esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Kinematic Data Analysis Results
#### 3.1.2 Cycle-Averaged Kinematic Parameters Related to Kinematic Synergies: Median and Ranges  
Figure 3a compares differences within each health condition group due to the test condition, and Figure 3b compares differences within each test condition group due to health condition. From the graphs, it can be observed that both patients and healthy participants perform the task by flexing their fingers, while the thumb remains extended and abducted. In general, no significant differences were identified between health conditions, except for a smaller range in PIP flexion and finger abduction in patients during RBBT, and a higher median PIP flexion during VBBT. However, significant differences were found between RBBT and VBBT in both health conditions for all parameters. Note that values in Figure 3 are expected to fall within the range [\(-\)1, 1], with negative values representing extension/adduction and positive values representing flexion/abduction relative to the neutral posture. However, because of normalisation to maximum values across subjects, values may occasionally exceed 1 if participants surpass these maximums, as seen in thumb abduction.  
Figure 3: \(Cont\).  
Figure 2: (**a**) Box-and-whisker plots of trajectory parameters: Efficiency Metric (EM, representing trajectory length) and Smoothness Metric (SM, representing the number of velocity peaks detected per trajectory) for healthy (**left**) and patient (**right**) groups, comparing test conditions (RBBT and VBBT). (**b**) Plots of trajectory parameters for real (**left**) and virtual (**right**) BBT, comparing health conditions (healthy and patient). Significant differences (\(p<0.05\)) are indicated by an asterisk (*) following the name of the variable.
## 3 Results  
### Effect of Water Temperature on Survival and Growth Rates of M. chinensis Larvae  
After three independent experiments with 30 larvae in each beaker, their sizes were noted. The growth rate of the larvae increased as the water temperature increased from 17 to 32 \({}^{\circ}\)C (Table 1). At water temperatures \(>\)22 \({}^{\circ}\)C, the larvae had grown by 220 \(\upmu\)m on day 15 of rearing, but at 17 \({}^{\circ}\)C, the average shell length was only 149.3 \(\pm\) 6.29 \(\upmu\)m by day 15. On day 15 of the experiment, significant differences in shell size were found between larvae kept at 32 \({}^{\circ}\)C (312.7 \(\pm\) 18.23 \(\upmu\)m in size) and 27 \({}^{\circ}\)C (265.5 \(\pm\) 14.55 \(\upmu\)m in size), and between the larvae kept at 22 \({}^{\circ}\)C (222.6 \(\pm\) 8.61 \(\upmu\)m) and 17 \({}^{\circ}\)C (149.3 \(\pm\) 6.29 \(\upmu\)m) (_p_ < 0.05). The daily growth rate at 32 \({}^{\circ}\)C (19.7 \(\pm\) 2.31 \(\upmu\)m) was significantly faster than that at 27 \({}^{\circ}\)C (12.6 \(\pm\) 2.25 \(\upmu\)m) and 17 \({}^{\circ}\)C (4.8 \(\pm\) 0.23) (_p_ < 0.05).  
The survival rate of the hen clam larvae decreased as the water temperature increased (Table 1), and an 82.5% survival rate was recorded at 17 \({}^{\circ}\)C, which was significantly higher than that recorded at 22 \({}^{\circ}\)C (65.4%). The lowest survival rate (23.6%) was observed at 32 \({}^{\circ}\)C (Table 1). Figure 2 shows the different larval developmental stages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Shell Length (\(\upmu\)m)**} & \multicolumn{2}{c}{**Daily Increment**} & \multirow{2}{*}{**Survival (\%)**} \\ \cline{3-3} \cline{5-6}  & & **Initial** & **Final** & & **of SL (\(\upmu\)m)** & \\ \hline  & 17 & 75.2 \(\pm\) 2.5 & 149.3 \(\pm\) 6.29 \({}^{\mathrm{a}}\) & 4.8 \(\pm\) 0.23 \({}^{\mathrm{a}}\) & 82.5 \({}^{\mathrm{d}}\) \\ Temperature & 22 & 75.2 \(\pm\) 2.5 & 222.6 \(\pm\) 8.61 \({}^{\mathrm{b}}\) & 9.8 \(\pm\) 1.52 \({}^{\mathrm{b}}\) & 65.4 \({}^{\mathrm{c}}\) \\ (\({}^{\mathrm{c}}\)C) & 27 & 75.2 \(\pm\) 2.5 & 265.5 \(\pm\) 14.55 \({}^{\mathrm{c}}\) & 12.6 \(\pm\) 2.25 \({}^{\mathrm{b}}\) & 50.5 \({}^{\mathrm{b}}\) \\  & 32 & 75.2 \(\pm\) 2.5 & 312.7 \(\pm\) 18.23 \({}^{\mathrm{d}}\) & 19.7 \(\pm\) 2.31 \({}^{\mathrm{c}}\) & 23.6 \({}^{\mathrm{a}}\) \\ \hline \hline \end{tabular} We intend a \(<\) b \(<\) c \(<\) d in shell length. Values in the same column with different superscript letters are significantly different (_p_ < 0.05).  
