esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, we investigated the effects of football shoes with different collar heights on ankle biomechanics and dynamic postural stability. Fifteen healthy college football players performed anterior and lateral single-leg jump landings when wearing high collar, elastic collar, or low collar football shoes. The kinematics of lower limbs and ground reaction forces were collected by simultaneously using a stereo-photogrammetric system with markers (Vicon) and a force plate (Kistler). During the anterior single-leg jump landing, a high collar shoe resulted in a significantly smaller ankle dorsiflexion range of motion (ROM), compared to both elastic (\(p\) = 0.031, dz = 0.511) and low collar (\(p\) = 0.043, dz = 0.446) types, while also presenting lower total ankle sagittal ROM, compared to the low collar type (\(p\) = 0.023, dz = 0.756). Ankle joint stiffness was significantly greater for the high collar, compared to the elastic collar (\(p\) = 0.003, dz = 0.629) and low collar (\(p\) = 0.030, dz = 1.040). Medial-lateral stability was significantly improved with the high collar, compared to the low collar (\(p\) = 0.001, dz = 1.232). During the lateral single-leg jump landing, ankle inversion ROM (\(p\) = 0.028, dz = 0.615) and total ankle frontal ROM (\(p\) = 0.019, dz = 0.873) were significantly smaller for the high collar, compared to the elastic collar. The high collar also resulted in a significantly smaller total ankle sagittal ROM, compared to the low collar (\(p\) = 0.001, dz = 0.634). Therefore, the high collar shoe should be effective in decreasing the amount of ROM and increasing the dynamic stability, leading to high ankle joint stiffness due to differences in design and material characteristics of the collar types.  
collar height; kinematics; kinetics; dynamic stability; ankle injury +
Footnote †: journal: Applied Soft. 2020, 10, 3362
# Human Activity Recognition (HAR) in Healthcare, 2 nd Edition
## 4 Future Developments  
The future of Human Activity Recognition (HAR) in healthcare encompasses the creation of adaptive, context-aware, and privacy-preserving systems. One promising direction is federated learning, which enables models to be trained across multiple devices without sharing raw data, thereby preserving user privacy. Transfer and self-supervised learning can also reduce the need for large labeled datasets, making HAR systems more scalable and generalizable.  
Another key area to explore is multimodal and predictive HAR. By integrating data from diverse sources such as movement, voice, facial expressions, and physiological parameters, researchers can develop predictive models that anticipate critical events, such as falls, epileptic seizures, or clinical deterioration.  
The evolution of natural human-machine interaction is also important. HAR systems will develop increasingly intuitive interfaces that are capable of understanding context and dynamically adapting to user behavior. This improvement will enhance the user experience and the effectiveness of assistance.  
Integration with smart environments is another crucial development. Homes and healthcare facilities will become sensitive and responsive, automatically adapting to users' needs to improve their comfort, safety, and autonomy.  
Furthermore, the combination of HAR with digital twins and virtual reality environments has the potential to revolutionize personalized rehabilitation and remote care. These technologies can simulate patient-specific scenarios and provide real-time feedback, enhancing therapies' effectiveness.  
Finally, the creation of explainable AI models will be vital for earning the trust of clinicians and patients. HAR systems must be accurate, transparent, and interpretable. These advancements require interdisciplinary collaboration and a strong commitment to ethical design principles.
## 3 Numerical Results
### A Sphere Pulsating at Internal Resonance Frequencies
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**KR**} & \(\pi\) & **4** & **5** \\ \hline Frequency (Hz) & 171.5 & 218.4 & 273 \\ \hline Max. element size/wavelength & 0.0654 & 0.0667 & 0.0695 \\ \hline Exact solution (Pa) & 395.48 & 402.6382 & 406.97 \\ \hline Numerical solution (Pa) & 392 & 397.5 & 400.8 \\ \hline Error (\%) & 0.89\% & 1.29\% & 1.54\% \\ \hline Exact phase angle of the pressure (deg) & 72 & 76 & 79 \\ \hline Numerical phase angle of the pressure (deg) & \(-85\) & 77 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison between the analytical and numerical results. Figure 5: Convergence history of the sphere pulsating at the eigenfrequency (KR = \(\pi\)). Figure 7 plots the convergence history for 218.4 Hz (KR = 4). In Figure 7, the time shift was also applied (\(F\) = 0.35), and the convergence was robust. The error between the numerical and analytical solutions at 218.4 Hz (KR = 4) is 1.29%, as shown in Table 2. In Figure 8, another time-shifting factor (\(F\) = 0.7) was tested, and the pulsating frequency was increased to 273 Hz (KR = 5), suggesting that the iterating process is stable and convergent. The difference between the numerical and analytical solutions is 1.54%, as listed in Table 2.  
