esa-sceva/satcom-chunk-collection huggingface dataset:
# A Literature Survey on Electrical-Current-Assisted Friction Stir Welding
## 5 EA-FSW of Similar Joints
### EA-FSW of Magnesium Alloys  
Some of the outstanding properties of magnesium alloys, that make them an interesting grade of alloys to be used in various industrial sectors and especially the transportation sector, are high strength-to-mass ratio, good heat dissipation performance, and good shock absorption performance. [PERSON] et al. [31] investigated EA-FSW of AZ31B butt welds with a thickness of 5 mm. they reported nano-size grains in the stir zone when the current intensity was less than 150 A. They also reported the presence of twin crystals and a more uniform microstructure in TMAZ in this range. However, the coarsening of the microstructure was reported in the HAZ.  
[PERSON] et al. [33] investigated the EA-FSW of the AZ31B magnesium alloy. They reported a larger welding zone and a more homogenous microstructure with a higher joint strength due to applying an electric current of up to 200 A. They used 0 A, 100 A, and 200 A electrical currents and demonstrated that all joints were formed without defects and voids. With an increasing electrical current, the heat input on the joint line increased, and bigger HAZ and stir zone (SZ) were formed in the 200 A case compared with other samples. The joint grain size decreases with increasing electrical current. The smaller grain size increased the joint  
Figure 3: Macroscopic morphology of EA-FSWed joints of AA2219 using a current of (**a**) 0 A, (**b**) 200 A, (**c**) 400 A, (**d**) 500 A, (**e**) 600 A. The white dotted lines present the material flow direction. Reprinted with permission from ref. [30]. Copyright 2022 Elsevier.  
hardness and mechanical properties of the AZ31B magnesium alloy joint. The FSW joint fractured from the HAZ area, while the EA-FSW cases fractured from the SZ area after the tensile test.  
[PERSON] et al. [32] investigated welding of the same alloy but using a pulse current, and reported similar results. In the experiments, they increased the electrical current to 400 A during EA-FSW of the AZ31B magnesium alloy. The SZ and HAZ thickness increased by increasing electrical current, and the biggest joint was formed in the 400 A case. They show that the intermetallic compounds (IMC) were formed with increasing electrical current in SZ. Increasing the temperature promotes the formation of IMCs. When an electric current is used, the temperature in the nugget increases, which leads to the formation of IMCs, or makes them thicker. According to X-ray powder diffraction (XRD) results, Mg and Al\({}_{12}\)Mg\({}_{17}\) were formed in a joint line. The IMC formation in this sample is related to the preheating joint that [PERSON] et al. [32] used. They used the preheating EA-FSW joint, while [PERSON] et al. [33] used tool Type c to conduct EA-FSW. Due to this difference, the fracture location of welded samples in [PERSON] et al.'s [32] tests differed. In this case, the fracture location of the AZ31B magnesium alloy was the HAZ area.
# Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine
## 2 Theory and Principle  
The architecture of the MFI scheme proposed in this paper is shown in Figure 1. Firstly, the signal constellation diagrams are divided by the WLS-FD algorithm, and the FD in each region is calculated, which is regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the GLCM, and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM. Meanwhile, to facilitate the learning of feature data by the SVM learning algorithm, it is necessary to change the feature data into one-dimensional data. The training feature data and test feature data are obtained by randomly extracting the feature set several times according to the ratio of 7:3, and the number of samples is 6370 and 2730, respectively.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 8 LLM Challenges
### Hallucination  
LLMs are said to hallucinate when they generate information that is fake or incorrect. The hallucination can either be intrinsic or extrinsic. In intrinsic hallucination, the model generates information that contradicts the content of the source text. In contrast, the generated content cannot be contradicted or supported by the source text in extrinsic hallucination. There are various reasons why a model can hallucinate or generate fake information during inference. For instance, if the model misunderstands the information or facts provided in the source text, it can lead the model to hallucinate. So, to be truthful, the model should have reasoning ability to correctly understand the information from the source text. The other reason why LLMs can generate false information is when the provided contextual information conflicts with the parametric knowledge acquired during pretraining. Additionally, it is observed that models have parametric knowledge bias, where the model assigns more importance to the knowledge acquired during pretraining over the provided contextual information.  
