esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 3 Text Similarity Methods
### Results  
The Split method was tested on a large number of input datasets from real student exams in the Institute of Applied Computer Science in Lodz University of Technology for a period of 5 years selected from 2015 to 2022, excluding the COVID-19 period.  
The number of student answers from these exams was 3054, gathered for 25 different questions. The dataset for each question consisted of more than 125 student answers with four or more teacher answers. All the students' answers were manually evaluated and marked by the teacher (Expert). Of the 3054 student answers, there were 1288 correct answers and 1766 incorrect answers.  
The new concept of the Split algorithm with the length check of both teacher and student answers and the calculation of the similarity measures was examined. A comparison of the previous and newly developed Split algorithms is shown as confusion matrices in Figure 1. A confusion matrix summarises the performance of a classification model by detailing the counts of True Positives (TP), False Negatives (FN), False Positives (FP), and True Negatives (TN) for each algorithm.  
Figure 1: Confusion matrices: comparison for Split methods, where TP—True Positives, FN—False Negatives, FP—False Positives, and TN—True Negatives.  
The new modifications of the Split algorithm improved the recognition of the correct answer (True Positives) from 58.16% to 74.3% and perfectly recognised the whole 1766 incorrect answers (True Negatives) in 100%.
## 2 Theoretical Background
### Business Reputation Management and Cybersecurity  
Business reputation is a key intangible asset that directly influences the success and sustainability of organisations. According to [16], reputation is defined as the collective perception of a company by its stakeholders, which is based on the evaluation of its past performance, its behaviour in critical situations, and future expectations. Business reputation affects various aspects, from customer loyalty to access to capital and talent attraction [17].  
In business environments, inadequate security practices can lead to data breaches that can have significant consequences for both the company and the entity whose data is affected. Cybercriminals take advantage of tactics such as ransomware and phishing through attack techniques such as zero-day, man in the middle, and identity theft to hijack company data and demand money through extortion for the rescue. The foregoing has led to government entities of governments such as that of the United States of America issuing a guide in 2023 to stop the ransomware-type attack through the National Security Agency [18]. Different academic studies have addressed the consequences of cybersecurity incidents in organisations [19; 20; 21; 22].  
Cybersecurity, in this context, is defined as the set of activities, processes, skills, and capabilities aimed at protecting information and communication systems, as well as the information contained therein, against damage, unauthorised uses, modifications, or exploitations [23]. A cybersecurity breach is an event that compromises the confidentiality, integrity, or availability of an information system or its security policies or procedures [24] and, depending on its magnitude, can have economic impacts, regulatory notification, repair and compensation to the client, litigation, the loss of market value or investments, regulatory fines, extortion payments, and the loss of business [25].  
One of the good practices used by incident management teams is risk management, which consists of identifying and controlling vulnerabilities in information systems (IS), including risk identification, risk assessment, and risk control [26]. A risk assessment evaluates what could go wrong, the likelihood of such an incident occurring, and the damage if the incident occurs [27]). Directly addressing the security of companies to identify threats and vulnerabilities in order to prevent future events through risk assessments is an important first step that should precede financial allocations [28; 29]  
Cybersecurity from a data breach perspective, considering risk management, has focused on models that emphasise mitigation and recovery [30], as well as the importance of implementing business continuity and cyber incident response plans [31]. Ref. [32] provide a clear example of the above statement by putting into practice the models of protection motivation theory (PMT) by technical teams to manage data breaches and the organisation's response strategies (ORS) to address security breaches through good corporate reputation practices.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 4 Performance Analysis and Simulation Results
### The Localization Performance  
In this section, we evaluate the localization performance of DRL algorithm from four aspects: the effect of range error; the relationship between nodes' number and localization error; the influence of reference nodes' topology; and the communication cost.  
