esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Linear Contact Law and Finite Element Model  
### Linear Contact Law  
The impact contact process includes three stages: elastic loading, elastoplastic loading, and elastic unloading. The linear contact loading law [37] ignores the initial elastic loading stage and linearizes the elastic-plastic loading stage. The relationship between the impact contact force \(F\) and indentation \(\delta\)[37] is as follows:  
\[F=K_{h}\delta, \tag{1}\]  
where \(K_{h}\) is the linear contact stiffness, and the slope of the loading curve is as follows:  
\[K_{h}=2\pi Rp_{0}, \tag{2}\]  
\(R\) is the radius of the sphere and \(p_{0}\) is the average pressure under completely plastic conditions. For metal materials the following is true:  
\[p_{0}=2.8\sigma_{Yh}, \tag{3}\]\(\sigma_{Yh}\) is the yield strength of the object being hit. The linear contact loading law only considers the diameter of the impact sphere and the yield strength of the object being hit.
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 3 Experimental Setup
### Data Acquiring and Processing  
Experiments were conducted using two nodes, one as transmitter and the other as receiver. The receiver was connected to a laptop through a serial port, as shown in Figure 2. All experiments were conducted on channel 26 and with 0 dBm transmit power. Antenna height was set to 1.2 m. The communication distance between transmitter and receiver was increased from 0 to 100 m, and the antenna directions of both nodes remained unchanged during this process. Considering that _RSSI_ and LQI change greatly when the distance is small, the measurement points were chosen as follows: the step is 0.2 m in the first 5 m, 0.5 m from 5 to 20 m, 1 m from 20 to 60 m, and 2 m from 60 to 100 m respectively. In brief, there is a total of 115 measurement points.  
Figure 1: Experimental environments: (**a**) Runway, (**b**) Artificial lawn, (**c**) Corridor.  
500 packets were sent at each distance, and the inter-packet interval was set to 25 ms. Each packet carried a sequentially increased serial number, so \(PRR\) could be calculated using the number of successfully received packets. \(RSSI\), LQI, background noise power, and serial number of successfully received packets were transmitted to the laptop for analysis, in which \(PRR\) and the mean values of \(RSSI\), \(SNR\), and LQI were calculated using MATLAB.  
CC2420 provides \(RSSI\) and LQI measurements, which can be obtained by accessing corresponding internal registers [13]. The received power can be calculated using the \(RSSI\) value according to the following equation:  
\[P=RSSI_{VAL}+RSSI_{OFFSET} \tag{1}\]  
where \(RSSI_{VAL}\) is the \(RSSI\) value provided by CC2420. \(RSSI_{OFFSET}\) is an empirical correction value, which is \(-45\) dBm according to the CC2420 datasheet. LQI presents the quality of received packets, and its values usually range from 50 to 110. The larger the LQI, the higher the quality of the received packet.
# Laboratory Evaluation of Tensile Creep Behavior of Concrete at Early Ages
## 5 Application  
Using the estimated relaxation modulus, which is shown in Table 4, the creep tests conducted were simulated with the finite element program ABAQUS (Hibbitt, Karlsson & Sorensen, Inc., Providence, RI, USA). Continuum material properties were specified for a viscoelastic material option with the Prony series parameters in ABAQUS. And a constant Poisson's ratio (\(\
u\) = 0.17) was used for the analysis. Since the tests lasted less than 6 h, the aging effect during the test was ignored in this simulation, and the relaxation modulus determined for each age of testing was regarded as a constant during the test periods. The upper half (50 \(\times\) 100 mm) of the creep test cylinders was modeled with bilinear axisymmetric solid elements (CAX8R element in ABAQUS). The stress level applied for each test was applied as a distributed pressure load on the surface of the top layer elements. The analysis was performed up to 5.1 h after the initial loading. The total analysis time was divided into 4 sub-steps at the time of 0.1, 0.5, 1.5, and 5.1 h with varying time steps (\(\Delta t\)) of 0.0001, 0.001, 0.005, and 0.01 h, respectively. Results of the finite element analysis are plotted with test results in Figure 9. As shown, the result of computational analysis agrees well with the result of tests at all ages of testing. In Figure 9, \(F_{0}\) denotes an applied test load at each age. Note again that, when the concrete experiences significant hardening during the analysis period, the material properties must be updated throughout the analysis in accordance with the relevant aging dependencies. This continuous update of material properties could be accomplished by an incremental analysis scheme [5].  