Also, teacher forcing is used during pretraining, where the decoder is conditioned on the ground truth prefix sequences to predict the next token. However, such a teacher forcing technique is missing during the inference, and such a discrepancy can also make a model hallucinate. Several techniques have been proposed to detect hallucinations in LLMs, such as  
1. sample not one but multiple outputs and check the information consistency between them to check which statements are factual and which are hallucinated;
2. validate the correctness of the model output by relying on and using external knowledge source;
3. check if the generated named entities or \(<\)subject, relation, object\(>\) tuples appear in the ground truth knowledge source or not, etc.  
Benchmarks such as TruthfulQA [102] have been developed to measure the truthfulness of language models. This benchmark can evaluate the risks of a model to generate misinformation or false claims that mimic popular misconceptions and false beliefs. It was observed in [102] that, generally, the largest models were the least truthful, so scaling up the model size increased performance but was less promising in improving the model's truthfulness.
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 5 Discussion
### Distance Metric  
Equation (12) uses a Euclidian distance metric. Other distance metrics could be also used, e.g., Manhattan or maximum distances. Choosing a different distance did not visibly change the example images, but an image with a lot of directed structures might be sensitive to the distance metric selection. We always recommend the Euclidian metric because it is rotationally invariant.  
The metric has to be invariant to translation, otherwise FFT could not be utilized for an efficient convolution calculation. While a position-dependent metric could be interesting, it would yield a much less efficient calculation scheme than the FFT-based convolution.
# Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM
###### Abstract
Treating COVID-19 as an endemic disease can be achieved when herd immunity has been formed in the population. Herd immunity refers to the indirect protection from infection offered to vulnerable individuals when a sufficiently significant number of immune individuals are present in a population [25]. Thus, efforts need to be made to create herd immunity related to COVID-19. Herd immunity will be achieved through vaccination or immunity gained from past infection [26]. When these conditions are achieved, even though COVID-19 persists and becomes endemic, the death rate caused by COVID-19 will decrease. As shown in Figure 2, the daily death cases caused by COVID-19 in early 2022 are not as high as death cases in 2021, even though the Omicron variant has entered Indonesia with very high positive cases. This study is conducted to forecast the COVID-19 condition in the future in Jakarta according to daily positive cases and deaths caused by COVID-19. Since hospital capacity is limited and there are limited health workers and vaccines in pandemic situations, governments should make plans based on predicted case numbers. Additionally, this study is also conducted to know whether COVID-19 in Jakarta, the capital city of Indonesia, can be said to have become endemic or not by looking at the pattern of daily positive cases, death cases, and forecasts for the next period. In this study, we will use machine learning models called Neural Network Auto-Regressive (NNAR) and Long Short-Term Memory (LSTM) to create a forecast model representing the number of COVID-19 positive and death cases in Jakarta. A time series, a chronological or time-oriented sequence of observations on a variable of interest [27], is used in this study. Furthermore, the findings of this study can be used as basic public information on prospective conditions in the future.
## 4 Results and Discussion  
### Defect Detection  
The phase image results processed by HA and FFT for each defect group are shown in Figures 8-11. Figure 8 shows the defect detection image results of group 1. In all phase images of the HA and FFT, no defects were detected at 1 Hz.  
However, from 0.5 Hz, defects are detected because the heat source provides enough energy to generate thermal contrast. In addition, the shape of the defect is somewhat blurred due to excessive heat transfer as it approaches the low modulation frequency of 0.01 Hz. Moreover, as the values of length, width, and depth for the samples decrease, the detectability decreases. For example, defects with a length of 100 mm, a width of 12 mm, and a depth of 2.2 mm were detected at all frequencies below the modulation frequency of 0.5 Hz. However, defects with a length of 5 mm, a width of 2 mm, and a depth of 0.2 mm were not detected in all modulation frequency bands. Figure 9 shows the defect detection image results of group 2 (according to defect length). In the case of the detection capability according to the defect length, all defects were detected in all modulation frequency bands of the HA and the FFT in the phase image, and the detectability started to drop from 0.2 Hz or higher and was not detected due to noise at 1.0 Hz. Figure 10 shows the defect detection image results of group 3 (according to defect width). The detectability according to the defect width showed a similar tendency to the group 2 results. Figure 11 shows the defect detection image results of group 4 (according to defect depth). In the case of detectability according to depth, it showed a different trend from groups 2 and 3. It can be seen that the depths of 2.2 mm, 1.8 mm, and 1.4 mm were detected from 0.2 Hz to 0.01 Hz and were not detected by noise at 1.0 Hz. In addition, it can be seen that the remaining 1 mm, 0.6 mm, and 0.2 mm depth defects start to be detected at 0.05 Hz or less. In other words, the lower the depth, the farther it is from the surface of the specimen, the more thermal energy must be provided with a lower frequency excitation to be detected.  
