esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Community Flood Management Strategies and Actions for Preparation  
Questionnaire surveys were conducted in the flood hazard areas. Since it is the most densely populated district and around 98% of its total area is categorized as being under high flood risk, PNSA was selected as representative of a high-risk area, whereas Sena and Bang Ban districts were considered to be representative of moderate risk and combined risk areas in the community surveys. According to the sampling distribution of the population proportion, 552 households in PNSA (\(n\) = 236), Sena (\(n\) = 162), and Bang Ban (\(n\) = 154) were randomly selected as target respondents. The respondents, comprising of females (72%) and males (28%), were ranged in ages from 18 to 103 years (mean age: 48). The survey results found that about half (43%) of the total respondents experienced more than 10 instances of flooding in their communities during the past 10 years. However, interestingly, the surveys reveal that approximately half the respondents did not receive information effectively, instructions regarding flooding (44%), or support from the local government in terms of community flood training (55%). In respect to financial support, it should be noted that close to 67% of respondents received no flood disaster relief payments from their local authorities. More worryingly, as depicted in Figure 4a,b, almost half the respondents (43%) were unable to access emergency health services during a flood disaster, while about 55% also reported inadequate drinking water provision during a flood event. Significantly, this present study reaffirms that half of the respondents (51%) reported that they had never taken proactive action to support flood risk responses. The results of Spearman's correlation coefficient demonstrate a significant positive relationship between flood forecasting and warning information and action preparation for floods (\(r\) = 0.394; \(p\) < 0.01). In addition, positive correlations exist between access to community flood hazard maps, flood disaster relief payments, flood preparedness training, access to emergency health services during floods, and actions for the flood preparation of local respondents. A strong positive correlation exists between access to flood hazard mapping and related flood training activities (\(r\) = 0.428; \(p\) < 0.01) (Table 3).  
Figure 4: Access to basic urban services: (**a**) emergency healthcare and (**b**) drinking water during floods (\(n\) = 522).
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 2 Materials and Methods
### Tandem Data Archive  
The S3A/B tandem phase ran for a period of roughly 6 months and began with an initial drift phase following the launch of S3B, as it moved onto the tandem orbit 30 s ahead of S3A. The tandem orbit period lasted from 7 June 2018 to 16 October 2018, and was followed by a second drift phase as S3B moved away to its nominal orbit. The final drift phase was divided into two parts, an initial slow drift until 24 October 2018, to facilitate further comparisons during this period increasing of separation, and a final fast drift until S3B reached its nominal orbit on 23 November 2018.  
From this full period, a data archive containing the S3A/B products for one full day per week was available for analysis at L1 and smaller subset of this for L2. This was considered sufficient to investigate the full scope of spatial and temporal interest. It should be noted that the tandem phase itself overlapped with initial S3B mission commissioning activities, so the best quality data came from the end of the tandem phase once the instrument had had chance to stabilise.  
The SSTs used in this study were derived using the ESA SST retrieval algorithms, which use a set of coefficients derived from modelled brightness temperatures and are a function of path length and total column water vapour taken from a meteorological model [3]. In this study, we have concentrated on the four most common retrievals which are N2 (nadir view only, S8, S9 channels only), N3 (nadir view only, S7, S8, S9 channels), D2 (dual view, S8, S9 channels only) and D3 (dual view, S7, S8, S9 channels). All SST measurements come with flags and quality level estimates based on the GHRSST (Group for High Resolution Sea Surface Temperature) recommendations [9], as well as with an estimate of the likelihood of cloudiness. Uncertainty estimates are also calculated as part of the operational processing and come in two forms: one as the GHRSST Single Sensor Error Statistic (SSES), which has a bias and a standard deviation estimate; and the other as a theoretical uncertainty which is based on trying to propagate different sources of error, including noise and retrieval errors, through to the SST measurement. The SSES and theoretical uncertainty therefore represent two different approaches to SST uncertainty with one based on statistics using observations matched to the drifting buoy network to derive an uncertainty, and the other taking a more metrological approach looking at possible sources of error.  
