esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 3 Background of BIM and Digital Twin
### From BIM to Digital Twin  
As mentioned before, Industry 4.0 technologies have significantly impacted the construction industry. Building Information Modelling (BIM) has been widely adopted and continuously improved recently. Integrating building component information into building models through parametric modelling extends the 3D model of real-world projects. The process and framework of BIM are used to establish a clear project vision in the early stages of the project design [30].  
As for three critical areas of technology, safety and management, BIM has been proven to assist in realizing digital twins (DT), significantly improving the prediction, management and monitoring of project quality and performance [31]. The integration of BIM and DT is based on the BIM model to create a virtual replica of a building or facility, combined with other software to fully simulate the physical environment of the building entity to create a Digital Twin [32].  
In simple terms, in construction, a digital twin can be considered an iterative and upgraded version that includes BIM. Digital Twin is dynamic, and its real-time nature allows the project to be optimized and adjusted during the construction and implementation phase, realizing the synchronization of the real and virtual models in the platform. This integration combines the advantages of BIM and digital twins, enabling stakeholders to increase process visibility, efficient information transfer and communication based on real-time data synchronized to the digital twin. DTs provide insight into the lifetime and characteristics of individual products and can optimize their sustainability, helping to improve future product generations [33].  
While current federated technologies are still limited by data transparency, concurrent viewing and editing of a single federated model, and controlled coordination of information access, [31] applications of BIM and digital twins are expected to play a vital role in smart building management.
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 7 Experimental Evaluation
### Experimental Setup  
The experiments were carried out in a real indoor environment. The monitored region covered by the sensor node was a cone with a 3 m radius. The sensor node is with seven PIR sensors, each of them equipped with fresnel lens arrays and a mask, as shown in Figure 4. The CC2430 module is used for data transmission between the sensor node and the sink based on ZigBee protocol. More details of the sensor node could be found in our previous work [3].  
There were eight volunteers that participated in our experiments, including three females and five males. The height of them ranges from 1.64 m to 1.80 m, and the weight of them ranges from 50 kg  
Figure 4: The wireless sensor node: there are seven PIR sensors on one sensor node. The sensor node is mounted at a height of 3m from the floor, looking down to classify human activities. Each PIR sensor is equipped with its own fresnel lens arrays and mask. The sampling frequency of PIR sensor is 25 Hz, and the resolution of the A/D converter is 8-bit. The CC2430 is the Radio Frequency (RF) transmission module based on ZigBee protocol, the transmission rate of which is 250 Kbps at 2.4 GHz.  
Figure 5: Different volunteers emulated different activities with their own speed and strategy: (**a**) Falling; (**b**) Jogging.  
to 70 kg. Each volunteer emulated five kinds of activities, including falling, sitting down, standing up from a chair, walking and jogging. Every activity was emulated ten times by each volunteer at a self-select speed and strategy, as shown in Figure 5. Totally, we obtained 400 samples, including 80 fall-simulated samples and 320 normal activity samples. In our experiments, fall-simulated samples are regarded as abnormal activities, and other samples are regarded as normal activities. Each sample is segmented automatically by thresholding Short-Time Energy, and all the normal training samples are unlabeled.
## 4 Analysis of Calculation Results  
The bearing capacity of the specimen calculated according to the theoretical formulae as well as the actual test results are shown in Table 5. The ratio of the theoretical to experimental values had a mean value of 1.031, a standard deviation of 0.32, and a coefficient of variation of 0.31. This indicates that, for UHPC-reinforced biased RC columns, the ultimate bearing capacity obtained using the theoretical calculation method is in good agreement with the test values, with low data dispersion, and can accurately predict the bias bearing capacity of the reinforced specimens. This shows that the design calculations for this type of reinforcement method according to the roll-out formula are on the conservative and safe side. However, the test values under eccentric pressure in this paper still deviate from the theoretically calculated values. Based on the observation of the phenomenon during the test, this gap may be caused by the relatively large difference in roughness between UHPC and the substrate material, and a small portion of the bonding surface detaches under eccentric pressure in some specimens. The cracks appeared on the bonding surface first. For this problem, the introduction of coarse aggregate into the UHPC system has gradually attracted the attention of scholars at home and abroad [32]. Adding a coarse aggregate can improve the traditional UHPC and reduce the amount of cementitious materials, as well as help improve the volume stability, reduce production costs, and increase the roughness of the UHPC [33], which allows the UHPC to fit the bonding surface better when used as a reinforcing material. However, the introduction of coarse aggregates has brought new challenges to the regulation of UHPC construction performance. Good homogeneity and densification are the basis for ensuring performance indexes, whereas unevenness is caused by layered settlement or uplift due to differences in the density of the concrete components. Scholars at home and abroad have researched concrete stability, exploring the effects of coarse and fine aggregates, mineral admixtures, external disturbances, and other factors. The conductivity method, visible slurry method, colored aggregate method, hydrostatic pressure test method, ultrasonic speed method, and other test methods and evaluation standards have been developed. These problems will be solved with the gradual maturation of UHPC preparation technology.
