esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Photoluminescence Proprteries  
Figure 4 presents the PL excitation and emission contour graph of Tb-doped pyrosilicate samples with excitation and emission ranges of 250-400 and 300-700 nm, respectively. All the samples have presented a similar emission characteristic of Tb\({}^{3+}\) 4f-4f transitions. The detail of each emission will discuss in the next result of scintillation spectra. The highest emission intensity of all the samples is 540 nm due to \({}^{5}\)D\({}_{4}\)\(\rightarrow\)\({}^{7}\)F\({}_{5}\) transition of Tb\({}^{3+}\). Despite the similarity in emission wavelength, the excitation characteristic of Tb-doped GPS is different from the rest of Tb-doped pyrosilicate counterparts. The main excitation wavelength of Tb-doped GPS is shifted from 270 nm in the other samples to 310 nm with a significantly higher \(QY\) at 68.9% than the Tb-doped LPS, YPS, and LaPS at 29.7, 13.6, and 21.6%, respectively. The possible reasons for the shifting of major excitation in Tb-doped GPS are effected by the intrinsic emission of GPS at 310 nm by Gd\({}^{3+}\) 4f-4f transition which observed in the study of undoped GPS crystal [67].  
Figure 4: PL excitation and emission contour graph of (**a**) Tb-doped LPS; (**b**) Tb-doped YPS; (**c**) Tb-doped GPS; (**d**) Tb-doped LaPS.  
Figure 3: Diffused transmittance spectra of Tb-doped LPS, YPS, GPS, and LaPS samples.  
Figure 5 exhibits the PL decay time profiles of each Tb-doped LPS, YPS, GPS, and LaPS sample. In the initial part of the profile, every sample in this study presents a slow rise in the signal intensity. This part of the profile is possibly influenced by the instrumental response function which is represented by cyan lines in all the four sample profiles. The fitting is done by excluding the influenced part by the IRF, and fitting results by a single exponential function is also as presented in Figure 4 below the name of each sample. The PL decay times of Tb-doped LPS, YPS, GPS, and LaPS samples are 2.62, 5.65, 5.29, and 3.04 ms, respectively. The results are consistent with the previous study on Tb-doped scintillator materials [68].
## 5 Performance Evaluation of Classification Algorithms
### How well can a Trained Model be Transferred between Different Image Types and Image Scenes?
NB shows a significant decrease in performance when transferred to other image types with almost a random classification performance on the WV2 test image. Training on the WV2 image and testing on IK (Figure 11, WV2 IK), all learning machines, except SVM, experience a significant performance decrease with respect to the image used to train them. QB (Figure 11, WV2 QB) shows comparable behavior of SVM, RT and KNN, however, with a significant decrease in accuracy. Tests of NB on the QB image show similar results as for the WV2 image on which it had been trained. Using the range \(R\)  
Figure 12: Classifier performance for different training-testing scenarios on MR image types. of the Area Under the Curve (_AUC_), calculated for each classifier over all VHR image types, as a measure of transferability, SVM (R=0.226) appears to be the best performing classifier in terms of transferability, followed by RT (0.243), KNN (0.311) and NB (0.793). In addition, for the MR image types, a generally high performance can be observed for all classifiers when trained and tested on the same image type. The best overall performance for all classifiers can be observed for the TM dataset (Figure 12, TMTM). For the ETM dataset (Figure 12, ETMETM), SVM and RT perform best, followed by NB and KNN. However, when trained on TM and tested on ETM (Figure 12, TMETM), a significant performance decrease appears for all classifiers. SVM, RT and KNN still perform well, whereas NB shows the worst performance when transferred. In addition, _models trained on ETM_ and applied to TM (Figure 12, ETMTM) show a decrease in performance for all the classifiers. Nevertheless, SVM, KNN and RT still perform reasonably well, whereas NB shows almost random classification behavior. The range \(R\) of the Area Under the Curve (_AUC_) for each classifier identifies SVM (R=0.143) as best performing classifier in terms of transferability also for MR images, followed by KNN (0.148), RT (0.249) and NB (0.338).
