esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Image Processing Analysis  
In this section, the structural damages of the samples that were punctured when the voltage was applied to their surfaces were evaluated. The image analysis method was used to evaluate the extent of these damages. For this purpose, sample surfaces were photographed at a 90\({}^{\circ}\) angle and a distance of 20 cm. The images obtained were analyzed with Image J software. In the analyses, the ratio of the area of structural damage caused by partial discharge and puncture events to the entire sample surface area was examined.  
The samples received damages of different sizes according to the type of material they were produced from. As the voltage increased, the polymer material carbonized and burned due to the breakdown event that occurred as a result of partial discharges. Figure 9 shows the image analysis of the ABS material after breakdown. Image analysis results of ASA in Figure 10, PC/ABS in Figure 11, PETG in Figure 12, and PLA in Figure 13 are shared.  
Figure 11: Results of image analysis for PC/ABS: (**a**) breakdown sample; (**b**) analysis of image.  
Figure 12: Results of image analysis for PETG: (**a**) breakdown sample; (**b**) analysis of image.  
Figure 10: Results of image analysis for ASA: (**a**) breakdown sample; (**b**) analysis of image.  
In Figure 9, it is seen that the ABS material is damaged superficially by 3.48% as a result of the breakdown. The damage pattern shows that the arcs occurring between the layers are intense.  
In Figure 10, it is seen that the ASA material is damaged superficially by 1.39% as a result of the breakdown. The damage pattern shows that the arcs that occur between the layers progress without disintegration.  
In Figure 11, it is seen that the PC/ABS material is damaged superficially by 3.49% as a result of the breakdown. The damage pattern shows that the arcs occurring between the layers are distributed in a limited way and progress by dispersion.  
In Figure 12, it is seen that the PETG material is damaged superficially by 0.72% as a result of the breakdown. The damage pattern shows that the arcs occurring between the layers follow a layer path and progress without dispersion. In this context, the breakdown event occurred on the material carbonized by the electric arcs.  
In Figure 13, it is seen that the PLA material is damaged superficially by 0.03% as a result of the puncture. The damage pattern shows that the arcs that occur between the layers propagate without breaking up. In this context, it is revealed that the electric arcs directly breakdown the PLA material.
## 6 Open Challenges, Future Prospects and Research Directions
### Supporting Demanding Applications
#### 6.4.7 Adaptive Optics  
Future research in AO for FSO communication is poised to significantly enhance system reliability and performance by addressing the challenges posed by AT and environmental variability. Key areas of focus include the development of advanced wavefront sensors, DMs, the integration of ML algorithms, miniaturization of AO components, and the exploration of hybrid AO techniques.
## 3 Experimental Results and Discussion  
### M-\(\kappa\) Relationships  
Figure 6 shows the \(M\)-\(\kappa\) relationship of the EMT carbon steel RHT under cyclic bending with \(\phi\) = 0\({}^{\circ}\) and \(d\) = 2 mm. The controlled curvature ranges from \(-\)0.3 m\({}^{-1}\) to 0.3 m\({}^{-1}\). The experimental results indicate that in the initial stage of loading, the EMT carbon steel RHT undergoes elastic deformation, presenting a linear trend in the \(M\)-\(\kappa\) relationship. However, upon entering the plastic region, the \(M\)-\(\kappa\) relationship becomes nonlinear, with the slope of the curve gradually decreasing, and the EMT carbon steel RHT undergoes permanent plastic deformation. When the curvature reaches the control peak value of 0.3 m\({}^{-1}\), the bending test machine initiates reverse bending, reducing the curvature from the peak value of 0.3 m\({}^{-1}\) to \(-\)0.3 m\({}^{-1}\). In the initial stage of the unloading process, the linear \(M\)-\(\kappa\) relationship is parallel to the linear \(M\)-\(\kappa\) relationship in the initial elastic range. Subsequently, the later part of the negative bending is similar to the positive bending, with the slope of the curve gradually decreasing, exhibiting a nonlinear trend as it enters the plastic range. After completing the first cycle, the \(M\)-\(\kappa\) relationship graph shows a loop, and with an increase in cycles, the loops almost overlap. Because the holes are small and localized, the \(M\)-\(\kappa\) relationships for different values of \(\phi\) and \(d\) are nearly identical. Therefore, only the \(M\)-\(\kappa\) relationship for the case of \(\phi\) = 0\({}^{\circ}\) and \(d\) = 2 mm is presented in this study. This phenomenon aligns with the experimental findings of [PERSON] et al. (2017) on 6061-T6 aluminum alloy RHTs under cyclic bending loads, where stable loops remain unaffected by \(\phi\) and \(d\).
