esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Perspectives
### Importance and Potential Impact  
Understanding the mechanisms of AAI-induced mitochondrial dysfunction and identifying specific biomarkers is crucial for the early diagnosis and effective treatment of AAN and BEN. These findings highlight the potential for novel therapeutic strategies targeting mitochondrial health, which could significantly improve patient outcomes and prevent the progression of kidney diseases related to AAI exposure.  
Recent research has shed light on the impact of Aristolchic Acid I (AAI) on mitochondrial function in kidney disease, particularly in the context of aristolchic acid nephropathy (AAN) [2]. AAI specifically targets proximal tubular epithelial cells (PTECs), which are known for their high mitochondrial content, making them particularly susceptible to mitochondrial dysfunction. Mitochondrial dysfunction is increasingly recognized as a critical factor in the pathogenesis of various kidney diseases, including AAN. However, despite this recognition, the precise status of mitochondrial function in PTECs following AAI exposure remains not fully elucidated.
## 2 Study Design
### Data Acquisition  
All MRI data in the experiment were collected from the MRI Research Center of UESTC and passed through 3T MRI scanner (General Electric Discovery MR750; Milwaukee, WI, USA) with 8-channel head coil acquisition. During scanning, foam pads and earplugs were used to reduce head movement and scan noise, respectively. The main scanning parameters of high-resolution, T1-weighted structural images are as follows: repetition time (TR) = 6.008 ms, echo time (TE) = 1.984 ms, flip angle (FA) = 9\({}^{\circ}\), matrix size is 256 \(\times\) 256, field of view (FOV) = 256 mm \(\times\) 256 mm, slice thickness = 1 mm, no gap, 154 slices in total. Gradient-echo planar imaging (EPI) sequence was used to obtain fMRI data. The main scanning parameters were as follows: TR = 2000 ms, TE = 30 ms, FA = 90\({}^{\circ}\), matrix size = 64 \(\times\) 64, FOV = 240 mm \(\times\) 240 mm, slice thickness/gap = 4 mm/0.4 mm, a total of 35 slices. Specifically, resting-state sequences were scanned for 250 s over 125 time points.
## 7 Limitations and Challenges  
The transition from legacy systems to blockchain-based solutions presents various limitations and challenges. These include a lack of enough number of real-world evaluations, the complexity of system integration, and insufficient research on consensus mechanisms.  
Additional issues such as security, legal compliance, and interoperability also need to be addressed.
## 2 LPWAN IoT Technologies
### Nb-IoT  
NB-IoT is 3 GPP or 4 GPP wireless access as a narrowband LPWAN technology that can collaborate in LTE or GSM under licensed frequency bands to achieve excellent performance. NB-IoT dominates a frequency bandwidth of 200 kHz for downlink and uplink communication [9], and its protocol is based on the LTE communication protocol. Thus, several concepts and building units of LTE's physical and upper protocol layers are reprocessed by NB-IoT's physical and upper protocol layers. In LTE M (Cat M1), the device receives a portion of the LTE carrier, as well as devices that are multiplexed across the LTE carrier, and the device takes advantage of the full leveraging capacity of the wideband LTE carrier. NB-IoT devices have these features in contrast to Cat M1: an NB-IoT carrier is present, shared capacity is given to all NB-IoT devices, and the capacity may be scaled by adding additional NB-IoT carriers. The implementation of NB-IoT can be offered through a software update and can be installed in three operating modes, as shown in Figure 3 and as follows:  
1. Standardone: This scenario deploys in a standalone 200-kHz spectrum. For increasing coverage, NB-IoT uses all the transmission power at the base station. The use of this mode will typically be as a substitute for GSM carriers.
2. In-band operation: This is deployed in a wideband LTE system, including one or more (180 kHz) LTE physical source blocks. NB-IoT signals must not be transmitted to the region's LTE-reserved time-frequency tools (including legacy-controlled area signals and reference signals). Wideband LTE and NB-IoT technologies share their transmission power at the base station and can be useful for consuming the same base station hardware with the output of either not being affected [16].
3. Guard band operation: This is deployed in the guard band of an LTE carrier and co-placed with an LTE cell, as the LTE channel shares the transmission power and the same power amplifier [17; 18].  
