esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Analysis of the Longitudinal Slope Linear Shield Tunnel Excavation Face
## 2 Materials and Methods for Longitudinal Slope Shield Tunneling
### Transparent Soil Model Test for Longitudinal Slope Shield Tunneling
#### 2.1.3 Test Procedure and Working Conditions  
During the test, the servo motor drives the piston at the excavation face to gradually retract, simulating the reduction in support force and inducing soil instability. The optical observation and post-processing system is used to compute soil displacement variations, generating a displacement contour map.  
The experimental procedure is as follows: (1) The pore solution was prepared by mixing n-dodecane and white oil in the ratio specified in Section 2.1. This solution was then poured into the model container. Subsequently, washed fused silica sand was gradually added into the liquid while gently stirring with a glass rod to minimize air bubble formation. The prepared transparent soil was then placed in a dark, temperature-controlled environment and left undisturbed for 24 h to ensure the mixture reached a stable and uniform state, suitable for subsequent experimental observation. (2) After the transparent soil had settled, the camera and laser system were installed and securely fixed. Prior to image acquisition, the camera aperture, focal length, and laser intensity were fine-tuned to ensure a clear and complete speckle field. (3) The power equipment was set to retract at a rate of 1 mm/min, while the camera was simultaneously activated to capture the deformation process. As the primary objective of this study is to investigate the influence of longitudinal slope gradients on excavation face stability, the displacement rate was kept constant across all tests to isolate the effect of slope. While displacement rate may influence strain rate-sensitive soils, the transparent soil used in this experiment has relatively low rate sensitivity, and the slow rate of 1 mm/min is consistent with quasi-static testing conditions commonly used in similar studies. Therefore, the influence of displacement speed on the  
Figure 3: Schematic diagram of MicroVec V3 software calculation principle.  
results is considered negligible in this context. (4) The captured laser speckle images were processed by selecting the pixel range to extract the target region. The extracted images were then analyzed using MicroVec V3 software for further image processing.  
In this experiment, a case with \(C/D=1\) was selected to investigate the displacement variation of cross-sections under different slopes and support plate positions. A bivariate control method was employed. The slope conditions were set to \(i=-15\%\), 0%, and 15%, while the setback amounts were set to \(S=3\) mm, 6 mm, and 9 mm. Using an orthogonal combination test design, the three slope conditions were paired with the three setback conditions, resulting in a total of nine test scenarios. The complete test matrix is summarized in Table 2, and the experimental layout is illustrated in Figure 4. (\(C\) represents the tunnel burial depth, \(D\) represents the tunnel outer diameter, and \(S\) represents the setback amount.)
# Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
## 2 Materials and Methods  
### Data and Preprocessing  
The experimental data were obtained from the BCI-IV-2a dataset in 2008 [44], which recorded four types of MI signals (including left hand, right hand, foot, and tongue MI tasks) from nine subjects (these subjects did not have any particular medical conditions according to the description of the data publisher). Figure 1 shows a MI experiment paradigm, where subjects wearing EEG caps sit in front of a computer screen. Before the start of the experiment, the computer screen shows a black cross symbol, and at the same time, the computer issues a prompt to remind the participant to pay attention. A second later, the screen gives the participant specific instructions that remind the participants to prepare themselves. At the third second, the subjects begin to perform the specified MI task. After 3 s, the subject is asked to rest for 1.5 s to prepare for the next task.  
Each participant underwent two experimental sessions, and the two sessions were performed on different days. An EEG recording session was divided into 6 runs, and 48 trials were performed in each run. Therefore, each subject performed 288 training trials and 288 testing trials. That is, 288 training (including validation data) and 288 testing MI-EEG signals were captured from each subject. We used all of the subjects' EEG data from the two sessions for our research. Due to the limited amount of EEG data, all of the data collected from the first session were used for training and validation, and the data collected from the second session were used for cross-session testing.  
Figure 1: Schematic diagram of an MI experiment paradigm.  
