esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of a Three-Dimensional-Printed Titanium Implant in Human Tissues: Case Study
## 2 Materials and Methods  
### Patient Information  
A 40-year-old man presented with two-week long paresthesia in his right hand and limited forearm rotation. The patient demonstrated a limited motion range of the elbow and forearm due to dislocation of the radial head that was attributed to a traumatic injury during childhood. Five years prior, he underwent orthopedic surgery employing an external fixator to repair a traumatic fracture in his right wrist. Although the range of motion of his elbow and forearm was already limited, further exacerbation was evident upon presentation. Magnetic resonance imaging (MRI) revealed an infiltrative tumor on his forearm that affected both the radius and ulna. The tumor, which was presumed to have originated from the external fixator pin site of the radius, was later confirmed as being a desmoplastic fibroma of the bone through core needle biopsy. No distant metastasis was observed. Although gain of function could not be guaranteed by reconstruction of both forearm bones, the patient nevertheless opted for anatomical reconstruction because of the already limited range of motion of the affected forearm prior to tumor presentation.  
Limb salvage surgery was performed with 3D-printed customized implants for both forearm bones, excluding the wrist and elbow joints (Figure 1A,B). The details of implant design, production, and verification for this patient are provided in our previous study [26]. A routine postoperative evaluation revealed local recurrence around the shaft of the proximal ulna within a year. Therefore, the patient underwent another limb salvage surgery, in which a single forearm bone was reconstructed in fusion of remaining distal radius and proximal ulna with a functional position following removal of the ulnar implant with the recurred tumor (Figure 2A-C). The institutional review board of our institute approved this study, and the patient provided written informed consent (NCC2017-0129).  
Figure 1: Primary surgery. (**A**) Intraoperative photograph and (**B**) postoperative plain radiograph showing the general configuration of an implant.
## 2 Materials and Methods
### Determination of Phenolic Acids  
Phenolic acids (chlorogenic acid, p-hydroxybenzoic acid, syringic acid, vanillic acid, p-coumaric acid, ferulic acid, gallic acid, protocatechuric acid and caffe acid) were determined chromatography according to the method described by [PERSON] et al. (2014) with slight modifications. Briefly, 150 mg of each lyophilized extract was mixed with 2.5 mL of 2 mol/L sodium hydroxide, 2.5 mL of 20 mL/L ascorbic acid solution containing 13.4 mmol/L ethylenediaminettetraacetic acid (EDTA), and 0.5 mL of 0.1 mg/mL of isoferulic acid used as internal standard. The mixture was then flushed with nitrogen and allowed to hydrolyze under agitation for 16 h at room temperature. Following hydrolysis, the sample was centrifuged at 2370\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C, and the resulting supernatant was acidified by adding 0.75 mL of acetic acid. The phenolic acids were then extracted using ethyl acetate (4 mL + 2 mL), and the organic layer containing the liberated phenolic acids was collected by pipetting off the upper organic (supernatant) layer from the bottom aqueous residue layer. The two organic layers were combined and evaporated to dryness in a speed-vac concentrator (Thermo Fisher Scientific, Courtaboeuf, France) for 1 h at 45 \({}^{\circ}\)C. The residue was then dissolved in 1 mL of methanol/water (75:25 \(v/v\)) and filtered through a 0.45 \(\upmu\)m filter before analysis by HPLC. Results were expressed as \(\upmu\)g/g sample. The limit of quantification was set at 4 \(\upmu\)g/g sample.  
Figure 1: Cereals (wheat, rice, and rye), pseudocoreals (quinoa, amaranth, and buckwheat) and chia seeds selected for the study.
