esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network-Based Bilingual Lexicon Induction for Indonesian Ethnic Languages
## 1 Introduction  
Indonesia's riches extend beyond its natural resources, such as minerals, vegetation, and fauna. The archipelago is highly diversified, with a variety of ethnic languages in Indonesia, which are mostly part of the Austronesian language family. Since prehistoric times, Indonesian ethnic languages have evolved, resulting in a different language for each ethnic group [1]. Based on the similarity matrix utilizing the ASJP database [2], most Indonesian ethnic languages are closely related and similar.  
Currently, the extinction of ethnic languages in Indonesia is a pressing issue that has attracted the attention of scholars, particularly linguists. The Summer Institute of Linguistics has stated that these local languages are endangered and may cease to be spoken in Indonesia. Therefore, we started the Indonesia Language Sphere project, which aims to create comprehensive bilingual dictionaries between the ethnic languages using aneural network and crowdsourcing approach, in order to conserve local languages on the verge of extinction [3].  
We expect that this project will expand the vocabulary of ethnic languages and that more people will learn and use them. The generated bilingual dictionary can be transformed into a bilingual dictionary service, and further integrated with the Google Translate service as a composite service on the Language Grid to enable a pivot-based hybrid machine translation, bridging the gap between high-resource languages and low-resource languages [4; 5].  
Indonesian ethnic languages are low-resource languages with a limited amount of language resources, such as bilingual dictionaries. We chose Minangkabau, Malay, Palembang, Javanese, and Sundanese as the languages to implement the proposed method in this study due to the availability of the bilingual dictionaries obtained from the results of our previous study [6]. Moreover, the Indonesian and Minangkabau languages have significant lexical similarities; thus, we presume they have several phonetic transformation rules, from Indonesian to Minangkabau and vice versa. For example, there appears to be a rule in Indonesian and Minangkabau where the last phoneme \"a\" in Indonesian tends to turn \"o\" in Minangkabau, while the middle phoneme \"ia\" appears to turn \"i\". There are many more patterns in these languages. Although this rule is not always applicable, it can help to predict a rough translation as a preliminary translation. To the best of our knowledge, no previous studies have explored approaches to bilingual lexicon induction using neural network targeting transformation rules between closely related languages. This study proposes a neural network-based bilingual lexicon induction for Indonesian ethnic languages with the following research goals:  
* Model a neural network-based bilingual lexicon induction between Indonesian and Minangkabau, using long short-term memory (LSTM).
* Evaluate how well the model detects the transformation rules between Indonesian and Minangkabau.
* Apply the model to Malay, Palembang, Javanese, and Sundanese languages.
# Biomechanical Characterization of Abdominal Aortic Aneurysm: The Rupture Mechanism
###### Abstract  
In this work, a four-week-old male CS7B/6 mouse model of abdominal aortic aneurysm (AAA) was developed to examine the AAA rupture mechanism. Immunofluorescence staining was adopted for quantifying the degradation of elastin, and Picrosirius Red staining was adopted for evaluating the density of collagen. Atomic force microscopy with two probe tip sizes of 5 \(\upmu\)m and 20 nm was adopted for mechanical characterization of the AAA. The microstructure changes and stiffness changes in both AAA samples and controlled samples were inspected. The degradation of elastin, wall thickening, formation of micro vessels, and increased density of collagen were observed in the AAA samples. The AAA samples also exhibited fragmented texture from AFM scanning. The histogram of stiffness measurements of the AAA samples with a 20 nm tip demonstrated two unique peak frequencies of stiffness intervals (0-10 kPa and 40-50 kPa). The stiffer regions were correlated with the increased density of collagen, as shown in the immunofluorescence images. The softer regions, combined with the fragmented texture, could be the key index contributing to the initiation and propagation of AAA rupture. Overall, the AAA group showed a higher stiffness than the control group (50.77 \(\pm\) 62.4 kPa vs. 40.6 \(\pm\) 51.86 kPa). The findings from this work may help in explaining ruptures in small AAA (<5.5 mm), which account for ten percent of all AAA ruptures. Additionally, the observations in this study may help develop early detection methods and innovative treatments for AAA.  