For the MI-EEG trials for the left and right hands were 3 s long and were selected according to the labels and MI experiment paradigm for the following binary MI-EEG decoding work. All of these trials are first fit to 8-30 Hz, from which n sliding windows with a step length of s are cropped. Each window is then fir into two larger frequency bands, that is, 8-13 Hz (\(\mu\)) and 13-30 Hz (\(\beta\)), which are again fit to two (8-10 Hz named \(\mu\)-1, 10-13 Hz named \(\mu\)-2) and three (13-18 Hz named \(\beta\)-1, 18-23 Hz named \(\beta\)-2, 23-30 Hz named \(\beta\)-3) smaller frequency bands.
# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 4 Experimental Evaluation and Discussion
Figure 10 shows that the output \(O\) produced more true positives regardless of the number of false positives. However, the output \(D\) can reduce the false-positive rate. This condition makes the proposed two-stage decision working as the goal that detection rates can be accelerated by the combining of two network outputs with a two-stage decision. In addition, the proposed algorithm still outperformed the mean operation between two network outputs for all areas. SOFN with the single output also gave worse results than the proposed one caused by no validation decision of post-processing for change detection. The proposed fusion network was employed with a two-stage decision to obtain a better prediction rate. Regarding time complexity, the proposed fusion network consumed more computational complexity than the existing algorithm by a factor of approximately two over the dual-path network and three with the single-path network. It was due to the proposed architecture designed with more network paths. In addition, the proposed two-stage decision required an additional prediction process in the inference stage. Let us see that the general total time complexity of dense convolutional network [35] was \(O\big{(}K^{2}\big{)}\) run-time complexity for a depth \(K\) network [38]. Dual-DCN [28] employed dual-path dense convolutional network with the depth of 6 that produced a run-time complexity of \(O\big{(}2\cdot 6^{2}\big{)}\). The proposed fusion network included three-path dense convolutional networks with the same depth by fusing back- and front-end differential network architectures, resulting in a run-time complexity of \(O\big{(}3\cdot 6^{2}\big{)}\). In the inference stage, a two-steps decision for the proposed made the run-time be \(O\big{(}2\cdot(3\cdot 6^{2})\big{)}\) that gave it an expensive computational complexity while producing a better result.
# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 2 System
### Operation Workflow  
The automated classification system functions through a sequential procedure that relies on camera input data and is managed by a PLC. Figure 8 illustrates a flowchart detailing the main stages of this procedure, designed to detect, identify, and categorize different waste materials in suitable containers.  
The process begins with the system initialization, indicated by the Start block. Once started, the system moves to the camera position, where it waits to detect the presence of a part. If no part is detected, the system is retriggered to continue monitoring the arrival of a new item on the conveyor. When an object is detected, the camera captures an image and the AI algorithm within the PC recognizes the object by analyzing the image data. The PLC then assesses the object and categorizes it based on predefined criteria: metal can, plastic bottle, paper, or reject. Based on the identified object, the system follows one of the following steps:  
1. Metal Can Identification: If the detected object is identified as a metal can, the PLC sends a command to the stepper motor, which then positions the object correctly and pushes it off the conveyor into the designated can bin.
2. Plastic Bottle Identification: If the detected object is identified as a plastic bottle, the PLC instructs the stepper motor to push the object into the bottle bin.
3. Paper Identification: If the part is identified as paper, the PLC sends a command to the stepper motor, enabling the object to move toward the paper bin located at the end of the conveyor.  
In each scenario, the stepper motor facilitates the precise mechanical relocation of detected items to their designated containers, ensuring accurate categorization. Components that do not conform to the predefined categories of metal, plastic, or paper are classified as rejects. Subsequently, the system directs these unidentifiable items to a dedicated reject  
Figure 8: Flowchart of part classification and sorting processes using camera-based recognition.  
container. This procedural framework improves the efficiency of the automation process, with the PLC managing the logical operations and the stepper motor executing the sorting tasks based on object classification.
