esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Image Quality Assessment  
When a field deployment of the vision-based system is conducted in a low-light or dark environment, no input image can be used as a reference image, i.e., an image that is captured under normal lighting conditions, so it is assumed to have good visual quality. Therefore, the assessment of output image quality from enhancement operations in this study is conducted based on the no-reference quality metrics, namely, the blind-referenceless image spatial quality evaluator (BR) [75], naturalness image quality evaluator (NQ) [76], and perception-based image quality evaluator (PQ) [77]. Essentially, BR, PQ, and NR metrics use similar NSS features but BR and PQ metrics use trained features based on natural and distorted images, in addition to human interpretation. Therefore, BR and PQscores are restricted to the assigned types of distortion, whereas NS is more independent in predicting the image quality.  
No reference quality metrics as described previously are used to estimate the quality of the output image from enhancement procedures. Meanwhile, the classical quality metrics, i.e., image entropy (\(E\)), peak-signal-to-noise ratio (\(PSNR\)), and structural similarity index (\(SSIM\)) are still used in this study to measure how each of these indexes changes following the enhancement process. The difference in image characteristics before and after implementing the enhancement algorithms can be estimated using these metrics.  
Automated Identification of Object Features and Significance in Close-Range Photogrammetry and SHM Procedures  
In automated close-range photogrammetry, the object detection is tested as a homogenous white area based on the predefined search window. Template matching based on normalized cross-correlation coefficient (NCC) computes all possible radii of the center of the white area in two directions within the search window. Overall, the adapted photogrammetry procedure in this study is computed automatically within all photogrammetry images by the self-calibrating bundle adjustment. When the photogrammetry is completed without error, the SHM is conducted, and the recorded videos or images are processed to generate the data. The sub-pixel registration of the pattern or template matching method [78] based on NCC is also used to track the object locations within the image sequences. Finally, using the relationship between two cameras (as a full-projection matrix) and the change in object location in each image (from the template matching method) as outlined in Figure 1, images are translated into time-domain response signals, i.e., displacement, velocity, or acceleration. The SHM accuracy is computed based on the difference between the vision-based measurement and reference values as the absolute or relative error based on the experiments.
## 2 Scratch Formation Reduction Strategy
### Velocity Synchronization  
Figure 2 shows a system in which a motor is attached to the roller to synchronize the velocity of the rollers and steel plate. To reduce the frictional force generated when the steel plate and roller come into contact, the rotational velocity of the roller (\(V_{r}\)) and transfer velocity of the steel plate (\(V_{m}\)) must be synchronized, as shown in Figure 2. To achieve this, the velocities of the rollers and steel plate must be accurately measured. However, because of the harsh hot rolling process environment, attaching velocity sensors to the steel plate is challenging. Accordingly, instead of directly measuring the rotational and transfer velocities of the roller and steel plate, respectively, an indirect method of synchronizing these velocities is proposed.  
Figure 1: (**a**) Conventional hot rolling method in finishing mill; (**b**) hot rolling in finishing mill using flying touch method.  
The frictional force exerted on the steel plate is caused by the velocity difference between the steel plate and rollers; this friction increases the load torque on the rotating motor of the roller. Therefore, if the load torque can be observed, then the friction force generated between the roller and steel plate can also be estimated. An SPO-based torque estimation method, which can estimate the load torque without using additional force/torque sensors, is used. To robustly control the system, the SMCSPO is applied to control the velocity of rollers.  
Figure 3 illustrates the velocity synchronization concept between the rollers and steel plate. Throughout the manuscript, \"\({}^{\prime\prime}\)\" refers to estimated quantities. If the roller does not come in contact with the steel plate, its load torque (\(\hat{T}_{l}\)) is 0, and the rotational velocity (\(V_{r}\)) is equal to the roller's reference velocity (\(V_{ref}\)). However, if the roller is in contact with the steel plate, then \(\hat{T}_{l}\) > 0. This means that the rollers and steel plate velocities do not synchronize, possibly leading to the formation of scratches on the plate. In this case, the new reference velocity of the roller (\(V_{r}\)) is determined by the sum of the previous reference velocity of the roller (\(V_{ref}\)) and velocity compensation value (\(V_{c}\)), as follows:  
\[V_{r}=V_{ref}+V_{c}, \tag{1}\]  
where \(V_{c}\) is proportional to \(\hat{T}_{l}\) and determined by selecting the appropriate gain, \(k_{p}\), as follows.  
\[V_{c}=k_{p}\hat{T}_{l}. \tag{2}\]  
The velocity synchronization method for the rollers and steel plates can be implemented by selecting a suitable \(k_{p}\) value.  
