esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Future Work with DCNN Hyperspectral Image Classification  
Deep learning, especially deep CNNs, should have great potential for tree species classification in the future. However, the extrapolation ability of deep learning is crucial for automation but has remained unsatisfactory in remote-sensing image recognition, especially if a source dataset varies significantly from a target dataset [19]. In this research, although the accuracy of a training set can reach 99.99%, the accuracy of a validation set is only 85.04%. In addition to the differences in the sample set itself, there is also an indication of insufficient generalization ability of deep learning. Thus, there is still much work to be done. For tree species classification, we plan to test different deep CNN frameworks and different CNN models, such as U-net, Segnet, and 3 DCNN, to improve our classification performance. Moreover, we do not consider the structure or multi-temporal correlation and only concentrate on the spectral signatures in the current work. We believe that some structure-spectral-temporal techniques can also be applied to further improve the CNN-based classification. Future research will, therefore, mine the tree structure information from multispectral and Synthetic Aperture Radar (SAR) data to characterize spatial distribution and dynamic changes of tree species.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 2 Basic Theory  
This paper will use the RF-CNN-LSTM model to predict the failure rate of the subway door system EDCU. In the process of subway vehicle operation and maintenance, there may be a situation where maintenance time is too long due to the untimely supply of spare parts, and the failure data cannot truly reflect the changes in component reliability, which affects the prediction results. Therefore, this paper proposes to use Random Forest (RF) to check and fill in the missing values of the failure rate data and ensure the completeness of the data. CNN-LSTM combines the Convolutional Neural Network (CNN) with the Long Short-Term Memory Network (LSTM) for processing the sequence data with spatial-temporal characteristics. CNN is used to extract spatial features from the input data, and LSTM is used to capture temporal dependencies for more comprehensive understanding and prediction of sequence data. The model prediction results are finally evaluated by R-squared accuracy and adjusted R-squared accuracy. The specific process is shown in Figure 1:  
Figure 1: Flowchart of RF-CNN–LSTM prediction model.
# Analysis of Multilayered Ground Amplification Characteristics by Scaled-Down Model Tests
## 3 Multilayered Flat Model Ground Test Results and Analysis
### Peak Ground Acceleration  
To evaluate the amplification characteristics of the multilayered flat model ground, the peak ground acceleration (PGA), spectral acceleration (SA), and amplification factor for the three input seismic waves were analyzed. Figure 10 shows the PGA by depth measured at the center of the flat, multilayered ground model. Under the three waveforms, the depth range of 0.3-0.6 m is dense ground, and the peak ground acceleration changes little with the decrease in depth. The depth range of 0-0.3 m is loose ground, and the peak ground acceleration increases obviously with the decrease in depth. Among them, the Sine 10 Hz wave does not show a steep shape even on the upper ground, because it is different from the natural period of the ground and cannot generate resonance. Artificial seismic waves and the sine wave resonate in the upper ground.  
Figure 10: Peak ground acceleration by depth of flat model under different seismic waves.  
Figure 9: The shear wave velocity modulus of loose ground and dense ground.
## 5 Experimental Results
### Results and Discussion
#### 5.3.1 Evaluation of Transmission Saving under CAT Approach  
Table 4 presents the percentage of savings (\(\eta\)) in the transmission of readings due to the implementation of the CAT approach, evaluated across three different CAT thresholds: 10%, 20%, and 30%. At a 10% CAT threshold, the savings percentage is 20.9%, indicating a 20.9% reduction in transmitted readings. When the CAT threshold is increased to 20%, the savings percentage rises to 44.9%, reflecting a significant reduction in the number of readings transmitted. At the 30% CAT threshold, the savings percentage further increases to 64.8%, demonstrating a substantial decrease in transmitted readings. These results underscore the efficacy of the CAT approach in minimizing data transmission, with higher CAT thresholds yielding greater savings. The rationale behind this is that increasing the CAT threshold reduces the frequency of data reporting, as fewer readings fall outside the defined threshold range. Consequently, this results in more significant savings in data transmission. However, this reduction in data transmission is accompanied by an increase in prediction errors and classification inaccuracy, as will be discussed in the next sections.
