esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, other methods are preferable during food processing. Besides heating, pH shifting has also been widely studied to improve the functionality of multiple plant proteins, such as rapeseed protein [24], soy protein [25], alfalfa protein [26], and pea protein [27]. [PERSON] et al. [16] combined high temperature with pH shifting (7.0 to 12.0) and successfully increased HPI solubility up to 97% at a neutral pH due to the weakening of hydrogen bonds, and electrostatic repulsion. However, treatment at high temperatures and extreme pH values negatively influences the colour, ultimate flavour, and nutrition value of food products [28]. This suggested that appropriate extraction conditions are crucial. According to the previous investigations, functionality modifications of HPI were primarily conducted after the protein isolation process, while the feasibility of the extraction protocol has not been adequately assessed. At present, similar to other plant protein systems, hempesed protein extraction is commonly based on the AE-IEP method. To maximise yield, hempesed proteins are solubilised in an aqueous phase at alkaline conditions (pH 8.5 to 10.0) [29; 30; 31; 32], temperatures between 24 to 40 \({}^{\circ}\)C [10; 33], and extraction times from 1 to 4 h [34; 35; 36]. Our hypothesis is that the combination of high pH and long extraction times might provide a suitable environment for disulphide bond formation that detrimentally affects the functionality of hempesed protein during the extraction, thus leading to the poor functionality of the ultimate HPI product. Thiol functional groups have been shown in previous investigations to undergo deprotonation at high pH, resulting in the formation of thiolate anions (S\({}^{-}\)) [37]. The thiolate group can participate in intermolecular bond formation reactions with other electrophilicreactive species [38], particularly intermolecular disulphide bond formation [39]. Therefore, it is reasonable to suspect disulphide bonding may occur between proteins during alkaline extraction. Thus, the conventional extraction protocol may not be suitable for high-thiol-content feedstocks such as hempseed. So far, the underlying mechanisms of protein extraction from hempseed has not been widely studied and there is a lack of systematic investigation into hempseed protein extraction kinetics as a function of alkaline pH. Consequently, the current study is aimed at systematically investigating the impact of the HPI alkaline extraction protocol on the structural and functional properties of hempseed proteins. For this purpose, the biochemistry and functionality of hempseed protein extracted under different alkaline extraction conditions and their corresponding HPI powder were analysed. In particular, the possibility of intermolecular disulphide bond formation during extraction was explored, and the functionality of HPI produced under different conditions compare to conventionally extracted HPIs. This study provides new insights into high-thiol-content-based plant protein extraction in the context of obtaining desirable functional properties for food processing applications.
## 4 The Carbon Emissions Assessment on the Life Cycle of Recycled Concrete Block
### Assumptions and Description of Life-Cycle Inventory (LCI)  
Five different concrete mixtures were specified to determine the life-cycle inventory. The assumptions and restrictions associated with the life-cycle inventory for each concrete component will be presented in detail in the following paragraphs.  
The carbon emission generated per ton of cement produced was 0.80 kg, and the transportation distance of cement was 150 km. The electricity and fuel consumption required to produce per ton of natural crushed stone were 1.17 kW/t and 0.723 L/t, respectively, and converted into carbon emissions of 3.12 kg. The carbon emission per ton of sand produced was 3.66 kg. The transportation distance of natural gravel and sand was 200 km [33].  
The typical preparation process of RA was as follows: The waste concrete was crushed by a forklift into the crusher, and then the broken concrete was screened by the sieving machine to obtain the recycled coarse aggregate with different particle sizes, and this process was assisted by some iron and impurity removal equipment. Statistical data on mass production of recycled aggregate were relatively lacking, as it was still in the stage of popularization in China. The data in reference [16] was referred to in this paper. The carbon emission generated per ton of RA produced was 1.61 kg, and the transportation distance of recycled aggregate was 30 km. The direct carbon emission coefficient of diesel truck transportation was 89.84 g/(km\(\cdot\)t). These data apply only to Shaanxi province in China.
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
## 2 Dataset Validation and Preprocessing
### Multicollinearity Detection  
The multicollinearity shows the dependence of multiple features on the output. The preliminary detection of the features that formulate the multicollinearity problem may avoid the use of unwanted features. The Pearson correlation matrix and the VIF factor are adopted to detect the multicollinearity of the input features impacting the output.  
