esa-sceva/satcom-chunk-collection huggingface dataset:
# Framework for BIM-Based Repair History Management for Architectural Heritage
## 5 Development of a BIM-Based Repair History Management Framework
### Data Mapping  
#### 5.2.1 Object Classification Code Generation  
In order to link the shape information of architectural heritage constructed using BIM tools and information in the repair history database, a common rule parameter, referred to as the mark parameter, acts as a mediator between the two datasets. The repair history records of each corresponding part are retrieved from the database based on the value of the mark parameter input to each part of BIM following the same naming convention. An object classification code for each part, conforming to the definition of Historic BIM 1.1.3 [32], consisting of the element type constituting the building and a seven-digit index number, is then input into the mark parameter. Figure 6 depicts the process of mapping the object classification code to the mark parameter using the proposed automated data synchronization system.  
The algorithm is implemented using Dynamo, a visual scripting tool for Revit, as shown in Figure 7. Following the naming convention designated for each part family (a Revit family is a group of components used to build a BIM model with a common set of properties and a related graphical representation; for example, walls, doors, and fixtures) of the BIM model, the element type code from level 1 to 3 and the location information on the part plane are analyzed, and the index number is input. The index number consists of two digits for the floor-level information of the part, three digits for the serial number according to the location on the plane, and two digits for the classification number according to the aggregation type. The serial number is automatically assigned based on the X-, Y-, and Z-axis position information of each part and numbered per the HBIM preparation criteria, as shown in Figure 8, by assigning numbers in a counterclockwise direction based on the lowermost and outermost left of the target heritage building.
## 4 Link Budget
### Link Budget under Realistic Atmospheric Conditions
#### 4.2.2 Study Periods  
For each area, we selected one month for the simulation. The ideal period is when there are fewer clouds and smaller _AOT_. The European Center for Medium-Range Weather Forecasts (ECMWF) model gives the probability of high, middle, low and total cloud cover at the global scale, at a spatial horizontal resolution of 0.75\({}^{\circ}\) and a temporal resolution of 6 hours [72]. The mean values and standard deviations of the probabilities of cloud presence over the considered areas of each month of 2011 are studied. The monthly _AOT_ is derived by using the MODIS Atmosphere Monthly Global Product MOD08_M3 at the horizontal resolution of 1\({}^{\circ}\)[46], which is also considered for the two areas. There is no _AOT_ value for the North-Asian site during the winter due to excessive cloud cover. We thus chose December and June for the Congo basin and Lake Baikal areas, respectively, when the average monthly probability of cloud is the lowest in order to promote the cloud free condition.  
Figure 13: **(a)** Simulated orbits of ISS (in pink) and ICESat (in blue) of 1 day over the simplified land-cover map derived from MCD12C1 (MODIS) of 2011 with a spatial resolution of 0.05\({}^{\circ}\); **(b)** Simulated orbits of 26 days over the Congo basin (Africa) zone (**left**) and lake Baikal (Asia) zone (**right**), land-cover maps were derived from MCD12Q1 (MODIS) of 2011 with a spatial resolution of 500 m. The different green colors indicate five dominant forest types as named in **(a)**.
## 2 Experimental Procedure
### Cytocompatibility and Cell Viability Assessment  
Cell culture experiments were performed on the scaffolds to evaluate cytocompatibility and cell viability. The scaffolds were autoclaved at 121 \({}^{\circ}\)C for 40 min. Subsequently, they were rinsed with aseptic phosphate buffered saline (PBS), pre-wetted with Dulbecco's Modified Eagle's Medium (DMEM) and then placed in 12-well plates. Osteoblast-like cells (MG-63) were seeded on the scaffolds at a density of 4 \(\times\) 10\({}^{4}\) cells per well. Incubations were run in DMEM supplemented with 10% fetal bovine serum (FBS) and 1% streptomycin/penicillin at 37 \({}^{\circ}\)C under a 5% CO\({}_{2}\) humidified atmosphere. The culture medium was changed every day for the duration of the experiment.  
