esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis
### Load-Displacement Hysteretic Loops  
The load-displacement hysteretic loops of MRPC assembled connections and the monolithic connection were similar. In the elastic stage, the specimens' hysteresis loops were nearly straight, and their residual deformation after unloading was small. As the cracks continued to develop, the displacement growth rate was higher than the load growth rate, the hysteresis loops' shape gradually tended to be full, and the residual deformation after unloading gradually increased. When the specimens entered the yield deformation stage, the reinforcements slipped to different degrees, and the hysteresis loops showed a certain degree of a pinching phenomenon. Finally, specimens formed a plastic hinge at the beam end. The main cracks alternately opened and closed under the cyclic load. The cracks continued to widen until the specimen failed.  
Figure 12(a) shows that the area of XJ's hysteresis loop was the smallest, and its seismic performance was the worst. Because there was an interface transition zone (ITZ) around the aggregate in ordinary concrete, which is a weak phase, cracks quickly penetrated the ITZ, reducing the concrete's compressive area and quickly forming plastic hinges at the beam end. Compared with specimen XJ, the hysteresis curve area of assembled connections was larger and had better ductility; MRPC significantly improved the seismic performance of connections. However, the degree of pinching varied under different steel fibers. SFZP-1's pinching was more significant, indicating that straight steel fibers had a lesser restraint effect on the cement matrix in the plastic stage. The steel fibers were quickly pulled out and exited the sample. The stress was transferred to the reinforcements, causing them to slip earlier. SFZP-4's hysteresis loop was the fullest, indicating that the MRPC with wavy steel fibers was better able to coordinate deformation with reinforcements and consume energy; because wavy steel fibers have a flat shape, they had a larger contact area with the substrate. In addition, the continuous and irregular shape was similar to ribbed steel bars, and the friction force between the steel fibers and the matrix was greater. Under repeated loads, the sliding of wavy steel fibers could consume more energy.  
Figure 12: Failure pattern of assembled beam–column connections. (**a**) SFZP-1. (**b**) SFZP-2. (**c**) SFZP-3. (**d**) SFZP-4.  
The slippage of the assembled MRPC joints can be improved by using copper-coated hook steel fiber, and the energy dissipation capacity of the assembled joints can be improved by using wavy steel fiber.
# Sampling Rate Impact on Precise Point Positioning with a Low-Cost GNSS Receiver
## 1 Introduction
### The Precise Point Positioning Method
The dual-frequency PPP is used to form the ionosphere-free combination of the carrier phase and pseudo range, this is expressed in Equations (3) and (4):  
\[\mathrm{P_{if}}=\frac{\hat{\mathrm{r}}_{\mathrm{i}}^{\mathrm{c}}\mathrm{P_{ 1}}-\hat{\mathrm{r}}_{\mathrm{i}}^{\mathrm{c}}\mathrm{P_{2}}}{\hat{\mathrm{r }}_{\mathrm{i}}^{\mathrm{c}}\mathrm{P_{2}}}=\rho_{\mathrm{j}}^{\mathrm{k}}+ \mathrm{d}_{\mathrm{orb}}^{\mathrm{k}}+\mathrm{c}\left(\mathrm{dT_{j}}- \mathrm{dt}^{\mathrm{k}}\right)+\mathrm{d}_{\mathrm{trop}}^{\mathrm{k}}+ \mathrm{d}_{\mathrm{hd},\mathrm{P_{it}}}^{\mathrm{j}} \tag{3}\]  
\[\mathrm{\Phi_{if}}=\frac{\hat{\mathrm{r}}_{\mathrm{i}}^{\mathrm{c}}\mathrm{P_{ 1}}-\hat{\mathrm{r}}_{\mathrm{i}}^{\mathrm{c}}\mathrm{P_{2}}}{\hat{\mathrm{r }}_{\mathrm{i}}^{\mathrm{c}}\mathrm{P_{2}}^{\mathrm{c}}\mathrm{P_{2}}^{ \mathrm{c}}\mathrm{P_{2}}^{\mathrm{c}}}=\rho_{\mathrm{j}}^{\mathrm{k}}+\mathrm{c }\left(\mathrm{dT_{j}}-\mathrm{dt}^{\mathrm{k}}\right)+\mathrm{d}_{\mathrm{ orb}}^{\mathrm{k}}+\mathrm{d}_{\mathrm{trop}}^{\mathrm{k}} \tag{4}\]  
where: \(\mathrm{P_{if}}\) and \(\mathrm{\Phi_{if}}\) are the ionosphere free for the code and phase combination in m, respectively; \(\mathrm{f_{i}}\) is the frequency of \(\mathrm{L_{i}}\) in Hz; \(\mathrm{N_{if}}\) is the combined ambiguity term in m; and \(\mathrm{\epsilon_{if}}\) is the noise measurement. Alternatively, a new approach could be used for the ionosphere combination (see [33] for further information).