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 2 Inverse Halftoning Technology for Digital Halftoning  
Inverse halftoning for digital halftone images can be considered as a nonlinear transformation from a halftone image to a continuous-tone image, which can be expressed as follows:  
\[I^{th}=D(I^{th}) \tag{1}\]  
where \(I^{th}\) and \(I^{th}\) refer to the digital halftoning image and the restored continuous-tone image by inverse halftoning method, respectively, and \(D\) represents the nonlinear transformation. Here, the DCNN is used as a nonlinear mapping function.  
According to the network architecture, inverse halftoning methods for digital halftoning can be divided into two types: end-to-end learning method based on a single network (EEBSN) and end-to-end learning method based on multiple sub-networks training (EEBMN).
## 4 Applications of WSN in Urban Areas
### Solid Waste Monitoring
The objectives include real-time monitoring of waste, assessing ecological factors, efficient waste administration, optimizing waste management operations, performing data tracking, and addressing waste gathering issues. The results show improved NL, E, Delay, R, T, LR, and Cost. However, the limitations include unminimized energy consumption, failure to reduce energy consumption based on ecological factors, inefficient solid waste management with minimal energy consumption, incomplete waste management for data monitoring, failure to carry out data tracking with minimal computational overhead, and unaddressed waste gathering problems. Each methodology tackles specific aspects of waste management but encounters distinct challenges in implementation. The \(\swarrow\) in Table 9 shows that the method increases the performance of the particular metric, whereas the blank space indicates that the method does not consider the performance of the particular metric.The summary conclusions of solid waste monitoring applications are listed below. * The studies address urban solid waste management challenges like inefficient collection schedules, high costs, and pollution through innovative technologies. * Different network topologies, including decentralized, centralized, and hybrid models, optimize waste collection and management. Decentralized networks enhance real-time monitoring, while centralized networks improve data tracking and route optimization. * The mesh network topology is valued for its reliability, scalability, and effective data collection in urban waste management. * Centralized architectures using tree or star topologies are efficient for data tracking and waste sorting but may require more setup resources. * Decentralized architectures, IoT, deep learning, and optimization algorithms improve waste management by enhancing efficiency, reducing costs, and minimizing environmental impact.
## 3 RE Process as \"Game Changer\": Clinical Applications
### Treatment Plan
#### 3.2.3 Digital Plan  
RE software is also helpful for the digital planning of skeletal anchorage systems. Skeletal anchorage is seeing widespread use among clinicians because it facilitates complex orthodontic biomechanics that can overcome the limitations of conventional treatment with fixed appliances [31]. Bone-supported anchorage makes it possible to increase the efficacy of orthodontic tooth movements, counteracting undesired dental side effects while enhancing orthopedic effects [32]. The extra-alveolar region has been recommended as a safe area for miniscrew insertion because of its adequate bone depth and the absence of fragile anatomical structures [33]. In this regard, the anterior palate region offers an excellent location for miniscrew placement with the paramedian region showing the lowest failure rate (about 1.3%). Although miniscrews can be safely inserted in this region with a free-hand approach, digital planning and the generation of surgical guides are encouraged for the following reasons: (1) control of miniscrew inclination and parallelism that facilitate orthodontic appliance placement and avoids undercuts; (2) control of miniscrew insertion depth, preventing bone trauma during insertion; and (3) precise planning of the relationship between the miniscrew and the cortical palatal and nasal bone (necessary especially for the application of orthopedic forces) (Figures 13 and 14).  
The information derived from CBCT can be beneficial in evaluating anatomical characteristics such as bone depth and cortical bone thickness. Moreover, the registration of CBCT data with digital models allows the superimposition of soft-tissue information and hard-tissue bone characteristics. This integration is crucial in designing a surgical guide that optimizes miniscrew positions within the skeletal anatomical structures; however, according to the ALARA and ALADA principles [8], CBCT scans using a digitally assisted miniscrew insertion system can be prescribed only in specific circumstances such as (1) tooth impaction, (2) a high risk for naso-palatal nerve injury (severely constricted palate or higher vault), (3) unerupted incisors, or (4) a cleft palate. Besides these conditions, digital planning should be performed superimposing the maxillary.STL file to the lateral cephalogram [34]. Alternatively, in the absence of the conditions mentioned above, clinicians could plan miniscrew placement and generate the surgical guide without using only the.STL file (i.e., without registering the radiograph), which guarantees better control compared to free-hand insertion (inclination/parallelism).  
