esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results: Monitoring and Slope Stability
### Measured Data  
Measured pore water pressure and ground temperature from PVTs are presented in Figure 12. PVT-1 and PVT-2 were embedded at 4.5 m (Unit I, silty clay) and 8.55 m (Unit II, sand) depth, respectively. Measured pore water pressure at PVT-1 was average 21 kPa, indicating the groundwater level was about 2 m from the slope surface, which was supported by CPT results in Section 2.1. Measured pore water pressure at PVT-1 increased from 15 kPa to 27 kPa proportionally to the decrease of ground temperature from 4.2 \({}^{\circ}\)C to 2 \({}^{\circ}\)C from November to January. It is estimated that the frozen ground absorbs unfrozen water developing a gradient of water in the same direction as the temperature gradient, which can increase the level of the groundwater table. Another reason for the pore pressure increase is the increase of precipitation in this period, according to the recording data from the weather station (NRK Meteorologisk institutt, Oysand, Norway) [22]. Pore water pressure at PVT-2 measured around 15 kPa in early October converged to less than 5 kPa because the pore pressure was dissipated after heavy rain in September at a depth of Unit II, consisting of gravelly sand. Measured temperature from PVT-1 decreased and converged to 2 \({}^{\circ}\)C at 4.5 m depth while the temperature from PVT-2 remained constant of 5 \({}^{\circ}\)C at a depth of 8.55 m.  
Figure 12: Measured pore water pressure and ground temperature by PVT 1 and PVT 2.  
Another measured data set from the integrated sensor pack I-1, shows the slope inclination, ground temperature, together with air temperature and humidity obtained from November 2019 to March 2020 in Figure 13. Air temperature fluctuated between \(-12\) to \(+18\)\({}^{\circ}\)C. Those values were slightly higher than the average temperature in 2017 and 2018 used in the parametric study (see Figure 10). Meanwhile, ground temperature was almost constant, ranging from 1.8 to 2 \({}^{\circ}\)C at 1.0 m below the surface from the ground. Air humidity varied between 30 to 100%. The values of slope inclination were recorded every hour. The inclination of the slope increased slightly from 0.2\({}^{\circ}\) to 0.45\({}^{\circ}\), which meant 2 mm displacement in a downward direction. Even though this change of inclination is negligible, it is important to keep monitoring the inclination of the slope depending on the changes of ground temperature. A threshold inclination was set to increase the interval of logging of inclinometer data from 1 h to every 10 min.
## 3 Materials and Methods
### BraTS-2023 Dataset  
The dataset employed in our experiments is the publicly available BraTS 2023 training dataset [12], which includes a multi-institutional cohort of 1250 multi-parametric MRI (mpMRI) scans of adult gliomas. For each patient, four MRI modalities are provided including pre- and post-gadolinium T1-weighted, T2-weighted, and T2-weighted FLAIR sequences, all of which are co-registered to the same anatomical template, interpolated to the same resolution (1 mm\({}^{3}\)), and skull-stripped. Ground truth annotations of the tumor subregions approved by expert neuroradiologists are also available for the evaluation, as documented in the related paper [40].  
The provided segmentation labels include:  
1. Contrast-enhanced tumor (ET): Regions that appear strongly highlighted in MRI after contrast medium administration.
2. Necrotic tumor core (NCR): Tumor regions which appear hypointense in TICE sequences.
3. Peritumoral edematous/invaded tissue (ED): Areas of diffuse hyperintensity in FLAIR sequences, which includes the infiltrative non-enhancing tumor as well as vasogenic edema in the peritumoral region.  
As previously described, the evaluation of segmentation performance of the BraTS 2023 Adult Glioma Challenge participants was based on the three composite tumor subregions, i.e., WT, TC, and ET, each derived from specific combinations of underlying labels. In contrast, for the purpose of our study, we chose to retain the original individual labels and compute the evaluation metrics separately for each class. However, to ensure clarity we also report the results of our model on the composite tumor regions, i.e., TC and WT.