Figure 3: Velocity synchronization concept for steel plate and rollers using SMCSPO.  
Figure 2: Hot rolling system configuration for velocity synchronization.
## 2 Data and Methods
### Statistic Calculation  
Due to the vector characteristic of wind direction, the numeric values of differences between the derived wind direction and that measured by the meteorological tower may not reflect the actual differences when the differences are greater than 180\({}^{\circ}\). This limitation is addressed by modifying the derived wind direction following [31; 32] when evaluating the derived wind direction, i.e.,  
\[WD_{\mathrm{lidar}}=\left\{\begin{array}{l}WD_{\mathrm{lidar}}\quad\quad if \ \left|WD_{\mathrm{lower}}-WD_{\mathrm{lidar}}\right|\leq 180\\ 360-WD_{\mathrm{lidar}}\quad\quad if\ \ WD_{\mathrm{lower}}-WD_{\mathrm{lidar}}<-180\\ 360+WD_{\mathrm{lidar}}\quad\quad if\ \ WD_{\mathrm{lower}}-WD_{\mathrm{lidar}}>180 \end{array}\right., \tag{11}\]  
Figure 5: The variation of RAE with GOF at **(a)** S3 and **(b)** S1. The blue points are RAEs of each VAD fitting result; the red dots are the averaged RAE within each 0.05 GOF interval with the standard deviation; the green dots represent the corresponding amount of available data.  
Figure 6: The flowchart of quality control.  
After this step, the differences in wind direction are within the range of \([-180^{\circ},180^{\circ}]\). This resolves the impact of the periodic change in wind direction on the results of regression analysis and statistic calculations. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R\({}^{2}\)) are used to evaluate the quality of derived wind. They are defined as follows:  
\[\text{MAE}=\frac{1}{t}\text{$\sum_{i=1}^{t}$}|WS_{\text{tower,i}}-WS_{\text{lidar,i}}|, \tag{12}\]  
\[\text{RMSE}=\sqrt{\frac{1}{m}\text{$\sum_{i=1}^{m}$}\big{(}WS_{\text{lidar,i} }-WS_{\text{tower,i}}\big{)}^{2}}, \tag{13}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{m}\big{(}WS_{\text{tower,i}}-WS_{\text{lidar,i} }\big{)}^{2}}{\sum_{i=1}^{m}\big{(}WS_{\text{tower,i}}-\overline{WS_{\text{tower,i}}}\big{)}^{2}}, \tag{14}\]  
where \(m\) is the number of paired samples, \(WS_{\text{tower}}\) is the wind speed measured by the meteorological tower, \(WS_{\text{lidar}}\) is the derived wind speed using the optimized or the traditional method.