Figure 8: Processed images with HA and FFT algorithms for detecting defects in group 1 of samples at different modulation frequencies: (**a**) 1 Hz, (**b**) 0.2 Hz, (**c**) 0.1 Hz, (**d**) 0.05 Hz, (**e**) 0.03 Hz, and (**f**) 0.01 Hz.  
Figure 10: Processed images with HA and FFT algorithms for detecting defects in group 3 of samples at different modulation frequencies: (**a**) 1 Hz, (**b**) 0.2 Hz, (**c**) 0.1 Hz, (**d**) 0.05 Hz, (**e**) 0.03 Hz, and (**f**) 0.01 Hz.  
Figure 9: Processed images with HA and FFT algorithms for detecting defects in group 2 of samples at different modulation frequencies: (**a**) 1 Hz, (**b**) 0.2 Hz, (**c**) 0.1 Hz, (**d**) 0.05 Hz, (**e**) 0.03 Hz, and (**f**) 0.01 Hz.
## 3 Research Methodology
### Procedure for Data Collection  
The questionnaire used in the study was based on UTAUT as this is a validated and widely used model for examining users' perceptions regarding the acceptance of a system and one that has been used in several domains, including education [49], as mentioned in Section 2. Other acceptance models could have been used, for example, TAM; however, it was observed that UTAUT is validated in the field of education, and the constructs it contains are relevant to this study. The UTAUT questionnaire uses the constructs of PE, EE, SI, FCs, and TC and any additional ones that are vital for the context, and in this case motivation was also used. The basic structure of the questionnaire was maintained; however, where necessary, it was adapted to the CodeLab context. The questionnaire consisted of 33 questions divided into 3 sections. The first section contained questions based on the UTAUT constructs, the second was about the usability of the system, and the third section captured demographic information. For the first section, a 7-point Likert scale ranging from strongly agree to strongly disagree was used to capture the responses. The questionnaire was developed using Qualtrics software [65] and was sent to the students via anonymous links on the classroom board, so that it was voluntary for them to answer. Qualtrics has been used in this context as it provides a variety of features that facilitate the development, distribution, and preparation of data for analysis as compared with other survey creation tools, such as Google Forms and SurveyMonkey. Additionally, it supports various languages and could easily be integrated with the learning environment of the university, facilitating the questionnaire-based research. As for the timing of sending the questionnaire out, it was sent in the final weeks of the semester, when the students were finishing the last module of activities, such that a post-usage evaluation of UTAUT was conducted.
## 4 Conclusions  
The performance of four adsorbents in terms of volume of water treated and the amount of nitrate removed in a SMAHS were in the order Dowex-Fe > Dowex > AG coconut copra > AG corn cob. Agricultural wastes, coconut copra and corn cob are mostly locally available materials and are the preferred adsorbents in terms of cost, especially in developing countries. The study showed that the amount of nitrate adsorbed and volume of water treated to maintain low nitrate concentration were higher for higher flux. A mathematical model incorporating membrane packing density and membrane correlation (MCC) coefficient in the homogeneous surface diffusion model applying the concept of CSTR reactor, demonstrated a good fit with the experimental data of nitrate removal in SMAHS. The application of the findings of this study to practical conditions may vary depending on the water quality, amount of water need/day, adsorption capacity of the adsorbent, its cost and availability. Furthermore, this study was a short-term study and needs to be extended with long-term experiments, and with repeated replacements of the exhausted adsorbents to obtain more information on practical applications.  
This study was financially supported by the Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE) (project number 4.1.17-13/14).  
Author Contributions: Conceived and designed the experiments: [PERSON], [PERSON] and [PERSON]. Experiments conducted: [PERSON]. Analysed the data and wrote the paper: [PERSON], [PERSON] and [PERSON]. Modelling: [PERSON], [PERSON], [PERSON], and [PERSON]. All authors read and approved the manuscript.  
