esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Granger Causality  
Granger causality is a statistical method employed to evaluate the causal link between two time series. It functions on the premise that if a time series, labeled as \(S\) (source or Granger-causative), influences another time series \(T\) (target), then historical values of \(S\) should contain information that aids in forecasting future values of \(T\), beyond what can be predicted solely by past values of \(T\). Essentially, Granger causality prediction determines whether previous values of one time series improve the forecast of another time series. When predicting the next sample in an observed time series by solely using its past values, a univariate autoregressive model is utilized. The univariate autoregressive model is characterized as  
\[T(n)=\sum_{i=1}^{M}\omega_{Ti}T(n-i)+e_{T}(n). \tag{1}\]  
The coefficients \(\omega_{Ti}\) represent the autoregressive model parameters for sequence \(T(n)\), where \(M\) denotes the regression order, and \(e_{T}\) represents the autoregression error.  
Extending this idea, bivariate autoregressive models are formulated to examine how one sequence causally influences another, defined as  
\[T(n)=\sum_{i=1}^{M}\omega_{Ti}T(n-i)+\sum_{i=1}^{M}\omega_{Si}S(n-i)+e_{TS}(n), \tag{2}\]  
where \(e_{TS}\) is the multivariate regression error. The coefficients \(\omega\) of the model are computed by minimizing errors across \(N\) provided samples, where \(N\) exceeds \(M\). Granger causality (\(GC\)) is subsequently defined as  
\[GC=\log\frac{Var[e_{T}(n)]}{Var[e_{TS}(n)]}. \tag{3}\]  
In the analysis of directed functional connectivity, predicting Granger causality serves as a robust method to deduce the directional influences among various brain regions using their time-series data. This approach evaluates whether prior activity in one brain area (the source) offers predictive insights into future activity in another (the target), surpassing what can be anticipated from the source's own past activity alone. Utilizing Granger causality prediction provides valuable insights into both the static and dynamic interactions and causal links within intricate systems, facilitating the comprehension of predictive associations and decision-making mechanisms.
## 2 Finite Element Modeling for Thermal Analysis
### Heat Source Model  
In the porous material as shown in Figure 4, the laser heat source penetrates through the powder bed and is gradually absorbed along the depth of the powder layer [28]. Therefore, it is important to select an appropriate heat source model in order to simulate the laser heat source of the LPBF process. In this study, the exponentially decaying equation method is applied as a heat source model, and the general form of the exponentially decaying equation method is that the 2D Gaussian distribution is on the top surface while the laser beam is absorbed exponentially along the depth of the powder layer [3]. The intensity of the laser energy at the optical penetration depth (OPD) reduces to \(1/\mathrm{e}\) (\(\cong 37\%\)) of the intensity of the absorbed laser beam at the powder bed surface [29]. The specific expression of heat source intensity is as follows and is implemented using the DFLUX user subroutine for ABAQUS.  
\[I(x,y,z)=\frac{2P}{\pi r^{2}}\exp\biggl{(}-2\frac{x^{2}+y^{2}}{r^{2}}\biggr{)} \frac{A}{H}\exp\biggl{(}-\frac{|z|}{H}\biggr{)} \tag{8}\]  
where \((x,y,z)\) are coordinates of the center of the heat source, \(P\) is power of the stationary laser source, \(r\) is the radius of the laser beam, \(A\) is laser beam absorptivity, and \(H\) is the optical penetration depth (OPD).  
The material used in this study is AISI 316L spherical powder with a distribution of average particle size of 45 \(\upmu\)m [4], and the absorption coefficient (A) for this material was found to be 0.52 at scan speeds above 50 mm/s [30]. Considering this result, the range of the absorption coefficient (A) is set to \(\pm 10\%\) based on 0.52. The OPD (H) of AISI 316L powder can be obtained from the OPD of Ni powder because the absorption of Ni and Fe is in the same range [4,31]. According to experimental investigations of [PERSON] et al. [32], the OPD in spherical Ni powder with size of 20 \(\upmu\)m is measured to be 20 \(\upmu\)m, while it is 200 \(\upmu\)m for the powder size of 50-75 \(\upmu\)m. If the OPD for the powder size 50 \(\upmu\)m is 170 \(\upmu\)m, then the OPD for the powder size 45 \(\upmu\)m is calculated to be 170 \(\upmu\)m, and if the OPD for the  
Figure 3: Thermal properties of AISI 316L solid and powder.  
