esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Chemical-mechanical planarization/polishing (CMP) has been known as a key process for global and local planarization in IC fabrication. Because of the urgent demands for conducting linewidth of IC device downsizing to nanometers, the stability and availability of CMP process have become critically significant [1, 2] for high volume production. The polishing pad used in CMP process is one of the most important consumables for affecting CMP process output [3]. The material removal rate (MRR) and planarization ability of the process are determined by the structure and material properties of polishing pads [4]. The slurry contains abrasive particles on the wafer surface for removal of the passivated layer after chemical activation. Currently, a CMP tool is not capable of fully monitoring the polishing pad on-line. Usually it only measures the groove depth and pad thickness or by empirical analysis [5, 6, 7]. Some efficiency indicators of pad performance can be established with measuring the surface topography, such as roughness and bearing area ratio so that the polishing pad could be efficiently utilized [8, 9, 10]. The asperities and profile of pad are associated with MRR and final quality of as-CMP wafer. The asperities and groove depth of pad are gradually worn with CMP processes. The pad conditioning or dressing process is necessary to restore the pad surface, but the profile and groove depth are changed with the numbers of conditioning. As the pad topography will effectively influence the MRR and polishing results, different kinds of measuring methods are developed to monitor the change of the pad surface [11, 12, 13]. Nowadays, the methods of analysis of pad topography are mostly for static and partial area [5]. Additionally, judging the efficiency of polishing pad and lifetime are based on groove depth and thickness. Better methods are those that establish a system which can achieve bigger area scanning and extract pad surface topography easily and faster. Accurate results can be obtained using optical microscope, but the polishing pad needs to be cut and comprehensive measurement cannot be achieved. The pad profiler can achieve a scan of a full pad, but still requires a high measurement time and the mechanical parts are easily affected by environmental pollution. Thus, there is not yet available for dynamic measurement of pad topography before and after CMP or pad conditioning processes.  
This study aims to develop a dynamic pad monitoring system (DPMS) for measuring the surface topography of polishing pad. A swing-arm type conditioner is widely used in modern polishing machines. In this study, a chromatic confocal sensor is attached on a designed fixture arm to acquire data from pad topography. Topography of total working area of the polishing pad is detected by rotation of polishing pad and arm motion. Because the mechanism is fixed on the swing arm, the entire area of polishing pad can be scanned and it can effectively reduce the measuring time. The DPMS then can provide a performance index of polishing pad to maximize the utilization of the polishing pad in a relatively shorter time.
## Case Study: Product Development
### System Validation
#### 6.4.3 Behavior Classification
For the final verification of the designed behavior classification method, the tuned decision tree was fed with 50 different behaviors with randomly-chosen timeframes. The test resulted with overall classification accuracy at a level of 100%, and all of the 25 normal, 7 suspicious and 18 dangerous behaviors were classified correctly. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ToD** & **SoA** & **ZoA** & **FoA** & **DoA (min)** & **CoB *** \\ \hline Morning & Bathroom & of standing & Standing & 3 & Normal \\ Afternoon & Living room & of standing & Sitting & 5 & Suspicious \\ Night & Bedroom & of lying & Standing & 17 & Danger \\ \hline \hline \end{tabular}  
* ToD, Time of Day; SoA, Section of Apartment; ZoA, Zone of Activity; FoA, Form of Activity; DoA, Duration of Activity; CoB, Class of Behavior.  
\end{table} 
Table 4: Classification of exemplary behaviors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{4}{c}{**Classification Validity (\%) of**} \\ \cline{2-5}  & **Overall** & _Normal_ & _Suspicious_ & _Danger_ \\ \hline Decision Tree I & 82.4 & 92.8 & 92.9 & 74.1 \\ Decision Tree II & 94.8 & 93.7 & 93.8 & 96.5 \\ SVM I & 54.1 & 60.0 & 0.0 & 50.5 \\ SVM II & 85.1 & 87.2 & 75.2 & 86.8 \\ KNN I & 96.0 & 97.9 & 90.9 & 96.8 \\ KNN II & 95.8 & 97.6 & 89.9 & 97.1 \\ Tuned Decision Tree & 99.1 & 98.8 & 98.4 & 99.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification validity of validated machine learning techniques.