## 4 Simulation Results and Analysis
### Simulation Experiments for Different Road Levels for a Heavy Vehicle  
In order to test the adaptability of the control strategy proposed in this paper to different vehicle models, simulation experiments were conducted for a heavy vehicle on B-, C-, and D-class roads by changing the vehicle speed to 15 m/s according to the actual situation, and the suspension parameters and road power spectrum are shown in Table 3 and Figure 5, respectively. Figure 8 shows the simulation results of the body acceleration on B- and C-class roads. The percentages of the RMS values of the body acceleration for the passive suspension and the semi-active suspension system based on the fuzzy and PPO algorithm under the same conditions are given in Table 7, and the corresponding RMSE values are given in Table 8. The experimental results showed that the PPO-based semi-active suspension system still had good performance under different vehicle models, vehicle speeds, and road conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Road Level**} & \multicolumn{3}{c}{**Body Acceleration**} & \multicolumn{3}{c}{**Suspension Deflection**} & \multicolumn{3}{c}{**Tire Dynamic Load**} \\ \cline{2-10}  & **Passive** & **Fuzzy** & **PPO** & **Passive** & **Fuzzy** & **PPO** & **Passive** & **Fuzzy** & **PPO** \\ \hline C & 0.5896 & 0.4735 & 0.3492 & 0.1275 & 0.06218 & 0.05513 & 12.71 & 9.624 & 11.93 \\ D & 0.8338 & 0.6844 & 0.6614 & 0.1424 & 0.08794 & 0.07839 & 18.77 & 16.86 & 13.61 \\ E & 1.179 & 1.058 & 0.9787 & 0.1671 & 0.1244 & 0.1109 & 25.45 & 24.23 & 19.25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The RMSE values of the two semi-active suspensions for a light vehicle under the same conditions.  
Figure 7: Simulation results of fuzzy, PPO, and passive suspension systems for a light vehicle on a D-class road.
# Management of Patients under Treatment with Monoclonal Antibodies and New Biological Therapies
## 4 Discussion
### Management of Complications Associated with Biological Therapies  
Oral toxicities are one of the adverse effects in treatment with biological therapies. The management of these oral toxicities consists of:  
Mucositis, stomatitis, and aphthous lesions: Basic oral care, corticosteroids (topical, intralesional, or oral), morphine rinses, systemic analgesics, LLLT (Low Level Laser Therapy), and change in medication and interruption of previously agreed radiotherapy sessions with the oncologist.  
Hyperkeratotic lesions and dyschromia of the mucosa: Monthly reviews and biopsy in case of suspicion.  
Geographic tongue: Avoidance of irritants, rinsing with corticosteroids in painful lesions.  
Dysgeusia: Nutritional recommendations such as abundant consumption of liquids and cold meals; Changes in the medication previously agreed with the dentist can be proposed.  
Lichenoid lesions: Topical corticosteroids for painful lesions and periodic reviews, as well as change in medication.  
Telangiectasias and mucosal bleeding: Basic oral care and change in medication.  
Xerostomia: Basic oral care, hydration, use of sialogogues such as pilocarpine or artificial salivary substitutes (palliative).  
Dysestesia: Basic oral care, avoidance of irritants and symptomatic treatment with analgesics. In case of neuropathy, use drugs such as clonazepum or gabapentin.  
MRONJ: This complication is divided by the AAOMS into four grades with a specific treatment for each of them [2]. (Table 3)  
It has been observed that the use of hyperbaric oxygen therapy as an adjunct to surgical and non-surgical treatment produces improvements in wound healing, pain, and quality of life. However, there are no statistically significant differences with respect to the control group [2; 62].  
Other studies mention the use of low-frequency lasers, parathyroid hormone and BMPs (Bone Morphogenetic Protein) as strategies in non-surgical treatment, but more studies are needed to verify their efficacy [2].
# One-Dimensional Fluid-Structure Interaction Models in Presurized Fluid-Filled Pipes: A Review
## 3 Numerical and Experimental Research
### Three Degree-of-Freedom Models  
Six-equation models aim at describing the 1,2,3-DOFs. As in the four-equation model, similar numerical schemes can be used for solving the six-equation system. However, the right-hand sides of the three continuity equations are not expressed in differential terms. A first or second-order approximation can be applied for integrating these equations.  
The six-equation model by an MOC approach was first proposed and solved by [5]. These authors have compared results from the frequency and time domains and carried out their validation using experimental data collected from a water-filled copper pipe excited by hammering the pipe-end.  
