esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contributions  
This research distinctly contributes to the astronomical classification domain by presenting a detailed analysis of numerous machine learning (ML) and deep learning (DL) models, specifically tailored for galaxy morphology categorization using the Galaxy Zoo dataset. Because previous studies lacked extensive comparative evaluations that simultaneously consider predictive performance, computational efficiency, and environmental impact, this investigation introduces an analytical schema integrating these dimensions. By systematically quantifying the classification efficacy of varied architectures, such as CNN, Vision Transformer, and classical ML algorithms, alongside their associated computational loads, a novel performance-energy trade-off framework is promoted. The introduction of Green AI principles into astronomical image classification significantly expands upon traditional approaches, explicitly inserting sustainability considerations into algorithmic assessment criteria. Moreover, by employing ResNet-50-derived features for classical ML models, this study facilitates a nuanced understanding of CNN-derived biases, thus delineating the portion of representational bias attributable to deep convolutional frameworks. Lastly, explicit hyperparameter tuning protocols coupled with energy-aware metric collections underline the comprehensive nature of this research, providing robust methodological benchmarks for future studies.
# Evaluation of Autonomous Mowers Weed Control Effect in Globe Artichoke Field
## 3 Results  
### Performances of the Three Autonomous Mowers  
3.1.1. The Percentage of Area Mowed  
The percentages of area moved (back-transformed mean values) after 30, 60, 90, 120, 150 and 180 min of AM1, AM2 and AM3 are shown in Table 2.  
The three autonomous mowers moved more than 40% of the area after 60 min, showing no statistical differences between them. After 120 min, the autonomous mowers managed more than 60% of the area. For this lapse of time, the percentage of area moved by AM2 was significantly higher (\(p<0.05\)) than the one achieved by AM1, with an increase of 12.8%. After 180 min, the autonomous mowers managed more than 78% of the area, and the area moved by AM1 was again significantly lower (\(p<0.05\)) compared to that managed by AM2, with a decrease of 6.1%. The performance of AM3 was statistically similar to that of AM2 and AM1, after both 120 and 180 min of moving.  
In accordance with the lack-of-fit test (\(p=1.0000\)), data recorded using the custom-built software fitted the asymptotic regression model.  
The 50% of the area moved was reached in a higher amount of time for AM1 compared to AM2 (95% confidence interval (CI) = 6.14-32.02) with a difference of 19.081 min. AM3 required a similar time as AM2 and AM1 (95% confidence interval (CI) = \(-\)2.97-18.12, and (CI) = \(-\)25.50-2.49, respectively). AM2 required less time than AM1 even for the moving of 80% of the area (95% confidence interval (CI) = \(-\)74.36--14.25), with a difference of 44.304 min. The time required by AM3 to move 80% of the area was again similar to that required by AM1 and AM2 (95% confidence interval (CI) = \(-\)59.21-5.78, and (CI) = \(-\)6.9-42.08).
# Influence of Contacts and Applied Voltage on a Structure of a Single GaN Nanowire
## 2 Experiment  
### Samples  
The samples with GaN NWs were prepared at the NanoLund Laboratory at Lund University, Sweden. The GaN NWs with the lengths from 3 \(\upmu\)m to 4 \(\upmu\)m and two different diameters of 200 nm and 350 nm were grown by selective area metal-organic vapor phase epitaxy (MOVPE), equipped with a 3 \(\times\) 2 close-coupled showerhead reactor [23]. First, a 1 \(\upmu\)m thick [0001]-oriented GaN template layer was grown on top of a Si (111) substrate, on which a 30 nm thick \(SiN_{x}\) film was deposited as a growth mask by low-pressure chemical vapor deposition (CVD). Further, hexagonal arrays of openings were defined in the \(SiN_{x}\) mask by e-beam lithography and reactive ion etching with the opening diameter of about 100 nm and the pitch of 1 \(\upmu\)m. The as-patterned templates were then loaded into the MOVPE reactor to grow the GaN nanowires. Due to the growth selectivity, the GaN growth only took place from the GaN surface exposed in the \(SiN_{x}\) openings. The continuously supplied flows of NH\({}_{3}\) and triethylgallium were used to synthesize GaN nanowires. A low V/III ratio had to be used in order to achieve the nanowire geometry with vertical m-plane side facets. The triethylgallium flow was 19 \(\upmu\)mol/min and the growth temperature was 1042 \({}^{\circ}\)C. The as-grown GaN nanowires, having a hexagonal cross-section, were about 200 nm thick and about 3.5 \(\upmu\)m long. In order to further increase the diameter of GaN nanowires, a GaN shell was grown around the GaN nanowires. This was achieved by increasing the V/III ratio, which enhanced the GaN growth on the m-plane side facets through forming low-growth-rate facets 1011 at the nanowire tip. By controlling the shell growth time, GaN nanowires with a diameter of 350 nm were obtained. From our previous study [22], we know that the structure of GaN NWs is pure wurtzite almost without dislocations and stacking faults.  
