esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
### Governing Equations  
Given two parallel discs with an inner radius \(r_{0}\), an outer radius \(r_{1}\) and a vertical distance \(h\). We assume that the flow between these two plates is stationary and laminar. Additionally, we assume an incompressible, Newtonian fluid. Then the Navier-Stokes equations, representing conservation of momentum, are given as:  
\[\rho\Big{(}\overrightarrow{v}\cdot\
abla\Big{)}\overrightarrow{v}=-\
abla p +\mu\Delta\overrightarrow{v} \tag{12}\]  
and the continuity equation, representing conservation of mass, is given as:  
\[\
abla\cdot\overrightarrow{v}=0. \tag{13}\]  
Here, \(\rho=\text{const.}\) is the density and \(\mu=\text{const.}\) is the viscosity of the fluid. The vector field \(\overrightarrow{v}\) is the velocity field and \(p\) the pressure. In cylindrical coordinates, the continuity equation for the velocity field \(\overrightarrow{v}(r,\varphi,z)\) is given by:  
\[\frac{\partial v_{r}}{\partial r}+\frac{v_{r}}{r}+\frac{1}{r}\frac{\partial v _{\varphi}}{\partial\varphi}+\frac{\partial v_{z}}{\partial z}=0. \tag{14}\]  
Due to the cylindrical symmetry, we assume that there is neither flow in azimuthal direction (swirl flow) nor in vertical direction resulting in \(v_{\varphi}=v_{z}=0\). Additionally, also all derivatives with respect to \(\varphi\) are zero. This results in the problem-adapted continuity equation:  
\[\frac{\partial v_{r}}{\partial r}+\frac{v_{r}}{r}=0. \tag{15}\]  
The Navier-Stokes equation for the radial velocity component then becomes:  
\[\rho\cdot v_{r}\frac{\partial v_{r}}{\partial r}=-\frac{\partial p}{\partial r }+\mu\Big{(}\frac{\partial^{2}v_{r}}{\partial r^{2}}+\frac{1}{r}\frac{ \partial v_{r}}{\partial r}-\frac{v_{r}}{r^{2}}+\frac{\partial^{2}v_{r}}{ \partial z^{2}}\Big{)}, \tag{16}\]  
and the remaining two equations become:  
\[\frac{\partial p}{\partial q}=\frac{\partial p}{\partial z}=0. \tag{17}\]  
Thus, the pressure varies only with the radius.  
Differentiating the continuity Equation (15) with respect to \(r\) and substituting it into the Navier-Stokes equation yields:  
\[\rho\cdot v_{r}\frac{\partial v_{r}}{\partial r}=-\frac{\partial p}{\partial r }+\mu\frac{\partial^{2}v_{r}}{\partial z^{2}}. \tag{18}\]
## 5 Conclusions  
The well-known neuroprotective effects of EPO in CNS injury [1; 2; 3] have a limited therapeutic time window [5; 6; 7; 8; 9], heterogeneity of extrahematopoietic receptors [12], and phylogenetic differences; thus, the clinical application of EPO remains challenging. Targeted delivery using MNPs under magnetic navigation, which has been widely studied in cancer models [15; 16; 17], can be introduced as a solution. To prove the feasibility of ENBs (alginate-encapsulated EPO-MNPs manufactured using the nanospray drying technique) as potential therapeutic tools against CNS injuries, the authors evaluated the release controllability and neuroprotective bioequivalence of ENBs compared with EPO in TG-treated SH-SYSY neuronal cells in vitro. ENBs subjected to one hour of preconditioning sonication (50-60 kHz) showed 84.1% EPO release by 24 h and resulted in a neuronal cell viability equal to that after treatment with EPO only over 24 h (more than 65.0%). Additionally, similar downward curves in the expression levels of biomolecular markers (JAK2, PDI, GRP78, PERK, ATF6, and CHOP), EPO receptor signaling pathway activation, and proapoptotic and apoptotic signaling pathway deactivation were observed; these changes were all attenuated after treatment with ENBs before 24 h, but the levels at 24 h were nearly the same between the ENBs and EPO. ENBs showed in vitro controllability of EPO release and in vitro feasibility for neuroprotection. These findings may lay a foundation for the potential in vivo use of ENBs as a neuroprotective therapeutic to treat CNS injury through adjusted preconditioning sonication, which balances fast release within the therapeutic time window of EPO and early engulfment of EPO by the mononuclear phagocytic system. To fully achieve the neuroprotective bioequivalence of ENBs to EPO in vivo, no less than 84.1% breakdown of the ENBs is required.  