Nodes in UANs are deployed in a 1000 \(\times\) 1000 2-dimensional area randomly. Set \(T_{s}\) be 1 ms and 11 ms for LM and HM modes receptively. We used 1000 Monte Carlo experiments with different network topologies in each test to average the effectiveness of topology variation.
## 2 Frequencies and Mode Shapes of the Double-Beam System
### Mathematical Formulation
#### 2.1.5 Fixed-Free Boundary Conditions  
The boundary stiffness is  
\[\left\{\begin{array}{c}k_{\
u,1}=k_{\
u,2}=\infty\\ k_{r,1}=k_{r,2}=0\end{array}\right. \tag{41}\]  
The boundary conditions are  
\[\left\{\begin{array}{c}\varphi_{1}(0)=0\\ \varphi_{1}^{\prime}(0)=0\\ \varphi_{1}^{\prime}(l)=0\\ \varphi_{1}^{\prime\prime}(l)=0\end{array}\right.^{\prime},\left\{\begin{array} []{c}\varphi_{2}(0)=0\\ \varphi_{2}^{\prime}(0)=0\\ \varphi_{2}^{\prime\prime}(l)=0\\ \varphi_{2}^{\prime\prime}(l)=0\end{array}\right. \tag{42}\]  
Equation (18) becomes  
\[\mathbf{\Phi}=\left(\begin{array}{cccccccc}1&1&1&1&0&0&0&0\\ 0&0&0&0&1&1&1&1\\ 1&-1&i&-i&0&0&0&0\\ 0&0&0&0&1&-1&i&-i\\ e^{\
u_{1}l}&e^{-\
u_{1}l}&e^{\
u_{1}l}&e^{-\
u_{1}l}&0&0&0\\ 0&0&0&e^{\
u_{3}l}&e^{-\
u_{3}l}&e^{\
u_{3}l}&e^{-\
u_{3}l}\\ e^{\
u_{1}l}&-e^{-\
u_{1}l}&ie^{\
u_{1}l}&-ie^{-\
u_{1}l}&0&0&0\\ 0&0&0&e^{\
u_{3}l}&-e^{-\
u_{3}l}&ie^{\
u_{3}l}&-ie^{-\
u_{3}l}\end{array}\right) \tag{43}\]  
Det \([\mathbf{\Phi}]=0\) results in  
\[\left\{\begin{array}{c}1+\cos(r_{1}l)\cosh(r_{1}l)=0\\ r_{5}=0\end{array}\right. \tag{44}\]  
or  
\[\left\{\begin{array}{c}r_{1}=0\\ 1+\cos(r_{5}l)\cosh(r_{5}l)=0\end{array}\right. \tag{45}\]  
Equations (44) and (45) are the same as that for a single beam with fixed-free boundary conditions. The solutions of the two equations are the same. If Equation (44) is chosen, the solutions of \(r_{1}l\) in the first six modes are as in Table 3.  
Solving Equation (16) results in the mode shapes of the secondary beam  
\[\varphi_{2,n}(x)=C\Bigg{[}\Big{(}\cos r_{1}^{(n)}x-\cosh r_{1}^{(n)}x\Big{)}- \frac{\cos r_{1}^{(n)}l+\cosh r_{1}^{(n)}l}{\sin r_{1}^{(n)}l+\sinh r_{1}^{(n)} l}\Big{(}\sin r_{1}^{(n)}x-\sinh r_{1}^{(n)}x\Big{)}\Bigg{]} \tag{46}\]  
It can be found that Equation (46) is similar to Equation (21). The mode shapes of the primary beam can be worked out by Equation (22).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Mode** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \(r_{1}l\) & 1.8751 & 4.6941 & 7.8548 & 10.9955 & 14.1372 & 17.2788 \\ \hline \end{tabular}
\end{table}
Table 3: First six solutions of \(r_{1}l\) for fixed-free double-beam systems.