Figure 9: Computational simulation of the creep test data.
## 3 Motion Vehicle Target Extraction
### MD-SILBP Shadow Removal Algorithm
The MD-SILBP operator trains the image background frames, and the texture features are used for background MD-SILBP modeling. The distance between the current frame texture histogram \(p_{s}\) and the background texture histogram \(p_{t}\) in the \(Q(p_{s},p_{t})^{i}\) candidate region is as follows:  
\[Q(p_{s},p_{t})^{i}=\frac{\sum\limits_{i=1}^{n}\min\big{(}p_{s}^{i},p_{t}^{i} \big{)}}{\sum\limits_{i=1}^{n}p_{s}^{i}} \tag{15}\]  
In the above equation, n is the number of bins in the candidate region. Then, the relationship between the two is further expressed by calculating the texture correlation between the candidate region in the current frame and the image background frame. \[S=\frac{\sum\limits_{j=1}^{n^{\prime}}H\left(T_{\Omega}-Q(p_{s},p_{t})^{i}\right)}{n ^{\prime}} \tag{16}\]  
In the above formula, \(S\) represents the texture correlation of the candidate region between the current frame and the image background frame; \(n^{\prime}\) is the number of pixels in the candidate region; \(H(\cdot)\) is the unit step function; and \(T_{\Omega}\) is the threshold. When the value of \(Q(p_{s},p_{t})^{i}\) is less than the value of \(T_{\Omega}\), the value of \(S\) is 1, indicating that the smaller the correlation between the current frame and the background frame, it is more likely to be judged as a shadow. Conversely, when the value of \(S\) is 0, it means that the more significant the correlation between the current frame and the background frame, it is more likely to be judged as the background, and the effect of the improved moving vehicle target extraction algorithm is shown in Figure 9. Ability to remove shadow effects on the foreground of moving vehicles is shown below.
## Materials and Methods
### Measurements of Sensitivity and Anti-Interference  
The sensitivity of spectral parameters refers to the drifting degree of the parameters in each spectral dimension when the properties of the object change [20]. What is more, the anti-interference is used to indicate the displacement degree of the value of parameters when the properties of a non-research object change in spectral space. The sensitive function is a common index employed to measure sensitivity and anti-interference. In this study, we have defined the calculation formulas of the sensitive function of derivative parameters on the basis of NDVI. \[\mathrm{S_{v}}=\left[\frac{\mathrm{d}(\mathrm{VNDVI})}{\mathrm{d}(\mathrm{ NDVI})}\right]*\left[\frac{\Delta(\mathrm{VNDVI})}{\Delta\mathrm{NDVI}}\right]^{-1} \tag{2}\]  
\[\mathrm{S_{A}}=\left[\frac{\mathrm{d}(\mathrm{A_{VNDVI}})}{\mathrm{d}(\mathrm{ NDVI})}\right]*\left[\frac{\Delta(\mathrm{A_{VNDVI}})}{\Delta(\mathrm{NDVI})} \right]^{-1} \tag{3}\]  
where VNDVI is the general term for GNDVI, BNDVI, GRNDVI, GBNDVI, and RBNDVI; \(\mathrm{A_{VNDVI}}\) is the general term for \(\mathrm{A_{NDVI}}\), \(\mathrm{A_{GNDVI}}\), \(\mathrm{A_{GNDVI}}\), \(\mathrm{A_{GNDVI}}\), \(\mathrm{A_{GNDVI}}\), \(\mathrm{A_{GBNDVI}}\), and \(\mathrm{A_{RBNDVI}}\); \(\mathrm{S_{V}}\) and \(\mathrm{S_{A}}\) are the sensitive functions of VNDVI and \(\mathrm{A_{VNDVI}}\), respectively; and \(\mathrm{d}(\mathrm{VNDVI})\), \(\mathrm{d}(\mathrm{NDVI})\), and \(\mathrm{d}(\mathrm{A_{VNDVI}})\) represent the first derivative of leaf area indices corresponding to VNDVI, NDVI, and \(\mathrm{A_{VNDVI}}\), respectively. \[\mathrm{d}(\mathrm{VNDVI})=\left[\mathrm{VNDVI}+\mathrm{o}(\mathrm{VNDVI}) \right]*\left[\mathrm{LAI}+\mathrm{o}(\mathrm{LAI})\right]^{-1} \tag{4}\]  
\[\mathrm{d}(\mathrm{NDVI})=\left[\mathrm{NDVI}+\mathrm{o}(\mathrm{NDVI}) \right]*\left[\mathrm{LAI}+\mathrm{o}(\mathrm{LAI})\right]^{-1} \tag{5}\]  
\[\mathrm{d}(\mathrm{A_{VNDVI}})=\left[\mathrm{A_{VNDVI}}+\mathrm{o}(\mathrm{A_ {VNDVI}})\right]*\left[\mathrm{LAI}+\mathrm{o}(\mathrm{LAI})\right]^{-1} \tag{6}\]  
