esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 4 Experiments
### Performance Evaluation
#### 4.4.2 Oversampled Data
Meanwhile, when the data were left imbalanced, the algorithm achieved a recall of 77.404%. Two good models with high recall and low FPR were obtained using the LightGBM and XGBoost algorithms using SVMSMOTE oversampled security alert data. LightGBM yielded the best FPR of 0.100%. However, XGBoost outperformed it in terms of recall and BCR, achieving values of 99.519% and 99.673%, respectively. When imbalanced data were used, the XGBoost algorithm yielded an FPR of 0.054%. However, when a high oversampling rate of >40% was chosen to generate sufficient synthetic positive alert samples to combat data imbalance, the FPR degraded to 0.173%. Similar degradation in performance of the FPR was observed in the LR, \(k\)-NN, and DT algorithms. Table 5 presents the results of applying SMOTE, ADASYN, and SVMSMOTE oversampling techniques to the benchmark dataset. Similar to the previous case, all algorithms achieved a performance improvement in the recall metric with LightGBM yielding the highest recall of 97.188% when ADASYN was used for oversampling. However, the algorithm exhibited a high FPR of 0.441%. For a security alert data analysis system to be effective, it is essential to maintain a low FPR such that security operators are not inundated with non-critical security alerts.
# Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering
## 6 Conclusions and Future Outlook
For now the SERS community should aim to provide experimental details including sample preparation, NP synthesis/properties (e.g., plasmonics and morphology), how NPs and analytes are brought into contact, excitation wavelength used, signal acquisition parameters and data processing employed, as part of the minimum reporting standards. Lastly, theranostics is an emerging interesting area of clinical diagnostics where SERS will play a crucial role as revealed by recent reports [159]. Theranostics combines diagnosis of disease and therapeutic treatment simultaneously. Ag- and Au-NPs have high optical activity and large surface area for functionalisation, and so they are attractive for diagnosis of specific diseases, photothermal therapy and TDM. This means that NPs can provide large optical enhancement and rapid quantitative assessment of disease at trace amounts whilst delivering therapeutic drugs to intended targets. Due togood biocompatibility, AuNPs can migrate across cellular membranes into the cytoplasm to give a snapshot of intracellular chemical processes and metabolic flux distribution without causing significant damage. When coupled to automated microfluidics or LoC devices and deep machine learning, SERS would permit in/off/online platform to monitor therapeutic drug activity and patients' response to new drugs in real-time. However, disease diagnosis by SERS should be validated carefully and comprehensively, which may include confirmatory inputs from techniques presented in Table 1. This is aimed to avoid costly and risky unpredictable analytics like the blood testing system which led to the 'Theranos scandal', perhaps the biggest science saga of the 21 st century to date. It is clearly evident that theranostics-SERS interface along with MVA will potentially revolutionise patient care, AMR biochemistry and drug discovery by expanding on the diagnostics and therapeutics toolbox for clinicians in the foreseeable future. We are hopeful this review will contribute to the ongoing efforts to translate vibrational spectroscopy to the clinic, spearheaded by the international society for clinical spectroscopy (CLIRSPEC) [160]. **Author Contributions:** Conceptualisation, M.C. and R.G. ; Writing--Original Draft Preparation, M.C. ; Writing--Review & Editing, M.C., H.M., D.I.E. and R.G. ; Supervision, R.G.
# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 2 Related Works
### Generation of Aerial Image to Map
In contrast, this study reinforces boundary information using edge detection, maintaining distinct geospatial elements while minimizing structural complexity, thus proposing a more efficient map generation process. SMAPGAN [16] utilizes two GANs to generate styled map tiles from remote sensing images and, conversely, to generate remote sensing images from map tiles. A key feature is its use of a semi-supervised learning strategy that leverages both paired and unpaired data. To achieve this, it divides its training into an unsupervised pretraining stage and a supervised fine-tuning stage, where initial weights are learned from unpaired data, followed by detailed refinement using paired data. In particular, it introduces a topological consistency loss function to preserve the spatial relationships between objects during map generation. The method proposed in this paper shares SMAPGAN [16]'s objective of preserving accurate spatial relationships between objects during the map generation process but differs significantly in its approach. Whereas SMAPGAN [16] focuses on enhancing structural relationships through topological consistency loss, this study integrates the Canny edge detector into the loss function to emphasize the preservation of key boundary information. This approach allows the proposed method to more effectively retain the edges and spatial positions of geographical features, ensuring high fidelity in the representation of object boundaries and topographical elements.
