esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electronic Short-Circuiting through the BCY10 Electrolyte  
In addition to the typical protonic and oxide-ion conductivity, the majority of proton-conducting ceramic electrolytes exhibit electronic p-type conductivity in oxidising atmospheres. The ionic-conducting phase here utilised, BCY10, is known to possess this electronic component in oxidising atmospheres, which can significantly impact the total conductivity in these conditions [24], as expressed by:  
\[\frac{1}{2}\text{O}_{2(\text{g})}+\text{V}_{\text{O}}^{\bullet\bullet} \longleftrightarrow\text{O}_{\text{O}}^{\text{x}}+2\text{h}^{\bullet} \tag{17}\]  
Figure 8 depicts the electronic transport numbers for hole conduction estimated for BCY10 as a function of \(p_{\text{H2O}}\) in the temperature range of 400-550 \({}^{\circ}\)C in O\({}_{2}\) atmosphere. The significant electronic contribution with increasing temperature and decreasing \(p_{\text{H2O}}\) is evident.  
Figure 8: Electronic transport number for hole conduction of the BCY10 electrolyte as a function of water vapour partial pressure (\(p_{\text{H2O}}\)) in O\({}_{2}\) atmosphere, calculated from data from [24].  
Therefore, the intercept of the impedance dispersion with the real axis at high frequency \(\left(Z_{\text{Q}}=1/T(j\omega C)^{n}\rightarrow\infty\right)\) corresponds to the electrolyte electrical resistance (\(R_{\text{oo}}\)), which corresponds to the parallel combination of the ionic (\(R_{\text{ion}}\)) and electronic (\(R_{\text{eon}}\)) resistances, given by:  
\[\frac{1}{R_{\text{ohm}}}=\frac{1}{R_{\text{ion}}}+\frac{1}{R_{\text{eon}}} \tag{18}\]  
At low frequency, \(\left(Z_{\text{Q}}=1/T(j\omega C)^{n}\to 0\right)\) corresponds to the total resistance (\(R_{0}\)) of the cell:  
\[\frac{1}{R_{0}}=\frac{1}{R_{\text{ion}}+R_{\text{p}}}+\frac{1}{R_{\text{eon}}} \tag{19}\]  
Finally, the true total polarisation resistance of the electrode is given by:  
\[R_{\text{p}}=\frac{R_{0}R_{\text{eon}}}{R_{\text{eon}}-R_{0}}-R_{\text{ion}} \tag{20}\]  
where \(R_{\text{eon}}=R_{\text{ohm}}/t_{\text{eon}}\).
## 5 Discussions, Conclusions, and Future Work
### Nomenclature
  
perpendicularity deviation vector of azimuth axis assembly in the system base coordinate, and its components around the x and y axes of the coordinate in [rad]  
Perpendicularity deviation vector of pitch axis assembly in the azimuth coordinate, and its components around the x and z-axes of the coordinate in [rad]  
R\({}_{\text{a}}^{\prime}\),R\({}_{\text{p}}^{\prime}\)  
Angular velocity vectors of the azimuth and the pitch in the inertial coordinate affected by errors of axis perpendicularity in [rad/s]  
axial shifts  
\(\Delta\)R\({}_{\text{o}}\)  
Vector of radial run-outs  
Oscillation vector of the azimuth, and its decomposed components of the maximum tilting oscillations around the x and y axes of the system base coordinate in [rad]  
Oscillation vector of the pitch, and its decomposed components of the maximum tilting oscillations around the x and z-axes of the azimuth coordinate in [rad]  
Real-time value expressions of the tilting oscillations of the azimuth along the x and y axes of the system base coordinate in [rad]  
Real-time value expressions of the tilting oscillations of the pitch along the x and z-axes of the azimuth coordinate in [rad]  
Rotation transformation matrixes affected by errors of run-outs of the azimuth and the pitch  
Angular velocity vectors of the azimuth and the pitch in the inertial coordinate affected by errors of run-outs in [rad/s]  
Vector of the azimuth gyroscope assembly deviation in the pitch coordinate, and its components around the x and y axes of the coordinate in [rad]  
Vector of the pitch gyroscope assembly deviation in the pitch coordinate, and its components around the x and z-axes of the coordinate in [rad]  
R\({}_{\text{g}}\),R\({}_{\text{pg}}\)  
Rotation transformation matrixes affected by assembly errors of the gyroscope locations  
Angular velocity vectors of the gyroscopes affected by errors of assembly errors of the gyroscope locations in [rad/s]  
K\({}_{\text{g}}\)  
Amplification coefficient of the gyroscopes  
Natural frequency of the gyroscopes in [rad/s]  
L\({}_{\text{g}}\)  
Damping coefficient of the gyroscopes  
Error sensitivity; the standard deviations of the experiment samples under different error levels  
n\({}_{\text{l}}\)  
Number of error levels  
\(\Delta\)\(\theta_{\text{i}}\)(x)  
Accuracy sample value of the ith error level of x  
\(\Delta\)\(\theta\)(x)  
Average value of accuracy sample values  
UAV  
Unmanned aerial vehicle  
FOS  
Field of sight  
LOS  
Line of sight  
BD  
Disturbances of the system base
# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 2 Materials and Methods  
### Method Overview  
Knee MR imaging produces a 3D representation of the knee joint, utilizing a sequence of 2D images taken laterally across the knee. Given the 3D nature of MR images, 3D CNN can be advantageous in evaluating the whole sequence of images as one unit. Through the implementation of 3D kernels, information from adjacent slices could be integrated. Therefore, 3D features that may not be detectable using 2D CNN could be potentially captured.  