## 3 Methodology  
### Data  
The Sentinel-1 satellite, which was launched by the European Space Agency in 2014, operates in a dual-satellite orbit with a C-band synthetic aperture radar (SAR). It offers frequent satellite imagery with a revisit period of 12 days and an extensive single-image overlay of 250 \(\times\) 250 square kilometers. In this study, 37 ascending orbit InSAR wide (IW)  
Figure 1: **(a)** The coverage of synthetic aperture radar (SAR) data in the study area; **(b)** Elevation conditions in the study area.  
In this study, the ALOS World 3D (AW3D 30) digital surface model (DSM) from JAXA was utilized as the external digital elevation model (DEM) to correct for terrain effects. Moreover, the precision orbit data (precise orbit ephemerides (PODs)) of the Sentinel-1 satellite were used to correct the orbital error.  
ENVI software (ENVI5.6) was applied for data processing. The image from 19 September 2021 served as the reference (super-master) image for the ascending orbit data, whereas the imagery from 12 September 2021 was used as the reference (super-master) image for the descending orbit data. The remaining images were used as auxiliary images to align with the super-master image. The temporal baseline for the ascending and descending orbit data was limited to 80 days, with spatial baselines of approximately 155 m and 145 m, respectively. The independent use of ascending and descending track data resulted in 179 and 181 interferometric pairs generated, respectively. Figure 2 illustrates the temporal and spatial baseline connections for the ascending and descending orbit data. In Figure 2, the green points depict valid data pairs, while the yellow points signify the super-master images. The X-axis corresponds to the time dimension, and the Y-axis indicates the extent of the spatial baseline separating each image from the respective super-master image.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Wavelength**} & **Ascending/** & \multirow{2}{*}{**Time**} & **Revisit** & \multirow{2}{*}{**Resolution**} & **Angle of** & **Imaging** & **Polarization** & **Number of Images** \\  & **Dscending** & & **Cycle** & & **Incidence** & **Mode** & **Mode** & **(Scenes)** \\ \hline
5.6 cm/C & Ascending & July 2021 to & & & & & & \\ Band & track & September 2022 & 12 Days & 20 m & 41.7\({}^{\circ}\) & IW & VV & 37 \\
5.6 cm/C & Descending & July 2021 to & & & & & & \\ Band & track & October 2022 & 12 Days & 20 m & 41.5\({}^{\circ}\) & IW & VV & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic parameters of Sentinel-1 data for the study area.  
Figure 2: \(Cont.\)
## 2 Related Work  
### Target Detection in Remote Sensing Images  
Different from conventional images, remote sensing images typically contain multi-channel, multispectral, and large-scale information. The target information usually contains characteristics such as multiple scales, dense arrangement, random direction, and high background noise. For rotating targets in remote sensing images, their morphology changes greatly, and detection difficulty is high. Many scholars have made improvements to existing algorithms to address these issues.  
Many authors have provided meaningful work on target representation and loss function optimization in rotating object detection problems. Rotated RetinaNet [2] detects rotating targets by directly regressing five parameters. Oriented R-CNN [10] avoids boundary problems caused by five-parameter representation by using Midpoint Offset Representation during the region proposal stage. CSL [11] avoids the issue of angle periodicity by transforming the regression process into a classification process. PSC [12] avoids angle period issues by adding angle encoding. GWD [13], KLD [14], and KFIoU [15] avoid the periodic problem of angles by converting rotating rectangles into Gaussian distributions. These works have to some extent solved the feature extraction problem of rotating targets, but have not considered the scale distribution of the targets.  
In addition to this work, other scholars have proposed feature extraction and representation methods that differ from horizontal object detection. The LSK module is used in LSKNet [5] to efficiently extract multi-scale features. Rotational convolution is used in ARC [7] networks to extract rotated features. ReDet [9] extracts rotation-invariant features through rotation equivariant networks. S\({}^{2}\)A-Net [8] extracts accurate angle features through two rounds of regression. PKINet [6] extracts features of different scales through convolutions with different kernel sizes. These methods solve the problem of multi-scale extraction of targets but do not consider the rotation features of the targets.