The authors declare no conflict of interest.
# Explainable Machine Learning for Lung Cancer Screening Models
## 2 Models for Lung Cancer Screening
We present one of the formulas of the screening models to emphasize that even a well-known model, such as a Cox model, could be very complex and require further explanations. This is a state-of-the-art model. Nevertheless, it \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **LCRAT Model** & **BACH Model** & **PLCO\({}_{m2012}\) Model** \\ \hline Age & + & + & + \\ Gender & + & + & + \\ Race/ethnicity & + & & + \\ Education & + & & + \\ BMI & + & & + \\ Smoking status & & & + \\ Quitted smoking (in years) & + & + & + \\ Years smoked & + & + & + \\ Cigarettes per day & + & + & + \\ Pack-years & + & & \\ Prior cancer & & & + \\ Lung disease & + & & + \\ Asbestos exposure & + & + & \\ Any relatives with LC & + & & + \\ Number of relatives with LC & + & & \\ \hline
**Total number of variables** & 12 & 6 & 11 \\ \hline
**Prediction of Lung Cancer risk** & 5 years & 10 years & 6 years \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of variables used in the LCRAT, BACH, and PLCO\({}_{m2012}\) models. The plus sign means that the variable is included in the model. \begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Expression** & **Coefficient** \\ \hline intercept & & \(-9.7960571\) \\ age & & \(0.070322812\) \\ age\({}_{2}\) & \((age-53.459001)^{3}\cdot I(age>53))\) & \(-0.00009382122\) \\ age\({}_{3}\) & \((age-61.954825)^{3}\cdot I(age>61)\) & \(0.00018282661\) \\ age\({}_{4}\) & \((age-70.910335)^{3}\cdot I(age>70)\) & \(-0.000089005389\) \\ female & & \(-0.05827261\) \\ qtyears & & \(-0.085684793\) \\ qtyears\({}_{2}\) & \((qtyears)^{3}\) & \(0.0065499693\) \\ qtyears\({}_{3}\) & \((qtyears-0.50513347)^{3}\cdot I(qtyears>0)\) & \(-0.0068305845\) \\ qtyears\({}_{4}\) & \((qtyears-12.295688)^{3}\cdot I(qtyears>12)\) & \(0.00028061519\) \\ smkyears & & \(0.11425297\) \\ smkyears\({}_{2}\) & \((smkyears-27.6577)^{3}\cdot I(smkyears>27)\) & \(-0.000080091477\) \\ smkyears\({}_{3}\) & \((smkyears-40)^{3}\cdot I(smkyears>40)\) & \(0.00017069483\) \\ smkyears\({}_{4}\) & \((smkyears-50.910335)^{3}\cdot I(smkyears>50)\) & \(-0.000090603358\) \\ cpd & & \(0.060818386\) \\ cpd\({}_{2}\) & \((cpd-15)^{3}\cdot I(cpd>15)\) & \(-0.00014652216\) \\ cpd\({}_{3}\) & \((cpd-20.185718)^{3}\cdot I(cpd>20)\) & \(0.00018486938\) \\ cpd\({}_{4}\) & \((cpd-40)^{3}\cdot I(cpd>40)\) & \(-0.00003847226\) \\ asbestos & & \(0.2153936\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Beta coefficients estimated in the BACH model. is still necessary to check how the model behaves on new data or different patient populations. For this reason, it is essential to analyze state-of-the-art models on a new dataset. We can empirically check that it works well on a specific population. In the next section of the article, we will present tools explaining how the result of the model depends on particular variables.
## 3 Results
### Principal Coordinates Analysis (PCoA)  
Principal coordinates analysis (PCoA) is an ordination technique that picks up the main elements and structures from reduced multidimensional data series of eigenvalues and eigenvectors. The method has the advantage over PCA in that each ecological distance can be investigated. Weighted Unifrac and Unweighted Unifrac are calculated to assist the PCoA analysis (Figure 9). Based on the unweighted UniFrac distance, mesophilic compost of Harmanli was significantly greater (\(p<0.05\)) than the thermophilic one (Figure 9A). The cluster dendrogram of the relative abundance in phylum level showed that both mesophilic composts possess a certain similarity. Based on the weighted UniFrac distance, the mesophilic compost of Harmanli site was significantly greater (\(p<0.05\)) than the other composts. The cluster dendrogram showed a similarity between both stages of the Yasno pole site. In contrast, the stages of Harmanli were reasonably distinguished during the composting (Figure 9B).  