powder size 75 \(\upmu\)m is 170 \(\upmu\)m, then the OPD for the powder size 45 \(\upmu\)m is calculated to be 102 \(\upmu\)m by linear interpolation. In this way, the OPD of AISI 316L powder with the size of 45 \(\upmu\)m can be estimated to be in the range of 102-170 \(\upmu\)m, and this is set as the range of the OPD (\(H\)) for sensitivity analysis and parameter optimization.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 3 Element Design  
The design of a single-switch RRA element is typically divided into passive and active parts. The design of the passive part depends on the equivalent parameters of the switch, which serves as the active part. These steps are crucial for achieving amplitude control RRA in the terahertz band.
## 4 Analysis
### Security
However, if the fog server joins in the collusion, the system may be vulnerable. With regard to man-in-the-middle attacks and messages that are re-encrypted at the proxy, they will be secure as long as the attacker does not satisfy the access structure. However, if the attacker is disguised as a legitimate user or a proxy, the system may be vulnerable momentarily. Table 3 shows whether or not the proposed model supports different security and attack items. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Security Items** & **Supports or Not** & **Attack Items** & **Supports or Not** \\ \hline Unidirectionality & \(\bigcirc\) & Collusion attack & \(\bigtriangleup\) \\ Data confidentiality & \(\bigcirc\) & User/attribute revocation & \(\bigcirc\) \\ Noninteraction & \(\bigcirc\) & Man-in-the-middle attack & \(\bigtriangleup\) \\ Intransitivity & \(\bigcirc\) & & \\ Multiple-uses & \(\times\) & & \\ Re-encryption control & \(\bigcirc\) & & \\ Master key security & \(\bigcirc\) & & \\ \hline \hline \end{tabular} Note: (\(\bigcirc\)—Excellent, \(\bigtriangleup\)—Normal, \(\times\)—Insufficient).  
\end{table} 
Table 3: The Security Analysis of Proposed Model.
## 4 Experiments and Results
### Standard of Evaluation  
The probability that the classifier categorizes the items correctly is referred to as accuracy. If a ship target is detected, the object will likely be accurately identified as a ship. Two types of accuracy measures were considered to evaluate the prospective performance of Auto-T-YOLO. \(Pd\) is the fault detection rate.  
\[P_{d}=\frac{TP}{N_{gt}} \tag{12}\]  
\(Pm\) is missing the alarm rate.  
\[P_{m}=\frac{FN}{N_{gt}} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensors** & **Polarization** & **Scale** & **Resolution** & **Position** \\ \hline RadarSat-2 TerraSAR-X Sentinel-1 & HH, VV, VH, HV & 1:1 1:2 2:1 & 1 m-15 m & In the sea and offshore \\ \hline \hline \end{tabular}
\end{table}
Table 3: SSDD dataset details.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Number of Areas** & **Number of All Area** & **Number of All Area** & **Total** \\  & **>64 \(\times\) 64** & **>32 \(\times\) 32** & **<32 \(\times\) 32** & **Total** \\ \hline Inshore & 82 & 150 & 64 & 213 \\ \hline Offshore & 206 & 647 & 300 & 947 \\ \hline Total & 288 & 797 & 363 & 1160 \\ \hline \hline \end{tabular}
\end{table}
Table 2: There are statistics about the ships’ target-size area in the SSDD dataset. When there is more than one target size area of the ship in the image larger than 64 \(\times\) 64, this study will count this image as “the number of areas > 64 \(\times\) 64”. The same as “the number of areas > 32 \(\times\) 32” is also counted like this. When the target size area of all ships in the image is smaller than 32 \(\times\) 32, we count this image as “the number of all area < 32 \(\times\) 32”.  