## 4 Measurement and Discussion  
The fabricated antenna was measured in an anechoic chamber with a Vector Network Analyzer 8510 system. Three scenarios were considered: free-space, on-body, and on-phantom. The measurement setups are shown in Figure 9. Since a wearable antenna is inevitably bent when being worn on the body in practice, bent antennas with curvature radii of 5 cm and 8.5 cm (i.e., conformal situations) were measured for the free space scenario. Further, the reflection coefficient of on-body scenarios, for example, on-arm and on-chest, was measured. As for the AR measurements, body tissue simulating liquid MSL2450v2 [40] was used to mimic the body loading due to the fact that a human may move during the measurement and cause unstable transmissions and reflections. A cylinder with a 10 cm diameter and a liquid bag packed with a 10-mm-thick foam were used as phantoms to mimic the on-arm and on-chest situations, respectively.
## 3 Results and Discussion
### GC Analysis  
#### 3.2.1 P&T GC-MS  
Regarding the analysis of potential volatiles by dynamic headspace (P&T) coupled to GC-MS, several aldehydes (butanal, hexanal, octanal, nonanal, decanal), alcohols (isobutanol, butanol), and aromatic compounds (composed of at least one benzene ring in their structures) were detected, as shown in Table 1. Six of the substances were confirmed by injection of the respective standard, while the others were tentatively identified using the mass spectra libraries. Only the best matches with the libraries were selected (direct matching factors and reverse search matching higher than 800). It is important to consider that, in the GC tests, the analysis of the material included both sides, internal and external. Figure 2: Raman spectra and corresponding, color-coded Raman image of the internal side of the sample CM2. Green: PET; blue: titanium oxide; red: polyester. Some of the solvents used in coating formulations were identified in the samples such as toluene, 2-butanone, 2-butoxyethanol, and 2-butoxyethyl acetate. Several compounds with an alkylbenzene and benzene (\(m/z\) 119, 134) structure were detected, which is in agreement with the type of compounds detected in thermoset polyester samples analyzed by [PERSON] et al. [15]. Xylenes and ethylbenzene are aromatic isomers with the chemical formula of C\({}_{8}\)H\({}_{10}\) (\(m/z\) 91, 106) used to synthesize plasticizers and polyesters. The xylenes consist of three isomers: o-xylene (OX; CAS 95-47-6), m-xylene (MX; CAS 108-38-3), and p-xylene (PX; CAS 106-42-3), which differ in the positions of the two methyl groups on the benzene ring. PX is first oxidized to terephthalalic acid or dimethyl terephthalate, OX is oxidized to phthalic anhydride, and MX is oxidized to isophthal acid, which are the base of polyester resins [16]. Diethylbenzenes, such as 1,4-diethylbenzene found in sample CM2, are used as solvent and precursors for cross-linking agents in producing resins such as polyesters [17]. Compounds with trimethylbenzene structures (\(m/z\) 105, 120) were also detected in all samples. 1,2,4-trimethylbenzene (CAS 95-63-6), 1,3,5-trimethylbenzene (CAS 108-67-8), and 1,2,3-trimethylbenzene (CAS 526-73-8) are used to produce 1,2,4-benzenetricarboxylic acid (trimellitic acid, TMA), 1,3,5-benzenetricarboxylic acid (trimesic acid), and 1,2,3-benzenetricarboxylic acid (hemimellitic acid), respectively, by aerobic oxidation. These acids are used to make plasticizers and as monomers for polyester and polyamide resins as they have high thermal resistance [18]. Cyclohexanone, detected in sample CM1, together with cyclohexanol, is oxidized with nitric acid to produce adip acid (AA, also called 1,6-hexanedioic acid), an intermediate in the production of polyester resins [19]. This information was very useful to try to find out which monomers were used in the manufacture of the polyester resins in the different samples analyzed. Dimethyl glutamate, identified in sample CM1 and CM2, is a dicarboxylic acid ester used in paints, enamel, varnish, lacquer, thinner, paint stripper, remover, polyamide, polyester, resins, and plasticizers [20].
