esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis Workflow
The sampled areas are numbered for reference in Figure 6.  
Indices were calculated for average extracted band values of the 20 highest NDVI pixels in each of the circular AOIs and to assess the relative performance of the three treatments for three indices, NDVI: NDVI = (NIR \(-\) Red)/(NIR \(+\) Red) [19] and two indices based upon the same normalized difference format but with blue and green bands substituted for the red band, designated here as NDBI and NDGI, respectively. Together, these three indices provide an indication of the relative utility of AC-processed data to support automated machine analyses of any kind. **Test 4. Qualitative evaluation in moderate-to-extreme Atm-I:** Multiple images representing moderate-to-extreme Atm-I were selected for correction by CMAC and LaSRC and visual inspection of the results in comparison to the TOAR view. Color balance and clearing of haze have been applied during CMAC development as indicators of the quality of image correction [7] that found clear images were generally close to SR. While not conclusory, attributes of clarity, natural appearing color balance, and absence of induced artifacts have proven to be valuable for R&D as a quick confirmation of the quality of the AC. Color balance can be a competent indicator of which bands are incorrect, presenting two possible states for the interpretation. As an example, an overly green appearance may indicate that the green band is over-represented (under-corrected) but also potentially that the blue and red bands are under-represented (over-corrected). Figure 5: The L8 3 August 2021 image (**left**) was affected by more extreme haze and smoke that settled into hydrologic drainages, while the 6 August 2022 image (**right**) was comparatively clear: TOAR (**a**,**b**), CMAC corrected (**c**,**d**) and LaSRC corrected (**e**,**f**). The polygons along the Okanagan River represent sampled AOIs. Each was affected by very low Atm-I (clear conditions) in (**b**); however, haze is present in other locations of the image. Figure 6: TOAR views of the three QIAs selected for statistical examination: 3 August 2021 (column **A**) experienced moderate-to-extreme smoke effects, while 6 August 2022 (Column **B**) was comparatively clear. In order of increasing Atm-I, from south to north (**top** to **bottom**), are the Penticton, Kelowna and Vernon AOIs. The smaller numbered circles denote sampled areas for statistical examination, each located over areas of cultivated vegetation.
# Low Cost Circularly Polarized Antenna for IoT Space Applications
###### Abstract  
This work presents the use of a three-element radiating structure for circularly polarized Low-Power Wide Area Network (LP-WAN) communication with space. The proposed structure has a 72 mm \(\times\) 72 mm \(\times\) 12 mm compact size with Right-Handed Circular Polarization (RHCP) and a 120\({}^{\circ}\) wide beamwidth radiation pattern. Printed on low-cost FR4 Epoxy substrate, a feeding network circuit based on Quasi Lumped Quadrature Coupler (QLQC), it achieves a \(-0.6\) dB insertion loss and a very compact size. The final structure has a 69% total efficiency and a 3.14 dBic realized gain.  
circular polarization; internet of things; miniature antenna +
Footnote †: journal: Electronics Electronics  
0000-0002-4919-2238-2238][PERSON] [1][PERSON] [2]
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 5 Experimental Simulation and Results
### Comparative Performance Analysis of Topologies in Terms of Packet Delivery Fraction (PDF)  
Figure 10 presents a boxplot of the results in terms of the PDF. As we can observe in Table 1 and Figure 10, the median PDF (i.e., the expected gain) is the highest in Topology 1, followed by Topology 3 and Topology 2. The median PDF is 0.43% higher in Topology 1 than it is in Topology 3 and 0.25% higher in Topology 3 than it is in Topology 2. Therefore, switching from Topology 1 to Topology 3 actually decreases network performance by just 0.43%, but switching from Topology 2 to Topology 3 increases performance by 0.25% in terms of the PDF. However, there appears to be no substantial overall change in the expected PDF gain (less than 1%) after switching topologies.  
Figure 8: Latency performance results.  
Figure 9: PER performance results.  