For this study, we built a machine-learning model capable of analyzing sequences of MR images for each knee as input, with output given as one of the five KL grades. We further trained another model by relabeling the samples into OA and non-OA categories according to the clinical standard, i.e., patients with KL \(\leq\) 1 are diagnosed as non-OA cases while patients with KL \(\geq\) 2 are considered as OA. An overview of the proposed models is described in Figure 1.  
Figure 1: Overview of the models proposed in this study. The input data for each patient are a sequence of MR images. Each sequence is preprocessed by cropping and then removing slices. This reduced data are fed into a 3D CNN. The second pipeline applies the same 3D CNN architecture but is trained separately for the binary OA/non-OA classification.  
In addition to MRI, we also studied traditional X-ray images, with an interest of finding out which imaging modality coupled with the modern CNNs can achieve better accuracy for knee OA diagnosis. We employed several state-of-the-art 2D CNN models, including VGG 16, ResNet50, DenseNet, etc. These models were trained to classify X-ray images into five KL categories. The one with the best accuracy was selected and further applied for the binary OA/non-OA classification. The pipelines for X-ray images are similar to those illustrated in Figure 1, except the 3D CNN is replaced by a 2D CNN and the preprocessing step for X-ray is to cut each pair of knees into individual ones. The X-ray images and MR images were obtained from the same group of patients, and the separation of training, validation, and testing sets were kept the same at the patient level for all the models trained and compared in this work.
# AE-CGAN Model based High Performance Network Intrusion Detection System
## 2 Related Works
### A Studies on Imbalanced Data  
In order to solve the data imbalance problem, previous research has used random oversampling, random undersampling, and the SMOTE (synthetic minority oversampling technique) [19] techniques are available. In the case of random undersampling, an important feature may be missed, resulting in inaccurate results. Random oversampling may cause the problem of overfitting because the same data is randomly copied [20]. A key idea in SMOTE is to find data that is close to the entered sparse class data, and randomly sample data within the range.  
[PERSON] et al. [21] performed sampling using SMOTE considering data levels using NSL-KDD datasets to optimize data and improve performance using sampling techniques. The sampled datasets were combined with several classification algorithms such as SVM, RF, and backpropagation neural network (BPNN) to compare performance. The SMOTE technique is widely used to solve the data imbalance problem, but it has a problem of degrading the classification performance because classes overlap or make noise.  
[PERSON] et al. [22] proposed an improved SMOTE-NCL (neighborhood cleaning rule) based on SMOTE. SMOTE-NCL calculates the ratio of each class, the average ratio calculated from it, the standard deviation of the class ratio, and the unbalanced scale divided by the class percentage, and uses SMOTE to sample sparse class data until the unbalanced scale exceeds the threshold. Finally, after sampling, a method was proposed to handle data considered to be noise through the neighborhood cleaning rule. The SMOTE-NCL improves the SMOTE. Its disadvantage, however, is that the performance of the classifier is greatly affected by the data sampled from the major data category when oversampling sparse data. The CGAN proposed in this study shows better performance because it is independent of the distance between data and is oversampled by the generation algorithm.  
