esa-sceva/satcom-chunk-collection huggingface dataset:
## Gene and Cell Technology
### Correction between Adiponectin Gene (Adipoa) Polymorphism and Coronary Artery Disease
#### 8.3.1 Background  
Coronary artery disease (CAD) is one of the most common cardiovascular diseases and is a major cause of morbidity and mortality worldwide. Adiponectin, an adipokine facilitating insulin action, has antiatherogenic effects. Adiponectin exerts its biological actions by binding two adiponectin receptors (adipoRs), adipoR1 and R2. Various studies have been done to investigate the role of ADIPOQ gene in the risk of CAD, yet their results have been inconsistent. So, there is a need of genotype analysis of ADIPOQ gene for further evaluation of the association between ADIPOQ gene polymorphism and CAD risk.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention  
The proposed model for intrusion detection and prevention (IDP), which integrates bAE + OCSVM + dAE + DBSCAN + ATT&CK, referred to as hybrid IDPS, has been meticulously designed with a comprehensive structure encompassing key phases in the realm of cybersecurity. The model's framework includes traffic data preprocessing, feature extraction, training the detection model, conducting anomaly detection, and clustering and preventing attacks. This model adopts a hybrid approach, integrating a deep learning algorithm that incorporates various techniques for enhanced cybersecurity measures.  
In the dimensionality reduction phase, the basic autoencoder (bAE) is employed to effectively reduce the number of dimensions. A one-class Support vector machine (OCSVM) is utilized for detecting anomalies, providing a robust mechanism during the training phase of the model. Additionally, the deep autoencoder (dAE) is employed for identifying abnormal behavior, with a specific focus on minimizing false negatives. The root mean square propagation (RMSP) algorithm serves as the optimization technique, contributing to the efficiency of the overall model.  
The model's capacity for handling anomaly attacks is strengthened through the application of the DBSCAN algorithm during the clustering phase. Moreover, prevention strategies are integrated into the model, drawing on the insights and methodologies provided by the MITRE ATT&CK framework.  
The architectural diagram of the proposed model, in Figure 4 further illustrates the intricate structure that integrates various techniques for a robust cybersecurity solution.  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Instance Class** & **Number of Instances** \\ \hline Benign & 2,359,087 \\ DoS Hulk & 231,072 \\ PortScan & 158,930 \\ DDoS & 41,835 \\ DoS GoldenEye & 10,293 \\ FTP-Patator & 7938 \\ SSH-Patator & 5897 \\ DoS Slowloris & 5796 \\ DoS Slowhttptest & 5499 \\ Bot & 1966 \\ Web attack–brute force & 1507 \\ Web Attack–XSS & 652 \\ Infiltration & 36 \\ Web Attack–SQL injection & 21 \\ Heartbleed & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CIC-IDS2017 dataset network traffic.
# GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
## 4 Experiments
### Experiment Steps
#### 4.2.4 Hyperparameters' Optimization by GA  
The hyperparameters of the base classifiers are different, and some hyperparameters affect the performance of the algorithm, while others do not. Thus GA is used to optimize the specific hyperparameters which are influential. Taking CICMalDroid2020 as an example, we set the value range of these parameters as in Table 4, and the parameters selected after GA are shown in Table 5.  
After parameter selection, the detection results on the two data sets are shown in Figure 6.  
The first step is setting the parameter selection ranges, then in the iterative process, the classifiers run with the given parameters, and the accuracies of various parameter combinations are compared in order to select the best combination. After this process, the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifiers** & **Hyperparameter Ranges** \\ \hline KNN & knn\_neighbors = [2, 4, 6, 8, 10, 12, 14, 16] \\ LGBM & lgbm\_max\_depths = [4, 5, 6, 7, 8, 9, 10, \(-\)1] \\ lgbm\_n\_estimators = [60, 80, 100, 120, 140, 160, 200, 240] \\ rf\_max\_depths = [4, 5, 6, 7, 8, 9, 10, 11] \\ rf\_n\_estimators = [50, 60, 70, 80, 90, 100, 110, 120] \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ranges of several hyperparameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & **Classifiers** & **Best Hyperparameters** \\ \hline CIC-AndMal2017 & KNN & knn\_n\_neighbors = 2 \\ CIC-AndMal2017 & LGBM & lgbm\_max\_depths = \(-\)1 \\  & KNN & knn\_n\_neighbors = 4 \\  & LGBM & lgbm\_max\_depths = 6 \\ CICMalDroid2020 & LGBM & lgbm\_n\_estimators = 160 \\  & RF & rf\_max\_depths = 11 \\  & RF & rf\_n\_estimators = 80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The selected hyperparameters by GA.  
