esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Objectives  
This study focuses on 12 major urban parks located on the Macau Peninsula. An innovative evaluation framework for Cultural Ecosystem Services (CES) is proposed and developed, integrating social media data with Natural Language Processing (NLP) techniques. Emotional expressions and perceptions of cultural values are analyzed from User-Generated Content (UGC). Based on this analysis, the framework quantitatively evaluates the characteristics and spatial distribution patterns of CES in urban parks, thereby revealing how the public perceives and uses these cultural services. The innovative contributions of this study are reflected in two main aspects:  
1. Methodological innovation: A data-driven analytical approach is adopted, integrating social media data, NLP techniques, and spatial analysis methods. This approach constructs a multidimensional CES evaluation framework specifically suited for urban green spaces. This framework requires no predefined assumptions and can flexibly extract patterns from complex multidimensional data. It systematically identifies emotional characteristics, value distribution, and spatial heterogeneity of CES in Macau's urban parks, thereby providing a novel perspective for exploring CES dynamics in complex urban environments.
2. Practical application value: This study provides practical tools and scientific evidence for planning urban green spaces and protecting cultural heritage in Macau. It addresses the research gap regarding CES evaluation in Macau's parks and serves as a reference model for CES studies in other cities.  
Specifically, this study aims to answer the following research questions:  
RQ1: How can social media data be employed to quantitatively evaluate CES values in urban parks?  
RQ2: Do Macau's urban parks show significant differences across various CES dimensions?  
RQ3: How do users' emotional evaluations of CES reflect their spatial distribution characteristics and usage preferences?  
A CES evaluation framework was developed based on social media data. This framework incorporated analyses of emotional expressions and perceived cultural values extracted from user-generated content (UGC). Additionally, spatial analytical methods were applied to quantitatively explore the spatial characteristics of CES within Macau's urban parks. This approach fills existing research gaps in assessing CES in Macau's urban parks. Furthermore, it provides scientific evidence for urban green space planning and cultural heritage conservation.
## 6 Conclusions  
Marine MLH detection along the ship's route is achieved and demonstrated by using a ship-borne CDWL. Before the ship-borne experiment, ground-based experiments are conducted at both inland and marine sites. The optimum TKEDR threshold for retrieving inland and marine MLHs is determined as a constant value of \(10^{-4}\) m\({}^{2}\) s\({}^{-3}\). The reliability of the constant threshold is then validated at the marine site. During the ground-based observation, the MLH diurnal variation at the marine site is found to be smaller than that at the inland sites in the case of clear day regardless of MLH maximum. In the future, the seasonal comparison of MLH maximums over the inland and marine sites is expected to be performed after long-term observation. Furthermore, using the ship-borne marine MLH detection here, it will be possible to evaluate and improve marine MLH simulation.  
Conceptualization, H.X.; methodology, L.W.; software, L.W.; formal analysis, H.X. and L.W.; investigation, L.W.; resources, J.Y., Y.W. and L.Z.; data curation, L.W.; writing--original draft preparation, L.W.; writing--review and editing, L.Z.; visualization, L.W.; supervision, H.X. All authors have read and agreed to the published version of the manuscript.  
Figure 6: **(a)** Horizontal wind speed, **(b)** horizontal wind direction, **(c)** vertical wind speed, **(d)** leg(TKEDR) and its MLH retrieval result measured by ship-borne CDWL along the ship’s route in the Sea. Pink circle denotes MLH\({}_{\text{TKEDR}}\).  
Funding: This research received no external funding. Data Availability Statement: Data underlying the results presented in this Technical Note are not publicly available at this time but may be obtained from the authors upon reasonable requirement. Conflicts of Interest: The authors declare no conflict of interest.
# Performance Evaluation of Silane in Concrete Bridge Decks Using Transmission X-ray Microscopy
## 3 Results and Discussion
### Cracks  
Figure 6 demonstrates horizontal concentration profiles with low values for silane-treated samples. For a few samples, the concentration profiles of the treated silane samples began to increase after depths of 2 mm. Two examples are shown in Figure 8. This means that, while silane was effective in protecting the first 2 mm, the salt solution was getting past the silane coating. This may have been caused by a crack in the silane coating or nonuniform performance by the silane.  