Moreover, the CVs of each topology are 0%, 23%, and 13%, respectively. In other words, switching from Topology 1 to Topology 3 increases risk by 13% and switching from Topology 3 to Topology 2 further increases risk by about 10%. Therefore, Topology 1 performed best again since it carried almost no risk (0%), and this performance was followed by Topology 3.
# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 3 Experiment and Comparison
### Analysis of Experimental Results
The more associated frame, the higher the detection accuracy should be. Still, the group delay and cache occupation of the detection result information also increased. Therefore, the appropriate number of associated frames should be selected in the engineering implementation process. For example, after analyzing the number of related frames and the recall rate and F1 number \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**Preprocessing**} & \multicolumn{3}{c}{**Feature Identification**} & \multicolumn{3}{c}{**Association Identification**} & \multicolumn{3}{c}{**S-Yolo Identification**} \\ \hline \multirow{2}{*}{Poposed} & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ \\ \cline{2-11}  & 0.68 & 0.59 & 0.63 & 0.81 & 0.68 & 0.74 & 0.88 & 0.93 & 0.91 & 0.93 & 0.97 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of calculation results of different discrimination steps of the design method in this paper. Figure 6: Comparison of ship target detection performance between the traditional Yolo algorithm and the proposed algorithm. Figure 7: Detection results of remote sensing ship targets in different scenarios (**a**) small target \(G_{1}\) (**b,c**) shore target \(G_{2}\) (**d**) cloud-penetrating target \(G_{3}\). of discriminant results, as shown in Figure 9, when the number of associated frames was greater than 7, the improvement of the index was less than 0.3%, so 7 can be selected as the number of associated discriminant frames.
## 2 Materials and Methods
### Spectra Collection and Analysis
VIP score trends were compared across all models, with special attention to peaks above 2, which were interpreted as key indicators of discriminative spectral features. We developed eight PLS regression (PLS-R) models to relate NIR reflectance spectra --acquired at four anatomical sites (biceps, triceps, brainstem, and cortical regions)--to clinical disability (EDSS) and fatigue (FSS) scores in patients. The mean EDSS score was \(3\pm 2\), and the mean FSS score was \(30\pm 15\). PLS is a multivariate chemometric technique ideally suited to cases where a large, collinear set of predictor variables (spectral wavelengths) must be used to predict one or more dependent variables. Model calibration and cross-validation were performed by employing the VBs method to determine the optimal number of LVs. Predictive performance was evaluated by the root mean square error (RMSE) for calibration and cross-validation, by the coefficient of determination (R\({}^{2}\)), and by assessment of bias in both calibration and cross-validation phases to ensure minimal systematic error. Additionally, to assess the effect size of the PLS-R models, we calculated [PERSON]'s \(\ell^{2}\)[22], a widely accepted metric for quantifying effect size in multiple regression frameworks where both the dependent and independent variables are continuous. [PERSON]'s \(\ell^{2}\) was computed for each regression model (EDSS and FSS) to evaluate the practical significance of the prediction. This index, calculated on R\({}^{2}\)cv in our case, allows for the evaluation of the relationship magnitude, with conventional thresholds indicating small (f\({}^{2}\) = 0.02), medium (f\({}^{2}\) = 0.15), and large (f\({}^{2}\) = 0.35) effects. Finally, VIP scores were examined to highlight the specific wavelengths that contributed most strongly to the prediction of EDSS and FSS scores. PLS-DA and PLS-R were selected due to their robustness in handling collinear, high-dimensional spectral data with small sample sizes, as well as their interpretability via latent variables and VIP scores.