Conceptualization, T.H.L., C.T.N., K.-i.K. and C.H.H.; data curation, K.-i.K.; formal analysis, T.H.L., C.T.N., K.-i.K. and C.H.H.; funding acquisition, K.-i.K. and C.H.H.; investigation, T.H.L. and C.T.N.; methodology, T.H.L., K.-i.K. and C.H.H.; project administration, T.H.L., C.T.N., K.-i.K. and C.H.H.; supervision, C.H.H; validation, C.H.H.; visualization, T.H.L. and C.T.N.; writing--original draft, T.H.L., C.T.N., K.-i.K. and C.H.H.; writing--review and editing, C.H.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Practical Technology Development Medical Microrobot Program (RS&D Center for Practical Medical Microrobot Platform, HII9C0642) funded by the Ministry of Health and Welfare (Republic of Korea) and the Korea Health Industry Development Institute (Republic of Korea).  
Not applicable.  
The authors sincerely thank [PERSON] for consultation on ER stress modeling.  
The authors declare that there are no conflicts of interest.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 3 Swarm Robotics Design and Simulation
### Simulated Hardware Design for Robots
#### 3.1.2 Sensor and Actuators  
Mounted on the front of the robot is a 360-degree LiDAR sensor that is 100% error-free and continuously scans the environment within a 1-m range. This sensor is essential for creating a real-time 2D map of the environment and enabling efficient navigation and obstacle avoidance in a mining environment. The robot also uses wheel encoders and an inertial measurement unit (IMU) to accurately track its orientation and position in the simulation. An onboard computer processes these sensory inputs in real time to make quick navigation decisions, which is essential to maintaining the effectiveness and safety of the swarm. The hook-lift system is a key component for ore-handling tasks and one of the robot's standout features. A vertical arm with a powerful hook is part of this system and is  
Figure 3: Different views of surface mining operations at BHP’s South Frank mine in Pilbara [32]: (a–c). (used with permission from BHP).  
driven by a software to operate the electric winch. To ensure accurate and stable loading and unloading, the hook is programmed to engage precisely with the lifting points on the block. To keep the robot stable during transport, the system can control the size and weight of the simulated blocks.
###### Abstract  
Aiming at the problem of accuracy degradation caused by the mixing of spatiotemporal disturbance patterns in sound speed profile (SSP) inversion using the traditional geographic grid division method, this study proposes an acoustic stability pre-clustering framework that integrates principal component analysis and machine learning clustering. Disturbance mode principal component analysis is first used to extract characteristic parameters, and then a machine learning clustering algorithm is adopted to pre-classify SSP samples according to acoustic stability. The SSP inversion experimental results show that: (1) the SSP samples of the South China Sea can be divided into three clusters of disturbance modes with statistically significant differences. (2) The regression inversion method based on cluster attribution reduces the average error of SSP inversion for data from 2018 to 1.24 m/s, which is more than 50% lower than what can be achieved with the traditional method without pre-clustering. (3) Transmission loss prediction verification shows that the proposed method can produce highly accurate sound field calculations in environmental assessment tasks. The acoustic stability pre-clustering technology proposed in this study provides an innovative solution for the statistical modeling of marine environment parameters by effectively decoupling the mixed effect of SSP spatiotemporal disturbance patterns. Its error control level (<1.5 m/s) is 37% higher than that of the single empirical orthogonal function regression method, showing important potential in underwater acoustic applications in complex marine dynamic environments.  
sound speed profile; transmission loss; inversion; South China Sea
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 4 The CHaChA Clustering Approach
### Online Cluster Adaptation
#### 4.4.1 Joining a Clustered Network  
For the initial cluster formation during network commissioning, as described in Section 4.3, a synchronous start of execution of CHaChA on all mesh nodes is assumed. However, in the practical operation of an urban backbone WMN, post-installation or relocation of devices is also to be expected. Further mechanisms are therefore used to subsequently incorporate nodes without having to perform a new initial cluster formation. They are defined as an extension of phase 0 of the CHaChA phase sequence (see entry point of the state diagram in Figure 5).  