Cell proliferation was quantitative estimated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. After cultivating for 3 days, MTT, a yellow tetrazolium salt, was diluted in DMEM and added to each well to form a purple formazan due to an enzymatic reaction by the living cell. After culturing for a further 4 h, dimethyl sulfoxide was blended to all wells to entirely resolve the purple formazan. Absorbance of each well at 490 nm was confirmed on a microplate reader (\(n=3\)).  
Cell viability was also qualitatively assessed by examining cells' attachment and spreading behaviors. The scaffolds were washed twice gently with PBS after incubating for 1, 2 and 3 days, and then fixed with 2.5% glutaraldehyde aqueous solution for 2 h and dehydrated twice in a series of ethanol solutions (70%, 80%, 90%, 95% and 100%), respectively. Afterward, the scaffolds were dried and coated with platinum, and then observed under SEM.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## Appendix A  
Assume that node A wants to send a quantum state \(\ket{\psi}\) to node B, the state of the system is \(\ket{\Gamma}\). The Bell pair shared between two nodes is \(\ket{\Phi^{+}}\).  
\[\ket{\Gamma}= \ket{\psi}\otimes\ket{\Phi^{+}}=(\alpha\ket{0}+\beta\ket{1}) \otimes\frac{1}{\sqrt{2}}(\ket{0_{A}0_{B}}+\ket{1_{A}1_{B}}) \tag{1}\] \[= \frac{1}{\sqrt{2}}[\alpha(\ket{0_{A}0_{B}}+\ket{0_{A}1_{B}})+\beta (\ket{10_{A}0_{B}}+\ket{11_{A}1_{B}})].\]\(|\psi\rangle\) is the control state, while the state with subscript A is the target state. Node A operates CNOT gate, and then applies the H gate to \(|\psi\rangle\). \(|\Gamma\rangle\) is transformed to \(|\Gamma_{1}\rangle\).  
\[\begin{split}|\Gamma_{1}\rangle&=\frac{1}{2}[|00_{A} \rangle(\alpha|0_{B}\rangle+\beta|1_{B}\rangle)+|01_{A}\rangle(\alpha|1_{B} \rangle+\beta|0_{B}\rangle)\\ &\quad+|10_{A}\rangle(\alpha|0_{B}\rangle-\beta|1_{B}\rangle)+|1 1_{A}\rangle(\alpha|1_{B}\rangle-\beta|0_{B}\rangle)].\end{split} \tag{10}\]  
Two qubits in node A are measured. Measurement results and the qubit state owned by node B are in Table 1. Quantum gate in node B can rebuild \(|\psi\rangle\) with entangled state. Relevant data based on GHZ states are also given in Table 1.
# Simulation Study on Thermal Wake Characteristics of Underwater Vehicle under Rotary Motion
## 4 Analysis of Simulation Results  
### Trajectory Analysis of Near-Field Cooling Water Movement  
Figure 12a,b show the cooling water movement track of the underwater vehicle under the condition of direct flight and 90\({}^{\circ}\) rotary motion, respectively. It can be seen that the trajectory of cooling water in the direct flight state is distributed along the central axis of the underwater vehicle, and the cooling water rises upward after being discharged from the discharge ports on both sides. The trajectory of cooling water in the rotating state is asymmetrical. Compared with the direct flight, the cooling water in the rotating side aggravates the upward movement trend and flows through the left rudder, while the cooling water trajectory measured by the backflow moves down obviously.  
Figure 11: Comparison of thermal wake trajectory.  
Figure 10: Temperature comparison at straight AB.  
In order to explore the cause of the difference in the trajectory of cooling water under the two states of the underwater vehicle, the velocity vector graphs of multiple cross sections under the two states were observed.  
Take the motion state of the model with a rotation angle of 90\({}^{\circ}\), and take cross-sections 1 and 2, respectively, at 0.1 m before and 0.2 m after the discharge outlet along the XY direction. Figure 13 is the velocity vector diagram of cross-sections 1 and 2 of the underwater vehicle under the rotation state, where the color variable in Figure 13b is temperature.  