# Effect of Nonlinear Hysteresis Details of Isolation System on In-Structure Response Spectra
###### Abstract  
To evaluate the seismic safety of components in a structure, an in-structure response spectrum (ISRS) must be obtained, and this also applies to seismically isolated structures. The main variables for designing seismic isolators are effective stiffness and effective damping, which can be given as the characteristic strength and secondary stiffness in seismic isolators for nonlinear behaviors. Many studies on the ISRS of isolated structures have been conducted to evaluate the effects of these two variables of isolators, but the effect of other variables related to the hysteresis curve of isolators also needs to be studied. This study focused on the effect of the initial stiffness of isolators on an ISRS because there were no clear criteria for determining the initial stiffness in isolator design standards, which has an effect on the ISRS that cannot be ignored. As a result, the initial stiffness contributed significantly to the ISRS not only at the natural frequency of the structure but also at low frequencies. An analysis was also performed in terms of the uncertainty of each variable. The sharpness of the yield point in the hysteresis curve was implemented using the Bouc-Wen model, and its impact was analyzed. In addition, the frequency content of the ISRS depending on the seismic intensity, which can considerably change the nonlinear hysteresis behavior, was examined. Through this study, although secondary stiffness and characteristic strength are the most important characteristics of seismic isolation design, it was confirmed that other variables also have a significant impact on the frequency content of an ISRS. Based on this study, the considerations when developing the ISRS of an isolated structure can be established.  
isolated structures; in-structure response spectrum; hysteresis behavior; initial stiffness of isolator
# Weighted Matrix Factorization Recommendation Model Incorporating Social Trust
## 5 Experimental Evaluation
### Comparison of Model Recommendation Performance
Unlike DiffNet++, which uses social networks to learn user representations, GDSRec improves model recommendation accuracy by correcting the final prediction scores by learning target users' preferences by utilizing the preferences of other users related to the target user in the social network. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Evaluation** & \multirow{2}{*}{**PMF**} & \multirow{2}{*}{**SoRec**} & \multirow{2}{*}{**SoReg**} & **Social** & **Trust** & **Neu** & \multirow{2}{*}{**DiffNet++**} & **GDSRec** & **TrustWMF** \\  & **Metrics** & & & & & **MF** & **MF** & **MF** & **DiffNet++** & **GDSRec** & **TrustWMF** \\ \hline \multirow{2}{*}{Epinions} & MAE & 0.9952 & 0.8961 & 0.9119 & 0.8837 & 0.8410 & 0.9072 & 0.8201 & 0.8047 & 0.8013 \\  & RMSE & 1.2128 & 1.1437 & 1.1703 & 1.1328 & 1.1395 & 1.1476 & 1.0635 & 1.0566 & 1.0527 \\ \hline \multirow{2}{*}{Ciao} & MAE & 0.9021 & 0.8410 & 0.8611 & 0.8270 & 0.7690 & 0.8062 & 0.7398 & 0.7323 & 0.7188 \\  & RMSE & 1.1238 & 1.0652 & 1.0848 & 1.0501 & 1.0479 & 1.0617 & 0.9774 & 0.9740 & 0.9562 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different approaches. 3. Compared to other models, the Trust-WMF model proposed in this paper has a lower MAE and RMSE, indicating that this model can effectively reduce the impact of noise when processing data. Additionally, the improved weighted matrix factorization method effectively controls the weight between different ratings, allowing the model to more accurately reflect user preferences and item characteristics when processing large-scale data. 4. The Trust-WMF model demonstrates superior recommendation accuracy on different scales in the Epinions and Ciao datasets compared to other models, indicating good robustness of the model and no bias toward specific datasets.
