esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Object Occlusion Analysis  
The appropriate threshold can help us judge whether the object is occluded or not. Therefore, how to select the appropriate threshold is very important. This paper selects the appropriate threshold by the SDM value of the response patch of all satellite video sequences. The distribution of the SDM value is unimodal in Figure 9. If the SDM value exceeds the occlusion threshold in five consecutive frames, the object can be considered partially or entirely occluded. Therefore, we can utilize the SDM value to judge whether the object is occluded or not.  
In order to select the appropriate threshold, we utilize the grid search from [2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0]. The success plot for per threshold is shown in Figure 10. We can see that when the threshold is 3.2, the AUC is the maximum. Therefore, 3.2 can be selected as the occlusion threshold.  
The visualization of the occlusion process in Figure 11 illustrates the relationship between the object occlusion's state and the SDM value. The threshold 3.2 can well judge whether the object is occluded. The SDM value exceeds the threshold when the object is partially or completely occluded. When the object is not occluded, the SDM value is less than the threshold in satellite videos. Some extreme cases may exist where the SDM value above the threshold is not caused by the object occlusion but caused by illumination variation or motion blur of the object. Since the duration of those extreme cases is very short, the impact on the final tracking results is negligible. Hence, our improvements are suitable not only for occluded objects but also for non-occluded objects.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Ours** & **KCF\_MF** & **KCF\_CNN** & **KCF\_SL** & **KCF\_AKF** & **KCF\_KF** & **KCF** \\ \hline AUC (\%) & **72.6** & 45.7 & 45.6 & 38.7 & 70.9 & 67.9 & 33.5 \\ \hline Precision score (\%) & **90.3** & 62.2 & 58.6 & 42.7 & 88.0 & 81.9 & 35.5 \\ \hline Success score (\%) & **95.1** & 64.8 & 64.6 & 58.4 & 93.9 & 92.5 & 47.6 \\ \hline FPS & 17 & 20 & 21 & 95 & 92 & 93 & **97** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental ablation results of all occluded video sequences (in bold the optimal results per tracker).  
Figure 9: Distribution of the SDM value of the response patch. We can see an obvious unimodal distribution. Hence, selecting an appropriate threshold to judge occlusion and non-occlusion.
## 2 Literature Review
### Communication Technologies for IoT Healthcare
#### 2.3.5 Cloud vs. Edge Computing in Healthcare IoT  
In healthcare IoT systems, the choice between cloud and edge computing architectures significantly impacts performance, particularly concerning latency and bandwidth [46, 47].  
_Cloud-Based IoT Platforms_: Cloud computing has significant scalability. However, because data must be transmitted from the IoT devices to the cloud and back, this centralized solution may cause latency problems and network dependency. Even though cloud computing enables scalability, in time-sensitive healthcare applications where instantaneous data processing is essential, such delays could be critical [48].  
_Edge Computing Solutions_: By processing data locally on embedded devices or nearby servers, edge computing overcomes these issues by lowering latency and bandwidth consumption. Real-time data analysis and decision making are made possible through this decentralized method, which is essential for applications like emergency response and patient monitoring [49].
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 2 Improved RANSAC for Plane Segmentation
### Improved RANSAC
#### 2.2.1 Optimization for Point Sampling  
The initial challenge is \"invalid sampling,\" as Figure 3a illustrates. This scenario depicts a situation where, instead of extracting the intended candidate plane, \(S_{1}\), an invalid plane, \(S_{2}\), is obtained as it outperforms \(S_{1}\) by including more inliers.  
Rule 1. Sample validation.  
In the sampling step (step 1) of the algorithm in Figure 2, any pair of the three sampled points must have similar normal vectors, which can be estimated with covariance matrices [21]. That is to say, for any three sampled points \(p_{1}\), \(p_{2}\), and \(p_{3}\), the sample is valid only when  
\[\alpha_{ij}=\arccos\left(\frac{\left|\mathbf{u}_{i}\cdot\mathbf{u}_{j}\right|}{\left\| \mathbf{u}_{i}\right\|\left\|\mathbf{u}_{j}\right\|}\right)<\sigma_{a},\ \ i,j\in\{1,2,3\},i\
eq j, \tag{7}\]  
where \(\sigma_{a}>0\) is the preset angle threshold and \(\mathbf{u}_{i}\) is the corresponding surface normal vector at \(p_{i}\) in Figure 3a.  