## 4 Regulation of Virulence Factors
### Quorum Sensing  
Quorum sensing systems have been described in _E. amylovora_, both QS-1, relying on an _N_-homoserine lactone autoinducer-1 signal (AI-1) [69, 70, 71] and QS-2 reliant on LuxS as the enzyme responsible for signal (AI-2) production [72]. However, a global regulatory function has not yet been explored. Although previous studies reported that _luxS_ did not affect quorum sensing in _E. amylovora_[71], recent results have indicated that some strains of _E. amylovora_ have _luxS_-dependent AI-2 activity, and that inactivation of _luxS_ affects some phenotypes, including virulence _in planta_[72], although the molecular basis of _luxS_-dependent regulation remains to be determined [72]. It was observed that AI-2 produced in _E. amylovora_ activated the expression of bioluminescence in the _Vibrio harveyi_ BB170 reporter strain. Findings showed that AI-2 in _Ea_1665 was dependent on _EaluxS_ because expression of _EaluxS_ in the _luxS_ frame-shift mutation of _E. coli_ DH5\(a\) demonstrated its importance in AI-2 synthesis. The pattern of AI-2 production with respect to growth phase and environmental changes deserves further research, and may give some insight into the function of this signaling system [72].  
Moreover, using comparative genomic analyses to search homologs of genes involved, several genes were identified corresponding to the Autoinducer-3/Epinephrine/Norepinephrine signaling system in _E. coli_. Particularly, the putative homologs to the two component system QseEF and the transcriptional regulator QseA are highly conserved in _E. amylovora_. Gene-knockout experiments revealed that the QseEFea system is involved in the regulation of swarming motility, biosynthesis of amylovoran, and biofilm formation [73].
## 4 Calculation Method of Equal Load Factor for Dual-Input Counter-Rotating Gearbox
### Backlash Test Verification  
The G\({}_{d1}\), G\({}_{d2}\), and G\({}_{o1}\) backlash was measured using a dial gauge. When measuring the side clearance, the input shaft and output shaft 1 positions were fixed, and the dial gauge was located at the G\({}_{d1}\) and G\({}_{d2}\) indexing circle. Then, G\({}_{d1}\) and G\({}_{d2}\) were turned and the dial gauge reading was the backlash value. The actual measurement method is shown in Figure 11.  
When measuring, the input shaft was rotated four times with the output shaft 1 position, and the backlash was taken as the average of eight measurements. The measurement data are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **Average** \\ \hline G\({}_{d1}\) & 0.24 & 0.25 & 0.25 & 0.24 & 0.25 & 0.26 & 0.26 & 0.26 & 0.251 \\ \hline G\({}_{d2}\) & 0.42 & 0.46 & 0.39 & 0.38 & 0.42 & 0.37 & 0.42 & 0.41 & 0.410 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Actual backlash measurement data.  
Figure 11: Dual-input counter-rotating gearbox backlash measurement physical diagram.  
Figure 10: Dual-input counter-rotating gearbox test stand.  
The actual measured G\({}_{d1}\) and G\({}_{o1}\) backlash value was 0.251 mm and the G\({}_{d2}\) and G\({}_{o1}\) backlash value was 0.410 mm. The actual measured backlash was within the theoretical calculated backlash range of 0.2443-0.4150 mm, and the three-dimensional tolerance analysis played a more important role in the study of the system's even load performance.  
With the actual measured backlash values, the two branch torques were calculated as 86.736 N m and 141.680 N m using Equations (18)-(21), and the uniform load coefficient was calculated as 1.241 according to the definition of the uniform load coefficient in Equation (22).
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 3 Case Analysis
### The Prediction of the NAR Model
#### 3.2.2 The Establishment of the NAR Model  
Because the traffic flow time series is a small scale of the data sample with only 60 cases, the proportions of the training set, verification set, and testing set were 70%, 15%, and 15%, respectively. Besides, the Levenberg-Marquardt training algorithm was used to construct the NAR dynamic neural network. By adjusting the number of neurons in the hidden layer and the delay order, the model's mean square error (MSE) and stability were compared to determine the optimal model parameter; the number of hidden layer neurons was determined using the empirical Formula (13).  
\[n_{1}=\sqrt{n+m}+a \tag{13}\]  
where \(n_{1}\) is the number of neurons in the hidden layer, \(n\) is the number of input samples  
Figure 6: Autocorrelation coefficient graph after NAR modelling. **(a)** Original traffic volume time series. **(b)** SARIMA model residual series.  