After the growth, GaN NWs were removed from the original substrate and deposited on a Si (111) chip with a 100 nm thermally grown SiO\({}_{2}\) layer on top, as an insulating layer. The SEM images of the free-lying NWs are presented in Figure 1. The SEM images of the NWs presented in this work were obtained in NanoLund and DESY NanoLab [24].
## 5 Optimization Techniques for Routing in Wireless Sensor Networks
### Location-based  
When this technique is used, a node decides the transmission route according to the localization of the final destination and the positions of some other nodes in the network.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 5 Conclusions  
The ultimate goal of this study is to develop a hybrid damper that combines VE dampers and RF dampers for use in boundary beams between coupled shear walls. As the first step, the hysteresis models for the VE damper and RF damper were constructed through element tests. Subsequently, the dampers were installed in a six-story frame with coupled shear walls, and their effectiveness was verified through shake table tests.  
The element tests successfully demonstrate the mechanical behavior and hysteretic response of both dampers. The damper characteristics obtained from these experimental tests provide reliable input parameters for subsequent analytical modeling.  
The shake table tests were conducted using both sinusoidal and Kokuji wave excitations. Results demonstrated that structural walls with VE dampers achieved greater story displacement reduction under sinusoidal loading, whereas RF dampers exhibited superior performance under Kokuji wave excitation.  
The analytical models of both dampers were constructed based on the element tests and installed to the STERA 3D software to simulate the shake table test of the six-story specimen. The analytical results demonstrated strong agreement with tests for both VE and RF dampers, confirming the accuracy and reliability of the STERA 3D models.  
Future research will focus on determining the optimal combination of damper quantities when both VE and RF dampers interact, analyzing the behavior of buildings with coupled shear walls equipped with the hybrid dampers, and developing corresponding design methods.  
Conceptualization, T.S. and Z.N.E.M.; methodology, Z.N.E.M. and R.M.; software, T.S.; writing--original draft preparation, Z.N.E.M.; writing--review and editing, T.S.; supervision, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study is available on request from the corresponding author.  
The authors gratefully acknowledge the support provided by [PERSON] (Hazama Ando Corp.), [PERSON] (Nishimatsu Construction Co., Ltd.), [PERSON] (Sato Kogyo Co., Ltd.), [PERSON] (Toda Corp.), [PERSON] (Maeda Co., Ltd.), [PERSON] (Kumagai Gumi Co., Ltd.), and [PERSON] (Cooperative Research Facility Center, Toyohashi University of Technology). The first author appreciates MEXT scholarship for support through scholarship of doctoral degree.  
Figure 29: Hysteresis of RF damper under Kokuji waves.
###### Abstract  
The problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because of its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although the good results achieved by the prior image constrained compressed sensing (PICCS) method, there may be some unsatisfactory results in the reconstructed images because of the image gradient L\({}_{1}\)-norm used in the original PICCS model, which leads to the image suffering from step artifacts and over-smoothing of the edge as a result. To address the above-mentioned problem, this paper proposes a novel improved PICCS algorithm (NPICCS) for SVCT reconstruction. The proposed algorithm utilizes the advantages of PICCS, which could recover more details. Moreover, the algorithm introduces the L\({}_{0}\)-norm of image gradient regularization into the framework, which overcomes the disadvantage of conventional PICCS, and enhances the capability to retain edge and fine image detail. The split Bregman method has been used to resolve the proposed mathematical model. To verify the effectiveness of the proposed method, a large number of experiments with different angles are conducted. Final experimental results show that the proposed algorithm has advantages in edge preservation, noise suppression, and image detail recovery.  
captured tomography (CT); sparse-view reconstruction; prior image constrained compressed sensing; image gradient L\({}_{0}\)-norm +
Footnote †: journal: Applied  
1
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 3 Materials and Methods
### Synthetic Data Generation
#### 3.2.5 Addition of Noise  
Random noise was independently added to each frame (Figure 2e). This step reproduces natural fluctuations due to camera noise that randomly appear on images. The noise (\(v\) in Equation (3)) was added to each pixel of a frame using a normal (Gaussian) distribution (mean: 0.5, standard deviation: 0.25). Performing a simple spatial averaging operation [39] on these small video patches produces synthetic PPG signals (see Figure 5c,d for typical examples) that are quite similar to realistic ones (Figure 5a).