## 2 Methods
### Robotic System for Thyroid Surgery
#### 2.4.2 Drive System for the Thyroid Surgical Robot  
To facilitate practical application, this paper designs a modular robotic arm drive system (Figure 6), consisting of a manipulator arm, transmission module, and drive module. The manipulator arm is integrated with the transmission module, allowing for quick assembly and disassembly (Figure 6a). The transmission module contains 7 drive shafts and 7 drive pulleys. The wires of each joint of the manipulator arm are arranged sequentially through the wire channels and drive pulleys and fixed onto the joint drive shafts. The drive module consists of 7 power transmission shafts, which control the six degrees of freedom of the manipulator arm as well as the opening and closing actions of the gripper. The drive module integrates a motor, reducer, and encoder and can be detachably assembled to the transmission module.  
Figure 6: Drive system for the thyroid surgical robot. (**a**) Structure of drive system. (**b**) The force analysis of the robot. (**c–e**) The tensile force of the transmission wire at shoulder 2, shoulder 1, and rotary joint based on a deflection of 20\({}^{\circ}\) for each joint.  
The total length of the surgical instrument at the end of the manipulator arm is 20.00 mm, and the diameter of the winding wheel is \(d=6\) mm. According to reference [30], the maximum gripping force of the surgical robot's manipulator arm when holding patient tissue is approximately \(f_{1}=9\) N. Considering the requirements of the surgical procedure, a safety factor needs to be incorporated. The safety factor of \(\delta=2\) is applied. The force analysis of the surgical robot is shown in Figure 5b.  
The wire tension of the manipulator arm during operation with full gripping \(F_{1}\), the tensile force of the transmission wire at the wrist joint 2 \(F_{2}\), the wrist joint 1 \(F_{3}\), and the elbow joint \(F_{4}\) can be calculated as follows:  
\[F_{i}=\frac{f_{1}\times\delta\times(L_{i}-\frac{L_{0}}{2})}{d_{i}}(i=1,2,3,4) \tag{16}\]  
where \(d_{i}\) denotes he effective winding length of the wire, \(L_{0}=15.33\).  
Considering the angular variation of the end effector during the surgical operation, based on a deflection of \(\theta=20^{\circ}\) for each joint, the tensile force of the transmission wire at shoulder 2, shoulder 1, and rotary joint for (Figure 6c-e) can be calculated as follows:  
\[F_{i}=\frac{f_{1}\times\delta\times L_{5}}{d_{i}}(i=5,6,7) \tag{17}\]  
\[F_{iX}=F_{5}\times\cos\theta(i=5,6,7) \tag{18}\]  
The torque of the joints controlled by the motor through the winding pulley in the transmission mechanism can be calculated as follows:  
\[M_{i}=F_{i}\times\frac{d}{2}(i=1,2,\cdots,6) \tag{19}\]  
\[M_{7}=F_{7}\times\frac{r_{1}}{r_{2}} \tag{20}\]  
The gear transmission ratio is 1:2; therefore, \(\frac{r_{1}}{r_{2}}=2\).  
According to the output functional requirements of the motors for each joint in Table 2, German FaULHABER (Schonaich, Germany) brushless DC motors (model 2232024 BX4) are applied as the actuation unit.
## 2 Experimental Methodology
### Slope Model Test
#### 2.1.3 Experimental Design  
In the model test of the expansive soil slope, the freeze-thaw cycle test was carried out using a custom-fabricated environmental chamber (Figure 4). The test temperature range of the equipment was \(-30\) to \(80\)\({}^{\circ}\)C, the temperature change accuracy was \(\pm 0.1\)\({}^{\circ}\)C, the heating rate was \(3\)\({}^{\circ}\)C/min, and the cooling rate was \(1\)\({}^{\circ}\)C/min. [PERSON] et al. [35] showed that the physical properties of expansive soils show dramatic shifts in negative temperatures, and setting the freezing temperature to \(-10\)\({}^{\circ}\)C and \(-20\)\({}^{\circ}\)C in sequence affects the physical properties of expansive soil the most. [PERSON] et al. [36] showed that a constant positive temperature field of freeze-thaw cycles can reduce the time when the entire slope reaches a stable temperature, and that when the absolute value of the thawing temperature is higher than the absolute value of the freezing temperature, the soil can quickly reach a stable state. There was a large range of expansive soil in the Yanji area of the Jilin province, China. A large amount of expansive soil was found in the process of local railway construction, which brought challenges to the construction of subgrade and roadside slope. To study the freezing and melting stability of the slope, two negative temperature levels were established to simulate the local winter temperature change. In this study, \(-10\)\({}^{\circ}\)C and \(-20\)\({}^{\circ}\)C were selected as the freezing temperature and \(30\)\({}^{\circ}\)C as the thawing temperature.  