\end{table}
Table 1: The initial and final shell lengths, daily increment of shell length (SL), and the survival rate of _Mactra chinensis_ reared at different water temperatures for 15 days.  
Figure 2: Developmental stages of _Mactra chinensis_—**(A)**: fertilized egg (50–60 \(\upmu\)m); **(B)**: D-stage larva (70–100 \(\upmu\)m shell length (SL)); **(C)**: early umbonal stage (110–140 \(\upmu\)m SL); **(D)**: umbonal stage (150–170 \(\upmu\)m SL); **(E)**: late umbonal stage (180–220 \(\upmu\)m SL); and **(F)**: settled (metamorphosing) stage (300–350 \(\upmu\)m SL). The scale bar indicates 50 \(\upmu\)m.
## 3 Results and Discussion
### X-ray Photoelectron Spectroscopy (XPS) Analysis
According to this criterion, all concentrations below 25 \(\upmu\)g/mL of both types of nanoparticles were hemocompatibility, as the percentage of red blood cell lysis remained below 5%. However, concentrations of 50 and 100 \(\upmu\)g/mL of Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles clearly induced erythrocyte lysis, thus proving toxic to these cells. To determine whether Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles induce an in vitro inflammatory response, nitrite production was measured upon the exposure of macrophages to different concentrations of nanoparticles. As illustrated in Figure 8b, concentrations below 10 \(\upmu\)g/mL of both types of nanoparticles induced small concentrations of nitrites (less than 20 \(\upmu\)M). However, concentrations above 25 \(\upmu\)g/mL of Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles induced higher amounts of nitrites (30 to 42 \(\upmu\)M), eliciting an inflammatory in vitro response comparable to the control, where the nitrites concentration is 50 \(\upmu\)M.  
These findings suggest that Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles exhibit promising hemocompatibility and low inflammatory potential at concentrations below 25 \(\upmu\)g/mL, making them suitable candidates for various biomedical applications [5]. The observed hemocompatibility, where the percentage of red blood cell lysis remained below the 5% threshold, indicates that these nanoparticles are unlikely to cause significant damage to erythrocytes at lower concentrations. Similarly, the relatively low levels of nitrite production, a marker of inflammation, suggest that these nanoparticles elicit a minimal inflammatory response in macrophages at lower doses. However, the study also revealed that higher concentrations of 50 and 100 \(\upmu\)g/mL of decorated Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag nanoparticles can induce significant erythrocyte lysis and higher levels of nitrites, suggesting a potential toxicity and a more pronounced inflammatory reaction. This highlights the importance of carefully considering the dosage and design of these nanoparticles to ensure their safe and effective use in biomedical contexts [59; 60]. Further optimization and comprehensive evaluation of their biocompatibility at different concentrations will be crucial to develop these nanoparticles as reliable and safe tools for various therapeutic and diagnostic applications [61; 62]. Figure 8: (**a**) Hemolysis of erythrocytes and (**b**) nitrite production in macrophages induced by Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles. Statistical analysis was performed using two-way ANOVA followed by [PERSON]’s multiple comparison test. Statistical significance was indicated as * \(p\) < 0.05, **_p_ < 0.01, and *** \(p\) > 0.001 and *** \(p\) > 0.0001.
## 6 Influence of Cavern Span on Stability of Surrounding Rock
### Model Test of 30 m-Span Cavern  
Figure 8a presents the size diagram of the cavern model with a span of 30 m, while Figure 8b illustrates the progressive collapse process of the surrounding rock in a cavern with the same span. In small-span caverns, the stress concentration in the surrounding rock is relatively low, and the stress distribution at the top of the cavern is more even. The surrounding rock is able to bear stress well, thus leading to higher stability.