Figure 13: Example of digitally assisted miniscrew insertion system using free-source software (BlueSkyPlan v. 4.9.4).
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 3 Energy Management for an Energy-Harvesting Sensor Node
### Energy Utilization Coefficient  
To evaluate the energy efficiency of different MAC protocols, the Energy Utilization Coefficient (EUC), denoted by \(\xi\), is defined as the ratio of the throughput to the energy budget:  
\[\xi(e_{B})=\frac{THR}{e_{B}}. \tag{9}\]  
It is expressed in packets per minute and per Joule. For notational simplicity, the slot number indication \"\([k]\)\" is omitted in the rest of this section, and all the following equations refer to a single time slot. The EUC quantifies the achieved throughput of a MAC protocol regarding the available energy budget and is similar to other energy efficiency metrics, e.g., [41; 42].  
By combining Equations (7)-(9), we obtain:  
\[\xi(e_{B})=\frac{1}{HT}-\frac{P_{S}}{H}\frac{1}{e_{B}}, \tag{10}\]  
where \(H\) (in Jules) is defined by:  
\[H=e_{T}-\tau_{T}P_{S}. \tag{11}\]  
\(H\) is a constant particular to a given hardware and MAC protocol. Indeed, the \(\tau_{i}\) values depend on the MAC protocol, while the \(P_{i}\) values depend on the hardware. Two remarks can be made regarding Equation (10). First, the EUC is not constant for a given hardware and MAC, but increases with the energy budget \(\varepsilon_{B}\). Secondly, the EUC is bounded, as:  
\[\xi_{\infty}=\lim_{\varepsilon_{B}\to\infty}\xi(\varepsilon_{B})=\frac{1}{HT}. \tag{12}\]  
From Equation (12), it can be observed that the maximum EUC \(\xi_{\infty}\) is higher for small values of \(H\). Therefore, the smaller \(H\) is, the better it is. For the rest of this work, the \(P_{i}\) values are assumed to be fixed, and the power consumption in sleep state \(P_{S}\) is supposed to be much smaller than the power consumption of the other states \(P_{i}\). This assumption holds true for all the WSN platforms. Hence, minimizing \(H\) is done by minimizing the \(\tau_{i}\) values. In order for \(H\) to be minimal, only the data frame should be sent at each packet transmission. However, most of the MAC protocols introduce an overhead to synchronize the nodes (e.g., the rendezvous process in pseudo-asynchronous MAC protocols) or for error control (e.g., ACK frames). As we will see in the next section, using ULP WuRx allows the minimization of \(H\) and hence the maximization of the EUC.
## 5 Performance Analysis
### Effect of PPDU Length  
We investigated how the length of PPDU affected throughput. Sum of throughputs per one link are shown in Figure 13(a),b. In the case of MLA-A, note that the length indicated the former PPDU, as shown in Figure 3(d) since the length of following PPDU in MLA-A could vary depending on the starting time of PPDU on the auxiliary link.  
In Figure 13(a), it seems that overall throughputs became saturated as PPDU length increased. This is because when a collision occurred, a whole length of TXOP became wasted without RTS/CTS. On the other hand, only the length of the RTS frame plus DIFS duration was dissipated with the help of RTS/CTS exchange. This is the reason why the sums of saturated throughput were continuously increasing when RTS/CTS exchange was on, as shown in Figure 13(b).  
