esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Crack Width  
Vernier Caliper readings were used to measure the width of the cracks during the pre-crack loading stages for all over-reinforced beams until the occurrence of failure, as seen in Figure 11. The development of cracks at each stage was measured and marked on the beams to observe the growth, sequence, and pattern of cracking. As a result, three types of crack were identified, namely flexure, flexural-shear, and horizontal cracks (spalling of cover concrete). Specifically, flexural failure was taken into account in this study to identify the flexural cracks in the beams specimens. Furthermore, the type of loading and mode of failures of the beam specimens are listed in Tables 3 and 4. Meanwhile, the crack widths for all the over-reinforced beams are shown in Figures 7 and 8.  
It was observed that the loads were initially increased up to 30%. Although the ultimate load of the CB beam possessed an elastic characteristic, 45% of the ultimate loads in the BCSP-6, BCSP-10, and BCSP-15 beams were dominated by this characteristic. This was followed by the appearance of flexural-shear cracks, which initially occurred at the mid-span of the beam sections. They gradually extended to the point-of-load application and finally to the supports. Then, the existing diagonal cracks widened and extended further with the increase in the loadings. Furthermore, the flexural cracks were observed at the point of load application. The applied load was approximately half of the ultimate load for the CB beams; however, it amounted to approximately 65% for the BCSP beams. For all the beams, the flexural cracks reached the mid-depth of the beam sections. At approximately 80%-90% of the ultimate load, the horizontal cracks appeared at the point-of-load application and widened significantly until the beam failure took place. Figure 11 illustrates the maximum width of the flexural cracks of all the beams with the corresponding loads. To be specific, the applied loads amounted to approximately 90% of the ultimate load. It was indicated from the figure that the imposed load, which corresponded to the first flexural cracks, decreased with as the thickness of steel plate to the effective stiffness ratio increased.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Resistance Capability of ECSSP-SC against Attacks
#### 6.2.2 Resistance to User Impersonation  
For impersonating the \(USR\), an _Attacker_\(A\) must be capable of generating the correct message \(\{c,T,w,\ T_{U}\}\). The value of the components \(c,\ T\), and \(w\) depends on the key \(K\), which in turn depends on the secret \(v\) and \(A_{RU}\), and as per the Assumption 5, an _Attacker_\(A\) cannot obtain them to create the key \(K\) and the correct message \(\{c,T,w,\ T_{U}\}\). Therefore, the ECSSP-SC is resistant against user impersonation. The process of resisting user impersonation by ECSSP-SC has been demonstrated in Figure 7.
###### Abstract  
Sun-induced chlorophyll fluorescence (SIF) is an important indicator of vegetation photosynthesis. While remote sensing enables large-scale monitoring of SIF, existing products face the challenge of trade-offs between temporal and spatial resolutions, limiting their applications. To select the optimal model for SIF data downscaling, we used a consistent dataset combined with vegetation physiological and meteorological parameters to evaluate four different regression methods in this study. The XGBoost model demonstrated the best performance during cross-validation (R\({}^{2}\) = 0.84, RMSE = 0.137 mW/m\({}^{2}\)/nm/sr) and was, therefore, selected to downscale GOME-2 SIF data. The resulting high-resolution SIF product (HRSIF) has a temporal resolution of 8 days and a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\). The downscaled product shows high fidelity to the original coarse SIF data when aggregated (correlation = 0.76). The reliability of the product was ensured through cross-validation with ground-based and satellite observations. Moreover, the finer spatial resolution of HRSIF better matches the footprint of eddy covariance flux towers, leading to a significant improvement in the correlation with tower-based gross primary productivity (GPP). Specifically, in the mixed forest vegetation type with the best performance, the R\({}^{2}\) increased from 0.66 to 0.85, representing an increase of 28%. This higher-precision product will support more effective ecosystem monitoring and research.  
Sun-induced chlorophyll fluorescence; machine learning; XGBoost model; downscaling
## 4 Example
### Extracting Feature Data
#### 4.2.1 Data Cleaning  
The data cleaning process includes outlier removal and missing value imputation.  
