esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes a new SSM for conflict identification and analysis based on the temporal and spatial proximity of conflicts and the severity of potential collisions. The novel SSM is called PCRI, and the comprehensive evaluation from multiple aspects enables PCRI to adequately measure conflict risk, achieving precise conflict identification. TTC is employed to compare the conflict identification performance with PCRI in different conflict types from the inD dataset. Results show that PCRI can identify conflicts more comprehensively, and the identification of the most dangerous moments is more accurate than that of TTC. Based on PCRI, the characteristics of conflicts in freeway merging areas considering the difference between trucks and cars are analyzed through the ORED. The results demonstrate that conflicts occur more frequently when trucks are involved, and the conflict locations are closer to the on-ramp. In summary, the proposed SSM can provide accurate conflict identification and analysis for traffic safety evaluation, especially in complex traffic environments. The conflict analysis results for freeway merging areas can better capture the differences in conflicts between trucks and cars, thereby providing a basis for the formulation of safety management strategies. For future work, PCRI can be extended to conflict scenarios involving a greater variety of vehicle types, such as the mixed traffic flow with human vehicles and autonomous vehicles.  
Conceptualization, Y.L. ([PERSON]) and K.C.; methodology, S.T., Y.L. ([PERSON]) and K.C.; software, S.T. and Y.L. ([PERSON]); validation, S.T., Y.L. ([PERSON]) and Y.L. ([PERSON]); formal analysis, S.T., Y.L. ([PERSON]) and Y.Z.; investigation, K.C. and Y.L. ([PERSON]); resources, Y.L. ([PERSON]) and Y.Z.; data curation, K.C. and Y.L. ([PERSON]); writing--original draft preparation, S.T., K.C. and Y.L. ([PERSON]); writing--review and editing, S.T., Y.L. ([PERSON]) and Y.L. ([PERSON]); visualization, S.T.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Author [PERSON] was employed by the company China Railway Siyuan Survey and Design Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 6 Discussion  
### Contribution  
To help researchers clarify the context of related technologies and clearly identify the relationship between various methods, this paper presented a comprehensive research map of the current practices about image-based 3D reconstruction. Following this, the fourth part of the paper focused on a critical synthesis of the main knowledge gaps and challenges in the 3D reconstruction process. Finally, main findings were summarized. In this process, the contribution of this paper is mainly divided into the following two aspects:  
1. A more comprehensive technology roadmap is created. Reading and summarizing the previous work, the authors find that the relevant literature only focuses on point cloud-based methods or perspective-based methods, which are applied to outdoor and indoor monitoring, respectively. Few people analyze them together in one article. This paper breaks the barriers between them and obtains a comprehensive technology roadmap. The technical roadmap is new and covers a wider range of methods,which provides a reference for the integration of indoor and outdoor construction progress monitoring.
2. The knowledge gap ignored by most scholars is highlighted. In the fourth part, the main knowledge gaps and challenges in the process of 3D reconstruction are analyzed, and the solutions are indicated. In addition to the problems concerned by scholars, this part also points out the problems of point cloud and prior knowledge, which are less concerned by scholars.
## 3 Related Literature Reviews  
In a survey paper, we investigated pressure-based routing protocols for underwater wireless sensor networks [4]. This paper is focused on a new classification that is based on the communication void. This article also provided a comparison between routing protocols based on their features, performance, and simulation setting, along with some future directions related to pressure-based routing algorithms. In [15], [PERSON] et al. have reported the energy efficiency and reliability of MAC and routing protocols for underwater wireless sensor network. This paper provides a detailed overview of the UWSN protocols that improve energy efficiency or reliability. [PERSON] et al. have classified protocols based on their routing strategies. Moreover, the protocols are compared based on different parameters along with their advantages and shortcomings. Simulation results have been carried out at the end of this paper, and the reviewed routing protocols have been compared considering energy efficiency and throughput.  