## 2 Methodology  
### Fuzzy Markov  
The fuzzy Markov method proposed is an integrated method that integrates the fault tree, fuzzy comprehensive evaluation and Markov model for the purposes of uncertainty data solving, risk identifying, reliability, availability and uncertainty evaluation. Integrated fault tree and fuzzy comprehensive evaluation provides the transfer rate for the Markov model, and the uncertain problems provides information for adjusting the \"Failure Cause Probability\" level to satisfy the real-time update function of the system evaluation. The steps of the proposed methodology are as shown in Figure 1.  
Figure 1: Fuzzy Markov integration framework. FTA: Fault Tree Analysis; MTTF: Mean time to failure.  
When the original \"Failure Cause Probability\" is 1, the new \"Failure Cause Probability\" can be changed to 2-5 according to the degree of the uncertain event; when the original level is 2, the new \"Failure Cause Probability\" can be changed to 3-5, and so on. For example, when the original \"Failure Cause Probability\" is 3, at this time, a failure event occurs seriously, the \"Failure Cause Probability\" is directly changed to 5, and then the evaluation is performed according to the steps of the comprehensive evaluation and the Markov method. The analysis of the impact of the uncertain problems on the system can be completed.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
###### Abstract  
Recent developments in machine learning algorithms are playing a significant role in wireless communication and Internet of Things (IoT) systems. Location-based Internet of Things services (LBIoTS) are considered one of the primary applications among those IoT applications. The key information involved in LBIoTS is finding an object's geographical location. The Global Positioning System (GPS) technique does not perform better in indoor environments due to multipath. Numerous methods have been investigated for indoor localization scenarios. However, the precise location estimation of a moving object in such an application is challenging due to the high signal fluctuations. Therefore, this paper presents machine learning algorithms to estimate the object's location based on the Received Signal Strength Indicator (RSSI) values collected through Bluetooth low-energy (BLE)-based nodes. In this experiment, we utilize a publicly available RSSI dataset. The RSSI data are collected from different BLE beacon nodes installed in a complex indoor environment with labels. Then, the RSSI data are linearized using the weighted least-squares method and filtered using moving average filters. Moreover, machine learning algorithms are used for training and testing the dataset to estimate the precise location of the objects. All the proposed algorithms were tested and evaluated under their different hyperparameters. The tested models provided approximately 85% accuracy for KNN, 84% for SVM and 76% accuracy in FFNN.  
a 2023 4, 651-668. 1
## 5 Experimental Results
### SMOTE Results
The AUC for the random forest (RF) classifier before applying SMOTE is depicted in Figure 6(a). On the other hand, the AUC for the RF classifier after SMOTE is equal to 1, as shown in Figure 6(b). It is clear that the performance of the RF classifier demonstrated a slight improvement following the a \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & _Precision_ & _Recall_ & _F1 Score_ & **Support** \\ \hline DDoS & 1.00 & 1.00 & 1.00 & 106,870 \\ BENIGN & 1.00 & 1.00 & 1.00 & 106,487 \\ Accuracy & & & 1.00 & 213,357 \\ Macro Avg & 1.00 & 1.00 & 1.00 & 213,357 \\ Weighted Avg & 1.00 & 1.00 & 1.00 & 213,357 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The classification report of the RF model after SMOTE. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & _Accuracy_ & _Precision_ & _Recall_ & **F1** \\ \hline Logistic Regression & 0.9969 & 0.9961 & 0.9977 & 0.9969 \\ KNN & 0.9999 & 0.9999 & 0.9999 & 0.9999 \\ XGB & 1.0000 & 0.9999 & 1.0000 & 1.0000 \\ Decision Tree & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ Random Forest & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance of the machine learning classifiers after SMOTE. Figure 7: The AUC for the RF model before and after SMOTE. The impact of the SMOTE on classifier performance is visualized in Figure 8. Figure 8 displays the confusion matrices for logistic regression, KNN, XGB, and RF classifiers prior to SMOTE application. Figure 8d shows the confusion matrix for the RF model following the SMOTE application. The confusion matrix of the RF classifier is free of both false positive (_FP_) and false negative (_FN_) following the application of SMOTE. Furthermore, a substantial reduction in FN is observed in the post-SMOTE RF classifier compared to its pre-SMOTE counterpart, as shown in Figure 5b. This indicates a significant improvement in the RF classifier's performance after SMOTE implementation. The performance of the logistic regression, KNN, XGB, and RF classifiers was evaluated before and after the implementation of the SMOTE. Figure 8 presents the confusion matrix for these classifiers prior to SMOTE application, while Figure 8d depicts the confusion matrix for the RF classifier after SMOTE was applied. A comparison of the false positive (_FP_) and true positive (_TP_) rates reveals that both were equal to 0 in the post-SMOTE scenario. However, the RF model demonstrated a more significant difference, with the TP rate being lower before SMOTE and higher after its implementation. Moreover, the false negative (_FN_) rate for the RF classifier was 946 times higher in the pre-SMOTE condition compared to the post-SMOTE condition. The overall performance of the RF classifier was substantially improved after the application of SMOTE, as evidenced by the enhanced confusion matrix metrics. Figure 8: The confusion matrix for the machine learning models after SMOTE. (**a**) Logistic regression model. (**b**) KNN model. (**c**) XGB model. (**d**) RF model.
