esa-sceva/satcom-chunk-collection huggingface dataset:
# Voting-Based Document Image Skew Detection
## 2 Skew Detection Algorithms
### Projection Profiling Row Align
#### 2.2.2 Improvements  
First, the image is binarized via standard bimodal Otsu thresholding.  
Then, we find the connected components in the image and compute their axis-aligned bounding boxes (AABBs) [27]. The purpose is that we only try to align points of interest into horizontal rows, landmarks of connected components which represent text symbols.  
The top and bottom midpoints of the AABBs' sides are the only points we need to rotate in order to find the skew angle. We illustrate the mechanism in Figure 5.  
To avoid computation with elements that are not text symbols, we compute the average width and height of the AABBs. If an AABB of a connected component is considerably larger than this average,  
Figure 4: (**a**) Input: skewed document; (**b**) points of connected components’ axis-aligned bounding boxes (AABBs): top (blue) and bottom (red) sides and their associated scanline histograms; (**c**) same as ’(b)’, but the histograms have maximum variance as the page is correctly oriented.  
Figure 5: Projection profiling of AABBs’ edge midpoints, on the histogram.  
we discard it from the rotation phase, as it does not represent a text symbol, or does not belong to a block of text.  
With a small step, such as a tenth of a degree, we compute the variance of the document's bottom and top histograms for all angles in the SASS and pick the winner skew angle as the one which gives the greatest sum of the aforementioned variances.  
It is unknown where the global maximum is for the document's scanline variance as a function of an angle, so it is safest to search the whole SASS, rather than using some fast convergence heuristic like a binary search or gradient descent. Unlike them, the brute force search guarantees to find the global maximum. The search space contains 200 or 300 angle candidates, and the scanline variance for one angle is computed in less than 100 microseconds, which is due to the fact that we only rotate at most a couple of thousand points per iteration. Therefore, the brute force search is the most recommended approach here.  
However, by sampling the variance for all angles in SASS, we are able to define a confidence metric, as described in Section 2.2.3.
###### Abstract  
The current study aims for the isolation and physicochemical characterization of inulin from defatted dandelion roots using green extraction techniques, including microwave extraction (MAE) and ultrasound-assisted extraction (UAE). The structure and degree of polymerization of inulin were elucidated by chromatographic techniques, as well as by FTIR and NMR spectroscopies. The color characteristics, water- and oil-holding capacity, solubility, swelling properties, wettability, angle of repose, flowability, and cohesiveness of dandelion inulin were evaluated. Moreover, the antioxidant and antibacterial potential of dandelion inulin were revealed. The results were compared with the conventional extraction and inulin from chicory. Dandelion inulin was evaluated as a powder substance with a degree of polymerization (DP) of 17-24. The highest yield (20%) was obtained by classical extraction; however, UAE and MAE demonstrated the highest purity. FT-IR and NMR spectra revealed that dandelion inulin is glucofructan with a molecular weight of 2.7-3.2 kDa that consists mainly of fructosyl units \(\beta\)-(2\(\rightarrow\)1) linked to one \(\alpha\)-D-glucose unit UAE was evaluated as the most perspective technique for the simultaneous extraction of inulin from dandelion roots, with the highest average DP 24 and high purity (82%), molecular mass, total fructose content, swelling index, and oil-holding capacity. Dandelion inulin exhibited intermediate cohesiveness, fair flowability, and moderate antimicrobial activity against _Listeria monocytogenes_ 863 and _Bacillus subtilis_ 6633. The physicochemical and functional properties of dandelion inulin reveal its future potential as an additive in food, cosmetic, and pharmaceuticals formulations as a texture modifier, a fat replacer, and a drug carrier.  
dandelion roots; inulin; microwave and ultrasonic extractions; FT-IR spectra; NMR spectroscopy; functional properties +
Footnote †: journal: Applied sciences  
Article
## 2 Thermal Analysis and Design Improvement of the WCLL COB Segment TC Region
### The TC Region Reference Design Configuration  
The reference geometric layout of the DEMO WCLL COB segment [4] is considered to assess its upper region, housing the TC, as depicted in Figure 1. The segment box (SB), namely the side walls (SWs) (first wall (FW) complex plus the back-plates and manifolds), the internal stiffening plates (SPs), the TC, the back-supporting structure (BSS) and the attachments, devoted to connecting the BB to the vessel, are made of Eurofer steel, whereas a 2 mm thick tungsten layer covers the FW straight and bend regions. In particular, a TC that is 25 mm thick equipped with 7 cooling channels with a square (7 \(\times\) 7 mm) cross section is initially considered. Specifically, six TC cooling channels follow a pure toroidal path, whereas one follows the radial-toroidal-radial path as well as the SB cooling channels (Figure 1) as was already done in [9] for the previous DEMO WCLL COB reference geometric configuration. Regarding the breeding zone cooling system, 16 double-walled tubes (DMTs) derived from the v06a DWTs geometric layout [10] are assumed for this reference configuration. Details on the assessed geometric configuration can be found in Figure 1.