The efficiency of the battery is conducive to its cost. Single-carrier frequency-division multiple access (FDMA) is utilized by the NB-IoT uplink, while orthogonal FDMA (OFDMA) and quadrature phase shift keying modulation (QPSK) are used in the downlink [9]. The NB-IoT message has 1600 bytes as a maximum payload size. The uplink data rate is restricted to 20 kbps, while 200 kbps is the restriction for the downlink.
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 3 Verification System Establishment
### Pressure Regulating Method of Pneumatic Braking System  
The proportional relay valve is the pressure regulator of the pneumatic braking system. The proportional relay valve is controlled by the PWM method, which adjusts the input current by changing the duty cycle, and then adjusting the outlet pressure.  
As the hysteresis characteristics of the proportional relay valve will affect the pressure regulation effect of the braking system. The PID method was adopted to control the outlet pressure of the proportional relay valve [25]. The PWM duty cycle was adjusted timely to keep the braking pressure at the target value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Models** & **Parameters** \\ \hline Proportional relay valve & WABCO 4802020040 & Range: 0–0.9 MPa \\ Pressure sensor & SMC PSE540A-R06 & Range: 0–1.0 MPa \\ Front brake chamber & RL 35190300170 & Spring stiffness: 5000 N/m \\ Rear brake chamber & RL 35300102810 & Spring stiffness: 5000 N/m \\ Tank & C 3513010-S0300 & Volume: 25 L \\ \hline \hline \end{tabular}
\end{table}
Table 3: Components of pneumatic braking bench.  
Figure 3: Structure of passenger car’s pneumatic braking system.  
The dynamic pressure regulation performance of the proportional relay valve was verified by the pneumatic braking bench. The target pressure range was set from 0 MPa to 0.7 MPa, while the step size was set as 0.1 MPa by using the ladder lifting mode. The experimental results were shown in Figure 4.  
As shown in Figure 4, the actual pressure at the outlet of the proportional relay valve can rise and fall according to the target pressure and can reach the target value. The experimental results show that the control algorithm can effectively eliminate the influence of the hysteresis characteristics of the proportional relay valve, and significantly improve the pressure regulation effect of the proportional relay valve. The experiment verified the pressure regulation performance of the pneumatic braking system, and provided a hardware basis for the rapid response of the AEB system.
## 2 Pump Model and Numerical Setting  
### Pump Structure  
The geometric model of the sand pump investigated is shown in Figure 1. The upper inlet of the pump assumes the function of receiving sand particles, and this inlet is exposed to the atmosphere. The density of the sand particles is 2700 kg/m\({}^{3}\), and the particle diameter is 0.2 mm. Upstream the upper inlet, a conveyor belt is deployed to provide sand particles for the pump. Sand particles fall toward the inlet of the secondary impeller through the convergent inlet segment. Particles mix with water immediately after they enter the flow passage of the pump. Under the action of the secondary impeller, the mixture travels outward in radial directions. Then, the upper part of the primary impeller is responsible for receiving and then imparting energy to the incoming mixture, which is eventually expelled into the volute chamber. The upper and lower parts of the primary impeller differ in the outlet diameter. The mixing in the volute chamber is enabled by the interaction of two streams of different velocities. Subsequently, the mixture is discharged from the volute chamber toward the pump outlet.  
The pump assumes dual functions, i.e., mixing sand particles with water and imparting energy to the mixture. With such a pump, generally, employed suction components like the blender and suction pipes are not necessitated [20]. The primary impeller serves as the core component of the pump. The upper part of the primary impeller receives and then transports sand particles toward the volute, and the lower part is responsible for inputting water into the pump chamber. The secondary impeller located under the sand inlet segment is responsible for receiving sand particles from the vertically mounted segment of the sand inlet. At the upper part of each blade of the secondary impeller, a forward cover is set to prevent solid particles from moving upward. The primary impeller encircles the secondary impeller, and the two impellers rotate synchronously. The blades of the two impellers are well-matched in terms of both blade number and blade setting angles, as indicated in Figure 1b. The lower part of the primary impeller provides energy for the water entering the pump from the bottom inlet. In this case, the lower part of the primary impeller is similar to the commonly used centrifugal impellers. The motor is located above the sand inlet segment. A coordinate system was predefined with the origin being set at the mid-span cross-section of the upper part of the primary impeller, and the \(z\)-axis overlapped the axis of the pump shaft, as indicated in Figure 1c. Main parameters of the sand pump are shown in Table 1.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Flow rate of water (m\({}^{3}\)/min) & 8.03 \\ Feed rate of sand (m\({}^{3}\)/min) & 0.89, 2.10, 3.12 \\ Rotational speed of the impellers (r/min) & 850, 950, 1050 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the sand pump.  
