esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, TMCs with either Fe, Cu, or Al metal centers were prepared and characterized using complementary methods. The IR spectra confirm the role of coordination of Al and Fe onto the biopolymer framework in the form of Al-O and FeOOH species. In contrast, Cu was complexed as Cu\({}_{2}\)(OH)\({}_{3}\)NO\({}_{3}\). Among the three types of metal centers, Fe coordinates with -NH\({}_{2}\) groups of the biopolymer framework, whereas Al and Cu systems do not show such coordination. Examination of the adsorption isotherms for these materials with phosphate and arsenate revealed more apparent and significant differences between the materials. The adsorption capacity of Al-TMC with P\({}_{1}\) (93 mg/g) and As\({}_{i}\) (80 mg/g) exceeded that for both oxyanions with Fe-TMC (P\({}_{1}\), 77 mg/g and As\({}_{i}\), 66 mg/g) and Cu-TMC (P\({}_{1}\), 17 mg/g and As\({}_{i}\), 31 mg/g). The predominant adsorption mechanism was ligand exchange (inner sphere complexation) of both oxyanions toward the metal centers, with Cu-TMC observing ligand exchange for arsenate adsorption. The selectivity studies further showed that Cu-TMC and Fe-TMC both are significantly more anion-selective toward arsenate (\(\alpha_{l/c}\) ca. 6.1 and 8.3 respectively; \(\beta_{t}\) ca. 4.4 and 5.0, respectively) versus Al-TMC (\(\alpha_{l/c}\) ca. 1.5 and \(\beta_{t}\) ca. 1.2) over orthophosphate. The results of this research are anticipated to contribute to the further development of sustainable biocomposite adsorbents with enhanced arsenate selectivity, even in the presence of the strongly competitive P\({}_{1}\) oxyanion species. Thus, the ability to distinguish arsenate from phosphate will serve to address global water security challenges related to arsenic contamination in aquatic environments that co-exist with orthophosphate.
# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 2 Materials and Methods
### Model Testing  
To effectively and quantitatively assess the performance of each binary classification model, we utilized the following metrics on the test set: accuracy, precision, sensitivity, specificity, and \(F1\) score. Accuracy is defined as the ratio of the number of samples correctlypredicted by the model to the total number of samples. Precision denotes the fraction of positively predicted samples that are indeed positive. Sensitivity is the proportion of actual positive samples that are correctly identified by the model. Specificity refers to the proportion of actual negative samples that the model accurately classifies as negative. The \(F1\) score, the harmonic mean of precision and sensitivity, considers both precision and recall. These metrics are calculated according to the following \"Equations (1)-(5)\":  
\[Accuracy=(TP+TN)/(TP+TN+FP+FN) \tag{1}\]  
\[Precision=TP/(TP+FP) \tag{2}\]  
\[Sensitivity=TP/(TP+FN) \tag{3}\]  
\[Specificity=TN/(TN+FP) \tag{4}\]  
\[F1\ score=2\times Precision\times Sensitivity/(Precision+Sensitivity) \tag{5}\]  
\[FPR=FP/(FP+TN) \tag{6}\]  
where _TP_ and _TN_ represent the counts of positive and negative samples that the model correctly identified, respectively. Conversely, _FP_ and _FN_ denote the counts of positive and negative samples that the model incorrectly identified, respectively.  
We constructed the receiver operating characteristic (ROC) curve to assess the overall performance of model. The ROC curve is derived from a series of varying classification thresholds for the model. At each threshold, we calculate the true positive rate (TPR) and false positive rate (_FPR_) of the model, which serve as the coordinates (_FPR_, _TPR_) for the curve [38]. The TPR, synonymous with sensitivity, and the _FPR_, defined by \"Equation (6)\", represent the proportion of negative samples incorrectly classified as positive by the model. The metrics in \"Equations (1)-(6)\" range from 0 to 1, with values closer to 1 indicating superior model performance. Notably, an AUC of 0.5 suggests that the classification capability of model is no better than random chance.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 3 Methodology
### Phase 3: Deep Belief Network-Based Ransomware Detection Model  
The feature subset extracted by IMIS in Phase 2 serves as the input for training a DBN designed for ransomware detection. A DBN comprises multiple layers of stochastic, latent variables, typically binary, organized into a directed belief network with the exception of the uppermost two layers, which constitute an undirected graphical model. This structural architecture empowers the DBN to efficiently model intricate data distributions and learn deep, hierarchical representations. DBN training is bifurcated into two primary stages: unsupervised pre-training, employing Restricted Boltzmann Machines (RBMs) to initialize network weights, and supervised fine-tuning, which refines these weights for classification. This framework mitigates the challenges inherent in directly training deep architectures via supervised learning, effectively capturing underlying data distributions and initializing the network in a parameter space proximal to the optimal solution.  