# City Information Modeling: State of the Art
## 5 Discussion  
### Current Issues  
Although CIM has been applied to some fields, it is still an emerging technique and faces technical and management issues, as follows:  
1. Technical Issues  
First, the accuracy and the automation level of existing methods regarding the data collection for CIM are still very limited, owing to the complex nature and large geographical areas of cities [81]. For example, how can we accurately obtain a high-fidelity 3D model of a city? How can we efficiently collect dynamic data of various massive sensors in a city? Second, the integration of massive multisource heterogeneous data remains a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Platform** & **Function of CIM** \\ \hline Skyline [71] & Integrate BIM models or other 3D models of \\ Smart World Pro [72] & buildings with the GIS environment to generate a 3D \\ WorldWind [47] & CIM model. \\ SuperMap [73] & Establish CIM models using CityGML standards. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Major platforms related to CIM.  
challenging issue in CIM. Currently, the integration of BIM and GIS is a good solution to this problem [37; 82; 83]. However, most existing buildings lack BIM models. In addition, more types of data (e.g., IoT data), except BIM and GIS, are also needed in the CIM, which requires a more compatible data integration method [84; 85]. Finally, research on CIM-based applications (e.g., evaluation, prediction, and management) has not been conducted extensively. Detailed models and algorithms related to specific application scenarios (evaluation, prediction, and management) and the nature of CIM are urgently required to broaden CIM applications.  
Management IssuesAs the foundation for smart cities, CIM platforms require a collaborative design with numerous participants from different industries in cities. Many industries can apply CIM, including but not limited to the construction [55], urban planning [51; 53; 54], energy [56; 57], and culture industries [50]. However, the absence of some industries leads to data silos in CIM, limiting the application of CIM [86; 87]. Collaborative design can efficiently break down data silos [88; 89], and thus, is required for creating CIM platforms. In addition, CIM platforms also require long-term maintenance to maximize their value throughout the life cycle of cities. Cities are changing continually; therefore, the data in their CIMs need to be updated in the long term. Through the long-term maintenance of CIM, big data of cities are generated, which may reveal the developmental trends of the cities and help in city management [90].
# Multivariate Analysis of Laser-Induced Tissue Ablation: Ex Vivo Liver Testing
## 1 Introduction
CCD is for fitting quadratic response surfaces by employing extra levels including the center point in addition to the two-level full-factorial. On the other hand, BBD is a three-level fractional factorial RSM design for fitting the second-order response surfaces, which is mostly useful when extreme treatment combinations are required to avoid. The purpose of the current research was to investigate the feasible application of RSM to reveal ablative interactions of multi-parameters and to optimize surgical parameters for the maximum tissue ablation efficiency. It was hypothesized that the statistical approach could not only reduce the number of tests but also could explore the optimal conditions for laser therapeutics. Due to easy procurement and tissue abundance, bovine liver was used as a surrogate model for the current feasibility study. Multivariate analysis with three-level BBD was employed to comprehend individual or interactive effects of various ablation parameters. Ex vivo tissue removal was investigated in light of three surgical variables for identifying the optimal ablation conditions.
## 4 Hardware Customization
### Accessories Detection and Health Mote  
Different modules are integrated into the \"accessories detection and health mote\" for monitoring the multiple PPE equipment statuses and the health of the construction workers on the site, to ensure their safety. Helmet and Goggle Detection Mote, Worker Health Monitoring Mote, Gloves' Detection System, and Shoe Detection System are the components that are part of the accessories' detection and health mote. Along with these motes, an additional system is placed at the entrance of the site for identification to identify the worker using RFID technology, as shown in Figure 6. It helps to identify and restrict unauthorized persons entering the construction site.  
It consists of a PIR Sensor, RFID Reader, Microcontroller, LCD, Power Supply Unit, Buzzer, and 2.4 GHz RF transceiver. This circuit is placed at the entrance of the construction site to monitor the entry of any person into the site. PIR is a passive infrared sensor, an input device used to monitor human movement and alerts the microcontroller whenever anyone enters the site, the microcontroller simultaneously reads the data from the RFID reader to check whether the person is a valid person or an unauthorized person.  