Figure 8: Examples of two cracked samples (**a**) concentration profiles in sample 2; (**b**) concentration profiles in sample 13.  
To learn more, Sample 13 was investigated in more detail with XCT to investigate local abnormalities that might have allowed the solution to pass the surface coating. Investigations showed a crack in the red area of Figures 9 and 10. This crack seems to have propagated from the surface to a depth of \(\sim\)6.3 mm. This crack provided a path for the solution to penetrate beneath the coating. After the solution moved past the coating, the solution could diffuse through the paste and cause an increase in the iodide concentration. This shows that the cracks could compromise the silane coatings. This observation of how cracks impact the performance is not well documented in the literature. The proposed method provides a systematic process to investigate this phenomenon. The impact of crack width on the long-term performance of silane will be the subject of future work.  
Figure 10: The 3D observation of a microcrack on the aggregate in one of the silane samples.  
Figure 9: The 2D observation of a microcrack on an aggregate in one of the silane samples (yellow lines show the cutoff region in the 3D image shown in Figure 10).
# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 4 Machine Learning
### Classification Methods
#### 4.1.3 K-Nearest Neighbor Method  
The k-nearest neighbor method (KNN method) is a classification method that does not produce a model [43]. This method is also known as a \"lazy\" algorithm because the class is predicted at the moment of introducing a new sample (instance) by calculating the distance from the new sample and other samples in a data set in \(n\)-dimensional space, where \(n\) is the number of variables. After calculating all distances, only \(k\)-nearest neighbors are considered in deciding which class the new sample belongs to by majority voting. The most frequently used distance metric in this method is Euclidean distance. An advantage of the KNN method is its easy implementation, as there is no training period. A disadvantage is its slow performance with large data sets and data sets with high dimensionality.
# A Novel Reduction Circuit Based on Binary Tree Path Partition on FPGAs
## 4 Binary Tree Path Partition  
### An Intuition  
For \(m\) accumulation tasks \(y_{a}=\sum_{i=0}^{n_{0}-1}a_{i},y_{b}=\sum_{i=0}^{n_{1}-1}b_{i},\cdots\), the idea of the BTPP is to divide the total \(\sum_{i=0}^{m-1}n_{i}\) elements in units of \(p\) (\(p\) is the pipeline stage of the adder). As shown in Figure 3a, the calculation process of \(y_{a}\) is divided into \(k\) iterations, where each iteration processes \(p\) data (\(p=8\)). When the data processed by an iteration is less than \(p\), 0 needs to be complemented, such as iteration \(k-1\). However, if \(p\) is large, the way of complementing 0 will cause performance degradation. To further improve performance, we use path partition, as shown in Figure 3b. According to the input data, the system realizes path partition by changing the connections between nodes. This means that, when the number of elements \(n_{i}\) in a single set is not an integer multiple of \(p\), the system can still provide high performance. For \(p\) operations in an iteration, if we arrange their execution time reasonably, it can be implemented using a \(p\)-stage pipeline adder. That is to say, the binary tree included in the iteration is the way BTPP processes data, but there is no binary tree in the hardware implementation of BTPP. BTPP only uses one reduction operator, which can effectively reduce area consumption. In the initial situation, the structure of different iterations is the same, so we only need to use the circuit to achieve the function of one iteration, as shown in Figure 3c.  
It can be seen from Figure 3 that the reduction circuit designed with this method has the ability to handle multiple data sets. The latter data sets have no effect on the previous data sets. For example, as shown in Figure 3b, regardless of the existence of data set \(B\), the path partition result of data set \(A\) does not change. Therefore, it does not matter whether a single data set or multiple data sets are handled. In addition, it can handle data sets with any number of elements. When the number of elements is greater than \(p\), these elements are calculated through multiple iterations. When the number of elements is less than \(p\), the path partition allows elements of different data sets to exist in the same iteration.
# Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
## 2 Materials and Methods
### Method
#### 2.3.3. Colorization  
Like other learning-based approaches, this step consists of two components: (1) a training component and (2) a prediction component, which are described below.  