abdominal aortic aneurysm; rupture prediction; atomic force microscopy; mechanical characterization; histological analysis +
Footnote †: journal: Applied Sciences
## 3 Parameter Optimization of the Current Controller and PLL
### Parameter Optimization of the Current Controller
As shown in Figure 13, the dynamic response characteristics of the output current are evaluated by analyzing the magnitude-frequency characteristic of \(H_{g}\), the cut-off frequency of \(H_{g}\) can be used to evaluate the dynamic response of the system [32]. The frequency corresponding to an amplitude of \(-3\) dB is defined as the cut-off frequency; the lower cut-off frequency represents a lower dynamic response for a grid-connected VSC. As \(m\) increases, the cutoff frequency of \(H_{g}\) decreases and the phase margin remains essentially the same. It means that the dynamic response of the system will be weakened with the increase of \(m\). Based on the above analysis, a larger \(m\) is beneficial to stability. However, if m is larger, the dynamic response speed of the system will decrease, and the stability margin of the fundamental frequency control will also decrease. Therefore, combined with the influence of \(m\) on the dynamic performance and the stability of VSC, it is more appropriate to choose \(m\) between \(0.6\) and \(1.5\) as shown in Figure 7:0 In this article, \(m\) is chosen as one as an example in the following verification. Figure 7:1 Damping coefficient \(m\) versus \(\phi\) change curve.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 4 The Proposed DNA Data Storage Readout Method Based on Heterogeneous Computing
### Hardware-Accelerated Kernel Workflow  
The execution of the hardware-accelerated kernel for DNA storage readout and recovery proceeds as follows.  
Step 1: Upon receiving the start signal from the host, the acceleration kernel simultaneously reads two sequencing reads from the off-chip memory. Each read is 224 bits long and is aligned to 256 bits for merging into a 512-bit-wide transmission. The sequencing reads are stored in a data buffer for subsequent module access. Each raw sequencing read consists of a 40-bit forward primer sequence, a 24-bit index sequence, a 120-bit data payload, and a 40-bit reverse primer sequence.  
Step 2: Each 512-bit-wide dual-read sequencing data group is split into two independent 256-bit signals, which are then fed into two separate primer identification modules. Each primer identification module is cascaded with an index check module, forming two parallel pipelined processing paths. The output of the primer identification module includes a 4-bit primer ID, the index sequence, the 120-bit data payload, and a 1-bit validity signal, which are passed to the index verification module. This module calculates the syndrome and preserves the information bits, ultimately outputting a 19-bit index information field, a 120-bit data payload, and a 1-bit validity signal.  
Step 3: Based on the identified primer ID and the index information bits, the corresponding position in the copy count memory is incremented. The memory write interface stores the data payload in the designated location in the off-chip memory.  
Step 4: Once all valid data payloads have been written to memory, the majority voting process begins. The data payloads are aligned to 128-bit widths and concatenated into 512-bit-wide blocks, which are sequentially read from off-chip memory BANK2. The  
Figure 4: Overall architecture of hardware-accelerated kernel.  
majority voting module produces a 120-bit consensus sequence, a 120-bit erasure flag sequence, and a 1-bit validity signal.  
Step 5: The 1-bit validity signal output from the majority voting module serves as the input to the valid-sequence filtering module. A table lookup is performed to retain only the consensus and erasure flag sequences associated with valid indices, where the homopolymer lengths meet the predefined requirements.  
Step 6: Two deinterleaving modules operate in parallel to process the consensus sequences and erasure flag sequences, producing two 360-bit-wide output signals that serve as inputs to the LDPC decoding module. The LDPC decoder performs error-and-erasure correction on codewords that still contain substitution and erasure errors to recover the original data. The 360-bit output signals from the LDPC decoder are then aligned to a width of 512 bits and sequentially written to the designated result storage region in off-chip memory BANK1. This completes the entire DNA storage readout and recovery process within the kernel.
## 3 Results and Discussion
### Bacteria and Moisturizing Test  
The spread plate method was used to examine the number of bacteria. Neither antibacterial agents nor antioxidants were added in the production processes of the emulsion products, and the bacteria were cultured under suitable growth conditions. Figure 7 shows that, when the sodium acrylates copolymer emulsifier was used, the number of bacteria within two days was lower than the standard of microbial tolerance in cosmetics, 1000 CFU/mL. Due to the better antibacterial properties of chitosan, when the amphoteric chitosan emulsifier was used, the number of bacteria was much lower than that resulting from the sodium acrylates copolymer emulsifier, as shown in Figure 7.  
