esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Canopy Height Model 2000  
In the preparation of CHM2000, the percentage of cells, with respect to the total number of terrestrial cells identified as clearing for the study area, was 3.9%, while growth was 4.5%. The percentages of clearing/growth are presented tilewise on a map for an extended area in Supplementary S3. The information may be of interest for the identification of the areas of deforestation and reforestation. However, the percentages are not directly applicable for the quantification of forest change, especially given that (1) growth is present in both agricultural areas and forested areas and (2) GFCH2019 and the canopy density model use different thresholds (3 and 5 m, respectively) to define vegetation. CHM2000 for the study area and its histogram are shown in Figure 9a,c. The difference between GFCH2019 and CHM2000 and the LVIS2005 survey contours used in the validation are plotted in (b). The histogram in Figure 9d illustrates how different the computed canopy height changes are for the cells identified as growth and clearing. As expected, the canopy heights for \"growth\" are comparatively low, while in the other case, the cleared vegetation is high, mostly between 10 m and 30 m.  
A validation of CHM2000 was performed using LVIS2005 and LVIS1998 canopy heights computed as the difference between the elevation of the highest detected signal and the mean elevation of the lowest detected mode (ZT-ZG). The statistics in Figure 10 show insignificant differences between GFCH2019 and CHM2000. The mean difference for both LVIS datasets is slightly smaller for CHM2000; however, the STD is smaller for GFCH2019 in both cases. The small difference may be explained by a few changes in vegetation coverage in the areas surveyed in the LVIS2005 and LVIS1998 campaigns. Also, 91.6% of the cells in CHM2000 are equal to GFCH2019. Furthermore, to determine how well the GFCH2019 performs, it was compared to the LVIS2019 dataset. The statistics for different values of LVIS canopy heights can be found in Supplementary S2. We confirmed the results of [17] that GFCH2019 coincides primarily with the 90 th percentile of the LVIS-based canopy height (RH90): the mean/ STD is \(-0.9/7.8\) m. If (ZT-ZG) canopy heights are used, the mean/STD is \(-5.7/8.6\) m, values similar to the statistics in Figure 10.  
Figure 9: (**a**) Canopy Heights Model CHM2000. (**b**) Difference in canopy heights between GFCH2019 and CHM2000. (**c**) Histogram of canopy heights for CHM2000; the min/max/mean height is \(3/49/15.8\) m, as in GFCH2019. (**d**) Histogram of the differences in canopy heights between GFCH2019 and CHM2000; the min/max differences are \(-42/39\) m.
# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
## 2 Related Work
Combined with the connected area algorithm, it judged whether there was a decimal point after the number. [PERSON] et al. [17] used Gaussian filtering and binarization methods to identify pointer circular meters. [PERSON] et al. [18] obtained the key point information of the pointer meter through the deep learning algorithm, determined the pointer rotation center and radius according to the key point information, and finally obtained the meter reading according to the pointer angle and the meter range. [PERSON] et al. [19] proposed a pointer meter recognition algorithm combining meter detection and localization, deep learning and image processing techniques. The algorithm used the Faster R-CNN algorithm to classify three types of meters (voltmeter, ammeter, and digital meter), followed by image processing for more accurate needle readings. [PERSON] et al. [20] introduced a new stage called corner detection and counter classification, which corrects meter regions and rejects illegible or wrong meters before the recognition stage. [PERSON] et al. [21] regarded the pointer as a two-dimensional vector whose initial point coincides with the end of the pointer and whose direction is along the direction from the end to the top. [PERSON] et al. [22] used Faster-RCNN and YoloV4 models to train military action images taken by drones. The results showed that the accuracy of Faster-RCNN reached 93%, while the accuracy of YoloV4 was 88%. [PERSON] et al. [23] used Faster-RCNN and SSD MobileNet V2 models to train military action images taken by drones and transplanted the trained models to the Raspberry Pi 4 Model B electronic board. The results show that the accuracy rate of the Faster-RCNN model reached 91%, while the accuracy rate of the SSD MobileNet V2 model was 88%. The basic principle of pointer meter recognition technology is to analyze and recognize the image of the pointer meter through image processing and machine learning technology, so as to read the meter data. The key steps of this technology include image preprocessing, feature extraction, pointer positioning, pointer recognition, and data reading. By analyzing the characteristics of the pointer in the image, such as pointer length, color, shape, etc., the position and direction of the pointer in the image are determined, and finally the meter reading is obtained by identifying the scale. This technology can be applied to different types of pointer meters, such as gauges, gas meters, etc. The following is the structure of this article, Section 2 introduces the related work of pointer meter recognition; Section 3 introduces the recognition algorithm of pointer meters, and introduces the related technologies used, including the principles of Yolov7, DeepLabv3+, PGNet, Thining, Hough transform, and reading; finally, Section 4 provides a detailed analysis of a large number of experiments.