# Research on the Trusted Traceability Model of Taishan Tea Products Based on Blockchain
## 5 Performance Test of Blockchain-Based Taishan Tea Traceability Model  
### Experimental Analysis  
In order to comprehensively assess the performance difference in data processing between the blockchain-based Taishan tea traceability model and the traditional Taishan tea traceability model, we encapsulated both of them into standardized API interfaces and executed exhaustive performance tests using Postman (v11.1.14.0), an interface testing tool. The tests cover three aspects: data writing (simulating concurrent submission of tea production information by multiple users), data querying (testing response time and accuracy under different query conditions), and throughput (evaluating the system's overall processing capability under high pressure). Through comparative analysis, the results in the response are verified to match the expected results. The experimental environment is based on an i9-12900K CPU (Intel, Santa Clara, CA, USA), a Centos7.9 operating system, and version 1.4.6 Fabric for testing.
## 2 Thermodynamic Analysis
* During the desorption phase, the adsorbent bed (operating as desorber in this phase) is regenerated, releasing the adsorbed refrigerant, exploiting the waste heat, QL', at temperature level, TL, as shown in Figure 1 The desorbed refrigerant is then condensed at the pressure p0, imposed by the sink temperature, T0, to which the condenser is connected. The first stage of the desorption process occurs under isosteric conditions (P-Q). Once the waste heat temperature level, TL, is attained, the refrigerant is isothermally desorbed down to the condenser pressure (Q-R). * During the adsorption phase, the waste heat, QL, is supplied to the evaporator at the temperature TL. The evaporated refrigerant is adsorbed in the adsorber and, due to the exothermic adsorption reaction, it is heated up until the upgraded heat temperature level, TA, is reached and the related energy QA is released as shown in Figure 1. Furthermore, in this process, a first isosteric stage takes place (R-S), which is prolonged until the upgraded temperature level, TA, is reached. At this stage, the sorption process continues isothermally (S-P), releasing the heat until the evaporator pressure is reached. To comparatively evaluate different operating conditions, the useful effect is defined as gross temperature lift (GTL) representing the temperature difference between the upgraded heat (TA) and the waste heat (TL). The main parameter to evaluate the AdHT efficiency, according to the first-law of thermodynamic, is the coefficient of performance (COP), defined as follows:  
\[\text{COP}=\frac{\text{Q}_{\text{A}}}{\text{Q}_{\text{L}}+\text{Q}_{\text{L}}^ {\prime}} \tag{1}\]  
Beside the overall AdHT efficiency, another relevant parameter needed to estimate the amount of sorbent material needed to upgrade the waste heat source, is represented by the specific energy, QA [I/g], whose definition is given by Equation (5) in Section 2.2.
## 3 Results and Discussion
### Experimental AHU Installations
It must be noticed that both circuits, as well as their interconnection, are managed by the corresponding dampers. speed is variable, and a value of 0 corresponds to a stopped fan while 1 corresponds to the fan working at full speed. In addition to the delay of the turning on, the signals show how the control algorithm of the target domain includes reducing the outdoor air intake during the hours when the outdoor temperature is maximum or how the turning off is conducted more smoothly in the target domain than in the source domain. Figure 6e shows the signal that controls the opening of the cooling coil valve, as in the other two, it takes values in the interval between 0 and 1. The main difference observed is the delay in the first opening of the valve and the presence of spikes in the source domain measurement. This kind of differences represents quite well the challenge that inspires the use of a transfer learning methodology, there are clear similarities in how the systems work and how the faults affect the variables measured, but in real applications there are also differences in the control and operation algorithms and these are the points that current studies in transfer learning for HVAC are not studying. Figure 6: Comparison between the measurements observed in the source and target datasets samples: (**a**) Outdoor Air temperature. (**b**) Supply Air temperature. (**c**) Outdoor Air Damper Control Signal. (**d**) Supply Air Fan Speed Control Signal. (**e**) Cooling Coil Valve Control Signal.