Figure 8: Convergence history of the sphere pulsating at the frequency KR = 5 (F = 0.7 in Equation (4)). Figure 6: The iterative results of one element for different iterating loops. Figure 7: Convergence history of the sphere pulsating at the frequency KR = 4 (\(F\) = 0.35 in Equation (4)). This computing sample illustrates that the present boundary element method can effectively improve numerical instability due to the internal eigenfrequency and robustly calculate the radiating wave at high frequency. The 0.35-0.7 range for the time-shifting factor (\(F\)) is recommended. The suggested ratio of maximum element side and wave length is under 0.069, showing that the numerical solution compares well with the analytical case; thus, the present method is both stable and reliable. In this paper, the midpoint rule was used to replace the surface integral. When the quadrangular elements were replaced by triangular elements, the error rate increased up to 0.63%, suggesting that quadrangular elements are more suitable for the present scheme.
## 5 Related Work  
The related work can be divided into harmonic suppression in non-backscatter systems and mirror frequency suppression/harmonic suppression of tags.  
**Harmonic suppression in non-backscatter systems.** In modern wireless communication systems, the suppression of harmonic interference has remained a prominent area of research. [PERSON] et al. [14] present a novel design of a dual-band antenna suitable for WLAN applications, capable of suppressing higher-order harmonics over a broad bandwidth without increasing the footprint of the design. [PERSON] et al. [15] propose and study a wideband 2 \(\times\) 2 filtering antenna array for fixed beam applications, achieving harmonic suppression up to three times the central frequency. However, these methods require additional resources, which are impractical for large-scale deployment.  
**Mirror frequency suppression/harmonic suppression of backscatter tags.** [PERSON] et al. [16] present the first single sideband backscatter architecture that produces a frequency shift on only one side of the single-tone Bluetooth transmission. This design effectively suppresses mirror frequency interference. However, this design ignores the harmonics generated by frequency shifting, which can introduce out-of-band interference. [PERSON] et al. [11] present a backscatter harmonic cancellation mechanism, which involves introducing additional voltage levels by imitating radios to generate an approximate sinusoidal signal for frequency shifting, thereby reducing harmonic interference. However, this mechanism is relatively complex to implement. [PERSON] et al. [10] present the use of IQ backscatter modulators for harmonic interference elimination. This hardware-level modification offers greater scalability, resulting in superior performance when addressing other types of interference, such as mirror frequency interference, as compared to our approach. However, due to the associated hardware costs, the deployment of such hardware modification solutions faces challenges in large-scale backscatter systems.
## 4 Discussion  
### AAS vs. XRF  
AAS vs. XRF regressions were interpreted in order to determine a conversion factor between XRF and AAS for total mercury in Batken, Kyrgyzstan. Semi-log and log-log regressions were performed to see if correlations improved, but it was found that linear regressions had the strongest R\({}^{2}\) and \(p\)-values (Table A1). The conversion factors can be used to establish XRF as the primary screening instrument for SHL. Based on the \(p\)-values of zero and the highest R\({}^{2}\) values (0.88 and 0.87, respectively), the linear regressions of ex situ-bulk and ex situ-sieved had the best correlations (Table 1). The in situ linear regression had a \(p\)-value of zero, but a R\({}^{2}\) value of 0.17, which is considered a poor fit to the data (Table 1). The in situ R\({}^{2}\) value did improve to 0.48 in the log-log regression (Table A1). Despite this improvement, the linear regression of AAS vs. in situ was used for the conversion factor, since the improvement was not significant enough to warrant the data manipulation in the log-log regressions. No comparable research was found in the literature review to either affirm or contradict the results of this study, making this study one of the first to find a correlation between XRF and AAS for determining Hg in soil. The conversion factor was determined to be 1.7 by finding the weighted mean between ex situ-bulk and ex situ-sieved.
