esa-sceva/satcom-chunk-collection huggingface dataset:
# Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images
## 2 Methods
### 2D CNN Model  
The 2D CNN designs the correct model of single views by reducing the bias of a single network, thereby effectively and accurately reducing the false positive rate of lung nodule [21]. This study proposes a 2D CNN architecture includes of two convolutional, two pooling and two fully-connected layers. The input image sizes \(50\times 50\times 3\), and the convolutional layer is based on the process of circulating a particular filter over the entire image. Kernel size, stride, padding, and filters can be different sizes, and the proposed 2D CNN architecture is described in Table 3.  
Figure 4: Sample CT images of lungs from the International Society for Optics and Photonics with the support of the American Association of Physicists in Medicine (SPIE-AAPM) dataset.  
Figure 3: Sample malignant tumor CT images from the LIDC-IDRI dataset.  
Figure 2: Sample benign tumor computed tomography (CT) images from the Lung Image Database Consortium and Infectious Disease Research Institute (LIDC-IDRI) dataset.  
We statistically analyzed the classification performance of the 2D CNN in terms of the Taguchi parametric optimization, accuracy, sensitivity, specificity, and false positive rate (FPR) derived from the confusion matrix. Accuracy is the correct prediction from the whole dataset, sensitivity is the ability of the test to correctly identify patients with the disease, and the specificity of a clinical test refers to the ability of the test to correctly identify patients without the disease. The following commonly used validation measurements are shown [3].  
\[\text{Accuracy}=\frac{(TP+TN)}{(TP+FN)+(FP+TN)} \tag{9}\]  
\[\text{Sensitivity}=\frac{(TP)}{(TP+FP)} \tag{10}\]  
\[\text{Specificity}=\frac{(TN)}{(TN+FP)} \tag{11}\]  
\[\text{FPR}=\frac{(FP)}{(TN+FP)} \tag{12}\]  
where true positive (\(TP\)) is malignant correctly identified as malignant, false positive (\(FP\)) is benign incorrectly identified as malignant, true negative (\(TN\)) is benign correctly identified as benign, and false negative (\(FN\)) is malignancy incorrectly identified as benign.
###### Abstract  
During blasting in engineering construction, the surrounding rock becomes unstable and is damaged under the impacts of multiple low-amplitude stress waves. It is of great practical significance to understand the damage evolution characteristics and the attenuation of the mechanical properties of rocks subjected to multiple stress waves. Single impact and repeated impact tests for sandstone were carried out using a split Hopkinson pressure bar (SHPB) loading system. The single impact test results showed that the sandstone materials were strain-rate-dependent, and the dynamic constitutive curve could be divided into four stages, namely the linear elastic stage, the new crack formation stage, the plastic strengthening stage and the unloading stage. The failure pattern mostly indicated splitting tensile failure, and the impact damage threshold was 45 J. The relationship between the damage and stress wave amplitude was D = 0.0029-exp \(\frac{\text{J}}{\text{J}\text{J}\text{J}}\) (5.4127-\(\circ\)/76.13) \(-\) 0.0504. The repeated impact test results showed that the dynamic compressive strength and the dynamic elastic modulus decreased, while the failure strain increased gradually as the number of impacts (n) increased. The sandstone specimen under repeated impacts had only one fracture surface compared with the single impact failure pattern. The cumulative damage presented the development form of 'rapid rise-steady development-rapid rise', and the damage evolution law could be expressed by D = 0.265 - 0.328-ln (ln3.989/n). Finally, a set of methods to determine the Holmquist-Johnson-Cook (HJC) model parameters for sandstone was proposed based on a single impact test, repeated impact test, uniaxial compression test and triaxial compression test. The numerical simulation results of the SHPB test showed that the dynamic constitutive curves of sandstone were in good agreement with the experimental results.  