By incorporating Rule 1, cases illustrated in Figure 3a are effectively bypassed. This further prevents the generation of unqualified candidate planes, reducing the effort of candidate plane validation.
## 2 Governing Equations
### Transport Equation for the Rate of Dissipation of Turbulent Kinetic Energy  
The corresponding governing transport equation for the modified Newtonian rate of dissipation of \(k\) is given by,  
\[\rho\frac{\partial\tilde{\varepsilon}^{N}}{\partial t}+\rho U_{ i}\frac{\partial\tilde{\varepsilon}^{N}}{\partial x_{i}}= \rho\frac{\partial}{\partial x_{i}}\left[\left(v_{\rm s}+\frac{f_ {\rm t}v_{\rm T}}{\sigma_{\varepsilon}}\right)\frac{\partial\tilde{ \varepsilon}^{N}}{\partial x_{i}}\right]+f_{1}C_{\epsilon_{1}}\frac{\tilde{ \varepsilon}^{N}}{k}P_{k}\] \[-f_{2}C_{\epsilon_{2}}\rho\frac{(\tilde{\varepsilon}^{N})^{2}}{k }+\rho E+E_{\tau_{p}}, \tag{22}\]  
with  
\[E=v_{\rm s}v_{\rm T}(1-f_{\mu})\left(\frac{\partial^{2}U_{j}}{\partial x_{i} \partial x_{k}}\right)^{2}. \tag{23}\]  
As mentioned in the previous sub-section, all terms are modelled in the Newtonian context (excluding \(E_{\tau_{p}}\)). The damping functions of Equation (22) are \(f_{1}=1\) and \(f_{2}=1-0.3\exp{(-(Re_{T})^{2})}\); with model coefficients \(\sigma_{\varepsilon}=1.3\), \(C_{\epsilon_{1}}=1.45\) and \(C_{\epsilon_{2}}=1.90\).  
The last term in Equation (22) is the viscoelastic contribution to the overall \(\tilde{\varepsilon}^{N}\) balance. This term acts as a Newtonian destruction to the dissipation and is given by,  
\[E_{\tau_{p}}=2\mu_{s}\frac{\mu_{p}}{\lambda(L^{2}-3)}\overline{\frac{\partial u _{i}}{\partial x_{m}}\frac{\partial}{\partial x_{k}}\left\{\frac{\partial}{ \partial x_{m}}[f(C_{mn})f(\hat{C}_{pp})c_{qq}C_{ik}]\right\}}. \tag{24}\]  
It has non-negligible effects on flow predictions for all DR regimes and thus requires a suitable model.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 3 Proposed Positioning Framework
### Positioning  
The framework of the LIDAR-camera positioning system is based on the RTAB-Map building method [38]. RTAB-Map is mainly used for ground scenarios with varying distances; however, from an aerial perspective (i.e., at high altitude), the ground appears uniform, which reduces the effectiveness of some RTAB-Map modules for UAV positioning. As shown in Figure 3, the positioning system framework proposed herein is divide into LIDAR positioning and camera positioning. For LIDAR positioning, an edge and planar feature detection method was implemented, which replaced an Iterative Closest Point (ICP) algorithm to estimate the UAV's motion based on LIDAR cloud sequences. Moreover, because the LIDAR point cloud feature is vulnerable to smooth terrain, detecting natural landmarks from images is essential. The stereo camera that RTAB-Map uses to assist the LIDAR is not applicable for long-distance ranges. Therefore, a depth estimation step is included for monocular association of the color point cloud map reconstruction to account for motion. The details of this proposed method are provided in the following subsections.