In this study at L1, we focus on the 3 thermal infrared channels used by the SST retrieval. The calibration of these channels is based on the observation of two internal warm blackbody calibration targets that are each viewed once per scan [2]. The data for these channels are provided as brightness temperatures, and come with the information required to evaluate independent (or random) and common (or systematic) components of uncertainty per pixel [10].
## 1 Introduction
However, both methods have their own limitations. For the non-convex natural gas flow equation, piecewise linearization techniques are used to deal with it. However, piecewise linearization models limit gas flow directions [21; 22]. Some researchers have proposed the mixed integer second-order cone relaxation technique to convexize the equation [17; 23; 24], but a large number of integer variables make the model complicated. The above research works have certain limitations on the treatment of non-convex terms in the OMEF problem. Aiming at the above problems, this paper proposes a decentralized optimization algorithm for OMEF based on PCCP-ADMM, which has the following main novelty and contribution. * The non-convex OMEF problem is transformed into a convex optimization problem by PCCP. For the power system model, based on the paper [25], this paper studies the method of convexity of the second-order power flow equation in the multi-period OMEF model, which effectively retains all the electrical quantities and is suitable for any topology. For the natural gas system model considering the dynamic characteristics, the positive and negative natural gas flow are introduced into the model, and the PCCP method is used to convexize the flow equation, which can efficiently solve the natural gas system model. * The decentralized optimization process is based on the obtained convex OMEF model. At each iteration of the PCCP, ADMM is used to establish and solve the decentralized OMEF problem of IEGS. During the solution process, each system only needs to exchange a small amount of boundary information, which achieves independent optimization and partition coordination. The solution process ensures the privacy and security of each system information. Finally, testing IEGS combined with an IEEE 39-bus power system and a Belgian 20-bus natural gas system is used to verify the proposed algorithm and can effectively solve the decentralized OMEF problem.
## 1 Introduction
The development of surgical guidelines and the increasing availability of clinical evidence on the efficacy of GTR have made this procedure popular for treating furcation involvement (Gottlow et al., 2018). Currently, GTR stands as the most commonly employed treatment measure for furcation involvement. Despite controversial evidence regarding its prognosis, previous studies have suggested that this procedure is effective in treating grade II furcation lesions in maxillary molars and grade III furcation lesions in mandibular molars (Gottlow et al., 2018; Gottlow et al., 2018). In these segmental foci, GTR consistently exhibited superior results when compared to open flap debridement, particularly in reducing open horizontal furcation depth, enhancing horizontal and vertical attachment levels, and decreasing pocket depth in mandibular or maxillary class II furcation involvement lesions (Gottlow et al., 2018). GTR has the capacity to induce significant tissue regeneration, even in intrabony defects, and the treatment prognosis often hinges on the anatomical morphology of the lesion, such as the presence of a residual bone wall. The regenerated tissue is notably influenced by the size of the initial defect, with deeper and narrower defects exhibiting higher levels of clinical attachment. Moreover, the angles between the bone wall and the base of the intrabony defects play a crucial role. Gentle angles necessitate lower degrees of bone filling compared to narrower and deeper defects, which, in turn, require proportionally greater bone regeneration (Gottlow et al., 2018). While numerous studies have compared graft and membrane types, few have focused on the morphology of the defect, and none have directly compared the amount of bone fill after GTR in intrabony defects and furcation involvement. Hence, the objective of this study is to compare the radiographic bone fill after GTR in mandibular and maxillary periodontal defects. Additionally, the study aims to assess the amount of radiographic bone fill after GTR in defects with distinct morphologic and etiologic characteristics, such as intrabony defects and trof the lesion. The study sample was then divided into four groups: maxillary truncation involvement, mandibular function involvement, maxillary intrabony defect, and mandibular intrabony defect. The amount of regenerated bone was measured using a radiograph and expressed as a proportion of the lesion depth.
# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
## 4 Experimental Verification and Analysis
### Experimental Results and Analysis  
#### 4.3.1 Results and Analysis on CWRU Dataset  
The experimental results on the CWRU dataset are shown in Figure 5. The average training time for the CDAN was 12.67 s, while the average training time for Models 1 to 4 was 30.65 s, 27.81 s, 19.27 s, and 16.89 s, respectively. The average accuracy of fault diagnosis for the comparison methods was 86.38%, 87.80%, 86.64%, and 91.36% respectively. The CDAN achieved an average fault diagnosis accuracy of 94.22%, which is more than 6% higher than Model 1, Model 2, and Model 3, and 2.86% higher than Model 4. Model 3 used an extractor to map features from both source and target domains, while the model proposed in this paper separately set different feature extractors for mapping laboratory samples and real fault samples. The CDAN achieved a fault diagnosis average accuracy 7.85% higher than Model 3, indicating that a model with two feature extractors performs significantly better in recognition performance than a single feature extractor.  
To better understand the cross-domain diagnostic performance of the CDAN, confusion matrices of the models under the A\(\rightarrow\)D operating conditions are presented to further elucidate the recognition results for specific fault patterns, as shown in Figure 6. It is  
Figure 5: Experimental results on the CWRU dataset.  
clear that CDAN can effectively detect the health status of bearings across six different cross-domain scenarios. The comparative results of the experiment show that the CDAN has good recognition ability for unlabeled bearing faults. Multiple sets of experimental data are superior to the compared methods, indicating that CDAN has better generalization performance and stability.  
To explore the advantages of using two types of feature extractors for mapping, the t-distributed Stochastic Neighborhood Embedding (t-SNE) method [34] was employed to visualize the features during the data transfer process. The feature visualization results using the CDAN method under experimental conditions A\(\rightarrow\)D are shown in Figure 7, while Figure 8 displays the feature visualization results using the Model 3 method under experimental conditions A\(\rightarrow\)D. A comparison between Figures 7 and 8 clearly shows that the clustering of samples from the same fault category in Figure 7 is more compact, with larger distances between fault category centers, indicating better performance compared to Figure 8. This
## 4 Experimental Results and Discussion
### LP Characters Recognition Results Analysis  
In this part, each character extracted from the license plate will be separately recognized. In total, 21717 characters of 36 (0-9, A-Z) classes having different length of each class are extracted in the previous part. The test dataset consists of different style characters with different background and foreground polarities are extracted from various VLP formats to evaluate the performance of proposed feature model under different conditions. As we have used SVM for classification, which is a supervised learning model, training images are required to get a decision boundary for performance assessment. Therefore, a database of 84058 characters of 37 (0-9, A-Z, EC) classes and almost 2250 characters of each class are used for training. One extra-class (EC) is also introduced to eliminate the falsely segmented characters (FS\({}_{\text{C}}\)). In order to support the adaptability constraint for recognition of multinational VLPs and to prove the effectiveness of the proposed framework, license plate characters from one country are gathered for training and made it suitable for the test data to have any background and foreground color and polarity. All training data consist of gray scale images to eliminate the effect of color and all images have dark foreground polarity to make it uniform. This approach will make this system more generic in nature to support multinational framework, as it is not feasible to gather training data from all targeted countries.  
Our proposed model contributes well at three different levels and achieved outstanding performance collectively for such a diversified data. The individual and collective performance of these levels is discussed in the next sections.
## 1 Introduction
This study aims to compare the energy use reduction when using several passive building design technologies, so as to understand how to use such technologies in the HHS climate regions, such as in Hong Kong. The study first searched for common and useful passive building design techniques in a similar climate via a literature review. Then, such techniques were compared based on a typical high-rise residential prototype building with a whole building simulation software for the Hong Kong climate. The study employs EnergyPlus, a widely-employed tool developed by by a team composed of the US Department of Energy (DOE), the US army construction engineering research laboratories (CERL), University of Illinois, Lawrence Berkeley National Laboratory and other institutions. This study compares the energy-saving effects of passive cooling techniques by discussing the annual consumption reduction and the hourly saving characteristics of the case with different passive design strategies. The findings will be helpful in the passive house and green building standard development in Hong Kong and contribute to further works to realize both energy efficiency and favorably built environments in residential buildings. A better understanding of passive design techniques in Hong Kong will guide the use of passive design technologies to reduce energy load. Moreover, new passive design methods and technologies emerged and were found to be helpful in other regions with similar climates.