As can be seen from Figure 13a, under the influence of the model shell and turtle back, the center of the vortex on both sides is asymmetrical, and the rotation direction is different. The rotary side vortex rotates counterclockwise, and the back flow measurement vortex rotates clockwise.  
As can be seen in Figure 13b, as the diameter of the contact surface between the model tail and water gradually decreases, due to the body volume effect, a low-pressure area appears at the location where the body diameter decreases, and the surrounding water quickly fills the \"volume gap\" left by the underwater vehicle movement. In addition, the disturbance effect of the shell and turtle back on the water decreases, making the vortex center offset upward. The trajectory of the high temperature cooling water discharged by the underwater vehicle is greatly affected by the vortex, which is similar to the trend of the vortex. The cooling water on the left side of the body is located in the whirling vortex and is driven upward. However, the cooling water on the right side of the body first rolls down  
Figure 12: Motion track of cooling water of underwater vehicle under different motion states: (**a**) Direct flight; (**b**) Rotary motion.  
Figure 13: Velocity vector diagram of sections one and two of the underwater vehicle under the rotating state: (**a**) Direct flight; (**b**) Rotary motion.  
with the deviating vortex, and then gradually moves toward the fuselage, which explains the reason for the asymmetric movement of the cooling water trajectory in Figure 12b.
## 1 Introduction
Environmental conditions, such as signal attenuation or obstacles, can also lead to data loss during transmission [17]. Detecting and addressing these types of data corruption is crucial for ensuring the accuracy and reliability of information in IoTs. IoT academies must learn how to handle these different types of corruption effectively to provide high-quality data for various applications. Previous research focused only on single types of damage, such as Gaussian noise, outliers, and missing values. However, addressing all types of data corruption is essential to ensuring accurate data reconstruction. This study proposes a new approach to addressing these challenges and makes significant contributions to the field. To the best of our knowledge, this is the first study to address complex corruption encompassing mixed noise, outliers, and missing values in multivariate sensing data within IoTs operating in 5G environments using a WTRPCA. The primary contributions of this study are as follows:  
1. This work presents an enhanced method for multivariate data reconstruction in 5G-operating IoTs. A TRPCA is combined with TC to enhance the method's ability to handle missing data, noise, or missing values. This unique approach leverages correlations among multiple attributes to improve reconstruction performance. 2. This study introduces a weighted approach to TC and the TRPCA, offering a means of handling singular values. In contrast to traditional methods, the proposed approach uses weighted tensor singular value thresholding (WTSVT) to shrink singular values based on their importance, potentially boosting reconstruction accuracy. 3. The proposed approach effectively tackles complex types of corrupted data, such as mixture noise, outliers, and missing values, and stands out in comparison to other models. This paper is organized as follows: Section 1 introduces the core concepts related to the study, while Section 2 surveys the existing literature and related works on IoTs, TC, and the TRPCA. Section 3 provides the necessary preliminaries, introducing tensors, TC, and the TRPCA in detail. The methodology is presented in Section 4, in which each step of the proposed model is thoroughly explained. An experimental validation of the model, including dataset descriptions, an understanding of the low-rank structure in the dataset, and a description of the experimental setup, is presented in Section 5. Section 6 concludes the paper, summarizes its contributions, and discusses potential directions for future research.
# Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence
## 4 Experiment
### Evaluation Metrics  
Fake news detection is essentially a classification problem, so the results are often evaluated using Accuracy (Ac), Precision (P), Recall (R), and F1-score (F1). The ultimate goal of the model is to detect whether the news is fake news or not. The news in the testing set can be classified into four categories based on their truth value labels and model prediction labels: true positive (TP), false positive (FP), true negative (TN), and false negative (FN). The labeling concepts are described in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Label** & **Real** & **Fake** & **Total** \\ \hline Train & 2492 & 2877 & 5369 \\ Test & 691 & 741 & 1432 \\ Total & 3183 & 3618 & 6801 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics for the Weibo dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Label** & **Real** & **Fake** & **Total** \\ \hline Train & 6840 & 5007 & 11,847 \\ Test & 833 & 573 & 1406 \\ Total & 7673 & 5580 & 13,253 \\ \hline \end{tabular}
\end{table}
Table 3: Statistics for the Twitter dataset.  