In the training component, image colorization is formulated as a regression problem and is solved using RF. The training data employ seven features of the input grayscale image corresponding to the unchanged regions extracted from the preprocessing step. At this time, these features are trained with the a and b components of the reference color image at the same pixel location. In other words, rather than establishing the color relationships between the L component and the a and b components of the reference color image like other colorization methods do [15; 18; 19; 23], this study establishes color relationships directly between the input grayscale image and the a and b components of the reference color image. Then, the RF regression parameters--mtry and ntree values--are defined. The mtry value is generally set to one-third of the number of features in the regression problem due to computational advantages, but, in this study, the total number of features is utilized, as in the original classification and regression trees procedure, since the number of features is not large enough to affect the computational time [32]. Furthermore, the ntree value is set to 32, which takes into account the performance and training time of the RF regression [35].  
The prediction portion of this step uses the RF regression obtained from the training component to predict the colors for the a and b components of the input grayscale image. Then, the input grayscale image is used as the L component, and it is combined with the predicted a and b components. Finally, the Lab image is converted to the RGB color space.
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 5 Software Interface
### High-Level Programming Interface
#### 5.1.2 Mapping Goroutines on DD-Threads
To adhere with the data-driven PXM, the actual read requires specifying the offset of the target location within the frame. Write operations are mapped into corresponding write (chID, TID, offset, data) operations. Similarly to the case of reads, the compiler substitutes the channel name with the tuple formed by the channel identifier (chID) and the DD-Thread identifier (TID). The compilation phase ends with all the information gathered except the actual identifier of the PEs that will execute the scheduled threads, while the mapping table can be discarded. Missing information (i.e., the identifier of the PEs) is acquired at run-time, every time the get_chID(), get_tID(), schedule(), read(), and write() functions are executed. Specifically, channel and thread identifiers are combined to select the target PE. The application entry point is represented by the instantiation of the _main_ thread. Without loss of generality, PE 0 within the VT is always selected for the execution of the main thread. Instead, scheduling the other threads requires to properly combine the chID and the TID to obtain the unique identifier (i.e., \(peID_{dst}\)) of the PE that will execute the thread. To this end, we allow to use an effective hash function, as follows:  
\[H(\cdot)\rightarrow\{peID_{dst}=(chID\oplus TID)\ mod_{K}\}.\]  
The \(\oplus\) is the concatenation operator, and the \(K\) is the total number of available PEs in the VT. At this point, it is possible to transfer the execution context (i.e., the \(TID\), the \(chID\), the channel size, and the pointer to the thread code) to the destination PE (i.e., \(peID_{dst}\)). In addition, read and write operations require determining the destination PE. Therefore, the same hash function is applied. In [31], the authors show a way to accelerate the generation of unique thread identifiers, along with an effective way for performing thread scheduling in hardware, by adding a simple extension on the router logic. Such mechanism can be easily incorporated into our design.
# An Updated Review: _Opuntia focus indica_ (OFI) Chemistry and Its Diverse Applications
## 2 Nutritional and Phytochemicals
### Nutritional Profile
#### 2.1.3 Fats  
The fatty acids are essential for human beings' health and are present in a lot of food stuff (plant-based and animal goods). Fatty acids help maintain good eye health, reduce age-related vision difficulties, lower joint pain, enhance joint flexibility, and are beneficial for brain function and growth. A general representation of fatty acids is reported in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fresh Weight** & **Dry Weight** & **References** \\ \hline Water & 88–95 &.... & \\ Ash & 1–2 & 19–23 & \\ Protein & 0.5–1 & 4–10 & [14; 17; 19; 21; 22; 23] \\ Lipids & 0.2 & 1–4 & \\ Carbohydrate & 3–7 & 64–71 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Opuntia_ cladodes chemical composition (g/100 g).  