## 7 Simulation Experiments and Comparative Study
### Case 1  
Figure 10 presents the graph when the PV array is operating in CCM mode and STC condition. It can be seen that the inductor current does not reach the zero value. More importantly, the signal value of the calculated \(V_{pv(CAL)}\) matches the measured \(V_{pv}\) value. Note that the duty cycle is fixed at a certain value and that the \(P\&O\) algorithm is not initiated to validate the proposed working model for identifying CCM and DCM operation.  
Next, the duty cycle was set at the value where the PV system starts operating in DCM mode, the waveforms of which are shown in Figure 11. It can be seen that the inductor current becomes discontinuous as it reaches 0 A. Operation in DCM can be detected based on the difference in values between the calculated \(V_{pv(CAL)}\) and measured \(V_{pv}\). These results confirm that the identification of CCM and DCM using the proposed method is accurate.  
Figure 8: Specifications of the PV Module.  
Figure 9: P–V Curves for PV Module.  
Next, to validate the MPP operation and convergence speed of the proposed algorithm and conventional _P&O_ algorithm, both systems were initially set to the open-circuit voltage of the PV module. Figure 12 shows the performance of the conventional _P&O_ algorithm; it can be seen that it requires several voltage steps to reach the MPP point. This is because the conventional _P&O_ becomes caught in the DCM of the converter. On the contrary, the effective operation of proposed method can be confirmed from Figure 13, where it can be seen that proposed methods needs only few steps to identify DCM operation. Thereafter, it reaches the MPP in fewer steps compared to the conventional _P&O_ algorithm. This simulation experiment indicates the high convergence speed and high efficiency of the proposed method.  
The time requirements of the proposed algorithm and conventional _P&O_ algorithm in CCM and DCM are provided in Table 1.  
Figure 11: Waveforms in Discontinuous Conduction Mode: (**a**) instantaneous photovoltaic voltage, (**b**) voltage across the inductor, and (**c**) duty cycle.  
Figure 10: Waveforms in Continuous Conduction Mode: (**a**) instantaneous photovoltaic voltage, (**b**) voltage across the inductor, and (**c**) current passing through the inductor.
# Fragility Curves for RC Structure under Blast Load Considering the Influence of Seismic Demand
## 4 Structural Model
### Capacity Curves  
The structure presented in Section 3 has been designed for permanent, service and earthquake load following the Italian [41] and European Standard [42] in each location, see Table 4. Then a finite element (FE) model of the framed structure has been developed using the commercial software JASP 6.5 [43]. This FE model was used to perform a static non-linear analysis with a uniform horizontal load. The lumped plastic hinge behavior has been modelled by standard approach [42; 43; 44], as illustrated in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Stress** & \multicolumn{2}{c}{**Reinforcing Bars**} & **Concrete** \\ \hline  & \(f_{d\omega}f_{\gamma}\) & \(f_{d\omega}f_{\mu}\) & \(f_{d\omega}f_{c}\) \\ Bending & 1.17 & 1.05 & 1.19 \\ Diagonal Tension & 1.00 & - & 1.00 \\ Direct Shear & 1.10 & 1.00 & 1.10 \\ Bond & 1.17 & 1.05 & 1.00 \\ Compression & 1.10 & - & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamic increase factor (DIF) for RC elements, extracted from [25].  
Figure 6: Materials constitutive law: (**a**) concrete, (**b**) steel.  