## 3 Problem Statement and the Dimension Reduction with Iovp
### Recursive Least-Squares Adaptation  
Here, we derive an adaptive reduced-rank RLS [2] type algorithm for efficient implementation of the MVDR-IOVP method. The reduced-rank beamformer \(\bar{\mathbf{\omega}}[i]\) is updated as follows:  
\[\bar{\mathbf{\omega}}[i]=\frac{\mathbf{R}_{D}^{-1}[i]\mathbf{\alpha}_{D}[i]}{\mathbf{a}_{D}^{H} [i]\mathbf{R}_{D}^{-1}[i]\mathbf{a}_{D}[i]} \tag{25}\]  
where:  
\[\tilde{\mathbf{k}}[i+1]=\frac{\alpha^{-1}\mathbf{R}_{D}^{-1}[i]\tilde\mathbf{{x}}[i+1]}{1+\alpha^{-1}\tilde{\mathbf{x}}^{H}[i+1]\mathbf{R}_{D}^{-1}[i]\tilde{\mathbf{x} }[i]} \tag{26}\] \[\mathbf{R}_{D}^{-1}[i+1]=\alpha^{-1}\mathbf{R}_{D}^{-1}[i]-\alpha^{-1} \tilde{\mathbf{k}}[i+1]\tilde{\mathbf{x}}^{H}[i+1]\mathbf{R}_{D}^{-1}[i] \tag{27}\]  
The columns \(\mathbf{s}_{d}[i]\) of the rank reduction matrix are updated by:  
\[\mathbf{s}_{d}[i]=\frac{\mathbf{R}^{-1}[i]\mathbf{a}_{d}[i]\mathbf{a}_{d}^{H}[i]\mathbf{\beta}_{d }[i]}{\mathbf{a}_{d}^{H}[i]\mathbf{R}^{-1}[i]\mathbf{a}_{d}[i]w_{d}[i]},d=1,\ldots,D \tag{28}\]  
where \(\mathbf{\beta}_{d}[i]=\sum_{d=1}^{D}\mathbf{s}_{d}[i]w_{d}[i]-\sum_{l=1,l\
eq d}^{D} \mathbf{s}_{l}[i]w_{l}[i]\) and:  
\[\mathbf{k}[i+1]=\frac{\alpha^{-1}\mathbf{R}^{-1}[i]\mathbf{x}[i+1]}{1+\alpha^{-1}\mathbf{x}^{ H}[i+1]\mathbf{R}^{-1}[i]\mathbf{x}[i]} \tag{29}\] \[\mathbf{R}^{-1}[i+1]=\alpha^{-1}\mathbf{R}^{-1}[i]-\alpha^{-1}\mathbf{k}[i+1 ]\mathbf{x}^{H}[i+1]\mathbf{R}^{-1}[i] \tag{30}\]  
where \(0\ll\alpha<1\) is the forgetting factor. The inverse of the covariance matrix \(\mathbf{R}^{-1}\) is obtained recursively. Equation (30) is initialized by using an identity matrix \(\mathbf{R}^{-1}[0]=\delta\mathbf{I}\) where \(\delta\) is a positive constant. From Equation (28), we can see that with the proposed IOVP approach, by orthogonalizing the columns of the projection matrix \(\mathbf{s}_{d}[i]\), the \(M\) weights can be independently updated in each recursion, and the rank-one problem in Equation (22) of [16] can be addressed. The computational complexity of the proposed adaptive reduced-rank RLS-type MVDR-IOVP method requires \(4M^{2}+3D^{2}+3D+2\) complex multiplications. The MVDR-IOVP algorithm has a complexity significantly lower than a full-rank scheme if a low rank (\(D\ll M\)) is selected.