Figure 7: Residuals after NAR modelling. (**a**) Original traffic volume time series. (**b**) SARIMA model residual series.
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 2 Materials and Methods  
The principle framework of the proposed HF-CRF hierarchical fusion SAR image change-detection method is shown in Figure 2. The HF-CRF method employs the CRF as the fusion framework. To establish the unary potential function and the pair-wise potential function, the mean-difference image [54] and the logarithmic-difference image [55] are used through neural networks and local sliding windows, respectively. In Branch I, the D-DRUNet neural network is adopted to fuse multimodal difference-image features. The network structure is a dynamic convolutional UNet with a dual encoder and a single decoder, and the encoder completes the semantic-feature fusion at the bottom layer, and the dual-jump connection structure obtains the fused segmented image during the decoding process. In Branch II, a CRF pair-wise local boundary entropy potential function is constructed by using a local sliding window to extract the boundary a priori information of the multi-modal difference image. Finally, the CRF model iteratively reasons the fused energy potential function to obtain the optimized change-detection results.  
Figure 2: The schematic diagram of HF-CRF hierarchical fusion structure.
###### Abstract  
A multiple quantum-well semiconductor saturable absorber mirror (MQW-SESAM) structure has been investigated by femtosecond pump-probe laser spectroscopy at a central wavelength of around 1050 nm. Coherent acoustic phonons are generated and detected over a wide frequency range from \(\sim\)15 GHz to \(\sim\)800 GHz. In the optical absorption region, i.e., in the multiple quantum wells (In\({}_{0.27}\)Ga\({}_{0.73}\)As), acoustic frequency combs centered at \(\sim\)365 GHz, with a comb spacing of \(\sim\)33 GHz, are generated. Most importantly, in the transparent region, i.e., in the distributed Bragg reflector, which is formed by a non-doped long-period semiconductor GaAs/Al\({}_{0.95}\)Ga\({}_{0.05}\)As superlattice, the mini-Brillouin-zone center, as well as zone-edge acoustic modes, are observed. The mini-zone-center modes with a fundamental frequency of 32 GHz can be attributed to the spatial modulation of the pump optical interference field with a period very close to that of the distributed Bragg reflector, in combination with the periodic spatial modulation of the electrostriction coefficient in the distributed Bragg reflector. The excitation of mini-zone-edge modes is attributed to the stimulated subharmonic decay of the fundamental center modes. Their subsequent back-folding to the mini-Brillouin-zone center makes them Raman active for the probe light.  
Keywords:coherent acoustic phonons; pump-probe spectroscopy; quantum well; semiconductor superlattice; mini-Brillouin-zone; high-speed asynchronous optical sampling +
Footnote †: journal: Applied Physics Letters
## 2 Input and Output Parameters for the Forward Radiative Transfer Problem
### Output Parameters  
Once the input parameters above have been specified, the solution of the RTE as described in Section 4, will provide two types of output, namely, (i) irradiances and mean intensities (scalar irradiances in Ocean Optics terminology) at specified vertical positions in the coupled system; (ii) total and polarized radiances in desired directions at specified vertical positions in the coupled system.
# End-Effectors Developed for Citrus and Other Spherical Crops
## 3 Picking Performance Tests
### Single-Factor Tests
#### 3.3.3 Effect of Picking Angle on Picking Performance  
The picking angle determines whether the end-effector can achieve complete clamping of citrus fruits and achieve shearing of the fruit stalk. In order to arrive at the optimal picking angle, the five factor levels of \(-15^{\circ}\), \(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\) were selected for a single-factor experimental study to analyze the effect of variation in picking angle on picking performance.  
According to Figure 15, the picking success rate was the highest when the picking angle was \(0^{\circ}\), reaching 94.29%; the single fruit picking time was the lowest when the picking angle was \(0^{\circ}\), at 1.65 s. In summary, the picking performance was the best when the picking angle was kept at \(0^{\circ}\).  
Figure 14: Effect of different end-effector speeds on picking performance.  
