esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Infrastructure and Additive Manufacturing: Synergies, Advantages, and Limitations
## 3 Key Applications of 3D Printing in Smart Infrastructure
### Sustainable and Self-Healing Materials
The adoption of 3D-printed infrastructure thus not only helps to preserve natural resources but also helps to lower the buildup of building and demolition debris, which makes up a sizable share of world landfill contents. Beyond mere material efficiency, 3D-printed infrastructure's sustainability depends much on energy savings. Energy-intensive operations including formwork creation, the transportation of prefabricated components, and heavy machinery operation define many traditional building techniques [129]. Large-scale 3D printing, on the other hand, allows automated, layer-by-layer deposition, therefore, lowering the need for high-energy-consuming operations including molding, welding, and unnecessary machining. This reduces direct energy usage as well as construction times, therefore, lowering the carbon footprint connected with extended site operations [130]. Furthermore, easily integrated into 3D-printed buildings are energy-efficient components as thermally insulating concrete and phase-change composites, therefore, enhancing their total energy performance [131]. The design of structures with ideal thermal characteristics is used to substantially minimize heating and cooling needs, thereby saving long-term energy for urban infrastructure [132; 133]. Another means of increasing the sustainability of 3D-printed smart infrastructure is by using renewable energy sources such as embedded energy-harvesting devices and solar-sensitive materials, thereby playing a role in the larger shift toward green and autonomous urban spaces [134].
## 3 Modal Analysis of a Suspended Piping System  
A galvanized water pipe is used as the experimental suspended piping system for testing the performance of PTMDs. The pipe with two fixtures is connected by two vertical steel bars to an  
Figure 2: Schematic of (**a**) double-sided bounding tuned mass damper (PTMD); (**b**) single-sided PTMD.  
aluminum beam (1220 mm, 80 mm, 40 mm), as shown in Figure 3a. The length of the steel pipe is 1133 mm; the outer and inner diameters are 115 mm and 110.8 mm, respectively. The steel bars used to suspend the pipe are 195 mm in length and 6.35 mm in diameter. The total mass of the pipe, bars, and fixtures is 9.3 kg.  
By applying a small impact load to the pipe, it can be found that the fundamental vibration mode is in the lateral direction. To determine the dynamic characteristics of the piping system, a finite element (FE) model is developed in the ABAQUS interface (Dassault Systems, Velizy-Villacoublay, France) [87]. The results of the FE analysis show that the first and the second mode shapes of the piping system consist of lateral and longitudinal motions, respectively. The mode shapes are shown in Figure 3b,c and the corresponding frequencies are 2.54 Hz and 8.14 Hz, respectively. The lower frequency lateral motion also experiences the larger displacement. Therefore, the lateral movement of the piping system is selected as the target motion for experimental studies.  
To verify the results of the FE model and also estimate the piping system viscous damping, a damped free vibration test is conducted. An initial displacement is applied to the middle of the pipe, and the displacement response of the pipe is recorded. With the help of the fast Fourier transform (FFT), the primary frequency of the piping system is 2.55 Hz, which is slightly different from the FE result (2.54 Hz). The slight difference between the obtained frequencies may be due to minor, unavoidable differences between the real and simulated model. The closeness of the obtained frequencies, on the other hand, demonstrates the accuracy of the FE analysis. Through the logarithmic decrement method, the damping ratio of the piping system is estimated to be 0.58%.
# Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
## 3 Materials and Methods
### Benchmark Dataset  
In classification tasks, the model must be evaluated on a dataset. We have performed our experiments on three datasets. The first dataset is CIFAR-10 [42], the second one is the Describable Textures Dataset (DTD) [43], and the last one is Flickr Material Database (FMD) [44]. CIFAR-10 consists of 60,000 images of 32 \(\times\) 32 pixels of ten different objects. DTD contains 47 classes of 120 images in the wild. This dataset is developed in different uncontrolled conditions. Initially, it includes 40 training images, 40 validation images, and 40 test images for each class. Finally, FMD is built with standard materials. It has ten classes of 100 images, and each image is hand-picked from Flickr.com (under Creative Commons license) to ensure a variety of lighting conditions, compositions, colors, texture, and material subtypes.  