## 2 Preliminary  
We first review some conclusions on the theory of solutions to linear differential equations.  
Equation (1) is said to be homogeneous if \(Q(x)\equiv 0\), and nonhomogeneous if not. Moreover, the equation \(y^{\prime\prime}+P(x)y=0\) is said to be the associated homogeneous equation of (1). Equation (1) is said to have constant coefficients when \(P(x)\) is a constant, and have variable coefficients if not. If Equation (1) has constant coefficients, it can be written as  
\[y^{\prime\prime}+py=Q(x), \tag{3}\]  
where \(p\) is a constant. The associated homogeneous equation of (3) is \(y^{\prime\prime}+py=0\) and the characteristic equation of (3) is \(\lambda^{2}+p=0\). Hence the characteristic roots of (3) are \(\lambda=\pm\sqrt{-p}\).  
Let \(y_{p}\) denote any particular solution of Equation (3) and \(y_{h}\) represent the general solution of its associated homogeneous equation, then the general solution of Equation (3) is \(y=y_{h}+y_{p}\).  
The following two lemmas are introduced for obtaining the general solution \(y_{h}\) and particular solution \(y_{p}\) of Equation (3), which will be used in the next section.  
Lemma 1 ([13]): _If \(p<0\), then \(\lambda\) is real, and hence the general solution is \(y_{h}=k_{1}e^{\lambda x}+k_{2}e^{-\lambda x}\). If \(p=0\), so is the value of \(\lambda\), then the general solution is \(y_{h}=k_{1}e^{\lambda x}+k_{2}xe^{\lambda x}=k_{1}+k_{2}x\). If \(p>0\), then \(\lambda\) is a pair of conjugated imaginary numbers, and hence the general solution is \(y_{h}=k_{1}\sin(\lambda x)+k_{2}\cos(\lambda x)\). Here \(k_{1},k_{2}\) are arbitrary constants._  
Lemma 2 ([13]): _Suppose that \(Q(x)\) is a polynomial of degree \(n\), then a particular solution of Equation (2) is_  
\[y_{p}=e^{\alpha x}(k_{n}x^{n}+k_{n-1}x^{n-1}+\cdots+k_{1}x_{1}+k_{0}),\]  
_where \(\alpha\) is a known constant and \(k_{i}(i=0,1,2,\cdots,n)\) are constants to be determined._  
_Specially, when the degree of \(Q(x)\) equals to 0, the particular solution \(y_{p}=c\) if \(\lambda\
eq 0\); and \(y_{p}=cx^{2}\) if \(\lambda=0\), where \(c\) is a constant._
## 3 Effect of Deformation Parameters on Microstructure Evolution
### Accumulative Strain  
It has been proved in numerous studies that accumulative deformation strain is the most important factor that affects the DRX, second-phase distribution and texture and, thus, the mechanical properties. According to Equation (1), the equivalent strain during MDF process is mainly determined by the deformation strain in per pass and the total deformation passes.  
Figure 4 shows the inverse pole figure maps and stress-strain curves of MDFed Mg-8.5 Gd-4.5Y-0.7 Zn-0.4 Zr alloys reported by [PERSON] et al. [34]. The results showed that the grain size refined with increasing deformation passes, but the grain refinement ef  
Figure 2: The microstructure of MDFed AZ80 alloys with different temperatures: (**a**,**d**) 380 \({}^{\circ}\)C, (**b**,**e**) 280 \({}^{\circ}\)C and (**c**,**f**) 180 \({}^{\circ}\)C [43].  