Figure 1: Geometric model of the sand pump: (**a**) whole view, (**b**) impellers and water inlet, (**c**) cross-sectional view.
## 4 Discussion
### Changes in the Co-Activation by Localized Vibratory Stimuli during Stair Climbing
In other words, the decrease in co-activation by vibratory stimulation during the FC phase is due to the increase in the TA activation, which enables the ankle joint to maintain dorsiflexion and achieve sufficient foot clearance. The effect of the local vibratory stimulation on the stability and function of stair climbing is apparent in the PU phase. Threshold and sub-threshold intensities seem to affect the degree of joint freedom and joint stiffness, respectively, and can be controlled to an extent by varying the vibration frequency. In particular, the Th-Max and Sub-min stimuli have the greatest influence. However, in previous studies, [PERSON] et al. [40] have reported that supra-sensory mechanical vibrations applied to construction workers' feet may increase the risk of loss of balance. [PERSON] et al. [41] haveconfirmed that a sensory stimulus above our tolerated level may cause discomfort. Furthermore, [PERSON] et al. [42; 43] have found that a sub-threshold mechanical vibrational stimulation increased the detection of plantar pressure changes, resulting in decreased postural shaking in elderly and peripheral neuropathy patients. In this study, changes in EMG activity and co-activation during stair climbing were identified when a mu rhythm-based vibratory stimulation was applied. These results suggest that localized vibratory stimulation can help improve the function and stability of stair climbing. However, since this study examined only the parameters of the muscles, it is difficult to identify changes in kinetic and kinematic factors such as joint angle, moment, and power during the performance of the motion. In future studies, if the analysis of kinetic, kinematic, and spatiotemporal variables is added using a three-dimensional motion analysis system, the effect of the local vibratory stimulation on stair climbing may be understood in more detail.
HSI is recognized as a powerful solution to classify and control the quality of materials in different fields, especially in the plastic waste sector [36; 37; 38; 39; 40; 41]. In recent years, researchers have used HSI to analyze floating microplastics collected from the sea surface [42]. Additionally, several studies have been published utilizing HSI methods to detect microplastics in water [43; 44]. These studies include techniques such as filtering water samples with a paper filter [45] and identifying microplastics as small as 100 \(\upmu\)m [46]. The effectiveness of HSI in detecting microplastics has been emphasized in recent review articles [47; 48]. In the present work, a hierarchical classification model based on short-wave infrared (SWIR)-HSI was applied. This approach was crucial for the optimal identification of polymers constituting marine microplastics sampled in seven sampling sites spread across the Mediterranean Sea and the Atlantic Ocean. Furthermore, for each microplastic particle, the morphological and morphometrical attributes were measured. The obtained results were compared with those of other published works.
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
## 2 Generalized Multicarrier Radar-Communication Coexistence Model
### Receiver  
Both the multicarrier radar and communication receiver in the coexistence system will obtain a radar response from the target reflection or an observation from an active transmitter in a communication system. For a radar receiver, assume that a single moving target is located at direction \(\theta\). By ignoring interference, a general form of the received signals before decoding may be written as follows:  
\[\mathbf{Y}_{\mathbf{R}}=\mathbf{H}(\boldsymbol{\theta})(\mathbf{\Omega} \circ\mathbf{I}_{N_{sc}\times N_{sc}})\mathbf{PCS}+\mathbf{W}, \tag{4}\]  
where matrix \(\mathbf{Y}_{\mathbf{R}}\) is an \(N_{sc}\times M\)-dimensional received signal matrix. \(\mathbf{W}\) denotes the \(N_{sc}\times M\)-dimensional complex white Gaussian noise at the receiver with covariance matrix, \(\mathbb{E}(\mathbf{W}\mathbf{W}^{H})=\sigma_{W}^{2}\mathbf{I}_{N_{sc}\times N_{ sc}}\). A detailed derivation of the model is presented in Appendix B.  