# Resource Capacity Requirement for Multi-Terminal Cooperation in Container Ports
## 3 Simulation Experiment
### Simulation Results with Low Transferring
This widened gap indicates increased variation of storage capacity requirement with the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(\overline{X}\)**Charts**} & \multicolumn{3}{c}{\(R\)**Charts**} \\ \hline  & **UCL** & **CL** & **LCL** & **UCL** & **CL** & **LCL** \\ \hline QC intensity & 9.71 & 7.92 & 6.13 & 5.65 & 3.10 & 0 \\ Storage space & 127,060.92 & 107,120.78 & 87,180.64 & 73,056.25 & 34,558.30 & 0 \\ Requirement & 2.52 & 1.69 & 0.86 & 3.03 & 1.43 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Control limits of \(\overline{X}-R\) charts for QC intensity, storage space requirement, and gate congestion corresponding to Figures 8–10. Figure 10: \(\overline{X}-R\) charts over time-shifts (x-axis) for gate congestion (truck queue) (y-axis) when trans-shipment and nontrans-shipment (inbound and outbound) TEUs are 80% and 20%, respectively, and 75% of arrived TEUs are transferred to other terminals. Figure 9: \(\overline{X}-R\) charts over time-shifts (x-axis) for storage space requirement (y-axis) when trans-shipment and nontrans-shipment (inbound and outbound) TEUs are 80% and 20%, respectively, and 75% of arrived TEUs are transferred to other terminals. mean difference between maximum and minimum storage requirements estimated at 34,558.30 TEUs. Meanwhile, the estimated truck queue length is 1.69 trucks with 2.52 in UCL for transferring TEUs (Figure 10). The gate processing requirement is increased by 8.3% in terms of the mean queue length as the transferring TEUs increase.
# Design of a Modified Compact Coupler with Unwanted Harmonics Suppression for L-Band Applications
## 2 Design Procedure  
This section explains how T-shaped stubs, radial subs, cross-shaped resonators and open-ended stubs can be used to create the proposed branch line coupler (BLC). At the first step, a conventional BLC is formed at the operating frequency of 1 GHz. The structure of a conventional coupler is shown in Figure 1a, and its frequency response is depicted in Figure 1b.  
Design procedure of the proposed BLC is illustrated in Figure 2. In step 1, the circuit of the initial resonator is presented. In step 2, the presented circuit is analyzed, and the related equations are extracted. Then, the transmission zeros of the resonator can be tuned to form the desired frequency response. The horizontal and vertical resonators, realized from the initial resonator, are obtained as illustrated in steps 3.1 and 3.2. Finally, the horizontal and vertical branches of the typical couplers are replaced with the proposed horizontal and vertical resonators, respectively.
## 2 Material and Methods
### SVR-GO-BFS\({}_{n}\) Model Development  
A hybrid model expressed by SVR-GO-BFS\({}_{n}\) was developed to improve the performance of the AOp forecast. The effective algorithms are combined into SVR-GO-BFS\({}_{n}\): SVR, GO for optimizing parameters, and BFS for input choice. The parameterization of the SVR-GO-BFS\({}_{n}\) model was based on \"n\", which showed the abundance of inputs engaging in developing the model in line with the \"n\" greatest importance values assigned by BFS. The most important inputs were selected using BFS. Next, GO was utilized to train SVR and optimize a pair of its hyperparameters (\(\gamma\) and C). Finally, the developed model was used to predict the AOp values. Figure 3 shows a fundamental flowchart of the model developed, which includes the main four steps.  
Figure 3: Flowchart of this study.  
Figure 2: The execution steps of BFS.  
Step one involved the preparation of data. The dataset included nine candidate inputs. In this step, the data were also normalized as previously described. The second step involved the selection of inputs using the BFS technique. This method contributed to a fair and logical determination and ranking of significant and insignificant inputs from the AOp dataset. In line with the significant input ranking, various input mixtures were built and expressed as n = 1, n = 2,..., n = all. If the \"n\" is one, it means that the input collection simply includes one input (the most significant input). If the \"n\" is two, it refers to an input set that involves the two most significant ones. If \"n\" equals \"all\", it means that an input assortment involves all significant ones. Obtaining the minimum optimal input's collection was the main objective of this method.  