In another study on GAN, [PERSON] et al. [23] proposed an IDSGAN using the GAN model to create an attack that could cheat and evade an intrusion detection system. IDSGAN leverages the generator to convert the original malicious traffic into a malicious traffic example. Considering that the internal structure of the detection system is unknown to attackers, examples of adversarial attacks perform the black-box attacks against the detection system. We used GAN to solve the data imbalance, and the above research differed in that it developed the IDS by making malicious data based on GAN.
# Robotic Systems and Navigation Techniques in Orthopedics: A Historical Review
## 2 Robotic Systems (RSs)
### Hands-On RS
#### 2.2.7 **P**axiteles \(\rightarrow\) iBlock  
Praxiteles (Praxim Ltd., Grenoble, France), presented in 2005, is also a semi-autonomous system and is in the category of MBARS [50]. The Praxiteles, as shown in Figure 6, was designed to guide the saw blade or to use a passive bone-milling process, and the risk of soft tissue damage can be expected to be reduced by less-invasive exposures [27].  
Figure 5: MBARS and HyBAR. (Reproduced with permission from _[PERSON] et al., MBARS: Mini Bone-Attached Robotic System for Joint Arthroplasty, International Journal of Medical Robotics and Computer Assisted Surgery, 1(2): 101–121, 2005, John Wiley and Sons;_ and with permission from _[PERSON] et al., HyBAR: Hybrid Bone-Attached Robot for Joint Arthroplasty, International Journal of Medical Robotics and Computer Assisted Surgery, 5: 223–231, 2009, John Wiley and Sons_).  
The OMNINAV iBlock robotic cutting guide (formerly known as Praxiteles, OMNIlife Science (now owned by Corin Group), East Taunton, MA, USA) is a motorized, bone-mounted cutting guide for all femoral resections [51], and was approved by the FDA (iBlock FDA 2010 file (accessed on 28 June 2023): [[https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K163338](https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K163338)]([https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K163338](https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K163338))) in 2010 for use in TKA applications [2; 27]. Similar to the NAVIO system, the iBlock is also mageless. Its OmniBiotics computer station can generate a 3D digital model of the patient's knee by using patented bone-morphing technology and intraoperative anatomic data [27]. Based on the 3D model, the surgeon can make the implant plan intraoperatively and see the planned bone cuts before they are executed.  
Although there are limited clinical data available for this system, some studies have shown that the iBlock system can obtain more efficient, accurate, and repeatable bone resections, as well as having a shorter bone preparation time [2; 52]. The limitations of the iBlock system include having no haptic feedback, being limited to TKA applications, having a closed platform, and having limited kinematic assessment after implantation [2].
## 3 Thermodynamic Approach for Obtaining the Surface Fractal Dimension from MIP  
The surface fractal dimension \(D\) describes the geometric complexity of the surface. Normally, the dimension is an integer number (1 for curves, 2 for surfaces, 3 for volumes). The usual topological dimension of a surface is increased over 2 as the surface gains complexity or roughness, up to a maximum value of 3. As a result of MIP, the value of \(D\) can be determined. As \(D=3\) correspond to the full volume filling, any value \(D>3\) has no physical justification. The usual interpretation of MIP results with a cylindrical-shaped pore network assumes the dimension of 2 and reads any increase in the intruded volume with the pressure as the intrusion of adjacent smaller pores. A significant part of this volume actually corresponds to the improved adjustment to the rough pore surface (Figure 1). In other words, the volume of the pore is a function of the scale at which this is measured.  
The relation between the pressure and the surface of the intruded mercury can be considered to quantify the complexity of the pore wall surface. During the MIP process, the increase in pressure causes a progressive increase in the volume of intruded mercury and, with it, an increase in the surface energy of the system. The increase in the energy in the system equals the work done for the mercury to intrude the sample. On the fractally rough pore surface of the cementitious material, the correlation between the area for a fractal surface (\(S\)) and the circumscribed volume (\(V\)) is given by [PERSON] [5] as in Equation (4). Considering the energy conservation during the MIP experiment, [PERSON] and [PERSON] [18] proposed a logarithm proportionality between the accumulated intrusion work \(W_{n}\) and the represented accumulated intrusion surface (\(Q_{n}\)) in Equation (5). Then, the surface fractal dimension \(D\) can be computed from Equation (6).  