# Effect of Strain Hardening on Increase in Collapse Pressure during the Manufacture of ERW Pipe
## Change in the Material Stress-Strain Curve during Manufactature
### Material Model  
Simulation was performed using API-5L-X70 steel, which is a popular choice for steel pipes. During the manufacture process, repeated cyclic loadings between tension and compression creates kinematic and isotropic terms of hardening, which results in translation and expansion of the yield surface, respectively, and changes the stress-strain curve of a steel plate. The modified Chaboche model, which is suitable for the simulation of cyclic loading, was adopted [3; 15]. The kinematic and isotropic hardenings of the yield surface are defined by the von Mises function as represented by Equation (1).  
\[f=\|\sigma-\chi\|-\eta \tag{1}\]  
In Equation (1), \(\sigma\) is stress and \(\chi\) and \(\eta\) are terms representing the kinematic and isotropic forms of hardening, respectively.  
The kinematic and isotropic terms of hardening are defined by Equations (2) and (3), respectively.  
\[\chi=\pm\frac{C}{\gamma}+\left(\chi_{0}\mp\frac{C}{\gamma}\right)exp\left(- \gamma\left|\varepsilon_{p}-\varepsilon_{p0}\right|\right) \tag{2}\]  
\[\eta=\left\{\begin{array}{c}\sigma_{0}+\left(\sigma_{a}-\sigma_{0}\right) \cdot\left(1-exp(-b_{a}p)\right),\;p\leq\Delta\varepsilon_{plateu}\\ \psi+Q\cdot\left(1-exp(-bp)\right),\;p\geq\Delta\varepsilon_{plateu}\end{array}\right. \tag{3}\]  
In Equations (2) and (3), \(C\), \(\gamma\), \(\sigma_{a}\), \(b_{a}\) and \(\psi\) are the material hardening parameters. \(Q\) and \(\sigma_{0}\) are the saturated isotropic hardening stress and the initial yield strength, respectively. \(\varepsilon_{p}\) and \(\varepsilon_{p0}\) are the plastic strain and the plastic strain at the beginning of the manufacture process, respectively. \(\Delta\varepsilon_{plateu}\) is the length of the yield plateau. \(p\) is the accumulated plastic strain.  
The kinematic and isotropic hardenings can be calculated by the strain that occurred during manufacture. The material model was calibrated by the stress-strain curve obtained from the tension-compression-tension cyclic loading test, as shown in Figure 1. The test was conducted for a dog bone specimen with a thickness of 10 mm. Maximum strains of \(\pm 2\%\) were applied. The material model showed good agreement with the experimental result including the Bauschinger effect and strain hardening. The yield strength at an offset strain of 0.5% and the tensile strength of the tested specimen were 555 MPa and 620 MPa, respectively.
## 5 Review of the Original GN-AK Protocol
### Security Properties  
The original GN-AK protocol satisfies several important security criteria:  
* Known-key security: Each session key is derived from pseudorandom values, ensuring compromise of one session does not affect others.
* Forward secrecy: Even if long-term keys are exposed, past session keys remain secure.
* Key confirmation: Optional third step confirms that both parties agree on the session key.
* Impersonation resistance: Encrypted hashes act as challenge-response values binding session keys to identities.  
While the protocol is secure, its efficiency is bottleneck by scalar multiplication using standard double-and-add methods. This becomes particularly critical in low-power devices or high-load servers where elliptic operations dominate runtime. The upcoming section introduces our enhanced protocol version using double-base chains and point halving to mitigate these limitations.
## 2 The Necessity of 3D Printing in Robotic Prosthesis Development  
Three-dimensional printing has revolutionized the field of robotic prostheses by providing a rapid, customizable, and cost-effective alternative to traditional manufacturing. Traditional prosthesis production often involves lengthy processes, from casting molds to assembling each component by hand, which can take weeks or even months. In contrast, 3D printing enables the swift production of tailored designs, significantly reducing wait times for patients and making prostheses more accessible [4].  
Another major advantage of 3D printing is the flexibility it offers in the customization of prosthetic components. Every patient has unique anatomical and functional requirements, and 3D printing allows for a high degree of customization to fit these needs. Adjustments, such as adapting the prosthesis shape to the patient's limb or incorporating specific functional mechanisms, can be rapidly iterated, helping to refine fit and comfort before final production. This capability is essential for the effective integration of robotic elements, such as sensors and actuators, that require exact placements and housings [5].  