## 6 Results
This indicates that these materials are more effective in absorbing electromagnetic waves, which could be attributed to their physical properties and composition. - The absorption values (\(A\)) for the materials MW (mineral wool), PT (hardened polystyrene), PE (extruded polystyrene), and PUR board (polyurethane board) are relatively low, remaining around the level of measurement uncertainty. This suggests that these materials are less capable of absorbing electromagnetic waves, which may correlate with their lower density or the nature of the materials which are not optimized for absorption. - The material concrete wall achieves the highest absorption values (\(A\)), followed by BW-MW and then BW. This hierarchy in absorption capacity may reflect the internal structure and density of the materials, with concrete wall having properties that enable it to dissipate more electromagnetic waves as heat or other forms of energy within its structure. - The maximum absorption (\(A\)) values for the materials are as follows: for BW, it is 11.8 dB; for BW-MW, it is 11 dB; and for concrete wall, it is 10.5 dB. These values might seem counterintuitive as one might expect that higher absorption would correspond to higher decibel values. However, these figures suggest a complex interplay between absorption, reflection, and transmission properties of the materials and how these properties affect the overall attenuation of electromagnetic waves. - These conclusions reinforce the understanding that the shielding effectiveness of a material is a result of both its reflective and absorptive properties. The materials with higher density or composite structures, such as concrete walls and brick walls with mineral wool, exhibit higher electromagnetic absorption, making them suitable for applications requiring effective electromagnetic interference (EMI) shielding. When measuring, reflections from the chamber may occur; however, these measurements fall into the category of comparative ones and are present both in measurements with shielding and in measurements without shielding. Therefore, their impact can be neglected even though they may have a minor effect on the results.
## 2 Theoretical Analysis  
### Bonding Mechanism  
Bond force refers to the shear force that occurs along the contact surface between a steel bar and concrete. The bond force between bolt and CBGM is mainly composed of chemical bonding force, frictional force and mechanical bite force. The chemical bonding force derives from the penetration of cement paste into the oxide layer on a bolt's surface during pouring, as well as the hardening of hydration of cement minerals during curing. The magnitude of chemical bonding force depends on the properties of the cement and the roughness of the bolt's surface. The frictional force is generated by the CBGM shrinking and holding the bolt tightly, the occurrence of which depends on the CBGM's shrinkage degree and the contact surface's roughness. The mechanical bite force is formed via the mechanical bite effect between the bolt's uneven surface and the CBGM. Among these three types of bonding forces, the chemical bonding force is generally very small. The frictional force of plain bolts is the main component of the bonding force, as it is the mechanical biting force for deformed bolts. The corresponding sliding process between the bolt and the CBGM can be viewed as the weakening and damage process of the chemical bonding force. After slippage, the bond force is mainly composed of frictional force. The specimens were composed of pre-cast concrete and high-strength bolts connected by CBGM in the square steel tube, and the confinement was effective in enhancing the bond and anchorage between reinforcement bars and CBGM [21]. The strain between the surface concrete and CBGM were changed by the bolt's pulling-out forms--that is, in the ascending section of the displacement-load curve, the surface of the specimen was subjected to tensile strain, and in the descending section, the strain gradually changed from tensile strain to compressive strain between the center and the edge. Due to the restrictive effect of the square steel tube on the CBGM, the ductility of the CBGM and the tensile stress of the bolt improved when the bolt pulled out from CBGM.
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 10 Conclusions  
This research employed the operational parameters of an EPB TBM and a 1D CNN to build a reliable normal and abnormal cutter wear prediction model. The EPB TBM advancement parameters and the pressure in the cutter chamber were considered, as we aimed to build a cost-effective, highly reliable model with fast computation. The 1D CNN model was used due to its abilities to optimize the parameters in the case of global and local minima and automatic feature extraction for the output. A four convolutional CNN model with 256 filters, 3 kernel sizes, 20% dropout, 2 fully connected layers, and the Adam optimizer was utilized to estimate cutter wear using machine parameters. The model had an overall correlation coefficient of 89.6%, a mean squared error (MSE) of 57.5, and a mean absolute error of 1.6. The model was able to successfully predict the cutter tool's life with a fast computational time, which was 3 min 22 s. We demonstrated that the proposed model could achieve a better prediction and processing time than that of traditional machine learning models and two deep neural network models. In application, the cutter wear prediction model could be used at the inspection points of the tunnel by the TBM operator to gain an understanding of the machine parameters and wearing relationship. Moreover, this study is the first to apply deep learning to cutter wear prediction in soft ground tunnelling. In this study, 18 different machine parameters were considered; in the future, more operational parameters can be used to develop deep learning models; hard rock and soft ground machine parameters can be used with pre-trained models to validate model computational ability.  
Conceptualization, K.K. and Y.K. ([PERSON]); methodology, K.K. and Y.K. ([PERSON]); software, K.K.; validation, K.K., Y.K. ([PERSON]) and H.T.; formal analysis, K.K.; investigation, K.K.; resources, Y.K. ([PERSON]); data curation, Y.K. ([PERSON]); writing--original draft preparation, K.K.; writing--review and editing, H.T. and Y.K. ([PERSON]); visualization, Y.K. ([PERSON]); supervision, Y.K. ([PERSON]) and T.A.; project administration, Y.K. ([PERSON]); funding acquisition, Y.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
JSPS KAKENHI Fostering Joint International Research (B), grant number 21 KK0070.  