## 2 Materials and Methods
### Data Contraction Detection Methods
#### 2.2.4 Squared RMS  
The Squared RMS of a signal is related to its variance. It must be noted that the used signals have zero mean. It is a power estimation which might contribute to the contraction detection and delineation accuracy by increasing the peak/valley amplitude differential.  
\[Squared\ RMS=\frac{1}{n}\sum_{i=0}^{n}x_{i}^{2} \tag{7}\]
# Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
## 3 Lasso Regression Theory
This paper uses cross-validation techniques to determine the value of \(\lambda\). Taking \(k\)-fold cross-validation as an example, the dataset is divided into \(k\) groups, with one group serving as the validation set and the remaining \((k-1)\) groups as the training set. Different values of \(\lambda\) are used to train the Lasso regression model on the training set, and the predictive ability of the model is evaluated using the validation set. This process is repeated \(k\) times to obtain the mean squared error for a specific \(\lambda\), as shown in Equation (5). \[MSE_{j}=\frac{1}{h}\sum_{i=1}^{h}(y_{i}-\hat{y_{i}})^{2} \tag{5}\]where \(h=N/k\), and \(\hat{y}_{i}\) represents the estimated value of the response \(y_{i}\). The mean squared error (MSE) of the cross-validation can be obtained by taking the average of the MSEs of the \(k\)-fold cross-validation, as shown in Equation (6). \[CV(p)=\frac{1}{k}\sum_{i=1}^{k}MSE_{j} \tag{6}\]  
In theory, the optimal value of \(\lambda\) for establishing the Lasso regression model can be chosen as the value corresponding to the minimum mean-squared error of the cross-validation. However, it is also necessary to consider the complexity of the regression model and the prediction accuracy based on the specific problem. It is generally recommended to select the \(\lambda\) value corresponding to the minimum mean squared error plus one standard deviation as the final parameter. This is because, as \(\lambda\) increases, the regression coefficients become sparser. Without significantly increasing the prediction error, it is preferable to choose larger \(\lambda\) values.
## 3 Preliminary Knowledge
### Object Detection Networks  
At present, object detection algorithms based on convolutional neural networks can be divided into two categories: one-stage object detection and two-stage object detection algorithm. The one-stage object detection algorithm is based on convolutional networks. The feature map directly returns the location of the target, and the model further classifies the target categories. The representative works are SSD [17] and YOLO [18; 19; 20; 21] series. In two-stage target detection algorithms, the first stage is to generate region proposals, and the second stage is to return to the target location and classify the target category based on the regional proposal, which is represented by the R-CNN [22] series algorithms, including R-CNN, Fast R-CNN [23], Faster R-CNN [24], and mask R-CNN [25]. This paper takes a one-stage mature representative network, YOLOv3, and a two-stage representative network, Faster R-CNN, for validating the effectiveness of our designs The process and experimental design of the clipping-based post-training 8-bit quantization method are shown in Figure 3. As shown in Figure 3, we apply our clipping base quantization method to two representative one-stage, two-stage target detection networks. After fine-tuning, we check whether the accuracy loss meets the expectation, and if the accuracy still decreases, we adjust the loss function considering the loss of clipping operation to obtain the result.
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
###### Abstract  
An encryption algorithm for hyperspectral data in fractional Fourier domain is designed. Firstly, the original hyperspectral image is separated into single bands and then each pair of bands are regarded as the real and imaginary part of a complex function by using an affine transform. Subsequently, the complex functions are encoded and transformed in fractional Fourier domain (FrFT). The parameters in affine transform and FrFT serve as the key of the encryption system. The proposed encryption scheme can not only protect the image information in spatial domains but also the spectrum information in spectral domains. Various experiments are given to demonstrate the validity and capability of the proposed encryption scheme.  
hyperspectral data; cryptography; optical transform +
Footnote †: journal: Applied Sciences
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 4 Simulation and Result Analysis
### Experimental Results
#### 4.2.2 Test Angle  
Figure 9a shows the test angles and the actual angles for each signal sample in a test dataset \(D\) before transfer learning. The blue circles are labels, and the red lines are the angles of the model prediction. Figure 9b shows the test angles and the actual angles after transfer learning. We can observe from Figure 9 that after transfer learning, the model's test accuracy is greatly improved. Before transfer learning, there is a large difference between the predicted azimuths and the actual azimuths, especially when the frequency increases. After transfer learning, the predicted azimuths are very close to the actual azimuths.