In the context of ransomware detection, the DBN functions as a classifier, discerning malicious software based on the IMIS-selected feature subset. The DBN is trained on a comprehensive dataset, encompassing known ransomware samples and benign applications generated in Phase 1. Consequently, the model learns to differentiate between these categories by recognizing the deep, hierarchical patterns that distinguish ransomware activities from legitimate software operations. Upon completion of training, the DBN can classify novel, unseen software samples with a high degree of accuracy, effectively distinguishing between malicious and benign entities. The full Python implementationsof the BBM-GAN data augmentation, IMIS feature selection, and UA-DES early stopping strategy can be found in Appendix B.
# A Novel Virtual Sensor for Estimating Robot Joint Total Friction Based on Total Momentum
## 3 Simulation and Analysis
### Velocity Step Experiment
#### 4.1.2 Motor High-Velocity Experiment  
The desired motor high-velocity step sent to the controller was 691 rad/s. As shown in Figure 6b and Table 4, the motor exhibits a better velocity step response performance when \(K_{\mu}\) is 50 and the velocity is high, similar to the results at low velocities. Additionally, the velocity step response performance is improved by 5.9%. Figure 9 shows that the actual motor torque exceeds the safety limit \(\pm\) 0.1 N\(\cdot\)m when \(K_{\mu}\) is 100 because the large \(K_{\mu}\) generates a large observed output, as shown in Figure 10. In addition, the actual motor torque includes more noise at high velocities compared to that at low velocities. Therefore, \(K_{\mu}\) equal to 50 yields a better gain coefficient than 10 or 100, and it is reasonable to conclude that the observed friction torque is more accurate when \(K_{\mu}\) is 50 than when \(K_{\mu}\) is 10 or 100. Therefore, 50 is selected as the final gain coefficient for both low-velocity and high-velocity motion in the experiments without payload.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{K_{\mu}}\) & **Low-Velocity Step Response Time [s]** & **High-Velocity Step Response Time [s]** \\ \hline No compensation & 0.051 & 0.051 \\ \(K_{\mu}\) = 10 & 0.043 & 0.051 \\ \(K_{\mu}\) = 50 & 0.041 & 0.048 \\ \(K_{\mu}\) = 100 & 0.045 & 0.050 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Motor velocity step response time.  
Figure 9: Actual motor torque at high velocities.  
The low-velocity and high-velocity step experiments indicated that the proposed algorithm can be effectively applied in different motion experiments. In addition, the controller velocity step response performance can be improved by compensating for the friction observed by the designed virtual sensor. Moreover, we give a simple and fast manual tuning rule for determining an appropriate observer gain coefficient for the problem of unknown priori knowledge of actual friction disturbance in robot joints.
## 4 Results and Discussion  
### Compressive Performance of Fiber Reinforced Geopolymer Concrete  
#### 4.1.1 Compressive Strength  
Table 5 and Figure 11 show the compressive strengths of geopolymer concrete reinforced with two types of fiber, including series P1, P2, H1, and H2. There were two results of compressive strength when the volume fractions of fiber were changed. In terms of polypropylene fiber-reinforced geopolymer concrete (PFRGC), the strength was improved at 0% and 0.5% of fiber and decreased at volume fractions of 1.0% and 1.5%. Besides, the strength of hooked end steel fiber-reinforced geopolymer concrete (HFRGC) increased from 0% to 1.0% of fiber and decreased at a volume fraction of 1.5%. The greatest values were 43.33 and 59.30 MPa for PFRGC and HFRGC, respectively, with a difference of 26.93%.  