## 3 Results and Discussion
### Eggs Quality Changes  
#### 3.3.1 Changes of Haugh Unit (HU)  
According to USDA's egg regulations [30], the HU values of AA eggs (According to USDA's egg regulation, eggs were classed into three grades: AA, A and B, among which grade AA is the highest quality) are above 72. In this study, the initial HU values of the eggs were 95.66 for n-3 PUFAs enriched eggs and 90.56 for conventional eggs (Tables 2 and 3) when measured two days after being laid and as soon as transported to the lab. Figure 4: Time-temperature fluctuation under different storage conditions. Figure 5: Time-humidity fluctuation of different storage conditions. There was a 40-50% decrease in HU values of n-3 PUFAs enriched and conventional eggs after 30 days of refrigeration (Figure 6a,b). However, for eggs under room temperature, the same level of decline happened after 18 days' storage (Figure 6c,d). The reduction of HU values was mainly due to the breakdown of carbonic acid in eggs albumen, which caused elasticity loss and structure change of mucin fibers. So the albumen became watery and albumen height reduced, which directly resulted in the decrease of HU values [36]. The data shows that the rate of HU decline varies significantly in different temperature and the higher temperature, the faster the HU decreased, which was in agreement with earlier studies [36; 37; 38]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Parameters** & **Initial Value** & **6 Days** & **12 Days** & **18 Days** \\ \hline \multirow{3}{*}{n-3 PUFAs enriched eggs} & Weight & 61.31 \(\pm\) 4.82 a & 59.67 \(\pm\) 4.91 a & 59.04 \(\pm\) 4.68 b,c & 57.53 \(\pm\) 4.32 c \\  & HU \(\tt\copyright\) & 95.66 \(\pm\) 1.41 a & 78.98 \(\pm\) 3.73 b & 67.35 \(\pm\) 2.17 c & 51.42 \(\pm\) 1.84 a \\  & YI \(\tt\copyright\) & 0.46 \(\pm\) 0.03 a & 0.28 \(\pm\) 0.02 c & 0.21 \(\pm\) 0.02 c & 0.18 \(\pm\) 0.03 c \\  & Albumen pH & 8.40 \(\pm\) 0.12 a & 9.18 \(\pm\) 0.11 b & 9.35 \(\pm\) 0.37 b & 9.42 \(\pm\) 0.14 c \\ \hline \multirow{3}{*}{Conventional eggs} & Weight & 64.33 \(\pm\) 3.13 a & 63.04 \(\pm\) 3.11 a,b & 61.01 \(\pm\) 2.67 b & 58.59 \(\pm\) 3.37 c \\  & HU \(\tt\copyright\) & 90.56 \(\pm\) 0.39 a & 75.66 \(\pm\) 4.14 b & 63.63 \(\pm\) 1.51 c & 48.25 \(\pm\) 3.73 d \\  & YI \(\tt\copyright\) & 0.45 \(\pm\) 0.01 a & 0.30 \(\pm\) 0.02 b & 0.23 \(\pm\) 0.01 c & 0.19 \(\pm\) 0.03 c \\  & Albumen pH & 8.24 \(\pm\) 0.18 a & 8.98 \(\pm\) 0.37 a & 9.28 \(\pm\) 0.17 b & 9.51 \(\pm\) 0.12 c \\ \hline \hline \end{tabular}  
* Means \(\pm\) SD of 5 measurements; \(\copyright\) Haugh unit; \(\copyright\) yolk index; Values with different letters (a–c) within the same row are significantly different (p \(<\) 0.05).  
\end{table} 
Table 2: The quality * changes of Omega-3 polyunsaturated fatty acids (n-3 PUFAs) enriched and conventional eggs under room temperature.
###### Abstract  
Contemporary indirect restorative materials vary in their physical and mechanical properties, necessitating additional research. This investigation aims to compare the mechanical properties (such as fatigue and compressive strength) of indirect dental restorative materials. In an in vitro study, the mechanical behaviour of monolithic only restorations made from a lithium dissilicate glass-ceramic (Group A), a ceramic-infiltrated composite (Group B), a polymer-based composite resin (Group C), and zirconia (Group D), bonded to a prepared tooth model, was evaluated after ageing and mechanical cycling. The average value of compressive strength (stage of cracking) in each group was as follows: Group A, 871 N; Group B, 728 N; Group C, 2655 N; and Group D, 2005 N. Moreover, the results of the compressive strength test (stage of destruction) in each group were as follows: Group A, 2516.5 N; Group B, 2266 N; Group C, 5670 N; and Group D, 3543 N. An analysis of variance (ANOVA) followed by [PERSON]'s (HSD) post hoc test was conducted to assess pairwise comparisons among group means. Statistical analysis revealed significant differences between Groups C and D and the others, highlighting the potential of these materials in clinical applications. Based on the average values, it can be concluded that the 3D-printed ceramic-infiltrated composite onlays exhibited the highest compressive resistance values among the materials evaluated. The lithium dissilicate glass-ceramic and the ceramic-infiltrated composite are brittle materials, which should be considered when covering teeth with high occlusal stress.  