## 2 Related Work
### A Brief on Multicast Protocol  
In MANET, multicast protocols play a crucial role in efficiently delivering data to multiple destination nodes. Multicast protocols involve two main activities: multicast group management and routing. Each node in MANET acts as both a router and a host, performing group management and routing. Every node receiving a multicast packet must forward the packet to two or more interfaces along the path to group members. The source node can send multicast packets to all groups even if it is not a member or does not know the destination nodes. Due to the dynamic nature of the network, information about multicast groups is frequently updated as nodes move.  
Routing protocols establish iterative and autonomous mechanisms to identify packet delivery paths. Path discovery methods, initialization, and maintenance approaches vary, and as well as the techniques used to create routing tables, they are classified as tree-based, mesh-based, or hybrid [7; 8].
## 3 Results and Discussion
### Dynamic Changes during Processing Analyzed via GC-IMS
#### 3.2.3 Multivariate Statistical Analysis  
In order to more effectively delineate and distinguish the variations among the black tea samples throughout all the processing stages, PLS-DA was implemented utilizing the volatile components identified through GC-IMS. A satisfactory result was obtained with the model parameters of R\({}^{2}\)Y = 0.996 and Q\({}^{2}\) = 0.992, respectively. Remarkably, the seven stages of black tea processing could be distinctly distinguished from each other, particularly between shaking and rolling, and between fermentation and first drying (see Figure 3B). These results suggested that the most striking changes in the volatile components occurred during the rolling and first-drying stages, compared with the other processing stages.  
Figure 3: Fingerprints of SFABT during the entire manufacturing process obtained from GC-IMS. (**A**) Fingerprints of all the samples. The suffix -M represents a monomer of volatile components, while the suffix -D represents a dimer. (**B**) Scores of PLS-DA with R\({}^{2}\)Y = 0.996 and Q\({}^{2}\) = 0.992. (**C**) Cross-validation through a 200-fold permutation examination, yielding an R\({}^{2}\) score of 0.169 and Q\({}^{2}\) at \(-\)0.688.  
An in-depth study of the model was conducted via a comprehensive permutation test encompassing 200 iterations, and the parameters (R\({}^{2}\) = 0.169 and Q\({}^{2}\) = \(-\)0.688) indicated a strong robustness of the model (Figure 3C). The above results indicated that the volatile metabolites changed greatly after rolling processing, which were in line with the results of PLS-DA on volatile fingerprints analyzed via the GC-E-Nose.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Determination of Target Variable and Threshold Value  
The target variable \"R_per\" in the online data set consisting of 129 lines and 40 attributes was obtained by evaluating the time spent answering each question by the researchers and experts. The online exam questions were read by the researchers, two linguists, and a total of 10 experts from different disciplines, and the arithmetic average of the time spent per question was obtained. The 13 th question of the Basic Information Technologies exam was given as an example within the scope of the exam.  
\"13- Which of the following is listed from largest to smallest in terms of speed?  
* RAM-HARDDISK-CACHE
* HARDDISK-CACHE-RAM
* RAM-CACHE-HARDDISK
* CACHE-RAM-HARDDISK
* HARDDISK-RAM-CACHE\"  
The expert who read question 13 the fastest by understanding it took 15 s, while the expert who read the slowest took 23 s. When the arithmetic average of all experts was taken into account, it was determined that question 13 could be read in an average of 18 s, but when the expert who read the fastest was taken into account, the value of 15 s was accepted. Considering the students' reading time for question 13, their prerequisite behaviors, academic achievements, and individual differences, the answering time for question 13 was accepted as 10 s as the critical value. Since students' answering the 13 th question in less than 10 s exceeded the critical value, it was considered either \"cheating\" behavior or \"suspicious\" behavior. The focus of the study is to determine threshold values and create scenarios based on this critical value. According to Scenario-1, the times answered below the critical values determined for each question were accepted as cheating behavior, and equal threshold times were given to each question. The threshold time was obtained by dividing 100 full points by the total number of questions. Thus, the numerical values obtained over 100 full points were used to create the target variable \"R_per\", and regression analysis was performed. According to Scenario-2, times below the critical value for each question are considered as \"cheating\" behavior. For example, if the critical value of question 13 is 10 s and the student answers in 9 s, it is considered as \"cheating\" behavior. Thus, binary classification data are obtained. For Scenario-3, the times below the critical value for each question were considered as \"suspicious\" behavior if they were approaching the critical value and as \"cheating\" behavior if they were moving away from the critical value. For example, if the critical value of question 13 was 10 s and the student answered it in 9 s, it was considered as \"suspicious\" behavior, or \"cheating\" behavior if it was answered in 5 s. Thus, triple classification data are obtained. Since the scenarios were obtained from the students' question answering times, the analysis was performed by removing the times from the data set.
