esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Part Identification Experiment
#### 4.2.2 Identification Experiment of Occluded Parts  
Images of the stacked sheet metal parts are acquired for experiments. The edge information of the part is detected using the Canny operator, as shown in Figure 16. In contour detection, it is impossible to effectively segment the overlapping areas of stacked parts. Therefore, multiple part contours of stacked parts are identified. As illustrated in Figure 17b, the part contours are retrieved for segmentation, and segment descriptors are generated. Utilizing a template-matching database of normalized Fourier descriptors, similar contour segments of the parts are identified. Figure 17 displays the identification outcomes (c).  
Different degrees of occlusion occur in randomly stacked and placed parts. To validate the robustness of the occlusion target recognition approach, one thousand photos of parts are gathered to generate detection data. The data collected contain photos of sections with occlusion rates of 10%, 20%, 30%, 40%, and 50% at a ratio of 1:1:1:1:1. Experiments were conducted on the data set, and the results are shown in Figure 18.  
Figure 18 shows the target recognition accuracies of algorithms with occlusion rates ranging from 10% to 50%. The algorithm shows in the case of local occlusion: the higher the occlusion rate, the fewer the contour features and the lower the accuracy rate. The recognition algorithm in this paper has an accuracy of higher than 90% at occlusion rates of less than 40% and an average detection accuracy of 93% at multiple occlusion rates. The method has a good recognition effect.  
Figure 16: Image of parts’ edges detected by the Canny algorithm: (a) stacked sheet metal parts; (b) contours of the part.  
Figure 15: Images depicting the QR code structure extraction process: (a) QR code, (b) result of grid segmentation, and (c) extracted structure of the QR code.
## 5 Discussion
### Compliance and Pertinent Legislation
#### 5.1.3 Data Protection Acts  
Not all states have enacted data protection laws, and some have yet to take effect, as seen in Table 6. These data may also be geographically visualized in Figure 14, helping illustrate the varying degrees of legislative coverage across different States.  
It is important to highlight that the dataset under examination covers the period between 2005 and 2015 and that the first data protection law took effect in 2020. Therefore, none of the breached data analyzed in this paper were subject to regulation by a data protection law. Another observation is that New York, the second-most-breached State in the dataset, has not enacted any data protection law. In contrast, Montana did not have any reported breach in the analyzed data and has enacted the Montana Consumer Data Privacy Act (MTCDPA). However, it has not yet commenced its effects.  
Figure 14: US States that have enacted a data protection law. None of these acts were in effect in the dataset time range.  
Figure 13: Breach notification statuses in U.S. States. The color map indicates the period in which the notification breach law took effect, and the pie charts indicate the distribution of breaches that occurred before (black) and after (green) the law took effect.  
Elements of the California Consumer Privacy Act (CCPA) include the rights of the data subjects, encompassing the right to be informed about the nature of collected data and its selling practices, to request data deletion, to opt out of data sales, to access their data, and to not be discriminated against in service and pricing when exercising their privacy rights.
## 2 Materials and Methods
### Diagnosis Methodology  
The proposed data processing and diagnosis mechanism aims to detect the heartwood MC level non-destructively at the point of measurement under the AE signal and environment parameters. Thus, to design the diagnosis methodology, tests over different types of tree trunks have been carried out by means of specific laboratory MC measuring instruments. The objective is to characterize the relationship among the AE signal feature, environmental temperature, and humidity data under different MC levels. The whole detection operates in the following two processes:  
1. Training process: In this stage, we first build up a dataset containing both AE and environment parameters, which are induced by the MC variations for 12 months. Note that target trees are required to distribute a safe circumstance for obtaining reliable features. Then, we select the optimal AE features and environmental data under the greatest correlation with the MC level by utilizing a dimension reduction algorithm as the next training input for ease of maximizing the expression from original data information and improving the hardware calculation efficiency. Afterward, a delicately designed machine-learning algorithm is adopted to fit the characteristics of sampled data, which would continuously vary with time. Finally, this diagnostic model should be carried out to detect the living tree heartwood's MC level.
2. Detection process: In this stage, we estimate the MC level of field woods based on the aforementioned achievement. Acquired parameters will be pre-processed and normalized. Next, it is input into an adaptive dynamic weighting module to calculate the individual influence of each feature on the MC level. Then, we combine the results in a weight fusion algorithm and finally predict the testing trunk's MC. The overview of the diagnostic system is shown in Figure 5.  