With a similar MOC numerical scheme, [PERSON]. [47] solved the equations and compared them to a four-equation model solved by FDM; the effect of Poisson coupling in each case was also analysed. The work of [5] was extended by [58], who proposed an added fluid mass term and solved the equations by a MOC-FEM approach. An MOC-FDM scheme was used by [59] in their numerical analysis, and the effect of initial axial tensional stress was included in their derivation.  
From the six-equation system, [PERSON]. [60] derived a four-equation model which included correction terms and factors accounting for the pipe-wall thickness (v.i. Figure 5). The model was validated with exact solutions in the time-domain [12; 54]. The authors concluded that, in the low-frequency range, a transient description of the 2-DOF is only important for very thick pipes (\(r/e<2\)).
# Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator
## 3 Controller Design
### Spo  
SPO estimates partial unknown states and perturbation. It helps the controller to reduce the chattering against uncertainties and disturbances. It is efficient in estimating perturbation in low-frequency vibration [13]. For the motor-driven robot, the main component of the perturbation \(\mathbf{\Psi}\) is the external disturbance \(\mathbf{d}(t)\) and gravity \(\mathbf{G}(\mathbf{x})\). The minor components include dynamic uncertainties and modeling errors.  
The design of SPO is based on the state space. The state variables with a new state variable \(x_{3j}\) for the \(j\)-th (\(j=1,2,\ldots n\)) joint of the robot can be represented as:  
\[\begin{array}{l}\dot{x}_{1j}=x_{2j}\\ \dot{x}_{2j}=\eta_{j}\overline{u}_{j}+\Psi_{j}\\ \dot{x}_{3j}=\eta_{j}\dot{x}_{2j}-\dot{\Psi}_{j}/\eta_{j}\\ y_{j}=x_{1j}.\end{array} \tag{13}\]  
where \(\eta_{j}\) is a positive constant. \(\overline{\mathbf{u}}=[\overline{u}_{1},\ldots,\overline{u}_{j},\ldots \overline{u}_{n}]\in R^{n}\) is the new equivalent control input to separate the perturbation from dynamics as follows:  
\[\mathbf{M}_{o}(\mathbf{x})^{-1}(\boldsymbol{\tau}(t)-\mathbf{C}_{o}(\mathbf{x },\dot{\mathbf{x}})\dot{\mathbf{x}})=\boldsymbol{\eta}\overline{\mathbf{u}} \tag{14}\]  
where \(\boldsymbol{\eta}=[\eta_{1},\ldots\eta_{n}]^{T}\in R^{n}\). The new state variable \(x_{3j}\) is defined as:  
\[x_{3j}=\eta_{j}x_{2j}-\Psi_{j}/\eta_{j}. \tag{15}\]  
The SPO for \(j\)-th joint is designed as follows:  
\[\begin{array}{l}\dot{\hat{x}}_{1j}=\hat{x}_{2j}-g_{1j}sat(\hat{x}_{1j})\\ \hat{x}_{2j}=\eta_{j}\overline{u}_{j}-g_{2j}sat(\hat{x}_{1j})+\dot{\Psi}_{j}\\ \hat{x}_{3j}=\eta_{j}^{2}(\overline{u}_{j}+\eta_{j}\hat{x}_{2j}-\hat{x}_{3j}) \\ \dot{\hat{V}}_{j}=\eta_{j}(\eta_{j}\hat{x}_{2j}-\hat{x}_{3j})\end{array} \tag{16}\]  
where \(g_{1j}\) and \(g_{2j}\) are positive constants, and the notation means the estimation; \(\sim\) means the estimation error, i.e., \(\hat{x}_{1j}=\hat{x}_{1j}-x_{1j}\); if \(\left|\xi_{1j}\right|\geq\epsilon_{0j}\), sat\((\hat{x}_{1j})=\left.\hat{x}_{1j}/\left|\hat{x}_{1j}\right|\); else, sat\((\hat{x}_{1j})=\hat{x}_{1j}/\left.\varepsilon_{cj}\right.\); \(\varepsilon_{0j}\) stands for the boundary layer of the estimation error.  
Figure 2: Block diagram of the controller.  