## 2 Study Region and Data Sources  
### Study Region  
The Hetao Irrigation District is the third largest irrigation district and an important food production base in China. The irrigation district is located in the western part of the Inner Mongolia Autonomous Region in North China. The area of Hetao Irrigation District is 1.12 million ha and the irrigated farmland is 0.57 million ha. In the present research, four counties (Dengkou, Hangjinhouqi, Linhe and Wuyuan) in the district were selected as the study region, which spans from 40.1\({}^{\circ}\)N to 41.4\({}^{\circ}\)N and from 106.1\({}^{\circ}\)E to 109.4\({}^{\circ}\)E (Figure 1). The region covers an area of 0.91 million ha, with 44% being irrigated land (Figure 2). The study region is characterized by arid continental climate with an annual precipitation of approximately 160 mm and pan evaporation (20 cm evaporation pan) of approximately 2240 mm. Mean annual temperature is 7.7-9.5 \({}^{\circ}\)C from 2009 to 2015, and daily mean temperatures ranging from \(-16.9\)\({}^{\circ}\)C in January to 29.1 \({}^{\circ}\)C in July. The study region belongs to an alluvial plain of the Yellow River with elevation varied from 1000 m to 1091 m above sea level.  
Figure 1: Location of the study area and sampling points in 2012 and verification points in 2014 and 2015.  
Dominant crops in the study area are summer maize, sunflower and spring wheat. The crop growing season spans from April to October. According to statistics of local government ([[http://www.bmagri.gov.cn](http://www.bmagri.gov.cn)]([http://www.bmagri.gov.cn](http://www.bmagri.gov.cn))), the planting area of three major crops has changed evidently in recent years (Figure 3). For summer maize and sunflower, the planting area increased from 2009 to 2015, while the planting area of spring wheat decreased significantly. Thus, summer maize and sunflower were considered in the following analysis.
# Structure Design, Kinematics Analysis, and Effect Evaluation of a Novel Ankle Rehabilitation Robot
## 2 Type Synthesis and Structure Design  
### The Movement Modes of the Human Ankle  
Ankle joint movements include plantar flexion and dorsiflexion movements around the frontal axis (\(x\) axis), inversion and eversion movements around the sagittal axis (\(y\) axis), and the abduction and adduction movements around the vertical axis (\(z\) axis). A coordinate system was established at the center of the ankle socket, taking a human's left foot as an example, as shown in Figure 1.  
The movement range of the ankle joint is limited by ligaments to protect the ankle from excessive damage, which are shown as Table 1[17].
# Neural Models for Measuring Confidence on Interactive Machine Translation Systems
## 4 Confidence Measures
### Metric for Evaluation of Translation with Explicit ORdering (Meteor)  
Meteor [37] appeared to address several weaknesses found in Bleu. Researchers thought that the brevity penalty used in Bleu does not compensate for the nonexistence use of recall. It also opted for a new method to capture the fluency of the translations through the word order. Meteor studies the precision and recall of the unigrams from the translations instead of considering multiple levels of n-grams as does Bleu. To study the fluency of the translation, Meteor calculates the minimum number of chunks that remains the same between the translation and the reference and divides it by the number of unigrams matched. In this case, the length of the translations does not matter as much as in Bleu--Meteor only counts the matched unigrams.  
We can expect similar results to those obtained with the Bleu CM. Both metrics consider the correctness of the word using unigrams, but the main difference between them is the process used to capture the context of each word. Meteor captures it through the word order, while Bleu 2, 3, and 4 n-gram matches. This difference is the central aspect that will differentiate the results obtained from both confidence measures.
# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 3 The Proposed Adaptive Fuzzy Weighted Mean Filter Model
### Fuzzy ROR Noise Detection  
First, the stop iteration condition in the original ROR algorithm is defined as Equation (4).  
\[PSNR_{cur}-PSNR_{pre}\leq 0.01, \tag{4}\]  
where the \(PSNR_{cur}\) represents the peak signal to noise ratio (PSNR) of the current iterative process, and \(PSNR_{pre}\) represents the PSNR of the previous iterative process.  