Figure 4: The proposed monitoring system’s organizational structure.
## 2 Materials and Methods
### Material Characterization and Analytical Methods  
The concentration of RhB was measured by UV-vis spectrophotometer (Shimadzu UV2550, Kyoto, Japan) with absorbance at 554 nm. Scanning electron microscope (SEM, ZEISS Gemini 300, Jena, Germany) was used to investigate the morphology, coupled with Energy disperse spectrometer (EDS) mapping to determine the elemental compositions. The microstructure of the materials was characterized by transmission electron microscopy (TEM, FEI Tecnai 12, Hillsboro, OR, USA). The crystalline structure of the catalysts was determined by X-ray diffraction (XRD, Bruker D8 Adance, Heidelberg, Germany) with Cu K\(\alpha\) radiation. The states of elements and elemental valence of the material before and after the reaction were determined by X-ray photoelectron spectrometer (XPS, Thermo Scientific Nicolet iS5, Waltham, MA, USA). Electron paramagnetic resonance (EPR, Bruker EMXplus-6/1, Heidelberg, Germany) spectroscopy was employed to in detect the generated ROS. The Brunauer-Emmett-Teller (BET) specific surface area was measured through analyzing the N\({}_{2}\) adsorption-desorption isotherm (Micromerities ASAP 2460, Norcross, GA, USA). The total organic carbon (TOC) of the solution was tested on a Thermo Scientific Vario TOC (Waltham, MA, USA).
## 2 Materials and Methods
### Design Procedure of the Proposed Antenna  
A monopole antenna is a type of radio frequency (RF) antenna that consists of a single radiating element. It is widely used in various applications, including radio broadcasting, wireless communication, and more. The length of a monopole antenna is typically defined as one-quarter (1/4) of the wavelength of the radio signal it is designed to transmit or receive. This length is chosen to ensure efficient radiation and reception of electromagnetic waves. The formula to calculate the length (\(l_{m}\)) of a monopole antenna is given by:  
\[l_{m}=\frac{c}{4f} \tag{5}\]  
where:  
\(l_{m}\) is the length of the monopole antenna (quarter wavelength) in meters.  
\(c\) is the speed of light in a vacuum, approximately 3 \(\times\) 10\({}^{8}\) m per second.  
\(f\) is the frequency of the radio signal in hertz (Hz).  
\[l_{m\_upper}=\frac{3\times 10^{8}}{4\times 2.4\times 10^{9}}=31.25\text{mm }(\text{for2.4 GHz})\]  
\[l_{m\_higher}=\frac{3\times 10^{8}}{4\times 3.5\times 10^{9}}=21.43\text{mm }(\text{for3.5 GHz})\]  
\[l_{m\_middle}\ =\frac{3\times 10^{8}}{4\times 3\times 10^{9}}=25\text{mm }(\text{for3 GHz})\]  
It is clear from the above calculations that monopole antenna lengths for different frequencies (2.4 GHz, 3.5 GHz, and 3 GHz) are varied, and the length is decreased when the resonance frequency is increased. However, in this study, the use of a ring resonator aims to reduce the antenna dimensions. Ring resonators are common components in RF and microwave engineering that can be used to create compact and efficient antenna designs. They are often used to enhance certain properties of antennas or to achieve specific resonant frequencies. By incorporating a ring resonator into the antenna design, the physical length of the antenna is potentially reduced while maintaining or enhancing its performance characteristics.
## 5 Object Detection Application Domains  
Object detection is applied in wide areas of CV, including defense (surveillance), iris recognition, face detection, human-computer interaction (HCI), robot vision, security, medical imaging, smart transportation, automated vehicle systems, image retrieval system, and machine inspection. For continuous video surveillance for a few hours, sensors generate petabytes of image data. The generated data is further reduced to geospatial data and then integrated with the other collected data to get a clear-cut picture of the current situation. One of the most important tasks involving object detection is to track vehicles/suspicious people from the collected raw data [88]. Crucial applications of object detection include detecting faulty electric wires, detecting unattended baggage, detecting driver drowsiness on highways, detecting vehicles parked in restricted areas, detecting objects present or coming onto the road (for self-driving vehicles), and also detecting stray animals present in industrial areas.  