The Pearson correlation matrix shows correlations between features. Covariance can be used to describe the correlation coefficients mathematically [13]:  
\[\rho_{xy}=\frac{E(XY)-E(X)E(Y)}{\sigma_{X}\sigma_{Y}} \tag{4}\]  
This formula has a few variables, each of whose definitions are as follows: \(E(XY)\) is defined as the mean value of the sum after multiplying between the corresponding elements in two sets of data; \(E(X)\) denotes the average value of the sample \(X\); \(E(Y)\) is the mean value of the sample \(Y\); \(\sigma_{X}\) denotes the standard deviation of the sample \(X\); \(\sigma_{Y}\) is defined as the standard deviation of the sample \(Y\); \(\rho_{xy}\) is the correlation coefficient between \(X\) and \(Y\), that is Pearson correlation coefficient. Generally \(\rho_{xy}\) defined as follow: \(0.8\leq\rho_{xy}\leq 1.0\), meansextremely strong correlation; \(0.6\leq\rho_{xy}\leq 0.8\), means strong correlation; \(0.4\leq\rho_{xy}\leq 0.6\), means moderate correlation; \(0.2\leq\rho_{xy}\leq 0.4\), means weak correlation; \(0.0\leq\rho_{xy}\leq 0.2\), means extremely weak correlation.  
The resultant correlation matrix of the normalized features Q-factor, SNR and BER is represented in Figure 1.  
The brighter the cell represents the highly positive correlation. The diagonal represents the auto correlation coefficient (between the feature and itself). The correlation matrix shows that a highly strong positive correlation exists between the SNR and the Q-factor (denoted as Q) with a value of \(\rho_{xy}=0.94\). A moderate correlation exists between the BER and Q-factor. A negative weak correlation exists between the SNR and BER.  
Moreover, the multicollinearity can be validated through _VIF_ using the method of [14]  
\[VIF=\frac{1}{1-R_{j}^{2}} \tag{5}\]  
where \(R_{j}^{2}\) is the multiple \(R^{2}\) for the regression of a feature on the other covariates. The higher the _VIF_, the lower the entropy of the information. The value of _VIF_ should not pass 10. If the value of _VIF_ is more than 10, then multicollinearity surely exists. The resultant _VIF_ for each feature is represented in Table 1.  
The Q-factor has the highest _VIF_ with 4.49. The SNR and BER achieve 3.88 and 1.34, respectively.
# Accident Risk Analysis of Gas Tankers in Maritime Transport Using an Integrated Fuzzy Approach
## 2 Backgrounds
### The Objective of the Present Study  
The International Regulations for the Safe Transport of Dangerous Goods by Sea, or IMDG Code, is an international regulation of the safe transport of dangerous goods by sea, including LPG and LNG, which are classified as dangerous due to their flammable, volatile, and pressurised properties [30]. By this regulation, standard response protocols, including those classified as Emergency Response Procedures (EmS Guide), Fire (F), and Leakage (S) procedures, are included for ships carrying dangerous goods (IMO, 2020). Each dangerous substance listed in the IMDG Code is assigned specific EmS codes reflecting the substance's fire (F-xxx) and spill (S-xxx) response characteristics. These codes summarise special measures to reduce risks associated with the transport of LPG (UN 1075) and LNG (UN 1972) [31].  
The primary accident scenarios involving LPG and LNG are gas fires and gas leaks. Gas fires present a substantial explosion hazard, while gas leaks have the potential to generate flammable vapour clouds [32]. Root cause analyses of accidents involving LPG and LNG frequently highlight errors in the implementation of EmS procedures, including inadequate fire isolation, inappropriate fire extinguishing strategies, delayed leak detection, and communication failures [33]. In this study, two primary accidents--a gas fire and a gas leak--were selected according to EMS procedures, and the primary incident of each accident, including the gas tanker and its environmental impact, was examined.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Impact of Content Motion  
In the Section 4.2.3, it has been shown that video bitrate for good QoE varies widely across different contents. Therefore, to guarantee satisfactory QoE for 360 video services, the characteristics of each content should be taken into account. In the following, we further discuss the impacts of the content motion on the QoE aspects.  