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
###### Abstract  
Owing to the outstanding sensing properties, especially high sensitivity and large stretchability, flexible piezoresistive strain sensors are advantageous for achieving intelligent sensing and have become a popular topic in the field of civil structural health monitoring (SHM). To explore advanced flexible strain sensors for civil SHM, this paper summarizes the recent research progress, achievements and challenges in flexible piezoresistive strain sensors. First, four common piezoresistive mechanisms are introduced theoretically. Sensor materials, including conductive materials, flexible substrates and electrodes, are explained in detail. Second, essential sensing parameters are interpreted and then followed by specific explanations of improvement strategies for the sensor performance in terms of each parameter. Third, applications of flexible piezoresistive strain sensors in the deformation measurement and damage detection of steel structures, concrete structures and fiber-reinforced composite structures are presented. Existing challenges and prospects in the practical application and large-scale production of flexible strain sensors are also reported. Last but not least, strategies for the selection of piezoresistive sensors for civil SHM are explained.  
flexible strain sensor; piezoresistive mechanism; nanoscale composites; civil structural health monitoring; intelligent sensing +
Footnote †: journal: Applied Sciences
# Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation
## 4 Experiments and Discussions on Part Segmentation  
### Dataset  
ShapeNet-Part [56] is a dataset specifically designed for 3D object part segmentation tasks. It is derived from the larger ShapeNet dataset, which contains 3D models from various categories. ShapeNet-Part provides fine-grained part annotations for a subset of these models, thus covering 16 object categories with 50 parts in total. Each object is represented as a point cloud, and each point is labeled with a part identifier. This dataset is widely used in the research community for developing and evaluating 3D part segmentation algorithms.
# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
## 3 Numerical Analysis
### Analysis of Noise Suppression by CRRTM  
Unlike traditional RTM, CRRTM relocates seismic records to the source location for excitation. Consequently, for each receiver point, it was necessary to extract and save the seismic data corresponding to each source. Figure 7a shows the seismic data received by the receiver at \(z=800\) m in the observation well. Figure 7b shows the upgoing P-waves retained after signal separation.  
Figure 5: Cross-correlation between source and receiver wavefields in traditional VSP RTM at various times.  
Figure 6: Single-shot imaging result in the simple model using the traditional W-VSP RTM method.  
After organizing the seismic data into a common-receiver gather, as shown in Figure 7b, the seismic records were excited at the source location. Simultaneously, the seismic wavelet was forward-propagated at the receiver location. Subsequently, the resulting wavefields were cross-correlated with obtain imaging results. The specific cross-correlation between the source and receiver wavefields is shown in Figure 8.  
It is evident that CRRTM effectively relocates migration artifacts, which are otherwise challenging to remove using conventional methods, to the edges of the image. These regions fall outside of the scope of theoretical imaging and, thus, do not affect the areas of interest. Figure 9 illustrates the single-image results obtained via CRRTM.  
Figure 8: Cross-correlation between source and receiver wavefields in CRRTM at various times.  
Figure 7: Reorganized receiver gather from different shot gathers in the simple model: (**a**) original data; (**b**) separated upgoing data.  
Unlike acoustic waves, the S-wave components in elastic waves often provide higher-resolution imaging results owing to their lower propagation speeds. CRRTM can also be applied to S-wave imaging. This principle is similar to that of compressional waves, which require the seismic wavelet propagated at the receiver location to be set as an S-wave source. Figure 10 shows the cross-correlation between the source and receiver wavefields when S-waves are used for imaging.  