Furthermore, 3D printing enables resource-limited regions and non-profit initiatives to provide prosthetic solutions at a fraction of the cost of traditional methods. Organizations like e-NABLE leverage 3D printing to create prosthetic limbs that are affordable and can be produced locally, reducing the logistical complexities associated with importing expensive devices. By using locally available materials and simplified assembly processes, 3D printing supports prosthetic distribution in underserved areas where advanced medical equipment may not be readily accessible [6].  
In addition to reducing costs, the material versatility offered by 3D printing allows for innovations in the use of lightweight, durable materials suited to withstand daily wear. As 3D printing materials and techniques advance, the potential to integrate sensory and adaptive technologies directly into the printed components themselves is becoming feasible, paving the way for even more sophisticated prosthetic designs [7].
# Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
## 3 Maximum Marginal Approach
### Feature Selection  
To find out the time interval where the segment of two classes signals are least similar, we calculate the dot product for the average of two classes signals. We assume that there are two classes of EEG signals with different emotions and the sets of detail components are formulated as \(S_{1}=\{d_{i}|i\in[0,I]\}\) and \(S_{2}=\{d_{j}|j\in[0,J]\}\), where \(i\) and \(j\) indicate number of EEG signals in two sets, \(S_{1}\) and \(S_{2}\), respectively. The average signals of two classes EEG are calculated as \(M_{1}=\frac{1}{I}\sum_{i=0}^{I}d_{i}\) and \(M_{2}=\frac{1}{I}\sum_{j=0}^{J}d_{j}\). The similarity between signals \(M_{1}\) and \(M_{2}\) is formulated as \(D(M_{1},M_{2})=\sqrt{|M_{1}-M_{2}||^{2}}\). The dot product of signals \(M_{1}\) and \(M_{2}\) is calculated to divide the time interval for EEG signal, which is formulated as \(O(M_{1},M_{2})=M_{1}\cdot M_{2}\).  
Figure 4 shows the dot product of two signals \(M_{1}\) and \(M_{2}\) at each time point \(t\in[0,T]\) where the x-axis is time, the y-axis is the dot product between two signals, and the 0-axis is the horizontal axis with a dot product of 0. The EEG is divided into four time intervals based on the area between the dot product of signals and the 0-axis. The similarity at each time interval is formulated as \(D_{t}=\sum_{t_{s}}^{t_{e}}D(M_{1},M_{2})_{t}\), where \(t_{s}\) and \(t_{e}\) are the start time and end time of the time interval, respectively. The segments with the least similarity between two signals \(M_{1}\) and \(M_{2}\) are selected as the features. The selected features indicate the components of the brain waves caused by different emotions.  
Figure 3: Example of two-level wavelet transform.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 4 Results and Evaluations
### Evaluation of the Solar Module  
As can be seem from Table 7, the solar module can generate enough power from the solar panel to meet the needs of the access point (powering the Wi-Fi hotspot module and satellite module). On the other hand, during the month with the largest radiation, it takes only 5.41 days to fully charge the battery while the system is in production. As the battery capacity is 720 Wh, and the system power consumption is 150.72 Wh/day, the system can last 720/150.72 = 4.78 days, even without solar energy. As a result, the proposed solar-powered satellite access point can be self-sustaining without an external power source, and is suitable for deployment in remote areas where electricity supply is unstable.  
There were limitations to the evaluation of solar panels and PWM solar controllers. Solar panel evaluation relied on the data sheet provided by the manufacturer, which might not match the actual characteristics of the solar panel. The charging efficiency of the PWM solar controller was estimated based on the characteristics of the PWM solar controller, and not by measurement. To determine the charging efficiency of a solar controller, a solar array simulator is needed, which is beyond the scope of this paper.