# Towards Climate Smart Farming--A Reference Architecture for Integrated Farming Systems
## 3 MiFarm-CSA Architecture
### Integrated Data Collection Network  
The provision of an advanced data collection network and autonomous monitoring is a major pillar of MiFarm-CSA, which is realized by the IDCON. Figure 2 illustrates IDCON, which aims at designing and deploying the technology means that will allow MiFarm-CSA to measure data from different farming systems, e.g., forests, crops and livestock, and to expand the capability of the MFS in rough and slope terrains and in large-scale lands. IDCON encloses six cutting-edge technology solutions, systems, devices and gateways: (a) Smart Sensing and Tracking (SST), (b) Aerial Sensing System and Monitoring (ASSM), (c) Weather and Climate Control and Forecasting (WCCF), (d) EMHS, (e) SMG and (f) CEDA. Figure 2: The IDCON layer of MiFarm-CSA architecture. Initially, the data capturing and recording takes place by using the SST, ASSM and WCCF. SST includes electronic sensors (passive and active wireless sensor devices) for environmental monitoring (quality of water, soil, air and nutrition concentration), photo-electronic sensors for measuring environmental and soil changes via metabolic changes in plants (e.g., pH level and phytohormones) and soil-derived GHG emissions sensors that measure the effects of regenerative grazing regime and mixed farming practices on GHG losses. In addition, low-cost IoT-enabled GHG sensors are utilized for measuring the effects of emissions on local and regional levels. Since animal tracking is of paramount importance in any MFS, specialized IoT devices (e.g., collars and ear-tags) are also utilized for locating, monitoring and managing livestock. Beyond the conventional information such as (i) location, (ii) motion, (iii) temperature and (iv) animal behavior, collars and ear-tags can show (i) the areas of the farm that have been most frequently occupied and (ii) the history of the areas where the animals have been grazing. As a result, MiFarm-CSA architecture is able to boost feed efficiency, increase food quality, mitigate risks regarding the animal loss, decrease the stakeholder's expenses and increase his income. Since the beginning of the development of new generations of UAVs, these platforms were considered a well suited tool, under different configurations, in agriculture, livestock and forestry because of their potential (size, weight, flight speed and altitude) [81]. ASSM provides a sophisticated technology by deploying single UAVs or groups of UAVs (FANETs) for implementing several monitoring and surveillance activities in the crops, livestock and
## 6 Discussion
The CV can be helpful to estimate how much time variability the signal has. The CV is a measure of dispersion. So, if a band has significant power above the general noise floor and the CV is high, the peak power is much higher than the average power. The high CV observed over Europe, the Arctic, and the coastlines of North America indicates a much larger spread in amplitude than for AWGN. A communication system designed to cope with AWGN at the noise levels given by a heatmap fails under these circumstances. The stationarity measurements and the CV show that the interference is not AWGN over populated areas. The window of stationarity can be used as the required length of an interleaver to obtain average coding performance for each interleaved interval. Even though a lot of measurements are required to increase the reliability of the estimation of the length of this window, we can identify regional trends. For frequencies in the range 434.75-434.83 MHz and 435.19-435.25 MHz, the length could be less than 14 ms for most cases and regions. For the Arctic and Europe, longer lengths are needed for more reliability. In the band 434.83-435.19 MHz, there is a significant portion of measurements over America, Europe, and the Arctic, where window lengths longer than 27 ms are needed. The results presented are part of the measurement campaigns performed in the first half of 2021. Calibration measurements to estimate absolute power levels are planned for the next phase. A ground station will transmit test signals, such as a CW, with a known power to calibrate the in-orbit power results. Furthermore, global measurements will be performed to obtain an overall view of the interference environment. These measurements will have a lower frequency resolution to decrease the data size of the campaign. The center frequency will also be modified to observe the frequency variability. It is important that future measurements include ECDF measurements of the interference IQ samples, as well as temporal characteristics to enable the choice of the right countermeasures to obtain robust uplink communication.