The customized board for the workers' identification detection system is shown in Figure 7.  
The reader reads the RFID card data and compares it with the stored valid card number, if the number is found in the saved data then it means the person is wearing a helmet and is an authorized person, otherwise he/she is not wearing a helmet or an unauthorized person is trying to enter the site. A buzzer is an output device used to alert through sound, it is connected to the microcontroller. If a person without a helmet or unauthorized person enters the site, then the controller rings the buzzer and alerts the nearby people, simultaneously the same information is updated to the RF and LoRa coordinator through the RF transceiver. LCD is an output device and helps to display the status of all of the operations performed by the controller. Table 1 provides all of the technical specifications of all of the components used in the customized board of entry node.  
Figure 6: System at the entry point of construction site.  
Figure 7: Customized Board of Entry Node.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 4 The Proposed MAC Address Randomization Covert Channel
### Covert Channel Metrics
#### 4.3.1 Covert Channel Throughput  
The throughput indicates how fast STA can actually send secret messages over the network channel. The covert channel throughput, as expressed in Equation (1), is measured as the total number of transmitted PR frames successfully acknowledged by AP multiplied by the number of covert bits carried in a single frame over the simulation time. In our case, since the covert payload is the MAC address, each frame carries 48 covert bits. The throughput is expressed in bits per second (bps). The throughput metric holds significant relevance in the performance evaluation of covert channels, as it quantifies the amount of covert data that can be transmitted within a specified timeframe. It serves as a vital factor for capacity planning, as it offers insights into whether the covert channel meets the required throughput to effectively function within a given scenario.  
\[Throughput=\frac{Rx\ PR\ Frames\times 48}{Simulation\ Time}\ \ [\text{bps}] \tag{1}\]
## 3 Materials and Methods
### Data Preparation and Experimental Setup  
We used the publicly available Chest X-ray Images (Pneumonia) dataset [30] for our experiments, which includes X-ray images of pediatric patients aged one to five years from Guangzhou Women and Children's Medical Center. The dataset contains 5856 images, categorized into two groups: pneumonia (4273 images) and healthy (1583 images) [30]. All chest radiographs were initially screened for quality control, and low-quality or unreadable scans were removed prior to analysis. Diagnoses for the images were graded by two expert physicians, with a third expert verifying the evaluation set to minimize grading errors and ensure the reliability of the dataset [30]. Samples of used images are shown in Figure 2. To ensure a balanced evaluation, we performed a stratified random split of the dataset into  
Figure 1: The study workflow for pneumonia recognition, utilizing a DenseNet architecture, Mish activation function, and multi-scale convolutions, proceeded through the following phases: (1) input data preparation, encompassing both healthy and pneumonia image classes; (2) implementation of the proposed deep learning model; and (3) evaluation of the implemented model’s performance in pneumonia recognition, including accuracy assessment.  
training and validation subsets in an 80:20 ratio, preserving the class distribution. Due to computational resource constraints and the large dataset size, we opted for a single 80:20 split instead of k-fold cross-validation, which would have significantly increased training time without providing proportional benefits in this context. Although we did not apply explicit measures such as class weighting or oversampling to address the class imbalance, the CNN inherently adapts to the data distribution through its training process. Additionally, data augmentation techniques, including rotation, shifting, zooming, and horizontal flipping, were applied during preprocessing to increase dataset variability and improve model generalization. All images were resized to 224 \(\times\) 224 pixels.  
We developed and trained the deep learning models using Python with the Keras-GPU [31] and TensorFlow-GPU [32] frameworks on the Google Colab platform. The platform's NVIDIA Tesla K80 GPUs with 12 GB of memory enabled efficient training. The models were trained over 20 epochs with a batch size of 32, utilizing the Adam optimization algorithm to update model parameters dynamically. The learning rate was automatically adjusted during training using a reduction mechanism that lowered it when validation performance plateaued, with a minimum of 0.5 \(\times\) 10\({}^{-6}\) to optimize convergence and prevent overfitting.