A good practice is to split our dataset using the Hold-Out Cross-Validation sampling technique [35]. The technique is used to test the model's predictive performance and how well it performs on the test or unseen data. The dataset is initially separated into two sets: training and test; then, the training set is split into two subsets: training and validation. The idea is that each set contains representative images of each class. Therefore, it is achieved to have balanced sets and random. In the case of CIFAR-10, the test set is initially left with approximately 16.66%, and the training set is divided into two subsets with the same distribution of images: training 80% and validation 20%. For DTD and FMD, the distribution is different because the dataset is small. The test set contains 15% of the data. Therefore, the rest is divided into the training subset with 82% and the validation subset with 18% of the data.  
The images have dimensions of 224 \(\times\) 224 pixels, except for the CIFAR-10 dataset, which has dimensions of 32 \(\times\) 32 pixels. Following convention, it is helpful to normalize  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & \\ \hline Learning rate & 0.001 \\ Minibatch & 30, CIFAR-10 = 64 \\ Loss function & ‘categorical_crossentropy’ \\ Metrics & ‘acc’, ‘loss’ \\ Epochs & 500 \\ Callbacks API & 4 \\ ModelCheckpoint & Monitor = ‘val_loss’, save_best_only = True, mode = ‘min’ \\ EarlyStopping & Monitor = ‘val_acc’, patience = 15, mode = ‘max’ \\ CVLogger & ‘model_history.csv’, append = True \\ ReduceLROnPlateau & Monitor = ‘val_loss’, factor = 0.2, patience = 10, min_lr = 0.001 \\ Optimizer & SGD—Adam \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameters of the proposed model.  
the pixel values to a range of 0 to 1 for our model to converge quickly because the inputs with large integer values can slow down the learning process. The number of images per class is shown in Table 2. As observed, the last two datasets have a few images, but one advantage is that they have a balance between the number of images per class.
## 4 Discussion
### Practical Applications  
Our findings suggest that the C-station is a reliable tool to measure isometric force production and evaluate the muscle strength of the lower limbs using the IMTP. The C-station is potentially an accessible alternative for clinicians due to being portable, user-friendly, and less expensive compared to stationary equipment. This could be helpful for practitioners, coaches, and clinicians aiming to serially monitor athletes throughout the season, in multiple locations. The calculations of CV, SEM, and MDC in the current study are also useful to ensure that the increase or decrease in lower limb strength when performing the IMTP during a season is beyond measurement error and clinically meaningful. Our data will help with the practical interpretation of IMTP results.
## 3 Results  
### Pull Strength  
Table 3 shows the pull strength (PS) data at \(t_{i}\) for all the test conditions. The pull strength decreases from the beginning to the end of a trial are shown in Table 4. The pull strength decreased rapidly during the first pull of all the test conditions. After the first break for all conditions, the pull strength fluctuated, and fatigue and recovery were found. The ANOVA results indicated that TT had a significant effect on the strength decrease (\(p\) < 0.01), but the number of breaks was insignificant. The pull strength decrease of 9 min (78.60 \(\pm\) 39.40 N) was significantly smaller (\(p\) < 0.05) than that of 12 min (102.13 \(\pm\) 30.18 N).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Conditions** & **Full Strength Decrease (N)** & **Full Strength Decrease (\%)** \\ \hline
9-1 & 65.70 \(\pm\) 42.92 & 23.29 \(\pm\) 12.47 \\
12-1 & 108.29 \(\pm\) 28.39 & 39.86 \(\pm\) 6.26 \\
9-2 & 74.97 \(\pm\) 31.06 & 23.39 \(\pm\) 6.
# Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST
## 2 Materials and Methods
### Feature Points Matching  
The algorithm depends on feature points; these are found within each image through using the AKAZE [22] algorithm. The parameters used for AKAZE were as follows--threshold: 0.0005, four Octaves, and five Octave Layers. AKAZE also generates a description vector for each feature point, and these descriptors can be used to compare to points through using the Hamming distance. To find the matching features between images, the Hamming distances between all feature descriptors of one image to all descriptors from the other image are calculated. Then, for every feature in the calibration image, the features  
Figure 1: Overview of the coordinate system, parameters, and the degrees of freedom [10].  
with the lowest Hamming distance \(d_{1}\), and the one with the second lowest distance \(d_{2}\) are selected. On these two distances, [PERSON]'s ratio test [23] is applied. This compares the distance with a ratio \(r\), and this is performed in order to check that the smaller distance is much smaller than the second best match (\(d_{1}<r*d_{2}\)). When the test succeeds, the feature point with the smaller distance is used to form a matching pair of feature points. If the distances do not comply with this test, no matching pair was found.  
Afterward, all found pairs are filtered by finding the ones that match to the same feature point and those are removed.  
The last step involves discarding pairs where the Euclidean distance between the points in the matched pairs is more than one standard deviation from the mean distance of all pairs.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 6 Analysis
### Security Analysis
#### 6.2.3 Lemma 3 for Liveness Attack  
A liveness attack in the context of CrossDeFi involves a malicious user (MU) attempting to delay the communication of messages within the network, thereby degrading user experience. This type of attack focuses on interrupting the normal operation and responsiveness of the system.  
Proof.: Consider the following scenario where BMiX, a miner from the blockchain of DeFi X, sends an asset transfer confirmation (ATC) message to the blockchain of DeFi Y. Similarly, BMiY from DeFi Y sends an ATC message back to DeFi X.  
To ensure the integrity and timeliness of these messages, CrossDeFi employs a hybrid cryptography scheme that includes the use of traceable ring signatures (TRS). This method not only secures the message but also ensures its traceability back to the sender, thus preventing anonymity from being exploited to delay the transmission. The system sets a threshold timeframe for receiving these ATC messages. Suppose BMiX receives the required number of verifications \(n\) from BMiY within this predefined period, and reciprocally, BMiY from DeFi Y also confirms the reception of \(n\) verifications from BMiX. In that case, the transfer is validated, and trust between the two chains is established. Any ATC messages received within this timeframe are considered successful, confirming the asset transfer effectively. Conversely, messages that fail to meet the deadline are automatically deemed unsuccessful.  
This strategic approach enables CrossDeFi to diminish the potential impacts of lineness attacks, ensuring that asset transfers are not only secure but also timely. By setting strict timeframes for confirmations and employing advanced cryptographic techniques, CrossDeFi enhances transaction finality and maintains a high level of user satisfaction within its network.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation
### Communication  
This study compares the internal low-speed oscillator integrated into the MCU with a clock frequency of 4.196 MHz with a 4 MHz external crystal oscillator. The internal oscillator is very energy efficient, does not require additional space, and enables the MCU to run in its lowest consumption mode. Its drawback is that it is less accurate and more unstable than the external crystal, i.e., the clock frequency includes significant jitter. In this section, we first investigate the jitter and deviation of the internal and external clocks and measure their influence on tag power consumption. We then evaluate how clock instability affects communication performance and if the presented mitigation strategy is effective.  
Low-Power Clock Stability  
To evaluate the clock stability, we record the square wave produced by the tag's MCU with an oscilloscope and compare it with an ideal square wave with the nominal carrier frequency. Figure 11 shows the measured CFO over time. We see that the MCU's supply voltage has a significant impact on the CFO of the internal oscillator, which is roughly 70 Hz larger--more than 800% increase--at \(V_{\text{DD}}=1.8\) V compared to 3.3 V. Additionally, even with constant supply voltage, the instantaneous CFO varies throughout a single message by more than 5 Hz. In theory, when using a 4-PSK modulation scheme, a phase offset of up to 45 \({}^{\circ}\) can still be decoded correctly, allowing an instantaneous CFO of 5 Hz for up to 20 ms. However, since we have to use a nonlinear equalizer to mitigate the strong ISI, already much smaller phase offsets lead to large errors in the equalizer output. Therefore, when using the internal oscillator, a receiver can not rely on a single estimate of the CFO, e.g., during the preamble but must dynamically track and correct phase offsets during message reception. Furthermore, as the tag generates the CFO from its main clock, the same deviation also affects symbol timing. Therefore, symbol timing must also be recovered and tracked throughout message reception. For comparison, we also measure the CFO when using the external crystal. The instantaneous CFO is much more stable and only varies less than a tenth of a hertz during a message transmission. Therefore, when using the external clock, PLLs are not necessarily required during message reception when the static CFO is estimated accurately once at the beginning of the message.