Not applicable.  
This work was supported by JSPS KAKENHI Fostering Joint International Research (B), grant number 21 KK0070.  
The authors declare no conflict of interest.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Link Cost Metric
#### 3.3.2 Cost Maintenance  
Link costs for GCS-routing were initialized based on drone geographic information. To determine the link costs correctly, we considered not only the distance but also obstacles that could exist. To detect  
Figure 4: Throughput dependency on distance between drones.  
Figure 5: Mean normalized throughput.  
a link in non-LoS (NLoS), each drone counts the number of beacon messages received from neighboring drone APs. We conducted experiments similar to that of Figure 4, but with 8 Mbps UDP flows with a 4.5 cm thick metal obstacle between drones to make the link NLoS. Each second, one drone monitored the average number of received beacons for the past 5 s, as shown in Figure 6. Throughput decreases more sharply in NLoS than LoS environments, with transmissions in NLoS suspended two times earlier than in LoS. The average number of received beacons decreased to fewer than 3, although the distance was close enough to communicate fully in LoS. Thus, we set the threshold count = 3, i.e., if the average number of received beacons is fewer than 3, the drone reports link failure to GCS.  
GCS-routing uses a beacon message broadcast in a different radio band from that used in the ad-hoc network, and periodic beacon broadcasts by AP interfaces are enabled by default. Thus, the process explained above does not affect ad-hoc network communication quality.
## 4 Results and Discussions
### Results of Clustering Algorithms
#### 4.1.2 Mean-Shift  
Mean-Shift was the second algorithm to be tested. The first step is to import the necessary libraries that Mean-Shift needs to perform the clustering process, then using scikit-learn's Mean-Shift class, a Mean-Shift model was created easily, and we ran the algorithm using a bandwidth of 120. After running we can see the information for each feature. For example, let's see the mean, standard deviation, minimum, and so on. Based on these values, the algorithm defines whether a student belongs to a specific cluster. In particular, we see that in cluster 0, we have 20 students, in cluster 1, there are 11 students, in cluster 2, there are 7 students, in cluster 3, there are 9 students and in cluster 4 there are 8 students.  
The results of the Mean-Shift are 5 clusters  
* Cluster 0: (1, 6, 7, 8, 9, 22, 23, 25, 29, 30, 33, 35, 36, 37, 41, 42, 43, 48, 49, 50)
* Cluster 1: (5, 10, 11, 12, 13, 14, 16, 26, 28, 31, 32)
* Cluster 2: (17, 24, 34, 38, 44, 52, 53)
* Cluster 3: (2, 3, 4, 20, 21, 40, 45, 51, 54)
* Cluster 4: (0, 15, 18, 19, 27, 39, 46, 47)
# Deep Eutectic Solvents as Extraction Media for
## 1 Introduction
### Flavonoids  
Flavonoids are a category of naturally occurring organic compounds found in fruits, vegetables, or grains. Flavonoids, as a group, include upwards of 8000 different identified compounds, which are responsible for a number of functions within plants, such as the coloration of the different parts of fruits or vegetables (leaves, flower, peel), as well as shielding against UV rays or external threats, such as pathogens. They can be located in other subsystems as well, such as the bark or the roots of the plant, where they might serve similar or different purposes [1].  
The interest around extraction of flavonoids stems from the multiple health benefits they provide. Cardiovascular benefits, anticancer activity, and neurological system fortification are only some of the many actions this group. Flavonoid intake has been involuntarily pursued in medicine since ancient times. Herbal medicine, such as traditional Chinese or Mediterranean medicine, has helped breedplants that are considerably rich in flavonoids. This legacy has fueled much of the research listed in this project, directing green extraction towards traditional herbs, fruits, or common foods in the hopes of isolating useful substances, including flavonoids [2; 3].  