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 4 Conclusions and Future Scope  
The present study presents a brief review of published research reporting MPC-led therapeutic automation in some biomedical conditions such as type 1 diabetes (including artificial pancreas), anaesthesia, fibromyalgia, HIV, and cancer (including oncolytic viral treatment and hyperthermia). A detailed review of MPC evolution, architectures, methodology, advantages, limitations, categories, implementation and softwares has also been presented. Automated therapeutic systems are sorely needed by medical professionals worldwide for the following reasons:  
* Better efficiency over manual monitoring and control;
* Automated and optimized drug delivery based on dynamic monitoring of therapeutic and patients' bio-parameters;
* Minimization of unintended under- or over-dosing;
* Real-time data-based decision support to medical personnel;
* Routine workload reduction of medical personnel;
* Cost effectiveness by minimizing repetitive treatments.  
Patient safety is the driving force behind the need for the wider implementation of automation in clinical therapeutic delivery. Medical specialists can concentrate on high-value jobs while computer-based drug delivery can manage routine tasks.  
Over the past few decades, researchers have explored MPC-led blood glucose level regulation, the automation of insulin delivery in blood, target controlled anesthesia infusion, impulsive oncolytic viral therapy for treating cancer, structured treatment interruptions to increase immunological responses to HIV, and more. However, from the point of view of the variety of issues being addressed and related to real-time applications, the field of automated biological therapeutic systems is still in its infancy. Hence, there is a vast scope to develop closed-loop control for biomedical therapy systems in a way that they can be translated into workable and scalable technologies. In this scenario, the MPC's receding horizon control concepts are particularly suited for effective medication delivery control. In particular, there is a large research scope in the MPC-based effective automation of hyperthermia therapy for cancer treatment, as well as that of adaptive intervention therapy for fibromyalgia. Currently, there is very limited research literature available that addresses these niche topics. Hence, this future scope is open to all MPC and biomedical therapeutic automation researchers.  
Conceptualization, S.P. and P.S.; methodology, S.P. and P.S.; software, S.P. and P.S.; resources, J.L.; writing--original draft preparation, S.P.; writing--review and editing, R.S. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the Symbiosis International (Deemed University), Pune, Maharashtra state, India.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Security Attacks
### Consensus and Incentive Layer
#### 4.3.3 Race Attack  
A race attack [27; 29; 31; 42] exploits the time gap between the initiation of a transaction and its subsequent confirmation. In this attack, an adversary creates two transactions using the same funds: one legitimate and one fraudulent. The attacker leverages the delay in network confirmation so that if the fraudulent transaction is confirmed before the valid one, the attacker effectively spends the same funds twice. Initially, the valid transaction is  
Figure 4: Attack where the attacker controls 51% of mining power, manipulating transactions.  
broadcast to build trust within the target network, but the attacker later exploits this trust by rapidly broadcasting the conflicting transaction.  
To mitigate race attacks, it is crucial to enforce a sufficient waiting period for transaction confirmations. Additionally, robust consensus mechanisms--such as Proof of Work or Proof of Stake--can further reduce the risk by ensuring the integrity of the ledger.
# A Comparison between Two Reduction Strategies for Shrouded Bladed Disks
## 3 Reduction Methods for Bladed Disks  
The application of the cyclic symmetry properties and of the CB-method to bladed disks for the generation of reduced order models suitable for nonlinear forced response analyses requires some preliminary definitions.  
Referring to Figure 3, where the fundamental sector of a shrouded bladed disk is depicted, the inner dofs \(\mathbf{q_{I}}\) of the fundamental sector include two sets of dofs. The first one, called \(\mathbf{q_{C}}\), includes the contact dofs and the second one, called \(\mathbf{q}_{NC}\), includes all the other non-contact dofs; it is noteworthy that, due to the local nature of contacts, the size of \(\mathbf{q}_{C}\) is always much smaller than the size of \(\mathbf{q}_{NC}\).