###### Abstract  
In this study, the effect of single-walled carbon nanotubes (SWCNTs) on the cross-linking of natural rubber (NR) using organic peroxides was investigated. NR-SWCNTs nanocomposites were prepared in an open two-roller mill followed by vulcanization with the compression molding process. Three different organic peroxides, 1,1-bis(tert-butylperoxy)-3,3,5-trimethylcyclohexane (T29), dicumyl peroxide (DCP), and 2,5-bis(tert-butylperoxy)-2,5-dimethyl-3-hexyne (T145), were used as vulcanizing agents. SWCNTs promote a remarkable reduction in the vulcanization time and increase the degree of cross-linking of vulcanized rubber when compared with neat or natural rubber-carbon-black composites; the same tendency was obtained in the NR-SWCNTs vulcanized with sulfur. Additionally, the mechanical performance of the NR-SWCNTs composites was significantly improved up to 75, 83, 27, and 10% for tensile strength, moduli, tear strength, and hardness. Raman spectroscopy studies evidence the occurrence of reaction between nanotube walls and free radicals generated from using organic peroxides during the vulcanization process. These results demonstrate that the incorporation of SWCNTs in combination with the use of organic peroxides for the NR vulcanization represents a potential alternative for the improvement of the physicochemical properties of NR composites.  
Keywords:natural rubber; vulcanization; single-walled carbon nanotubes; organic peroxides; nanocomposites; mechanical properties +
Footnote †: journal:
# A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection
## 1 Introduction
Additionally, a number of studies have been done for HVAC control automation based on the usage of artificial neural networks (ANN) and other machine learning (ML) algorithms for personal thermal comfort evaluation [26]. In recent years, the field of non-invasive bio-sensing technology has made huge progress, creating good conditions for research aimed at studying ways for such technology introduction within the field of indoor thermal comfort. This combination has a potential to reduce bias in the HVAC system that has originated from the continuous request of personal feedback on thermal conditions or the need for manual user adjustment of the thermostat or opening of the windows. Such a personal bio-sensing system may provide a better user experience, while reducing energy consumption and prevention of the overheating/over-cooling period within the individual's thermal environment. The main research question for this study is: What are the options for determining thermal discomfort by non-invasive means using bio-sensing technology? Within the given paper, the approaches and (or) models which were developed in order to track comfort or to predict/prevent thermal discomfort events within office work space are evaluated. If proven that thermal discomfort can be tracked and prevented in advance, the deployment of such a system can revolutionize indoor environment quality and personal experience within office space.
## 3 Experiment and Results Discussion
### COD Detection Compensation Based on Fusion of UV-Vis Spectroscopy and Environmental Factors  
#### 3.2.1 Data Fusion Theory  
Data fusion, also known as information fusion, is a method that comprehensively evaluates data based on a certain fusion algorithm using the object or environment data obtained by multiple sensors. It involves the multi-level processing of multi-source data, including data detection, correlation, estimation, and combination. According to the degree of abstraction at the data processing level, data fusion can be divided into three levels: data-level fusion, feature-level fusion, and decision-level fusion [37].  