Studies on the extracted lipids from the seed, pulp, and skin of OFI [33] reported that the maximum quantity of palmitic acid (C16:0) was found in cladode skin (20.76 g/100 g). A large quantity of oleic acid (C18:1) exists in pulp (23.26 g/100 g); the maximum amount of linoleic acid (C18:2) was found in pulp (48.86 g/100 g), while a quantity of 11.44 g/100 g of linolenic acid (C18:3) was found in skin (Table 3). Trace amounts of polyunsaturated fatty acids, eicosenoic acids, and eicosatetraenoic acids were observed in the skin, seed, and pulp, respectively. The unsaturated acids (Z)-octadec-9-enoic acid (C18:1) and cis, cis-9,12-Octadecadienoic acid (C18:2) represent 90% of all fatty acids calculated [31; 33].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Amino Acids** & **Cladodes** & **Seeds** & **Fruits** & **References** \\ \hline Glutamine & 36.12 & Trace & 12.59 & \\ Glutamic acid & 5.43 & 21.68 & 2.40 & \\ Arginine & 5.01 & 6.63 & 1.11 & \\ Leucine & 2.71 & 9.94 & 0.74 & \\ Lysine & 5.22 & 6.79 & 0.63 & \\ Isoleucine & 3.97 & 6.20 & 1.13 & \\ Phenylalanine & 3.55 & 5.25 & 0.85 & [31; 32] \\ Valine & 7.72 & 6.02 & 1.43 & \\ Proline & Trace & Trace & 46 & \\ Taurine & Trace & Trace & 15.79 & \\ Citrulline & Trace & Trace & 0.59 & \\ Glycine & Trace & Trace & 5.06 & \\ Ornithine & Trace & Trace & Trace & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The compositions of the amino acids content in OFI fruit, cladodes, and seeds in (g/100 g).  
Figure 3: General representation of (**A**) saturated and (**B**) un-saturated fatty acids.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Case Studies: Non-Optimal Results
#### 3.1.2 California Investor-Owned Utilities (IOUs)  
In 1976 the California Public Utilities Commission (CPUC) mandated that California Investor-owned utilities (IOUs) limit delivery voltage to residential and commercial customers to the range of 114-120 volts, effectively reducing the voltage bandwidth by half. The utilities complied and, through the end of 1978 it was estimated that more than 1 billion kWh (1 Million MWhs) were conserved through this practice [24].  
\begin{table}
\begin{tabular}{l r r r r r} \hline
**Utility** & **\#Cust** & **Savings** & **V\%** & **CVR\({}_{f}\)** & **Period** \\ \hline SCPUD & 290 k & 53,856 MWh \({}^{\ddagger}\) & 2.3\% & 0.7 & 1 year \\ IOU & \textgreater{}10 M & 1 M & – & – & 2 years \\ CFEC & 30 k & 20\% \({}^{\dagger}\) & \textless{}3\% & \textless{}0.75 & 1 month \\ CEC & 165 k & 1.7\% \({}^{\dagger}\) & 1.7\% & 1 & 10 years \\ JEA & 417 k & 65 MW \({}^{\dagger}\) & 5\% & – & one time \\ GP & 2.35 M & 264 MW \({}^{\dagger}\) & 2.4\% & – & on demand \\ \hline \end{tabular} \({}^{\dagger}\) peak only; \({}^{\ddagger}\) 6.15 MW avg.  
\end{table}
Table 1: Utility CVR Savings Summary.