(1) Outlier Removal  
Outlier removal is an indispensable step in data preprocessing, as the collected data series may contain abnormal values that affect the overall feature extraction of the data series. When removing outliers, the upper and lower threshold limits (U and L) of each parameter must be read first. For each data point in the original parameter data series, the decision to delete the data point is made based on the upper (U) and lower (L) threshold limits. Figure 13 provides a visualization example of outlier removal.  
\begin{table}
\begin{tabular}{c c} \hline \hline Number of Entities in the Test Set & 173 \\ Identified Entities & 139 \\ Correctly Identified Entities & 131 \\ Precision (P) & 94.24\% \\ Recall (R) & 75.72\% \\ F1 Score & 83.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation results of the entity annotation model.  
Figure 13: Outlier removal visualization example.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Entity Type** & **Count** & **Relationship Type** & **Count** \\ \hline Anomaly Event & 112 & Time & 358 \\ Anomaly Time & 358 & Phenomenon & 284 \\ Anomaly Phenomenon & 243 & Device & 116 \\ Anomaly Device & 93 & Parameter & 145 \\ Anomaly Parameter & 111 & Cause & 142 \\ Anomaly Cause & 134 & Plan & 157 \\ Handling Plan & 144 & Belong & 109 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of entity and relationship counts in the spacecraft abnormal event graph.  
Data missing is a common problem in data collection, transmission, and storage processes. Missing data occur randomly, so current methods for handling missing values are generally divided into three categories: deletion, imputation, and no processing. For spacecraft time series data, direct deletion would result in the loss of data at certain time points, while not processing would affect subsequent feature data extraction. Therefore, we use imputation to handle missing values. As spacecraft parameters have a certain degree of stability and generally do not experience large fluctuations in a short period of time, missing values at short time points can be filled directly with the value of the previous time point. For long periods of missing time point data, filling the missing values directly with the value of a non-missing time point prior to the occurrence of the missing data would result in a loss of information contained in that time series and may even affect subsequent modeling. Therefore, we use regression imputation, establishing a regression equation and using its predicted values for missing value imputation. Figure 14 provides a visualization example of missing value imputation using regression imputation.
## 2 Materials and Methods
### Statistical Analysis  
We presented continuous data as median, mean \(\pm\) standard deviation (SD). Correlations were assessed using [PERSON]'s or [PERSON]'s method for normally or non-normally distributed data, respectively. The Chi-square test ([PERSON]'s exact test for small numbers) was used for comparisons of categorical variables. We firstly carried out univariate regression logistic models, specific for each variable, to evaluate potential risk or protective prognostic factors related to pituitary tumor surgery. Subsequently, we carried out a stepwise backward regression analysis to select the variables mostly influencing the outcome of interest, i.e., the incomplete tumor resection at follow-up. The significance threshold was fixed at 0.05. Finally, a multivariate regression analysis, including variables that remained significant, was performed. A \(p\)-value \(<\) 0.05 was considered statistically significant. Statistical analysis was performed using STATA (Statistical software, Version 13--StataCorp LP., College Station, TX, USA).  
Parameters included in the analysis were sex, age, tumor type, previous treatment, 2D-based parameters (HD, ICD, Zurich Pituitary Score, Hardy and Wilson grade, Knosp grade), as well as 3D-based indexes (Vol, Area, Spher, Conv). For statistical analysis, the endpoint (tumor removal) parameter was dichotomized as \"total\" or \"incomplete\" (including subtotal, partial, or incomplete resection).
# Effects of 450 MeV Kr Swift Heavy Ion Irradiation on GaN-Based Terahertz Schottky Barrier Diodes
###### Abstract  
GaN-based terahertz (THz) Schottky barrier diodes (SBDs) are critical components for achieving high-power performance in THz frequency multipliers. However, the space applications of GaN-based THz SBDs are significantly constrained due to insufficient research on the effects of space irradiation. This work investigates the effects of 450 MeV Kr swift heavy ion (SHI) irradiation on the electrical characteristics and induced defects in GaN-based THz SBDs. It was found that the high-frequency performance of GaN-based THz SBDs is highly sensitive to Kr SHI irradiation, which can be attributed to defects induced in the GaN epitaxial layer by the irradiation. Low-frequency noise analysis reveals trap states located at an energy level of approximately 0.62 eV below the conduction band. Moreover, the results from SRIM calculation and photoluminescence spectra confirmed the presence of irradiation-induced defects caused by Kr SHI irradiation.  