The three fusion methods have different characteristics. Data-level fusion prediction is the fusion prediction that directly combines the raw data of various sensors in a certain way, belonging to the lowest level. The main advantage of this method is that it has the most real data, which is beneficial for further analysis and the processing of the data. However, this method has a large amount of data, so the processing time increases, and the real-time performance is poor. Feature-level fusion prediction is an intermediate-level type of fusion that extracts features from the raw data information of multiple sensors, combines them in a certain way, and finally comprehensively analyzes and predicts the target [38]. Feature-level fusion prediction effectively compresses the amount of information by extracting features from the original data, which is beneficial for real-time data processing [39]. Decision-level fusion prediction is the highest-level fusion prediction. Each sensor first establishes preliminary prediction models for the same target. Then, the results are combined in a certain way, and decision-level predictions are finally made. Decision-level fusion prediction has good real-time and fault tolerance, but its preprocessing cost is high [40].  
Feature-level fusion takes into account the advantages and disadvantages of data-level fusion and decision-level fusion. To ensure the accuracy and real-time performance of data processing, this paper uses feature-level fusion to fuse spectral data and environmental factor data. Feature-level fusion refers to first extracting features from UV-Vis spectroscopy and preprocessing environmental factor data. Finally, fusion and modeling are performed. The process is shown in Figure 9.
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
## 3 Proposed VSFCM Algorithm
### Framework of the VSFCM Algorithm  
The execution process of the CDCI method and the VSFCM algorithm is shown respectively in Algorithms 1 and 2.  
```
Input: Data set \(X\)= \(\{x_{k}\}_{k=1}^{N}\), number of clusters \(C\). Output: cluster center matrix \(H=\{h_{i}\}_{i=1}^{C}\). procedure CDCI (Data \(X\), Number C)
1. \(H=[\ ]\);
2. Calculate the cut \(-\) off radius \(cd\) according to (12);
3. Calculate the local density \(\rho_{j}\) of each point according to (14);
4. Calculate the distance \(\delta_{j}\) of each point according to (9);
5. Calculate \(\tau_{j}\) according to (15);
6. Rearrange \(\tau=\left\{\tau_{j}\right\}_{j=1}^{n}\) from large to small, and get the corresponding \(X^{\prime}\) after the original data set \(X\) is re-sorted by \(\tau\);
7. Select \(\tau_{1}\) corresponding to \(x_{1}^{{}^{\prime}}\) as the first cluster center \(x_{e}\), and let \(H=H\cup x_{e}\);
8. Let \(tt=1\), \(k=2\);
9. Repeat
10. while \(\left\|x_{k}^{\prime}-H\right\|<cd\) // If the distance between \(x_{k}^{\prime}\) and selected cluster center is smaller than \(cd\), //then skip directly
11. \(k=k+1\);
12. \(H=H\cup x_{k}^{\prime}\);
13. \(tt=tt+1\);
14. Until \(tt=C\)
15. return \(H\)
16. end procedure
```  
**Algorithm 1** Cut-off Distance-induced Clustering Initialization (CDCI)  
```
Input: Data set \(X\)= \(\{x_{i}\}_{i=1}^{n}\), number of clusters \(C\). Output: Membership matrix \(U\)= \(\{u_{ij}\}_{i=1}^{C,n}\), cluster center matrix \(H=\{h_{i}\}_{i=1}^{C}\), weight matrix \(W\)= \(\{w_{ik}\}_{i=1}^{C,n}\). procedure DVPPCM (Data \(X\), Number C)
1. Set threshold \(\varepsilon\) and maximum number of iterations \(iM\);
2. Run Algorithm 1, and get \(H^{(0)}\) and the point \(x_{e}\) with the highest density;
3. Do
4. \(iter=iter+1\);
5. Update \(U^{(iter)}\)= \([u_{ij}]\) by calculating memberships \(u_{ik}\) using (26);
6. Update \(H^{(iter)}=[h_{i}]\) by calculating centers \(h_{i}\) using (32);
7. Update \(W^{(iter)}=[w_{ik}]\) by calculating weights \(w_{ik}\) using (38);
8. while \(\left\|H^{(iter)}-H^{(iter-1)}\right\|\geq\varepsilon\) and \(iter\leq iM\);
9. return \(U^{(iter)}\), \(H^{(iter)}\),\(W^{(iter)}\); end procedure
```  
**Algorithm 2** Viewpoint-driven subspace fuzzy C-means (VSFCM) algorithm