Keywords:lithium dissilicate; zirconia; ceramic-infiltrated composite; polymer-based composite resin; compressive strength; fatigue test +
Footnote †: journal: Applied sciences
## 4 Minimal Subgroups and \(D_{p}(G)\)  
The main aim of this section is to to prove the following theorem.  
**Theorem 3**.: _Let \(q\) be a prime. Assume that every element of order \(q\) lies in \(D_{p}(G)\), and in addition, if \(q=2\) and the Sylow \(q\)-subgroup of \(G\) is nonabelian, then every element of order \(4\) lies in \(D_{p}(G)\). Then \(G\) is \(q\)-soluble and \(l_{q}(G)\leq 1\)._  
Proof.: Let \(\Omega=\langle x\in O^{p}(G)\mid x^{q}=1\rangle\), if \(q\
eq 2\) or the Sylow \(q\)-subgroup of \(G\) is abelian; \(\Omega=\langle x\in O^{p}(G)\mid x^{q}=1\rangle\), if \(q=2\) and the Sylow \(q\)-subgroup of \(G\) is nonabelian. Then \(\Omega\leq O^{p}(G)\cap D_{p}(G)\) by hypothesis.  
Assume \(p\
eq q\). By Theorem 1.3, \(\Omega\) is a \(p^{\prime}\)-group and by Theorem 1.4, \(\Omega\leq Z_{\infty}(O^{p}(G))\). If \(O^{p}(G)\) is not \(q\)-nilpotent, then there exists a minimal non-\(q\)-nilpotent subgroup \(H\) of \(O^{p}(G)\). By the structure of the minimal non-\(q\)-nilpotent groups, we have that \(H=[Q]R\), where \(Q=O_{q}(H)\) and \(\exp(Q)=q\) or \(4\) (if q = 2 and \(Q\) is non-abelian) and \(R\) is a cyclic \(r\)-group with \(r\
eq q\). However, \(Q\leq\Omega\leq Z_{\infty}(O^{p}(G)\), so \(Q\leq H\cap Z_{\infty}(O^{p}(G)\leq Z_{\infty}(H)\). It follows that \(H\) is nilpotent, a contradiction. This contradiction shows that \(O^{p}(G)\) is \(q\)-nilpotent. Thus, \(G\) is \(q\)-soluble and \(l_{q}(G)\leq 1\) since \(G/O^{p}(G)\) is a \(p\)-group.  
Assume \(p=q\). If \(O^{p}(G)\) is of order \(p^{\prime}\) then \(G\) is \(p^{\prime}\)-closed and so is \(p\)-nilpotent. In particular, \(G\) is \(p\)-soluble with \(l_{p}(G)\leq 1\). If \(O^{p}(G)\) is not a \(p^{\prime}\)-group, then \(\Omega\
eq\emptyset\) and by Theorem 1.3, \(O_{p^{\prime}}(\Omega)\) is the Hall \(p^{\prime}\)-subgroup of \(\Omega\). Let \(T\) be any \(p^{\prime}\)-subgroup of \(G\). Then \(\Omega\leq N_{G}(R)\). Since, clearly, \(\Omega\) is normal in \(G\), we see that \([\Omega,T]\leq\Omega\cap T\leq O_{p^{\prime}}(\Omega)\). Since \(O^{p}(G)=\langle T\leq G\mid p\
mid|T\rangle\), \([\Omega,O^{p}(G)]\leq O_{p^{\prime}}(\Omega)\). Now, considering on the quotient \(O^{p}(G)/O_{p^{\prime}}(\Omega)\), we have that \(\Omega/O_{p^{\prime}}(\Omega)\leq Z(O^{p}(G)/O_{p^{\prime}}(\Omega))\). By a same argument as above (or by [PERSON]'s theorem), it can be obtained that \(O^{p}(G)/O_{p^{\prime}}(\Omega)\) is \(p\)-nilpotent. Therefore, \(O^{p}(G)\) is \(p\)-nilpotent and so is \(G\). Thus, is \(p\)-soluble with \(l_{p}(G)\leq 1\). The proof is completed.  
Methodology, B.L.; Supervision, B.L.; Writing--original draft, Y.H. and L.G.; Writing--review and editing, T.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grants number 11471055 and 11601245.  