where VNDVI, NDVI, \(\mathrm{A_{VNDVI}}\), and LAI refer to the values of VNDVI, NDVI, \(\mathrm{A_{VNDVI}}\), and LAI, respectively; and \(\mathrm{o}(\mathrm{VNDVI})\), \(\mathrm{o}(\mathrm{NDVI})\), \(\mathrm{o}(\mathrm{A_{VNDVI}})\), and \(\mathrm{o}(\mathrm{LAI})\) represent the infinitesimal value change of VNDVI, NDVI, \(\mathrm{A_{VNDVI}}\), and LAI, respectively. \[\Delta(\mathrm{VNDVI})=\mathrm{VNDVI}_{\mathrm{max}}-\mathrm{VNDVI}_{\mathrm{ min}} \tag{7}\]  
\[\Delta(\mathrm{NDVI})=\mathrm{NDVI}_{\mathrm{max}}-\mathrm{NDVI}_{\mathrm{min}} \tag{8}\]
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods  
This research extracted significant features for fitting the classification model of NSR, PAF, and AF signals based on the procedures in Figure 2. First, the data were collected from ECG recordings in a hospital in Thailand. Then, data preprocessing removed noise and adjusted base wander. Next, the wave components were decomposed using the continuous wavelet transform (CWT) and calculated feature extraction using RR irregularities. Next, feature selection found significant variables for the classification model using a statistical test. Then, ANN and ANFIS techniques were applied to train and test datasets. Finally, performance evaluation was compared among significant variables to find the best AF diagnosis model.  
Figure 2: Research flow diagram.
# Efficient Melody Extraction Based on Extreme Learning Machine
## 4 Experimental Results and Discussions
### Experimental Results on Test Sets
#### 4.3.1. Accuracies on Different Collections  
As mentioned before, the three collections were mixed together and divided into training, validation, and testing sets. Thus, only the results on the testing sets were reported. To be fair, the results of the reference methods were also based on the testing set. The results are illustrated on individual collections to provide some deep insights.  
The OAs, RPAs, and RCAs on ISMIR2004 are shown in Figure 5. It can be observed that the accuracies did not vary much among different methods. The proposed method obtained the highest OA, while its RPA and RCA were not high. This phenomenon implies that the proposed method was superior for voicing detection, while inferior for pitch estimation. Table 1 shows that recordings from ISMIR2004 contributed 2.7% to the whole training set. Hence, it can be concluded that the pitch estimation results relied more on the training set than voicing detection.  
The OAs, RPAs, and RCAs on MIREX05 are depicted in Figure 6. It can be seen that the OA of the proposed method was much higher than all other compared methods. However, its RPA and RCA were much lower than others. Only one recording of this collection was covered in the training set, while 12 others were involved in the testing set. This great gap confirms our conclusion that pitch estimation relied more on training data than voicing detection. Moreover, both RPA and RCA of the other methods were higher than OA, while the OA of the proposed method was higher than the other two. Similar results can be observed in ISMIR2004.  
The OAs, RPAs, and RCAs on MIR-1K are provided in Figure 7. Results were very diverse on this collection. The OAs ranged from 26% to 64%. RPAs varied between 31% and 66%. RCAs covered the range of 39% to 69%. The proposed method obtained the highest OA, while Melodia achieved the highest RPA and RCA. This result may be due to the fact that it involved some strategies to preferably select the singing melody. RPA and RCA indicate the accuracies among the voiced frames, while the OA took into account of both voiced and unvoiced frames. It can be inferred that the voicing detection performance of the proposed method still outperformed the others on this dataset. Its RPA and RCA on this dataset was much better than those on ISMIR2004 and MIREX04. Table 1 reveals that the training set was mostly contributed by this collection, and that may be the reason for the RPA and RCA improvement.  
Figure 5. Accuracies on ISMIR2004.
# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
###### Abstract  
Air quality monitoring systems based on Internet of Things (IoT) technology are critical for addressing environmental and public health challenges, but their energy efficiency poses a significant challenge to their autonomous and scalable deployment. This study investigates strategies to enhance the energy efficiency of IoT-based air quality monitoring systems. A comprehensive analysis of sensor types, data transmission protocols, and system architectures was conducted, focusing on their energy consumption. An energy-efficient system was designed using the Smart Air sensor, Zigbee gateway, and Mini UPS, with its performance evaluated through daily energy consumption, backup operation time, and annual energy use. An integrated efficiency index (IEI) was introduced to compare sensor models based on functionality, energy efficiency, and cost. The proposed system achieves a daily energy consumption of 72 W\(\cdot\)h, supports up to 10 h of autonomous operation during outages, and consumes 26.28 kW\(\cdot\)h annually. The IEI analysis identified the Ajax LifeQuality as the most energy-efficient sensor, while Smart Air offers a cost-effective alternative with broader functionality. The proposed architecture and IEI provide a scalable and sustainable framework for IoT air quality monitoring, with potential applications in smart cities and residential settings. Future research should explore renewable energy integration and predictive energy management.  
Internet of Things; air quality monitoring; energy efficiency; sensor technologies; data transmission protocols; LoRaWAN; alternative energy sources; smart cities; integrated efficiency index; environmental monitoring
# Correction of Field-Measured Wind Speed Affected by Deterministic Interference Factors
## 3 Wind Speed Revise
### NLDI Site
#### 3.1.1 Wtt  
The WTT was carried out in the Wind Tunnel Laboratory of South China University of Technology, and a uniform air flow wind field was used. The maximum span of NLDI is approximately 4 km, and the maximum altitude is approximately 300 m. The scale ratio of the island model in the test is 1:1000, and the maximum blockage ratio of the model in the tunnel is 3.7%, thereby satisfying the test requirements. Figure 4a shows the test model located in the wind tunnel. Two Cobra probes were installed at the same height; one was at the anemometer position of the site, and the other was outside the upstream turntable away from the model (marked as the reference point). Both probes were set 65 mm over the floor, corresponding to the anemometer elevation of 65 m in the prototype. Two probes were always parallel to the axis of the wind tunnel as the turntable rotated to change the wind direction during the entire test. The sampling frequency of the probe was 400 Hz and the sampling time was 102.4 s during the experiment, as well as the accuracy of wind speed was usually \(\pm\)0.5 m/s.  
Figure 3: Observations of wind speed and direction at the sites of NLDI and PAFC during Typhoon Manghut: (**a**) NLDI; (**b**) PAFC.  
With regard to the impact of typhoon and geographical characteristics, WTT was only conducted for wind directions ranging from 0\({}^{\circ}\)-180\({}^{\circ}\). Meanwhile, wind speeds of the upstream and downstream probes were compared without the island (Figure 4b). The figure shows that the wind speed of the downstream probe is 10% lower than that of the upstream in the empty wind tunnel mainly due to the shielding of the magnetic base that fixed the upstream probe. In view of the influencing factor, the wind speed at the downstream measured point would be revised.
###### Abstract  
Optical microsurgery of the retinal pigment epithelium (RPE) requires reliable real-time dosimetry to prevent unwanted overexposure of the neuroretina. The system used in this experiment implements optical coherence tomography (OCT) to detect the intentional elimination of RPE cells. We evaluated the performance of OCT dosimetry in terms of its ability to detect RPE cell damage caused by microsecond laser pulses of varying duration. Therefore, ex-vivo porcine RPE choroid sclera explants were embedded in an artificial eye and exposed to single laser pulses of 2-20 \(\upmu\)s duration (wavelength: 532 nm, exposure area: 120 \(\times\) 120 \(\upmu\)m\({}^{2}\), intensity modulation factor: 1.3). Simultaneously, time-resolved OCT M-scans were recorded (central wavelength: 870 nm, scan rate: 33 kHz). Post-irradiation, RPE cell damage was quantified using a calcein-AM viability assay and compared with an OCT-dosimetry algorithm. The results of our experiments show that the OCT-based analysis successfully predicts RPE cell damage. At its optimal operating point, the algorithm achieved a sensitivity of 89% and specificity of 94% for pulses of 6 \(\upmu\)s duration and demonstrated the ability to precisely control radiant exposure of a wide range of pulse durations towards selective real-time laser microsurgery.  