Figure 6: **(a)** CIC-AndMal2017: Comparison of GA_StackingMD and Initial Stacking Model; **(b)** CIC-MalDroid2020: Comparison of GA_StackingMD and Initial Stacking Model.  
best combination of these parameters is selected within the given range. The results in Figure 6 show that the optimized hyperparameters could effectively improve malware detection. Among them, in the data set CIC-AndMal2017, four evaluation indicators have significantly improved. In the data set CICMalDroid2020, although recall decreased by 0.03 percentage points, accuracy, F1-scores, and precision have improved to varying degrees, of which accuracy has increased by 1.88 percentage points and 0.1 percentage points, respectively.
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.7 Time  
On average, existing systems spend several months to make an accurate medical decision. Oppositely, the proposed scheme collects opinion and creates a block from several parties within hours.
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 5 Experiments and Analysis
### Results and Analysis
#### 5.2.2 Defenses Against Communication Delays  
Since communication delay model attacks mainly target the communication process between ENs and ZSs, they exhibit minimal dependence on the type of datasets. Therefore, the average waiting time per round of FL training and the final model accuracy in CIFAR-10 are recorded as an example. As shown in Figure 7, with an increasing number of PCA nodes, the average waiting time for FedAvg and Oort, which lack delay defense mechanisms, increases rapidly and approaches the maximum waiting time \(T_{m}\) for each FL round. Additionally, due to the increasing channel noise power, the final model accuracy is notably compromised. In contrast, CS-FL and FedCS achieve shorter average waiting time for model collection while maintaining a relatively stable model accuracy. These results demonstrate that the CRI value in CS-FL effectively measures the real-time communication quality of ENs.  
As shown in Figure 8, without a penalty mechanism for dishonest resource reporting by malicious ENs, the defense efficiency of FedCS declines rapidly under CCA threats. The average waiting time and the model training accuracy of FedCS are similar to those of FedAvg and Oort, which lack communication monitoring mechanisms. This indicates that the original defense mechanism of FedCS can be bypassed by deceptive behaviors from ENs. On the contrary, CS-FL, enhanced by the SCI incentive mechanism introduced after the formal training phase, significantly strengthens its ability to identify and defend against CCA attacks. Consequently, it effectively safeguards the accuracy of the global model and maintains training efficiency with minimal disruption.  
Figure 5: The accuracy of FL tasks using the DPA model on two datasets. (**a**) The variation trend of the accuracy rate along with the number of malicious nodes among the total nodes on MNIST. (**b**) The variation trend of the accuracy rate along with the number of malicious nodes among the total nodes on CIFAR-10.  
Figure 6: The accuracy of FL tasks using the MPA model on two datasets. (**a**) The variation trend of the accuracy rate along with the number of malicious nodes among the total nodes on MNIST. (**b**) The variation trend of the accuracy rate along with the number of malicious nodes among the total nodes on CIFAR-10.
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 2 LDPC Codes Constructed from BIBDs and Their Tanner Graphs
### Balanced Incomplete Block Designs (BIBDs)  
Let \(\mathbf{X}\) be a finite set of points and \(\mathbf{B}\) be a collection of subsets of \(\mathbf{X}\), where the elements of \(\mathbf{B}\) are referred to as blocks. The number of elements in \(\mathbf{X}\) is denoted by \(v\), i.e., \(|\mathbf{X}|=v\). Let the number of blocks in \(\mathbf{B}\) be \(m\), and let \(K\) denote the set of possible sizes for the blocks. Specifically, each block in \(\mathbf{B}\) contains a number of elements that belong to \(K\). This means that the size of any block in \(\mathbf{B}\) is one of the values in set \(K\), i.e., for \(B_{j}\subset\mathbf{B},1\leq j\leq m\), \(|B_{j}|\in K\). A BIBD is an incidence structure defined by pair \((\mathbf{X},\mathbf{B})\), which satisfies the following properties:  
