esa-sceva/satcom-chunk-collection huggingface dataset:
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 4 Numerical Examples
### Comparison with Previous Methods  
When the potential facility candidates are not given, the general strategy to determine the set of facility locations is the application of clustering-based algorithms. Among them, the K-means clustering algorithm is one of the most popular methods in handling continuous multi-facility location problems. However, the minimizer of the total weighted Manhattan distance is the weighted median rather than the weighted mean in each cluster. In this sense, the weighted median is used to update the cluster center in each of the iterations in the clustering-based algorithms [18]. In addition, [PERSON], [PERSON] [40] also developed a clustering-based genetic algorithm for the continuous multi-facility location problem considering the Manhattan distance. To illustrate the outperformance of the proposed approach, different methods are compared for Instances 24-27 in handling the continuous multi-facility location problems. Given different numbers of facilities, the comparison results including objective function values and the facility locations are presented in Figure 13 and Tables 6-9, respectively. Since different randomly generated initial solutions result in different clustering results, we repeat each simulation five times and pick up the best one among them. As shown in Figure 13, the performance of minimizing the total transportation cost using different meth  
Figure 13: Comparison of transportation costs using different methods for Instances 24–27. (**a**) Instance 24; (**b**) Instance 25; (**c**) Instance 26; (**d**) Instance 27.  
ods is presented. The proposed method outperforms other clustering-based algorithms. Besides, given any number of facilities from 2 to 12, the method proposed by [PERSON] [65] demonstrates better performance in reducing the total transportation cost compared with the k-means method and clustering-based genetic algorithm, since the weighted median is used to update the cluster center. Besides, from Tables 6-9, the facility locations obtained using the method proposed by [PERSON] [65] are closer to the globally optimal solution compared with the other two existing methods. In this sense, the method proposed by [PERSON] [65] is more appropriate to determine the optimal solution when the problem size is quite large. As a consequence, regarding the Manhattan distance, there is definitely strong evidence that the proposed exact method can determine the globally optimal facility locations for the continuous multi-facility location problems. Moreover, the application of the weighted median in the clustering-based methods is also reasonable when the Manhattan distance is considered.
## 3 Implementation Method
### Determining the Frequency Range of \"Weak\" FIV  
CCA can eliminate the disturbance of random noise. However, owing to the coupling effect of the system dynamics, periodic noise with the same frequency in the vibration and sound pressure signals cannot be denoised, such as the dynamic response of the mechanical parts. Thus, further denoising of the CCF was required to determine the frequency of the FIV. First, the frequency distribution of the CCF was determined by spectrum analysis. Based on the results [25; 26], the frequency of \"weak\" FIV was up to thousands of hertz. Thus, high-frequency components were extracted by HWPT to eliminate the interference of low-frequency noise. Then, the mean value of the frequency amplitude was set as a threshold to determine the frequency range of \"weak\" FIV, i.e., the frequency whose amplitude exceeded the threshold was considered as the potential \"weak\" FIV and its distribution characteristics were determined based on the statistical results. Finally, according to the statistical results, the frequency range of \"weak\" FIV was determined.
## 5 Strength and Limitations  
The first limitation of this study is that the number of MPs was much higher in the Dhaka district, and all key regulatory agencies were located there; hence, the majority of our interviews were conducted in Dhaka, which might have reduced our ability to obtain a much broader perspective on the pharmacists and stakeholders working outside Dhaka. Second, due to a low number of Grade B pharmacists in the professional group, we were unable to increase the number of interviews with Grade B pharmacists. However, this study included a wide mix of pharmacists and stakeholders, including representatives from regulatory bodies, academicians, pharmacy owners, and representatives from medicine sellers' organizations, which rendered a better representation of the pharmacy community in Bangladesh. Further, despite the low number of interviews, we were able to reach data saturation with the IDIs and KIIs of each group. This allowed us to obtain a broader knowledge base on the practical challenges of attaining good pharmacy practices in the MPs and potential opportunities for strengthening model pharmacy initiatives in Bangladesh.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 3 Architecture of the Integrated IoT Application Development Platform
### Characteristics of the IoT Application Development Platform  
[PERSON] [7] proposed an IoT middleware reference model that included an interoperability module that operates on a gateway for integrating; a persistence and analytics module that stores and processes data; a context module that provides relevant information for services and users; a semantic operation module; and a resource and event module that manages the device capability and generates events according to the capability and context. Compared with this proposal, the system architecture proposed here has the following characteristics:  
1. Cloud-fog collaborative computing mode. The system can be deployed in the IoT environment near the sensor devices in a fog computing form. The data do not need to be transmitted to a  
Figure 4: The IoT application development platform, based on open source technology.  