Step three engaged in the optimization of SVR utilizing GO. A pair of SVR's parameters, including C and \(\gamma\), were optimized by applying GO. Ultimately, various regression models (SVR-GO-BFSn) were developed based on various input mixtures.  
Step four involved performing predictions using SVR-GO-BFSn. Utilizing the testing set, the corresponding built predictive models were assessed, and the forecast outcomes were reported. The most suitable model was picked following its precision and error performance. Therefore, the most suitable input set was regarded as the optimal collection of inputs and its C and \(\gamma\) were the optimal SVR values.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 4 Methodology
### Network Architecture of SSI-PQM
PQC-TLS is applied once to share the pre-master secret between the CN and the IoT device. This pre-master secret is then relayed to the 802.1X authentication server. Subsequently, the pre-master secret is transmitted back to the CN, where it is reused for generating a Pre-Shared Key (PSK) for encrypted communication using EAP-PSK. Figure 5: SSI-PQM platform process: The process from IoT device authentication by AS-1 \(\rightarrow\) L2 TP tunneling over PQC-TLS \(\rightarrow\) 802.1X authentication over PQC-TLS \(\rightarrow\) MACsec Key Agreement. The process ultimately leads to encrypted communication between the IoT device and the IoT server through the SSI platform.
# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 4 Results and Analysis
### Experimental Results and Evaluation
#### 4.3.2 Compared with Other Feature Selection Algorithms  
Based on the above experimental results, we can find that when \(K\) equals 1, the NC-AD and the MCI-AD groups' data obtain the optimal feature subset. While \(K\) equals 2, the NC-MCI group data obtain the optimal feature subset. To prove the effectiveness of the RF-AUC-Cor algorithm proposed in this paper, we evaluated with two other strategies. One is the classification schema without feature selection, and the other is the classification schema using the PCA (principal components analysis) dimensionality reduction technology. PCA is one of the most widely used dimension reduction algorithms, and its performance has been proven in many of the literatures. The experimental results are the average classification results of 10-fold cross-validation, which are presented in Figures 8 and 9.  
From Figures 8 and 9, it can be found that the proposed method achieved a superior AUC and accuracy than the PCA method, and the superiority is significantly relevant in the NC-AD group data, which proves that the random forest base RF-AUC-Cor feature selection algorithm is effective in high dimensional imbalanced data scenarios. Consequently, the algorithm proposed in this paper not only improves the recognition rate of the minority class however additionally guarantees a higher accuracy rate.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Advances on the MAC Layer
#### 4.2.1 Multi-User RTS/CTS Handshake  
Wi-Fi 6 extends the legacy RTS/CTS handshake to a multi-user one. Indeed, OFDMA implies that several transmitters/receivers may be active simultaneously using different RUs. Thus, the RTS/CTS has to inform all the neighbors about the RUs and TXOP selected for the next ongoing transmissions [33]. The mechanism also has to accommodate multiple receivers and transmitters. For both RTS and CTS, the lowest modulation is used so that all stations can decode them.  
To accommodate multi-rate environments, Wi-Fi 6 uses a duration-based threshold to trigger the RTS/CTS handshake. Indeed, transmission time is a better resource measure than packet size.The access point tunes the RTS/CTS threshold effectively to avoid interference and maximize efficiency [5; 11]. The access point is always awake to have a complete view of medium activity, whereas some power-saving stations may provisionally turn off their radio chipsets.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## Appendix A  
**Proof of Lemma 1.** We consider a coordinate system that is centered at the eavesdropper as shown in Figure 1. Since jammers are placed in a grid, each friendly jammer is \(2d\) away from its nearest neighbor in the same axis. The transmitting power of each jammer is assumed to be \(P_{l}\), which is same as the transmitting power of legitimate transmitters. From the channel model defined in Section 3.2, the radio signal received at an eavesdropper experiences both Rayleigh fading and the path loss. We consider the path loss effect first and then extend our analysis with consideration of Rayleigh fading effect.  