The authors would like to thank the anonymous reviewers for their useful comments that have improved the final version of this manuscript.  
The authors declare no conflict of interest.
# Sentiment-Aware Word Embedding for Emotion Classification
## 3 Methodology
### Constructed Emotional Word Vector Based on the Emotional Lexicon
Similarity in \(Y\) means that an item in E and an item in T have the same context; meanwhile, the emotional intensity provided in the dictionary represents the emotional information of the item in E and constructs the emotion vector for all items in T by combining the two kinds of information. The emotional word vector for each word is calculated as a weighted sum rather than a simple average operation:  
\[EmotionVec_{i}=\frac{1}{n}\sum_{i=1}^{n}weight_{i}*V_{e_{i}}. \tag{2}\]  
Then, Formula (3) is used to compute the weight of every word; based on this formula, the higher ranked nearest neighbors will receive higher weights:  
\[weight_{i}=Y_{ij}*score_{j}. \tag{3}\]The Dalian University of Technology Information Retrieval (DUTIR) is used to represent sentiment lexicon in this study, which is a Chinese ontology resource labeled by the Dalian University of Technology Information Retrieval Laboratory [38]. It contains 27,466 words, and each word is associated with a real-valued score of 1, 3, 5, 7, or 9. The score represents the degree of sentiment, where 9 indicates the maximum strength, and 1 is the minimum strength. Then, we rank the emotional words in \(E_{w}\) by strength scores. The \(score_{j}\) is defined as the reciprocal rank of \(e_{j}\) in the \(E_{w}\), that is  
\[score_{j}=\frac{1}{rank_{j}} \tag{4}\]  
where \(rank_{j}\) denotes the rank of \(e_{j}\) generated by the intensity-based ranking process. The weights have to be normalized to sum to one. This can be obtained with the following relation:  
\[weight_{i}=\frac{weight_{i}}{\sum_{j=1}^{n}weight_{j}}. \tag{5}\]  
In the process of constructing the emotional word vector of words, the shortcomings of the context-based word embedding result in words with opposite sentiment polarity having a fairly high cosine similarity in the vector space. There are great errors generated in the construction of emotional word vectors using word embedding mapping and cosine similarity, so sentimental lexicons are used to correct the word representations mapping from the word embedding space. It is a good way to optimize existing word vectors by using real-valued sentiment intensity scores provided by the emotional lexicons and word vector refinement model. In this way, the words are closer to semantically and emotionally similar words in the dictionary (that is, those with similar intensity scores) and stay away from words that are not emotionally similar but are similar in semantics.
## Materials and Methods
### Instrumental Settings  
ESR measurements were conducted at room temperature on an X-band (\(\sim\)9.4 GHz) spectrometer JES-FA 100 (JEOL Inc., Chiba, Japan) with an ES-UCX2 standard cavity using a 5 mm sample tube. Spectra acquisition parameters were as follows: 1 mW microwave power; 10 mT sweep width; 30 s sweep time; 0.5 mT modulation width; time constant 0.03 s; number of scans 10. During spectra acquisition, the MgO: Mn\({}^{2+}\) internal reference sample was used for calibration. The ESR signal intensities were measured as the peak-to-peak amplitude (App) of the main \"singlet\" of the ESR spectra (i.e., BKG-singlet, RIS-singlet), as described by [PERSON] and [PERSON] [36]. The ESR spectra were adjusted in linear baseline correction within the analytical software of the spectrometer. The ESR spectra were recorded before and after the chemical treatment and exposure to radiation. ESR measurements were also repeated at different times to test the stability of the signal after radiation exposure.
# Western Mediterranean Precipitation Extremes, the Result of Quasi-Resonant Sea-Atmosphere Feedbacks
## 2 Materials and Methods
### Coherence and Phase of Rainfall and SST Anomalies According to Harmonic Modes
#### 2.3.1 Exceptional Rainfall Episodes in the Period Range of 11.4 to 45.7 Days  
The eleven most significant events with the highest amplitude in the period range of 11.4 to 45.7 days are represented in Figure 2b. Defining EREs from this criterion amounts to hypothesizing that they result from the co-evolution of the clustering of mesoscale lows and the harmonization of SST anomalies, that is to say the formation of the synoptic convective system. This period range is therefore representative of the amount of atmospheric water conveyed by the cyclonic system. In contrast, the concentration of precipitation resulting from the deep convection that occurs in faster period ranges depends on local factors such as orography and the temperature gradient between the sea and the land.  