remote cloud computing platform and can be processed locally. Then, the processed data can be transmitted to intelligent processing functions which are deployed in the remote cloud computing platform to implement collaborative computing between the cloud and the fog.
2. The northbound interface is utilized to shield the underlying heterogeneity and support different kinds of devices to meet system scalability requirements. At the same time, a southbound interface is provided to the user application for development. The southbound interfaces support different styles of API interfaces, such as Web Service, the open source Platform API, RESTful style interface, and SDK.
3. Multiple open source platforms are integrated. In a large-scale IoT application environment--for example a smart city--different subsystems may adopt different open source platforms or self-development platforms. One of the goals of the system is to use Web of Things technology to implement service interactions between different platforms.
4. The architecture proposed here implements the context processing mechanism through application layer business logic, such as complex event processing, which provides more intelligent application processing capabilities.
5. Distributed resource management and blockchain-based resource billing.
# Perception of Familiar Second Language Accents and the Role of Linguistic Background
## 2 Methodology
### Stimuli  
Eight talkers were selected from the Speech Accent Archive [23]. The talkers represented an English accent, that is, Standard Southern British English, which is also regarded as Received Pronunciation, and Greek-accented English. The English accent included talkers from London, Hertfordshire, and Surrey, and the Greek-accented English included talkers from Greece. The two accents were represented by four speakers each (two males and two females). The choice of the specific talkers was made on the basis of the following criteria. First, the speech samples were free of exaggerated background noise. Second, the talkers had clear speech without any disfluencies or reading errors. Third, there was an attempt to select talkers from proximal geographic locations. For example, the English talkers lived in London and the Home counties. English talkers had an age range of 18-72 (\(M=37.5\); \(SD=17.7\)) and had lived either all or a large part of their lives in the UK and acquired the local accent at birth. Greek-accented English talkers had an age range of 19-55 (\(M=34.25\); \(SD=15.69\)). They spoke L1 Greek and had learned English as an L2 academically between the ages of 3-9 (\(M=7.5\); \(SD=3\)). These speakers were living in Greece by the time of the recordings and reported living in an English-speaking country in the range of 0.25-1.5 years (\(M=0.99\); \(SD=0.9\)).  
The phonetic transcriptions provided by the database, where available, were examined to determine the acoustophoretic features of sounds in the two accents. Several features differed between English and Greek-accented English. The recordings lasted between 10 and 17 s and included the following passage, which was the same for all talkers: \"Please call [PERSON]. Ask her to bring these things with her from the store: Six spoons of fresh snow peas, five thick slabs of blue cheese, and maybe a snack for her brother [PERSON]\". All recordings were adjusted for peak intensity in Praat [24].
## 2 Materials and Methods
### Ultrasound Image Acquisition and Protocol  
The subjects were asked to refrain from performing any strenuous physical activity 24 h before the experimental session, during which B-mode ultrasound patellar tendon images were acquired. Both the dominant and the non-dominant side were studied. The same experienced sonographer (M.G.) performed all image acquisitions, for a total of 20 images for each subject. Specifically, while the subject had the quadriceps muscle relaxed and was lying in a supine position, 8 images were acquired with a knee angle of 30\({}^{\circ}\), and then with a knee angle of 0\({}^{\circ}\), 12 scans were acquired. For the purpose of this texture-analysis study, 6 images on each side were analyzed as reported in Table 1. The remaining 4 images on each side were employed also for qualitative assessments of neovascularization and tendon structure as reported in [23]. According to the definition as cited in the Introduction [23], 5 subjects out of the 14 were found to be affected by subclinical tendinopathy (i.e., 5 subjects demonstrated the presence of light structural changes in association with at least mild neovascularization or moderate/severe structural changes with/without neovascularization).  