# Attosecond Time Delay in Photoionization of Noble-Gas and Halogen Atoms
## 1 Introduction
For instance, methyl iodide possesses a giant resonance at a similar photon energy as its constituent atom, iodine [56]. Therefore, understanding the giant dipole resonance in non-noble-gas atoms is highly relevant to streaking and RABBIT experiments on molecules, which have only recently become available due to advances in data acquisition and analysis [14]. Moreover, to better understand the nature of the giant dipole resonance, a comparison of Wigner delays between halogen and noble-gas atoms is instructive. In this paper, we calculate partial photoionization cross sections, asymmetry parameter, phase shift, and Wigner time delay for both noble-gas and halogen atoms. The results are shown in graphs for each pair of neighbor atoms, revealing the great resemblance between the two group. Behaviors of the time delay are discussed for photon energy just above the ionization threshold, in the vicinity of giant resonance and Cooper minimum. In the following sections, we introduce the theoretical methods in Section 2, then present our results in Section 3, reveal the resemblance between noble-gas and halogen atoms, and discuss the features of Wigner time delay near the giant dipole resonance and Cooper minimum. Finally, a conclusion is drawn in Section 4 with possible future work.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Environment
#### 5.1.4 Flight Path Encompassed  
The UAV must visit all the nodes and collect data from them during its flight time. Hence, the flight path must encompass all the CH locations in the WSN deployment region. We performed optimization to minimize the UAV path. The UAV's distance in the proposed UWSN application during its mission is calculated and then compared with other algorithms' results. The path encompassed by the UAV during its mission is calculated in meters.
## 4 Discussion
### The Volume of the Chambers  
There are two oil chambers, as shown in Figure 2: the outlet chamber (\(V_{0}\)) and the actuator piston chamber (\(V_{3}\)). In Figure 7a,b, the amplitude and phase of the swash plate oscillation change dramatically with the volume of the outlet chamber and the actuator piston chamber. Conversely, the chambers before or behind the valve spool (\(V_{1}\) and \(V_{2}\)) have little influence on oscillation, because of the narrow intervals of the changing volumes. When \(V_{0}\) is near \(6\times 10^{-6}\) m\({}^{3}\) or \(V_{3}\) is near \(5\times 10^{-7}\) m\({}^{3}\), the amplitude of the oscillation reaches the maximum, and the phase drops remarkably; approximately 170 degrees and 130 degrees, respectively.  
Hence, a reasonable interval of the volume of the outlet chamber \(V_{0}\) and the actuator piston chamber \(V_{3}\) will minimize the oscillation amplitude of the swash plate. For the outlet chamber, it should be larger than \(3\times 10^{-5}\) m\({}^{3}\). It should not be too large, otherwise it can involve large bulk  
Figure 6: Comparison of simulated and experimental results. (**a**) Comparison of the improved and common model and (**b**) comparison of the improved model and experimental results.  
Figure 7: The influence of the volume of the chambers. (**a**) The outlet chamber (\(V_{0}\)) and (**b**) the actuator piston chamber (\(V_{3}\)).  
and heavy weight of the pump assembly. When the volume exceeds \(6\times 10^{-5}\) m\({}^{3}\), the effect of a larger volume on the reduction of vibration is not obvious. The volume \(V_{3}\) of the actuator piston chamber should be set between \(3\times 10^{-7}\) m\({}^{3}\) and \(7\times 10^{-7}\) m\({}^{3}\). However, a large volume may slow down the response speed of the swash plate, which also needs to be taken into consideration.
# Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
## 4 Conclusions  
In this paper, we presented the application of a convolutional neural network (CNN) for robust quality inspection of metal additive manufacturing (AM) parts. The Missouri S&T dataset, including optical microscope images of real-world metal AM parts were used to train and test the CNN model. This work contributed to the development of a CNN model with excellent performance in recognition of good quality, crack, gas porosities, and lack of fusion categories. To generate the appropriate model, extensive experiments were investigated on hyper-parameters including kernel size and the number of layers, data augmentation operations, and regularization. Our final model achieved an accuracy of 92.1% with 8.01 milliseconds recognition time of one image. The results indicate the promising  
Figure 8: (**a–d**) Additive manufacturing build metal parts images, (**e–h**) attention maps corresponding to (**a–d**).  
Figure 9: Examples of wrongly classified images in the test dataset of metal additive manufacturing defects. Results highlighted in black and red indicate correct and incorrect classification results, respectively. (**a**) Gas—Good, (**b**) Gas—LoF, (**c**) Gas—LoF, (**d**) LoF—Gas.  
application of the CNN method in quality inspection in the AM industry. It would be interesting to explore more CNN architectures and include a variety of materials in the future.  
