficiency of photocatalytic air purifiers, therefore, depends on the intensity and duration of light exposure, which can vary depending on the installation setting.  
Furthermore, the longevity and effectiveness of the photocatalytic materials are critical for assessing long-term economic viability. Over time, photocatalysts may lose their activity due to factors such as particle agglomeration, surface poisoning, or deactivation [289]. This necessitates periodic maintenance, which may involve replacing or regenerating the photocatalytic materials. Regeneration techniques, such as heating or ultraviolet (UV) light treatment, may reduce the frequency of replacement but still incur additional operational costs. The cost of maintaining or replacing photocatalytic materials should be factored into the overall lifecycle cost analysis.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Link
#### 4.2.2 Implementation of a Time-Varying Connection Relationship
Upon the emulation starts, flow entities are issued by the main controller. As shown in Figure 7, each dynamic link corresponds to a timer and two flow entities. At the start time of emulation, several timers are activated and the on/off time points of each dynamic link are set within them. When the timers expire, the link connection or disconnection functions are executed to deploy corresponding connection or delete corresponding flow entities on virtual switches. A problem which must be taken into consideration is to emulate the dynamic link connection at the distributed virtual server cluster. When deploying two emulation nodes in a connection link, there are two relative position situations: two nodes located at the same  
\[r=R_{E}+H \tag{9}\]1 Realization of dynamic link connection. server, and two nodes located at different servers. For the former, the main controller can directly deploy two flow entries at the same virtual switches. Meanwhile, this deployment method is no longer applicable when two emulation nodes are linked to different virtual switches. To solve this issue, the placement of the emulation node in the server cluster and the corresponding host name is recorded when creating the emulation node. Integrated with the fields of srsnode_port and dstnode_port, the corresponding relationship between flow entries and virtual switches can be cleared. Figures 8 and 9 show the change of link connection in timeslots TS1 and TS2. When Node3 disconnects with Node2 and connects with Node1 directly, the main controller first deletes the bidirectional flow entries between S1-4 and S2-6 ports, and then adds the bidirectional forwarding rules between S1-2 and S2-5 ports in S1 and S2 separately. Because the forwarding rule is a map between two physical ports in the programmable switch, we can emulate the connectivity of link connections in the physical layer easily.
###### Abstract  
Accurate time period estimation (TPE) between sensor signals is essential for vehicle speed measurement in intelligent transportation systems (ITSs). In this context, we focus on time period estimation using signals acquired from a dual pyroelectric infrared (PIR) sensor setup. To estimate the time period between these signals, this paper analyzes and compares two correlation-based methods--conventional cross-correlation (CCF) and Hilbert transform-enhanced cross-correlation (CCFHT). An analytical framework is developed to quantify the bias and variance of each method under practical conditions, including sensor mismatch and noise. The PIR sensor signals are modeled based on their dynamic response characteristics, enabling theoretical analysis supported by simulations and field experiments. Results show that although both methods yield negligible bias under ideal conditions, CCFHT significantly reduces estimation variance in noisy or mismatched scenarios. These findings confirm the advantages of CCFHT for achieving robust and precise vehicle speed estimation using low-cost PIR sensor systems, and provide insights into practical deployment within ITSs.  
Keywords:time period estimation; pyroelectric infrared sensor; vehicle speed measurement; cross-correlation function; Hilbert transform; bias and variance analysis _Article_
## 4 Results from the Literature Review
### Autonomous Trucks  
Autonomous trucks are equipped with technology that enables driverless cargo transport. Using advanced systems, such as artificial intelligence (AI), sensors, cameras, LIDAR data, and GPS, these vehicles can independently navigate roads, plan routes, and make safety-related decisions [36; 162]. Progress in autonomous truck technology can substantially improve the efficiency of logistics and supply chains while reducing the environmental impact, aligning with sustainable transport objectives. When paired with electric or hybrid engines, autonomous trucks can also cut CO2 emissions, supporting climate action efforts and improving air quality [163]. Autonomous trucks provide the benefit of real-time control over driving parameters and rapid responsiveness to changing road conditions, resulting in lower fuel consumption, resource conservation, and cost savings for transport companies. Without the need for drivers, these vehicles can operate continuously, reducing transport times and enhancing supply chain efficiency [164]. Research into autonomous convoys also underscores their potential in military or humanitarian crisis settings. Fully autonomous convoys can deliver essential supplies to areas where traditional transport is unsafe or inaccessible, thereby increasing logistics safety in high-risk areas and minimizing human exposure to risk [165; 166].