Based on the above concepts, the formulas for accuracy (Ac), precision (P), recall (R), and F1 score (F1) are shown in Equations (39)-(42).  
\[Ac=\frac{TP+TN}{TP+FP+TN+FN} \tag{39}\]  
\[P=\frac{TP}{TP+FP} \tag{40}\]  
\[R=\frac{TP}{TP+FN} \tag{41}\]  
\[F1=2\frac{P\cdot R}{P+R} \tag{42}\]  
Ac is used to assess the overall performance of the detection model and indicates the ratio of correctly predicted samples to overall samples. P is used to assess the model's ability to correctly predict false news, indicating the ratio of samples correctly predicted as false news out of all samples predicted to be labeled as false news. R is used to measure the sensitivity of the fake news detection classifier, denoting the proportion of samples that are correctly predicted as fake news out of all samples whose truth value is labeled as fake news. F1 is used to measure the predictive performance of the fake news detection classifier, F1 denotes the reconciled mean of P and R. A higher F1 score ensures that both P and R of the classification model are also higher.
# Key Technologies for THz Wireless Link by Silicon CMOS Integrated Circuits
## 3 Features and Applications of 300 GHz Band Communication
### The Future of Terahertz Communication Spreading in Space
How can we maintain a sustainable society while covering the enormous power consumption of such supercomputers? Research on space photovoltaic power generation using a 2.5-km-square solar panel on a geostationary orbit is proceeding as one of stable candidates for renewable energy [30]. This solar panel can generate 1 GW. Electricity produced by space photovoltaic power will be transmitted to the ground using microwaves. The target efficiency of power transmission is 50%. If a supercomputer is built together with the space photovoltaic panel, and electric power generated by space photovoltaic power is used in place, power can be effectively utilized without transmission loss [30]. Ultra-high-speed terahertz communication can be used for large-volume data exchange for space supercomputers that cannot lay optical fiber. On the other hand, it will be an era of monitoring the real world with enormous sensors exceeding 1 trillion units and processing that information with artificial intelligence. Although the information obtained from the sensor cannot be stored as it is, the amount of stored data will exponentially increase. On the other hand, cold data in which access frequency decreases with time will also increase. Since the access frequency of cold data is low, the access speed is not important. When slow access is allowed, it may be possible to construct a cold storage data center on the moon, for example, rather than on Earth [31]. Since the light propagation time to the moon is 1.3 s, the access speed will be about 3 s in a round trip. Nonetheless, a lunar cold storage data center will be one of the candidates for utilization on the moon. Furthermore, manned exploration of Mars and migration of mankind are currently being studied [32]. The means of communication of the migrants to Mars is limited to radio. It is noted that one petabyte can be transmitted per day when information can be sent at a data rate of 100 gigabits per second. When Mars is furthest from Earth it takes 21 min to propagate electromagnetic waves. Hence, although communication will not be real time, terahertz communication makes it possible to exchange large volumes of information between Earth and Mars. It is noted that the moon's atmosphere is very low (i.e., almost vacuum). The atmospheric pressure of Mars is about 0.6% of the Earth, and most of the atmosphere is carbon dioxide. Carbon dioxide absorbs infrared to cause a greenhouse effect but does not absorb terahertz. Therefore, terahertz absorption on the moon and Mars is negligible. Performance required for achieving terahertz communication with geostationary orbits of the moon and Mars is estimated in Figure 18. Although it may not be a technology that can be realized immediately, the space utilization of ultra-high-speed terahertz communication will be a meaningful challenge to overcome for the sustainable development of mankind.