Figure 2: (**a**) Precipitation depth averaged over the region (2\({}^{\circ}\)E, 8\({}^{\circ}\)E) \(\times\) (42\({}^{\circ}\)N, 45\({}^{\circ}\)N), which is considered representative of EREs. The dates of occurrence of EREs refer to the highest peaks of precipitation in the period range of 11.4 to 45.7 days; (**b**) amplitude of rainfall depth in the period range of 11.4 to 45.7 days. The red line indicates the level of confidence of 95%; (**c**) coherence of the average precipitation depth \(P_{m}\) and the average SST anomalies \(\text{SSTA}_{i}\) observed in each of the 12 marine regions. Only the coherence of pairs \((P_{m},\text{SSTA}_{i})\) such that the confidence level is higher than 95% and 0 day \(<\) Phase \(<\) 2 days are represented; only the peaks are visible. The asymmetric phase tolerance margin is justified by the fact that, in some cases, the ERE occurs when the SST anomaly has not yet reached its maximum amplitude.  
As shown in Figure 2c and Table 1, each of these 11 events, whose return period is close to 2 years, correspond to one or more SST anomalies. However, events 1\({}^{\prime}\) to 3\({}^{\prime}\) have an infra-annual return period despite the presence of SST anomalies coherent and in phase with the precipitation event.  
The most influential SST anomalies (more than 2 occurrences) when the cyclogenesis leads to EREs 1 to 11 come from the region 2 of the Mediterranean and the regions 7 to 10, i.e., the western and eastern Black Sea, the Baltic Sea, and the North Sea.
# Ultrasound-Guided Procedures in Common Tendinopathies at the Elbow: From Image to Needle
###### Abstract  
Elbow pain is a prevalent condition in musculoskeletalal physicians' settings. The majority of cases present with periarticular pathologies (varying from tendinopathy to nerve entrapment syndrome). Nevertheless, in some cases, the underlying cause can be intra-articular, e.g., loose bodies or rheumatic disease. Progress in ultrasound (US) technology has yielded high-resolution assessment of the elbow and, importantly, allows real-time, radiation-free guidance for interventions. Particularly in ambiguous cases, US imaging is necessary to arrive at the correct diagnosis. The following four clinical conditions are covered: tennis elbow, golfer's elbow, distal biceps, and distal triceps' tendonopathy. The present review illustrates cadaveric elbow anatomy, corresponding US images, and exemplary pathologies. Additionally, the authors also discuss the existing evidence on ultrasound-guided procedures in the conditions mentioned above.  
tentonopathy; golfer's elbow; tennis elbow; distal biceps; distal triceps; ultrasonography; steroid; injection +
Footnote †: journal: Medicalangiogenesis and continuous muscle contraction can lead to tendon ischemia, further aggravating tendinois development [10]. The elbow joint is a complex hinge joint between the humerus and the radius and ulna, providing pronation/supination of the forearm and flexion/extension of the elbow.  
Due to the elbow's complex anatomy, the underlying etiology's determination is sometimes far from straightforward. Understanding the elbow's anatomy, pathophysiology, and biomechanics is essential for managing pertinent pathologies [11]. The role of physical examination is essential; however, it is also limited as physical signs are often nonspecific [12; 13]. Ultrasound (US) imaging has proven to be a valuable method to provide a specific diagnosis and a convenient tool to guide interventions [14; 15; 16; 17]. Numerous interventional procedures (e.g., injections of corticosteroids and local anesthetics, dry needing, or regenerative medicines) are commonly performed to treat painful conditions at the elbow [18].  
This review aims to describe the anatomy, US imaging/guidance, and the literature evidence about the most common interventional procedures targeting tendons in the elbow region, i.e., tennis elbow, golfer's elbow, distal biceps, and distal triceps tendinopathy.