These two PSNRs are compared with the original image; however, the process of restoration does not recognize the original image or the noisy image. Therefore, this study modifies PSNR for stopping iteration. This new stopping condition is called the peak signal-to-removal noise ratio (PSRNR); it can be calculated using fixed images of the previous iteration and the current iteration. PSRNR is defined by extensive experiments and shown as Equations (5) and (6):  
\[PSNR_{cur}\leq PSRNR_{pre}\text{ or }PSNR_{cur}\text{ is }infinite, \tag{5}\]  
\[PSNR=10\log_{10}\frac{255^{2}}{\frac{1}{RC}\sum_{i=1}^{R}\sum_{j=1}^{C}\left( x_{i,j}^{n-1}-x_{i,j}^{n}\right)^{2}}, \tag{6}\]  
where \(n\) is the iteration number, \(x_{i,j}^{n-1}\) is the image restoration pixel value in the previous iteration, \(x_{i,j}^{n}\) is the image restoration pixel value of the current iteration, \(PSNR_{cur}\) is the current \(PSNRNR\), and \(PSNR_{pre}\) is the previous \(PSNR\). By the above \(PSNR\) criteria, the noise detection skill is named the No-Reference ROR (NR-ROR) algorithm in this study.
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## Appendix A Data Acquisition Details  
EEG signal acquisition followed a semi-automatic protocol that integrated manual event activation with automated processing via BrainVision Recorder 1.27 software. The procedure included the following steps:  
1. Recording (triggers): Triggers were configured within the BrainVision Recorder program, manually activated by the researcher through keypresses at the start and end of each recording. These triggers were labeled in the raw EEG signals, allowing for later identification of the analysis window.
2. Start and End Margins: The recording included a 3 s margin before the start and 3 s after the end, resulting in a total window of 36 s, of which 30 s corresponded to the experimental period of interest.
3. Justification for Temporal Margins: The implementation of these temporal margins aimed to minimize artifacts associated with the initiation and termination instructions of the task. The participant took approximately 3 s to process the start instruction, which could introduce transient artifacts in the signal. Similarly, the recording automatically stopped at 36 s with the trigger, reducing the variability associated with manual intervention in the recording termination.
4. Preprocessing Segmentation: During preprocessing, the 30 s window was extracted, discarding the first and last 3 s to eliminate transient artifacts and ensure that the analysis was performed on stationary data, free from transient influences.  
This methodological approach reduced variability and increased data reliability without requiring additional hardware for event activation.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Smart Cities and Automotive Applications  
#### 3.3.1 Sensor Nodes for Smart Cities  
The sensor nodes proposed by [13] were developed for smart cities and remote sensing applications. Pertaining to the processor and memory specifications, the nodes were designed around the 8 MHz Waspmote microcontroller (ATmega1281) consisting of 8 kB SRAM, 4 kB EEPROM, 128 kB FLASH and 2 GB SD card memory. Two ZigBee radio modules are utilized for realizing communication between the sensor motes. A 2.4 GHz Zigbee module (XBee-PRO version) is chosen for communication between ground nodes and the other ZigBee module operating at 868 MHz is used for linking with the UAV node. A radio expansion interface is used for facilitating a node to simultaneously connect to two different ZigBee transceivers. The 2.4 GHz XBee-PRO can operate at a transmission power of 18 dBm with sensitivity of \(-\)100 dBm while achieving a communication range of 1500 m. The 2.4 GHz XBee S2 version can transmit at 3 dBm power with a receiver sensitivity of around \(-\)97 dBm covering up to 120 m.  
The sensor nodes utilize external sensor boards with sockets for accommodating the HC ultrasonic sensor and the DHT11 sensor (temperature and humidity), Grove-Gas Sensor (MQ2) (for detecting the leakage of gases that include hydrogen, liquefied petroleum gas (LPG), methane, CO, alcohol, smoke or propane) and the D-Sun Hc-Sr501 pyroelectric infrared (PIR) motion sensor. In addition, external sockets are available for connecting new types of sensors. Batteries were utilized to power the nodes during the first implementation of a WSN by the authors. During the second implementation, the Waspmote board that can accommodate a solar panel was utilized. The low power consumption during data communication was realized using ZigBee modules.