[PERSON] designed and performed the experiments, wrote the manuscript; [PERSON], [PERSON] and [PERSON] assisted the analysis; [PERSON] supervised the research. All the authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the financial support from NSF (National Science Foundation) grants CMMI-1625736 and EEC-1937128, and Intelligent System Center (ISC) at Missouri University of Science and Technology.  
The materials preparation and characterization were supported by the Materials Research Center (MRC) at Missouri University of Science and Technology. [PERSON] would like to thank [PERSON] and [PERSON] from Missouri University of Science and Technology for their discussion and suggestions.  
The authors declare no conflict of interest.
## 5 Cutter Spacing Optimization and Bit Rock Breaking Simulation
### Analysis of Sampling Results  
For this study, scenarios involving a front row of axe cutters and a rear row of triangular prism cutters were examined. The design variables considered were the longitudinal distances (1(A) and 2(B)) and the transverse distances (1(C) and 2(D)) of the front and rear rows of the cutters, as depicted in Figure 6. The target variable was the mean cutting force applied to the whole cutter (E). Utilizing the Box-Behnken method for sampling, 17 sets of numerical simulations were conducted, with the results illustrated in Table 2.  
As shown in Table 2, there is a notable fluctuation in the cutter's force values across differing spatial cases that underscores the significance of optimization. A comprehensive analysis, conducted utilizing MINITAB 2021 software, yielded the Pareto weight plot, which provides elucidation on the impact exerted by each distinct design variable on the objective. This is illustrated in Figure 15, where variables exceeding the delineated red line have a considerable bearing on the design objective. In particular, the interaction between transverse distances 1 and 2, coupled with the interaction between longitudinal distance 2 and transverse distance 2, proves consequential in influencing the aggregate mean value of the cutter's force.  
In order to streamline the analysis of factor B and D interactions, a contour plot juxtaposing the two factors and the mean value of the cutting force was constructed, as shown in Figure 16. The data indicate that an augmentation of both transverse distance 2 and longitudinal distance 2 is associated with an escalation in the cutter's cutting force. As such, to curtail the cutting force exerted on the cutter, the transverse distance between the anterior and posterior rows of cutters should be meticulously maintained at 7 mm to 7.5 mm, while the longitudinal distance should range between 10 mm and 12.5 mm. With respect to the impact factor's weight on the target value, the MINITAB software was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multicolumn{4}{c}{**Factors**} & \multicolumn{2}{c}{**Cutting Force**} \\ \cline{2-6}  & **A/mm** & **B/mm** & **C/mm** & **D/mm** & **Average Value E/kN** \\ \hline
1 & 15.0 & 12.5 & 12.0 & 9.5 & 16.7153 \\
2 & 12.5 & 15.0 & 7.0 & 9.5 & 14.4970 \\
3 & 12.5 & 12.5 & 12.0 & 1  
employed to develop a correlation between the design variables and the target values. The derived expression is as follows:  
\[\begin{array}{l}E=-0.7+1.52A-1.44B-0.5C+2.35D-0.0139A^{2}+\\ 0.114B^{2}+0.0374C^{2}+0.0145D^{2}-0.0018 AB-0.0264 AC\\ -0.082 AD+0.0051 BC-0.1347 BD+0.0654 CD\end{array} \tag{10}\]
## 3 Communication in IoT
Other characteristics that allow an integrated, low-power system to be chosen for use are its longer-term reliability, the number of digital and analogue inputs/outputs that reduce the number of peripheral devices (sensors and actuators) to be supported, the capacity of power harvesting modules, and the effort needed to program the system. IoT is a dominant type of IT wireless technology in the seven major groups, i.e., the Global System for Mobile Communication (GSM), Wireless Personal Area Network (WPAN), the Cognitive Radio/Wireless Regional Area Network (WRAN), Wireless Radio, mesh, Point-to-Point (P2P), and the Low-Power Network (LPN), Low-Power Wide Area Network (LPWAN). Further GSM standard is divided into GSM EDGE Radio Access Network (GERAN) and Universal Mobile Telecommunication System (UMTS) and Universal Terrestrial Radio Access Network (UTRAN) [88]. Many wireless devices are produced in compliance with different wireless standards. One prominent problem is the interference between devices that run on the same band (for example Bluetooth, ZigBee, and WiFi) or adjacent bands [89]. Table 1 summarizes the IoT wireless networking which offers a wide range of bandwidth, operating frequency, range, and power consumption. The various technologies and specifications as well as the discrepancies between IoT projects and their unique criteria impede network interoperability. Two very common phenomena are high temperatures and high humidity when it comes to agricultural deployments. Based on observations by [PERSON] et al. [90], when the temperature increases from 25 \({}^{\circ}\)C to 65 \({}^{\circ}\)C, it has a major effect on the signal intensity obtained. [PERSON] et al. [91] presented similar findings. In addition, humidity in agricultural deployments can also be very high. For open fields, the wireless nodes are exposed directly to rain or irrigation systems. Relative humidity in greenhouses can also reach 80% for long periods. Dampness has been shown to greatly impact the distribution of radio waves [74; 92]. It is necessary to take the specs of the number of nodes into account, i.e., the distance between them, the antenna height, and the operating frequency based on the appropriate size of the messages when selecting a wireless transceiver for the agricultural purpose. The next subsection discusses the sensors which are used or have the potential to be deployed over agricultural fields for soil assessment and monitoring soil pollutants such as heavy metal toxics. In addition, the next subsection also highlights the various wireless nodes and the associated microcontroller and transceiver.