In [13], [PERSON] et al. conducted a survey of routing protocols for underwater acoustic sensor network. The aim of this survey was to classify the routing protocols based on cross-layer design methods, and it is considered the first paper that provides this classification. Moreover, they reviewed the intelligent algorithm-based routing protocols that can provide a broad range of concepts for routing protocol design for effectively improving the routing performance. Furthermore, this paper investigated the development trends of UASN routing protocol design in recent years to offer researchers clear and direct insights for the development of underwater acoustic sensor network routing protocols. In [3], [PERSON] and [PERSON] introduced underwater sensor networks as a new challenge for Opportunistic Routing (OR) protocols. This article comprehensively reviewed OR protocols in UWSNs. Moreover, the critical issues faced when using OR in different kinds of networks were also discussed. Furthermore, OR protocols were classified and explained in depth, along with their advantages and disadvantages. Lastly, this paper has been summarized with a comparison of all discussed protocols highlighting future research directions that must be further investigated for OR protocols in UWSNs. In this review article, we focus on next-hop node selection techniques and challenges for routing protocols in UWSNs, which is quite different from the aforementioned surveys focusing either on a particular category of routing protocols or complete routing protocols design issues for overall performance improvement.
## 4 Calibration and Correction of the Additive Phase
### Additive Phase Correction  
It can be clearly observed how there is a piston in the phase maps in the characterization of a sample as the IR laser power is increased. Figure 19a-e present in phase the damage that the sample receives when the IR laser is positioned at the glass-air interface, and the sample is characterized at powers of 110, 140, 175, 205, and 230 mW.  
Figure 15: Phase shifting (in profiles) relative to power shifting from 110 to 230 mW.  
Since the applied power for each damage caused to the paint is known, then it is possible to remove the corresponding additive phase from the calibration presented in the reference of Figure 18. If the additive phase map of the calibration is constant, it will be enough to also remove that constant value from the phase map of the measured three-dimensional form; in this case the damage caused by the optical trap. If the additive phase map were not constant, then a correction should be implemented by relating both phase maps, point to point. Figure 20 shows the Figure 19d phase map; from 0 to 4.78 \(\upmu\)m corresponds to the uncorrected phase and the half from 4.78 to 10.27 \(\upmu\)m is the corrected measurement. It can be observed, in the corrected half, how the area of the paint that has not been damaged by the optical trap remains with values close to those in Figure 19a. It is expected that the correction in each of the other phases will behave in the same way.  
Figure 16: Phase piston behavior in glass using 0 mW power as reference: (**a**) phase mapping; (**b**) profiles.  
Figure 17: Phase piston behavior in paint using 0 mW power as reference: (**a**) phase mapping; (**b**) profiles.  
Figure 18: Phase-shifting behavioral trend shown in interferograms as well as in phase average (point) depending on the power.
# Online Optimization of Pickup and Delivery Problem Considering Feasibility
## 2 Pickup and Delivery Problem
### Constraints
#### 2.2.1 Constraints on Agent Location  
First, let \(\mathcal{N}\) denote the finite set of agents. We introduce a binary variable vector \(p_{l}(t)\in\{0,1\}^{|\mathcal{V}|}\) for the agent \(l\in\mathcal{N}\), where \(t\in\{0,1,2,\dots,\}\) is discrete time. The \(j\)-th element \(p_{l,j}(t)\) of \(p_{l}(t)\) is 1 if the agent \(l\) is located at the vertex \(v_{j}\); otherwise, it is 0. The constraint on the location of the agent \(l\) is given by  
\[\sum_{j=1}^{|\mathcal{V}|}p_{l,j}(t)=1,\;\;l\in\mathcal{N},\;\;t\in\{\tau_{i},\dots,\tau_{i}+T_{f}\}, \tag{3}\]  
which implies that at time \(t\), the agent \(l\) is located at only one vertex.
## 2 Materials and Methods
### Testing of Optimization Results  
The quality of the results of our optimization method was tested on four spatial geodetic networks, with random variations in the coordinates of the points within the network, total station accuracies, and requirements for the resulting point accuracies. The longest semiaxis of the error ellipsoid (LSEE) was employed as the accuracy characteristic \(K\) in our testing.  
Hereinafter, the five employed algorithms will be designated (in line with Figure 1) as follows:  
* A1--The numbers of measurement repetitions are increased standpoint by standpoint; initial network configuration is used.