Figure 8: Beta diversity analysis of four compost stages (A1, thermophilic of Harmanli site; A2, mesophilic of Harmanli site; B1, mesophilic of Yasno pole site; B2, thermophilic of Yasno pole site).  
Figure 9: **(A)** PCoA based on Weighted Unifrac distance. **(B)** PCoA based on Unweighted Unifrac distance (A, A1 thermophilic of Harmanli site; B, A2 mesophilic of Harmanli site; C, B1 mesophilic of Yasno pole site; D, B2 thermophilic of Yasno pole site).
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 2 Methods
### Joint Loss Function and Transfer Learning  
To enable the network to be trained efficiently on the dataset, we designed a joint loss function and introduced the transfer-learning technique. Transfer learning is mainly applied to initialize networks via fully pretrained networks on the famous MS COCO [47] dataset, and then the initialized network is directly applied to train the optical and SAR image datasets to enhance the network's learning efficiency and effectiveness. The loss function consists of four aspects during the training phase:  
\[L_{total}=L_{mask}+L_{reg}+L_{cls}+L_{giou} \tag{7}\]  
where \(L_{mask}\), \(L_{reg}\), \(L_{cls}\), and \(L_{giou}\) represent the segmentation loss, the regression loss, the classification loss, and the intersection-over-union (IoU) loss, respectively.  
To overcome the problem of unbalanced samples, the gradient-harmonizing mechanism [48] was used for both classification and regression losses. It reduces the weights of easy samples (the images that are easily distinguished by the model) and outliers, and the  
Figure 7: Multistage cascade task head schematic.  
dynamic properties of its loss make training more effective and robust. The \(L_{reg}\) and \(L_{cls}\) are defined as (8) and (9):  
\[\begin{array}{c}L_{\text{reg}}=\frac{1}{M}\sum\limits_{i=1}^{M}\lambda_{i}ASL _{1}(d_{i})\\ =\sum\limits_{i=1}^{M}\frac{ASL(d_{i})}{GD(gr_{i})}\end{array} \tag{8}\]  
where \(GD\) is the designed gradient density function and \(ASL_{1}\) is the modified form of smooth-\(L_{1}\).  
\[\begin{array}{c}L_{cls}=\frac{1}{M}\sum\limits_{i=1}^{M}\lambda_{i}*L_{CE}\\ =\sum\limits_{i=1}^{M}\frac{L_{CE}}{GD(g_{i})}\end{array} \tag{9}\]  
in which  
\[L_{CE}=\left\{\begin{array}{ll}-\log(p),\quad if\ p*=1\\ -\log(1-p),\ if\ p*=0\end{array}\right. \tag{10}\]  
where \(M\) represents the overall sample count and \(\lambda_{i}\) can be regarded as the loss weight of the \(i\)-th sample; \(p\) represents the probability distribution predicted by the model, and \(p*\) is the true label. The IoU loss \(L_{giou}\) is defined as [49]:  
\[L_{giou}=1-IoU+\frac{A^{c}-U}{A^{c}} \tag{11}\]  
where \(U\) represents the union between the ground truth and the predicted bounding box, and \(A^{c}\) represents the area of the smallest enclosing box. The \(L_{mask}\) can be represented as Equation (12), which is the dice loss form in the V-Net [50].  
\[L_{mask}=1-\frac{2|X\cap Y|}{|X|+|Y|} \tag{12}\]  
Similarly, the operator \(\cap\) represents the elementwise dot product, and the operator \(\mid\mid\) represents the numerical square. Therefore, Equation (12) can be formulated as:  
\[\begin{array}{c}L_{mask}=1-\frac{2\sum\limits_{pix}Y_{T}\cdot Y_{F}}{\sum \limits_{pix}Y_{T}^{2}+\sum\limits_{pix}Y_{F}{}^{2}}\end{array} \tag{13}\]  
where \(Y_{T}\cdot Y_{F}\) is the dot product between the pixels of the ground-truth label image and the pixels of the predicted result.