## 4 Results and Discussion
### Significant Benefits of Integrating AI-Based DTs in the Construction Industry  
There are only three benefits to integrating AI-based DTs. They are ranked based on the contribution value, as shown in Figure 4 as B-DT-AI-3 (AI capabilities to improve analytics), B-DT-AI-1 (AI facilitates digital twins in modeling complex real-world systems), and B-DT-AI-2 (facilitates strategic choice-making). The contribution coefficients of B-DT-AI-3, B-DT-AI-1, and B-DT-AI-2 are 0.335, 0.333, and 0.330, respectively, as shown in Figure 4.  
The CA results reveal that the integration of AI significantly enhances DTs' ability to improve analytics (B-DT-AI-3). That is, construction projects are inherently complex, involving a wide range of interrelated elements, including materials, equipment, personnel, weather conditions, and regulatory requirements. Consequently, integrating AI tools, such as machine learning (ML) and deep learning (DL), with DTs technology enables the real-time analysis of large-scale data collected from diverse sources. This integration facilitates project status tracking, provides recommendations for improvements based on environmental conditions, predicts potential system faults, and delivers real-time feedback to project stakeholders [10]. In contrast, traditional modeling techniques often struggle to manage this complexity effectively. Consequently, this highlights the transformative potential of AI-integrated DTs for the construction sector.  
Facilitating strategic decision making (B-DT-AI-2) is another significant benefit of integrating AI with DTs. Specifically, AI algorithms further enhance the capabilities of DTs by creating dynamic and evolving scenarios. These scenarios present users with unexpected challenges, thereby providing opportunities to evaluate and improve decision-making processes [57; 61; 77]. DTs process and model the complex interactions inherent in construction projects with a high degree of precision [28]. This integration, in turn,  
Figure 4: Contribution value of B-DT-AIs.  
transforms the decision-making process from a reactive to a proactive and data-driven process [28]. The combined capabilities of DTs and AI offer access to extensive real-time and historical data collected from diverse sources. AI algorithms analyze this data to uncover trends, patterns, and often undetectable correlations through manual analysis [20]. Consequently, this data-driven approach provides a robust foundation for making more informed and strategic decisions, significantly enhancing project outcomes and efficiency.  
The research findings align with the literature, confirming that integrating AI-based DTs offers significant benefits. These include enhancing the modeling of complex real-world systems, e.g., Attaran and Celik [10], facilitating strategic decision making, e.g., Rane (2023) [9], Attaran and Celik (2023) [10], and D. J. Wortley (2024) [60], and improving analytical capabilities through AI-driven insights, e.g., Rane (2023) [9], and Attaran and Celik (2023) [10].
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 6 Conclusions  
The current dense BSs placement that is driving the small coverage area concept has resulted in unpredictable traffic patterns for each BS, lending credence to the proposed BSs switching-on/off algorithm. The proposed strategies conserve energy by monitoring the traffic load of the network and deciding whether to switch certain BSs off or on. This study investigated the possibility of achieving a balance between network performance and energy savings in dual radio access cellular networks--LTE and 5G--by having the 5G-BSs switched off/on, reflecting the instantaneous traffic load situation, while guaranteeing service and coverage. The simulation results demonstrated that the proposed  
Figure 16: Data rate versus power consumption over time.  
cooperation management approach can achieve an energy savings of up to 3.52 kW per day. However, the energy savings in this approach depends on the number of BSs that will be turned off. Future work will investigate the 5G BSs switching-off/on under an offloading mechanism, which is one of the crucial issues in the 5G network.  
**Author Contributions:** As the first author, [PERSON] has conducted simulation, and wrote the main parts and the first draft of this paper. [PERSON] revised the first version of paper. [PERSON] wrote the introduction section. [PERSON] revised the final version of paper.  
**Funding:** This work was supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Trade, industry and Energy (No. 20164030201340).  