## 3 Design of In-Situ Test
### Slt  
Three types of PHC piles were driven into different bearing soil layers, including coarse sand ((d)) and medium sand (c & c ). The distribution of the investigated PHC types is illustrated in Figure 1, and the characteristics of the PHC piles are presented in Table 3. The D-type pile with a length of 12.5 m was embedded in coarse sand (d). Both C-type and K-type PHC piles were penetrated into medium sand (c & c). The soil below the K-type PHC pile tip was hollowed out with a drill to obtain pure shaft friction capacity, as shown in Figure 4(Aa). The test pile installation process is presented in Figure 4(Ab,Ac). Before SLTs, the pipe heads were pre-treated to protect the pile head from damage in the SLT. For C-type and D-type PHC pile pipes, the pile heads were cut, and new reinforced concrete pile heads with a diameter of 600 mm were fabricated, as shown in Figure 4B. For K-type PHC pile pipes, the pile heads were strengthened by the bind with hoops because the counterforce could only be provided by skin friction, as shown in Figure 4C.  
Figure 5 shows the schematic figure of the SLT. The SLT was performed with a loading method for individual piles step-by-step, in line with a local pile testing specification [54]. The load applied on the pile head was increased by 10% of the design load, and the load of the first step was twice that of the other steps. Settlement of pile head was measured and recorded at intervals of 5 min, 10 min, 15 min, 15 min, 15 min, 30 min and so on after each loading step until the settlement is smaller than 0.1 mm within two hours. The corresponding pile head settlement was monitored and recorded with four digital dial gauges fixed to the reference beams. Most of the investigated piles in this study failed before reaching the maximum load, and the pile capacity could be determined from load-displacement curves obtained from SLTs.  
Figure 3: (**a**) Schematic of static cone penetration test: (**b**) Schematic of double bridge probe; (**c**) Reaction system; (**d**) Data acquisition system.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
\begin{tabular}{c} \textless{} \textless{} \textgreater{} \\ medium sand \\ \end{tabular} & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of investigated pre-stressed high-strength concrete (PHC) piles.
## 3 Results and Discussion
### Sensory Parameters as Discriminators of Cold-Pressed Camelina Oils
The groups obtained by HCA demonstrated that a ratio of n-6:n-3 can contribute more to the hydrolytic status than the oxidative status of CPCO samples, whereas oxidative stability determined by IP impacted OFI more than OSQ (Figure 2b). Therefore, there were positive (\(p\) = 0.001752-0.2041) relationships between IP and OFI (r = 0.6195), OSQ (r = 0.5028), P content (r = 0.7149) and n-6:n-3 ratio (r = 0.9091) (Figure 3). Figure 2: Dendograms of hierarchical cluster analysis for (**a**) the investigated cold-pressed amelina oil (CPCO) samples and (**b**) the studied variables. However, PV and \(\Sigma\)4 PAHs variables created the inter-cluster because these results were positively correlated (r = 0.4471, \(p\) = 0.2667) (Figures 2b and 3). Interestingly, two sensory characteristics of CPCO samples had a positive (\(p\) = 0.08451-0.2255) association with P level (r = 0.4980 and r = 0.4829 for OSQ--P and OFI--P, respectively) and a ratio of n-6:n-3 (r = 0.5460 and r = 0.6445 for OSQ--n:6:n-3 and OFI--n:6:n-3, respectively). A significant positive correlation between OSQ and OFI as was found (r = 0.9423, \(p\) = 0.000460), whereas these two sensory parameters of investigated oils were negatively associated with the content of secondary oxidation products (r = \(-\)0.8906, \(p\) = 0.003009 and r = \(-\)0.8668, \(p\) = 0.005335 for OSQ--AnV and OFI--AnV, respectively). Additionally, correlation matrix results indicated significant negative correlations between MUFA and PUFA (r = \(-\)0.9485, \(p\) = 0.000329) and SAFA (r = \(-\)0.7742, \(p\) = 0.02413) determined in the studied oils (Figure 3). Furthermore, IP was negatively correlated to PUFA (r = \(-\)0.6883, \(p\) = 0.05911) and AnV (r = \(-\)0.6239, \(p\) = 0.09834). Meanwhile, P level and concentrations of \(\Sigma\)4 PAHs (r = \(-\)0.6220), secondary oxidation products (r = \(-\)0.5429), and PUFA (r = \(-\)0.5277) also showed strong negative relationships (\(p\) = 0.09961-0.1789). In contrast, there was a significant positive correlation between the P amount and n-6 to n-3 ratio in oils (r = 0.7399, \(p\) = 0.03588). The CPCO samples with high acceptance sensory quality regarding OSQ and OFI showed high values of n-6:n-3, IP, and P but low amounts of undesirable components such as secondary oxidation products and PAH contaminants.