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 4 Performance Analysis
### BLER Analysis of the Algorithm with Robustness Mechanism  
The proposed algorithm with robustness mechanism is compatible with different block lengths (\(N_{1}=64\), \(N_{2}=32\), and \(N_{3}=16\)). The proposed decoder for \(N_{3}=16\) with a 2-bit CRC has a generator polynomial of \(g(x)=x^{2}+1\). Figure 19 demonstrates that the BLER performance of the proposed algorithm and optimized algorithm is close for the three block lengths. The optimized algorithm has generalization ability and can break the block length constraint during neural network training. The large-scale processing capability of the neural network is also exploited.
## 3 The Communication Program for the Semi-Trailer ECPB In-Loop Testing System
### Data Flow of the ECPB Semi-Trailer In-Loop Testing System  
The cloud server is utilized to construct the Internet distributed architecture, as shown in Figure 3. During the simulation of the semi-trailer transporter braking process, the ECPB hardware test bench transmits the pressure signal to the cloud server. Then, the cloud server calculates the required braking torque for the hub motor based on the air pressure-braking torque conversion model and sends it to the twin test vehicle to execute the braking operation. The twin test vehicle releases driving information in real-time to the cloud. After receiving the information, the cloud server feeds the vehicle dynamics parameters back to the test bench according to the vehicle dynamics model. The test bench generates new braking signals based on the latest vehicle motion characteristics. This process is repeated cyclically until the twin test vehicle completes braking.
## 3 Results and Discussion
### Pore Analysis  
Image analytical pore analysis using LOM made it very difficult to distinguish pores from the oxide inclusions previously mentioned. Therefore, both were combined in the analysis in Table 2. It can be seen that the percentage of pores and inclusions together is 2.87 vol.-% before HIP and 0.77 vol.-% after HIP. However, higher-resolution SEM images (Figure 10) show that these detected 0.77 vol.-% are almost exclusively oxide inclusions and residual pores are very rare. Therefore, the authors assume that the 0.77 vol.-% measured in the HIP specimens are almost exclusively non-metallic inclusions. Theoretically, this proportion could be subtracted from the results before HIP, resulting in a pore fraction of 2.1 vol.-% before HIP. However, it was not sufficiently investigated whether the amount or size of the oxide inclusions increased after HIP. This point is the subject of further research. Therefore, it can only be stated with certainty that the porosity before HIP is at least 2.1 vol.-% and at most 2.87 vol.-%. This is a relatively low porosity for MBJ. Porosities in the range of 2.6-8 vol.-% are reported in the literature for MBJ specimens of 316L after optimization of powder characteristics or the sintering process [43; 44; 45; 46]. The porosity comparison with the literature confirms that the concept of sintering with a high proportion of bcc phase is effective here and that this material is well adapted for sintering.  
The pore size before HIP is on average 4.38 \(\mu\)m, although larger pores occur: 10% of all pores in the as-sintered condition are larger than 15.52 \(\mu\)m. After HIP, where it can be assumed that almost exclusively oxide inclusions are present, the mean size is 3.2 \(\mu\)m.
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
## 3 Results and Discussion
### The Stability of Free and Immobilized Laccase
#### 3.2.2 The pH Effect  
The effect of pH on free lacase and nanoparticle-immobilized lacase was investigated in the range of 3.0 to 9.0, and the results are presented in Figure 3b. According to Figure 3b, free lacase and immobilized lacases show their maximum enzymatic activity at a pH of 5.0. So, the optimum pH of free and immobilized lacases is 5.0. Regardless of the pH of 5.0, the free lacase exhibited lower enzymatic activity than the immobilized lacases, which indicated the resistance of immobilized lacases on Fe\({}_{3}\)O\({}_{4}\) and CuFe\({}_{2}\)O\({}_{4}\) nanoparticles to the pH change compared to the free lacase. This wide pH range of immobilized lacase high activity could result from intermolecular interactions between the glutaraldehyde surrounding the active sites and the amino acid side chains of lacase. This interaction also prevents the lacase from unfolding, and subsequently resists the change in pH. Moreover, the CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized lacase showed more resistance to pH change  
Figure 3: Storage stability (**a**), pH effect (**b**), and temperature effect (**c**) on free and immobilized lacase.  
than the Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase, due to the presence of copper ions, as discussed in the previous subsection.