Figure 3: Slope freezing model and freeze-thaw monitoring system.  
When the deformation rate of the expansive soil slope was less than 0.01 mm within 2 h, the test was complete. Define \(n\) times complete freeze-thaw cycles as FTn. A single freeze-thaw test was divided into three stages: freezing at \(-10\)\({}^{\circ}\)C, freezing at \(-20\)\({}^{\circ}\)C and thawing at \(30\)\({}^{\circ}\)C. The samples were basically stable at the end of six days (144 h) at \(-10\)\({}^{\circ}\)C, and then the temperature of the model box dropped to \(-20\)\({}^{\circ}\)C for three days (72 h). Subsequently, the thawing temperature was maintained at \(30\)\({}^{\circ}\)C for two days (48 h). In 11 days (264 h), a freeze-thaw cycle of the slope model was completed, and four freeze-thaw cycles were tested totally (FT1 to FT4). This is because the slope displacement is small after four freeze-thaw cycles, and the test is terminated.  
Figure 4: Custom-fabricated environmental chamber. (**a**) freeze-thaw environmental chamber, test temperature range: \(-30\) to \(80\)\({}^{\circ}\)C, temperature change accuracy: \(\pm 0.1\)\({}^{\circ}\)C, heating rate is \(3\)\({}^{\circ}\)C/min, cooling rate is \(1\)\({}^{\circ}\)C/min; (**b**) slopes are arranged parallel in the test, and the ratio of three groups of slopes is different.
# Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells
## 2 Horizontal Well Drilling Characteristics  
### Characteristics  
A typical horizontal borehole trajectory diagram is shown in Figure 1. The borehole changes from a vertical borehole to a horizontal borehole and extends through the reservoir, causing the horizontal well to have different characteristics than a vertical well.  
(1) Drag and torque. The direct contact of the BHA with the wellbore under the action of gravity from the build-up section leads to large drag and torque. Drag counteracts  
Figure 1: Schematic diagram of the horizontal well drilling process.  
part of the weight applied to the bit by BHA, causing the weight on bit (WOB) reflected by the surface WOB (SWOB) to be inconsistent with the downhole WOB [23].
2. Eccentric annulus and hole cleaning. The drill string in the horizontal part of the wellbore is eccentric to the lower side by gravity, forming an eccentric annulus, while in the eccentric annulus, the flow velocity is not uniform, and the cuttings settle radially to form a cutting bed in the lower wellbore, which affects the friction coefficient between the drill string and the wellbore and subsequently affects the WOB transfer [24,25].
3. Trajectory control. Horizontal wells require directional operations due to the need for a well incline and azimuth adjustment [26]. The positive displacement motor (PDM) is a commonly used tool for trajectory control. In a PDM, the power section converts the hydraulic energy of mud flow into mechanical rotary power to drive the drill bit to rotate rapidly at the bottom hole to achieve trajectory control [27]. Rotary Steerable Systems are employed as tools for trajectory control, which enables directional drilling while the drill string is rotating. However, in order to enhance drilling efficiency, it is commonly deployed in tandem with a PDM. As a result, the bit's RPM surpass that of the surface RPM.  
The above analysis shows that due to the special characteristics of the horizontal well drilling process, the drilling parameters measured at the surface are significantly different from the actual drilling parameters at the bottom hole, and the surface measurement data cannot be used directly to build ROP models in horizontal wells. In order to achieve an accurate prediction of ROP in horizontal wells, the surface measurement data must be pre-processed considering the characteristics of horizontal wells, and then an ROP prediction model build based on a data-driven approach.