* A2--The number of measurement repetitions is increased for the entire network simultaneously; initial network configuration is used.
* A3--The numbers of measurement repetitions are increased standpoint by standpoint without initial network configuration.
* A4--The number of measurement repetitions is increased for the entire network simultaneously without initial network configuration.
* A\({}_{\text{OPT}}\)--The final optimal result selected as the best-performing result from all four algorithms.
# Image Noise Reduction by Means of Bootstrapping-Based Fuzzy Numbers
## 5 Filtering Images
### Fuzzy Similarity Value  
In this section using a fuzzy metric, we measure the amount of fuzzy similarity between the color vectors where the color in each pixel is expressed as a fuzzy number. This function is defined as follows.  
Suppose \(\tilde{u}_{k}=(\tilde{u}_{k}^{1},\tilde{u}_{k}^{2},\tilde{u}_{k}^{3})\) represents the color vector of the image pixels at position \(k\) comprising its \(R\), \(G\), and \(B\) components, where \(\tilde{u}_{k}^{i}\) is a fuzzy number for each pixel and represents red, green, or blue with a fuzzy number. Furthermore, \(c\) is a positive real parameter used to control the spread of the function. This function, denoted by \(R\), is as follows:  
\[R(\tilde{u}_{i},\tilde{u}_{j})=\frac{c}{c+\|\tilde{u}_{i}-\tilde{u}_{j}\|}.\]
## 3 Results and Discussion  
### Density and Porosity of Al-BiSn Composites  
Figure 3 depicts the measured density and porosity of extruded pure Al and Al-BiSn composites with different BiSn contents. The average measured density values of the extruded Al-BiSn composites increase with the incremental BiSn content due to the high density of BiSn alloy (8.12-8.56 g/cm\({}^{3}\)) which is almost 3 times that of pure Al (2.70 g/cm\({}^{3}\)). Further, with an increasing amount of BiSn in Al-BiSn composites, the percentage of porosity also increased. This increment in the porosity can be regarded as the pronounced effect of particle agglomeration due to the high amount of reinforcement and pore nucleation between the Al matrix and BiSn interfaces. These findings are consistent with previous studies [15; 21].  
Figure 3: The fluctuation of measured density and porosity of Al-BiSn composites as a function of BiSn content.
# Exploring the Potential of Sensing for Breast Cancer Detection
## 5 Biosensors for Breast Cancer Detection
### Electrochemical Impedance Spectroscopy
In the alpha dispersion bandwidth, the magnitude of electrical impedance spectra maintains a substantial difference for different cancer cell lines. So, impedance spectroscopy is suitable for identifying low concentrations of cancer cells. HER2 is considered one of the most important biomarkers for identifying the proliferation of breast cancer. In [149], authors designed an electrochemical immunosensor for analyzing the HER2 extracellular domain (ECD) in human serum, and a screen-printed carbon electrode was considered as a transducer surface in the presence of gold nanoparticles. The linear sweep voltammetry (LSV) method was applied to analyze the enzymatic reaction and the antibody-antigen interaction. They observe the following: (a) the quantification and the patient's response to the therapy of the biomarker were determined for a wide range of concentrations; (b) the dilute sample with buffer was sufficient for a high HER2 ECD concentration; (c) the LOD was determined at 4.4 ng/mL; (d) the cut-off value was 15 ng/mL, so the LOD, as well as the diagnosis of HER2, was easily determined for the patients.
## Appendix A Experimental Reproducibility Details
### Human Evaluation Protocol  
**Evaluator Qualifications:**  
* Native Tarifit speakers
* Fluent in target languages (Arabic/French/English)
* Linguistic or translation background preferred  
**Evaluation Criteria:**  
1. **Adequacy (1-5):** Does the translation convey the meaning of the source text?  
* 5: Complete meaning preserved
* 4: Most meaning preserved, minor gaps
* 3: Essential meaning preserved
* 2: Some meaning preserved
* 1: Little or no meaning preserved
2. **Fluency (1-5):** Is the translation natural in the target language?