## 2 Modeling
### Solar Radiation Model
The module normal vector \(\overrightarrow{n}_{ij}\) and the unit cell area \(A_{ij}\) in the inertial coordinate system can be calculated as  
\[\overrightarrow{n}_{ij}=R\cdot\frac{\left(\frac{\partial F}{\partial x_{ij}} \frac{\partial F}{\partial y_{ij}},\frac{\partial F}{\partial z_{ij}}\right) }{\sqrt{\left(\frac{\partial F}{\partial x_{ij}}\right)^{2}+\left(\frac{ \partial F}{\partial y_{ij}}\right)^{2}+\left(\frac{\partial F}{\partial z_{ ij}}\right)^{2}}} \tag{12}\]  
\[A_{ij}=rd\theta dx\sqrt{1+r^{\prime}(x)^{2}} \tag{13}\]  
where \(x_{ij}\), \(y_{ij}\), and \(z_{ij}\) are the \(x\), \(y\) and \(z\) coordinates of central point of the module \(ij\), respectively. \(r(x)\) is the airship cross-section. \(R\) is the transformation from the airship coordinate system to the inertial coordinate system, given by  
\[R= \begin{bmatrix}C_{\varphi}\cdot C_{\psi}&S_{\phi}\cdot S_{\varphi}\cdot C _{\psi}-C_{\phi}\cdot S_{\psi}&C_{\phi}\cdot S_{\varphi}\cdot C_{\phi}\\ C_{\varphi}\cdot S_{\psi}&C_{\phi}\cdot C_{\psi}+S_{\phi}\cdot S_{\varphi} \cdot S_{\psi}&S_{\psi}\cdot S_{\varphi}\cdot C_{\phi}-S_{\phi}\cdot C_{\psi} \\ -S_{\varphi}&C_{\psi}\cdot S_{\phi}&C_{\phi}\cdot C_{\varphi}\end{bmatrix}  \tag{14}\]  
where \(C=\cos\), \(S=\sin\), and \(\psi\), \(\varphi\), and \(\phi\) are the yaw angle, pitch angle and roll angle, respectively. The unit vector of solar direct radiation \(\overrightarrow{n}_{s}\) can be calculated as  
\[\overrightarrow{n}_{s}=\left(\overrightarrow{n}_{sx},\overrightarrow{n}_{ sy},\overrightarrow{n}_{sz}\right)=\left(-\cos\theta_{ele}\cdot\cos\theta_{ azi},-\cos\theta_{ele}\cdot\sin\theta_{azi},\sin\theta_{de}\right) \tag{15}\]  
where \(\theta_{azi}\) is the azimuth angle, defined as  
\[\theta_{azi}=\arccos(\frac{\sin(\theta_{ele})\sin(\Phi)-\sin(\theta_{dec})}{ \cos(\theta_{ele})\cos(\Phi)}) \tag{16}\]The projection coefficient of the solar direct irradiance \(\omega_{p}\) on the module surface is defined as [37]  
\[\omega_{p}=\left\{ \begin{matrix}\overrightarrow{n}_{s}\cdot\overrightarrow{n}_{ ij}\Big{|}&\overrightarrow{n}_{s}\cdot\overrightarrow{n}_{ij}<0,-\theta_{ DIP}<\theta_{ele}<\pi+\theta_{DIP}\\ 0&\text{others}\end{matrix} \right. \tag{17}\]  
where \(\theta_{DIP}\) is the angle of view at the altitude \(h\), as shown in Figure 2.  
\[\theta_{DIP}=\arccos(r_{0}/(r_{0}+h)) \tag{18}\]  
where \(r_{0}\) is the radius of Earth. The intensity of direct irradiation \(I_{d0,ij}\) received by the PV module is given by  
\[I_{d0,ij}=\omega_{p}\cdot I_{d0} \tag{19}\]  
The scattered radiation \(I_{dh,ij}\) unit is expressed as  
\[I_{dh,ij}=\frac{1}{2}(1-\cos\alpha_{ij})\cdot I_{dh} \tag{20}\]  
where \(\alpha_{ij}\) is the angle between the module and the horizontal plane, which can be obtained by  
\[\alpha_{ij}=\arccos(\left.\overrightarrow{n}_{ij}\cdot\overrightarrow{n}_{ z}\right. \tag{21}\]  
Figure 1: Schematic diagram of stratospheric airship PV array [36]. The total irradiance power of the PV module \(Q_{sun,ij}\) can be calculated as  
\[Q_{sun,ij}=(I_{d0,ij}+I_{d0,ij})\cdot A_{ij} \tag{22}\]
# Practical Submodule Capacitor Sizing for Modular Multilevel Converter Considering Grid Faults
## 5 SM Capacitor Selection
### Design Flowchart  
Based on the analysis above, a design procedure flowchart is shown in Figure 8. First, some basic parameters are required, which includes the DC-link voltage (\(V_{dc}\)), number of SMs in each arm (\(N\)), arm inductance (\(L_{a}\)), AC-side fundamental angular frequency (\(\omega_{1}\)), gain of the MMC control loops (\(K_{op},K_{cp},K_{i}\), and \(K_{r}\) ) allowable peak-to-peak SM voltage ripple (\(V_{rip}\)), maximum SM voltage threshold (\(V_{th}\)), expected SM capacitor lifetime (\(L_{exp}\)), and system operating points in normal and grid fault conditions. Then, the minimum SM capacitance that satisfies the energy storage, voltage ripple, and transient SM voltage requirements can be calculated using the Equations (33) and (35)-(37). After that, the SM capacitance \(C_{sm}\) can be decided by adding a proper redundancy to the highest value among \(C_{sm\_cm}\), \(C_{sm\_rip}\), \(C_{sm\_ij}\). Based on the selected capacitance, the rated voltage of SM capacitor \(V_{rated}\) can be obtained using Formula (45). Since the capacitance and rated voltage have been decided, a certain type of capacitor product can be selected in the market. In the end, the lifetime of the selected capacitor product needs to be checked using Equations (38) and (43). It should be noted that the MMC system may encounter different fault conditions. Thus, this design process may be conducted multiple times based on different grid fault operating points. Through comparison, the maximum calculation value of \(C_{sm}\) and \(V_{rated}\) can be chosen as the final result.