\[Pf\] is the false alarm rate. \[P_{f}=\frac{FP}{TP+FP}\] (14) _recall_, _precision_, _map_, and \[FPS\] are defined by: \[Precision=\frac{TP}{TP+FP}\] (15) _recall_ means the chance of judging objects. It is the rate that the model predicts correctly in data of true. \[Recall=\frac{TP}{TP+FN} \tag{16}\]  
Sometimes these two metrics do not change in a synchronized manner. If it is not intuitive to determine which algorithm is superior, the F1 indicator can be used:  
\[F1=\frac{2\times P\times R}{P+R} \tag{17}\]  
In the above formulas, _precision_ is \(P\), and \(recall\) is \(R\). \(Ngt\) is the number of the ground truth. \(TP\) represents a true positive number; \(FP\) represents a true negatives number; \(FN\) is a false positives number; and \(TN\) is a false negatives number. In general, _precision_ and _recall_ are detection metrics used by a single image. For the whole dataset, this study needs to introduce one evaluation standard \(AP\). It represents the accuracy of the whole dataset and the regional curve under accurate recall.  
\[AP=\int_{0}^{1}P(R)dR \tag{18}\]  
The relationship between the detection time (\(s\)) and frames per second (\(FPS\)) of a SAR image is:  
\[FPS=\frac{1}{Time} \tag{19}\]
## 5 Results and Analysis
### Spatial Heterogeneity of Built Environment Factors Under the MGWR Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Independent Variable**} & \multirow{2}{*}{**Moran’s I**} & \multirow{2}{*}{_p_**-Value**} & \multirow{2}{*}{**Z-Value**} & \multirow{2}{*}{**Autocorrelation**} \\  & & & & & **Feature** \\ \hline Road network density & 0.495 & 0.000 & 13.739 & ++ \\ Population & 0.365 & 0.000 & 10.195 & ++ \\ Accessibility & 0.454 & 0.000 & 12.729 & ++ \\ Bus lines & 0.064 & 0.049 & 1.968 & ++ \\ Bus stops & 0.128 & 0.000 & 3.676 & ++ \\ Land use mix & 0.087 & 0.011 & 2.558 & ++ \\ Office types & 0.273 & 0.000 & 8.029 & ++ \\ Residential types & 0.241 & 0.000 & 8.178 & ++ \\ \hline \hline \end{tabular} Note: ‘\(\star\)+’ represents positive spatial autocorrelation.  
\end{table} 
Table 7: Spatial autocorrelation test. Figure 3: Multicollinearity test results. The local regression coefficients of inbound and outbound ridership were calculated by using Equation (3) of the MGWR model. The distribution of local regression coefficients was visualised by ArcMap 10.8, and spatial heterogeneity distribution maps were obtained in Figures 4 and 5, which show the spatial heterogeneity of built environment factors on inbound and outbound riderships of Shenzhen rail transit under the MGWR model. Figure 4 shows that there is spatial heterogeneity in the effects of population, bus stops, and residence types on inbound passenger flows. Density, accessibility, land use mix, and office types are globally negatively correlated with inbound traffic, while bus lines are globally positively correlated. Figure 5 shows that road network density, accessibility, bus lines, land use mix, office types, and residential types have spatially heterogeneous effects on outbound passenger flows. Population and bus stops are globally negatively correlated with outbound passenger flows. By analysing Table 8, Figures 4 and 5 comprehensively, it can be observed that, for inbound ridership, population, bus stops, and residential types have local impacts. Accessibility, bus lines, land use mix, and office types exhibit global effects. For outbound ridership, accessibility, bus lines, and office types have local impacts. Road network density, population, bus stops, and residential types demonstrate global effects. These findings reveal the spatial heterogeneity of built environment factors on both inbound and outbound ridership. Figure 5: Spatial heterogeneity in the impact of built environment factors on outbound ridership.
