esa-sceva/satcom-chunk-collection huggingface dataset:
# Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
## 2 Materials and Methods  
_Proposed Method_  
This paper builds deep learning-based NIDS to create a state-of-the-art adversarial robust and explainable deep learning-based NIDS. The focus was on white-box untargeted attacks: FGSM, PGD, and DeepFool attacks. Throughout the adversarial learning literature [14], it has been outlined that an efficient way to make deep learning models robust against adversarial attacks is to train them with a mixture of adversarial samples and clean training data. This process is known as adversarial training, as shown in Figure 5.  
The first stage of the proposed framework is to collect the network security dataset that contains different attacks. NSL-KDD dataset was chosen for the purposed of carrying out the experiments. After collecting the dataset, it is then preprocessed, where feature modification is performed and redundancy is lowered. At this stage, separation of train and test set is then performed to set aside the dataset for model evaluation.  
After preprocessing, baseline model training then starts. In this study, we built four baseline models, three classical machine learning multi-class classifiers, and a multi-layer perceptron mode, representing a deep learning model. After model training, model evaluation is performed using AUC.  
The third stage is the generation of adversarial samples. Again, the baseline multi-layer perception model generates adversarial examples using different types of adversarial attacks. This study generated three different adversarial samples from three different adversarial attacks: FGSM, DeepFool, and PGD. The adversarial samples include pairs of the adversarial test samples and adversarial train set, which were generated using benign test sets and train set, respectively. An adversarial train set will be used for adversarial training, while an adversarial test set will be used for a robust adversarial test. Following adversarial sample generation, the next stage will be to evaluate the adversarial robustness of baseline models using our proposed adversarial robust evaluation measure.  
After baseline model adversarial robust evaluation, the next stage is the adversarial training of the baseline multi-layer perceptron using the available adversarial attacks. We developed three distinct adversarial-trained models in this case: FGSM adversarial-trained model, DeepFool adversarial-trained model, and PGD adversarial-trained model.  
After adversarial training, we apply our proposed adversarial robust evaluation measure to check which of the adversarial-trained model is more robust. The final stage is a robust model explanation using the SHAP method.  
All our models were implemented using TensorFlow 2.8.0 and Keras. The experiments were performed on a Virtual machine with GPU acceleration with 32 GB memory and three Intel core processors at 3.00 GHz. For generating adversarial samples, we used the open-source IBM Robustness Toolbox (ART) framework [21].
## 4 Investigations and Discussion
### Characteristics of Effective Thermal Conductivity of SCW System with Single- and Two-Well Types
Figure 12 shows the characteristic of effective thermal conductivity under no bleeding and bleeding conditions for single- and two-well type SCW systems with respect to the influences of the key factors, including injection rate, inlet temperature, injection-production interval, and geological conditions, on the open-loop geothermal system (OLGS) performance [43]. From the results of Figure 12, the application of bleeding to the single-well SCW system showed that the effective thermal conductivity was 162% higher than that without bleeding. In addition, the two-well SCW system also showed a 400% improvement in performance when bleeding was performed. Based on the thermal performance results of the single-well and two-well SCW system, it may be advantageous to select and use an appropriate SCW. In particular, the two-well SCW system is expected to be applicable in a very efficient underground heat exchanger design if the influence of groundwater is used well. Characteristics of Effective Thermal Conductivity of SCW System Single/Two-Well SCW System with/without Bleeding Water  
Table 7 presents the results of effective thermal conductivity measurements of the closed borehole single-well SCW system and single- and two-well SCW system for each return pipe location. In these results, the position of the single-well SCW return pipe without bleeding increased by approximately 9% compared to the lower return pipe installation position. In the case of the single-well type SCW system, the effective thermal conductivity value obtained by evaluating thermal performance in bleeding and non-bleeding conditions was 1.6 times higher in the non-bleeding condition than in the 10% bleeding condition. However, as a result of evaluating the thermal performance of the geothermal  
Figure 12: Effective thermal conductivity characteristic under no bleeding and bleeding conditions for single- and two-well type SCW systems: (**a**) k-slope; (**b**) effective thermal conductivity. heat exchanger of the two-well SCW system, the effective thermal conductivity value was evaluated as approximately four times higher with 25% bleeding than without bleeding. From these results, it was proven that high thermal performance can be obtained by improving the groundwater flow of the groundwater suction at the well drainage well.