When shear waves are used for imaging, the significant difference in propagation speed between S- and P-waves leads to a weaker correlation between the source and receiver wavefields. This results in a reduction in low-frequency noise in the imaging results. However, owing to the same propagation speed disparity, the direct wave from the source wavefield and the reflected wave from the receiver wavefield correlate incorrectly, creating additional arc noise. These artifacts appear around the observation well and accumulate over multiple imaging iterations. Figure 11 shows a single imaging result obtained using S-waves.  
Figure 10: Cross-correlation between source and receiver wavefields using S-wave in CRRTM at various times.  
Figure 9: Single imaging result in the simple model with CRRTM.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 1 Introduction
### Description of the Problem  
Historically, the construction industry has perpetuated unchanged principles for a long time; the design and construction processes are embedded in paradigms deeply rooted in the culture of this industry. In contrast to traditional ways to develop construction projects, new trends aimed at improving the conception of production processes are emerging [7]. Technologies applied to this industry have a lower performance due to technical, organizational, and economic obstacles. Labor efficiency is low compared to other industrial sectors; the accident rate in construction sites is significant, there is a relatively low quality of work, and the control of the construction site is difficult, so manual labor is decreasing little by little [8]. Automation in construction processes seems to be the answer.  
Currently, the 3D printing of concrete using robotic systems has been carried out with own-designed codes or with software accompanied by plugins associated with the equipment involved (robots). BIM models consist of digital data that allow the realization of a structure where, by using this data, it is possible to elaborate trajectories for an automated system, such as a robot [9]. However, there is limited evidence on the existence of computer routines that allow fluid and simple communication between BIM models and a robotic arm to print 3D elements of concrete, which, in practice, correspond to machine-to-machine communication protocols (BIM-robot), the main objective of this research.
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 2 Efficient Fuzzy MPC Algorithms with the Advanced Construction of the Dynamic Matrix
### Generation of the Dynamic Matrix
#### 2.2.3 Advanced Dynamic Matrix Generation
Next, the appropriate elements of the dynamic matrix, for each \((k+i)\)th time step from the prediction horizon, will be calculated using the formula:  
\[u_{k}^{j_{u}}\text{ is }C_{1}^{f,j_{u}}\text{ and }u_{k-1}^{j_{u}} \text{ is }C_{2}^{f,j_{u}}\text{ and }\dots\text{ and }u_{k-m+1}^{j_{u}}\text{ is }C_{m}^{f,j_{u}}\]4  
Now, the dynamic matrix can be generated (and updated in each sampling instant):  
\[\mathbf{A}_{k}=\left[ \begin{array}{cccc}\tilde{A}_{k}^{11}&\tilde{A}_{k}^{12}& \dots&\tilde{A}_{k}^{1n_{u}}\\ \tilde{A}_{k}^{21}&\tilde{A}_{k}^{22}&\dots&\tilde{A}_{k}^{2n_{u}}\\ \vdots&\vdots&\ddots&\vdots\\ \tilde{A}_{k}^{n_{g}1}&\tilde{A}_{k}^{n_{g}2}&\dots&\tilde{A}_{k}^{n_{g}n_{u }}\end{array} \right], \tag{33}\]  
\[\tilde{A}_{k}^{jm}=\left[ \begin{array}{cccc}\tilde{a}_{1,k+1}^{j,m}&0&\dots &0&0\\ \tilde{a}_{2,k+2}^{j,m}&\tilde{a}_{1,k+2}^{j,m}&\dots&0&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ \tilde{a}_{p,k+p}^{j,m}&\tilde{a}_{p-1,k+p}^{j,m}&\dots&\tilde{a}_{p-s+2,k+p}^ {j,m}&\tilde{a}_{p-s+1,k+p}^{j,m}\end{array} \right]. \tag{34}\]  
Note that in the \(i\)th row of each matrix \(\tilde{A}_{k}^{jm}\), the elements calculated for the \((k+i)\)th time step from the prediction horizon are used. Assume that the firing strengths for a given fuzzy rule are grouped in the following vector:  
\[\widetilde{\mathbf{w}}^{f}=\left[\widetilde{w}_{k+1|k^{\prime}}^{f}\widetilde{w}_ {k+2|k^{\prime}}^{f}\dots,\widetilde{w}_{k+p|k}^{f}\right] \tag{35}\]  
and define:  
\[\mathbf{A}^{jmf}=\left[ \begin{array}{cccc}a_{1}^{j,m,f}&0&\dots&0&0\\ a_{2}^{j,m,f}&a_{1}^{j,m,f}&\dots&0&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ a_{p}^{j,m,f}&a_{p-1}^{j,m,f}&\dots&a_{p-s+2}^{j,m,f}&a_{p-s+1}^{j,m,f}\end{array}  \right]. \tag{36}\]  
Then, the matrices \(\tilde{\mathbf{A}}_{k}^{jm}\) can be calculated using the relatively simple formula:  
\[\tilde{\mathbf{A}}_{k}^{jm}=\sum_{f=1}^{l}\widetilde{\mathbf{w}}^{f}\cdot\mathbf{I}\cdot \mathbf{A}^{jmf}\ \, \tag{37}\]  
where \(\mathbf{I}\) is the identity matrix of dimension \(p\times p\), and the matrices \(\mathbf{A}^{jmf}\) remain the same in each time step.