We first calculate the cumulative interference emitted from jammers at the 1 st layer, which is shown as follows,  
\[I_{j}(1)=4P_{l}\Big{(}\sqrt{2}d\Big{)}^{-\alpha}.\]  
Similarly, we have the interference from jammers at the 2 nd layer as follows,  
\[I_{j}(2)=4P_{l}\left[2\Big{(}\sqrt{10}d\Big{)}^{-\alpha}+\Big{(}3\sqrt{2}d \Big{)}^{-\alpha}\right].\]  
The interference from jammers at the 3 rd layer is given by  
\[I_{j}(2)=4P_{l}\Big{\{}2\cdot\Big{[}\Big{(}\sqrt{10}d\Big{)}^{-\alpha}+\Big{(} 3\sqrt{2}d\Big{)}^{-\alpha}\Big{]}+\Big{(}5\sqrt{2}d\Big{)}^{-\alpha}\Big{\}}.\]Following the similar analysis, we have the interference from jammers at the \((n-1)\)-th layer as follows,  
\[\begin{array}{ll}I_{j}(n-1)&=4P_{J}\Bigg{\{}2\cdot\left[\left(d\cdot\sqrt{1+ \left(2n-3\right)^{2}}\right)^{-\alpha}\right.\\ &+\left(d\cdot\sqrt{9+\left(2n-3\right)^{2}}\right)^{-\alpha}+\cdots\\ &+\left(d\cdot\sqrt{\left(2n-5\right)^{2}+\left(2n-3\right)^{2}}\right)^{- \alpha}\right]\\ &+\left(d\cdot\sqrt{2\left(2n-3\right)^{2}}\right)^{-\alpha}\Bigg{\}}.\end{array} \tag{10}\]  
Then, the interference from the \(n\)-th layer is given by  
\[\begin{array}{ll}I_{j}(n)&=4P_{J}\Bigg{\{}2\cdot\left[\left(d\cdot\sqrt{1^{2 }+\left(2n-1\right)^{2}}\right)^{-\alpha}\right.\\ &+\left(d\cdot\sqrt{9+\left(2n-1\right)^{2}}\right)^{-\alpha}+\cdots\\ &+\left(d\cdot\sqrt{\left(2n-3\right)^{2}+\left(2n-1\right)^{2}}\right)^{- \alpha}\Bigg{]}\\ &+\left(d\cdot\sqrt{2\left(2n-1\right)^{2}}\right)^{-\alpha}\Bigg{\}}.\end{array} \tag{11}\]  
Summarizing them all, we then have  
\[I_{j}(n)=4P_{J}\sum_{k=1}^{n}\Bigg{\{}2\bigg{(}d\cdot\sqrt{\left(2k-3\right)^{2 }+\left(2k-1\right)^{2}}\bigg{)}^{-\alpha}+\bigg{(}d\cdot\sqrt{2\left(2k-1 \right)^{2}}\bigg{)}^{-\alpha}\Bigg{\}}. \tag{12}\]  
We next have the cumulative interference from all the jammers as follows,  
\[I_{j}=\sum_{m=1}^{n}I_{j}(m).\]  
Considering the Rayleigh fading effect, we finally prove the expectation the cumulative interference from all the jammers as given in Lemma 1.