fect was weakening when exceeding six passes. Meanwhile, the ultimate strength, yield strength and elongation increased with increasing deformation passes. Similar conclusions were investigated by [PERSON] et al. [45], and the grain size of AZ31 alloys approached 1 \(\upmu\)m with repeated MDF at a strain of 5. [PERSON] et al. [46] prepared a bulky Mg-5.95 Gd-3.21Y-0.83 Zn-0.32 Ag-0.44 Zr alloy through MDF at 480 \({}^{\circ}\)C with an accumulative strain of 4.4 after 27 forging passes. The results showed an inhomogeneous microstructure with different levels of DRX, and the severe strain observed at the center of the workpiece inhibited an average grain size of 2.91 \(\upmu\)m, while the edge area was 17.78 \(\upmu\)m. The optimum comprehensive mechanical properties were obtained in the fine-grained center zone with an ultimate strength of 364 MPa, yield strength of 295 MPa and an elongation of 4.8%, which mainly attributed to the grain refinement and dynamic precipitates. [PERSON] et al. [47] investigated the effect of small-strain MDF with a cumulative strain of 2.7 on the microstructure, texture and mechanical properties of AZ31 alloys. The results showed that a uniform fine-grained structure with an uncommon bimodal basal texture was achieved. Continuous DRX, discontinuous DRX and twinning DRX occurred during deformation, which are the main grain refinement mechanisms. Furthermore, [10; 11; 12] twinning and the formation of \(<\)0001\(>\) be paeallel to last forging direction were dominant to the increasing yield strength.
## 4 Discussion
### Analysis of Trade-Off According to Patch Coverage Change  
As discussed previously in Section 2.3, patch coverage has two opposing effects on model performance. When patch coverage decreases, the contextual information per patch diminishes, whereas the number of patches extracted per parcel increases, enhancing the aggregation effect. In this section, we comprehensively analyze the trade-off between these two effects and examine how varying levels of patch coverage impact model performance, including under conditions of severely reduced coverage.
# Managing Redundancy in a Multiple-Robot Orienteering Problem for Equilibrium and Robustness
## 4 Discussion  
The paper simultaneously considers robustness and workload equilibrium in orienteering tasks for the first time. It is strictly expressed as a three-stage optimization problem called RMOPWB. The approach extends the traditional MOP framework by integrating robust optimization principles and workload balancing. Our contributions include the development of heuristic strategies and the utilization of Monte Carlo sampling techniques to mitigate redundancy. The numerical simulations show that the algorithm can maintain the total rewards, while effectively reducing the excessive redundancy. Building on the comprehensive validation presented here, our immediate future work will focus on deploying and evaluating the RMOPWB algorithm on a physical fleet of UAVs. The reliability and performance demonstrated in our high-fidelity ROS2 environment establish the necessary foundation for this important next step.  
Conceptualization, [PERSON] and Z.M.; methodology, [PERSON]; software, [PERSON]; validation, T.J., [PERSON] and W.L.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] 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 due to privacy or ethical restrictions.  
The authors declare no conflicts of interest.  
Figure 10: Influence of the number of attackers on algorithm performance. Results from 50 simulations, \(|\mathcal{S}|=6\), \(B=26\), \(|\mathcal{V}|=14\) and \(|\mathcal{E}|=13\).
## 5 Deterioration Features and Data Description of the Tokyo Region  
### Deterioration Features of the Tokyo Region  
The Tokyo expressway is over 330 km long and is under the control of Metropolitan Expressway Co., Ltd. Inspection data on parts of the Metropolitan Expressway's steel bridge deck slab, the Route 3 Shibuya Line and Route 5 Ikebukuro Line, were used for analysis. The construction years of both routes comprised around 1965 to 1975. By processing longitude/latitude coordinates and the management ledger, a detailed environmental/structural dataset was prepared.  
Fatigue damage caused by repetitive vehicular traffic is the main reason for RC slab deterioration in the Tokyo region ([PERSON] et al. (1986) [19]). After decades of use, these slabs have deteriorated a great deal. Usually, during visual inspection, one and two-dimensional cracks, water leakage, and efflorescence were observed on the bottom of the slab. As [PERSON] (1987) stated, in the initial state, deterioration of the slab starts with the appearance of transverse cracks, which is considered as related to shrinkage and construction joints. Since they undergo a heavy traffic volume of around 45,000 vehicles per day on average, transverse cracks gradually increase, and longitudinal cracks also start to appear. As two-dimensional cracks develop further, mesh cracks can be observed. Then, as crack density and crack width increase, punching shear failure ultimately occurs. The process is shown in Figure 12. As stated earlier, during this process, water such as rainfall significantly accelerates the deterioration process. In addition to the development of two-dimensional cracks, the interlock effect becomes weak, and shear capacity is decreased. Eventually, punching shear failure occurs. According to this theory, a quantitative deterioration assessment has been done by [PERSON] et al. (2018) to predict the remaining fatigue life of bridge decks based on their site inspected cracks [20; 21]. However, the research is based on mechanics aspects, which did not consider the environment effect. Therefore, this research can be considered a risk assessment by integrating both mechanics and ambient effect. As in East Japan, other factors such as structural features (slope, slab thickness, etc.) also affect the slab degradation rate.  