Provided that at least one active mesh link exists at the beginning of phase 0, a CFN first checks over the time duration [CH_THRESH_CH_PERIOD] if CH broadcast messages with complete cluster information (mesh ID and channel for the secondary interface) are sent on the base channel. These indicate that an initial cluster formation has already been performed, and the CFN has arrived subsequently. Alternatively, the receipt of MCH phase announcements signals a clustering that is still in progress and needs to finish first. For cluster joining, the CFN then sends a JOIN message to the CH with smallest ALM cost. Again, CHs in one-hop proximity are preferentially chosen. After configuring its secondary interface according to the cluster information (mesh ID and channel) announced by the CH, the CFN becomes a CM and switches directly to phase 7.  
If there are neither CH messages nor phase announcements in phase 0, CFNs proceed with the normal phase sequence for initial cluster formation. If errors occur during the phases, detected by exceeding a timeout due to missing MCH phase announcements, nodes jump back to phase 0. Errors and connectivity problems that occur after successful clustering are also handled by jumping back to phase 0, as described in detail below.  
Figure 5: State diagram of the dynamic cluster adaptation mechanisms for long-term network operation (CFN: Cluster-Free Node, CM: Cluster Member, CH: Cluster Head). Reprinted from ref. [18].  
There, nodes attempt reintegration into the network or alternatively start a new clustering phase sequence.
# A Probabilistic Hyperspectral Imagery Restoration Method
## 2 Related Work
combine the TV-regularization term with low-rank matrix representation [32]. [PERSON] et al. combine the non-local similarity with LRMR and thus proposed a method named group low-rank representation [33]. Superpixel segmentation is used together with low-rank representation to improve the performance of image denoising results [34]. [PERSON] et al. propose a structured low-rank representation, which achieves the state-of-the-art denoising performance by utilizing the structure among low-rank matrix [35]. Though these low-rank based methods have been proposed, two deficiencies limit the their usage. 1) In real applications, hyperspectral image will be influenced by multiple kinds of noise such as Gaussian noise, sparse noise, stripe, dead lines, and their combinations. However, above mentioned methods model noise deterministically, which can only handle one to two kinds of noise and lose flexibility to handle complex noise. 2) The rank number of all existing low-rank based method need to be assigned manually, and automatically method to determine the rank number is desired. To deal with these two problems, we propose a new HSI-oriented denoising method in this paper.
## 3 Analysis Model for Cracking and Flexural Behavior of HPFRCC Beams
### A Layered Section Analysis Method  
A layered section analysis method, which has been widely used for the analysis of flexural behavior of a concrete beam [21; 22; 23], has been adapted to describe the deformation and crack width of a HPFRCC beam.  
In the section analysis model without axial stress, the deformation of a segment can be described by the curvature (\(\varphi\)) and the depth of the neutral axis (\(n\)). Based on [PERSON]'s principle that the longitudinal strain through the height in a beam is proportional to the distance from the neural axis [21], the following relationship is easily derived:  
\[\varphi=\frac{\varepsilon_{c}}{n}=\frac{\varepsilon_{c}+\varepsilon_{r}}{h} \tag{1}\]  
where \(h\) is the height of the beam, \(\varepsilon_{c}\) is the strain at the top fiber, and \(\varepsilon_{t}\) is the strain at the bottom fiber. Hence, the strain (\(\varepsilon_{c}\)) at a specific depth (\(x\)) can be calculated by using Equation (2), as shown in Figure 7:  
\[\varepsilon_{x}=\varphi\cdot x-\varepsilon_{c} \tag{2}\]  
The depth of the neutral axis can be found from the force equilibrium at the cross section as the sum of the longitudinal forces at each layer should be equal to the external axial force. In this research, a false position method [24] was used to find the nonlinear solutions.  
Figure 7: Layered model subjected to flexure: (**a**) un-deformed shape; (**b**) deformed shape; and (**c**) strain distribution over the height.