## 4 Results
### High-Frequency Monitoring of Methane Point Sources  
In this section, we demonstrate the capability of the Sentinel-2 MultiSpectral Instrument (MSI), with its fine pixel resolution of 20 m and rapid revisit rate of 2-5 days, to detect and quantify significant methane point sources. We employ the multi-band multi-pass (MBMP) method, utilizing shortwave infrared (SWIR) measurements from MSI spectral bands 11 (approximately 1560-1660 nm) and 12 (approximately 2090-2290 nm) to identify methane plumes in the atmosphere. The MBMP approach combines data from two bands with a non-plume reference observation to retrieve methane columns. This method is capable of quantifying point source methane emissions up to approximately 3 t/h. We applied these techniques to the high-frequency monitoring of strong methane point source plumes from a coal facility in Shanxi, China (from January 2020 to August 2024), and a compressor station in an oil and gas field in the Permian Basin (from January 2020 to August 2024).
# Semantic Communication on Digital Wireless Communication Systems
## 3 IER--A Novel Physical Layer Semantic Metric
### Definition of IER (Integer Error Rate)  
First of all, we should consider how to define the error distance between the sending/received message for the semantic codec. As illustrated in Figure 8, assume that the sending message is sequence S and the received message is sequence R. Then, the error distance between the sending/received message for the semantic codec can be defined as the distance between sequences S and R. In mathematics, there are many ways to define the distance between two sequences, e.g., Euclidean distance, Manhattan distance, Hamming distance\(\ldots\), etc. Hamming distance is the commonly adopted metric in the current communication system, but it is not the best one for semantic communication. We are going to analyze this in the following.  
Figure 7: Semantic JSCC vs. SSCC simulation result. (**a**) BER vs. SNR; (**b**) spectrum efficiency (\(\text{bps}/\text{Hz}\)); (**c**) PSNR vs. SNR; (**d**) SSIM vs. SNR.
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)
### Future Development Directions
#### 3.4.1 Intelligent Networks and Integrated Networks  
With the rapid development of network technology and information technology, the IoT, cloud computing, and big data have penetrated the daily applications of humans. People can use Radio Frequency Identification (RFID), sensors, monitoring, and other equipment to sense, measure, and capture potential areas of natural disasters. This allows advance deployment and quick decision making, and reduces losses caused by disasters. For example, in May 2018, when floods occurred in Maryland, the US Department of Homeland Security used automatic sensors to provide an early warning and reduce losses. In November 2018, when a forest fire broke out in California, Silvia Terra, a San Francisco based company, used AI technology to map the forest, including the density and type of trees, to identify the areas with the highest fire risk and make a plan. In response to the COVID-19 outbreak in 2019, the communication network industry made outstanding contributions to the prevention and control of the epidemic. At present, the most mature and large-scale schemes are the communication big data travel card provided by operators,  
Figure 11: System topology of the “LTE + Mesh” network.  
the health codes based on big data analysis, and the Exposure Notifications System issued by Apple/Google Corporation.  
The 6G network can be referred to as the \"AI + ground communication + satellite network\". Using AI technology to build 6G networks will be an inevitable choice, and using intelligent dynamic spectrum-sharing technology between ground communication and satellite communication can improve spectrum efficiency. At the same time, intelligent seamless switching technology and intelligent interference elimination technology are being used to realize real space-air-ground-sea intelligent communication.