# Optimal Arrangement Strategy of IoT Sensors in Urban Drainage Networks: A Review
## 3 Arrangement of IoT Sensors in the Drainage Network  
In contrast with the relatively mature methods for optimizing the layout of monitoring points in surface water and water supply networks, there is still a lack of comprehensive guidance for optimizing the placement of IoT sensors in drainage networks. The optimized layout of sensors in drainage networks is similar to the selection of monitoring points in other applications, but there are significant differences in topology, flow characteristics, and water quality monitoring: 1 drainage networks are usually simplified as tree-like structures [13, 23], with inspection wells and connections represented by nodes [12]; 2 the internal environment of pipelines is complex, usually exhibiting alternating open and full flow states; 3 the rainwater and sewage transported inside the pipeline are complex in composition and corrosive. These characteristics account for the difference in the design and layout of IoT sensors in drainage networks compared to other applications.  
Sensors should be arranged to provide as much valid data as possible on the targets to be monitored, which is the first step in information analysis [24], planning and management [25, 26, 27], or the adoption of appropriate measures [28]. The accuracy of decision making depends more on the precision and reliability of data, rather than the volume of the data. Poor-quality datasets may lead to decision-making biases [29, 30]. It has been shown that an increase in the number of sensors leads to a sharp decline in the return on scale [2, 23]. In Figure 3, we explicitly present a general process applicable to the optimal placement of IoT sensors in UDNs, as well as a description of the content and framework of this review.  
Firstly, the study area and monitoring objectives should be identified for the selection of indicator parameters and sensor types. Topological information and data on the drainage network should be collected and organized. Based on various optimization theories and methods, the optimization model and framework were established by incorporating prior knowledge and engineering experience. The monitoring plan was then generated and quantitatively evaluated. The operational status of the drainage network can then be  
Figure 3: Process of optimizing the placement of IoT sensors in UDN.  
obtained using the IoT monitoring data. Thus, the drainage network can be maintained and promptly managed.
## 3 Overview and Theoretical Foundations
### Big Spatial Multidimensional Data Analytics
#### 3.2.4 Spatial Query Optimization  
The plain version of spatial join is expensive, which caused the introduction of optimized methods, such as filter-and-refine algorithm [39]. It operates in three steps as follows. First, spatial access methods (SAMs) are applied to restrict the search space. SAMs organize minimum bounding rectangles (MBRs) as geometric keys. It is not yielding the exact result of the spatial join, but a set of candidate pairs that contain all the answers and pairs of objects that do not fulfill the join predicate. In the second step, the candidate pairs are fed to a geometric filter that is applied to examine the pairs, for example, if the predicate is 'contains' then it checks the objects that are contained completely within other objects (geohash that is contained completely inside another geohash). The output is composed of three classes, true hits fulfilling the join predicate, false hits not fulfilling the predicate and remaining pairs that are possibly fulfilling the join predicate. The third step works on the exact geometry of the remaining pairs, which is expensive, and means applying ray casting algorithm (Point-in-Polygon). The filter-and-refine strategy entails a big improvement in efficiency of the inclusion spatial query. In this paper, we apply an adapted version of this approach to the execution of the spatial join between meteorological, pollution and mobility data. Figure 2 displays the workflow of the filter-and-refine algorithm (a.k.a. multi-step processing of spatial joins).