## 1 Introduction
After investigation of various parameters relating to the quality of extra virgin olive oils (acidity, peroxide index, and spectrophotometric data), the organoleptic properties (sensory panelists examination), and the nutritional properties (phenols, o-diphenyl, secioriidoids, tocopherols, volatile compounds, antioxidant capacity), they stated that there were no differences in nutrient content and sensory properties between organic and conventional foods or they were specific over time [6]. Other observations were made by [PERSON] et al. in 2015 [7] that suggested that fatty acid composition, phenol, tocopherol content, and oxidative stability were affected by cultivation practices. The phenol, total tocopherol, \(\alpha\)-tocopherol, \(\beta\)-tocopherol, and linolenic acid contents were significantly higher in organic products. On the other hand, the authors declared that \(\delta\)-tocopherol content, oleic acid, and oxidative stability were higher in the conventional oils than in the organic oil. Moreover, organic agronomical practices did not affect the acidity, peroxide index, or spectrophotometric constants of the extra virgin olive oils [7]. Other studies by [PERSON] et al. in 2014 [8] reported that the content of tyrosol and hydroxytyrosol was higher in the conventionally -farmed olive oils, except for the organic olive oils produced from olives in the last two stages of maturity [8]. Based on the data presented above, whether the cultivation method significantly influences the quality of extra virgin olive oil remains uncertain. This study aimed to analyze the composition of selected extra virgin olive oils from organic and conventional farming systems available on the Warsaw market (Poland) and determine whether oils from organic production exhibit superior quality compared to those from conventional systems. Furthermore, the impact of different cultivation practices--whether organic or conventional--on oil quality remains a subject of debate, with limited research available. Therefore, the influence of agronomic practices on oil quality needs to be clarified.
# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 4 Results
### Performance Evaluation  
To elaborate on the accuracy of our simulation model, we selected a \"hygienic door opener\" object, on which we performed pick-point analyses for the selected robotic gripper. We conducted 50 pick-point evaluations for the chosen object with our simulation model by varying the object's translation and rotation. For each pick-point, we elaborated on grasp quality, a metric derived from three pointers: (a) normalized grasp duration (\(t_{\text{grasp}}\)), (b) rotation around the main (\(Y\)) axis \(\varphi_{max}\), and (c) the time of absence of contact force between the object and the surface during a successful grasp \(t_{\text{grasp}-NC}\). Normalized grasp duration (\(t_{\text{grasp}}\)) is a metric derived from the time contact force between when the gripper fingers and when the object reaches the desired value and then drops back to zero. The rotation (between 0 and 180\({}^{\circ}\)) around the main axis (\(Y\)) indicates that the set and the actual pick-point are not equal, meaning that it is best to avoid such pick-points since we cannot be sure if the pick-point will be reached.  
The total grasp quality score \(S_{\text{grasp}}\) for the selected pick-point for a single object can be expressed as:  
\[S_{\text{grasp}}=0.33\cdot t_{\text{grasp}}+0.33\cdot\left(\ \frac{180^{\circ}-\ \varphi_{max}}{180}\ \right)+0.33\cdot t_{\text{grasp}-NC}\, \tag{2}\]  
where \(S_{\text{grasp}}\) is the grasp quality (score) for the selected pick-point, \(t_{\text{grasp}}\) is normalized grasp duration, \(\varphi_{max}\) is rotation around the main (\(Y\)) axis, and \(t_{\text{grasp}-NC}\) is the time of absence of contact force between the object and the surface during successful grasp.  
After the simulations were completed, we selected ten pick-points which had the highest grasp quality (highest score) and ten with the lowest grasp quality (lowest score). To validate the simulation model, we performed the picking of the object in those pick-points and compared the simulation results with a physical robotic cell workstation. Initial verification of the simulations was carried out by comparing the visual response of the actual bin-picking robotic cell workstation with the simulation model. Still, additional measurement equipment will be needed for accurate verification.  
The object had to be placed in the same exact location as in the simulation model, which was achieved with a preprinted template. The template was exported from the GUI and placed directly underneath the robotic cell workstation's robotic gripper. This ensured that the pick-points were set identically in the simulation and physical setup, preventing false input data.  
Figure 12: The time needed for a single simulation compared to the total number in a batch. Source: own.