# Finite Difference/Collocation Method for a Generalized Time-Fractional KdV Equation
## 2 Preliminaries of Fractional Calculus and Jacobi Polynomials
### Numerical Discretization in the Time Direction
By using the [PERSON] interpolation method, we interpolate \(U\left(\eta\right)\) on \([z_{i-1},z_{i}]\) as_  
\[I_{U}\left(\eta\right)=w_{i-1}u_{i-1}+\frac{w_{i}u_{i}-w_{i-1}u_{i-1}}{z_{i}- z_{i-1}}\left(\eta-z_{i-1}\right), \tag{15}\]  
_and according to the theory of [PERSON] interpolation, there exists \(\epsilon\in[z_{i-1},z_{i+1}]\) such that_  
\[U\left(\eta\right)-I_{U}\left(\eta\right)=\frac{U^{\prime\prime}\left( \epsilon\right)}{2!}\left(\eta-z_{i-1}\right)\left(\eta-z_{i}\right). \tag{16}\]  
_Then, (14) can be rewritten as_  
\[R_{k} =\frac{\left(w_{k}\right)^{-1}}{\Gamma\left(1-\alpha\right)} \sum_{i=1}^{k}\int_{z_{i-1}}^{z_{i}}\frac{1}{\left[z_{k}-\eta\right]^{\alpha} }\left[U\left(\eta\right)-I_{U}\left(\eta\right)\right]^{\prime}d\eta\] \[=\frac{\left(w_{k}\right)^{-1}}{\Gamma\left(1-\alpha\right)} \sum_{i=1}^{k}\int_{z_{i-1}}^{z_{i}}\frac{U^{\prime\prime}\left(\epsilon\right) }{2\left(z_{k}-\eta\right)^{\alpha}}\left[\left(\eta-z_{i-1}\right)\left( \eta-z_{i}\right)\right]^{\prime}d\eta\] \[=\frac{\left(w_{k}\right)^{-1}}{\Gamma\left(1-\alpha\right)} \sum_{j=1}^{k}\int_{z_{i-1}}^{z_{i}}\frac{U^{\prime\prime}\left(\epsilon \right)}{2\left(\eta-z_{i-1}\right)\left(z_{i}-\eta\right)}\left[d\left[z_{k} -\eta\right]^{-\alpha}\right.\] \[\leq\frac{\alpha\max_{t_{0}\leq\epsilon\leq t_{k}}\left|U^{ \prime\prime}\left(\epsilon\right)\right|\max_{1\leq i\leq k}\left(z_{i}-z_{i- 1}\right)^{2}}{8w_{k}\Gamma\left(1-\alpha\right)}\sum_{i=1}^{k-1}\int_{z_{i-1} }^{z_{i}}\left(z_{k}-\eta\right)^{-\alpha-1}d\eta\] \[\left.+\frac{\alpha\max_{t_{0}\leq\epsilon\leq t_{k}}\left|U^{ \prime\prime}\left(\epsilon\right)\right|}{2w_{k}\Gamma\left(1-\alpha\right)} \int_{z_{k-1}}^{z_{k}}\left[\left(\eta-z_{k-1}\right)\left(z_{k}-\eta\right) \right]\left(z_{k}-\eta\right)^{-\alpha-1}d\varsigma \tag{17}\]  
\[\sum_{i=1}^{k-1}\int_{z_{i-1}}^{z_{i}}\left(z_{k}-\eta\right)^{- \alpha-1}d\eta=\frac{1}{\alpha}\left[\left(z_{k}-z_{k-1}\right)^{-\alpha}- \left[\left(z_{k}-z_{0}\right)^{-\alpha}\right]\right.\leq\frac{1}{\alpha}\left( z_{k}-z_{k-1}\right)^{-\alpha} \tag{18}\]  
\[= \frac{1}{(z_{k}-\zeta)^{\alpha}}.\]0 \[= \frac{1}{(z_{k}-\zeta)^{\alpha}}.\]1 \[= \frac{1}{(z_{k}-\zeta)^{\alpha}}.\]2  
_Thus,_  
\[= \frac{1}{(z_{k}-\zeta)^{\alpha}}.\]3  
_Denote \([t_{i^{*}-1},t_{i^{*}}]\) as the interval satisfying \(\max_{1\leq i\leq k}\left(z_{i}-z_{i-1}\right)=z_{i^{*}}-z_{i^{*}-1}\), then the truncation error (14) has the form_  
\[= \frac{1}{(z_{k}-\zeta)^{\alpha}}.\]4_when \(z(t)\) satisfies the Lipschitz condition on \([t_{i^{*}-1},t_{i^{*}}]\) and \(L\) is the Lipschitz constant._  
According to the finite difference approximation (12) and replacing the nonlinear part \(u\left(x,t_{k}\right)\) with the former step \(u\left(x,t_{k-1}\right)\), we have the semi-discretization form of the generalized fractional KdV Equation (10) as  
\[= \frac{1}{(z_{k}-\zeta)^{\alpha}}.\]5  
where \(\eta_{i}=w_{i}\Gamma\left(2-\alpha\right)\).