When embedding 0.5% of fiber to a geopolymer concrete mixture, the difference between specimens using polypropylene and steel fibers was small, and this difference varied significantly at higher volume fractions. These compressive strengths were 36.18-59.30 MPa and 32.45-49.44 MPa with differences of 34.36% and 38.99% at 1.0% and 1.5% of fiber, respectively. Steel fiber had better properties than those of polypropylene fiber, and the adhesion between steel fiber and the base material was also better  
Figure 11: Compressive strength.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Volume Fraction of Fiber (\%)**} & \multicolumn{4}{c}{**Compressive Strength (MPa)**} \\ \cline{2-5}  & **P1** & **P2** & **H1** & **H2** \\ \hline
0.0 & 32.05 & 32.05 & 32.05 & 32.05 \\
0.5 & 43.33 & 38.59 & 43.89 & 45.20 \\
1.0 & 36.18 & 35.50 & 57.02 & 59.30 \\
1.5 & 32.45 & 27.13 & 43.33 & 49.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compressive strength.  
than polypropylene because of the special shape and rough surface of steel fiber. As a consequence, the compressive strength of HFRGC was always higher than those of PFRGC, as indicated by the experimental results.
# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 6 Conclusions  
In this paper, an OCSVM-based method for tunnel construction personnel's safety status detection is proposed, and three sets of data on the physical signs and environmental characteristics of tunnel construction safety personnel are collected. Several variables in each dataset were selected separately to build the corresponding OCSVM safety warning model. The model was applied to detect the personnel safety information of the test set. By analyzing the detection results of the data with different feature distributions, we can obtain a model accuracy above 90%, which verifies that the OCSVM algorithm has good scientific generalization ability and scalability for early warning based on the tunnel personnel safety information. We conducted ablation experiments with different data ratios in different warning states, i.e., data experiments with different balance degrees, which showed better performance and proved the good scientific generalization ability and scalability of the model. Moreover, this paper compares the OCSVM model with the KNN algorithm. In the three datasets, the evaluation indexes precision, recall and F-score are all nearly 10% higher for the OCSVM model than the KNN algorithm, which proves that the OCSVM algorithm is more suitable for tunnel personnel safety detection and is conducive to guaranteeing the safety of tunnel construction personnel.  
For future research on the tunnel personnel safety early warning problem, the next step could be to expand the data features, multi-dimensional classification and prediction of the OCSVM algorithm. The research direction is to expand the amount of data and data dimensions for experiments, combine them with deep learning models, complete data unsupervised training, and compare with a variety of machine learning prediction and classification methods to better serve the tunnel and civil construction.  
Conceptualization, G.H.; methodology, G.H.; software, G.H.; validation, G.H., J.C. and L.L.; formal analysis, J.C.; investigation, L.L.; resources, L.L.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, G.H.; visualization, G.H.; supervision, G.H.; project administration, G.H.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Materials used for experiments.  
The authors declare no conflict of interest.
## 2 System Descriptions and Problem Statements
The system (10) is equivalent to the following representation._  
\[\widetilde{x}(k+1)=\mathbb{E}_{Rij}^{-1}(h)\mathbf{A}_{Rij}(h)\widetilde{x}(k) \tag{13}\]  
Obviously, one can find the augmented system (13) is a normal state-space model since the controller gain \(\mathbb{F}_{dj}\) and the observer gain \(\mathbb{L}_{di}\), i.e., the regularity and causality of the system (3) are held. **Lemma 1**. : _[_34_]_ **(Projection Lemma)** Given matrices \(\mathbf{\Omega}\), \(\mathbf{\Lambda}\) and symmetric matrix \(\mathbf{\Phi}\) which satisfies \(rank(\mathbf{\Omega})<rank(\mathbf{\Phi})\) and \(rank(\mathbf{\Lambda})<rank(\mathbf{\Phi})\), if and only if there exists arbitrary matrix \(\mathbf{\Pi}\) such that_  
\[\mathbf{\Phi}+\mathbf{\Omega}^{\mathrm{T}}\mathbf{\Pi}\mathbf{\Lambda}+ \mathbf{\Lambda}^{\mathrm{T}}\mathbf{\Pi}^{\mathrm{T}}\mathbf{\Omega}<\ 0 \tag{14}\]  
_and the following inequalities are held simultaneously._  
\[\mathbf{\Omega}_{\perp}^{\mathrm{T}}\mathbf{\Phi}\mathbf{\Omega}_{\perp}<\ 0\text{ and }\mathbf{\Lambda}_{\perp}^{\mathrm{T}}\mathbf{\Phi}\mathbf{\Lambda}_{\perp}<\ 0 \tag{15}\]  
_where \(\bullet_{\perp}\) denotes the null-space matrix of \(\bullet\)._  
**Lemma 2**. : _[_35_]_ **A scalar \(\alpha>0\) exists to satisfy the following inequality if matrices \(\mathbf{U}\) and \(\Xi<0\) with appropriate dimension such that \(\mathbf{U}^{\mathrm{T}}\Xi\mathbf{U}\leq 0\) is held._  
\[\mathbf{U}^{\mathrm{T}}\Xi\mathbf{U}\leq-\alpha\Big{(}\mathbf{U}^{\mathrm{T}} +\mathbf{U}\Big{)}-\alpha^{2}\Xi^{-1} \tag{16}\]  
In the next section, the stabilization issue of the system (13) is proposed by using the Lyapunov stability theory. Moreover, the stability conditions will be converted into LMI form through the above lemmas. Then, the convex optimization algorithms [36] can be employed to solve these LMI stability conditions.