## 2 Related Work
They aimed to control the reliability and delay values of the considered flows. The controller entity defines the adequate schedule and reserves the required resources for each flow. SDN-TSCH also adopts the concept of control and data traffic isolation and used dedicated timeslots to construct reliable paths from the nodes to the controller and vice versa. Each traffic flow is also accommodated using dedicated timeslots. The authors considered up to 15 nodes and compared their approach with Orchestra [29]. The simulation results showed that the proposed approach improved the delivery rate and delay in comparison with Orchestra, especially for the network size of 15 nodes. [PERSON] et al. [20] addressed the scalability issue in the industrial WSNs (IWSNs). They considered that the sink is the only node that connects the controller with the SD-IWSN. Thus, the sink suffers from congestion. This, in turn, limits the number of nodes. The proposed approach used the advantages of SDN to add a virtual sink, which allows for network extension. Normally, each node has a single radio interface. This means that the sink node (as well as the other nodes) can receive from only one transmitter node during a timeslot. The virtual sink could be considered as a set of nodes, each one with a radio interface, making the controller consider them as a single sink. In other words, this work considered sinks with several radio interfaces to extend the capabilities of the real sink. A sink node with three radio interfaces, for instance, is capable of receiving from three different nodes at the same timeslot. The results showed that virtual sinks allowed for network extension and helped to meet the application's requirements. [PERSON] et al. [16] aimed to investigate the effect of application's traffic isolation on network performance. The proposed application traffic isolation (ATI) approach also adopts the concept of control and data traffic isolation. The ATI approach uses the shared timeslotsand assigns a single timeslot per application traffic. The reference case for comparison was the control and data traffic isolation (CDTI) approach [9], and the results showed that the ATI approach increased the delivery rate and reduced the delay in the application layer. Table 1 represents a comparison among the related works. It confirms that the present work is the first to consider the application requirements and supports the network scalability using shared timeslots.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 2 Overview of RPL  
RPL [1] is a proactive, distance-vector routing protocol for low-power and lossy networks (LLNs) which are characterized by high loss rates, low data rates, and instability. In LLNs, routers typically operate with constraints of processing power, memory, and energy.  
RPL supports multipoint-to-point, point-to-multipoint, and point-to-point traffic flows which correspond to upward, downward, and any-to-any traffic patterns, respectively. Typical application scenarios such as monitoring in LLNs require multipoint-to-point traffic flows from the sensing devices to the central control point. Applications such as actuation and selective sensor queries generate point-to-multipoint traffic flows which need downward routes. Since RPL is based on LLNs comprising IPv6 addressable nodes, we can envision diverse point-to-point IoT applications where a device interacts with another device within an LLN.
# Design and Application of New Aeration Device Based on Recirculating Aquaculture System
###### Abstract  
This study optimized the design of an aeration device for pond engineered recirculating aquaculture systems (RASs) whose application is aimed at increasing dissolved oxygen (DO) levels in RAS aquaculture practice. DO is a key factor in aquaculture productivity, and oxygenators are the power devices used for regulating its levels in aquaculture ponds. In this study, grass carp (_Clenopharyngealod itellus_) aquaculture trials were conducted in a self-built RAS by using the new aeration device (NAD); the microporous and impeller aeration components were individually tested in terms of performance, and then combined for the orthogonal testing of their operating parameters in order to assess the NAD's oxygenation capacity. The test results show that the device effectively increased the dissolved oxygen levels in the RAS tank, enhanced the upper-lower water layer exchange and directional flow, and met the design and parameter selection requirements. Compared with the existing RAS oxygenation equipment, the NAD operated with the optimal parameters and increased the oxygen transfer rate in the pond water tank by 122%.  
aquaculture engineering; pond; aeration device; dissolved oxygen (DO); test +
Footnote †: journal: Applied Sciences
# Study on Characteristic Strength and Constitutive Model of Red Sandstone under Hydraulic Coupling
## 5 Damage Constitutive Model Considering Seepage Pressure  
### Effective Stress Principle  
The applicable constitutive model is very important for simulating the stress-strain relationship of materials [37]. The statistical damage constitutive model has the characteristics of clear parameter meaning and wider applicability. Therefore, this paper selects the statistical damage constitutive model to simulate the stress-strain curve of the rock. In this paper, the hydraulic coupling effect is considered, and the connection between stress and seepage pressure is established through the principle of effective stress [38; 39]:  
\[\sigma_{ij}^{{}^{\prime}}=\sigma_{ij}-\alpha P_{w}\delta_{ij} \tag{5}\]  
where \(\sigma_{ij}^{{}^{\prime}}\) is the effective stress tensor, \(\sigma_{ij}\) is the total stress tensor, \(\alpha\) is the effective stress coefficient, \(P_{w}\) is the seepage pressure, and \(\delta_{ij}\) is the second-order tensor.