# Seismic Performance of LRS-FRP-Concrete-Steel Tubular Double Coupling Beam
## 2 Materials and Methods  
### Specim Design  
A total of five coupling beams were manufactured and tested by considering the cross-section form, diameter of the inner steel tube, and construction technology. As shown in Figure 1, the geometry of the coupling beam is 200 mm in width, 900 mm in length, and 600 mm in height. Figure 2 illustrates the brief manufacturing processes of specimens. The control specimen is one double RC double coupling beam (RC-DCB). The specimen, LRS-DCB, is one double RC coupling beam wrapped with one PET sheet. The difference between RC-DCB and LRS-DCB is the extra wrapped PET sheet. The details of these two specimens are illustrated in Figure 1a. The other three coupling beams are LRS-FRP-concrete-steel DST double coupling beams, as shown in Figure 1b, the differences between them are the diameter of the inner steel tube and the manufacturing method. Thus, DST-DCB-89 is one double coupling beam with an inner steel tube of 89 mm diameter and wrapped with one PET-FRP sheet; DST-DCB-76 is one double coupling beam with an inner steel tube of 76 mm diameter and wrapped with one PET-FRP sheet; DST-DCB-89P is one precast double coupling beam, as shown in Figure 2, with an inner steel tube of 89 mm diameter and wrapped with one PET-FRP sheet. The details of all specimens are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **PET ply** & \
\begin{tabular}{c} **Longitudinal** \\ **Reinforcement** \\ \end{tabular} & **Stirrup** &
\begin{tabular}{c} **Geometry of** \\ **Steel Tube** \\ \end{tabular} & **Construction** \\ \hline RC-DCB & - & 4 \(\blacklozenge\) 18 & \(\blacklozenge\) 8075 1 & - & Cast \\ LRS-DCB & One & 4 \(\blacklozenge\) 18 & \(\blacklozenge\) 8075 & - & Cast \\ DTS-DCB-89 & One & 4 \(\blacklozenge\) 18 & - & \(\blacklozenge\) 89 \(\times\) 3.5 \(\times\) 1900 & Cast \\ DTS-DCB-76 & One & 4 \(\blacklozenge\) 18 & - & \(\blacklozenge\) 76 \(\times\) 3.5 \(\times\) 1900 & Cast \\ DTS-DCB-89P & One & 4 \(\blacklozenge\) 18 & - & \(\blacklozenge\) 89 \(\times\) 3.5 \(\times\) 1900 & Precast \\ \hline \end{tabular}  
[FOOTNOTE:1]Footnote 1: The symbol \(\blacklozenge\) expresses the diameter; the symbol \(\
# The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
###### Abstract
Moreover, this approach enables the prediction of the rheological properties of magnetic particles [17]. Another application field of magnetic nanoparticles is anemia treatment and diagnostics, including Computed Tomography (CT) and MRI [18; 19; 20; 21; 22]. In the case of such iron-based compounds, The Food and Drug Administration (FDA) has approved different formulations, where the colloidal suspension of the magnetic nanocarriers is proposed [19]. Ferumovytol (trade named Feraheme(r) and Rienso(r)) composition is based on the colloidal iron having a hydrodynamic diameter (\(D_{H}\)) ranging from 17 to 31 nm coated with an organic stabilizer like polyglucose sorbitol carboxymethyl ether [23; 24; 25]. In turn, in Ferumovide (Feridex(r), Endorem(r)), the dextrans are used to coat iron oxide nanoparticles (IONPs) [26; 27] similarly to Ferumovatran-10 (Sinerem(r), Combidex(r)) [28]. Another product called Ferisat(r) is based on iron dextran nanostructures, and Injectafter(r) contains ferric carboxamidtose [26]. Ferucarbotran (Ciavisit(r), Resovist(r)) stabilizes IONPs with carbox dextran, where \(D_{H}\) is up to 180 nm [29; 30]. In turn, in Ferumovsil (Lumiren(r), GastroMARK(r)), IONPs are coated with siloxanes, where \(D_{H}\) is up to 300 nm [31], and in Nanotherm(r) with aminosilanes, and VSOP C184 ultrasmall IONPs are coated with citrates [29]. As these agents contain solid-state iron-based compounds having magnetic properties, they reveal tremendous potential to be used in magnetic hyperthermia [31]. Depending on the shape, size, and concentration of nanoparticles in the solution and the magnetic properties of magnetic carriers, including coercivity, magnetic remanence, magnetization saturation, and hysteresis loop area, the particles' effectiveness in magnetic hyperthermia can vary. The ability to predict these properties with Artificial Intelligence-based algorithms can provide the opportunity to optimize the parameters of magnetic hyperthermia [5]. The AI-based algorithm was also proposed to increase the effectiveness of microbubbles, which are used in clinical diagnostics as a contrast nanocarriers in ultrasound imaging techniques and as a transport carrier in targeted delivery [32; 33]. In this paper, we briefly overview the significance of the combination of AI-based methods, in particular, the Machine Learning technique with magnetic hyperthermia, to shed light on their application in the field of nanotechnologies, in particular nanomedicine, including targeting strategies and optimization of the effectiveness of existing anticancer therapies. Algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Finally, the direction of future research has been indicated.