## 2 Materials and Methods
### Sample Characterization  
Customized photoluminescence (PL) spectroscopy was used in these experiments. A 532 nm green diode-pumped solid-state (DPSS) laser was used as an excitation source. The spectroscopy set up was equipped with a double monochromator (Horiba (HR320)), lock-in amplifier, chopper and InGaAs detector. In detail, the emitted PL signal was collected by a lens and then focused onto the entrance slit of the monochromator. During the measurement, all of the samples were drop-casted on the glass substrate. The integration time was set at 1 s and the scanning range was fixed from 800 to 1600 nm. In this experiment, the PbS QD and PbS/MnS core shell QD (CS 0.3, CS 0.6, CS 0.9, CS 1.2 and CS 1.5) samples were exposed to the laser light for continuous irradiation time for 40 min with the power density of 5.66 W/cm\({}^{-2}\).
## 4 Selection of Services
### Adaptable WSD Security Certification
Figure 3: An example of a hierarchy of security properties for Domain Y.  
Figure 2: An example of a hierarchy of security properties for Domain X. * _ServiceModel_: A reference (_i.e._, URI), named (_ModelLink_), to the location where the model of the service is stored, and the type of the service model (_i.e._, WSDL-based, WSCL-based, implementation-based) is declared in the _Type_ element. * _TestEvidence_: All artifacts related to the test cases executed on the service for its certification. It includes test class (_TestClass_), type (_TestType_), attributes (_TestAttribute_), specifications (_TestSpecification_), and the result of test case execution (_TestResult_). _TestSpecification_ is the specification about the real test case that is declared through test id, description, and a link to the test model that is used to generate the test case. _TestResult_ is a set of pairs holding a reference to the test case and a pass-fail result. * _TestMetrics_: A set of metrics representing the quality of the test cases executed on the service. These measurements are used in WSD matching to compare the services by representing the result of different tests on various services. Figure 4: Certificate Schema. The above-mentioned information is used by WSD to find the certified Web services that comply with the user policies.