All the above requirements for applications may vary according to the circumstance, and detection is performed either offline or online. Factors such as inter-class and intra-class variations, occlusions, rotation invariance, and multi-pose are also the main challenges in object detection.
## 4 Optimizations for Structural Parameters  
### Objective Function and Constraints  
In practical applications, the noise main frequency fs should be considered first and used as an equation constraint for design. The structural parameters are targeted to be designed according to the resonance frequency \(f_{s}\), and the mapping relationship between the structural parameters and \(f_{s}\) is completed by the BP network, which is \(f_{s}=g^{\text{BP}}\left(l_{1},l_{2},a\right)\). In this optimization case, the \(f_{s}\) is kept the same as that of the analytical case, which is 378.5 Hz.  
The ventilation capacity of the acoustic metamaterial labyrinth flat plate proposed in this paper is often the priority to be considered. It can be intuitively seen that the ventilation capacity \(\Omega\) is directly related to the parameters \(l_{1}\), \(l_{2}\), and \(a\). To optimize the ventilation capacity, \(l_{1}\) and \(l_{2}\) should be small enough, while the parameter \(a\) should be large enough. Thus the optimized objective function \(\Omega\) is defined as \(\Omega=\left(l_{1}+l_{2}\right)/a\). The convergence speed of the algorithm and the generation of optimal solutions are directly affected by the initial population. A randomly generated initial population can easily lead to falling into a local optimum. To avoid the algorithm's population diversity decreasing in subsequent iterations, and to improve the quality of the initial solution, the range of optimization objectives should be limited, as shown in Table 3. Thus, the geometric parameter optimization problem with inequality constraints and equational constraints can be expressed as  
\[\left\{\begin{array}{c}\min\,\Omega(l_{1},l_{2},a)\\ s.t.\,lb_{1}\leq l_{1}\leq ub_{1}\\ lb_{2}\leq l_{2}\leq ub_{2}\\ lb_{3}\leq a\leq ub_{3}\\ f_{s}=378.5\;\text{Hz}\end{array}\right. \tag{15}\]  
According to the parametric constraints, the relationship between the resonant frequency and \(l_{1}\), and \(l_{2}\) is obtained, as shown in Figure 10a when \(a=20\) mm. The resonant frequency tends to decrease with increasing parameters \(l_{1}\) and \(l_{2}\), and the effect of \(l_{1}\) is greater in the set parameter range. The variation in ventilation capacity \(\Omega\) with \(l_{1}\) and \(l_{2}\) is shown in Figure 10b, and the value of \(\Omega\) increases as \(l_{1}\) and \(l_{2}\) increase.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(ub_{1}\) (mm) & \(ub_{2}\) (mm) & \(ub_{3}\) (mm) & \(lb_{1}\) (mm) & \(lb_{2}\) (mm) & \(lb_{2}\) (mm) \\ \hline
220 & 120 & 35 & 180 & 80 & 5 \\ \hline \end{tabular}
\end{table}
Table 3: Constrained range of optimized parameters.  
Figure 9: Absorption, transmission, and reflection coefficients of LC.
# An Improved Dictionary-Based Method for Gas Identification with Electronic Nose
## 4 Conclusions and Outlook  
In this article, a novel dictionary learning method was proposed for gas classification. The comparison results between the improved algorithm and different classification algorithms demonstrated that the proposed algorithm could achieve a high recognition accuracy and a relatively lower running time. In the training phase, the algorithm performed a multi-column atomic update, which reduced the running time better than the traditional single-column atomic methods. Meanwhile, in the testing phase, a novel classification model was proposed to effectively increase the discriminative power and the average recognition accuracy of the whole algorithm. When the dimension of the sample was larger than 10, the average recognition rate of the algorithm was maintained above 92%, and the average training time was controlled within 4 s. As a result, it is a feasible means to detect VOC gases in the environmental industry, chemical industry, and petroleum industry.  