1. Each block in \(\mathbf{B}\) contains exactly \(k_{i}\) points for \(k_{i}\in K\).
2. Every pair of distinct points in \(\mathbf{X}\) is contained in exactly \(\lambda\) blocks.  
This incidence structure \((\mathbf{X},\mathbf{B})\) is denoted by \(\text{BIBD}(K,\lambda;v)\). For \(K=\{\kappa\}\), \(\text{BIBD}(K,\lambda;v)\) is denoted by \(\text{BIBD}(\kappa,\lambda;v)\) for short. In combinatorial design theory, \(\text{BIBD}(K,1;v)\) is also referred to as the Steiner 2 design. These properties ensure a balanced and structured relationship between the points and blocks, making balanced incomplete block designs (BIBDs) a cornerstone of combinatorial design theory. Their well-defined and symmetric structure is particularly valuable in the construction of LDPC codes, where BIBDs are employed to achieve desirable properties such as large girth and controlled cycle distributions in Tanner graphs. This makes BIBDs a powerful tool for designing LDPC codes with enhanced iterative decoding performance.
###### Abstract  
Planar optical waveguides were fabricated in Pr:YLF crystals by ion implantation. In a further step, ridge waveguides were fabricated using precision diamond dicing. These enable strong light confinement and have propagation losses as low as 0.4 dB/cm. To study the influence of ion implantation on the spectroscopic properties, fluorescence and lifetime measurements were conducted in the ridge waveguides. Under blue pumping, small-signal optical gains of 6.5 dB/cm and 5 dB/cm were demonstrated at wavelengths of 607 nm and 639 nm, respectively. These results make ion-implanted ridge waveguides in Pr:YLF promising candidates for compact integrated lasers in the visible spectral region with high output powers in the watt range.  
ion implantation; rare earth ions; ridge waveguide; optical amplifier Article
###### Abstract  
Phase-coherent underwater acoustic (UWA) communication systems typically employ multiple hydrophones in the receiver to achieve spatial diversity gain. However, small underwater platforms can only carry a single transducer which can not provide spatial diversity gain. In this paper, we propose single-carrier with frequency domain equalization (SC-FDE) for phase-coherent synthetic aperture acoustic communications in which a virtual array is generated by the relative motion between the transmitter and the receiver. This paper presents synthetic aperture acoustic communication results using SC-FDE through data collected during a lake experiment in January 2016. The performance of two receiver algorithms is analyzed and compared, including the frequency domain equalizer (FDE) and the hybrid time frequency domain equalizer (HITDE). The distances between the transmitter and the receiver in the experiment were about 5 km. The bit error rate (BER) and output signal-to-noise ratio (SNR) performances with different receiver elements and transmission numbers were presented. After combining multiple transmissions, error-free reception using a convolution code with a data rate of 8 kbps was demonstrated.  
Keywords: underwater acoustic communications; SC-FDE; channel equalization; synthetic aperture +
Footnote †: journal: Sensors
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
## 2 Materials and Methods
### Boundary Conditions  
The problem was modeled using a displacement-based model, and we imposed Dirichlet boundary conditions for the displacement of the structure. For the source on the top of the protruding element of the rock bolt, we assumed that the main displacement acted along the same axis as the impact of the hammer (z-axis). The bottom surface could have had displacements different from zero along the z-axis, but the displacement along the lateral surface of the grouting was assumed to be equal to zero. This was consistent with the assumption that most of the energy propagated within the steel rod and on the surface between the rod and the grouting.
# Entropy-Guided Distributional Reinforcement Learning with Controlling Uncertainty in Robotic Tasks
## 4 Experiments
### Comparative Evaluation in Robotic Task  
To verify the applicability of our method across various robotic environments, we evaluated both the vanilla TQC and our EG-TQC on six benchmarks from the Deep-Mind control suite. The benchmarks comprised various manipulation tasks using Hopper, Humanoid, and Walker. The EG-TQC dynamically adjusts the discount factor values from \(\gamma_{\min}=0.97\) to \(\gamma_{\max}=0.99\) based on the uncertainty provided by the entropy. In contrast, the TQC was evaluated with fixed discount factor values of \(\gamma=0.97\), \(0.98\), and \(0.99\). In Figure 3, the frames represent the amount of data the agent has processed during training, while the evaluation returns reflect the cumulative rewards received by the agent based on how well it performs the required actions. Our experimental results confirm that the  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Hyper Parameters** \\ \hline Optimizer & Adam \\ Learning rate & \(3\times 10^{-4}\) \\ Replay Buffer size & \(10^{6}\) \\ Number of critics N & 2 \\ Learning rate of discount factor \(\eta\) & \(10^{-8}\) (DM Control Suite) \\  & \(10^{-7}\) (Fetch) \\ Number of Hidden Layer in critic networks & 2 \\ size of hidden layers in critic networks & 256 \\ Number of hidden layers in policy network & 2 \\ Size of hidden layers in policy network & 256 \\ Minibatch size & 256 \\ Entropy target \(\mathcal{H}_{T}\) & \(-\dim\mathcal{A}\) \\ Number of atoms M & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters used for evaluation.  
