esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 2 The Proposed Method for Cable Force Determination
### Vibration Video Data Collection
#### 2.1.4 Data Acquisition Parameters of Smartphone  
The data acquisition parameters of the smart terminal such as image resolution, acquisition frame rate and sampling duration can affect the sampling accuracy of the collected vibration video data. Taking the maximum viewing range of 2 m \(\times\) 3 m as an example, when the image resolution is set to 1080 \(\times\) 1920, the pixel accuracy is 1.85 mm/pix. The amplitude of two pixels is set to be the displacement extraction resolution; thus, when the cable vibration displacement is equal to or more than 3.7 mm, the vibration displacement of the cable can be extracted with the resolution of 1080 P. Therefore, the image resolution is recommended to be 1080 P (1920 \(\times\) 1080) or 4 K (3840 \(\times\) 2160) for video data collection.  
According to the sampling theorem, for the signal with the highest frequency \(f_{B}\), the sampling rate \(f_{s}\) should fulfill requirement \(f_{s}\geq f_{B}\) in order to avoid distorting the signal. Generally, \(f_{s}=(6\sim 10)f_{B}\) is used in engineering [17]. To meet the test requirements, a frame rate of 24 FPS can collect cable vibration signals within 4 Hz for long cables, while for short cables, the frame rate can be increased to be appropriately 30 FPS or 60 FPS. The video sampling duration is recommended to be 20 s, the corresponding frequency resolution is calculated to be 0.05, as shown in Equation (1):  
\[\Delta f=\frac{f_{s}}{N}=\frac{1}{T} \tag{1}\]  
where \(\Delta f\) is the frequency resolution, \(f_{s}\) is the sampling frequency, \(N\) is the number of sampling points, and \(T\) is the sampling duration. The sampling frequency error equals to \(\Delta f/2=0.025\) Hz.
## 3 Materials and Methodology
### Preprocessing the Categorized Signals
#### 3.3.2 Steps in Preprocessing Explicit Signals  
Preprocessing involves steps such as data cleaning, transformation, and feature engineering, which are the essential steps as mentioned in Table 8 to ensure data quality and readiness for analysis. Data cleaning is performed to handle missing values by imputing them with averages or frequent categories and removing outliers used to standardize the data format. Then, normalization and scaling, involved in transformation, are applied to ensure numerical values such as scores and durations are uniform, typically within a range of 0-1 as mentioned in Equation (1). For categorical data, conversion techniques are used to convert textual feedback into numerical formats.  
\[\bullet\quad\text{Normalization:}\] \[\text{val}^{\prime}=\text{val}-\min_{\text{val}}x/\max_{\text{val}}x- \min_{\text{val}}x \tag{1}\]  
where  
\[\text{val: The original value of the feature.}\] \[\min_{\text{val}}x\text{: The minimum value of the feature in the dataset.}\] \[\max_{\text{val}}x\text{: The maximum value of the feature in the dataset.}\] \[\text{val}^{\prime}\text{: The normalized value.}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Signal Type** & **Signal Name** & **Description** & **Example Source** \\ \hline \multirow{3}{*}{Implicit} & Engagement trends & Frequency of interactions with quizzes, videos, or simulations. & Interaction trackers \\ \cline{2-4}  & Revisit Count & Number of times the learner revisits specific content. & System logs \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Step** & **Category** & **Description** \\ \hline Data Cleaning & \(-\) & Handle missing values (e.g., imputation with mean/median). Remove duplicate records. \\ \hline \multirow{3}{*}{Transformation} & Feature Scaling & Normalize or standardize numerical features to bring them to the same scale. \\ \cline{2-3}  & Conversion & Convert categorical variables into numerical representations. \\ \cline{2-3}  & Outlier Detection & Identify and remove extreme values that may skew the model’s performance. \\ \hline \multirow{2}{*}{Feature Engineering} & \(-\) & Extract features such as normalization and score improvement to enhance model performance. \\ \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Steps involved in preprocessing explicit signals.
# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 4 The Proposed Method
### Optimization of Cluster Centers  
This step's primary purpose was to select a cluster center from each cluster based on a set of defined criteria. The following combination of four criteria is used to select cluster centers:  
* The total energy of selected cluster centers;
* The total distance of the nodes of that cluster from the center of the cluster;
* No central node collision inside the cluster;
* The total distance of the selected cluster centers from each other.  
As the first criterion is increasing, and the other three criteria are lowered, the cluster centers are better positioned. As a result, the proposed method is a multi-objective algorithm that needs to be optimized. With the multi-objective nature of the fit function problem for selecting cluster heads, the proposed method is defined as follows:  
-_f_ 1: Total energy of selected cluster centers;  
-_f_ 2: The total distance of the nodes of that cluster from the center of the cluster;  
-_f_ 3:No collision of the center node of the cluster with the nodes inside that cluster;  
-_f_ 4:The total distance of the selected cluster centers from each other.  
The ultimate fitness function is defined as Equation (6):  
\[Fitness=\alpha f_{1}+\beta\frac{1}{f_{2}}+\gamma f_{3}+\lambda\frac{1}{f_{4}} \tag{6}\]  
Here \(\gamma\), \(\beta\), \(\alpha\) are four fixed parameters that determine the impact of these four different criteria. The goal of optimization is to maximize the above objective function. Since the second and fourth criteria must be minimized, they are placed inversely in the fitness function. Using the ant colony optimization algorithm is to select a cluster head node in each cluster based on different parameters.  
When clustering is performed, the distance between all nodes is measured with other cluster nodes, and the node with the least distance is selected as the cluster node. Nevertheless, the most important thing is not to collide in routing, and thus, the cluster head node may be changed and not measured only by distance and energy. Therefore, at each routing step, the amount of residual energy of the cluster head node will be compared to a predefined threshold, and if the energy level is less than this amount, another node in the same cluster will be selected with more residual energy than the cluster head and replaces the previous cluster head and acts as a member node. If there is no node with more residual energy than the cluster head in a cluster due to non-collision, re-clustering will be applied to the grid, which includes high energy overhead and reduces the grid's life.
# Special Issue on Intelligent Processing on Image and Optical Information II
### Image Processing Applications  
Several practical solutions are emphasized in this subsection. The automatic estimation of tortuosity is essential in medical diagnosis. In the paper entitled Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images', [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], and [PERSON] [15] proposed an automated framework for tortuosity estimation for corneal nerves and retinal vessels.  
The paper entitled 'The Influence of Image Processing and Layer-to-Background Contrast on the Reliability of Flatbed Scanner-Based Characterisation of Additively Manufactured Layer Contours' by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [16] evaluates the influence of image processing and layer-to-background contrast characteristics upon contour reconstruction quality under a metrological perspective.  
Strain gauges are commonly used for tension tests to obtain the strain of metal t specimens. In the paper entitled 'A Study on Tensile Strain Distribution and Fracture Coordinate of Nanofiber Mat by Digital Image Correlation System', [PERSON], [PERSON], and [PERSON] [17] employed the digital image correlation method to measure displacement and calculate strain for all areas of the specimen.  
Drones have played significant roles in security and surveillance. The paper entitled 'Moving Vehicle Tracking with a Moving Drone Based on Track Association' by [PERSON] and [PERSON] [18] proposes a new data association scheme for target tracking comprising the hypothesis testing and track fusion.
## 4 Conclusions  
A nanostructured photocatalytic TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane was successfully fabricated using a POEM polymer template through a sol-gel process. The hydrophilic POEM chains interact strongly with the TiO\({}_{2}\) precursor (TTIP) to form uniform POEM/TTIP hybrids. Upon calcination at 450 \({}^{\circ}\)C, TTIP is transformed into highly crystalline anatase TiO\({}_{2}\) while the organic POEM is completely decomposed. The well-organized structure and morphology of the TiO\({}_{2}\) film were confirmed by HR-TEM, SEM, XRD, and AFM analyses. A homogenous distribution of the catalytic sites on the membrane surface was achieved, exhibiting a higher adsorption capacity toward Congo red (a model organic dye) than that of the bare Al\({}_{2}\)O\({}_{3}\) membrane. Under UV illumination, the TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane showed 96% organic removal efficiency along with effective fouling mitigation.  