## 2 Materials and Methods
### Frequency-Domain DAS  
For the signal processing of ultrasonic guided waves with a certain bandwidth, by obtaining their dispersion relationship through numerical calculations or experimental methods, the delay and sum can be implemented in the frequency domain, thereby better inverting the propagation process of the wave field. The signal is transformed into the frequency domain for processing using Fourier transform:  
\[U_{n}^{s}(\omega)=\mathcal{F}[u_{n}^{s}(t)]=\int_{-\infty}^{+\infty}u_{n}^{s}(t )\cdot e^{-j\omega t}dt \tag{4}\]  
For a specific frequency component \(\omega\), the time-domain delay given by Equation (1) can be expressed in terms of the phase delay as:  
\[\Delta_{p}\Big{(}\overset{\rightarrow}{r}\Big{)}=\overset{\rightarrow}{k}_ {m}\cdot\Big{(}\overset{\rightarrow}{r}-\overset{\rightarrow}{s}_{m}\Big{)} -\overset{\rightarrow}{k}_{n}\cdot\Big{(}\overset{\rightarrow}{s}_{n}- \overset{\rightarrow}{r}\Big{)},m,n=1,2,\ldots,M \tag{5}\]  
In the above equation, \(\overset{\rightarrow}{k}_{m}=|k(\omega)|\overset{\rightarrow}{\xi}_{m}\) and \(\overset{\rightarrow}{k}_{n}=|k(\omega)|\overset{\rightarrow}{\xi}_{n}\), where \(\overset{\rightarrow}{\xi}_{n}\) and \(\overset{\rightarrow}{\xi}_{m}\) are unit vectors pointing in the direction of wave propagation. In the calculation of phase delay, the dispersion relation must be substituted as a known condition. For the probing point located  
Figure 2: Flowchart of the time-domain DAS method.  
at \(\overset{\rightarrow}{r}_{s}\) in the imaging area, the phase delay for each frequency component is calculated based on the above equation and loaded onto \(U_{m}^{s}\left(\omega,\overset{\rightarrow}{r}\right)\):  
\[U_{DAS}\left(\omega,\overset{\rightarrow}{r},\overset{\rightarrow}{r}_{s} \right)=\sum_{m=0}^{M-1}\sum_{n=0}^{M-1}\frac{A(\omega)}{\sqrt{|\mathbf{r}-\mathbf{s} _{m}||\mathbf{r}-\mathbf{s}_{n}|}}e^{\left|\overset{\rightarrow}{|\mathbf{r}-\mathbf{s}_{m}|} \right|}e^{\left|\overset{\rightarrow}{|\mathbf{r}-\mathbf{s}_{m}|}\right|} \tag{6}\]  
The signals from each array element are synchronized when the condition \(\overset{\rightarrow}{r}_{s}=\overset{\rightarrow}{r}\) is satisfied. The inverse transformation to the time domain is as follows:  
\[u_{DAS}\left(t,\overset{\rightarrow}{r}_{s}\right)=\mathcal{F}^{-1}\left[U_{ DAS}\left(\omega,\overset{\rightarrow}{r},\overset{\rightarrow}{r}_{s}\right)\right] \tag{7}\]  
The imaging index is:  
\[I_{2}\left(\overset{\rightarrow}{r}_{s}\right)=u_{DAS}\left(t,\overset{ \rightarrow}{r},\overset{\rightarrow}{r}_{s}\right)\Big{|}_{t=0} \tag{8}\]  
When the probe point is exactly located at the damage site, the above equation will yield the maximum amplitude, thereby enabling frequency-domain damage imaging. The flowchart of frequency-domain DAS method is shown in Figure 3.