# Establishing Reference Data for Electromyographic Activity in Gait: Age and Gender Variations
## 3 Results  
No significant differences in height, body mass, or BMI were observed between males and females in the children's group. However, in the adult group, all parameters demonstrated significant differences (\(p\) < 0.001), with males being taller and heavier compared to females. SPM analysis for the effect of age (Figure 2) indicated that the main differences in EMG activity between children and adults occurred during the midstance phase of the gait cycle for the biceps femoris (17-31% of the gait cycle, \(p\) < 0.001; and 77-80% of the gait cycle, \(p\) = 0.039). Additionally, significant differences were observed for the semimembranosus during the first 5% of the gait cycle (\(p\) = 0.017) and 22-37% of the gait cycle (\(p\) < 0.001); for the rectus femoris during 63-76% (\(p\) < 0.001) and 93-96% (\(p\) = 0.032) of the gait cycle; and for the soleus during 34-43% (\(p\) < 0.001), 62-69% (\(p\) = 0.003), and 87-94% (\(p\) < 0.001) of the gait cycle. Table 2 presents the mean and standard deviation (SD) of the gait and spatiotemporal characteristics across different age groups. The effect of age on both peak ankle dorsiflexion moment and power was statistically significant (\(p\) < 0.001), with greater values observed in adults. A similar effect was found for peak plantar flexion power (\(p\) = 0.012) and the knee flexion moment (\(p\) = 0.004). However, children exhibited greater hip flexion and cadence during gait, as well as reduced values for the hip extension moment, walking speed, stride time, and stride length. The gender effect, also analyzed using SPM (Figure 3), revealed that females exhibited greater EMG activation for the biceps femoris during 66-68% of the gait cycle (\(p\) = 0.049), while it was greater for males during 93-97% of the gait cycle (\(p\) = 0.036). For the semimem-branchous, males exhibited increased activation around 95% of the gait cycle (\(p\) = 0.049), whereas for the tibialis anterior, females showed significantly greater activation between 37% and 41% of the gait cycle (\(p\) = 0.029). The vastus lateralis exhibited the longest durations of significant differences, occurring during 54-63% (\(p\) < 0.001), 66-75% (\(p\) < 0.001), and 97-100% (\(p\) = 0.029) of the gait cycle for females, with more activated EMG. The influence of gender on gait measures was also significant (Table 3), with females showing a greater plantar flexion angle (\(p\) = 0.023) and larger dorsiflexion power (\(p\) = 0.041) at the ankle compared to males. Additionally, females exhibited an increased peak of hip flexion, greater hip extension power, and an earlier foot-off phase. Figure 2: Average (solid line) and standard deviation (dashed line) of EMG envelopes along with SPM results for two age groups: children (red) and adults (blue). Shaded areas indicate gait cycle periods with statistically significant differences. The \(p\)-value was set at 0.05 to determine statistical significance.
# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 2 Methodology  
### Preliminary  
Since transmission towers are made of steel metal materials with stronger reflective properties than their surrounding background, and appear as strongly bright small elongated targets in high-resolution SAR images. Meanwhile, according to the geographical conditions on which transmission towers are situated, it can be known that the background around the transmission towers can be usually viewed as distributed targets, such as land and forest. Therefore, the transmission tower pixels have a stronger correlation each other compared to the background pixels in spectral and spatial. In conclusion, there are the following characteristics of transmission towers.  
(1) The backscattering intensity of background clutter is required to be much lower than the peak signals of transmission towers, so it is reasonable to select potential transmission tower pixels by the SCRs.  
(2) The spatial density of potential transmission tower pixels detected on a transmission tower area is greater than for ones on background area.  
(3) The transmission towers in the SAR images appear as simple and recognizable long strips.
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 5 Performance Evaluation in Practical Tests
### Average Power Consumption Per Node  
This test is allocated to the investigation of the average energy consumption for each network node. By the acquired simulation results presented in Figure 13, it is observed through this practice test that the energy consumption of network nodes is reduced. According to Figure 21, the average energy consumption of each network node for traffic rates 5, 10, and 15 packets per minute is respectively 3.37, 6.14, and 17.4 milliwatts.  
Figure 21: Comparison of average energy consumption of each node, while the traffic rate is 5, 10, and 15 packets per minute.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Sent** & **Receive** & **Drop** & \
\begin{tabular}{c} **Packet Delivery** \\ **Ratio(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Aggregated** \\ **(\%)** \\ \end{tabular} \\ \hline RPL(Indoor) & 81,874 & 75,313 & 6561 & 91.9 & 0 \\ RPL(Outdoor) & 81,430 & 77,987 & 3443 & 95.7 & 0 \\ BD-RPL(Indoor) & 83,326 & 80,334 & 2992 & 96.4 & 0 \\ BD-RPL(Outdoor) & 81,450 & 79,004 & 2446 & 96.9 & 0 \\ M-RPL(Indoor) & 78,830 & 73,369 & 5461 & 93.1 & 6.78 \\ M-RPL(Outdoor) & 77,329 & 73,650 & 3679 & 95.2 & 9.83 \\ A-RPL(Indoor) & 80,993 & 78,132 & 2861 & 96.4 & 11.03 \\ A-RPL(Outdoor) & 82,584 & 79,931 & 2653 & 96.7 & 13.26 \\ LA-RPL(Indoor) & 83,718 & 82,665 & 1053 & 98.7 & 34.11 \\ LA-RPL(Outdoor) & 82,880 & 82,151 & 729 & 99.1 & 37.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of practical tests comparing the proposed method and other approaches in indoor and outdoor environments.