From a chemical structure standpoint, all flavonoids stem from a main skeleton that they share, and differentiate from each other based on the substituents attached to any part of the structure (Figure 1). They possess phenolic and pyrane rings in their structures and have many subclasses, such as flavanols, flavones, flavanones, chalcones, and anthocyanins. The flavonoid skeleton is comprised of an aromatic ring linked on one side with a six-membered heterocyclic ring, which bears an oxygen atom instead of carbon next to the common side. The two rings connect to another aromatic ring to form the skeleton, as shown below:
# Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO
## Materials and Methods
### SPME-GC-MS Analysis  
The volatile components of the EVOOs were extracted and identified by the headspace GC-MS using the SPME method. It was used in parallel with the S3 analysis as a validation technique in order to characterize the headspace of the analyzed samples. A Shimadzu Gas Chromatograph GC2010 PLUS (Kyoto, KYT, Japan) interfaced with a single quadrupole mass spectrometer Shimadzu MS-QP2010 Ultra (Kyoto, KYT, Japan) and the HT280T autosampler were used for the analysis. The fiber used for the adsorption of the volatile compounds was DVB/CAR/PDMS-50/30 \(\upmu\)m (Supelco Co. Bellefonte, PA, USA), which was exposed to the headspace of the vials after heating the samples in the autosampler oven at 35 \({}^{\circ}\)C for 20 min, with the aim of creating a headspace equilibrium. The desorption of the compounds took place in the GC-MS injector and the extracted compounds were separated using a capillary column MEGA-WAX (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA). Ultrapure helium (99.99%) was used as a carrier gas, at a constant flow rate of 1.3 mL/min. After the collection of the mass spectra in the total ion current (TIC) mode, the identification of the volatile compounds was carried out using the NIST11 and the FFNSC2 mass spectra libraries.
## 4 Timekeepers in Other Species of Cyanobacteria  
Not all cyanobacteria are lucky to have all three oscillator components. Another common species of cyanobacteria, _Prochlorococcus marinus_, managed to keep _kaiC_ and _kaiB_ but not _kaiA_, choosing KaiBC hourglass-like damped oscillator, which is an alternative to the bona fide circadian clock of _S. elongatus_' [63]. _P. marinus_' gene expression rhythm oscillates under a periodic light/dark cycle; however, it dampens when exposed to a constant light environment [63]. _P. marinus_' KaiC stays constitutively phosphorylated even without KaiA [64], and in agreement with this phenomenon, has an A-loop sequence that is different from that of _S. elongatus_. Furthermore, _P. marinus_ lacks _cikA_ homologs, and this is consistent with the notions that CikA is the newest addition to the circadian clock and that entrainment through quinone-sensing, along with the ability to sustain a free-running rhythm, may have become obsolete, at least in this organism [65]. Different environmental pressures might have slowly forced a modified survival method in this strain, and perhaps, such a high level of clock sophistication was not necessarily paying off.  
Another familiar species _Synechocystis_ sp. PCC 6803 is known to have a _kaiA_ homolog and three _kaiC_s and _kaiBs_[66]. Just like _S. elongatus_, _Synechocystis_' KaiA induces autophosphorylation of KaiC1; however, KaiC2 and KaiC3 stay constitutively phosphorylated without KaiA, as in the case of _P. marinus_ KaiC. The rhythmic gene expression in _Synechocystis_ also seems to be governed by KaiA-KaiB1-KaiC1, implying its conserved circadian timekeeping function. It would be interesting to find out whether the other oscillator components have any functional variance and significance in the clockworks. Unlike _S. elongatus_, which requires light as a primary energy source, _Synechocystis_ is able to grow under continuous dark conditions with only 5 min of light pulse each day in the presence of glucose [67]. It would be intriguing to investigate the biochemical details behind its unique clock circuit that may be entrained by a light pulse application [68].  
Originally isolated from a Beppu hot spring in Japan, _Thermosynechococcus elongatus_ BP-1 has an optimal growth temperature around 57 \({}^{\circ}\)C [69; 70]. Its thermophilic nature makes _T. elongatus_' clock proteins suitable for biochemical and structural analyses that can be used for studying those of _S. elongatus_[71; 72; 73], since _T. elongatus_ has all three _kai_ genes--_sasA_, _cikA_, and _rpaA_--which are all similar in size and sequence to _S. elongatus_. In addition, _T. elongatus_' oscillatory gene expression pattern is temperature invariant from 30 to 60 \({}^{\circ}\)C [74]. Further work is needed to evaluate how _T. elongatus_ not only managed to survive in such a harsh environment but also developed a circadian timekeeping system that is immune to such a wide range of temperatures.