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 3 Experimental Results and Discussion
### Comparison with Other Encoding Schemes  
The comparison of the proposed R-VLAD vector and Hybrid Feature Vector (HFV) encoding method with other encoding schemes is presented in Table 1. The parameters for the proposed encoding scheme are fixed as discussed previously. The vector quantization follows the hard assignment and a histogram of codeword occurrences is formed. The results show that these assignments can cause loss of information, which ultimately affects recognition performance. The soft assignment (SA) [32] is another encoding method, which considers all the codewords for final voting. The \(\beta\) parameter is used for controlling of softness. We set \(\beta\) as 1 in evaluation. The improvement is seen as compared to VQ as more information is encoded related to other codewords. VLAD and fisher vectors are used in standard form with a codebook size of 256. R-VLAD vector outperforms the standard VLAD representation despite being a lower dimensional (more compact) representation. However, the R-VLAD residual  
Figure 5: Impact on accuracy by varying the codebook size.  
Figure 6: Comparison of different normalization methods on accuracy.  
vector contains high order statistics, as discussed earlier. Fisher vector encoding performed poorly, which shows super vectors do not necessarily improve accuracies. Best accuracy is obtained on both datasets with the proposed HFV, which combines the R-VLAD with the LLC descriptor.
## 4 Performance Analysis and Discussion
### Throughput  
Figure 19 shows the measured throughput using three protocols, revealing that the throughput falls as the number of nodes in the network rises. Heavy routing overhead leads to the congestion and crowding out of the data packets. However, RRM exploits the cluster structure and multi-path feature to prevent bottlenecks. Therefore, the RRM slope decreases slowly. Hence, RRM still has a good throughput in a large network.
## 3 Test Results and Analysis
### Rock Removal  
According to the macroscopic states of broken rock debris in Table 2, the large-particle rock debris first increases and then decreases during the cutting of the basalt samples using the reduced-scale TBM cutter. This is because certain microwave irradiation can induce microcracks in the rock, which renders the rock more prone to breakage, so the formed fragments are larger. When microwaves further heat the rock, more intergranular and transgranular fractures occur, and the mineral structure in the rock is changed, which leads to decreased cohesion in the rock [25]. Therefore, the broken rock debris begins to become smaller. Hence, with increasing exposure to microwave irradiation, the removal of rock from the basalt samples increases when using the reduced-scale TBM cutter and exhibits an approximately linear increasing trend over time (Figure 10). After 15 min of microwave irradiation, the removal of rock using a reduced-scale TBM cutter increases by 42%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial No. of Cutters**} & **Microwave** & \begin{tabular}{c} **Increasing Exposure** \\ **to Microwave Irradiation** \\ **(min)** \\ \end{tabular} & \begin{tabular}{c} **The First** \\ **Cutting** \\ **Mark (mg)** \\ \end{tabular} & \
\begin{tabular}{c} **The Second** \\ **Cutting** \\ **Mark (mg)** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **(mg)** \\ \end{tabular} \\ \hline W-5 & 2100 & 0 & 2 & 3 & 5 \\ W-6 & 2100 & 3 & 2 & 2 & 4 \\ W-7 & 2100 & 6 & 2 & 2 & 4 \\ W-8 & 2100 & 9 & 1 & 2 & 3 \\ W-9 & 2100 & 12 & 1 & 2 & 3 \\ W-10 & 2100 & 15 & 1 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Changes in the mass loss of the reduced-scale TBM cutter with increasing exposure to microwave irradiation.  
Figure 8: Changes in the average mass loss of the reduced-scale TBM cutter with increasing exposure to microwave irradiation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Increasing** \\ **Sarial No. of** \\ **Samples** \\ **Irradiation (min)** \\ \end{tabular} & **Initial State** & \begin{tabular}{c} **The First** \\ **Cutting Mark** \\ \end{tabular} & \
\begin{tabular}{c} **The Second** \\ **Cutting Mark** \\ \end{tabular} & **Rock Debris** &
\begin{tabular}{c} **Mass of Rock** \\ **Debris (g)** \\ \end{tabular} \\ \hline X-1 & 0 & & & & & \\ X-2 & 3 & & & & & \\ X-3 & 6 & & & & & \\ X-4 & 9 & & &