**Acknowledgments: This work was supported by an Inha University research grant.**  
**Author Contributions: [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.**  
**Conflicts of Interest: The authors declare no conflict of interest.**  
Figure 9: Organic dye removal efficiency of bare Al\({}_{2}\)O\({}_{3}\) membrane and POEM-directed TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane in the (**a**) absence and (**b**) presence of UV illumination.
## 2 Dataset
### Seismic Attributes  
Seismic attributes can refer to any quantitative measurement derived from seismic data. [PERSON] et al. [10] divided attributes into geometrical and physical categories. The first group is generally used to enhance the visibility of geometrical characteristics of seismic data, such as dip, azimuth, and continuity. The second group refers to the physical parameters of the subsurface and hence is related to impedance. The main subgroups for this type of attribute are amplitude, phase, and frequency. [PERSON] [11] recognized amplitude and phase as fundamental attributes from which other attributes are derived. He classified attributes by dimensionality, in depth or time, and as instantaneous or local [12].  
[PERSON] [13] stated that complex seismic trace analysis treats the seismic trace as a continuous set of sinusoidal segments whose properties are recorded by the trace's instantaneous amplitude, phase, frequency, and relative amplitude change. Instantaneous amplitude is a measure of reflection strength and is commonly used. However, its interpretation is not straightforward when dealing with thin beds. The instantaneous phase and cosine of the phase permit reflection continuity to be better described. While the instantaneous phase shows sharper vertical breaks due to phase wraparound, the cosine of the phase is a continuous waveform analogous to perfect automatic gain control (AGC). It removes all amplitude information that may increase apparent reflection continuity.  
[PERSON] [14] introduced the relative geologic age volume. To generate such volume, each seismic sample is assigned an estimated geologic age by imputing previously interpreted reflectors and interpolating between them. This methodology assumes that relative geologic time values increase with depth. The volume has the same dimensions and coordinates as the input seismic data, with each color representing a constant age. The age estimation here is relative, as we can determine which horizon is older or younger in a group but not what is the actual geologic age value of it. [PERSON] [14] presented a detailed explanation of the workflow that generated the relative geologic age volume, including some applications of it. The present study considers relative geologic age volume as simply another seismic attribute to be included in the multi-attribute process.
# Grousers Effect in Tracked Vehicle Multibody Dynamics with Deformable Terrain Contact Model
## 4 Simulations and Results
### Tracks with Grousers
#### 4.2.2 Traction Force-Slip Curves  
The grousers action contribution to the traction effort is reported and discussed. The procedure to obtain traction force-slip curves in MTB model was the same reported in Section 4.1.2. Traction force developed by tracks with grousers was investigated for two different types of soil: sand and compact agricultural terrain. The effect of grousers height was also highlighted. The maximum traction force value was reached on sand with 25 mm height grousers (Figure 19). It was about 250% of the value reached with smooth tracks (on sand). Comparing the results obtained for different grouser height on sand (12 mm and 25 mm), it is clear that the variation of traction force maximum values was not proportional to grousers height variation. The mean term of Equation (6) reported below is proportional both to grouser height sunk into the soil (\(h_{b}\)) and surcharge \(q\). Moreover, it is about 100 times greater than other two terms. The height of the grouser part sunk into the soil (\(h_{b}\)) on sand was almost equal to the total grouser height, both for 25 mm and 12 mm grousers height (Figure 18). On the contrary pressure \(q\) exerted by links bases was greater for 12 mm height grousers on sand, due to the greater global sinkage. For this reason, maximum traction effort reached with 12 mm height grousers was about 80% of the value referred to 25 mm height grousers (Figure 19).  
On compact agricultural soil the maximum value of the traction force was smaller than the value reached with smooth grousers (Figure 20). Grousers contribution in terms of traction effort is limited, because soil stiffness does not allow them to sink. Shearing action between tracks and terrain had the main role in drawbar pull development, but the sum of the grousers shear base areas is smaller than the total area of smooth tracks.  