The channel frequency response matrix denoted by \(\mathbf{H}(\boldsymbol{\theta})\in\mathbb{C}^{N_{sc}\times N_{sc}}\) could be estimated for each \(L\) symbol duration under the block fading channel assumption. When an OFDM signal is employed, the channel state matrix is a diagonal matrix \(\mathbf{H}(\boldsymbol{\theta})=\text{diag}\{h_{1}(\theta),h_{2}(\theta) \cdots,h_{N_{sc}}(\theta)\}\), where \(h_{1}(\theta)\) is the channel frequency response at the first subcarrier.  
Considering the radar task at hand, the channel state matrix describes the combined effects of target response, scattering, channel fading, Doppler shift, and power decay with distance. Among these parameters, the direction of arrival (DoA), Doppler shift, and target response are of particular interest for the radar system. Note that if multiantenna transceivers are used, spatial processing, such as DoA estimation may be performed. If a single-antenna system is employed, the DoAs can be estimated using a mechanically rotating directional antenna, as in many classical radar systems [57]. The communication receiver treats Doppler shifts, multipath effects and power decay as channel distortion. To ensure reliability of data transmission, channel coding and frequency precoding are typically employed. One needs to design a channel coding matrix \(\mathbf{C}\) and a precoding matrix \(\mathbf{P}\) to address the dynamic nature of \(\mathbf{H}(\boldsymbol{\theta})\).  
In particular, for a colocated radar-communication node as shown in Figure 2, the communication subsystem could easily share a channel state matrix with a radar subsystem. Moreover, if the target also carries a cooperating communication system, the performances of the channel and target estimations may both benefit from a joint estimating procedure. A practical example of such a system is a vehicle radar and vehicle-to-vehicle communication network.
## 4 Results
### LCFs Selection and Dynamic Factor Change Trend Analysis in the Study Area
#### 4.1.2 Analysis of Dynamic Factor Changes in China from 2001 to 2022  
We used ArcGIS 10.8 software to generate spatial distribution maps of precipitation, NDVI, and land cover type in China from 2001 to 2022 and tested the trends (Figure 7). The results showed that between 2001 and 2022, precipitation reach a maximum of 13,463.5 mm and a minimum of 2.95 mm. Overall, south-central China received more precipitation each year, with the highest amounts in southern Tibet and lower amounts in northwest China (Figure 7a). During these 22 years, there was no significant trend in precipitation  
Figure 6: The _MI_ calculation results of condition factors.  
in most regions of China. This stability suggests that the use of annual precipitation data remains a valid method for assessing long-term landslide susceptibility trends. However, a few regions, such as northern Xinjiang, central Henan, parts of Yunnan, and Taiwan, exhibited a significant decreasing trend in precipitation (\(p<0.05\)), while some areas, including southwestern Xiang, Heilongjiang, the border regions of Inner Mongolia and Jilin, northeastern Zhejiang, and parts of Qinghai, showed a significant increasing trend (\(p<0.05\)) (Figure 7b). The NDVI showed a clear north-south divide, with the Heihe-Tengchong line as the boundary and a lower NDVI in the north and a higher NDVI in the south. This could be because the southern region, with more precipitation and dense vegetation, had a higher NDVI than the northern region, which had less precipitation and sparse vegetation (Figure 7c). Over 22 years, the NDVI increased significantly (\(p<0.01\)) in most regions of China, while it decreased significantly in few regions (Figure 7d). The predominant land cover type in China's northern and northwestern region is non-vegetative, with shrub and grassland ecosystems dominating the central regions, and a variety of tree-based and other vegetative cover types dominating the south. Cities, buildings, water bodies, permanent snow, and ice were sparsely distributed (Figure 7e). During the 22-year observation period, China's rapid economic development caused significant changes in land cover types across the country. These changes indicate a gradual expansion of land use and a discernible trend towards the transition between different land cover types. Regions without noticeable change trends, however, were primarily characterized by distinct topographic features that rendered them difficult to utilize. These include deserts and wastelands in non-vegetated areas, alpine forests in vegetated regions, and construction land that has already been used (Figure 7f).  