## 3 Evaluation Results  
To evaluate the automated extraction of tumor regions using the evaluation cases, the tumors were detected for all of the cases. The average value of the similarity index using DSC between the tumor-region extraction results and ground-truth image was 0.839. Figures 4 and 5 show examples with high- and low-similarity indices, respectively, in the extraction results obtained using the proposed method. No training time was needed because a pretrained model was used as a 3D CNN for tumor region extraction. However, the training time was 2.87 days at 460 epochs during the development at NVIDIA, and the inference time was approximately 4 s per case. As for evaluating the grading accuracy, the accuracy and AUC was observed to be 91.3% and 0.927. The confusion matrix of the grading and processing results are presented in Tables 1 and 2. Furthermore, the ROC curve, AUC, and the examples of correctly classified and misclassified tumors by our proposed method are shown in Figures 6 and 7. In grades II and III, 69.2% of the lesions were correctly classified as grade II and IV, and for grade IV, all the lesions were correctly classified. Training time of the 3D CNN for grading was 27.1 min, and the inference time was 0.11 s for all 46 VOIs.  
Figure 4: Example of a case with a high similarity index. (**a**) Input image, (**b**) ground-truth image, (**c**) output image, and DSC values calculated in the volume data.  
Figure 5: Example of a case with a low similarity index. (**a**) Input image, (**b**) ground-truth image, (**c**) output image, and DSC values calculated in the volume data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Estimated Grade**} \\ \cline{2-4}  & **II/III** & **IV** \\ \hline \multirow{2}{*}{**Actual grade**} & II /III & 9 & 4 \\ \cline{2-4}  & IV & 0 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of the grading result.  
Figure 6: ROC curve and AUC for proposed method.
## 5 Experimental Results and Discussion
### Security Analysis  
Our solution embeds invisible and robust watermarks in video frames on the data source side. Thus, data users can easily extract watermarks and verify the authenticity of video data at the destination side. Due to the fact that an adversary cannot control watermark embedding and extraction procedures, he/she cannot create fake frames containing valid watermarks. The security features of the digital watermarking scheme can effectively protect against forgery attacks in IoV networks. In addition, metadata containing audit-proof information is stored on the immutability distributed ledger. An adversary cannot tamper with these on-chain data without controlling the majority (51%) of miners within an Ethereum network. Even data owners cannot insert, replace, and delete watermarked frames that have been published. A Merkel tree structure of metadata can easily detect any content inconsistency. This can prevent integrity attacks to both on-chain and off-chain data. Furthermore, a DDS system atop an IPFS network relies on a distributed hash table (DHT)-based P2P protocol for data distribution and storage among distributed sites. It is difficult for an adversary to disable data service by launching DoS/DDoS attacks on some sites. As a result, our solution can mitigate the impact of availability attacks.
## 3 Tire-Road Contact Force Equations through Numerical Analysis
### Establishment of Tire FE Model
#### 3.1.2 Tire Section and Material  
The 11.00R20 truck tire was taken as an example to demonstrate the tire modeling process. The radial tire is mainly composed of the tread, belt, carcass, bead, sidewall, apex, ply, and other parts, as shown in Figure 5. The Yeoh material model was chosen for simulating the tire rubber according to its uniaxial tensile test results [27]. Unlike the isotropic rubber material, the ply and belt of the tire are composite structures with anisotropy properties. The rebar reinforcement layer was selected to simulate the tire's reinforced skeleton structure. The parameters of the Yeoh model and rebar model are referred to in the literature [40].  
Figure 3: The equivalent contact area of truck tires.  
D Tire Model  
The 3D geometric model of the tire was generated through rotating the tire section shown in Figure 5 by 360\({}^{\circ}\) about the central axis, and different material properties were assigned to the corresponding parts. The total number of cells and nodes were 185,710 and 214,070, respectively. The rubber used the eight-node linear hexahedron element (C3D8R), and the skeleton structure used the four-node quadrilateral membrane element (M3D4R). The tire inflation pressure was realized by exerting pressure on the inner surface of the tire. The rim and the road were regarded as rigid bodies without deformation. The surface-to-surface contact type was used between the tire and the road, and binding constraints were adopted between the tire and the rim. The FE model of the truck tire in ABAQUS is shown in Figure 6. The modeling process of the passenger car tire is similar and not elaborated here.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Type** & \multicolumn{2}{c}{**Tire Specification**} \\ \hline Passenger Car & 195/55R16 & 205/55R16 \\  & 225/55R16 & 225/60R18 \\  & 235/65R18 & 245/60R17 \\ \hline  & 8.00R20 & 9.00R20 \\ Truck & 10.00R20 & 11.00R20 \\  & 11.00R22 & 12.00R20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of tire.  
Figure 4: Tire identification: (**a**) passenger car tire; (**b**) truck tire.  
Figure 5: Tire section.