# Electrical Monitoring under Transient Conditions:
## 4 Results
### Results in Field Motors
#### 4.2.1 Cage Motors in Water Intake Facility (3.8 MW, 6.6 kV)  
Several High Voltage (H.V.) cage motors were diagnosed in a water intake facility [41]. To this end, starting current analyses were employed. The results revealed that all diagnosed motors had a certain level of damage in their rotor cages. However, the severity of the damage significantly changed from one motor to another. Figure 8 shows the time-frequency analyses for two of the diagnosed motors: while the motor A was diagnosed as healthy (no fault components evolutions were found in the time-frequency map), motor B was diagnosed with a severe level of rotor failure (see clear V-shaped pattern caused by the transient evolution of the \(f\)-\((1-2s)\) component). This latter motor was inspected in the repair shop and a significant number of broken bars were found in the rotor cage (more than 1/4 of the rotor bars were damaged) [41]. On the other hand, note how the time-frequency map for motor A shows a vertical line that is indicative of the commutation of the supply voltage under starting (the motor was started using an auto-transformer). This example clearly shows how the approach is highly reliable to detect rotor damages in cage motors.  
Figure 8: Time-frequency maps of the starting current for two H.V. cage motors of the water intake facility.  
Figure 7: Time-frequency maps of the emf signals induced by the stray flux at sensor position B for the two considered fault conditions.
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 2 Optical Imaging System Principles for Large-Size Stepped Shaft Diameter Detection
### Parameter Computation  
Based on the symmetric dual-telecentric optical path detection method used in the system and considering the detection range of the large stepped shaft diameter detector (600 mm-800 mm), the maximum detection range has been increased to 820 mm in order to improve fault tolerance. The calculation of the object-side linear field of view can be expressed as follows:  
\[2y=(820-600)/2=110\:\mathrm{mm} \tag{1}\]  
The TCD1501D CCD is selected, with a photosensitive receiving area of 35 mm. Additionally, the image-side linear field of view is as follows:  
\[2y^{\prime}=35\:\mathrm{mm} \tag{2}\]  
Figure 1: Schematic diagram of the double-telecentric optical path.  
Therefore, the magnification ratio [17] is the following:  
\[\beta=\frac{2y^{\prime}}{2y}=-0.32 \tag{3}\]  
The TCD1501D CCD has a pixel size of 7 \(\upmu\)m \(\times\) 7 \(\upmu\)m. Based on the Nyquist frequency [18] theory, a clear image display requires a minimum of two sampling points for each spatial frequency. Consequently, the CCD's maximum resolution is at least twice the pixel size. Therefore, the spatial frequency is defined as follows:  
\[\text{spatial frequency}=\frac{1000}{2\times\text{ pixel}}\text{lp/mm} \tag{4}\]  
In the equation, \"pixel\" represents the size of each pixel. From the above equation, it can be deduced that the maximum resolution spatial frequency of this type of CCD is 71.4 lp/mm. In order to be distinguishable by the photosensitive device, the resolution power of the designed double-telecentric system should be greater than 0.3 at 71.4 lp/mm.  
The formula for calculating the Airy disk size [19] is as follows:  
\[d_{\text{min}}=1.22\lambda_{0}F \tag{5}\]  
where \(\lambda_{0}\) represents the wavelength of the laser and \(F\) is the F-number. In order to fully utilize the CCD's resolving power, it is required that the Airy disk size be smaller than the CCD pixel size. Therefore, \(F\) can be set to 10.  
Figure 2: Schematic diagram of the diameter detection system.  
In a telecentric optical system, the depth of field determines the imaging clarity of the system [20] and can be represented by the object-space depth. The formula is as follows:  
\[\Delta=\frac{F\times\text{ pixel }\times k}{\beta^{2}} \tag{6}\]  
where \(\Delta\) represents the depth of field, \(\beta\) is the magnification factor, and \(F\) is the working \(F\)-number of the system. The parameter \(k\) is a specific value (commonly taken as (0.015-0.008)). Based on calculations, the depth of field for the system is determined to be 8.2 mm. Therefore, the main technical specifications of the object-image telecentric system can be found in Table 1.