* 5: Perfect fluency
* 4: Good fluency, minor issues
* 3: Acceptable fluency
* 2: Disfluent but understandable
* 1: Very disfluent
## 6 Applications in Medical and Food Systems
### Food Applications
#### 6.2.3 Bakery and Cereal Products  
Ashwagandha powders, including many products, are evaluated at different concentrations, such as 5% in Namakpara, Pap Chakal, and Muruk, and up to 10% in Missy Roti and Chutney powders. Containing up to 5% of _Withania somifera_ root powder, biscuits are rich in energy, minerals, fiber, and protein, enhancing their medicinal benefits [139]. Ashwagandha root powder at 2% in baked goods such as Thepla and breads reduces diabetes [140].
## 3 Results
### Heat Transfer Coefficient in the Tropical Island Region
#### 3.3.3 Seasonal Average Heat Transfer Coefficients  
The heat transfer coefficients of different seasons in the tropical island region are listed in Table 3, and the meteorological division method is used to delimit four seasons in this study. From Table 3, we can know that the difference between THTC-Y and THTC-N is minimum in spring and maximum in summer, and they are 1.5 W/(m\({}^{2}\cdot\)K) and 7.2 W/(m\({}^{2}\cdot\)K) respectively. Except for this, the difference is 5.2 W/(m\({}^{2}\cdot\)K) for the whole year. Thus, the effect of evaporation heat transfer on THTC is significant and cannot be ignored directly. Additionally, because the current Chinese standard only gives THTC recommended values in summer and winter, we mainly make a comparison between the THTCs provided by this study and the THTCs recommended by the standard here. It should be noted that THTC recommended values in the current Chinese standard are a consequence without distinction for wind speed size and consideration for evaporation heat transfer, and they must have a certain gap with the THTCs considering wind speed size and evaporation effects in this study. Furthermore, the differences are 19.9 W/(m\({}^{2}\cdot\)K) in summer and 10.4 W/(m\({}^{2}\cdot\)K) in winter, respectively. Therefore, as far as the tropical island building is concerned, strong wind contributes to large CHTC, together with heavy rainfall, then EHTC cannot be ignored. Consequently, THTCs in the tropical island region are much higher than the recommended values in the current Chinese standard aimed at inland climates.  
Here, it should be noted that convective, radiation, and evaporative heat transfer are three forms of heat transfer between an envelope surface and the ambient environment in the heat balance equation of external building surfaces. When the envelope surface is wet, the three forms of heat transfer occur simultaneously, but the interaction effects among the three forms of heat transfer are not considered in the heat balance equation of external building surfaces. Therefore, it is unnecessary to take into account the influence of rain on CHTC and RHTC.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{\(u\) **[m/s]**} & \multirow{2}{*}{**Rain Hours [h]**} & **CHTC** & **RHTC** & **EHTC** & **THTC-Y** & **THTC-N** & **THTC (Standard)** \\ \cline{3-8}  & & & & & & **[W/m\({}^{2}\cdot\)K]** & \\ \hline Spring & 3.02 & 45 & 19.1 & 1.5 & 26.4 & 24.9 & — \\ Summer & 4.56 & 118 & 26.0 & 7.2 & 38.9 & 31.8 & 19.0 \\ Autumn & 2.91 & 175 & 18.6 & 5.8 & 6.9 & 31.3 & 24.4 & — \\ Winter & 3.76 & 126 & 22.4 & & 5.2 & 33.4 & 28.2 & 23.0 \\ Whole year & 3.56 & 464 & 21.5 & & 5.2 & 32.5 & 27.3 & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: Heat transfer coefficients of different seasons in the tropical island region.
## 3 Related Work
### OCR Technology and Platforms
#### 3.2.2 Other OCR Technologies  
Other OCR technologies also exist and are used for different purposes. Camera is a toolkit for building document image recognition systems [26]. The images or symbols must be assigned to the classes manually, and the result of this step creates a database in the Extensible Markup Language (XML) format. Camera uses the K Nearest Neighbors (KNN) algorithm as its classifier. Camera is available on [[https://gamera.informatik.hsnr](https://gamera.informatik.hsnr)]([https://gamera.informatik.hsnr](https://gamera.informatik.hsnr)). de/index.html (accessed on 7 August 2021).  
Figure 1: Tesseract Timeline [25].  