Table 6 shows the statistical results about the effect of content motion on four QoE aspects based on the Kruskal-Wallis test. We can see that no statistically significant effect of content motion is found for the acceptability (i.e., \(p>0.05\)). Meanwhile, statistically significant effects with the \"small\" sizes are found for the presence and the perceptual quality (i.e., \(p<0.05\), \(0.06>\eta^{2}>0.01\)). This implies that factors having significant impacts on the presence and the perceptual quality may not cause considerable influences on the acceptability. For the cybersickness, the effect of content motion is found  
Figure 8: Perceptual quality scores vs. bitrates (Device set D1). (**a**) Content #1; (**b**) Content #2; (**c**) Content #3.  
Figure 7: Presence scores vs. bitrates (Device set D1). (**a**) Content #1; (**b**) Content #2; (**c**) Content #3.  
Figure 9: Acceptability rates vs. bitrates (Device set D1). (**a**) Content #1; (**b**) Content #2; (**c**) Content #3.  
to be \"large\" (i.e., \(p<0.05\), \(\eta^{2}>0.14\)). These findings are in agreement with a statement presented in [17] that the content motion has statistically significant impact on the perceptual quality and cybersickness.  
Since the impacts of content motion on the presence, the perceptual quality, and the cybersickness are statistically significant, a post-hoc test using Mann-Whitney tests with Bonferroni correction is additionally conducted. The Mann-Whitney test is used to compare the statistical difference between pairs of video contents. The obtained results are presented in the following subsections. Note that, in this study, we consider three content motion types of camera motion, object motion, and background motion.
# New Nonlinear Second-Order Phase-Locked Loop with Adaptive Bandwidth Regulation
## 1 Introduction
Nevertheless, the parameters of the loop filter cannot be optimized rapidly because of the implementation complexity of the algorithm. Similarly, the method presented in Reference [21] is based on real-time estimations of the dynamics and signal-to-noise ratio of the incoming signals, which are used to compute the better compromise between the equivalent noise bandwidth and the steady state error factor of the loops. The performance depends on an appropriate selection of tracking loop parameters and the characteristics of the PLL itself does not change. Without the necessity of switching circuit elements in the loop filter, [PERSON]. [23] proposed an adaptive PLL tuning system architecture. However, a high Signal-to-Noise Ratio (SNR) is always demanded. Those algorithms can improve the robustness of the PLL. Their performances depend on an appropriate selection of tracking loop parameters. On the other hand, they could be external auxiliary factors that will increase the complexity of implementation. All existing tracking algorithms do not change the characteristics of the traditional PLL itself to balance the contradiction between low noise and fast locking performance. In this study, a nonlinear element is coupled into the traditional second-order PLL based on the working principle of traditional PLL and the nonlinear control idea. The new nonlinear second-order PLL can conduct adaptive regulation over loop noise bandwidth by the control effect of the nonlinear element. When a large input-output phase error occurs, the proposed PLL decreases the phase error quickly by taking advantage of the loop bandwidth. When the phase error decreases gradually due to the control effect of the loop, the method suppresses noises by using the small bandwidth and finally the loop locks accurately. Such new PLL breaks restrictions, updates performance, and balances the tracking speed and accuracy of the traditional PLL.
## 2 Materials and Methods
### Statistical Analysis
#### 2.5.8 Sample Size  
The adequacy of the sample size was evaluated using a \(t\)-test for independent samples, based on the lowest observed effect size (0.197), a significance level of 0.05, and a sample size of 10,752 per group. This resulted in a statistical power greater than 0.99, confirming its sufficiency to detect significant differences. However, this sample size does not represent independent specimens but derives from repeated measurements on each aligner. Each aligner was tested at three measurement points (gingival, middle, and occlusal) with three repeated tests per point to refine force estimation and account for intra-aligner variability. Statistical analyses accounted for data non-independence by applying appropriate methods that consider within-subject variability, ensuring precision in force estimation without artificially inflating statistical power.
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
## 2 Materials and Methods
### Model Optimizations  
As mentioned in the introduction, edge devices have limited resources for computation and memory. To address this problem, efficient native models were created by considering the model size and computational power. This is the case with several models, such as MobileNet and SqueezeNet. Another approach to increasing the performance of an edge device (faster inference and memory access) is the application of quantization techniques, where the model becomes simpler by reducing the precision of the weights and activation functions of the model (e.g., from 32-bit floating-point representations to 8-bit representations) [13]. Quantization approaches can broadly be divided into two categories. The first category is post-training quantization (PTQ), which quantizes the floating-point models. This technique reduces the size of the models by a factor of four and reduces inference time [13]. However, PTQ leads to degradation in model performance during inference. One reason for this is the smaller number of bits allocated [43].  