\[S^{1/D}\sim V^{1/3} \tag{4}\]  
\[\ln(W_{n})=\ln(Q_{n})+C \tag{5}\]  
\[\ln\left(\frac{W_{n}}{r_{n}^{2}}\right)=D\ln\left(\frac{V_{n}^{1/3}}{r_{n}} \right)+C \tag{6}\]  
where \(V_{n}\) and \(r_{n}\) are the accumulated intruded volume and smallest pore radius for the intrusion phase n, and \(C\) is a constant. This approach has been applied in [8; 19] to characterise the microstructure of cement-based materials, where a scale-dependence of the surface fractal dimension was confirmed. This means that the pore structure changes in complexity depending on the scale at which it is analysed. A microfractal region was associated with the microstructure of C-S-H, whereas the macrofractal region was associated with the capillary porosity. In other words, the self-similarity maintains for certain pore size, and then it transitions to another self-similarity, with a different value of the surface fractal dimension. An important corollary of the previous finding would be that modelling cannot be established to consider only one type of physical pore structure, but it would need to change depending on the value for the surface fractal dimension.
## 2 Materials and Methods
### Methodology
To achieve greater confidence in the estimated slopes, we used a criterion of sufficient data availability, according to which: (a) each time series should have a availability of monthly values of more than 75% of the total period of each network and (b) the first two and last two years should have more than 10 months available. This was decided because of the sensitivity of linear trends to the initial and latest parts of time series (as done in [91]). These  
Figure 2: The steps of the applied methodology of assessment/evaluation of MERRA-2 GDB. criteria were satisfied for 232 GEBA and 22 BSRN stations (shown in the graphs presented in the next section, i.e., results). The slope values were calculated using the more robust, nonparametric Theil-Sen's slope estimator [92; 93], which is the median of all possible linear slopes. The SSR changes (\(\Delta\)SSR) were computed by multiplying the computed slopes, which measure the changes per month, by the temporal duration (in number of months) for each time series. Along with the slopes, the statistically significant slopes at 95% confidence level were calculated using the non-parametric Mann-Kendall Test [94; 95], which is used to determine whether a time series has a monotonic upward or downward trend or not. Then, the sign of the slope values for each station was compared with the sign of the slope of the corresponding MERRA-2 pixel (including the station). Same signs of slopes of the MERRA-2 and stations' time series of SSR anomalies indicate a qualitative agreement of \(\Delta\)SSR (or GDB) between MERRA-2 Reanalysis and GEBA/BSRN stations, for a specific pixel, whereas opposite trends indicate a disagreement. This evaluation is useful since it quantifies the degree to which the MERRA-2 based GDB agrees, in sign and magnitude, with the reference ground-based stations, providing confidence about the MERRA-2 GDB.
## 2 Mathematical Formulation
### Set of Constraints  
To establish the location and size of D-STATCOMs in radial and meshed distribution networks, it is necessary to observe several constraints regarding the physical operation of the distribution network. Some of these have to do with active and reactive power balance, voltage grid regulation characteristics, and the sizes of the reactive power compensation devices. These constraints are listed below and defined for \(\forall k\in\mathcal{N}\) and \(\forall h\in\mathcal{H}\).  
\[P_{kh}^{g}-P_{kh}^{d}=\sum_{m\in\mathcal{N}}Y_{km}V_{kh}V_{mh} \cos(\delta_{kh}-\delta_{mh}-\theta_{km}), \tag{4}\] \[Q_{kh}^{g}-Q_{kh}^{d}+Q_{k}^{st}=\sum_{m\in\mathcal{N}}Y_{km}V_{ kh}V_{mh}\sin(\delta_{kh}-\delta_{mh}-\theta_{km}),\] (5) \[V_{\min}\leq V_{k,h}\leq V_{\max},\] (6) \[Z_{k}Q_{\min}^{st}\leq Q_{k}^{st}\leq Z_{k}Q_{\max}^{st},\] (7) \[\sum_{k\in\mathcal{N}}Z_{k}\leq N_{A}^{st}, \tag{8}\]  
where \(Q_{k}^{st}\) is the reactive injection generated by the D-STATCOM at node \(k\); the variables \(P_{kh}^{g}\) and \(Q_{kh}^{g}\) are the active and reactive power injection in power sources connected to \(k\) in the period \(h\); the parameters \(Q_{kh}^{d}\) and \(P_{kh}^{d}\) are the dynamical loads associated with node \(k\) at time \(h\), which are defined as a function of the residential, industrial, and commercial users connected to the distribution grid; \(Z_{k}\) is a binary variable (i.e., it takes values of 0 or 1) that indicates the connection or not of a D-STATCOM at node \(k\); and \(N_{A}^{st}\) corresponds to the maximum number of D-STATCOMs that can be installed in the distribution network.  