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 5 Conclusions  
In this paper, we proposed a name disambiguation scheme based on heterogeneous academic search sites. The proposed scheme integrated and collected research outcomes provided by heterogeneous academic search sites. Using the collected data, name disambiguation was performed using clustering schemes based on necessary attributes. Moreover, the proposed method was compared with and evaluated against traditional rule-based name disambiguation schemes and deep learning-based name disambiguation schemes. Considering the metadata provided by academic search sites, we proposed a multi-classifier capable of selecting a more accurate name disambiguation scheme. The proposed multi-classifier selects a more precise name disambiguation scheme. The performance evaluation of the proposed method showed an exceptionally high F1-measure value of 0.99, confirming its suitability as the most apt scheme for name disambiguation. In performance evaluation of the proposed multi-classifier, we showed that very high performance was obtained by using W2V. We verify the excellence of the proposed scheme through various performance evaluations. The proposed name disambiguation scheme considers various academic search sites. In addition, the proposed scheme proved that expandability through a multi-classifier is possible. In future, we plan to expand the proposed method to a multi-language-based name disambiguation scheme. In addition, we will conduct research on performance improvement through the combination of rule-based and GCN-based name disambiguation scheme.  
Conceptualization, D.C., J.J., S.S., H.L., J.L., K.B. and J.Y.; methodology, D.C., J.J., S.S., H.L., J.L., K.B. and J.Y.; validation, D.C., J.J., H.L., J.L. and K.B.; formal analysis, D.C., J.J., S.S., H.L., J.L. and K.B.; writing--original draft preparation, D.C., J.J., S.S. and K.B.; writing--review and editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A2B5B02002456, No. RS-2022-00166906), the Korea Association of University, Research Institute and Industry (AURI) grant funded by the Korean Government (Ministry of SMEs and Startups; MSS) (No. S3047889, HRD program for 2021), and by \"the Ministry of Science and ICT (MSIT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01462) supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP).  
Figure 15: Comparison of multi-classifier performance based on W2V.
## 3 System Model and Description
### Optimal Structure for Shortest Path Selection  
The property of shortest path selection is explained as: if \(P(i,j)=(V_{i}...V_{k}..V_{s}...V_{j})\) is the shortest path from node \(i\) to the node \(j\). Node \(k\) and \(s\) are the intermediate relay nodes then \(P(k,s)\) must be the shortest path from \(k\) to \(s\). Suppose, \(P(i,j)=(V_{i}...V_{k}..V_{s}...V_{j})\) is the shortest path from node \(i\) to node \(j\), then \(P(i,j)=(P(i,k)+P(k,s)+P(s,j))\). If \(P^{\prime}(i,k)\) is the shortest distance from node \(i\) to node \(k\), then there must be a shortest path \(P^{\prime}(i,k)\) from node \(i\) to node \(k\) and \(P(i,j)=(P^{\prime}(i,k)+P(k,s)+P(s,j))\leq P(i,j)\). Meanwhile, \(P(i,j)\) is the given shortest path from node \(i\) to node \(j\) in contradiction. The \(P(k,s)\) is the shortest distance from node \(k\) to node \(s\) and \(P(k,j)\) is the shortest distance from node \(k\) to \(j\).