# Parametric Analysis on Landing Gear Strut Friction of Light Aircraft for Touchdown Performance
## 2 Landing Gear Modeling and Validating
### Drop Test and Model Validation
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline Shock absorber & & & \\ \(\gamma\) & Gas polytropic coefficient & 1.3 & - \\ \(C_{d}\) & Orifice discharge coefficient & 0.8 & - \\ \(\rho\) & Fluid density & 860 & kg m\({}^{-3}\) \\ \(P_{0}\) & Initial gas pressure in shock strut & 0.85 & MPa \\ \(V_{0}\) & Initial gas volume & \(129.5\times 10^{-6}\) & m\({}^{3}\) \\ \(\mu_{ee}\) & Seal friction coefficient & 0.06 & - \\ Structure & & & \\ \(L_{b}\) & Distance between upper and lower & 0.106 & m \\ \(L_{d}\) & Distance between lower bearing and & 0.168 & m \\  & wheel axis & & \\  & Distance between the lower bearing and & & \\ \(L_{t}\) & intersection of the upper and lower & 0.088 & m \\  & torsion arm & & \\ \(L_{n}\) & Length of the lower torsion arm & 0.083 & m \\ \(r_{0}\) & Radius of wheel & 0.098 & m \\ \(r_{k}\) & Half-axle distance & 0.1 & m \\ \(I_{w}\) & Wheel moment of inertia about the & \(5.94\times 10^{-3}\) & kg m\({}^{2}\) \\  & rotational axis & & \\ \(d_{A}\) & Upper bearing width & 0.02 & m \\ \(d_{B}\) & Lower bearing width & 0.04 & m \\  & Outer cylinder equivalent area moment & & \\ \(I_{1}\) & of inertia about the strut axis & & \\  & Inner cylinder equivalent area moment of & & \\ \(I_{2}\) & inertia about the strut axis & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Landing gear parameters definition and value. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Info.**} & \multicolumn{3}{c}{**LC-1**} & \multicolumn{3}{c}{**LC-2**} \\ \cline{2-6}  & **Numerical** & **Test** & **Error (\%)** & **Numerical** & **Test** & **Error (\%)** \\ \hline Ground vertical force (kN) & 2.72 & 2.77 & \(-1.51\) & 4.96 & 4.97 & \(-0.52\) \\ Ground longitudinal force (kN) & 1.54 & 1.53 & 1.19 & 1.50 & 1.46 & 2.51 \\ Shock absorber stroke (mm) & 38.21 & 37.88 & 0.88 & 71.67 & 73.85 & \(-2.95\) \\ Sprung displacement (mm) & 51.91 & 49.63 & 4.58 & 92.93 & 92.52 & 0.44 \\ Unsprung displacement (mm) & 15.92 & 15.49 & 2.72 & 21.35 & 21.56 & \(-0.97\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of numerical results and test results.
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 4 Overview and Fundamental Results  
This section presents a brief overview of the localized solution along with some fundamental results that will be used later. Our localized algorithm can be implemented as a network maintenance service and can be periodically invoked to identify all the trigger nodes. As discussed earlier, the identification of trigger devices can provide an efficient defense against the reactive jamming threats by converting triggers to receivers only in the overlaying routing protocols, thus avoiding activating jammers. We will use testing to identify these trigger nodes by allowing them to send out a test message and listen if there is any noise. However, individual testing is too time-consuming, thus we often test a number of nodes, called group testing (GT). However, testing a group of nodes simultaneously encounters several difficulties. For example, if some _Noise_ is sensed after performing testing, we may not know which ones in the tested nodes triggered the jammers. Moreover, scheduling nodes in a testing group to perform the testing synchronously may result in a lot of communication overhead in the network if tested nodes are far from each other. In addition, if two group of nodes are testing at the same time and the jammer triggered by the first group can jam nodes in the second group, the testing result may be inferred incorrectly. Hence, an efficient grouping and scheduling is essential to reduce the overall latency.  
We utilize two principles to efficiently reduce the number of testing rounds i.e., overall latency:  
1. If two nodes \(u\) and \(v\) are at the distance at least \(R+r\) they cannot trigger the same jammer. This enable us to test \(u,v\) in a same round without having the outcome of testing \(u\) and that of  
Figure 1: Reactive Jamming Attack and Mitigation by Identification of the Triggers.  
interfered each other. In general, we can perform testing in parallel for two sets of nodes \(U\) and \(V\) that are \(R+r\) far away from each other.
2. If \(u,v\) and \(w\) are identified triggers, then all nodes inside the triangle whose vertices are \(u,v,w\) are also triggers. Furthermore, if \(T=\{t_{1},\ldots,t_{k}\}\) is a set of triggers, then all the nodes inside the convex hull of \(T\) are also triggers. This holds as long as \(R>2r\).