GaN-based THz SBDs; heavy ions irradiation; trap states; low-frequency noise; photoluminescence +
Footnote †: journal: Micromachines  
0000-0001-5002-2276-1198][PERSON]  
2
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Handover Blocking Probability  
#### 3.3.1 Handover Blocking Probability of the Proposed Approach  
For the proposed approach, the handover blocking probability presented in References [8; 30; 31; 32] is used to examine the failures in terms of the handover blocking probability in this study. Many factors can cause a handover failure for the MN, such as the non-availability of a wireless network resource, signal-to-noise deterioration, and handover latency. If \(T_{HO}\) represents the handover latency for the proposed approach, let \(E[T_{HO}]\) denote the mean value of \(T_{HO}\). Let \(T_{E}\) be the time when MN exists in the network along with its probability density function \(f_{E}(t)\). We assumed \(T_{HO}\) to be exponentially distributed with the cumulative function \(F_{T}\left(t\right)\) for the sake of simplicity. If \(T_{HO}\) is the only factor blocking the handover, then the handover blocking probability \(p_{b}\) is given as:  
\[\rho_{b}=\Pr(T_{HO}>T_{E})=\int\limits_{0}^{\infty}\left(1-F(u)\int\limits_{E }\left(u\right)d(u)=\frac{\mu_{C}E[T_{HO}]}{1+\mu_{C}E[T_{HO}]} \tag{18}\]  
where \(\mu_{C}\) is the rate of boundary crossing for the MN, i.e., its mobility rate. Supposing that the BMA's coverage area is circular, then \(\mu_{c}\) is calculated as follows [8; 32; 33; 34]:  
\[\mu_{C}=\frac{2v}{\pi R} \tag{19}\]  
where v represents the average velocity of the MN and \(R\) represents the radius of the BMA's coverage area.
## 4 LEACH Protocol Improvement Based on the Differential Evolution Algorithm
### Optimizing Algorithm and Determining Cluster Heads with the DE
Adjusting \(\alpha\) and \(\beta\) will adjust the contribution rate of \(e_{j}\) and \(\overline{e_{j}}\) to the adaptation value. In this paper, \(\alpha=\beta=0.5\). The average equivalent energy \(\overline{e_{j}}\) is defined as:  
\[\overline{e_{j}}=\frac{1}{n-1}\sum_{i\sim i\sim j}^{n}\tilde{e_{i}} \tag{8}\]  
where \(\tilde{e_{i}}=f(e_{i},r_{i})\) is the equivalent energy of node \(i\). Suppose \(r_{i}\) is the distance from node \(i\) to node \(j\), and \(e_{i}\) is the remaining energy of node \(i\). The remaining energy of node \(i\) and its distance to node \(j\) should be taken into account together for the structure of \(f(e_{i},ri)\). The following should be satisfied: the farther the distance from node \(i\) to node \(j\), the less the equivalent energy of node \(i\), and _vice versa_. The function reflects the characteristics of problems in simulation. In all, the target function is:  
\[f(i)=\frac{1}{n}\sum_{j\sim i}^{n}[\alpha e_{j}+\beta\frac{1}{n-1}\sum_{i\sim i \sim j}^{n}e_{i}\cdot\exp(-\frac{r_{i}}{l})] \tag{9}\]The experiment vector \(U_{i,G+1}\) resulted from the crossover operation is compared with the objective vector \(X_{i,G}\) substituted into the vector objective function. If the objective function value of \(U_{i,G+1}\) is greater than that of \(X_{i,G}\), then \(X_{i,G}\) is replaced with \(U_{i,G+1}\). And if not, \(X_{i,G}\) will be kept, thus the next generation swarm is created. By now, a swarm has finished a generation of evolution. The DE algorithm repeatedly executes the steps of variation, crossover and selection until the termination condition is satisfied. The termination condition is usually the evolution generations, and the element with the absolute dominant amount position in the experiment vectors is just the corresponding number of the final cluster head.