However, there are still many problems associated with its practical application. First, electronic noses are extremely sensitive to the environment, which results in issues such as the inability to collect reliable data. Furthermore, most sensors can induce aging over time. Therefore, the algorithm must be predictive. Another difficult challenge is how to obtain a good sample for practical applications. These problems have a significant impact on the evolution of electronic noses and will be an important direction for future work. With the rising number of researchers in the fields of materials science and data analysis, gas recognition based on e-nose technology might soon reach new heights.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 15: The comparison results between the improved method and other methods. (**a**) The residual of the KSVD-DL algorithm. (**b**) The residual of the improved algorithm.
## 3 Nonlinear FE Analysis  
### General Description  
From Section 2.3, it can be seen that the concave square frustum-shaped wet joint has superior shear capacity compared with the conventional wet joint. The finite element (FE) software ABAQUS/Standard was employed to conduct the numerical simulation of S-SWJ to further explore the shear capacity of the concave square frustum-shaped wet joint. A 3D nonlinear FE model was adopted to accurately simulate the behavior of S-SWJ, and the established FE method was verified by the experimental data.  
As shown in Figure 7, a 3D FE model of S-SWJ was established, and the geometric parameters of the FE model were kept the same as those in the test. The wet joint was sandwiched between two bridge panels, and the interaction between the wet joint and bridge panels is described in detail in the following subsection. The steel bars as reinforcement in specimen were embedded within the concrete using the embedded element technique available in ABAQUS. The bottom surface of the supports was coupled to a reference point, and the two supports were fixed by constraining all degrees of freedom of the reference point. To make the two steel bars on the top of S-SWJ move downward, the upper surface of the two steel bars was coupled with a reference point, and the load was applied on the reference point. The supports were connected to the bridge panels by a tie constraint, which was also defined between the two steel bars and the wet joint. Solid elements of type C3D8R were employed to model the concrete, and truss elements of type T3D2 were used to model the steel bars embedded in concrete. A mesh size of 25 mm was used for the concrete and the reinforced steel bars to achieve accurate results and high computational efficiency. The constitutive models for concrete and steel bars, and the concrete-concrete interface (Figure 7) behavior are also important to obtain accurate results, and the details are presented as follows.  
Figure 6: Load–displacement curves of S-SWJ and S-CWJ.  
Figure 7: Finite element (FE) model of S-SWJ.
## 2 Materials and Methods
### Characterization of Physical Properties of Spray-Dried Anthraquinone-Loaded Casein Micelles
#### 2.3.3 Color Measurements  
Spray drying might cause thermal degradation of casein micelles. Hence, color measurements were used to evaluate the degree of browning level, as a correlation has been reported between the color intensity and formation of advanced Maillard reaction products during spray-drying [25]. So, the color measurements were carried out using the Chroma Meter CR400 (Konica Minolta Sensing Inc., Osaka, Japan), employing the parameters \(L^{*}\) (lightness), \(a^{*}\) (redness), \(b^{*}\) yellowness) defined in the CIE Lab system as per method given by [PERSON] et al. [26], [27]. Where \(L^{*}\) is the lightness coordinate ranging from \(L^{*}\) = 0 (no reflection for black) to \(L^{*}\) = 100 (perfect diffuse reflection for white), describes the light-reflecting of the transmittance capacity of an object. \(A^{*}\) represents red (\(-\)100) to green (100) and \(b^{*}\) represents yellow (\(-\)100) to blue (100) components. All measurements were taken as an average of at least three points on the center and the periphery of each powder layer 1 cm high. The standard white tile's lightness, redness and yellowness were used as reference (\(L_{s}\) 97.14, \(a_{s}\) 0.075, \(b_{s}\) 2.23). The following equation calculated the total color difference:  
\[\Delta E=[(L^{*}s-L^{*}c)2+(a^{*}s-ac^{*})2+(b^{*}s-bc^{*})2]1/2 \tag{2}\]  
where s and c stand for standard and spray-dried casein micelles samples, respectively.  