## 5 Analytical Modeling
### The Proposed Function and Its General Optimization Procedure  
The goal of our reliable transport scheme was to optimize the round-trip delay of the file and the corresponding energy consumption. In this section, we propose a new function which combined the round-trip delay and energy consumption, and optimized it using general optimization algorithms.  
Let \(TE\) denote the estimated value of total energy of the network, and \(DC\) denote the duration of the communication service. In some applications, \(TE\) and \(DC\) are priori information that can be obtained in advance. For example, in an ON formed by scholars attending a conference, the duration of the communication service is the length of the conference. We can estimate the range of total energy from the number of participants (the wireless devices are mostly mobile phone, equipped with 1500-2500 mAh lithium batteries). Similarly, under an emergency scenario, the ON is formed by the rescue team and survivors. The best rescue time can be seen as the duration of the communication service, and the range of total energy can be estimated by the permanent population and popularity of mobile phones of the affected area, as well as the energy of devices carried by rescue works. Therefore, we assume that \(TE\) and \(DC\) are known.  
The proposed metric is expressed as follows:  
\[NDM=\alpha\cdot\frac{TE}{EC_{K}}+(1-\alpha)\cdot\frac{DC}{ED_{K}} \tag{25}\]  
Recall that \(EC_{K}\) and \(ED_{K}\) are the mean energy consumption and round-trip delay of a file, respectively. To understand Equation (25), \(\frac{TE}{EC_{K}}\) can be interpreted as the mean number of files that can be delivered under the current energy of the network; similarly, \(\frac{DC}{ED_{K}}\) can be interpreted as the mean number of files that can be delivered during the period when the network provides communication services. With this interpretation, the \(NDM\) can reflect the average number of successful deliveries that the current opportunity network can support. By setting the weight parameter \(\alpha\) (\(\alpha\in\) [0,1)), users can adjust the contributions of energy consumption and latency to the network performance, respectively.  
To achieve an optimal trade-off between energy and delay, we maximized \(NDM\) by selecting the optimal parameters \(\{M_{r},T_{s,r},T_{w,r}\}\), (\(r\in(1,2,\ldots,K)\)). There are many general algorithms that have been developed to solve the above nonlinear optimization problems. In this paper, a simple and fast stochastic optimization method with good robustness--the differential evolution algorithm--was used [43]. The details of the algorithm are not be described here.  
It is important to note that the above optimization process does not need to be performed on the source node in real time, but in an offline manner. All possible cycles \(r\) (\(r=1\), \(2\),..., \(K\)) are calculated and then stored in the memory of source node for actual use.
# Kilohertz Pixel-Rate Multilayer Terahertz Imaging of Subwavelength Coatings
## 3 Results
### Layer Thickness Measurements with Rotating Sample and Stationary Head  
To demonstrate the repeatability of the layer thickness measurements, we present data acquired with rotating samples but stationary reflection head (fixed radius). The results of 10 consecutive turns are shown in Figure 4. The thicknesses of all layers fall in the range of about 30 \(\upmu\)m to 100 \(\upmu\)m. For each sample, the polar-coordinate plots of the thickness readings overlap perfectly, which illustrates the excellent repeatability of both the raw data and the evaluation results. In the plot of the metallic substrate disk (see Figure 4a), the slit of the disk, used for consistency checks of the image reconstruction, is seen at an angle of about 10 degrees. At an angle of 205 degrees, some deviating measurement results appear for the filler and basecoat, where the first one exhibits larger values around 110 \(\upmu\)  
Figure 2: (**a**) Representative waveforms acquired at 1.6 kHz. Black: Reference waveform. Red: Normalized measurement waveform of a sample with three layers (shifted vertically and horizontally for clarity). To the eye, the sample waveform does not reveal any individual echos, which points towards the subwavelength nature of the applied layers. (**b**) Dynamic range of the measurement system at 1.6 kHz measurement rate. A “single-shot” measurement (625 \(\upmu\)s acquisition time) attains a peak dynamic range of 40 dB and a bandwidth above 3 THz. The absorption lines caused by water vapor in the ambient air are symmetric, indicating a good linearity of the delay axis provided by the system.  