## 3 Materials and Methods
### Data Analysis  
To conduct the analysis of the GC-MS data, the profiles with VOCs obtained in five biological replicates for each medium-phase treatment were analyzed with the MSD ChemStation D.02.00.275 (Agilent technologies), and automatic integration using a threshold of 14 units was performed. Only those peaks absent in any of the three replicates of each medium control were considered to be volatiles released by _M. furfurfur_. Tentative annotation of compounds was done using NIST MS search 2.0 with the NIST 14 database and comparing experimental RI with the RI of compounds reported in the literature from the same (or an equivalent) column. Exported data files of chromatograms in.csv format were used for the subsequent plotting of chromatograms. Automatic integration results were carefully reviewed, and peak areas were used to construct the matrix in which tentatively annotated volatile compounds are reported as columns/variables and estimated peak areas as rows/observations. As grouping variables, we included the four media (mDixon, OA, OA+PA, and PA), both growth phases (exponential and stationary), and the combination medium-phase of the eight experimental treatments (De, Ds, Oe, Os, OPE, OPs, Pe, and Ps). Each peak area was transformed using a Hellinger transformation adequate for matrices with many zeros [70], and afterwards Pareto scaling was applied, which is useful for chromatographic analysis on a GC-MS platform [71]. In order to investigate the hypothesis that supervised techniques could afford more informative models than unsupervised analysis for the differential data, and thus unravel variables that influence class separation, each data set was analyzed by three techniques: Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Projection to Latent Structures Discriminant Analysis (PLS-DA). The data were scaled to have unit variance and were zero-centred. HCA is a statistical tool to pool samples based on similarities by measuring distances between all possible pairs of samples and plotting a topology for objects (e.g., samples for each treatment) and variables (e.g., VOCs). PCA was performed to reduce the redundancy between correlated VOCs. To support this analysis, PERMANOVA with 999 permutations was used using Euclidean distances in order to determine which grouping variable (medium, growth phase, or both) better explained the volatiles profiles differentiation. The PLS-DA model intended to find components or latent variables that correlate variation from a VOCs matrix to a response variable Y. The eight experimental medium-phase treatments (De, Ds, Oe, Os, OPE, OPs, Pe, Ps) were used as the Y response variable of the model. PLS-DA was cross-validated using seven groups (Q2 >0.5 was considered significant [72]) and performing 1000 random permutations of the VOCs dataset to evaluate the Q2 value.
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
## 2 Macroscopic Saturated Porous Media Model
### Porous Media Flow Models
#### 2.6.2 Darcy-Brinkman Flow  
In this work, the database for the ML model is generated by applying the LBM to simulate fluid flow on the microscale. The boundary conditions of the corresponding 3D pore-scale samples are designed to maintain laminar flow at a low Reynolds number. Consequently, [PERSON]'s law is applied to inversely determine the intrinsic permeability. On the macroscopic scale, incorporating the Brinkman model allows the approach to captureviscous forces when necessary by introducing an additional effective fluid stress term. This ensures that the model can account for both permeability and viscous effects in cases where they become significant.  
The Brinkman or Darcy-Brinkman flow model extends the Darcy flow model by incorporating the fluid viscous shear stress. This extension allows for a more accurate representation of non-linear flow and improves flow modeling near free-flow boundaries. To account for both the drag force, exerted by the porous matrix, and the viscous shear stress of the pore fluid, [PERSON] [50] suggested combining [PERSON]'s law with the Navier-Stokes equation. A similar result can be realized within the TPM by not neglecting the term \(\operatorname{div}\left(\mathbf{T}_{E}^{F}\right)\) in the fluid momentum balance equation. Thus, considering the definition of the effective fluid stress (11) in the fluid momentum balance in (13) and assuming creeping flow conditions with \(\left(\mathbf{v}_{F}\right)_{F}^{\prime}\approx\mathbf{0}\) yields  
\[\operatorname{grad}p=-\mu^{F}\left(\mathbf{K}^{S}\right)^{-1}\mathbf{w}_{F}+ \mu^{\text{FR}}\,\Delta\mathbf{v}_{F}+\rho^{\text{FR}}\mathbf{g}\,, \tag{16}\]  
which is considered a refined version of the Brinkman or Darcy-Brinkman flow equation. In this, \(\Delta\mathbf{v}_{F}\) represents the Laplacian of the velocity field, which accounts for the viscous shear stresses under the assumption of incompressible flow. As in [PERSON]'s flow (15), having \(\mathbf{K}^{S}\) in the above formulation provides a fluid-independent measure of a porous medium's ability to transmit fluids. Thus, deriving \(\mathbf{K}^{S}\) from \(\mu\)-CT images together with knowing the fluid properties allows for the modeling of nonlinear flow within porous media.  