The chroma that defines the degree of color saturation and hie angle (\(H^{*}\)), derived from the two coordinates \(a^{*}\) and \(b^{*}\) were determined by the following equations:  
\[C^{*}=(a^{*2}+b^{*2})^{1/2} \tag{3}\]  
\[H=\tan^{-1}\left(b^{*}/a^{*}\right) \tag{4}\]
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 4 Case Study
### Implementation
#### 4.2.4 Simulating Expected Revenue Using Monte Carlo Simulation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Rule**} & \multirow{2}{*}{**Consignees: Shippers**} & \multirow{2}{*}{**F(x)\({}_{C/G\ Ratio}\)**} & \multirow{2}{*}{**F(x)\({}_{R}\)**} & \multicolumn{2}{c}{**Fitness of**} & \multicolumn{2}{c}{**Fitness of**} & \multirow{2}{*}{**Accuracy**} \\  & & & & **Association Model** & **Classification Model** & & **Revenue (THB)** \\ \cline{1-1} \cline{6-6} \cline{6-6}  & & & & **SA** & **CA** & **RC** & **AC** & \\ \hline
1 & 1:13 & & 3884 & & 1 & & 1038 \\ \cline{1-1} \cline{6-6}
7 & 1:3 & Triang & 11,429 & & & 1 & & 3063 \\ \cline{1-1} \cline{6-6}  & & & (4.05, 6.5015) & Pareto & 0.09 & 0.80 & & 0.624 & \multirow{4}{*}{**Accuracy**} \\ \cline{1-1} \cline{6-6}
4 & 6:40 & & (N = 2400) & (0.9681, 1076) & & & 0.60 & & 12,266 \\ \cline{1-1} \cline{6-6}  & & & (N = 6) & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 8: Probability distribution functions of input parameters. Figure 8: Segmentation of consignees according to Rules 1, 7, and 4.  
likely expected revenue was 12,266 THB, and a range of 334-22,200 THB/year. Based on these rules, revenue is expected to grow by 16,368 THB/year, equivalent to a 36% revenue increase from eight consignees. Even more revenue can be generated by considering all rules and all product categories and creating classification rules for suggesting supply-demand relationships.
###### Abstract  
Surface reconstruction for freehand 3D ultrasound is used to provide 3D visualization of a VOI (volume of interest) during image-guided tumor ablation surgery. This is a challenge because the recorded 2D B-scans are not only sparse but also non-parallel. To solve this issue, we established a framework to reconstruct the surface of freehand 3D ultrasound imaging in 2011. The key technique for surface reconstruction in that framework is based on variational interpolation presented by [PERSON] for shape transformation and is named Variational Surface Reconstruction (VSR). The main goal of this paper is to evaluate the quality of surface reconstructions, especially when the input data are extremely sparse point clouds from freehand 3D ultrasound imaging, using four methods: Ball Pivoting, Power Crust, Poisson, and VSR. Four experiments are conducted, and quantitative metrics, such as the Hausdorff distance, are introduced for quantitative assessment. The experiment results show that the performance of the proposed VSR method is the best of the four methods at reconstructing surface from sparse data. The VSR method can produce a close approximation to the original surface from as few as two contours, whereas the other three methods fail to do so. The experiment results also illustrate that the reproducibility of the VSR method is the best of the four methods.  
freehand 3D ultrasound; surface reconstruction; variational surface reconstruction; ball pivoting; Power Crust; Poisson reconstruction; Hausdorff distance +
Footnote †: journal: Applied Physics Letters
# Route Prefix Caching Using Bloom Filters in Named Data Networking
###### Abstract  
This paper proposes an elaborate route prefix caching scheme for fast packet forwarding in named data networking (NDN) which is a next-generation Internet structure. The name lookup is a crucial function of the NDN router, which delivers a packet based on its name rather than IP address. It carries out a complex process to find the longest matching prefix for the content name. Even the size of a name prefix is variable and unbounded; thus, the name lookup is to be more complicated and time-consuming. The name lookup can be sped up by using route prefix caching, but it may cause a problem when non-leaf prefixes are cached. The proposed prefix caching scheme can cache non-leaf prefixes, as well as leaf prefixes, without incurring any problem. For this purpose, a Bloom filter is kept for each prefix. The Bloom filter, which is widely used for checking membership, is utilized to indicate the branch information of a non-leaf prefix. The experimental result shows that the proposed caching scheme achieves a much higher hit ratio than other caching schemes. Furthermore, how much the parameters of the Bloom filter affect the cache miss count is quantitatively evaluated. The best performance can be achieved with merely 8-bit Bloom filters and two hash functions.  
prefix caching; Bloom filter; named data networking; name lookup; routing table +
Footnote †: journal: Applied