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 7 Discussion  
Table 4 provides a comparison table highlighting a selection of state of the art solid-state photon counting image sensors in the three different technologies (CIS, EMCCD and SPAD). This section discusses and compares the performance of SPAD-based image sensors based on analogue integration. SPAD based image sensors have the highest CVF of solid-state SPC image sensors. Moreover, the pixel size of the SPAD analogue-based imagers is commensurate with EMCCD and sCMOS scientific imagers, although FF is lower. With the exception of the LOFIC pixel which has dual CVF's, like the recent CIS DSERN pixels, the increase in CVF of SPAD pixels yields a reduced full well in the order of 100's photo-electrons or integrated SPAD events.  
SPADs have the advantage of picosecond temporal resolution. Analogue pixels with low transistor counts permit nanosecond and sub-nanosecond time-gated SPC imaging to be realized where digital pixels further permit TCSPC imaging with 10's ps time resolution at the cost of low spatial resolution.  
In terms of RN, SPAD analogue integrators share a similar noise characteristic with 3 transistor (3T) CIS pixels in that the integration node is not fully depleted and so suffers from kT/C noise. Our test structure [15] cancels the kT/C noise by implementing 3T-pixel true CDS timing and furthermore implemented 4096 sample CMS to yield <0.01 e\({}^{-}\) equivalent RN in the best case. However, both 3T timing and >1 k sample CMS is very restrictive in an image sensor design preventing, for example, the global shutter or global time-gated operation that our recent work and [37] implements. Therefore delta-reset sampling CDS [43] is implemented in our SPAD analogue counter image sensor which adds a noise component of 100's \(\upmu\)V RMS kT/C to the RN. However, the equivalent CVF of the SPAD-based analogue pixels in the 10 mV range is high enough to compensate, as demonstrated by the 0.06 e\({}^{-}\) RN figure which is the lowest in the published SPC image sensor literature.  
In comparison to other works, analogue integrators suffer from cumulative noise limiting the photon number resolution. Spatio-temporal oversampling, at a few photons per pixel level, mitigates the noise integration whilst extending the photon number resolution although high frame rates are required.  
Figure 9: Measured RMS noise in multi-photon threshold oversampled binary imaging.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & **[12]** & **[16]** & **[23]** & **[21]** & **[27]** & **[18]** & **[16]** & **[26]** \\ \hline
**Photodetector** & PIN FD + LOFC &
\begin{tabular}{c} “Pump-gate lot” \\ PIN FD \\ \end{tabular} & PIN FD & EMCCD & SPAD & SPAD & SPAD & SPAD \\ \hline
**Final Circuit** & ST + LOFC & 4T & 4T & CCD & Active CTA NT & Passive CTA 11T & Passive CTA 9T & \(\mathcal{P}\) Counter -100T \\ \hline
**Array Size**
## 2 Method  
The design of the dual receivers tracking system and the 4-D tracking model based on the MDM are proposed in this section. The FEM model used to verify the tracking algorithm is also introduced.
## 4 Experiments
### Discussion  
In the previous studies in this area, researchers have presented various methods based on traditional machine learning techniques to accurately identify multiple garments in real-time, thus enhancing online virtual clothing style selection and benefiting the broader clothing industry.  
[PERSON] et al. [41] introduced a method employing CNN and VGG16 for cloth classification based on feature extraction, which achieved an accuracy of 90%. On the other hand, [PERSON] et al. [42] utilized R-CNN with YOLOv5s for feature extraction and fabric classification, thus attaining the highest accuracy of 97%. However, both approaches encountered limitations due to the small size of their datasets, leading to a restricted number of images. Further, some types of cloth fell outside the camera's capture area, resulting in an inability to accurately identify certain cloth objects. As presented in Table 7, the proposed approach combining ResNet50 for feature extraction with the Transformer model exhibited performance that was superior to that of any of the other methods. In our assessment, the deep learning model achieved enhanced accuracy in recognizing and classifying cloth simulations while significantly reducing the time for confusion compared to alternative models. This efficiency is attributed to the Transformer model's parallel execution, which conserves device resources during execution.  
The higher recognition and classification accuracy of the proposed model can be attributed to three main features: (i) Robustness to variations in physical properties, encompassing cloth color, the brightness of the cloth object in the video, random rotation angles of the video on the screen, and image resolution in the videos. (ii) Effective utilization of transfer learning techniques, enabling the model to be trained efficiently with a substantial amount of datasets. (iii) Efficient execution during the training process, thus obtaining significant time savings and a cost-effective solution. This is achieved through a fully automated end-to-end architecture for feature extraction and classification.