The moisture retention of the emulsion was measured with the system shown in Figure 1. The spatial humidity was detected continuously after the emulsion was placed in the system. Figure 8 shows the changes in humidity over time for different emulsifiers. A lower spatial humidity indicates greater moisture retention. Since chitosan contains more hydrophilic functional groups, such as -OH and -NH, more moisture was retained in the emulsion made with chitosan. Figure 8 confirms that the moisture equilibrium time between the emulsion and the gas phase was about 40 min, and the moisturizing effect of the modified chitosan emulsion was better than that of the sodium acrylates copolymer emulsion.  
Figure 6: Change of transmittance with time.
## 2 Data and Methods
### Methods
To address this issue, a geographically weighted regression (GWR) model was used to explore the spatially varying relationships between the dependent variable and the potential influencing variables [56; 13]. The spatial regression coefficients of each grid in GWR were not the regression results of itself, but the spatial coefficients in the local area based on optimal bandwidth. Of course, spatial regression methods are not limited to GWR; there are other spatial regression methods, such as kriging, spatial error regression, etc. In the OLS multiple regression model, the dependent variable \(y\) (population redistribution) is statistically related to a set of \(N\) independent variables \(x\) as follows:  
\[y_{i}=\beta_{0}+\sum_{j=1}^{N}x_{i}\beta_{j}+\varepsilon_{i}, \tag{1}\]  
where \(i=1\) to M, where \(i\) is an index of the number of cells for which data are available, M is the total number of cells of the country, \(\beta_{0}\) is the intercept, \(\beta_{j}\) represents the beta coefficients for each dependent variable, and \(\varepsilon\) is a randomly distributed error term. An OLS regression model can be converted into a GWR (geographically weighted regression) model by substituting each beta coefficient (the intercept and the dependent variable coefficients) with its local counterpart such that the beta coefficients can vary across space. \[y_{i}=\beta_{0(u_{i},v_{i})}+\sum_{j=1}^{N}x_{i}\beta_{j(u_{i},v_{i})}+ \varepsilon_{i}, \tag{2}\]  
where \(i=1\) to M, and \((u_{i},v_{i})\) is the location in geographic space of the \(i\)-th observation. A set of beta coefficients (and, hence, a regression model) is estimated at each location based only on neighboring, geographically weighted data cells. A key feature of this approach is the ability to calibrate the spatial weighting function to identify the bandwidth, i.e., the number or proximity of neighboring cells included that results in a \"best-fit\" model. In this paper, AICc (corrected Akaike information criterion) is used as the bandwidth method. If an environmental change variable (MeanYield, MeanFlow, ConsecLowYield, or ConsecLowFlow) was found to have a significant correlation with population redistribution consistently across both crop-water model datasets and both decades, it could be considered as having a robust effect on population redistribution. Urbanization level (Built-up) and population (Pop) were included as control variables. The effects of environmental change on population redistribution may differ between urban and rural areas and among areas with different population densities. The robustness of environmental changes on population redistribution was analyzed by comparing different control variables.
## 5 Results and Discussion
### Baseline System  
We consider a typical baseline system that performs geospatial join using a conventional method. The method requires to join tuples from pollution and mobility data at a granular scale, where geographic coordinates (longitude/latitude) of each tuple from each data set is checked against geographic coordinates of each tuple from the other dataset, resulting thus in a highly costly Cartesian product type of join. Add to this the fact that we are joining multidimensional data and the problem is exaggerated as a type of join that is better known as a Theta-join. Those types of joins require joining data on non-equal keys (longitudes and latitudes in this case). They entail high computational cost and result in very big intermediate result sets. We selected this baseline to compare with our system in terms of time-based and accuracy-based QoS constraints, such as running time and result accuracy, respectively. It is worth mentioning that the baseline system offers the utmost accuracy, but at the cost of higher computational cost, and thus an elevated latency. By selecting this baseline, we quantify the optimization provided by our system in terms of time-based QoS by abandoning only tiny statistically insignificant loss in the accuracy as discussed shortly in the results. This is so as time-based and accuracy-based QoS constraints are always contradicting in such a way that optimizing one of them tampers with the other. So, finding a balance between them both should be the target, which is one of the main contributions of this paper.
## 4 Power Data Virtual Planet Layer  
### The Problem to Be Solved  
In the actual application scenario, developers in the information field such as network and software are not familiar with the background of power application and message content. Meanwhile researchers in the power industry are generally unable to efficiently complete the task of programming and development of communication networks. Since each discipline has different concerns and technology overlaps, interdisciplinary research collaboration is needed. However, in the process of research and development, there are still problems in that it is difficult to give full play to the expertise of each party.  