# Area-Time Efficient Hardware Architecture for CRYSTALS-Kyber
## 5 Conclusions  
An area-time efficient hardware architecture for CRYSTALS-Kyber is introduced in this paper. By implementing the proposed dual-path delay feedback NTT and INTT architectures in key generation, encryption, and decryption, these operations are noticeably improved in terms of processing time and area-time efficiency. Specifically, the key generation operation in the proposed architecture is accelerated by twofold compared with existing works. In addition, the proposed architecture offers the best value of area-time product among the state-of-the-art Kyber architectures. Therefore, the proposed architecture can be applied in designs where the high balance between hardware complexity and processing time is strictly considered.  
[PERSON], S.K. and Y.E. conceptualized the idea of this research, conducted experiments, collected data, and prepared the original version. T.T.N. reviewed, analyzed data, and updated the manuscript. H.L. supervised, validated, reviewed, and supported the research with funding. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and ICT (MSIT) under the ITRC support program (IITP-2021-0-02052) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP), in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011232), and in part by the IITP grant funded by the Korea government (MSIT) (No. 20210007790012003).  
Not applicable.  
The authors declare no conflict of interest.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 7 End-User Study
### Analysis of the Results
#### 7.4.1 Efficiency  
We measured the efficiency as the task completion time required per approach. Modeling the given process description with BPMN.io took participants between 11 and 29 min (M = 17.6, SD = 5.6), whereas the time needed with the BPMNE4 IoT modeler was between 4 to 10 min (M = 6.0, SD = 1.3). Figure 20 illustrates the results.  
The results reveal a longer task completion for modeling the IoT process with BPMN.io. Most of the problems that emerge when modeling IoT-driven BPs with BPMN 2.0 were related to the use of the different modeling elements. As there are no IoT-specific modeling elements in BPMN 2.0, study participants were unsure how to model the IoT involvement. While some participants represented IoT involvement in terms of _pools_ or _lanes_, others used different task types (i.e., service and script task) or event types (i.e., message and signal event). As another aspect noticed during the review of the models, some participants used business rule tasks to represent sensors and service tasks for actuators, while others used service tasks for both sensors and actuators. A third group used script tasks for modeling sensors and actuators. Due to the ambiguous BPMN modeling elements (cf. Figure 1: IoT-driven service task vs. non IoT-driven service task) and the fact that there are no IoT-specific modeling elements in BPMN 2.0, modeling IoT-driven BPs was more time consuming and complex. In BPMNE4 IoT (cf. Section 5), there are specific elements (i.e., artifacts and events) for modeling sensors and actuators. As our BPMN extension (BPMNE4 IoT) can explicitly represent specific IoT behavior (push and pull interaction and IoT conditions), the participants completed the given task faster compared to the use of BPMN 2.0. Note that the process models created by the study participants were checked for their semantic by two study guides. It was noticed that none of the study participants caused semantic errors in either BPMN or BPMNE4 IoT. This can be explained with the fact that the participants already were experienced with modeling business processes in terms of BPMN 2.0. Moreover, the BPMNE4 IoT extension conforms with BPMN 2.0. Consequently, we may conclude that BPMNE4 IoT is more efficient than BPMN 2.0, i.e., less time is needed for modeling IoT-driven BPs compared to BPMN 2.0, which confirms Hypothesis H1.