# Technologies and Fabrication of Intelligent Packaging for Perishable Products
## 2 Intelligent Packaging Techniques
### RFID Coupling with Sensing Techniques
#### 2.3.1 Active Tags Coupling with Sensors  
Active tags can be easily coupled with sensors, as they operate using batteries that can be used to drive the sensors. For example, a product called Device-to-Business (D2B(tm)), developed by the HK Poly U RFID team, is a typical active RFID tag coupled with sensors and applied in Intelligent Packaging. The product eases communication between RFID devices and other devices. The D2B(tm) contains 433-MHz active RFID tags, a communication module, and sensors. The different types of sensors are chosen based on the requirements of the applications. The communication distance can be up to 60 m. This device can be connected to various other devices, such as smartphones, RFID readers, personal computers, etc.  
Environmental factors such as container boxes can be monitored using D2B(tm) devices embedded in packaging. If any extreme changes in the environmental conditions occur in the container, the operators will receive a warning from the device. This allows the operators to take the necessary measures to prevent loss or damage. A mobile terminal and D2B(tm) device embedded in a container box is shown in Figure 4. In addition to monitoring the environment in container boxes, a D2B(tm) device can also be used to locate and select specific products through utilizing its Pick-to-Light function. The function benefits the warehouse management in cases where the first in, first out (FIFO) principle is used [33].
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 5 Results
### Performance Analysis of Proposed Method
#### 5.6.2 Analysis on CIC-IDS2017 Dataset
The F1-score and accuracy excel, with the lowest FNR, showcasing its remarkable effectiveness in reducing both false positives and false negatives, establishing it as the top-performing method in this assessment. Hence, the proposed detection model successfully identified and classified seven distinct cyber-attacks. Subsequently, the prevention model effectively mitigated four of these detected attacks by promptly blocking them and routing the associated data traffic to the security enforcement gateway, due to their high-priority designation within the Cyber Threat Repository. Concurrently, the remaining three attacks, deemed as lower priority, were redirected to the Incident Log Database for further analysis and subsequent mitigation efforts. These findings highlight the effectiveness of the prevention model, as it successfully mitigated all detected attacks. The priority-based blocking strategy is highly effective, successfully blocking all high-priority attacks, as shown in Table 7. Furthermore, Figure 21 demonstrates an impressive success rate in preventing all detected attacks, emphasizing the resilience of the prevention model. These findings confirm the model's ability to uphold security without any compromises. Figure 20: Performance analysis of stage 3 OCSVM + dAE + DBSCAN on CIC-IDS2017. The evaluation and results of the proposed cybersecurity solution reveal key insights into its effectiveness and robustness. The assessment of OCSVM underscores the critical influence of the \"nu\" parameter on detection accuracy, essential for optimizing performance. Similarly, evaluation of the deep autoencoder (dAE) demonstrates its effectiveness in anomaly detection, utilizing MSE across datasets. The detection model's overall performance across both \"Normal\" and \"Attack\" data in CIC-IDS2017 and CSECIC-IDS2018 datasets shows significant efficacy, consistently delivering notable results. Moreover, the proposed approach outperforms alternatives in clustering tasks, exhibiting superior precision, recall, and overall accuracy. Comparative analysis across datasets highlights its superiority, with high correctness of clustering, F1-measure, and recall scores. Successful identification and classification of cyber-attacks, coupled with the prevention model's remarkable success rate in mitigating them, underline the solution's robustness in enhancing cybersecurity resilience. Implementation insights elucidate practical deployment strategies, leveraging various techniques like autoencoders, OCSVM, DBSCAN, and the MITRE ATT&CK framework. Overall, these evaluations collectively demonstrate the comprehensive efficacy of the proposed cybersecurity solution in detection, prevention, and overall resilience enhancement, promising in tackling contemporary cybersecurity challenges.