Constraints (4)-(7) can be interpreted as follows. Equations (4) and (5) defines the power balance constraints at each node of the network for each period. These are known as the power flow constraints [28]. Inequality constraint (6) refers to the voltage regulation imposed by the regulatory entities for the adequate operation of the distribution network [29]. Inequality constraint (7) concerns the upper and lower bounds assigned for the D-STATCOMs that are to be installed in the distribution network. Finally, inequality constraint (8) is associated with the maximum number of D-STATCOMs that can be placed in the distribution grid.
## 3 System Model and Problem Statement
### System Model
#### 3.1.3 Energy Model  
The energy consumption model we use here is called first order radio model [5-8,14], as is shown in Figure 2.  
Each sensor node will consume the following \(E_{Tx}\) amount of energy to transmit a \(l\)-bits message over distance \(d\):  
\[E_{t_{l}}(l,d)=\begin{cases}l\cdot E_{\mathit{dec}}+l\cdot\varepsilon_{\mathit{ fi}}\cdot d^{2},&\text{if }d<d_{0}\\ l\cdot E_{\mathit{dec}}+l\cdot\varepsilon_{\mathit{app}}\cdot d^{4},&\text{if }d \geq d_{0}\end{cases} \tag{1}\]  
Figure 2: Radio energy dissipation model.  
\(E_{Rx}\) amount of energy to receive this message:  
\[E_{Rx}(I)=l\cdot E_{elec} \tag{2}\]  
and \(E_{Rx}\) amount of energy to forward this message:  
\[E_{F_{x}}(l,d)=E_{Tx}(l,d)+E_{Rx}(l)=\begin{cases}2l\cdot E_{dec}+l\cdot\varepsilon _{h}\cdot d^{2},&\text{ if }d<d_{0}\\ 2l\cdot E_{dec}+l\cdot\varepsilon_{mp}\cdot d^{4},&\text{ if }d\geq d_{0}.\end{cases} \tag{3}\]  
The definition of the radio parameters is listed in Table 1.
## 3 Methodology
### Analysis and Validation of Results  
A validation of this method was performed by comparing experimental and analytical results according to the following methodology:  
1. Observation of frequency response peaks to determine if they align with the expected natural frequencies in the theoretical model.
2. Verification of whether the transducer achieves the expected potential for generating electrical energy in experiments for the proposed cases: resistive circuit, circuit with diode bridge, and DC-DC buck converter.
3. Assessment of relative error percentages concerning analytical results to verify the conformity of the experimental model.
4. Comparison of the potential generated by the transducers with recent works in the literature, considering power density, voltages, and natural frequencies.
5. Evaluation of the monitoring system's compliance with mode change requirements, ensuring that the Attiny85 microcontroller operates correctly with low power consumption.  
Figure 5: Schematic of the circuits: (**a**) Case I—resistive and (**b**) Case II—bridge rectifier circuit.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 5 Conclusions
The study's integration of iRAP risk assessments and GIS-based black spot analysis is directly compatible with the EU's Network-Wide Road Safety Assessment methodology (2023), ensuring policy coherence and practical applicability across European cities. Given limited public budgets, local governments can prioritize interventions using a layered approach. For instance, the following can be considered:  
* High-impact, low-cost measures first--solutions like traffic signal upgrades, pedestrian refuge islands, and speed management (e.g., Berlin cushions) should be implemented as they offer quick safety benefits with minimal investment;
* Data-driven prioritization--cost-benefit analysis (CBA) helps rank interventions by maximizing accident reduction per money spent, ensuring resources are directed toward the most cost-effective solutions;
* Phased infrastructure upgrades--larger interventions (e.g., road redesigns) can be implemented in stages to align with budget cycles and political feasibility;
* Leveraging external funding--governments can seek EU road safety grants and public-private partnerships to finance larger-scale projects. Future research could explore refining risk models for complex urban intersections, where iRAP's current methodology has limitations, and further validate the approach in larger metropolitan areas with diverse traffic conditions. Expanding the integration of machine learning techniques within this framework could also enhance predictive capabilities, providing even stronger decision-making tools for urban planners and policymakers. Conceptualization, E.T. and D.R. ; methodology, D.R. ; software, D.R. ; validation, E.T. and D.R. ; formal analysis, N.N. and D.R. ; investigation, N.N. ; resources, D.R. ; data curation, N.N. and D.R. ; writing--original draft preparation, M.K. ; writing--review and editing, M.K. and E.T. ; visualization, M.K. ; supervision, E.T. and D.R. ; project administration, D.R. and E.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.