The above problem can be effectively solved by the virtual plane proposed in this paper. This plane is mainly to provide a platform for power personnel and software programmers to work together. After receiving power packets from different underlying devices and based on different protocols, the virtual plane unifies the data format of the message and provides it to the distribution network advanced application. This enables a perfect combination of power infrastructure and advanced applications. As shown in Figure 2, in the actual power grid, the data such as current, voltage and power collected by smart distribution network equipment such as feeder terminal unit (FTU) and transformer terminal unit (TTU) will be uploaded to the virtual plane through wireless or fiber-optic communication links. In the power communication of the communication field, the virtual plane extracts the power information in the data packets transmitted by different protocols, and unifies the format. At the same time, the data is cleaned according to the demand and then the high-quality information is uploaded to the cloud server. For reprocessed data, the virtual plane offloads the analysis tasks of these data to the edge platform according to business requirements, or repackages and encrypts them before transmitting them to the control center.  
Figure 2: Bridge between communication and power; TTU: transformer terminal unit, FTU: feeder terminal unit.  
In the process of power data acquisition, transmission, processing and analysis, the virtual plane of power data plays a role as a bridge. Under the premise of being safe and efficient, it not only minimizes the communication costs of power technicians and software developers, but also better integrates the hardware of the power equipment with the software of the communication protocol.
## 3 Results
### Impact of Inter-Seasonal Training on Cardiac Morphology and Function
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**All Athltes (\(n\) = 59)**} & \multicolumn{2}{c}{**Non-Linemen (\(n\) = 39)**} & \multicolumn{2}{c}{**Linemen (\(n\) = 20)**} \\ \cline{2-6}  & **Pre-T** & **Post-T** & **Pre-T** & **Post-T** & **Pre-T** & **Post-T** \\ \hline Age, y & 21.9 \(\pm\) 1.4 & & 21.8 \(\pm\) 1.4 & & 22 \(\pm\) 1.5 & \\ Caucasian & 40 (68\%) & & 26 (67\%) & & 14 (70\%) & \\ Afro-Caribbean & 19 (32\%) & & 13 (33\%) & & 6 / (30\%) & \\ Prior university ASF seasons & 1.6 \(\pm\) 1.1 & & 1.6 \(\pm\) 1.1 & & 1.7 \(\pm\) 1.1 & \\ Height, cm & 183.6 \(\pm\) 7.2 & & 181.2 \(\pm\) 6.1 & & 188.3 \(\pm\) 7.1 \({}^{\ddagger}\) & \\ Weight, kg & 102.3 \(\pm\) 22.6 & 101 \(\pm\) 20.4 & 887 \(\pm\) 9.8 & 89.3 \(\pm\) 9.7 & 128.4 \(\pm\) 16.9 \({}^{\ddagger}\) & 123.9 \(\pm\) 16 ***, \({}^{\ddagger}\) \\ BMI, kg/m\({}^{2}\) & 30.1 \(\pm\) 5.5 & 29 \(\pm\) 4.8 & 27.1 \(\pm\) 3 & 27.2 \(\pm\) 2.9 & 36.1 \(\pm\) 4 \({}^{\dagger}\) & 34.8 \(\pm\) 3.8 ***, \({}^{\ddagger}\) \\ BSA, m\({}^{2}\) & 2.28 \(\pm\) 0.28 & 2.26 \(\pm\) 0.25 & 2.1 \(\pm\) 0.1 & 2.1 \(\pm\) 0.1 & 2.6 \(\pm\) 0.2 \({}^{\ddagger}\) & 2.5 \(\pm\) 0.2 ***, \({}^{\ddagger}\) \\ Body fat, \% & 19 \(\pm\) 7.4 & 18.2 \(\pm\) 6.6 & 15.3 \(\pm\) 4.7 & 15.2 \(\pm\) 4 & 26.2 \(\pm\) 6.4 \({}^{\dagger}\)\({}^{\dagger}\) & 24.2 \(\pm\) 6.7 \({}^{\dagger}\)\({}^{\dagger}\)\({}^{\ddagger}\) \\ HR, beats/min & 58.9 \(\pm\) 7.9 & 60.2 \(\pm\) 8.1 & 57.8 \(\pm\) 7.8 & 60.4 \(\pm\) 8.4 \({}^{\star}\) & 60.4 \(\pm\) 8.2 & 59.7 \(\pm\) 7.7 \\ SBP, mmHg & 121.6 \(\pm\) 10.6 & 122.4 \(\pm\) 9.4 & 118.2 \(\pm\) 8.8 & 122.5 \(\pm\)9.5 & 128.2 \(\pm\) 11.1 \({}^{\dagger}\)\({}^{\dagger}\)\({}^{\ddagger}\) & 121.8 \(\pm\) 9.5 \({}^{\star}\) \\ DBP, mmHg & 67 \(\pm\) 8.3 & 73.9 \(\pm\) *** & 64.5 \(\pm\) 7.3 & **74.1 \(\pm\) 7*** & 72.2 \(\pm\) 7.8 \({}^{\dagger}\)\({}^{\ddagger}\) & 73.6 \(\pm\) 9.8 \\ \hline Values are mean \(\pm\) SD. Abbreviations: BSA: body surface area, BMI: body mass index, HR: heart rate, SBP: systolic blood pressure, DBP: diastolic blood pressure. \({}^{\star}\)\({}^{\ddagger}\)\
# Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis
## 2 Methods
### Implementation of the Solution
#### 2.5.2 Real-Time Computing Process  
This process remains latent, awaiting the reception of images to be processed. Real-time processing involves multiple pre-processing steps before applying the trained deep learning model and various post-processing algorithms to enhance system response. The processing workflow is illustrated in Figure 12, and it is explained below.  
Figure 11: Solution architecture graph.  
Figure 12: Real-time image processing workflow.  
Pre-Processing Block  
Pre-processing algorithms are carried out before executing the Deep Learning model. These algorithms serve as a preliminary filtering steps to avoid processing images that do not meet the quality criteria for evaluation. First, probe movement is verified, because acquisitions where the probe is not held still cannot be analyzed. Using optical flow algorithms [27; 28], probe movement is estimated between two consecutive images, using reference points located in the upper part of the image (muscle and fat). A movement threshold of 2 mm/s was established. If the image does not meet the criteria, it is not processed, and it will be displayed on the screen with a red label indicating movement. If, on the contrary, it meets the requirements, a green label will be displayed on the screen and the rest of the processing will be executed.
## 4 Experimental Results
### Statistical Significance of Hyper-Heuristic Algorithms
#### 4.2.1 Friedman Test  
The median ofvs obtained by the hyper-heuristics, including the SSHH, were subjected to the Friedman test at a significance level of 0.05. The test result returned a \(p\)-value less than \(2.2\times 10^{-16}\), which is an insignificant value. This result means that there is a significant difference between the median ofvs obtained by the EA-ILS hyper-heuristic across the three problem domains. The Friedman rankings are presented in Table 9 and indicate that the EA-ILS hyper-heuristic is the best with a rank of 1.67 while AdapHH and SSHH finished second and third, respectively. The two variants of ILS, which are FS-ILS and NR-FS-ILS, finished sixth and seventh, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Hyper-Heuristic** & **\(\mu\)-Norm** & **\(\mu\)-Rank** & **Best** & **Worst** \\ \hline
1 & EA-ILS & 0.0727 & 1.30 & 9 & 0 \\
2 & NR-FS-ILS & 0.1036 & 2.90 & 0 & 0 \\
3 & AdapHH & 0.1063 & 3.40 & 1 & 0 \\
4 & FS-ILS & 0.1071 & 3.80 & 0 & 0 \\
5 & EPH & 0.1369 & 4.60 & 0 & 0 \\
6 & SR-AM & 0.1486 & 4.90 & 0 & 0 \\
7 & SR-IE & 0.6355 & 7.00 & 0 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation Results on the Quadratic Assignment Problem.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Hyper-Heuristic** & **\(\mu\)-Norm** & **\(\mu\)-Rank** & **Best** & **Worst** \\ \hline
1 & EA-ILS & 0.0608 & 1.20 & 27 & 0 \\
2 & AdapHH & 0.1341 & 2.70 & 6 & 0 \\
3 & SR-AM & 0.1795 & 3.80 & 2 & 0 \\
4 & EPH & 0.2347 & 4.07 & 4 & 1 \\
5 & NR-FS-ILS & 0.2766 & 4.03 & 1 & 6 \\
6 & FS-ILS & 0.2950 & 4.67 & 1 & 4 \\
7 & SR-IE & 0.5653 & 6.37 & 0 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Overall Performance on the Extended Hyflex Test Suite.