Google Drive OCR is a multilingual and online OCR service provided by Google Incorporation and supporting up to 200 languages in almost 25 writing systems. According to [PERSON] and [PERSON] [27], Hidden Markov Model (HMM) has been employed to work potentially with all languages and writing systems. Although it is a powerful OCR system, it does not support the Kurdish language [28]. Google drive can partially recognize Kurdish documents based on its Arabic model, but it misses the special Kurdish characters that do not exist in the Arabic alphabet.  
GOCR/JOCR is an open-source OCR tool under the GNU license [29] which can read and convert images files such as PNM, PGM, PBM, and PP into text format. GOCR does not require training data that results in a fairly low accuracy rate for different languages. The recognition process happens in two stages. In the first stage, GOCR takes the whole document as input, and in the second one, it processes misidentified characters from the same document [29].  
[PERSON] et al. [11] provided a comparative analysis on various OCR engines, their capability, working platform, and other attributes. Recently, a new survey with a broader coverage was also published [30].  
Finally, literature reports on research about handwritten Kurdish texts. For example, [PERSON] and [PERSON] [31] and [PERSON] [32] have reported on handwritten character recognition in Kurdish, focusing on single and isolated characters. However, as we mentioned, our focus is on machine-type documents, and therefore, we are not reviewing those studies in detail.  
To summarize, although OCR is a well-developed technology in language processing, most less-resourced languages have not benefited from the advances in the field. Furthermore, as most modern OCR engines use artificial intelligence methods such as LSTM, they require a large amount of data for their training. We are interested in showing that leveraging the script similarity between languages is a solution to that issue. Although the recent literature presents some work on using limited data in OCR adaption, particularly on LSTM-based engines [33; 34; 35; 36], it does not show script-similarity-based approaches based on using well-resourced language to the advantage of less-resourced ones. That is what we intend to show in this research. In our method, the emphasis and concentration are on a specific engine (Tesseract).
# Prediction of Corroded Pipeline Failure Pressure Based on Empirical Knowledge and Machine Learning
## 2 Methodology
### Constraining Optimization by Empirical Formulas-Incorporated Loss Function  
Artificial neural networks (ANNs) have demonstrated considerable accuracy in predicting the failure pressure of corroded pipelines. The integration of physical formulas to guide the optimization of neural networks was initially proposed by [PERSON] [18], and it has been shown to enhance the model's ability to extract information from data, thereby improving performance in failure pressure prediction tasks [14]. However, empirical formulas often yield results with lower accuracy and are typically insufficient for directly guiding the optimization of neural networks. This limitation necessitates complex feature extraction steps to bridge the gap between empirical insights and model optimization. In this study, we introduce a novel approach to employing empirical formulas for guiding the optimization of artificial neural networks. By designing an appropriate loss function, we aim to balance the strong fitting capabilities of neural networks with the instructive nature of empirical formulas, thereby leveraging the strengths of both methodologies to achieve more accurate predictions of failure pressure in corroded pipelines.  
Figure 1 illustrates the optimization process of a neural network guided by an empirical formula. On the left are pipeline data obtained from experiments or simulations, comprising seven features: outer diameter, inner diameter, yield strength, tensile strength, flaw length, flaw depth, and flaw width. These features are utilized in two parallel pathways: one inputs the data into an artificial neural network (ANN) to predict the failure pressure  
\(\hat{P}_{FA}\), while the other employs the empirical formula to predict the failure pressure \(\hat{P}_{FE}\). In Figure 1, the prediction results from the empirical formula in Constraint 1 are directly compared with the prediction results from the neural network to compute the loss. Here, Constraint 1 serves as a direct optimization constraint imposed by the empirical formula. When Constraint 2 is applied, the neural network outputs an additional term \(\hat{P}_{Defect}\), which can be interpreted as the prediction error arising from variations in flaw dimensions as captured by the empirical formula. The introduction of \(\hat{P}_{Defect}\) effectively compensates for the inherent errors of the empirical formula and enhances the overall optimization effect. This additional term allows for the model to adapt more flexibly to discrepancies between the empirical formula and real-world data, thereby improving the accuracy and robustness of the optimization process.