and the latter one shows lower values around 30 \(\upmu\)m. The pattern of the mask imprint is seen in the lowest layer of each sample, i.e., in the thickness of the filler in the case of the metallic substrate, and in the thickness of the base coat on the non-metallic substrate, the latter exhibiting a more pronounced and uniform thickness contrast (see Figure 4b). This is attributed to the improved hand-spraying skills that the experimentalist developed during the project.
## 3 Results and Interpretation
### Interpretation of Diversity  
The results of the quantitative and qualitative analyses of the eight glass beads showed that one type was potash glass and the other seven were soda glass. Based on the findings, the study compared and analyzed the differences in fluxes, stabilizers, and coloring materials, and identified the origins of Na for the seven soda glass beads.  
The quantitative and qualitative analyses of the glass beads showed that the blue glass bead from tomb No. 8, the brown and green glass beads from tomb No. 11, the brown glass beads from tomb No. 12, and the light-blue, brown, and green glass beads from tomb No. 18 were all soda glass. However, the blue-green glass beads from tomb No. 11 were potash glass. There was more soda glass from potash glass, and this may be because soda glass shows color more easily, is glossier, and resists weathering better than potash glass.  
The stabilizers used for the glass included Al\({}_{2}\)O\({}_{3}\), CaO, PbO, and MgO, and different concentrations of these were used according to whether the glass was soda or potash glass, and whether the color of the glass was brown, green, or blue. In addition, the blue glass beads from tomb No. 8, the brown and blue-green glass beads from tomb No. 11, the brown glass beads from tomb No. 12, and the light-blue glass beads from tomb No. 18 did not contain PbO.  
Prior studies have stated that potash glass generally contains LCA (low CaO, Al\({}_{2}\)O\({}_{3}\)) properties and has few stabilizers. However, soda glass shows various stabilizer compositions depending on its color. The stabilizer concentration differs depending on the color of the glass. For the glass beads from the Suchonri site, blue-green potash glass was distributed along the LCA-B (low CaO < Al\({}_{2}\)O\({}_{3}\)) areas. However, green and blue-green glass was found to contain a lower concentration of CaO and a larger a concentration of Al\({}_{2}\)O\({}_{3}\), and blue glass had less Al\({}_{2}\)O\({}_{3}\) and more CaO.  
All glass beads from the Suchonri site contained Fe, regardless of their color. Fe is yellow or reddish-brown in color, and it is estimated to affect the color of brown glass beads. Green glass beads contain Cu, as Cu has a green color in an oxidation firing environment. In addition, although Fe can be red or brown in an oxidation firing environment, it changes into green in a reduction firing environment. When comparing the concentrations of coloring materials among green colored glass beads, the green glass beads and blue-green glass bead of tomb No. 11 and the green glass beads of tomb No. 18 were found to contain more Cu than the green glass beads from tomb No. 18. This shows that there is a possibility that Cu and Fe were both used as coloring materials for green glass beads.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 3 Uncertainty Analysis
### Pentaprism Setup
Based on the calibration experiment, the angle setup error of PSD at the horizontal and vertical direction, \(\theta_{ph}\) and \(\theta_{pv}\), can be assumed to be less than \(\pm\)2.5\({}^{\circ}\), the PSD measurement error \(F\) can be assumed to be less than \(\pm\)100 \(\upmu\)m, and the setup errors of the measuring distance, \(D_{lp}\) and \(D_{lt}\), can be both assumed to be 1 m. Thus, the measurement uncertainties, \(\sigma_{\delta_{h}}\) and \(\sigma_{\delta_{p}}\), were estimated to be \(\pm\)1.02 and \(\pm\)3.06 \(\upmu\)m from Equations (12) and (13), respectively.