All images were acquired using a ClearVue 550 ultrasound machine (Philips Medical Systems, Milan, Italy) equipped with a linear array transducer (Philips L12-5, central frequency = 5-12 MHz). The time-gain compensation was kept equal for all scans (neutral), dynamic image compression was turned off, and the gain was set at 50% of the total range. The image acquisition preset was kept the same for each subject and throughout the study. The ultrasound images were stored as DICOM files and analyzed offline.
# Isoprenoids Production from Lipid-Extracted Microalgal Biomass Residues Using Engineered E. coli
## 3 Methods and Materials
### Biosynthesis and Analysis of Isoprenoids Produced Using the Engineered E. coli Strains  
To verify whether the hydrolysate of LMBRs could be utilized to produce bio-based chemicals, several engineered _E. coli_ strains were employed to produce isoprenoids. YJM25 (_E. coli_ BL21(tm (DE3)/pYJM21, pYJM14), containing the optimized pathway and isoprene synthase, was used for isoprene fermentation [4]. YJM29 (_E. coli_ BL21(DE3)/pYJM28), harboring a more efficient pathway for \(\alpha\)-pinene production with the heterologous hybrid MVA pathway, GPPS2 and \(\alpha\)-pinene synthase (Pt30), was utilized in \(\alpha\)-pinene biosynthesis [5]. And FHR-2, including the _Artemisia annua_\(\beta\)-pinene synthase gene (QH6) and a heterologous hybrid mevalonate (MVA) pathway (_E. coli_ BL21 (DE3) (pACYDuet-1-_mvaE-mvaS-GPPS2-QH6_, pTcHis2B-_ERG8-ERG12-ERG19-IDII_), was applied for \(\beta\)-pinene production [28]. The fermentation procedure was carried out as reported in the previous study [29] with some modifications. Shake-flask experiments were performed in triplicate using a series of sealed 600 mL shake flasks containing 100 mL of fermentation medium including glucose 2 g/L or a suitable concentration of acid hydrolysates. Optical density (OD) of the bacteria was measured with a spectrophotometer (Cary-50, Varian Inc., Palo Alto, CA, USA) at a wavelength of 600 nm. The isoprene, \(\alpha\)-pinene and \(\beta\)-pinene production were analyzed as described earlier [4; 5; 28] by a gas chromatograph (GC) equipped with a flame ionization detector (FID) and a HP-1 column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent, Palo Alto, CA, USA). Based on the retention time of standard samples, isoprene, \(\alpha\)-pinene and \(\beta\)-pinene were identified, respectively. The concentration of target production was calculated from the peak area using a standard curve.
## 2 Overview of Machine Learning Techniques
### Introduction to Machine Learning  
Machine Learning (ML) algorithms and neural networks are both subsets of artificial intelligence (AI) and are often used to interpret complex data. While they have similarities, they also possess distinct characteristics. Here, we explore ML algorithms, which can generally be grouped into three main categories, and draw some comparisons with neural networks (Table 1).  
1. Supervised Learning: This method relies on labeled data, which means that both the input and output are provided to the algorithm during training. The algorithm learns to predict the output from the input data during this training process. Once the model is adequately trained, it can predict the output when given new input data. Common supervised learning algorithms include linear regression, decision trees, and support vector machines (SVMs) [26]. In contrast, a neural network would use layers of nodes(neurons) to process and transform the input into output, often excelling in tasks where the relationship between the input and output is too complex to be captured by traditional supervised learning algorithms.