## 2 Interconnection of the TFBN
### Higher Level Networks of the TFBN  
Connecting \(2^{2m}\) lower networks with a 2D-Torus network in \(2^{m}\) rows and \(2^{m}\) columns analogous to the BM, the higher levels of the TFBN are initiated. Thus, with \((m=2)\), 16 Level-1 networks can be connected to form Level-2, and 16 Level-2 networks can be connected to form Level-3, and so forth. Figure 2 depicts the lay out of the higher-level networks of TFBN. \(q\) is the inter-level connectivity. In each BM, \(4\times 2^{q}=2^{(q+2)}\) free wires and their corresponding ports are used for the higher-level connections. The \(2(2^{q})\) links are used for both vertical and horizontal connections, where \(q\in 0,1,\ldots,m\) is the inter-level connectivity. Figure 2 shows a \((4\times 4)\) BM with \(2^{(2+2)}=16\) free ports, with \(m=2\). If \(q=0\), there are four free ports for each higher level--two for horizontal and two for vertical interconnections. Bidirectional links are formed by tying incoming and outgoing links (both vertical and horizontal) to connect two adjoining BMs of the higher levels of TFBN.  
Figure 1: A \(4\times 4\) basic module of a TFBN as a flattened butterfly network.  
The value of \(m\), the inter-level connectivity (\(q\)), and the level of hierarchy (\(L\)), determine the size of the BM, which is denoted by \((m,L,q)\). For higher granularity, \(m=2\) has been taken. The highest hierarchical level that can be generated from a \((2^{m}\times 2^{m})\) BM is \(L_{max}=2^{(m-q)}+1\).  
For example, if \(m=2\) and \(q=0\), then \(L_{max}=2^{(2-0)}+1=5\). Thus, Level-5 is the highest level obtainable in a TFBN with \((4\times 4)\) BMs connected hierarchically. In each level, the number of nodes is \(N=2^{2 mL}\). So, for the 5-level TFBN considered here, the number of nodes is \(N=2^{2m(2^{(m-q)}+1)}=2^{20}=\) 1,048,576. Thus, more than a million nodes can be connected in a 5-level TFBN for an MPC system.
## 2 System Model  
### Network Model  
As shown in Figure 1, the network model in this paper consists of vehicles, roadside units (RSUs), edge servers, and blockchain network. Each RSU can act as a relay node to realize the information interaction between vehicles and edge servers within its communication coverage. The roadside has RSUs at equal distances, and an MEC server is deployed in each RSU, which are connected by fiber-optic wired links. The set of vehicles is defined as \(m=\{1,2,\cdots,m\}\). The set of RSUs is defined as \(n=\{1,2,\cdots,n\}\). Within the blockchain system, all RSUs constitute blockchain nodes, which can be divided into two categories. One category is ordinary nodes, which only undertake transmission and reception and do not participate in the consensus process; the other category is consensus nodes elected by virtue of voting, and such nodes are responsible for generating new blocks and implementing the consensus process. When the vehicle generates computationally intensive and delay-sensitive tasks to be processed, the system determines the offloading decision based on energy consumption, delay, and overhead. If the task is assigned to be processed locally, the vehicle will rely on its own computational resources to perform the computation, and after the end of the computation, the results will be transmitted to the RSU for on-chain storage and sharing; if the task is assigned to be processed by the MEC server, the computation results will be fed back to the RSU to realize on-chain storage and data sharing between the RSUs. Therefore, regardless of whether the task is offloaded to the local computing or edge server, the calculation results will not be tampered with, and safe and reliable data sharing between RSUs can be realized.