split Hopkinson pressure bar; repeated impact test; damage evolution law; failure pattern; HJC model; determination of parameters +
Footnote †: journal: Applied Sciences
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 4 Results and Analysis
### Evaluation Index  
The experiments use mean average precision (mAP) as an evaluation metric for detection accuracy. mAP is the average of the detection accuracy of multiple categories. The average precision (_AP_) of each category can be obtained by the precision-recall curve, which is obtained by calculating precision (_P_) and recall (_R_). The \(P\), \(R\), and _AP_ are defined as follows:  
\[P =TP/(TP+FP) \tag{12}\] \[R =TP/(TP+FN)\] (13) \[AP =\int_{0}^{1}p(r)dr \tag{14}\]  
where _TP_ stands for true positive value, _FN_ stands for false negative value, and _FP_ stands for false positive value.
## 1 Introduction
This phenomenon is called plasticity induced crack closure and tends to decrease the effective stress intensity range thereby resulting in slower crack propagation rates [8]. In compact tension (CT) specimens, the crack progresses more rapidly in center than at the surface conducting to a crack tunneling effect, due to the prevailing tri-axial state of stress at the center, promoting plain strain in contrast with plain stress at surface. Striation spacing between beach marks on fatigue crack surfaces is also affected by shot and laser peening effect. [PERSON] et al. [9] have observed a decrease in striation spacing with increase in the number of laser peening impacts for Ti6 Al4V specimens'. For the same alloy, [PERSON] et al. [10] studied the effect of shot peening and laser peening on the fatigue crack propagation and compared with the untreated one with respect to the striation spacing; this was done using \(R=0.1\) and \(R=0.7\). Both peening surfaces presented a reduction on the striation spacing when compared to the untreated specimens. Overloads can lead to significant interaction effects on crack propagation, as has been reported in many studies [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Crack growth retardation can be explained by many mechanisms, including models based on crack closure, residual stresses, crack tip blunting, strain hardening, reversed yielding and crack branching. The residual plastic deformation effect leads to compressive stresses in the wake of the crack and raises the crack opening load on subsequent crack growth (crack closure), becoming the most important phenomena for what concerns the explanation for the variation of characteristic features of post-overload transients [19; 20; 21; 22; 23]. [PERSON] and [PERSON] [23] observed for 6061-T6 and 2024-T3 aluminum alloys that closure measurements produced good data correlation between distinct stress ratio crack growths obtained in tests with increasingly K conditions. However, in the near-thresholdregime with crack growth data obtained by the K-decreasing method, measured opening loads were excessive. This discrepancy was justified by [PERSON] et al. [24], who suggested the \"partial closure model\". [PERSON] et al. [25] concluded that crack closure explained the bias of stress ratio on the fatigue crack growth rate for the 6082-T6 aluminum alloy and the influence of several load parameters for overloads interactions if the partial crack closure model is included in the analyses. The present work analyzes the effect of shot peening, specimen thickness and stress ratio on the fatigue crack propagation of 7475 aluminum alloy with T7351 heat treatment. T7351 provides an aged material abler to resist to stress-corrosion, the heat-treatment produces stress-relieved by control stretching and after artificially overaged to achieve the best stress corrosion resistance. A more extensive analysis of the crack growth following periodical tensile overloads blocks is also evaluated.