# Trajectory Tracking with Adaptive Robust Control for Quadrotor
## 5 Discussion  
The problem of trajectory tracking with a quadrotor after several simulations is that the PD adaptive controller with the adjustment mechanism based on the MIT rule methodology, and the model reference adaptive control for MRAC, is not enough to resolve the problem due to the oscillations at the beginning of the desired trajectory. This implies that the quadrotor flight is so far from the desired trajectory and the convergence time is too slow (see Figures 6 and 7); for that reason, it is necessary to design a robust adjustment mechanism with a robust theory like that based on sliding modes.  
Then, the adjustment mechanism MIT rule with sliding mode (MIT-SM) reduced the oscillations in the trajectory tracking with the quadrotor and a better convergence time. Besides, the MIT rule with the twisting (MIT-Twisting) adjustment mechanism presents a better response in convergence time and oscillation reduction. To obtain the best response in this work to reduce the oscillations (almost eliminate), a short convergence time to the desired trajectory was obtained with the adjustment mechanism based on the MIT rule with high order sliding modes (MIT-HOSM) (see Figures 6 and 7).  
Even analyzing the pitch and roll angles, the MIT rule with high order sliding modes (MIT-HOSM) has better error correction. An exception occurs in the yaw angle where the MIT rule with sliding mode (MIT-SM) has a better response in error. On the other hand, we took into account the control effort response with the four proposed adjustment mechanisms. The MIT rule with high order sliding mode (MIT-HOSM) presents the smallest control effort compared to the adjustment mechanisms MIT rule, MIT-Twisting and MIT-HOSM proposed for the pitch and roll angle, except for the yaw angle where the MIT-HOSM applies the biggest control effort to steer the quadrotor for the desired trajectory (see Tables 1-3).  
Thus, for future work, we have to develop a construction and electronic embedded system for a quadrotor to do real flight tests with the PD adaptive control law with the different robust adjustment mechanisms proposed in this work.  
Conceptualization, T.E.-F. and A.S.; methodology, T.E.-F.; software, A.S.; validation, F.S., R.J. and A.S.; formal analysis, W.G.; investigation, T.E.-F.; resources, F.S. and R.J.; data curation, A.S. and W.G.; writing--original draft preparation, T.E.-F.; writing--review and editing, A.S.; visualization, R.J.; supervision, T.E.-F., W.G.; project administration, T.E.-F.; funding acquisition, T.E.-F. All authors have read and agreed to the published version of the manuscript.  
PROFEXCE 2020.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data available on request due to restrictions.  
Figure 26: Convergence \(\hat{k}_{d}\) with MIT rule with high order sliding modes (MIT-HOSM).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Probability Distribution of Loss Degree Based on Three-Point Estimation Method
### Uncertain Variables Model
#### 4.1.2 Probabilistic Model of Wind Power Generation  
Wind speed PDF follows Weibull distribution [26], which is as follows:  
\[f(v)=\frac{k}{c}\Big{(}\frac{v}{c}\Big{)}^{k-1}\exp(-\Big{(}\frac{v}{c}\Big{)} ^{k}) \tag{15}\]  
where \(v\), \(k\), and \(c\) represent wind speed, shape parameter, and scale parameter, respectively.  
The relationship between the actual active power output of wind power and wind speed is shown in Equation (16):  
\[P_{w}=\left\{\begin{array}{l}0\hskip 142.26378 ptv\leq v_{in}\;or\;v\geq v_{out }\\ P_{r}\frac{v-v_{in}}{v_{r}-v_{in}}\hskip 142.26378 ptv_{in}\leq v\leq v_{r}\\ P_{r}\hskip 142.26378 ptv_{r}\leq v\leq\;v_{out}\end{array}\right. \tag{16}\]  
where \(v_{in}\), \(v_{r}\), and \(v_{out}\) represent cut-in wind speed, rated wind speed, and cut-out wind speed, respectively, and \(P_{w}\) and \(P_{r}\) represent the actual output power and rated power of wind power, respectively.