### Literature Review
The above eleven models include: (1) simple linear models, such as Multiple Linear Regression; (2) nonlinear models, such as Extreme Learning Machines and weighted k-Nearest Neighbors; and (3) ensemble methods, such as Random Forests and Extreme Gradient Boosting. To improve real-time control performance and reduce possible negative impacts of PV systems, [PERSON] et al. [37] proposed a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output with the application of Self-organizing Map (SOM), Learning Vector Quantization (LVQ) and Support Vector Regression (SVR). [PERSON] et al. [38] used auto-encoder to reduce the dimension of historical data, and employed LSTM to forecast solar power. In the field of solar forecasting, a few researchers have also paid attention to the prediction of solar irradiance due to its important influence on PV power output. For example, [PERSON] et al. [39] applied a simple and linear statistical forecasting technique named ARIMA to day ahead hourly forecast of solar irradiance for Abu Dhabi, UAE. In another relevant study, five novel semi-empiric models for hourly solar radiation forecasting are developed and then compared with the Angstrom-Prescott (A-P) type models [40]. Differently, a multi-level wavelet decomposition is applied by [PERSON] et al. [41] to preprocess the solar irradiance data in order to further improve the day-ahead solar irradiance forecasting accuracy. In [PERSON]'s another paper, a new day-ahead solar irradiance ensemble forecasting model was developed based on time-section fusion pattern classification and mutual iterative optimization [42]. With the emergence of deep learning (DL) models, [PERSON] et al. [43] turned to Long Short Term Memory (LSTM) to catch the dependence between consecutive hours of daily solar irradiance data. In general, the DL algorithm is more promising compared to the abovementioned traditional machine learning. Recently, DL approaches have been not only successfully applied in image processing [44], but also utilized to address the classification and regression issues of one-dimensional data [45]. In the DL system, there are various branches, including LSTM, Convolutional Neural Networks (CNN), and Recurrent Neural Network (RNN) and so on. In spite of the superior performance of DL algorithms, few studies have applied the DL methods in the day-ahead solar irradiance forecasting. Researchers need to validate whether the introduction of DL can improve the solar irradiance forecasting accuracy. Moreover, there are various versions of DL models just like those mentioned above. Different DL models have their own advantages and disadvantages. Therefore, in the practice of solar irradiance forecasting, three important issues should be taken into consideration, namely how to select the rational DL models, how to well combine them, and how to further improve the performance of the hybrid DL model.
# On Analyzing Beamforming Implementation in O-RAN 5G
## 6 Conclusions  
This paper has positioned the research to be performed for the implementation of beamforming in O-RAN while meeting the requirements of low latency. We started by presenting the O-RAN architecture and the configurations of the interfaces between O-RAN units and presented the Split Option 7-2x. We have analyzed beamforming methods in O-RAN and discussed the best method for ultra-reliable low-latency communication. The discussion concludes that the channel information method seems to be the best positioned for minimizing latency since it uses digital precoding implemented in the O-RU (O-RU Category B) and makes feasible the implementation of ZF-BF that may reduce decisively the interference in received signals, so that the O-RU may operate without interferences. Moreover, we introduced open issues to be studied when using the channel information method in order to optimize channel status information.  
Conceptualization, investigation, validation and writing: [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; [PERSON], [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was undertaken under the \"Context-Aware Adaptation Framework for eMBB services in 5G networks\" project supported by the National Science Centre, Poland, under grant Nr. 2018/30/E/ST7/00413.  
The authors declare no conflict of interest.
# Impact of Direction Parameter in Performance of Modified AODV in VANET
## VANET Component Model
### Two-Ray Interference Model  
The Two-Ray Ground Reflection Model is a radio propagation model used to predict the path losses between transmitting and receiving antennas of the nodes. The received signal has two components: the line-of-sight (LOS) component and the multipath component, which is formed by a single ground-reflected wave. This model considers the impact of both the direct path and the ground reflection during informationpropagation. The path loss is included to model the propagation of information in the vehicular network. The received power is  
\[P_{r}\propto\frac{1}{d^{4}} \tag{3}\]  
where \(d\) is the distance between the transmitting and receiving antennas.  
For any nonlinear (not smooth) unobstructed stretch of road, depending on the distance, the transmission faces constructive or destructive interference for its ground reflection. The two-ray ground model only considers that path loss increase for distances over approximately \(900\,\mathrm{m}\). Therefore, to capture ground reflection, VANET is comprised of a Two-Ray Interference model [29].