## 5 Experiments
### Computational Efficiency  
Theoretically speaking, suppose original data and stego data in the \(i\)-th sample are \(H(i)\) and \(S(i)\), and embed data in the \(i\)-th sample \(b(i)\) are embedded; then, stego data are \(S(i)=2H(i)+b(i)\) with the hiding computational cost for \(N\) samples as \(O(N)\). Since the computational complexity for the preliminary process of intDCT-IV is \(2.5N\log_{2}N-N\), then the total computational cost is \(O(N\log N)\).  
We also evaluated the time complexity of the proposed method to show the computational efficiency. Since the matrix calculation exists in intDCT, we calculated the computational cost for embedding. Even though calculation time depends on the computer's performance and the programming language for implementation, as a reference, we calculated the computational cost for embedding 12 data with the proposed three alternative expansion strategies. The system configuration is listed in Table 3. We conducted the embedding calculation 10 times and summarized the time required to process one second of the input speech. Figure 7 shows the results. The error bars of the figure show the standard error. These results show that the effect of the additional computation is subtle and the computation efficiency of the proposed method is almost the same as that of the conventional method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Conventional** & **Solution 1** & **Solution 2** \\ \hline EER (\%) & 25.0 & 62.5 & 70.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: EER of the hiding methods.  
Figure 6: FAR and FRR values of the proposed method to determine the EER threshold.
# Investigation of Critical Gap for Pedestrian Crossing Using Fuzzy Logic System
## 2 Methodology
### Detection of Critical Gaps Using Fuzzy System  
The fuzzy system process that is implemented in this analysis follows the approach adopted by [PERSON] (1995), which includes three steps:  
1. Fuzzification process;
2. Inference process based on IF-THEN rules;
3. Defuzzifcation process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Variable**} & \multicolumn{3}{c}{**Linguistic Terms and Range Values**} \\ \cline{2-4}  & **a (Low)** & **b (Medium)** & **c (High)** \\ \hline Length of accepted gap & 0–1.75 s & 1.75–5.62 s & 5.62–7.6 s \\ Number of rejected gaps & 0–1.933 & 1.933–3 & 3–4 \\ Length of rejected gap & 0–0.9 s & 0.9–2.65 s & 2.65–3.5 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Membership functions used with name of input variables and range values.  
Figure 2: View of the location.  
These are explained below.  
The first step is the fuzzification process, which can be achieved by converting the input data to a fuzzy set through fuzzy linguistic variables and membership functions. Then, based on IF-THEN rules, the inference process is carried out. Lastly, the defuzzification step is implemented, which is then completed using membership functions to crisp fuzzy outputs (Figure 3). Each of these steps will be further illustrated below.
## 2 Results and Discussion  
This research has been designed to follow the nectar (NE)/honey-sac (HoS)/honey (HO) pathways of the headspace, volatiles, and semi-volatiles in order to characterize Satsuma mandarin (_Citrus unshiu_ Marc.) honey. Two complementary methods for VOC isolation (without artefact generation) were applied: HS-SPME (with two fibres: divinylbenzene/carboxene/polydimethylsiloxane (DVB/CAR/PDMS) and polydimethylsiloxane/divinylbenzene (PDMS/DVB)) and USE (using two solvents: pentane/Et\({}_{2}\)O 1:2 (\(v/v\)) and CH\({}_{2}\)Cl\({}_{2}\)). The applied chemical screening methodologies indicated striking differences in the obtained chemical profiles (depending on the volatility/solubility/sample). As was previously mentioned, the melissopalynological analysis cannot be used ambiguously to authenticate this honey. To ensure the unifloral honey origin, all of the samples were collected under controlled bee-hive locations in the area where _C. unshiu_ predominantly grows. In investigated samples of the local beekeepers Satsuma mandarin pollen ranged from 1%-10%, and in other samples 3%-41%. The pollen grains from other nectar plant species in the samples were: _Capsela bursa pastoris_ L., Asteraceae (_Taraxacum_ form), _Citrus sinensis_ L., _Fabaceae_ spp., _Rhamnus_ spp., _Colutea arborescens_ L., _Diplotaxis erucoides_ L., _Centaurea_ spp. and _Cerastium_ spp.