Figure 15: The effect of different picking angles on picking performance.
## 3 Mobile Communications Background
### Evolution of Mobile Technology  
Since the first wireless network was established more than four decades ago, the mobile communication system has undergone a noteworthy transformation. The launch of the first-generation mobile network in the early 1980s, coupled with the spontaneous increase in the need for effective communication systems, increased the demand for more connections globally and sped up mobile communications development [40; 41]. Wireless communications have, thus, grown to constitute a significant aspect of modern civilization. It has equally changed how society functions, especially after introducing satellite communications, television (TV), and radio transmission. A brief overview of the various stages of technological advancement is given below.
## 3 Mathematical Formulation  
The proposed work aims to simultaneously minimize tolerance cost and machining time, represented in Equation (1). Tolerance cost (\(TC_{i}\)) and machining time (\(MT_{jk}\)) are calculated using Equations (2) and (3). Equations (4) and (5), respectively, determine the critical dimension of the sub-assembly (\(Y\)) and its tolerance (\(t_{Y}\)). Machine engagement time (met) is estimated using Equation (6). The constraints considered in this work are expressed by Equations (7)-(9). The allocated tolerance (\(t_{i}\)) should be within the process limits, represented by Equation (7). The calculated sub-assembly tolerance within the given sub-assembly tolerance; this constraint is given by Equation (8). The total individual machine engagement time to manufacture the product should be less than the given available machine time (amt) represented in Equation (9). In addition, it is assumed that the following data are known well in advance before manufacturing the product:  
* Number of sub-assemblies and their dimensional chain details;
* Allowable tolerance for each sub-assembly (\(at_{y}\));
* Number of parts in the assembly (\(nc\));
* Number of sub-stages required to make the parts (\(no\));
* Number of possible processes for each sub-stage of the components (\(np\));
* Number of possible machines for each process (\(nm\));
* Number of hours available for each machine (\(amt\));
* Possible process--machine combination details (\(opno\));
* Mathematical model to compute tolerance cost and machining time;
* Constants to compute tolerance cost (A and B) and machining time (X1 and \(Y1\));
* Process tolerance limits (\(tmin\) and \(tmax\)).  
\[Z=\min(TC,MT) \tag{1}\]  
\[TC_{ijk}=\eta_{jk}\left(A_{j}+\frac{B_{j}}{t_{ijk}}\right) \tag{2}\]  
\[MT_{ijk}=\eta_{jk}\left(X1_{j}+\frac{Y1_{j}}{t_{ijk}}\right) \tag{3}\]  
\[Y=\sum_{l=1}^{nc}\pm d_{l} \tag{4}\]  
\[t_{Y}=\sum_{i=1}^{no}t_{ijk} \tag{5}\]  
\[met_{k}=\sum_{i=1}^{no}MT_{ijk} \tag{6}\]  
\[t_{\min}\leq t_{i}\leq t_{\max} \tag{7}\]  
\[t_{aY}\geq t_{Y} \tag{8}\]\[amt_{k}\geq met_{k} \tag{9}\]  
\(Z\) Objective function  
\(TC_{ijk}\) Tolerance cost of \(i\)th sub-stage using \(j\)th process on \(k\)th machine  
\(MT_{ijk}\) Machining time of \(i\)th sub-stage using \(j\)th process on \(k\)th machine  
\(t_{ijk}\) Allocated tolerance of \(i\)th sub-stage in \(j\)th process and \(k\)th machine  
\(A_{j}\), \(B_{j}\) Tolerance cost function constants for \(j\)th process  
\(X1_{j}\), \(Y1_{j}\) Tolerance machining time function constants for \(j\)th process  
\(\eta_{jk}\) Efficiency factor using \(j\)th process on \(k\)th machine  
\(d_{l}\) Dimension of \(l\)th component  
\(Y\) Critical dimension of sub-assembly  
\(t_{y}\) Calculated tolerance of critical dimension  
\(t_{ay}\) Specified tolerance of critical dimension  
\(i\) Sub-stage number index  
\(j\) Process number index  
Machine number index  
\(l\) Component number index  
\(met_{k}\) kth machine engagement time  
\(amt_{k}\) Available time of \(k\)th machine