The detailed stability analysis for the SPO has been proven in previous research [14]. Herein, we only use SPO to protect the controller against lesser uncertainties, which is reduced from \(\Psi_{j}\) to \(\widetilde{\Psi}_{j}\). The perturbation estimation error has a relation to the real perturbation [13], as shown in a Laplace transform as:  
\[\frac{\widetilde{\Psi}_{j}(p)}{\Psi_{j}(p)}=-\frac{p(p+\mathsf{g}_{2j}/g_{1j})} {p^{2}+(\mathsf{g}_{2j}/g_{1j})p+(\mathsf{g}_{2j}/g_{1j})\eta_{j}^{2}} \tag{17}\]  
where estimation error \(\widetilde{\Psi}_{j}=\widetilde{\Psi}_{j}-\Psi_{j}\), and (17) shows a high-pass filter from the real perturbation to the perturbation estimation error. It signifies that the SPO is effective for the system with a low-frequency variability.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 2 Proposed Approach
### Level 1--Similarity-Based Correlation
#### 2.3.1 Event Aggregation  
The purpose of aggregation is to summarize events that convey the same knowledge within some defined observation window so that one aggregate event is sufficient to represent all the transactions recorded in the individual events. This model defines an event as an n-tuple \(e=[attr\_1,attr\_2,\ldots,attr\_n]\), where each \(attr\_i\in e\) represents an extracted attribute or feature out of all the possible features of the event. The aggregated event E is the fusion of all such events having all similar features, except the timestamp. Since one aggregate event can represent several individual events, the result of aggregation is fewer events with no loss of information. Which features to mine to form the event depend on the attacks that need to be detected by the model.  
In the sample entries in Table 3, all events record the same fact, that is, the host 192.168.204.69 is accessing the service on UDP port 67 (DHCP) running on the target host 192.168.204.1. The only difference in these events is the timestamp. Ignoring the timestamp, these events can be represented by the aggregate event E shown in Table 4 below.  
In the aggregate event, all the features are the same, except the number of bytes (bytes), which is extracted from the length field of the IP header. The count and packets (PKTS) are calculated fields which track the number of packets that make up the aggregate event. To maintain the information from the elementary events in the aggregate, the bytes attribute of the aggregate is the total of the individual byte fields from the constituent events. This is done so that there is no loss of information from the individual events. In this way, analysis and detection can be based on this aggregate in the same way it would have, based on the individual events. The output of the aggregation step is an aggregated event set \(AES\) comprising a set of the aggregate events \(E\) as defined by the following equation.  
\[AES=[E_{1},E_{2},\ldots,E_{n}] \tag{1}\]
## 3 Hybrid Business System Model  
This paper focuses on the resource scheduling problem for URLLC and mMTC services in the AIoT. Given the presence of a security-oriented URLLC short data packet transmission service in the access scenario of massive AIoT devices, we employ Orthogonal Frequency Division Multiplexing (OFDM) to facilitate the collaborative transmission of URLLC and mMTC services. Figure 1 shows the architecture used, in which the F-AP collects data information from the previous cycle and estimates the channel state in the next cycle, before performing bandwidth resource scheduling based on the number of tasks transmitted by the mMTC service and URLLC service in the downlink. The coordinated transmission of mixed services is implemented to ensure that the QoS requirements of different services are met.  
In our paper, the F-AP is responsible for transmitting \(H\) URLLC services, while there are also \(J\) mMTC services within the coverage area of the F-AP, facilitating communication between AIoT devices. Among these services, the URLLC service is provided with a higher transmission rate by connecting to the F-AP, whereas the mMTC service is primarily designed for the mutual transmission of information between specific AIoT devices. Since AIoT devices typically use a single antenna, we assume that all devices employ a single antenna in this scenario. Then, the URLLC service set in the scenario can be expressed as \(H=\{0,\cdots,h\}\), and the mMTC service set can be expressed as \(J=\{0,\cdots,j\}\).
# Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine
## 4 Results and Discussion
### Qualitative Study
#### 4.2.3 Analysis on Decision Boundaries  
To provide an intuitive understanding of the superiority of our HGCELM, we visualize the decision boundaries of ELM, SS-ELM, GCELM, and HGCELM. In Figure 4, we synthesize three types of representative data distributions, i.e., linear separable data (the first row), linear inseparable data with half circles (the second row), and linear inseparable data with concentric circles (the third row), each of which contains 100 samples with two classes. The complexity of these datasets gradually increases from top to bottom. In this experiment, we select 10 samples for training and initialize each classifier with 10 hidden neurons. Owing to the simplicity, all the four classifiers can correctly find a reliable decision boundary on the linear separable dataset. However, for the first linear inseparable dataset, semi-supervised methods (i.e., HGCELM, GCELM, and SS-ELM) can obtain more generalized decision boundaries than the supervised method (i.e., ELM). However, spectral convolution-based methods (HGCELM and GCELM) are superior to the graph Laplacian-based method (SS-ELM) in terms of decision boundary, as well as test accuracy. From the second linear inseparable dataset, HGCELM can accurately classify the data points with a better decision boundary, while the other three methods show relatively poorer ones. In particular, ELM fails to work on this dataset under the same experiment settings. This is because the ELM cannot use the unlabeled samples, making it more likely to overfitting the limited training set. Although SS-ELM shows same accuracy as HGCELM, its decision boundary cannot separate two circles. We can naturally conclude that HGCELM has a better generalization ability than the other methods. The ability is benefited from the fact that the high-order relationship among the whole data points can improve the quality of decision making. It should be noticed that due to the geometric property of data distribution, the Euclidean-distance-based graph construction strategy would result in an inappropriate graph structure on the linear inseparable datasets. Therefore, a small neighbor size is desired when constructing a graph or hypergraph for these datasets.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Data Sets** & **HGCELM** & **GCELM** & **SS-ELM** & **ELM** & **KELM** & **TSVM** & **ST-ELM** & **LapSVM** & **GCN** \\ \hline austra & \(\mathbf{82.57}+3.72\) & \(80.78\pm 2.99\) & \(76.24\pm 8.14\) & \(73.32\pm 10.48\) & \(79.47\pm 4.12\) & \(77.43\pm 10.59\) & \(75.17\
# A Trust-Influenced Smart Grid: A Survey and a Proposal
## 6 Trust: State of the Art in Substations
### Research Areas  
Trust has been stated as an important reflection of the state of the substation, the execution of legitimate commands of devices within the substation and the dissemination of sensitive substation information [51]. To detect malicious nodes in the protection zones of substations, trust was implemented in wireless sensor nodes [52,53] by using their wireless range. It must be noted that most substations that exist, at the time of this paper, do not use wireless sensor nodes in protection zones for substations but rather use IEDs which are serial-based or Ethernet-based.  
Figure 4: Substation automation system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Trust Components**} \\ \hline
**Direct Trust** & **Indirect Trust** & \
\begin{tabular}{c} **Tested Against** \\ **Trust Attacks** \\ \end{tabular} & **Risk Component** &
\begin{tabular}{c} **Knowledge** \\ **Component** \\ \end{tabular} \\ \hline
[29, 30, 31, 39] & [29, 31, 33] & [29, 31, 33] & & & [29, 30, 31, 39] \\
[34, 35, 38, 36, 42, 43] & [34, 35, 42, 43] & [32, 37] & - & [36, 38, 42, 43] \\
[32, 40] & [32, 36, 37, 40] & & & [32, 37, 40] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Research on trust in Smart Grid categorized by trust components.  
Another paper presented the measurement of trust between substations by the use of behavioural pattern analysis [54, 55]. The analysis used machine learning and statistical tools and used logs from the security gateway of substations as the source of data. These logs contained communication between substations. They computed a threat value to substations based on which the inverse was the trust value. However, the analysis is external to the substations, and therefore, an attack within a substation is likely to be over before an analysis is completed. Furthermore, most attacks originate from SCADA with legitimate commands, and these can go undetected.  
[PERSON] et al. [56] built a system to secure SCADA from deontological threats. The system aims to limit the access of an attacker or a naive/unskilled operator to a critical substation. The performance of an operator in controlling remote substations and resolving alarms is considered in determining the operator's trustworthiness.  
[PERSON] et al. designed a trust system for securing IEC 61850 GOOSE communication [57]. The untested trust system comprised modules that mimicked firewall policies, checked frame formats and access control.