Figure 12: Crack progress and deterioration process of an RC slab.
# A Chinese-Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization
## 4 Experiments
### System Environment and Model Parameters  
In our experiment, we used Ubuntu 20.04.6 as the operating system environment and trained the model on a Tesla V100 GPU. The system had 16 GB of memory capacity. To build a shared vocabulary, we utilized the subword-nmt tool and set the vocabulary size to 10k to ensure consistency in word representation between the source and target languages [40, 41]. We chose the fairseq framework as the underlying framework, which provides rich functionalities and efficient operations, supporting model training and execution effectively with high computational performance [42].  
Here are the detailed descriptions of the model parameters: the model architecture is the Transformer, the label smoothing coefficient is set to 0.1, the dropout rate is 0.1, the weight decay coefficient is 0.01, and the dropout rate of the attention mechanism is 0.1. The Adam optimizer is used for parameter update, and the beta parameter of the Adam optimizer is set to 0.9 and 0.98 [43]. The first 4000 update steps use linear learning rate pre-heating. The initial learning rate in the preheating stage is \(1\times 10^{-7}\), the basic learning rate during training is 0.0003, and the maximum number of tokens per training batch is 4096. The model parameters were not manually or automatically tuned. sacreBLEU is a popular tool for computing BLEU (Bilingual Evaluation Understudy) and chrF++ (chrF enhanced with word bigrams) scores, measuring the similarity between machine-translated output and reference translations [44, 45, 46, 47]. BLEU is a commonly used metric to evaluate machine translations. It measures the similarity between machine-translated outputs and reference translations at the word level. The calculation formula for BLEU is as follows:  
\[\text{BLEU}\ =BP\cdot\exp\left(\sum_{n=1}^{N}w_{n}\log p_{n}\right) \tag{4}\]  
\[BP=\left\{\begin{array}{l}1\ \text{if}\ c>r\\ e^{1-r/c}\ \text{if}\ c\leq r\end{array}\right. \tag{5}\]  
The variables \(c\) and \(r\) represent the length of the translation and reference text, respectively, to be evaluated. chrF++ is a metric that evaluates machine translation quality by combining both character-level and word-level matching and calculating their average value. It addresses the limitations of traditional metrics like BLEU when evaluating long sentences or sentences with more errors.  
\[\text{chrF}\,\beta=\left(1+\beta^{2}\right)\frac{\text{chr}\,P\cdot\text{chrR }}{\beta^{2}\cdot\text{chr}\,P+\text{chr}\,R} \tag{6}\]  
In this context, the default value of \(\beta\) is 2.chrP refers to Precision, which measures the proportion of character-level n-grams that match the candidate and reference translations. chrR represents Recall, which measures the proportion of character-level n-grams matched between the candidate and reference translations.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 7 Experimental Tests
### Constraint due to Reclosing in 1 s  
In order to improve the quality parameters of their service, electrical distributors require themselves to close the tie recloser within 1 s from the occurrence of the fault (so that the interruption, for users connected to the healthy section of the line, is classified as transitory and subjected to lower penalties [43]). This condition is met in all the considered case studies except in the third  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interval** & **Measurement Method** & **Max Value [s]** & **Measured Value [s]** \\ \hline \(T_{SR\_BLIND}\) & \(t^{r}_{SR\_RTDone}\)=\(t^{r}_{FAULT}\) & 0.33000 & 0.21579 \\ \(T_{PR1\_BLIND}\) & \(t^{r}_{PR\_RTDone}\)=\(FAULT\) & 0.33000 & 0.21580 \\ \(T_{PR2\_TRIP}\) & \(t^{r}_{PR2\_CB}\)=\(t^{r}_{FAULT}\) & 0.33000 & 0.32957 \\ \(T_{PR3\_TRIP}\) & \(t^{r}_{PR3\_CB}\)=\(t^{r}_{FAULT}\) & - & 0.54589 \\ \(T_{IB\_CLOSED}\) & \(t^{r}_{TR\_IB}\)=\(FAULT\) & - & 0.76248 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Estimated time intervals during the fault 67N.S1 between PR2 and PR3.  