Figure 19: Traction force-slip curves on sand.
# Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell +
## 3 Interference Evaluation in Downlink
### Fundamentals of the Multi-Elliptical Propagation Model
Since these characteristics meet the definition properties of probability density [36], in the simulation procedure, the directions of departure of propagation paths are generated on their basis. A detailed description of determining the radiation angle distribution is given in [35]. In step 6, based on the MPM geometry structure, AOAs are calculated for each angle of departure (AOD). The sets of the obtained AOAs for each time-cluster are the basis for determining the histograms in step 7. For each time-cluster, we choose appropriate powers defined in the analyzed PDP (step 8). Next, in step 10, we multiply the AOA histograms with the proper powers to obtain the PAS seen around the Rx [33; 35]. At this stage, the local scattering components and direct path for LOS conditions are also considered (step 9). Using spatial filtering by the Rx antenna pattern, we calculate the PAS seen on this antenna output (steps 11 and 12) [33]. During interference analysis, we launch the MPM simulation procedure twice for the serving and interference Tx beams, respectively.
# Research on Permanent Magnet Synchronous Motor Control System Based on Adaptive Kalman Filter
## 3 Simulation Results and Discussions
### Simulation of PMSM Control System Based on PAEKF and Existing Parameter Adaptive Methods  
In reference [22], the author constructed a parameter adaptive function. The adaptive adjustment of noise parameters is realized by adjusting the values of two parameters \(\alpha\)  
Figure 5: (**a**) When the Q value is \(\copyright\)–\(\copyright\), the speed is estimated by the PAEKF; (**b**) when the Q value is \(\copyright\)–\(\copyright\), the speed is estimated by the EKF.  
Figure 6: (**a**) When the Q value is \(\copyright\)–\(\copyright\), the difference between actual speed and estimated speed based on PAEKF. (**b**) When the Q value is \(\copyright\)–\(\copyright\), the difference between actual speed and estimated speed based on EKF.  
and \(\beta\) in the function, to improve the dynamic and steady-state performance of the PMSM closed-loop control system. Figure 7 shows the simulation results of the PMSM control system based on the adaptive law proposed in reference [22].  
The simulation results from Figure 7 show that as the \(\beta\) value increases, the greater the floating of the PMSM estimation speed. In addition, although the adaptive method proposed in the literature [22] completes the adaptation of noise parameters, it also introduces additional parameters \(\alpha\) and \(\beta\). Furthermore, the selection of these parameters is also a significant work. However, the speed estimation of PMSM based on PAEKF stays within plus-minus 0.5. Therefore, the method in [22] is not as stable and accurate as the method based on PAEKF.
# NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation
## 4 Experimental Analysis
### Datasets
#### 4.1.1 Argoverse-HD  
Argoverse-HD [9] dataset, where HD stands for High-framerate Detection, was introduced for Streaming Perception Challenge competition in August 2020 and is a derivative of Argoverse 1.1 dataset [39]. It offers diverse urban outdoor scenes composed of 204 linear kilometers in the city of Miami and 86 linear kilometers in the city of Pittsburgh using a high frame-rate camera sensor data at 30 FPS.  
To satisfy the purpose of monocular sparse-to-dense streaming perception evaluation of the proposed object detector, only images from the center ring RGB camera of the Argoverse 1.1\({}^{\prime}\)s ego-vehicular camera setup is utilized for training and testing purposes. This dataset contains 8 annotated object classes related to common world driving scenarios such as bicycle, bus, car, motorcycle, person, stop sign, traffic light, and truck with a total of 66,953 images of a fixed 1200 \(\times\) 1920 resolution and 1.26 million bounding boxes.