Furthermore, it should be noted that the longer the length of the PPDU, the better the performance of the MLA-A regardless of RTS/CTS exchange. This is because the additional transmission on the auxiliary link entirely relied on the residual time of ongoing TXOP. Hence as the duration of the PPDU got longer, the TXOP duration held by the transmitter was longer as well, and there was more opportunity to get a chance of access on the auxiliary link.
## 2 Materials and Methods
### Mesocosm Design and Sampling  
Four mesocosms (each one ton) were set up to evaluate the impacts of _T. striata_ cultivation on the surrounding bacterial community and the microbial ecosystem--see below (Figure 2). Mesocosms were floated by buoys and tied to the pier using ropes (Figure 2B). Each mesocosm was filled with one ton of natural seawater and then left for two days for acclimation. A 20-L bioreactor containing _T. striata_ enclosed by a semi-permeable membrane was then installed into two of the mesocosms and secured with ropes. These are termed \"bioreactor installed mesocosms\" (BMs) for the duration of the study. The other two adjacent mesocosms had no bioreactors installed, and they are termed \"control mesocosms\" (CMs) (Figure 2A,C,D). All mesocosms (BMs and CMs) were then spiked with 30 uM nitrate and 2 uM phosphate to induce sufficient growth of all biota living in mesocosms. On days 0, 2, 3, 4, 5, 6, 7, 9, 11, 13, and 15, four liters of seawater was subsampled from each mesocosm for DOC concentration, as well as various biotic factors (bacterial abundance and composition, abundances of heterotrophic nano-flagellates (HNF), phytoplankton, ciliates, and zooplankton, and the nutrients ammonium, nitrate, nitrite, phosphate and silicate) (see below). On each day, a 50-mL sample of _T. striata_ was also collected using a suction flask through tubes which connected to the faucet of each bioreactor (Figure 2C,E). This water sample was subsequently preserved with 5% Lugol's solution [33] and returned to the laboratory for cell enumeration. Water temperature (\({}^{\circ}\)C), salinity, pH, and dissolved oxygen (DO, mg\(\cdot\)L\({}^{-1}\)) were also measured within and adjacent to (3 m away) mesocosms using an YSI 6600 sonde (YSI inc., Marion, MA, USA).
# Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events
###### Abstract
Long-term datasets of vegetation satellite products, land surface temperature and soil moisture, at moderate spatial resolution [35; 36; 37], enable the assessment of ecological responses to hot and dry events. Furthermore, datasets of vegetation cover can provide extended information about the responses of different land covers [10]. The present work also analyses the vegetation's functional recovery following the hot and dry episodes by quantifying the lag time of the re-establishment of normal productivity values.
###### Abstract  
This study proposes a comprehensive methodology for accurate State of Charge (SOC) estimation in lithium-ion batteries by optimizing equivalent circuit model (ECM) parameters under varying temperature conditions using the Taguchi method. Analysis of Variance (ANOVA) was employed to evaluate the influence of these parameters on ECM accuracy. Experiments were conducted at \(-10\)\({}^{\circ}\)C, \(25\)\({}^{\circ}\)C, and \(40\)\({}^{\circ}\)C to evaluate the effects of pulse time gap, discharge pulse time, and C-rate on SOC estimation accuracy. A genetic algorithm-based multi-objective optimization technique was employed to minimize RMSE in the extended Kalman filter (EKF) SOC estimation process. The results showed that temperature significantly impacts SOC prediction, with deviations most pronounced at low (\(-10\)\({}^{\circ}\)C) and high (\(40\)\({}^{\circ}\)C) temperatures. When assessments are conducted for different SOC levels (SOC90, SOC50, SOC30), the key results highlight the substantial influence of pulse time gap and discharge pulse time on model accuracy. Also, it was observed that there is a significant reduction in RMSE, indicating improved performance under optimized conditions. The findings are particularly relevant for real-time applications, such as electric vehicles, where accurate SOC estimation is crucial for battery management.  
Li-ion battery; optimized HPPC profile; equivalent circuit model; Taguchi method; extended Kalman filter +
Footnote †: journal: _sciences_  
_Applied_