The results in terms of horizontal force and top horizontal displacement (capacity curves) are reported in Figure 8 for the structures designed in the selected locations. The equivalent bilinear SDOF constitutive curves have been calculated equating the area underneath bilinear and capacity curves assuming that the ending point and the first elastic slope should be the same for the two curves.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \(Ac\) & \(As_{s}x\) & \(As_{s}y\) & \(r_{s}x\) & \(r_{y}y\) & \(A^{s}s_{s}x\) & \(A^{s}s_{y}y\) & \(r^{\prime}_{s}x\) & \(r^{\prime}_{s}y\) \\ \hline
**Location** & **[mm2]** & **[mm2]** & **[mm2]** & **[-]** & **[-]** & **[-]** & **[mm2]** & **[mm2]** & **[-]** & **[-]** \\ \hline L’Aquila & 90,000 & 770 & 770 & 0.0086 & 0.0086 & 770 & 770 & 0.0086 & 0.0086 \\ Catania & 90,000 & 616 & 616 & 0.0068 & 0.0068 & 616 & 616 & 0.0068 & 0.0068 \\ Bari & 90,000 & 462 & 616 & 0.0051 & 0.0068 & 462 & 616 & 0.0051 & 0.0068 \\ Cagliari & 90,000 & 308 & 462 & 0.0034 & 0.0051 & 308 & 462 & 0.0034 & 0.0051 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reinforcents details for columns in each structure, \(Ac\) represents the concrete area, \(As\) the tensile reinforcement, \(A\)’s the compressive reinforcement, \(r\) is the reinforcement ratio (\(r=As/Ac\)) while the letter \(x\) or \(y\) denotes the reinforcements for the bending moment around x axis or y axis respectively, see Figure 2.  
Figure 8: Capacity curves and SDOF constitutive law for each structure location. The total horizontal load Vb is plotted versus the horizontal displacement of the top floor.  
Figure 7: Moment-rotation constitutive behavior for a frame member-end considered in JASP 6.5, where \(M_{u}\) is the ultimate bending moment, \(M_{\sigma}\) is the first cracking moment, \(\theta_{y}\)’ is the yielding rotation and \(\theta_{u}\)’ is the ultimate rotation.
## 3 Results and Discussion
### Diagnostics  
The MRONJ diagnostic process, according to the AAOMS guidelines, is based on medical history, clinical examination, and imaging tests [2]. A typical symptom of MRONJ is pain, the odontogenic etiology of which should be excluded, and which may radiate to the temporomandibular joint and maxillary sinus [2]. Neuros sensory sensations other than pain are also possible [2]. One of the prodromal factors of MRONJ may be chin numbness, which must be differentiated from malignant changes each time [16]. In a clinical examination, one of the symptoms is loosening of the teeth, for which periodontitis should be excluded [2]. Moreover, the presence of exposed bone or fistulas is a significant clinical symptom in determining the severity of MRONJ [2; 10]. The latter should be differentiated from fistulas resulting from pulp necrosis [2].  
Orthopantomogram (OPG) is a common type of examination used in diagnosing MRONJ. Taking an OPG radiogram allows for making an initial diagnosis and serves as a helpful tool in monitoring the progress of the disease and the therapeutic process. In questionable cases, referring the patient to three dimensional imaging diagnostics (i.e., a conventional computed tomography [CT] as well as cone beam computed tomography [CBCT]) is the gold standard in MRONJ diagnosis [17; 18]. Those examinations enable more accurate bone density assessments in the area affected by necrotic lesions and allow for more precise determination of its extent and presence of possible pathological fractures. These factors are crucial for qualifying for potential surgery and planning its scope [17].
## 2 Miniaturization of the Microwave System
### Microwave Transmission System Miniaturization Design
#### 2.2.3 Reduced Length Waveguide Plus Miniaturized Ridge Waveguide  
Taking into account changes in the microwave system's working environment, the microwave system can be further reduced. The high-voltage accelerator steel tank is typically filled with SF6, and the gas environment is significantly more electrically insulated than the atmospheric environment. As a result, the insulation size of the high-voltage isolation waveguide can be reduced, as can the size of the excitation cavity, and the system can finally be installed in a high-voltage spherical shell. After optimization, the size of the entire microwave system can be reduced to 234 mm, and the length from the power source to the bottom of the discharge chamber is only 306 mm finally.  
Figure 4c and plan 3 curves in Figure 5 represent the simulation results of further downsizing the microwave system. The microwave can still enter the discharge chamber very effectively while generating an electric field with a maximum field strength of 6011 V/m. Additionally, at 2.45 GHz, the scattering parameter S11 is \(-\)23.752 dB, and the transmission efficiency is higher than 90%, further demonstrating the microwave system's superior microwave transmission capability. Thus, the two key designs of the ion source system, namely, small size and high transmission efficiency, are achieved, and the system installed in the head of a high-voltage accelerator is satisfied.