Future Internet_**2012**, \(4\), 688-699; doi:10.3390/fi4030688  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Traditional Practice _vs._ New Tools and Routines in Stroke Treatment  
**[PERSON] and [PERSON] ***  
University Hospital of North Norway, Norwegian Centre for Integrated Care and Telemedicine,  
Tromso 35, 9038, Norway; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +47-9093-2183; Fax: +47-7775-4098.  
_Received: 19 April 2012; in revised form: 23 July 2012 / Accepted: 25 July 2012 / Published: 6 August 2012_  
**Abstract:** In Norway, it is a national goal to provide more patients with thrombol treatment. A referring hospital and a specialist hospital have implemented videoconferencing (VC) equipment to share knowledge and discuss stroke patients, regarding thrombol treatment. VC has only been used four times within the 19 months that the service has been available. The objective in this article is to increase the understanding of the contradiction between the need for knowledge-sharing through VC technology, as well as the reasons for low frequency of use when discussing stroke patients. Semi-structured interviews were conducted with 13 professionals. The results illustrate how the technology _per se_ is not the reason for the low frequency use. Health care is shaped by behavior, traditional rules, standards and division of labor. By using cultural historical activity theory (CHAT) as a framework, we illustrate the importance of understanding the historic way of performing an activity to be able to expand the treatment activity in the future.  
**Keywords:** collaboration; medical discussions; knowledge-sharing; videoconference; activity theory; stroke
## 5 Results and Discussion
Our results also align with findings from previous studies. Similar to other studies that identified the performance degradation of Modbus in high-BER environments [19], our observations show that the protocol's resilience is further reduced under real-world failure conditions. Moreover, the minimal additional latency introduced by modern OT security appliances supports the position advocated by [17] that security enhancements can be applied without sacrificing system performance.
###### Abstract
First, quantitative prediction of strong ground motions in a wide frequency range is provided based on the Green's function summation technique using a statistical Green's function valid from as low as 0.1 Hz up to 20 Hz derived from the observed strong motions in Japan [17]. Subduction-zone mega-thrust earthquakes along the Nankai Trough attract much concern and may lead to a chain rupture to adjacent fault segments (cascade model). Therefore, strong ground motions from hypothesized individual and chain ruptures of the Tokai, Tonankai, and Nankai segments in southwestern Japan are predicted in this paper. The predicted strong ground motions are compared with the seismic intensity distributions of past earthquakes. Their peak values in acceleration and velocity are also compared with empirical predictions from the well-calibrated GMPE [10]. Second, the predicted strong ground motions are used to estimate the damage of building structures using nonlinear response analysis models reflecting the characteristics of the seismic input and building types and their construction ages [2; 3; 4]. Finally, the merit of the proposed procedure for building damage prediction is discussed with special reference to the retrofitting policies for earthquake resistance performance of existing buildings based on the findings in this paper.