Other models exist in the literature that describe flow through porous media, including the Forchheimer or Darcy-Forchheimer model, which extends [PERSON]'s law by incorporating nonlinear effects that become significant at higher flow velocities. Specifically, the Forchheimer equation adds terms to account for these nonlinearities, which depend on factors such as tortuosity and velocity (see, e.g., [1,32]). While these models are important for accurately capturing flow behavior under specific conditions, a detailed exploration of them is beyond the scope of this work, which focuses primarily on determining intrinsic permeability.
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 2 Evacuation Wayfinding Systems
### Integrated Systems Based on Wireless Sensor Networks and Cloud Computing  
The major drawback of the WSN-based evacuation wayfinding systems is the limited computing capacity, which does not allow them to compute optimal evacuation plans in a timely manner so as to forward this information to evacuees in the presence of time-varying hazards. Hence, some evacuation wayfinding systems have integrated cloud computing technologies that are accessible via on-site WSN, to offload intensive computations to remote cloud servers. For instance, the research in [29] proposed a hazard surveillance system to detect unusual events in an environment and alert residents; the system was composed of a number of static sensors, several mobile sensors and an external cloud server; when static sensors detect unusually high temperatures, mobile sensors would be dispatched by the cloud server to take snapshots and upload to the server for further analysis; if a fire emergency was confirmed, the cloud server would notify the residents in the vicinity to evacuate. the study in [30] proposed a multi-cloud-based evacuation system that integrated an on-site WSN and remote cloud servers to calculate evacuation paths for users; when a disaster occurred, the system launched an instance for each user to compute the desired evacuation route; owing to the limited I/O capability of cloud providers, several cloud platforms were employed to launch sufficient instances for evacuees and a dynamic programming algorithm was used to minimise the overall latency and service maintenance cost of the system.
## 3 Issues in Implementing URLLC  
### Quality of Service (QoS) for URLLC  
URLLC-focused applications require an end-to-end (E2E) delivery of data with reliability, security, and minimum latency. Such requirements have driven the 3 rd Generation Partnership Project (3 GPP) to set desired quality of service (QoS) requirements such as an air interface latency of 1 ms and 99.999% system reliability for URLLC [11]. These QoS requirements for URLLC, depending on its various applications, are shown in Table 3.  
The channel quality and lack of dedicated bandwidth can be an obstacle to meeting the desired latency requirement for URLLC [13]. To achieve the desired reliability in URLLC is also a challenge. As several mobile applications rely on different methods of retransmission, the retransmission of data in URLLC can degrade latency [11], unless the retransmission methods are designed as per URLLC requirements. The current 4G long-term evolution (LTE) and NR hybrid automatic repeat request (HARQ) are not quite appropriate to handle URLLC requirements. These methods depend on the complete or partial retransmission (RTX) of the error packet with additional time for HARQ processing, which is not suitable for time-critical applications [14]. An alternative method to achieve a low block error rate (BLER) and avoid RTX is to allocate high resources to the system; however, this might result in poor system capacity and low spectral efficiency. Therefore, there exists a trade-off between reliability and latency, which can be based on the application requirements. The physical layer plays a major role to achieve such a low latency and reliability; however, three major concerns exist [15]. First, system overhead in term of channel access, user schedule, and allocation of resources should be minimized. Second, the packet error probability should be minimized to achieve lower latency because the retransmission of packets can affect the latency, as mentioned earlier. Third, the transmission of URLLC packets should be prioritized, and they should be transmitted as soon as they are generated. Although the requirements mentioned by 3 GPP and ITU for URLLC are based on one-way communication, latency should be defined on the basis of E2E communication [16].
## 3 Results and Discussion
### Characterization of Zr-EPRC
#### 3.2.2 Energy-Dispersive X-ray Spectroscopy (EDS)  
The EDS analysis results (Figure 10) indicated that zirconium accounted for 20.4% of the elements in the material, confirming the successful loading of zirconium onto the material. Apart from zirconium, numerous other metallic elements such as Ca, Mg, and Fe were detected, indicating their roles in the adsorption of phosphate ions.