## 7 Performance Evaluation
### Numerical Results and Discussion
#### 7.2.2 Implementing Overlapping Constraints Based on Critical Time Deadlines
respectively; the period of every TT queue (\(Q_{m}^{h}\)) in \(h\) as \(T_{m}^{h}\); the duration of open-window intervals for \(Q_{m}^{h}\) in \(h\) as \(W_{m}^{h}\); data out of \(h\) by a rate \(C_{h}\); offsets between any adjacent nodes as \(OS_{k}^{h,h-1\cdots}\); and end-to-end latency deadline for the \(Q_{k}^{h}\) frame as \(e2\epsilon_{k}\). Then, the window overlapping constraint can be given as  
\[\forall\;WCD\Big{(}\left.OR_{k,P(m)}^{h,edge}\right|_{max}\Big{)}\leq e2 \epsilon_{k}:\]  
\[\forall\;WCD\Big{(}\left.OR_{k,P(m),P(n)}^{h,edge,alg2}\right|_{max}\Big{)} \leq e2\epsilon_{k}:\]  
\[\Big{(}\left.OR_{k,m}^{h,edge}\leq\left.OR_{k,P(m)}^{h,edge}\right|_{max}\Big{)} \;\wedge\;\Big{(}\left.OR_{k,m,n}^{h,edge1,alg2}\right.\leq\left.OR_{k,P(m),P (n)}^{h,edge1,alg2}\right|_{max}\Big{)}\]  
where \(m\) and \(n\) represent the priorities of queues that overlap with the \(Q_{k}^{h}\) window in every node \(h\), \(P(m)\), \(P(n)\in\{L,\,H\}\) (\(L\) lower priority and \(H\) higher priority), and \(edge1\), \(edge2\;\in\{B,\,E\}\) (\(B\) opening edge and \(E\) closing edge).
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 5 Conclusions, Extensions, and Open Questions
### Query Model  
Some of our algorithms use only certain types of queries. In particular, we have the following:  
* While Phase 1 of the algorithm seems already quite expensive in terms of high-cost operations, we note that it makes use of much simpler queries \"\(d(x,y)>d(z,w)\)?\" like in [14]. This is also the case for the Single-Linkage algorithm (Phase 2 naive), which uses very few such high-cost queries (Table 1). While our implementation combines exact (high-cost) and noisy (low-cost) queries, the algorithms in [14] use only noisy queries, though under a different noisy model related to certain _planted_ instances.
* The first exact implementation of Single-Linkage++ (Phase 2) requires computing/ estimating the cost of the clusters given their centroids (3). Instead, the dynamic programming version makes use of the full power of the model. Indeed, our approximation (Phase 2 APX in Table 1) is based on the idea that queries/algorithms that approximately compute the centers (medois) are enough. Definition 4 and Theorem 4 suggest that a query model/algorithm which allows for approximating the centers, and their costs (3), yields an approximate implementation of Phase 2 of Single-Linkage++.
* The Coupon-Collector Clustering algorithm using same-cluster queries (Section 4) uses very simple comparison-queries, namely \"\(d(x,y)<d(x,z)\)\" (second and third steps), in addition to the same-cluster queries (first step). The latter can either be available or can be simulated by a richer class of \"scalar\" queries in (6). As already observed, these are slightly more complex than those in [8; 9].
* The model can be refined by introducing a cost dependent on the _set_ of distances involved, e.g., how many they are. Whether comparing just _two_ pairwise distances is easier then comparing _groups_ of distances seems to depend on the application at hand; sometimes comparing groups is easier because they provide a richer \"context\" which helps.
## 2 Acquisition and Characterization of Pore Structure
### CT Scanning  
Both thin section and SEM scanning require pre-treatment of the rock samples, such as slicing, grinding, and polishing. This series of processes can cause some degree of damage to the sample surface, leading to changes in the pore structural features of the sample. In contrast to these two methods, CT scanning technology, which reconstructs the sample's internal structure using projection data obtained through X-ray imaging, can be considered a non-contact, non-destructive 3D imaging technique [40].  
An X-ray CT scanning system consists of three main components: the X-ray source, the sample stage, and the detector [7]. In the X-ray source, an electron beam is emitted from the cathode and strikes the anode target, exciting the atoms in the target material and releasing energy in the form of X-ray photons. These X-ray photons pass through the sample placed on the sample stage and are used to image the internal structure of the sample. The detector collects the varying attenuation signals, presenting a two-dimensional X-ray image (spectrum) of the current scanning position based on the X-ray absorption intensity. By rotating the sample stage, 2D projection data from different positions of the sample are acquired. Combining these projection data ultimately allows for the reconstruction of the sample's 3D structure, as shown in Figure 3.