# Viability of ABO Blood Typing with ATR-FTIR Spectroscopy
## 3 Results and Discussion
### Single-Antigen Identification
Bearing these results in mind, we decided to evaluate whether the independent single identification of A and B antigens would lead to an improvement in the classification rate. Therefore, we used an independent multiple antigen classification (IMAC), consisting of independently training four algorithms (linear discriminant analysis for A (LDA-A) and for B (LDA-B) antigens; Naive Bayesian Classifier for A (NBC-A) and B (NBC-B) antigens). Every tested sample was assigned an IMAC number based on which of the four algorithms was + or--(Table 8), and the class was decided according to this number. The correspondence IMAC number--blood type was decided by studying the histogram of samples vs. IMAC number for every blood type. IMAC numbers 4, 8, and 12 did not appear in any of the tested training datasets. In fact, only number 4 appears only once in the assayed test datasets. Therefore, the blood type for these numbers was determined based on a probability study. This was particularly challenging in the case of IMAC 8, as the probability of being A or O was very similar and there were neither experimental nor probabilistic reasons to prefer one over the other. The results presented in Table 9 clearly indicate that IMAC yields superior outcomes when compared to random classification for each class, achieving similar results to those obtained by the previous algorithms. Regarding the confusion matrix, it is interesting to note that the A samples are mistaken for O (and vice versa) to a greater extent than a mere random classification. A similar situation occurs with B and AB samples (and vice versa). Once again, it becomes apparent that the presence of the B antigen is particularly influential in the classification. \begin{table}
\begin{tabular}{c c c c c c} \hline
**IMAC Number** & **LDA—A** & **LDA—B** & **NBC—A** & **NBC—B** & **Blood Type** \\ \hline
0 & - & - & - & - & - & O \\ \hline
1 & - & - & - & + & O \\ \hline
2 & - & - & + & - & O \\ \hline
3 & - & - & + & + & A \\ \hline
4 & - & + & - & - & B \\ \hline
5 & - & + & - & + & B \\ \hline
6 & - & + & + & - & B \\ \hline
7 & - & + & + & + & B \\ \hline
8 & + & - & - & - & O/A \\ \hline
9 & + & - & - & + & A \\ \hline
10 & + & - & + & - & A \\ \hline
11 & + & - & + & + & A \\ \hline
12 & + & + & - & - & B \\ \hline
13 & + & + & - & + & AB \\ \hline
14 & + & + & + & - & AB \\ \hline
15 & + & + & + & + & AB \\ \hline \end{tabular}
\end{table} 
Table 8: IMAC classification table.
# Design of OCC Indoor Positioning System Based on Flat Panel Light and Angle Sensor Assistance
## 6 Conclusions  
In this paper, we propose an indoor positioning algorithm based on flat panel light and angle sensor assistance, which contains fuzzy positioning and precise positioning. This LP-VLP system has an extremely low modification cost and deployment cost, and the receiver uses a cell phone as the receiver, which has certain practical value. In this paper, experiments are conducted based on this design, which show that the recognition rate is as high as 96.4% in fuzzy positioning and the average minimum error in precise positioning is 6.5 cm, which achieves centimeter-level positioning accuracy. It proves that this LP-VLP system has a certain feasibility.  
However, this method has some shortcomings and areas that can be improved. For example, the location of each lamp is coded to create a graphic code, which requires that each graphic code is unique, and once damaged, it will be difficult to replace. To address this problem, we believe that we can use laser engraving on lamp shades to modify the flat panel lamp: when the flat panel lamp needs to be replaced, the original lamp shade can continue to be used, avoiding unnecessary waste. In the experiment, we realized the \"pseudo-two-light positioning\" algorithm and the expected positioning effect was achieved. In the future, we can also design the \"pseudo-three-light positioning\" and select three points on the flat lamp for calculations, to further improve the positioning accuracy.  
Conceptualization, M.F. and P.L.; methodology, M.F. and P.L.; software, M.F. and P.L.; validation, M.F. and M.L.; formal analysis, S.L. and G.H.; investigation, M.F.; writing--original draft preparation, M.F.; writing--review and editing, M.F.; supervision, Y.W.; project administration, Y.W. and P.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The experimental data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 2 Methodology  
This section briefly discusses the study's main components: the dataset, data preprocessing and feature selection techniques, classification techniques, and the hyperparameter tuning approach.  
Figure 1: Overview of dataset characteristics and model parameters for detecting GPS spoofing attacks targeting UAVs.  
Figure 2 illustrates the process of the proposed approach. This process comprises several phases: dataset building, dataset assessment, model training, and model evaluation. Authentic signals were collected from real-time experiments and malicious signals generated by simulating 3 types of GPS spoofing attacks [2]. We performed two steps during data assessment: data preprocessing and feature selection, then applied several techniques to clean the input data, perform data transformation, and encode the input data. We used a feature correlation technique, Pearson's Correlation Coefficient, and a feature importance technique known as the Chi-Squared Test.  
These techniques led to the identification of correlated features of low importance and their removal from the dataset. Bayesian Optimization was used during the hyperparameter tuning phase to determine each model's best parameters for training, guaranteeing optimal performance. This study targeted six well-known and frequently used classification algorithms: SVM, ANN, RF, GNB, CART, and LR. We evaluated the selected models in terms of _ACC_, _PD_, _Pfa_, and _Pmd_ in the last phase.