2. Unsupervised Learning: Unlike supervised learning, unsupervised learning algorithms are only given input data, and are left to find structures, relationships, or patterns on their own. These algorithms are often used for clustering or association tasks, like grouping customers based on purchasing behavior. Common unsupervised learning algorithms include K-means clustering, hierarchical clustering, and the Apriori algorithm [33]. In a similar vein, certain types of neural networks, such as Autoencoders, can also perform unsupervised learning tasks, reconstructing the input data and identifying patterns without explicit labeling.
3. Reinforcement Learning: Reinforcement learning algorithms learn from the consequences of actions, much like the way a child learns to walk. They aim to maximize some sort of reward or minimize a penalty. Reinforcement learning is typically used in navigation, gaming, and robotics [33]. Interestingly, neural networks can be a part of reinforcement learning systems as well, as function approximators to predict the quality of actions, seen, for instance, in Deep Q-Networks (DQNs).  
It is important to note that the choice between Machine Learning algorithms and neural networks depends on the specific task at hand. While Machine Learning algorithms offer a range of models each suitable for different types of tasks, neural networks, particularly deep learning models, are usually favored for tasks involving large volumes of unstructured data, like image and speech recognition. However, the computational requirements and the need for large datasets can sometimes be a limitation for neural networks.
## 2 Background  
### Basic Ide of Circular Hough Transform Function (CHT)  
[PERSON] in 1962 proposed the representation of a line in space (r, \(\Theta\)), so that straight lines could be detected in an image. Subsequently, this transform was extended to the detection of regular shapes. Practical cases of application of the Hough transform (HT) for linear elements can be found in line detection in low-level vision systems [22] and in the detection of straight lines in images with optimized algorithms [23].  
The circular Hough transform (CHT) function is derived from the Hough transform (HT) for linear object detection. CHT is based on the same principle: a point (x,y) is represented by a curve in space (r, \(\Theta\)) [24].  
The equation of a circle answers to  
\[(x-a)^{2}+(y-b)^{2}=r \tag{1}\]  
Now, each centroid of circle (a, b) will be represented on a surface in the space (a, b, r) where r is the radius of the circle.  
The circle is thus defined by three parameters and, consequently, the parameter space will have three dimensions. A circle in image-gen-space will be a point in parameter space [25].  
The parameter space is defined in each case by the shape of the object of interest to be detected in an image [26].  
For each contour pixel there is a family of circumferences passing through that pixel and given by the parametric representation of circles  
\[x=a+r^{*}\cos{(t)} \tag{2}\]  
\[y=b+r^{*}\sin{(t)} \tag{3}\]  
The angle t is evaluated in 0-360\({}^{\circ}\).  
The algorithm is based on a voting accumulation system [27]. Each contour pixel votes for all possible circumferences passing through that pixel. If a point appears in the parameter space that has accumulated many votes, the parameters of that point are interpreted as corresponding to the circle that passes through a large number of contour points [25].  
Thanks to the cumulative voting system (maximum likelihood election) the CHT function is robust to noise and to the lack of data for some incomplete circles.
###### Abstract  
Background: The purpose of this study was to identify the epidemiologic characteristics and prognostic factors for malignant pleural mesothelioma in Korea, which are currently insufficient. The data were derived from malignant mesothelioma patients who registered under the Asbestos Injury Relief Act; Methods: A total of 728 patients received compensation from the Asbestos Injury Relief Act due to malignant mesothelioma between 2011 and 2015. Of these, 313 patients (43.0%) with malignant pleural mesothelioma were included in the study. The study variables were sex (male, female), age at diagnosis (\(<\)59, 60-69, \(\geq\)70), smoking history (yes, no), surgery (yes, no), chemotherapy (yes, no), occupational exposure to asbestos (yes, no), and histological subtype (epithelioid, nonepithelioid); Results: Median survival of mesothelioma was 8.0 months (95% confidence interval: 6.2 to 9.8). The 1-year, 2-year, and 5-year survival rates (%) were 43.5%, 23.6%, and 12.5%, respectively. In multivariate analysis of [PERSON]'s proportional hazards model; sex, age, smoking history, occupational asbestos exposure, and histological subtype were not significant prognostic factors, but surgery and chemotherapy combined was a significant predictor; Conclusions: Although the representativeness of these data is limited, our study estimates the epidemiologic characteristics of malignant pleural mesothelioma. Non-occupational exposure had a similar prognosis to occupational asbestos exposure, and there was no sex difference. In addition, it was found that receiving a combination of surgery and chemotherapy affects the survival rate, but there is a limitation in that factors such as performance status, comorbidities, and stage that contribute to survival are not considered.  