**Conflicts of Interest:** The author declare that they have no competing interests.
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 3 Results
### GA Hyperparameter Tuning Experiments for Okada-GA Inversion Model
#### 3.1.1 Experimental Scenarios and Settings
At the end of the tuning process, the optimal set of hyperparameters along with the optimal objective function were selected for each fault model. The values set for each hyperparameter tuned in the experiments are outlined in Table 2. According to the number of values of each hyperparameter, 108 hyperparameter combinations were generated for each combination of fault model and objective function. Hence, a total number of 864 experiments were run in the hyperparameter tuning process, with different hyperparameter settings for each. The Okada-GA inversion model was run for each experiment. All experiments in this study were run using Python programming on a computer with the following hardware specification: 16 GB RAM DDR4 and 4.2 GHz processor. Figure 4: Distribution of observation points, focal mechanism of the 2011 earthquake, and aftershocks.
## 2 Materials and Methods
### Multi-Feature Extraction of Pile Dynamic Signal
#### 2.4.1 Time Domain Feature Extraction  
The signal in the time domain is the original signal that has been obtained directly, i.e., time is taken as an independent variable that reflects the correlation between the signal in terms of amplitude and the temporal variable [38]. Time domain features often reflect the information more intuitively, such as the mean square value characterizing the energy of the signal and the variance reflecting the dispersion between data. Moreover, in recognizing technical failures, [PERSON], [PERSON] extracted variance, kurtosis, and square root amplitude in the time domain feature extraction [39]. [PERSON], [PERSON] extracted mean square and variance features [37] as part of the multi-feature extraction, which was input to the intelligent classification and identification method with good results. In this paper, five characteristics were chosen according to the statistical aspect of periods, average value, mean square value, variance, skewness factor, and kurtosis factor. The time series xi(t) = {x1,x2,...,xn} of signal length N is denoted., xn}. Therefore the variables for each feature are calculated according to Table 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic Index** & **Expression** \\ \hline average value & \(\overline{x}=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}\) \\ mean square value & \(x=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}^{2}\) \\ variance & \(x_{\rho^{2}}=\frac{1}{N}\sum\limits_{i=1}^{N}(x_{i}-\overline{x})^{2}\) \\ root mean square value & \(x_{rms}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}^{2}}\) \\ skewness & \(\alpha=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}^{3}\) \\ skewness factor & \(x_{\alpha}=\frac{\alpha}{x_{rms}^{2}}\) \\ kurtosis & \(k=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}^{4}\) \\ kurtosis factor & \(x_{k}=\frac{x_{rms}^{2}}{x_{rms}^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time domain characteristic index.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pile Number** & **Length of the Piles (m)** &
\begin{tabular}{c} **Soil Boundary** \\ **Position (m)** \\ \end{tabular} & **Defect Position (m)** \\ \hline
1\# & 0.95 & 0.32 & - \\
2\# & 0.97 & 0.26 & 0.44–0.54 \\
3\# & 0.97 & 0.28 & 0.44–0.55 \\
4\# & 0.97 & 0.73 & 0.42–0.53 \\
5\# & 0.97 & 0.77 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison table between finite element simulation results and experimental results.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece  
### Non-Device-Centric Biometric Authentication Context and Threat Model  
We consider a non-device-centric authentication architecture composed of client devices, a database server, an authentication server and a computation server, as in [3]. It is assumed that each user employs his/her client device, such as a smartphone, that acquires the biometric characteristics and implements all the steps of the proposed scheme to protect the biometric data before sending them to the database server at enrollment and to the computation server at verification. The user provides his/her unique identifier \(ID\) to the client device. If the \(ID\) is sensitive, it is mapped to a public index, as proposed in [33]. At the enrollment phase, the client device maps the user \(ID\) to a non-sensitive index \(x\) that is stored in the database server. At the verification phase, the client device again provides the index \(x\) and the computation server uses a Private Information Retrieval (PIR) [34] to retrieve data from the database without revealing to the database which data are retrieved.  
It is assumed that all the operations executed in the client device are carried out in a trusted way, for example, by using a Trusted Execution Environment (TEE). It is also assumed that no attacks can be carried out at the enrollment phase. All the servers are honest at the enrollment phase. If an attacker gains access to the client device at the verification phase, which is known as a stolen-device scenario, the attacker cannot manipulate the client device to carry out unauthorized operations or change the processed data because the client device is tamper-resistant. What the attacker can do is to provide impostor or synthetic biometric characteristics to the client device, known as presentation attacks.  