The second category, quantization-aware training (QAT), attempts to mitigate the error caused by quantization by simulating the effects of quantization on weights and activation functions during the training process. This means that the model compensates for the loss due to the application of quantization. For this reason, QAT provides higher accuracy than PTQ [13]. We used QAT in all of the implementations of the detection models used in the experiments.
## 4 Discussion
An insertion torque superior to 80 Ncm is a very high torque and it may be that good results could be obtained also with lower torque values, as suggested by the present study or by another trial in which implants inserted with a torque of 32 Ncm were also loaded immediately [35]. Most likely, an insertion torque superior to 35 Ncm for loading four-to-five splinted implants, as in the present study, is adequate to ensure good success rates of immediately loaded implants. Most of the early implant failures affected immediate post-extractive implants (8 out of 12). This observation is in line with other studies that showed trends for post-extractive implants to fail more often than delayed-placed implants [13; 36]. During the follow-up, two implant fractures occurred, which may indicate an excessive loading on the implant in relation to its robustness. Complications may seem very frequent when compared with other similar studies. A special effort was made to detect and report all events, also those less commonly. In the majority of studies, complications are under-reported, if not reported at all, which can generate a great bias in implantology. Knowledge of complications may help to solve them. Compared with the 1-year follow-up publication, it was decided to consider soft tissue decubitus, occlusion adjustments, early phonetic problems prosthetic relining, prosthetic screw replacement, and vestibular-lingual prosthetic flange reduction as normal prosthetic modifications instead of real prosthetic complications. The same consideration was made for peri-implant mucositis, which were solved with professional cleaning and chlorhexidine. This decision was made since, once solved, they rarely recurred during the follow-up period. The real complications were divided into biological complications and prosthetic complications (Tables 6 and 7). The decision of replacing the original metal-resin prostheses with the monoblock white resin ones led to a reduction of fracture percentage from 76% to 12%. The more common complications of monoblock prostheses were prosthetic chippings, which were easily repaired in the lab without the necessity of making a new prosthesis. The major limitation of the present study is the retrospective design. In addition, treatments were delivered under normal clinical conditions, using broad patient inclusion criteria, therefore the results of this could be extrapolated to wider patient populations with similar characteristics. However, the number of patients treated could be further expanded to have a more representative sample and different types of implants could be used.
## 5 Discussion and Conclusions  
In this study, a novel algorithm to find the mandibular condyle was developed by classifying dental panoramic X-ray images through image detection techniques. Detection algorithms were compared using various learning models and the algorithm achieved an 82% weighted precision. This study therefore confirms the possibility of developing an algorithm to find a condylar region and identify abnormal diseases. The study also provides evidence of the possibility of learning by sufficiently extracting features with a small amount of data.  
Using the developed algorithm, the expert system could provide additional information that allows clinical experts and local doctors to more quickly filter through dental panoramic images. The system developed in this study will help clinical doctors make decisions regarding diagnosis and further help patients and doctors save time spent in repetitive imaging and diagnostic processes to ensure smooth treatment.  
It is important to detect osteoarthritis as early as possible and begin treatment. However, making a diagnosis only by clinical symptoms or panoramic radiographs has limitations. According to previous studies, a comparison of the presence or absence of bone change between the mandibular condyle in panoramic radiography and computed tomography showed a degenerative bone change in the computed tomography of 24% of the patients who did not exhibit significant condylar bone changes in panoramic radiographic images [19,20]. Separation of condyle and diagnosis of only one side of condyle has another limitation. The symmetry of condyle is an import criterion of TMJ disease [18]. We will develop a comparison algorithm and include it the expert system. In addition, the disease classification was determined only by the panoramic X-ray images, which are not the gold-standard reference. Therefore, to improve accuracy and clinical use, it is necessary to supplement the diagnostic record of computed tomography images.  
In the future, we plan to develop an algorithm with a higher accuracy using larger datasets because the sensitivity of this study was not high. Furthermore, because the design of this learning algorithm was limited to the condylar region, another algorithm could be developed through expansion to other regions or diseases.  