asbestos; malignant pleural mesothelioma; surgery; chemotherapy; survival +
Footnote †: journal: Applied Sciences
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Emulator  
As discussed in Section 2, the end-to-end channel for visible optical communication is exceptionally complex. In the transmission model, for example, in atmospheric environments, gas molecules and aerosol particles in the atmosphere absorb and scatter light radiation in the near-infrared band, resulting in a loss of signal received power. In addition, the change of atmospheric turbulence causes severe distortion to the optical signals. For another example, in the underwater environment, the attenuation of underwater light depends on the wavelength, where the attenuation of the signal increases with frequency. Moreover, there are other propagation effects such as temperature fluctuations, salinity, scattering, dispersion, and beam steering. For underwater VLC applications whose bandwidth is not too high (tens of MHz), the power attenuation with frequency can be approximately modeled as a linear relationship, allowing the modeling of underwater VLC multipath channels using compressive sensing (CS) method [43]. Traditional methods for high-speed point-to-point VLC cannot support accurate VLC end-to-end channel modeling, but machine learning is able to simulate the complicated nonlinear dynamics of VLC channels [44]. In massive multiple-input multiple-output (m-MIMO) VLC, the machine learning-based methods enable accurate estimation of the channel matrix [45].
## 5 Experimental Results and Discussion
### End-to-End Delay  
To measure the quality of service (QoS) of the network, it is important to know the end-to-end delay or latency metric. This metric is crucial because it indicates how long it takes a packet to travel across a network from source to destination. Figure 9 displays the proposed ACO-PT and competing algorithms' end-to-end latency (ACO-FL, RACO, and AODV-ACO). According to the testing data, the suggested ACO-PT had an end-to-end latency of 226.3 milliseconds with 200 vehicles. The competing methods, on the other hand, caused a substantially larger end-to-end latency of 262.3-363.3 milliseconds. When the number of vehicles was extended to 400, the end-to-end time for our suggested ACO-PT was similarly shorter than for other competing approaches, as depicted in Figure 9. The suggested ACO-PT had a time delay of 250 milliseconds, whereas competing methods  
Figure 8: (**a**) Throughput of the proposed ACO-PT and contending algorithms (ACO-FL, RACO, and AODV-ACO) with a maximum of 200 vehicles, (**b**) throughput of the proposed ACO-PT and contending algorithms (ACO-FL, RACO, and AODV-ACO) with a maximum of 400 vehicles.  
had a time delay of 300.8-388.7 milliseconds. It is concluded that the proposed ACO-PT algorithm was robust, resulting in lower end-to-end delay.  
\[E\_e_{d}=\sum_{i=1}^{R}\biggl{(}\frac{V_{d1}}{P_{r1}}+\frac{P_{si}}{T_{r}}\biggr{)} +\biggl{(}\frac{V_{d2}}{P_{r2}}+\frac{P_{si}}{T_{r}}\biggr{)}+\biggl{(}\frac{V_{ d(V_{u})}}{P_{r(V_{u})}}+\frac{P_{si}}{T_{r}}\biggr{)} \tag{39}\]  
where \(R\) is the total number of routers, \(V_{d1}\) is the distance between vehicle 1 and vehicle 2, \(V_{d2}\) is the distance between vehicle 2 and vehicle 3, \(V_{dn}\) is the distance between \(V_{n}\) vehicles, \(P_{r1}\) is propagation rate 1, \(P_{r2}\) is propagation rate 2, \(P_{r2}\) is the propagation rate for \(n\) packets, and \(T_{r}\) is the transmission rate.