selective retina therapy; viability assay; photocoagulation; microbubble formation; thermomechanical damage; fringe washout; coherence-loss +
Footnote †: journal: Applied Sci. **2021**, _11_, 5535. [[https://doi.org/10.3390/app11125535](https://doi.org/10.3390/app11125535)]([https://doi.org/10.3390/app11125535](https://doi.org/10.3390/app11125535))
# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
## 3 Methodology
### Proposed Approach Framework  
We illustrate our proposed approach for CPDP employing machine learning models (Bi-LSTM, Bi-GRU and Attention mechanism) combined with a feature selection method (CFS with best-first-search) and data sampling method (SMOTE-ENN) in this section. The datasets were obtained from the AEEEM and PROMISE as source (S) and target (T) projects. In order to select the features that are more relevant to the target class, feature selection is employed. Then a data balancing method is applied to balance the training dataset and remove noisy and irrelevant instances in source and target domains. Selected balanced source project (Ss) is then used to train the model. Finally, the trained model is utilized to predict the label of the selected balanced target project (Ts), and the result is then compared based on _AUC_ and F1-measure performance metrics. Figure 1 demonstrates the full workflow of the proposed approach.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 8 Discussion
### Perceptual Awareness to Intent Understanding  
The integration of computer vision and AI has advanced cobots from basic perception tasks--like object detection and human pose estimation--to richer understanding of scenes. Despite this, moving from perception to understanding intention remains an underdeveloped frontier that is crucial for proactive collaboration.  
Intent prediction requires not only recognizing the current scene but anticipating human goals and future actions--enabling robots to assist before being explicitly commanded. Several barriers explain its limited progress:  
* Sparse Contextual Data: Existing datasets rarely capture labeled human intentions over time, making supervised learning of intent inference difficult.
* Ambiguous Visual Cues: Similar gestures may signal different intentions depending on task context, making disambiguation hard from vision alone.
* Limited Multimodal Integration: Audio, gaze, force sensing, and verbal cues are critical for intent inference but remain challenging to fuse robustly in real time.
* Poor Generalization: Models often overfit to specific tasks or environments, failing to adapt to new users, layouts, or cultural practices.
* Real-Time and Safety Constraints: Predicting intent must happen quickly and reliably to avoid errors that compromise trust and safety.  
Addressing these challenges requires developing multimodal, context-aware learning frameworks, data-efficient training strategies, and probabilistic models that can reason under uncertainty. Advancing intent understanding is essential to enable cobots to transition from reactive tools to proactive, intuitive collaborators in shared workspaces.
## 4 Results and Discussion
### Deformation Capacity  
Ductility is an important indicator to measure the elastoplastic deformation ability of a structure. It refers to the ability of a structural member to withstand deformation without a significant reduction in bearing capacity, and displacement ductility can also be defined as the ability of a structural member to withstand elastoplastic deformation before final failure. In general, the displacement ductility of a structural member can be quantified in terms of displacement ductility coefficients.  
According to Ref. [13], the ductility of the strengthened beams is quantitatively evaluated by the ductility (\(\mu\)), which is defined as \(\mu=D_{\mathrm{u}}/D_{\mathrm{y}}\), wherein \(D_{\mathrm{u}}\) is the deflection at the ultimate load, and \(D_{\mathrm{y}}\) is the deflection at the yielding load.  
From the load-displacement curve of Figure 12, it can be observed that the strengthened beam exhibits no distinct yield point. Hence, the equivalent energy method is employed in this paper to determine the yield point \(D_{\mathrm{y}}\) of the specimen. The calculation model is depicted as shown in Figure 13. The original curve is substituted by an ideal elastic-plastic two-segment line with equivalent envelope areas (A1 = A2), and the displacement at the inflection point of the two-segment line is regarded as the yield displacement, with the corresponding point on the curve being the yield point \(D_{\mathrm{y}}\). Commonly, in the descending segment of the load-displacement curve, the displacement corresponding to 85% of the peak load is considered as the ultimate load Du of the component. Nevertheless, as indicated by the load-displacement curve, the specimen undergoes sudden failure after reaching the peak load. Therefore, the ultimate displacement in this paper is taken as the ultimate load \(D_{\mathrm{m}}\). The calculation results are shown in Table 4. The application of high-toughness epoxy mortar for reinforcement on both sides of the beam does not improve the ductility of the beam. This is because the definition of ductility is highly sensitive to the yield displacement, and the reinforcing material, while increasing the ultimate displacement of the beam, also increases its yield displacement.