Figure 16: Digital signals acquired during the fault 67N.S1 between PR2 and PR3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Study** & **Interval** & **Measured Value [s]** \\ \hline Fault clearing time for fault 67N.S1 between SR and PR1 & \(T_{SR\_TRIP}\) & 0.32839 \\ Fault clearing time for fault 67N.S1 between PR2 and PR3 & \(T_{PR2\_TRIP}\) & 0.32957 \\ Reclose time for fault 67N.S1 between SR and PR1 & \(T_{IB\_CLOSED}\) & 1.19899 \\ Reclose time for fault 67N.S1 between PR2 and PR3 & \(T_{IB\_CLOSED}\) & 0.76248 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Time intervals for fault clearing and interconnection reclosure for the fault 67N.S1.  
case, i.e., the fault 67N.S1 between SR and PR1, when the IB is actually commanded after 1.19899 s. This additional constraint was thus added during the design of the logic selectivity system.  
The largest \(T_{packet}\) that allows the boundary to close in less than 1 s is that with a duration of 103.42 ms, which can be obtained with the pairs of SF and BW equal to (9;125), (10;250), and (11;500), to which, respectively, correspond receiver sensitivity values of \(-\)129, \(-\)129 and 128.5 dBm. Therefore, in order to always guarantee the best receiver sensitivity and link budget, values of SF equal to 9 and BW equal to 125 kHz were set to configure the LoRa package during the tests.  
In Figure 17, a time-frequency diagram of a LoRa package with a nominal duration of 103.42 ms, acquired with the spectrum analyzer RSA 3408A, is reported.  
The acquired digital signals are reported in Figure 18.  
Figure 17: Time–frequency diagram of a LoRa packet with a duration of 103.42 ms.  
The measured time intervals of interest are, then, reported in Table 18.  
It can be noticed that this time, \(T_{IB\_CLOSED}\) is lowered to 0.76795 s, compliant with the considered constraint.
## 3 Results and Discussion
### Nanocoated Fabric Pull-Out Properties
#### 3.2.5 Single and Multiple Nanocoated Fabric Pull-Out Energy Results
In contrast, the single-yarn total pull-out energy of the GPO was observed to be 28.39% lower than that of the KPO. Furthermore, the two-yarn total pull-out energy of the BPO was found to be significantly greater, exceeding those of the MPO, GPO, and KPO structures by factors of 4.02, 11.98, and 7.92, respectively. Conversely, the two-yarn total pull-out energy of the GPO was 33.94% lower than that of the KPO. Moreover, the three-yarn total pull-out energy of the MPO structure exhibited a remarkable enhancement, exceeding that of the GPO and KPO configurations by factors of 5.03 and 3.17, respectively. Conversely, the three-yarn total pull-out energy of the GPO structure was observed to be 37.09% lower compared to that of the KPO structure. The three-yarn total pull-out energy for the BPO structure was excluded from analysis due to premature yarn breakages during the pull-out tests, attributed to the generation of significantly high resistive frictional forces. Nonetheless, the three-yarn total pull-out energy of the BPO structure remained several times higher than that of both the GPO and KPO structures. In conclusion, the highest energy absorption regions in the single-yarn and multiple-yarn pull-out energy of nanocoated fabric structures were identified, in descending order, as stick-slip energy (stage III), crimp extension energy (stage I), and initial interlacement rupture energy (stage II). The contributions of B\({}_{4}\)C and MWCNTs demonstrated enhancements across all these regions. An additional noteworthy observation is the enhancement of crimp extension energy absorption through nano-reinforcement. Interestingly, the improvement in the energy absorption capacity of yarns within the fabric structure, without disintegration, is considered significant at this stage. For instance, the crimp extension energies of B\({}_{4}\)C-reinforced fabrics, compared to the control fabric, were higher by factors of 4.28, 15.14, and 9.86 for single, two-yarn, and three-yarn configurations, respectively. These findings suggest a potential performance enhancement for prospective soft ballistic applications.