# Time Domain NMR in Polymer Science: From the Laboratory to the Industry
## 3 Applications
### Discovery and Characterization of New Materials
An interesting correlation has been found out between the active molecules decay rates and the T\({}_{2}\) of a series of polyacrylates [55]. For the sensitizer molecule, the local rigidity that characterizes the polyacrylates is not sufficient to suppress its decay rate, because of inelastic processes, while an increase of mobility, reflecting on an increase of \({}^{1}\)H T\({}_{2}\), that happens in acrylates with long side chains, favors the decay rate of the emitter, increasing the overall efficiency of the device.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 5 Simulation Results
### Scenario Description  
To point out the benefits of an ADR-based attitude tracking controller, a comparison with respect to a quaternion tracking controller described in (17) is carried out:  
\[\Gamma_{c}=[(\boldsymbol{\omega}_{e}+\boldsymbol{\omega}_{r})^{\times}] \mathbf{J}(\boldsymbol{\omega}_{e}+\boldsymbol{\omega}_{r})+[\boldsymbol{ \omega}_{e}^{\times}]\boldsymbol{\omega}_{r}+\mathbf{R}^{T}(\tilde{\mathbf{q} })\dot{\boldsymbol{\omega}}_{d}-\tilde{\lambda}(\boldsymbol{\omega}_{e}+ \rho\tilde{\mathbf{q}}_{v}) \tag{17}\]  
with \(\tilde{\lambda}=\text{diag}(\tilde{\lambda}_{1},\tilde{\lambda}_{2},\tilde{ \lambda}_{3})\) and \(\rho=\text{diag}(\rho_{1},\rho_{2},\rho_{3})\) with \(\lambda_{i},\rho_{i}\in\mathbb{R}_{+}\). Note that (17) is composed of a feedforward compensation and a proportional and derivative feedback. The inertia matrix has been taken as \(\mathbf{J}=\text{diag}(1.09,1.10,2.11)\times 10^{-5}\) kg m\({}^{2}\), which is representative of the very small VTOL-UAV Crazyflie 2.0 [53]. According to physical parameters, the maximum torque that can be generated by the propulsion system is \(\tilde{\Gamma}=(9.6\ 9.6\ 4.9)^{T}\times 10^{-3}\) Nm. Then, the saturation functions for the proposed control are chosen as \(M_{12}=M_{22}=9.6\times 10^{-3}\) and \(M_{32}=4.9\times 10^{-3}\).  
The external disturbance torque is assumed to be a time-variant signal and a pulse signal actuating around the three principal axes; that is, \(\boldsymbol{\Gamma}_{d}=\boldsymbol{\Gamma}_{d}^{slow}+\boldsymbol{\Gamma}_{ d}^{pulse}\), with  
\[\boldsymbol{\Gamma}_{d}^{slow}=\begin{pmatrix}(1+\exp(-\sin(0.5t)\sin(0.3t))) \cos(0.1t)\\ -(1+\exp(-\sin(0.5t)\sin(0.3t)))\cos(0.2t)\\ \cos(0.3t)\end{pmatrix}\times 10^{-3}Nm\]  
and  
\[\boldsymbol{\Gamma}_{d}^{pulse}=7(\tilde{\Upsilon}(t-\tau_{1})\ \ -\tilde{ \Upsilon}(t-\tau_{2})\ \ \tilde{\Upsilon}(t-\tau_{3}))^{T}\times 10^{-3}Nm\]  
where \(\tilde{\Upsilon}(t)\) is a smooth, differentiable pulse function with unit amplitude and pulse width PW = 0.25 s. \(\tau_{1}=2.5\) s, \(\tau_{2}=5\) s, \(\tau_{3}=7.5\) s designate the instants when the  
Figure 2: ISS interpretation for the ADRC.  
pulse function takes place. The desired attitude is obtained by integrating the following kinematic equation  
\[\mathbf{\dot{q}}^{d}=\frac{1}{2}\binom{-\mathbf{q}_{0}^{dT}}{I_{3}q_{0}^{d}+[ \mathbf{q}_{v}^{d\times}]}\boldsymbol{\omega}_{d} \tag{18}\]  
with initial condition \(\mathbf{q}_{d}(t=0)=(1\;0\;0\;0)^{T}\). Moreover, the desired angular velocity \(\boldsymbol{\omega}_{d}\) is established as  
\[\boldsymbol{\omega}_{d}=(\sin(2t)\;\sin(0.5t)\;-\sin(0.2t))^{T}\times 10^{-1}\; \text{rad}/\text{s}\]  
The initial angular velocity and quaternion for the simulated quadrotor are \(\boldsymbol{\omega}(t=0)=(0\;0\;0)^{T}\) and \(\mathbf{q}(t=0)=(-0.36\;0.56\;-0.39\;-0.62)^{T}\), respectively.
# Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection
## 3 Validation Experiment  
In this study, the LMWT method proposed in this paper is used to test vibration signals under three conditions. These conditions include vibration signals under a simulated high noise interference environment, single fault signals in the Case Western Reserve University (CWRU) bearing dataset, and compound fault signals in the Paderborn University bearing dataset. In these experiments, the proposed method is used to process the vibration signals and compare them with the EWT method regarding their performance in fault detection, specifically comparing the LMWT method with the EWT method under different signal conditions to evaluate the practicality and effectiveness of the proposed method in bearing fault detection.