EG-TQC method outperforms the basic fixed discount factor approach. The EG-TQC method enhances sample efficiency in the early stages of learning by adjusting future beliefs to modulate the contribution of learning data. Additionally, it positively impacts final learning performance and stability. Furthermore, the EG-TQC method eliminates the need for manually determining the appropriate discount factor for each task. It adjusts future reliability based on the specific situation, thereby improving overall performance.
# Study on Microwave Deicing of Carbon-Fiber-Modified Concrete under Multi-Factor Coupling Effect
## 1 Introduction
### Research Significance  
To date, few factors have been considered in the study of microwave deicing of concrete. There are many factors to be considered in the microwave deicing process. Variation of factors affects deicing efficiency, which has research value for the study of the microwave deicing effect of carbon fiber concrete under the combined effect of multiple factors.  
We studied the law of deicing caused by the temperature rise of carbon-fiber-modified concrete absorbing electromagnetic waves. Carbon fiber modification on the surface of concrete in cold areas (airport applications) to improve deicing efficiency, as well as the geographical location of the airport, can, to a certain extent, resolve the issue of concrete surface ice for planes landing and taking off so as to ensure the normal operation of the airport and to solve the problem of insecurity caused by ice on airport road surfaces in high-altitude and high-latitude areas.
## 2 Problem Formulations
### Definition of Synchronization Error Cross-Coupling Error  
In synchronization control, there are two error types, and they are called the synchronization error and cross-coupling error. These error types are determined as in Reference [57].  
The error of the position tracking on each active joint is calculated as follows:  
\[e_{\alpha}=\theta_{da}(t)-\theta_{\alpha}(t) \tag{6}\]  
where \(\theta_{da}(t)\in R^{3\times 1}\) denotes the angle of the desired position on each active joint.  
In the synchronization matter of the error tracking, the position error of \(e_{ai}(t)\) not only achieves zero it but also tunes the motion relationship among multiple active joints during the tracking operation:  
\[e_{a1}(t)=e_{a2}(t)=e_{a3}(t) \tag{7}\]  
where \(e_{ai}(t)\) is the position error at the \(i\)th joint.  
The synchronization errors are defined in the case of three active joints as follows:  
\[\left\{\begin{array}{l}e_{1}^{s}(t)=e_{a1}(t)-e_{a2}(t)\\ e_{2}^{s}(t)=e_{a2}(t)-e_{a3}(t)\\ e_{3}^{s}(t)=e_{a3}(t)-e_{a1}(t)\end{array}\right. \tag{8}\]  
where, the vector of synchronization error is \(e^{s}=\left[\begin{array}{cc}e_{1}^{s}(t),&e_{2}^{s}(t),&e_{3}^{s}(t)\\ \end{array}\right]^{T}\). Therefore, the goal of synchronization error can be achieved if \(e_{i}^{s}(t)=0\) for all active joints.  
The cross-coupling error was defined as follows:  
\[\left\{\begin{array}{l}\varepsilon_{a1}^{c}=e_{a1}+\lambda\int\limits_{0}^ {t}(e_{1}^{s}-e_{3}^{s})d\upsilon\\ \varepsilon_{a2}^{c}=e_{a2}+\lambda\int\limits_{0}^{t}(e_{2}^{s}-e_{1}^{s})d \upsilon\\ \varepsilon_{a3}^{c}=e_{a3}+\lambda\int\limits_{0}^{t}(e_{3}^{s}-e_{2}^{s})d \upsilon\end{array}\right. \tag{9}\]  
where \(\lambda\) is the positive constant, \(\upsilon\) is the variable from time zero, and the vector of cross-coupling errors is \(e_{\alpha}^{c}=\left[\begin{array}{cc}e_{a1}^{s},&e_{a2}^{s},&e_{a3}^{s}\\ \end{array}\right]^{T}\).  