## 3 The Test Results  
### Carrying Capacity Analysis  
The measured values of the bearing capacity of the specimen take the maximum value collected by the force sensor. The measured values of 32 test pieces are summarized here, as shown in Table 4. It can be calculated from Table 4 that, on the premise that the length of the specimen, the thickness of the steel tube wall, the steel grade and the concrete grade are the same, the carrying capacities of CFST filled with three types of concrete are compared as follows: regarding 16 specimens with lengths of 300 mm and 400 mm, the load-carrying capacity of CFST with high titanium slag concrete exceeds that of CFST with half-high titanium slag concrete. The load-carrying capacity of CFST with half-high titanium slag concrete exceeds that of CFST with normal concrete, but with an insignificant difference of about 2%. Table 1 indicates that although three types of concrete have the same grade, the axial compressive strength is slightly different. The compressive strength of full-high titanium slag  
Figure 3: Loading and testing: (**a**) Test setup; (**b**) Schematic diagram of the layout of displacement transducer and strain gauge. concrete is slightly greater than that of half-high titanium slag concrete, and the compressive strength of half-high titanium slag concrete is slightly greater than that of normal concrete, which is consistent with the tendency of the bearing capacities of three kinds of CFST. For 16 specimens with lengths of 500 mm and 600 mm, the bearing capacity of full-high titanium slag CFST is 3% larger than that of half-high titanium slag CFST and 2% smaller than that of normal CFST. The bearing capacity of half-high titanium slag CFST is 4% smaller than that of normal CFST. The carrying capacities of CFST filled with three types of concrete do not follow the above tendency. The reason is that achieving the ideal axial compression state is difficult when installing specimens, often with slight eccentricity. This small eccentricity has no obvious impact on the bearing capacity of short CFST, but has a relatively large impact on that of tall CFSTs. In short, the compressive strengths of these three kinds of concrete with the same grade are very close, as are the bearing capacities. Therefore, it can be proved that the replacement of normal coarse or fine aggregate by high titanium slag aggregate has little effect on the bearing capacity of CFST.
###### Abstract  
Recent growth and diversification of sheep milk products means more sophisticated methods are required to ensure their flavour quality. The objective of this study was to compare four extraction techniques for the analysis of volatile compounds in sheep milk by gas chromatography-mass spectrometry (GC-MS). Solvent Assisted Flavour Evaporation (SAFE), Solid Phase Microextraction (SPME), Headspace Sportive Extraction (HSSE) and Stir Bar Sportive Extraction (SBSE) were evaluated for their sensitivity, selectivity, reproducibility, and overall efficiency. A total of 48 volatile compounds from nine compound classes were identified in the spray-dried sheep milk. Alcohols, aldehydes, alkanes, carboxylic acids, ketones, lactones, sulphur compounds, nitrogen compounds, and terpenes were all present, but the differences between the methods were most apparent for lactones. SBSE extracted eight lactones, SAFE extracted four lactones and HSSE and SPME only detected trace levels of two lactones. Six of the lactones--6-hexa-lactone, \(\delta\)-octalactone, \(\gamma\)-decalactone, \(\gamma\)-dodecalactone, \(\delta\)-tetradecalactone, and \(\delta\)-hexadeca-lactone--were identified for the first time in spray-dried sheep milk. The present work demonstrated that SBSE is an effective tool for the extraction and analysis of volatiles, especially lactones, in sheep milk and dairy products in general. A discussion of the benefits and limitations of each method is included.  
sheep milk; volatiles; flavour; lactone; extraction method Article
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 2 Methodology
### Detailed Assumptions of Simulation Study (Simulation Scenarios)  
The simulation study utilized models of real ships; their parameters can be found in any generally available database of merchant ships. Maneuvering variants for the constructed models of ships, corresponding to the actual navigational situations in the studied waters, were determined with the use of an expert survey method. The study consisted of assigning five different types of maneuvers to the given units, using the existing devices of the integrated bridge and the developed spatial decision support system with the implemented method of imaging in AV technology, which, according to the expert, were the most appropriate to be performed in the studied area, which was the port of Swinoujscie, located in the north-western part of Poland, in the Baltic Sea basin. The number of questionnaires was defined as \(n=20\). Each time the study was preceded by the expert acquainting with the developed method.  
The analysis of the results of the expert study made it possible to identify variants of maneuvers carried out under the scenarios in the simulation experiment in question. The scenarios were built, taking into account the maneuvers with the highest scores for a given unit. For the towed train, 2 variants with the highest scores were adopted: unberthing and turning. The unit-simulation maneuver configuration matrix is presented in Table 1.