## 2 Materials and Methods
### Protocol  
#### 2.4.1 Home-Based Sleep Studies and SOSD Calculation  
All participants completed home-based polysomnography studies at baseline (before CBT-I/waitlist control randomisation) and 6-week follow-up. Participants were set up with recording equipment at sleep laboratories (the Adelaide Institute for Sleep Health and the Queensland University of Technology) by trained sleep technologists. Two electroencephalogram (C3 to M2 and C4 to M1), one electromyogram, two electrooculogram, and two leg electromyogram channels were recorded. Finger pulse oximeters, nasal cannulas, oro-nasal thermistor, and chest and abdominal bands recorded oxygen saturation, flow, and respiratory effort. Trained technicians scored the home studies according to American Academy of Sleep Medicine (2007) [32] \"alternate\" criteria for respiratory events for AHI scores, using an AHI cut-off \(\geq\) 15/h to define OSA [32; 33]. The trained technicians were blind to participants' condition. The sleep window on the polysomnography was based on a combination of participant self-reported lights off/on time, and objectively derived measures (e.g., position sensor reading, attachment time of oximeter). On the same night as the polysomnography study, each participant self-reported time in bed, time out of bed, total sleep time, sleep onset latency, and wake after sleep onset on an overnight sleep-wake diary. Sleep efficiency was calculated as the percentage of time in bed spent sleep.  
Overnight diary parameters were compared to polysomnography parameters to calculate SOSD for total sleep time, sleep onset latency, wake after sleep onset, and sleep efficiency at baseline and 6-week follow-up. SOSD was calculated by subtracting the polysomnography measures from the overnight sleep diary measures (subjective minus objective) for each sleep/wake parameter. For example, if a participant reported a total sleep time of 420 min, but had an objective total sleep time of 480 min, their total sleep time SOSD would be \(-\)60 min. A sensitivity definition of SOSD was used to align with previous literature, in which SOSD was dichotomously defined as overestimation or underestimation of each parameter (total sleep time, wake after sleep onset, and sleep onset latency) by at least 60 min [34].
###### Abstract  
A method is proposed to expand the Z-directional measurement range of a fiber-based dual-detector chromatic confocal probe with a mode-locked femtosecond laser source. In the dual-detector chromatic confocal probe, the _Z_-directional displacement of a measurement target is derived from the peak wavelength in the normalized intensity ratio from the two light intensities obtained by the two identical fiber detectors. In this paper, a new method utilizing the main-lobe and side-lobes of axial responses acquired from both the normalized intensity ratio \(I_{\text{a}}\) and the invert normalized intensity ratio \(I_{\text{n}}\), which is the inverse of \(I_{\text{a}}\), is proposed to obtain the seamless relationship between the peak wavelength and the _Z_-directional displacement of a measurement target. Theoretical calculations and experimental investigation are carried out to demonstrate the feasibility of the proposed measurement range expansion method.  
chromatic confocal probe; femtosecond laser; measurement range expansion; side-lobe
## 4 Application
### Empirical Predictive Models
One for predicting the dimensional deviations of the linkages and one for predicting the resulting joint clearances of the mechanism. In order to determine the forecast quality of the predictive models and to check their applicability to the training data set, the RMSE values are automatically calculated and the CoP values (see Equation (3)) are calculated using the kept out data set. The advantage of CoP over Root-Mean-Squared-Error (RSME) is its automatic scaling of the value. For example, a value of 0.8 corresponds to a forecast quality of 80% for the forecast accuracy of new data points and in general represents a suitable value [37; 44]. As a consequence, the CoP values in combination with the RMSE values of the different models can be compared more precisely. The results are illustrated in following Table 3. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Factor Levels** \\ \hline Layer height & 0.1778 mm; 0.2540 mm \\ Seam style & align; random \\ Build orientation \(Z\)-direction & 0\({}^{\circ}\); 90\({}^{\circ}\) \\ Linkage length & 53 mm; 87 mm; 161 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Design of experiment (DoE) for experimentally gathering the training data sets. Figure 9: Non-assembly test specimen for gathering training data for the predictive models using FDM machine Stratasys F370. Regarding the results in Table 3 it becomes evident that both GP and SVM indicate suitable values and thus good forecast quality, which seems plausible as both are especially suitable for operating in smaller training data sets because of their good model adaption and their low implementation effort [44].