We define vectors of \(\Delta e^{s}=\left[\begin{array}{cc}e_{1}^{s}-e_{3}^{s},&e_{2}^{s}-e_{1}^{s },&e_{3}^{s}-e_{2}^{s}\\ \end{array}\right]^{T}\) and \(\dot{\Delta}e^{s}=\left[\begin{array}{cc}\dot{e}_{1}^{s}-\dot{e}_{3}^{s},& \dot{e}_{2}^{s}-\dot{e}_{1}^{s},&\dot{e}_{3}^{s}-\dot{e}_{2}^{s}\\ \end{array}\right]^{T}\).  
Then, the first and second derivative of the cross-coupling error can be computed as follows:  
\[\dot{e}_{\alpha}^{c}=\dot{e}_{\alpha}+\lambda\Delta e^{s} \tag{10}\]  
\[\dot{e}_{\alpha}^{c}=\ddot{e}_{\alpha}+\lambda\dot{\Delta}e^{s} \tag{11}\]
# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 5 Experimental Results
### Experiment on Data Augmentation and Transfer Learning  
In order to verify the effects of data augmentation and transfer learning technologies, we carried out the ablation experiment.  
(1) Experiment on Data Augmentation Technology  
With no transfer learning, we compared accuracy convergence curves of two SE_Xception models. One of them uses data augmentation and the other model does not. The results are shown in Figure 7a. It can be seen that the data augmentation technology can effectively improve the accuracy and convergence performance of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Number of Parameters (M)** & **FLOPs (M)** &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline SE\_Xception & 23.85 & 48.15 & 99.40 \\ SE\_Xception\_0.75 & 13.47 & 27.29 & 99.26 \\ SE\_Xception\_0.50 & 6.04 & 12.31 & 98.59 \\ SE\_Xception\_0.25 & 1.55 & 3.21 & 98.49 \\ SE\_Xception\_0.1 & 0.27 & 0.59 & 95.78 \\ \hline MobileNetV1 & 3.24 & 6.67 & 95.41 \\ ShuffleNet & 1.95 & 3.99 & 95.79 \\ SE\_miniXception & 0.61 & 1.27 & 97.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of compressed SE_Xception and other light-weight models.  
(2) Experiment on Transfer Learning Technology  
The effect of transfer learning on model performance is verified on the SE_Xception model without data augmentation. Figure 7b shows that transfer learning can improve the starting point of convergence of the model.  
To summarize, data augmentation can improve the final accuracy and transfer learning can improve the starting point of convergence. The combination of these two technologies can effectively improve the final performance of the model.
# Calculation of the Rearfoot Angle Representing Flatfoot from Comparison to the Cavanagh Arch Index
## 2 Materials and Methods
### Rearfoot Angle (RFA) Measurement  
This section presents a proposed methodology, i.e., RFA measurement as an alternative flatfoot detection. According to the previous studies, a Cavanagh's AI is a typical method used to determine the flatfoot; however, the calculation stages of either manual or digital AI measurement are time-consuming. This study is then proposed as an alternative method to determine flatfoot based on the RFA measurement. The RFA measurement is much simpler than the manual AI measurement (Figure 1a) and the digital AI measurement (Figure 1b).  
To obtain the RFA, each subject was asked to stand relaxed on a flat platform so that the subtalar joint in a neutral position could be felt by palpation. The subtalar joint is formed between the talus and calcaneus and was used as a reference point for rearfoot measurement.  
Three locations are marked using a marker pen, as presented in Figure 3a. These are: (1) the base of the calcaneus, which is indicated as point 1, (2) the subtalar joint that is marked as point 2, and (3) the top end of the bisection of the lower one-third of the leg or 15 cm above point 2 that is marked as point 3 [13]. The next step is to draw a straight line with the marker from point 2 down to point 1 (line 1) up to point 3 and extend this line to  
Figure 2: Digital AI measurement method: (**a**) footprint scanning; (**b**) image processing of the footprint using MATLAB;(**c**) FL, FW, FAC, and AI results.  
the floor (line 2), as illustrated in Figure 3a. The RFA is the angle formed between line 1 and line 2; if the angle shows that the ankle leans inward, it is called flatfoot or valgus foot, and otherwise, it is called high arch or varus foot.  
The RFA was measured using a goniometer as presented in Figure 3b. The goniometer arms were aligned with line 1 and the other arm with line 2. RFA is measured as the angle between the projections of line 1 and line 2. RFA \(\geq 5^{\circ}\) valgus represents a flatfoot type. In this study, we find the minimum value of RFA, which indicates flatfoot, by using the measurement results of [PERSON]'s AI as a basis.