## 2 State-of-the-Art PWL Method
### Basic Principles of PWL  
The nonlinear function f(x) is divided into n segments with PWL method. The independent variables in the ith section range from p\({}_{i}\) to q\({}_{i}\), and the ith segment of f(x) is approximated by:  
\[\text{h}_{i}(\text{x})=\text{k}_{i}\times\text{x}+\text{ b}_{i} \tag{1}\]  
Approximate absolute error (AE), influenced by expressed by:  
\[\text{AE}=\mid\text{f}(\text{x})-\text{h}(\text{x})\mid \tag{2}\]  
MAE and AAE are the maximum and the average AE, defined, respectively, as:  
\[\text{MAE}=\text{max}(\text{AE}) \tag{3}\]  
and  
\[\text{AAE}=\frac{\sum\text{AE}}{\text{N}} \tag{4}\]  
where N is the number of inputs that are sampled to participate in the computation.  
Apart from AE, relative error distance (RED) is another standard and metric for approximate designs. RED is defined as:  
\[\text{RED}=\frac{\text{AE}}{\text{f}(\text{x})} \tag{5}\]  
MRED and NMED represent mean and normalized relative error distance, defined respectively, as:  
\[\text{MRED}=\frac{\sum\text{RED}}{\text{N}} \tag{6}\]  
and  
\[\text{NMED}=\frac{\text{AAE}}{\text{max}(\text{f}(\text{x}))} \tag{7}\]
## 4 Simulations and Performance Evaluations
### Increasing the Number of Clients  
The subsequent phase, aimed at enhancing the observance of FL and its scalability, entailed increasing the number of clients participating in the simulation to a maximum of 10. Of these, 2 were Raspberry clients, and 8 were virtual clients, coexisting with the server on the computer. The 10-client threshold could not be exceeded due to the restricted RAM on the laptop and the considerable resources required to train neural networks. Nevertheless, this number of clients proved adequate in inducing a noteworthy decline in the model's performance as the number of devices involved escalated. In addition, it can be seen that the accuracy calculated by the server of the global model after each federated round is progressively sluggish as the number of clients used grows.  
Figure 13 portrays the accuracy value for each federated round for five different simulations, each divided equally across all clients, using the internal training set (50,000 images). The specific parameters for each test are as follows: two clients with 25,000 samples each, four clients with 12,500 samples each, five clients with 10,000 samples each, eight clients with 6250 samples each, and ten clients with 5000 samples each. The simulation employing only two clients is the best, whereas the test with ten devices yields the worst performance, requiring 40 federated rounds before achieving accuracy comparable to the former. However, it should be noted that, in the first case, Raspberries must operate with 25,000 samples each, resulting in relatively slower local periods, while in the second case, each client uses only 5000 samples, thereby completing its local epochs much faster.  
Figure 12: FL with different amounts of randomly chosen samples for two clients.  
Figure 13: FL with different amounts of randomly distributed samples among different number of clients.  
Examining Figure 14, representing the same performance while in the time domain, it is evident that the actual difference in performance between the simulations is much lower and sometimes even nonexistent. For instance, the test with ten clients completed its 40 rounds at a time, close to the tenth round of the test with only two clients. The same applies to the other curves, with the simulation involving four clients overlapping perfectly with that of two. This implies that, in the context of a system with limited computational capacity, as represented by the Raspberry Pi, a federated approach generates relatively similar performance to centralized ones.  
A similar pattern can be witnessed even with random samples, as illustrated in the two simulations depicted in Figure 15. The clients work with 30,000 and 12,000 images randomly selected from the dataset, and the algorithm trained with five clients obtains lower accuracy per round. However, since these simulations are completed twice as fast as the two-client simulation, the result is that the accuracy values are very similar moment by moment.
# A Method to Accurately Estimate Fish Abundance in Offshore Cages
## 2 Proposed Fish Counting Algorithm
### Algorithm
#### 2.2.1 Length Frequency Key  
The average TS of fish in a cage can be estimated by sampling some fish. To calculate \(<\)TS\(>\) from the sampled fish, the algorithm takes length frequency key (LFK) as an input. LFK is a table of two columns that records the number of fish of particular length, as shown in Table 1. Note that the length in the table is the total length of fish round up to a nearest cm.