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 5 Experiments
### Machine Learning Algorithm Performance Comparison

Table 4: Phase 1 examination results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifier** & **Accuracy** & **TPR** & **FPR** & **Precision** & **Recall** & _F_**-Measure** & **ROC Area** \\ \hline
**NB** & 89.51\% & 0.881 & 0.109 & 0.942 & 0.894 & 0.915 & 0.948 \\ \hline
**Logistic** & 94.58\% & 0.948 & 0.091 & 0.953 & 0.961 & 0.954 & 0.966 \\ \hline
**MLP** & 95.60\% & 0.956 & 0.077 & 0.96 & 0.969 & 0.962 & 0.964 \\ \hline
**SMO** & 94.83\% & 0.949 & 0.086 & 0.955 & 0.962 & 0.956 & 0.944 \\ \hline
**IBK** & 96.93\% & 0.960 & 0.050 & 0.971 & 0.973 & 0.971 & 0.969 \\ \hline
**J48** & 96.99\% & 0.962 & 0.048 & 0.974 & 0.975 & 0.972 & 0.970 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Phase 2 examination results. The ranker search method ranks the attributes according to their individual evaluations, after which it is possible to specify the number of attributes to retain. Using the InfoGainAttributeEval algorithm, we ranked the attributes of the GTCS dataset by their evaluations, which resulted in selecting 44 out of 84 features of the GTCS dataset. The final selected features are summarized and ranked in Figure 2. Next, we replicated the phase 1 experiment using the reduced and normalized GTCS dataset with those 44 features. The results of this phase are summarized in Table 3. Figure 3 compares the classification accuracy of each classifier in the two phases. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Class** & **Phase I** & **Phase II** \\ \hline \multirow{4}{*}{**SMO**} & Normal & 92.80\% & 93.70\% \\  & Botnet & 93.60\% & 94.50\% \\  & Brute Force & 83.70\% & 84.60\% \\  & DDoS & 91.01\% & 92.89\% \\  & Infiltration & 72.45\% & 83.35\% \\ \hline \multirow{4}{*}{**IBK**} & Normal & 95.50\% & 96.40\% \\  & Botnet & 95.60\% & 96.50\% \\  & Brute Force & 95.10\% & 96.00\% \\  & DDoS & 96.32\% & 97.22\% \\  & Infiltration & 79.67\% & 80.57\% \\ \hline \multirow{4}{*}{**J48**} & Normal & 94.60\% & 95.10\% \\  & Botnet & 95.30\% & 96.20\% \\ \cline{1-1}  & Brute Force & 87.70\% & 91.60\% \\ \cline{1-1}  & DDoS & 95.53\% & 96.43\% \\ \cline{1-1}  & Infiltration & 83.43\% & 84.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 2: Selected features of GTCS dataset. The experimental results show that IBK outperformed other classifiers and had the best accuracy in both phases. Moreover, the results shown in Table 6 indicate that IBK outperformed other classifiers in classifying normal, botnet, and brute force classes, while MLP and J48 performed best at classifying DDoS and infiltration classes, respectively. These results are illustrated in Figure 4.
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 4 Fundamentals of Zigbee
### Zigbee Communication Process  
The process of communication in a Zigbee network begins with the network initialization and joining procedure, followed by data transmission and reception. When a Zigbee device powers on, it scans for available networks using a process called active scanning. During this process, it listens to beacon frames broadcast by the coordinator or routers, which contain information about the network's identity, channel, and capacity for new devices. Once a suitable network is found, the device requests to join. The coordinator or router evaluates the request and, if accepted, assigns a network address to the device.  
A critical part of the joining process is the secure distribution of the network key, which ensures that only authorized devices can participate in encrypted communication within the network. When a new device joins the network, the coordinator sends the network key to the device using a secure key exchange mechanism. This exchange typically employs a pre-shared key (PSK) or a link key established during device pairing. The network key itself is encrypted using the pre-shared key to protect it from interception. Once the device successfully receives the encrypted network key, it decrypts it using the PSK, allowing it to communicate securely with other devices in the network.  
Figure 1: Zigbee network topologies.  
This initial network key exchange is crucial for maintaining network security. Without it, unauthorized devices could potentially intercept or inject data into the communication stream. After the network key is successfully exchanged, the joining device can participate in encrypted communication, ensuring that data payloads are protected from eavesdropping or tampering. Additionally, network key updates may occur periodically or when the security of the network is compromised. These updates are also encrypted and securely distributed to all devices within the network, maintaining the integrity and confidentiality of the communication over time.  