## 2 Propagation of an Apertural BGCSM Beam through a Paraxial ABCD Optical System
\begin{array}{l}W(\rho_{s},\rho_{d})=\left(\frac{k}{2\pi B}\right)^{2}\exp \left[-\frac{ikD}{B}\rho_{s}\cdot\Delta\rho\right]\\ \times\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty} \int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\sum\limits_{m=1}^{N}\sum\limits _{n=1}^{N}A_{m}A_{n}^{*}\exp\left[-\left(\frac{1}{2\sigma_{0}^{2}}+\frac{B_{m} }{a^{2}}+\frac{B_{n}}{a^{2}}\right)\mathbf{r}_{s}^{2}\right]\\ \times\exp\left[\left(\frac{ik}{B}\Delta\rho-\frac{ikA}{B}\Delta\mathbf{r}- \frac{B_{m}}{a^{2}}\Delta\mathbf{r}+\frac{B_{n}^{*}}{a^{2}}\Delta\mathbf{r} \right)\cdot\mathbf{r}_{s}\right]d^{2}\mathbf{r}_{s}\\ \times\exp\left[-\left(\frac{1}{8\sigma_{0}^{2}}+\frac{1}{2\delta_{0}^{2}}+ \frac{B_{m}}{4a^{2}}+\frac{B_{n}^{*}}{4a^{2}}\right)\Delta\mathbf{r}^{2}\right] \\ \times I_{0}\left(\beta\frac{|\Delta\mathbf{r}|}{\sigma_{0}}\right)\exp\left[ \frac{ik}{B}\rho_{s}\cdot\Delta\mathbf{r}\right]d^{2}\Delta\mathbf{r}\ \.\end{array}  \tag{6}\]  
After integrating over \(\mathbf{r}_{s}\) and \(\Delta\mathbf{r}\), we obtained the analytical cross-spectral density expression of an apertured BGCSM beam passing through a paraxial \(ABCD\) optical system as follows:  
\[ \begin{array}{l}W(\rho_{s},\rho_{d})=\left(\frac{k}{2B}\right)^{2}\exp \left[-\frac{ikD}{B}\rho_{s}\cdot\Delta\rho\right]\sum\limits_{m=1}^{N}\sum \limits_{n=1}^{N}\frac{A_{m}A_{n}^{*}}{\varsigma\gamma}\exp\left(-\frac{\beta^ {2}}{4\zeta_{0}^{2}\gamma}-\frac{k^{2}}{4\xi_{0}B^{2}}\Delta\rho^{2}\right)\\ \times\ \exp\left(-\frac{1}{4\gamma}\left|\frac{k}{B}\rho_{s}-\frac{k}{2B\xi} \left(\frac{ikA}{B}+\frac{B_{m}}{a^{2}}-\frac{B_{n}^{*}}{a^{2}}\right)\Delta \rho\right|^{2}\right)\\ \times\ I_{0}\left(\frac{\beta}{2\sigma_{0}\gamma}\left|\frac{k}{B}\rho_{s}- \frac{k}{2B\xi}\left(\frac{ikA}{B}+\frac{B_{m}}{a^{2}}-\frac{B_{n}^{*}}{a^{2}} \right)\Delta\rho\right|\right),\end{array}  \tag{7}\]  
with  
\[ \begin{array}{l}\varsigma=\frac{1}{2\sigma_{0}^{2}}+\frac{B_{m}}{a^{2}}+ \frac{B_{n}^{*}}{a^{2}}\,\ \ \ \ \ \gamma=\frac{\varsigma}{4}+\frac{1}{2\delta_{0}^{2}}-\frac{1}{4\varsigma}\left( \frac{ikA}{B}+\frac{B_{m}}{a^{2}}-\frac{B_{n}^{*}}{a^{2}}\right)^{2}.\end{array}  \tag{8}\]  
In the above derivations, we applied the following integral formulae [49]:  
\[ \begin{array}{l}\int_{-\infty}^{\infty}\exp\left(-p^{2}x^{2}\pm qx\right)dx= \frac{\sqrt{\pi}}{p}\exp\left(\frac{q^{2}}{4p^{2}}\right),\ \ \ \ \ \ (p>0),\end{array}  \tag{9}\]  
\[ \begin{array}{l}J_{\
u}(\chi)=\frac{(-i)^{\
u}}{2\pi}\int_{0}^{2\pi}\exp[i \upsilon\theta+i\chi\cos(\theta)]d\theta,\end{array}  \tag{10}\]  
\[ \begin{array}{l}\int_{0}^{\infty}x\exp\left(-\gamma x^{2}\right)J_{\
u}(px)J_ {\
u}(qx)dx=\frac{1}{2\gamma}\exp\left(\frac{p^{2}+q^{2}}{4\gamma}\right)I_{ \
u}\left(\frac{pq}{2\gamma}\right),\ [\mathrm{R}\mathrm{e}\mathrm{r}\mathrm{y}>0,\mathrm{R} \mathrm{e}\mathrm{r}\mathrm{y}>-1],\end{array}  \tag{11}\]  
where \(J_{\
u}\) (\(x\)) and \(I_{\
u}\) (\(x\)) are the \(\
u\)-order Bessel and modified Bessel functions of the first kind, respectively.