# Design of Advanced Human-Robot Collaborative Cells for Personalized Human-Robot Collaborations
## 4 User-Aware Collaboration
### Integrated Task and Motion Planning
#### 4.2.2 Proactive Motion Planner  
The proactive motion planner solves the motion planning problem related to each basic movement of a task, as decomposed by the Task decomposition module. The term proactive distinguishes this module from the reactive speed modulation module. The proactive planner is intended to find a collision-free trajectory according to a prediction of the user's actions and movements. Once a trajectory has been found, its execution starts, and the reactive layer monitors and adjusts it according to real-time scene information. Moreover, the path is sent to the Human-System Interaction Module for visualization so that the user will foresee the robot's movement in the short run.  
The proactive trajectory planner has been implemented by using the standard path-velocity decomposition paradigm, in which a path planner finds a collision-free path from a start to a goal state, and a path parametrization algorithm (e.g., TOPP [41]) optimizes the velocity profile along the path. Regarding path planners, sampling-based algorithms are preferred, for they can efficiently deal with high-dimensional search space [42].  
User awareness is embedded in the path planner using a cost function that depends on the human state. The typical approach minimizes a weighted sum of an efficiency term (e.g., the path length) and user-aware terms, such as human-robot distance [43], trajectory repeatability [44], or human visibility [45].
# A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
## 2 Methods
### Positioning Model in 5g
#### 2.2.2 TOA-Based Distance Estimation  
The distance between two nodes depends on the traveling time and the traveling speed of the signal transmitted from one node to the other. Here, we assume that a timestamp \(t_{0,ji}\) is attached to the signal from node \(j\) to node \(i\) before node \(j\) transmits it, i.e., the departure time of the signal can be obtained. The arrival time at node \(i\) is shown as  
\[t_{ij}=t_{0,ji}+\frac{d_{ij}}{c}+\delta_{ij} \tag{2}\]  
where \(d_{ij}\) is the true distance between node \(i\) and \(j\), and \(\delta_{ij}\) is a zero-mean random variable with a variance of \(\sigma_{t}^{2}\), which is caused by the measurement error. To be general, we assume that \(\sigma_{t}^{2}\) for different nodes is the same and obeys the Gaussian distribution [23]. Besides, the signal travels at the speed of light, which is denoted as \(c\). Denote \(\Delta t_{ij}\) as the traveling time of the positioning signal between node \(i\) and \(j\) with error \(\delta_{ij}\) based on the above analysis. Then, we have  
\[\Delta t_{ij}=t_{ij}-t_{0,ji}=\frac{d_{ij}}{c}+\delta_{ij} \tag{3}\]  
\[\Delta t_{ij}\sim\mathcal{N}\Big{(}\frac{d_{ij}}{c},\sigma_{t}^{2}\Big{)} \tag{4}\]  
According to the above analysis, the estimated distance between node \(i\) and \(j\) is expressed as  
\[\begin{array}{ll}\dot{d}_{ij}&=c\Delta t_{ij}\\ &=c\Big{(}\frac{d_{ij}}{c}+\delta_{ij}\Big{)}\\ &=d_{ij}+c\delta_{ij}\end{array} \tag{5}\]  
Obviously, the estimated distance between node \(i\) and \(j\) consists of the true distance between the two nodes and an additional error from the measurement error.