The communication channels between the parties are assumed to be secure so that the messages are confidential and their integrity can be checked. Thus, an external adversary cannot extract or employ the information transmitted by the communication channels. Further, the servers are assumed to be authenticated so that an attacker cannot impersonate the servers.  
The servers can be honest-but-curious, that is, they carry out their steps as expected but can try to obtain information about the users' biometric data from the protected data they process or store. It is assumed that this curiosity can lead the servers to collusion to try to obtain information.
## 2 Material and Method
### Atlas (Alt08)  
ICESat-2 carries an ATLAS laser altimetry instrument capable of delivering pulses at a repetition rate of 10 kHz (10,000 per second), with single-photon detection, and samples at about 0.7 m. It calculates the distance between the satellite and the Earth's surface by measuring the travel durations of laser pulses. For each photon, it includes information such as time, latitude, longitude, and height in the World Geodetic System 1984 (WGS84) datum. The ICESat-2 (ALT08) measures terrain height and canopy heights at precise spatial scales along the track with a predetermined segment size of 100 m to maintain data parameter continuity [5; 6; 7]. The recent footprints (122 numbers) acquired by ICESat-2 on 30 May 2021 were retrieved for the study site.
## 3 Tuning the Voltage FPR
### Tuning Method Based on Phase Margin Selection  
This procedure is based on choosing the controller in the function of phase margin specifications. Thus, it builds on robustness criteria. All the possible controllers (considering different \(\alpha\) values) will have the same bandwidth at a specified design frequency. So, the proposed steps to tune the controller are:  
**Step 1**. Select the system specifications. It is used to choose the desired phase margin value, \(\phi_{m}\), for the controller at a specified gain-crossover frequency, \(\omega_{c}\).  
**Step 2**. Tune a set of different proportional and integral \(k_{p}\)-\(k_{i}\) gains for a desired \(\phi_{m}\) and different \(\alpha\) values from 0 to 2. In [31]\(\phi_{m}\) at \(\omega_{c}\) can be defined as  
\[G_{C}(I\omega_{c})G(I\omega_{c})=e^{-\mathrm{I}(\pi-\phi_{m})}=-\cos(\phi_{m} )-\mathrm{I}\sin(\phi_{m}) \tag{11}\]  
where \(G_{C}(I\omega_{c})\) and \(G(I\omega_{c})\) are the isochronous transfer functions of the controller and the plant, respectively. Applying (1) and (2) to (11), the \(k_{i}\) gain is  
\[\frac{C(\sin(\phi_{m})r_{C}\,C\,\omega_{c}+\cos(\phi_{m}))\big{(}\omega_{c}^{2 }-\omega_{0}^{2}\big{)}\omega_{c}^{-\alpha}}{\big{(}C^{2}r_{C}^{2}\omega_{c}^{ 2}+1\big{)}\omega_{0}\cos\big{(}\frac{\pi\alpha}{2}\big{)}} \tag{12}\]  
and the \(k_{p}\) gain results  
\[-\frac{C\omega_{c}\big{(}(\cos(\phi_{m})r_{C}\,C\,\omega_{c}-\sin(\phi_{m})) \cos(\frac{\pi\alpha}{2})+(\sin(\phi_{m})r_{C}\,C\,\omega_{c}+\cos(\phi_{m})) \sin\big{(}\frac{\pi\alpha}{2}\big{)}\big{)}}{\big{(}C^{2}r_{C}^{2}\omega_{c}^ {2}+1\big{)}\cos\big{(}\frac{\pi\alpha}{2}\big{)}} \tag{13}\]  
**Step 3**. Decision stage. Depict the different Bode plots for the different \(\alpha\) values. As all the plots have the same \(\phi_{m}\), choose the more suitable one for the system application regarding excitation region, bandwidth or delay at some strategic frequency tones.