Figure 7: Spatial distribution of precipitation, NDVI, and land cover types (**a**,**c**,**e**) and their change trends test (**b**,**d**,**f**) during 2001–2022.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 5 Proposed Method
### Routing Using MOSOA  
In this phase, the collision-free optimal transmission path from the source node to the destination is discovered using the MOSOA. The routing path generation is optimized using the following fitness metrics such as queue length, link quality, communication cost, and residual energy. This path generation has various phases such as representation, initialization, fitness function derivation, and iterative process. These processes are detailed subsequently.
# Chemical Speciation of Antarctic Atmospheric Depositions
## 2 Materials and Methods
### Data Processing  
The chemical model applied to the atmospheric deposition samples considers the species reported in Tables 2 and 3. The formation constants of the relevant species were derived from literature data (see refs. reported in the two tables). Because all the samples had low ionic strength values, the formation constants selected from the literature were those estimated at quite low ionic strength (see Table 3). If necessary, the Extended Debye-Huckel (EDH) equation [16] was applied to the original literature values, to obtain the log\(K\) values at \(I=0.0\) mol L\({}^{-1}\) and \(t=25\)\({}^{\circ}\)C. These values were used to derive speciation diagrams. Both original and extrapolated values are shown in Tables 2 and 3, with the original experimental conditions indicated as reported in the respective literature. Moreover, because weak interactions between anions and alkaline cations were considered in the chemical model, whenever possible, the selected formation constants were those estimated with non-interacting cations. The species distribution diagrams were drawn using the ES4 ECI software [17], which takes into account the possible variation of the ionic strength associated to the variation of the charged species in solution as a function of pH, and corrects the values of the formation constants by applying the EDH equation. OriginPro 2020 SR1 (by OriginLab Corporation, Northampton, MA, USA) was used for descriptive statistics, data processing, and presentation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Protonation Constants**} \\ \hline
**Species** & **logK a** & **log\(\beta\) b** & **Reference** & **log\(K\)** & **log\(\beta\)** \\ \hline  & \multicolumn{4}{c}{**Values Estimated by the Application of**} \\  & & & & **EDH Equation**[16] \\ \hline  & \multicolumn{4}{c}{\(I=0\) mol L\({}^{-1}\), \(t=25\)\({}^{\circ}\)C} & \multicolumn{4}{c}{\(I=0\) mol L\({}^{-1}\), \(t=25\)\({}^{\circ}\)C} \\ \hline \(\text{HSO}_{4}\)1  & 1.987 & 1.987 & 1.987 \\  & \multicolumn{4}{c}{\(I=0\) mol L\({}^{-1}\), \(t=25\)\({}^{\circ}\)C} & \multicolumn{4}{c}{} \\ \hline \(\text{HPO}_{4}\)2  & 12.35 & 12.35 & 12.35 & 12.35 \\ \(\text{H}_{2}\text{PO}_{4}\)3  & 7.20 & 19.55 & 7.20 & 19.55 \\ \(\text{H}_{3}\text{PO}_{4}\) & 2.15 & 21.70 & 2.15 & 21.70 \\ \hline  & \multicolumn{4}{c}{\(I=0\) mol L\({}^{-1}\), Nelta1 e, \(t=25\)\({}^{\circ}\)C} \\ \(\text{Hac}\)4  & 4.74 & 4.74 & 20 & 4.74 & 4.74 \\ \hline  & \multicolumn{4}{c}{\(I=0.16\) mol, Nelta1, \(\text{L}^{-1}\), \(t=25\)\({}^{\circ}\)C} \\ \(\text{Hfor}\)5  & 3.55 & 3.55 & 21 & 3.72 & 3.72 \\ \hline \hline \end{tabular}  
\end{table} 
Table 2: Overall (log\(\beta\)) and partial (logK) protonation constants of the species considered in the model. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Formation Constants**} \\ \hline \multicolumn{4}{c}{**Hydrolytic Species**} \\ \hline
**Species** & **logK\({}^{\text{a}}\)** & **log\(\beta\)\({}^{\text{b}}\)** & **Ref.