Once the network is formed and devices are connected, data are transmitted using a simple yet effective routing protocol. In mesh topology, Zigbee implements the Ad hoc On-demand Distance Vector (AODV) routing algorithm, which dynamically builds routing tables based on current network conditions. This allows Zigbee networks to self-heal, as alternative paths are discovered automatically when a link fails. End devices communicate with each other through either direct or indirect data transmission. Direct communication occurs when two devices are within range, while indirect communication involves routing through the coordinator or routers. Acknowledgments and retransmissions ensure data reliability, even in environments with interference or packet loss.
## 3 Methodology for Dynamic Risk Evaluation and Early Warning of Crest Cracking  
### Outline of the Methodology  
The first step of the methodology is to define the abnormal state associated with crest cracking of high earth-rockfill dams. As discussed in the introduction, crest cracking is closely related to the settlement inclination of the dam crest. If the settlement inclination exceeds a certain threshold, the dam crest is susceptible to cracking. The settlement inclination is defined as [3]:  
\[\gamma_{S}=\frac{\Delta S}{\Delta L} \tag{1}\]  
where \(\gamma_{S}\) is the settlement inclination, \(\Delta S\) is the settlement difference of two points at the same elevation, and \(\Delta L\) is the horizontal distance between the two points.  
The settlements can be computed using FEM. In order to improve the accuracy of the calculated settlements, the material parameters of the finite element model are calibrated based on the in-situ deformation monitoring data. The determination of threshold \(\gamma_{C}\) of settlement inclination (when \(\gamma_{S}>\gamma_{C}\), crest cracking possibly occurs, and vice versa) is an intractable problem and there is no consensus on the specific value of \(\gamma_{C}\) for high earth-rockfill dams. The suggested values of \(\gamma_{C}\) for high earth-rockfill dams in the literature [3,10] roughly range from 0.2% to 1%. In this study, the value  
Figure 4: The revealed cracks in exploratory pits.  
of \(\gamma_{C}\) is taken as 0.5%. Accordingly, the abnormal state, which is indicative of the possibility of crest cracking, is defined as \(\gamma_{S}>\gamma_{C}\).  
Recall that the duration \(T_{AS}\) of abnormal state before crest cracking is indeterminate, hence \(T_{AS}\) is treated as a random variable and is assumed to comply with exponential distribution in this study. Consequently, the probability of crest cracking can be formulated as a function of the duration of abnormal state, \(T_{AS}\). Once the abnormal state emerges (i.e., \(\gamma_{S}>\gamma_{C}\)), the probability of crest cracking can be estimated in terms of \(T_{AS}\). Moreover, the cracking time corresponding to a given cracking probability can be derived. Because there is no prior knowledge about the probability corresponding to the occurrence of crest cracking, the probabilities for estimating cracking time are selected and updated through the statistical analysis of the computed probabilities at the observed cracking times.  
The parameter in the formulation of crest cracking probability is epistemic uncertain. How to estimate the probability distribution of the parameter is a dominant problem in the application of the methodology. Bayesian theory is applied to estimate and update the probability distribution of the parameter based on the observed time intervals from the emergence of abnormal state until the occurrence of crest cracking.  
The procedure of the methodology is shown in Figure 5.  
Figure 5: Flowchart of the methodology for dynamic risk evaluation and early warning of crest cracking.
## 4 Experiments
### Data Drift Evaluation  
Phishers continuously modify or migrate the URLs of their phishing webpages to evade security mechanisms and countermeasures, enabling them to deceive users and engage in malicious activities. This strategy makes the detection and prevention of phishing webpages increasingly challenging, as the dynamic changes in webpage addresses render learned rules and patterns from the training set ineffective. This phenomenon is commonly referred to as data drift. To evaluate the model's resilience to data drift, we collected an evaluation dataset over a period of 3 months after completing the training data collection. Each month, we continued to gather 100 samples, forming the data drift evaluation dataset consisting of three subsets, C1 to C3, representing phishing webpages collected one month, two months, and three months after model training, respectively. Since the evaluation dataset solely consists of phishing samples, we report only the accuracy.  
The results in Figure 4 indicate that our proposed method outperforms all six baseline approaches in handling data drift in phishing websites. Upon analyzing the results in detail, we observe several key factors that contribute to the superior performance of our method. Firstly, our approach incorporates advanced feature engineering techniques and learning  
Figure 3: Comparison of ROC and AUC between proposed method and baseline method in imbalanced data experiment.  
algorithms that capture both static and dynamic characteristics of phishing websites. By considering features such as URL structure and behavioral patterns, our method can effectively identify subtle changes introduced by data drift.
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 4 Performance Evaluation  
In this paper, all of the experiments are carried out in MATLAB R2012b (The MathWorks, Natick, MA, America). As the energy efficiency is of major concern, we evaluate the performance of the DAHC protocol in terms of the number of alive nodes, the total residual energy, and the throughput of the BS. It is compared with the LEACH-C protocol [11] and the O-LEACH protocol [12]. LEACH-C protocol adopts the centralized strategy to select CHs. All nodes have to report energy and location to the BS each round. The O-LEACH protocol considers the residual energy to generate CHs. In this method, the node whose residual energy is greater than ten percent of the average value is likely to be a CH. In this way, the load balance is relatively improved.
## 1 Introduction  
Shiikuwasha (_Citrus depressa_ Hayata) is a small citrus cultivar with strong acidity and a particular aroma that has been cultivated in Okinawa, Japan, for many years. The shiiikuwasha peels contain polymethoxylated flavones, such as nobiletin and tangeretin, with various health benefits, including anticancer and antibobesity effects [1; 2; 3]. Their volatile organic compounds (VOCs) are effective for stress relief and aromatherapeuticrelaxation, and as anti-inflammatory agents [4; 5]. In Okinawa, shiikuwasha fruits are used to season or decorate food or are processed into beverages and conditions such as jams and sauces [6]. Calamansi (_C. microcarpa_ Bunge) is a citrus fruit widely grown in tropical to subtropical areas, including Okinawa, Taiwan, and Southeast Asian countries [7; 8]. In general, shiikuwasha and calamansi juices have high acidity and a similar appearance. A chemical marker, namely 3\({}^{\prime}\),5\({}^{\prime}\)-di-C-\(\beta\)-glucopyranosylphoretin, can be used to discriminate the two citrus cultivars; this substance has no direct contribution to the flavor properties of the two citrus cultivars [7].  
In terms of the flavor characteristics of citrus juices, consumers generally focus on three factors. First, color contributes greatly to citrus fruits as the primary information that conveys attractiveness [9]. Citrus fruit pigments, known as carotenoids, such as \(\beta\)-cryptoxanthin, \(\beta\)-carotene, and lycopene, are important metabolites that become vitamin A in the body and are necessary for humans [10]. Carotenoid levels are related to color indices such as the L*a*b* color spaces [11; 12]. Second, substances related to taste, such as sugars and organic acids, and the sugar-acid ratio are essential factors when considering the quality of citrus juices [13; 14; 15; 16]. In addition to known conventional analytical methods, proton nuclear magnetic resonance (\({}^{1}\)H-NMR) can be applied to analyze taste-related metabolites in a highly reproducible and convenient manner [17; 18]. Multivariate analysis of taste/nutrient metabolite data is an effective approach for discriminating citrus cultivars based on metabolite composition [18]. Third, VOCs, which greatly contribute to the aroma traits of citrus, are mainly composed of monoterene hydrocarbons. However, information on the differences in the VOC profiles of the two citrus cultivars is lacking [7; 8; 14; 16].  
This study aimed to clarify the flavor characteristics of shiikuwasha and calamansi by comparing their physicochemical traits and carotenoid, metabolite, and VOC contents. The three major cultivation lines of shiikuwasha (\"Izumi kugani\", \"Izumi kugani-like\", and \"Ogimi kugani\") were evaluated, as they are commonly consumed in Japan. In addition, to evaluate the impact of peeling on the VOC profile of fruit juice, the fruits were prepared, and their contents were analyzed as whole juice (with the peel) and flesh juice (without the peel); this approach contributes insights related to food processing.