[PERSON], responsible for the entire paper (Conceptualization, methodology, analysis and writing--original draft preparation); [PERSON] and [PERSON], data collection and analysis; [PERSON], methodology review; [PERSON], methodology review, editing, and supervision. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Dongguk University Research Fund of 2016 and Gerontechnology research center.  
This work was supported by the Dongguk University Research Fund 2020 (S-2020-G0001-00050).  
The authors declare no conflict of interest.  
Figure 9: Actual usage of expert system.
## 2 Pms6
### Overview  
PMS6, which is presently the fastest exact algorithm to compute \(M_{l.d}(S)\) for large \((l,d)\), was proposed by [PERSON], [PERSON] and [PERSON] [23]. This algorithm (Algorithm 1) first computes a superset, \(Q^{\prime}\), of the motifs of \(S\). This superset is then pruned to \(M_{l,d}(S)\) by the function \(outputMotifs\)which examines the \(l\)-mers in \(Q^{\prime}\) one by one determining which are valid motifs. This determination is done in a brute force manner.  
```
PMS6(\(S,l,d\)) //Determineasupersetofmotifs\(Q^{\prime}\) foreach\(x\in_{l}s_{1}\) { for\(k=1\)to\(k=\lfloor\frac{n-1}{2}\rfloor\) { \(Q\leftarrow\emptyset\);  Classes\(\leftarrow\emptyset\);  foreach\(y\in_{l}s_{2k}\)and\(z\in_{l}s_{2k+1}\) { Compute\(n_{1},\cdots,n_{5}\)for\((x,y,z)\);  if\(C(n_{1},\cdots,n_{5})\
ot\in Classes\) {  Createtheclass\(C(n_{1},\cdots,n_{5})\) with\((x,y,z)\);  Add\(C(n_{1},\cdots,n_{5})\)toClasses; } elseadd\((x,y,z)\)toclass\(C(n_{1},\cdots,n_{5})\); } foreachclass\(C(n_{1},\cdots,n_{5})\)inClasses \(Q\gets Q\cup B_{d}(C(n_{1},\cdots,n_{5}))\) if\(k=1\)then\(Q^{\prime}=Q\) else\(Q^{\prime}=Q^{\prime}\cap Q\); if\(|Q^{\prime}|<\)thresholdbreak; } //Prune\(Q^{\prime}\) \(outputMotifs(Q^{\prime},S,l,d)\); }
```  
**Algorithm 1**PMS6[23].  
To compute \(Q^{\prime}\), PMS6 examines triples \((x,y,z)\), where \(x\) is an \(l\)-mer of \(s_{1}\) and \(y\) and \(z\) are \(l\)-mers of \(s_{2k}\) and \(s_{2k+1}\), respectively for some fixed \(k\). These triples are first partitioned into equivalence classes based on the number of positions in the \(l\)-mers of a triple that are of each of 5 different types (see below). Next, we compute the \(B_{d}\) for all triples by classes. This two step process is elaborated below.  
**Step 1:**_Form Equivalence Classes._ Classify each position \(i\) of the triple \((x,y,z)\), into one of the following five types [22]:  
Type 1: \(x[i]=y[i]=z[i]\).  
Type 2: \(x[i]=y[i]\
eq z[i]\).  
Type 3: \(x[i]=z[i]\
eq y[i]\).  
Type 4: \(x[i]\
eq y[i]=z[i]\).  
Type 5: \(x[i]\
eq y[i],x[i]\
eq z[i],y[i]\
eq z[i]\).  
The triples \((x,y,z)\) of \(l\)-mers such that \(x\in_{l}s_{1}\), \(y\in_{l}s_{2k}\) and \(z\in_{l}s_{2k+1}\) are partitioned into classes \(C(n_{1},\cdots,n_{5})\) where \(n_{j}\) denotes the type \(j\) positions in the triple \((x,y,z)\) (for \(1\leq j\leq 5\)).
**Step 2:**_Compute \(B_{d}\) for all triples by classes._ For each class \(C(n_{1},\cdots,n_{5})\), the union, \(B_{d}(C)\), of \(B_{d}(x,y,z)\) for all triples in that class is computed. We note that the union of all \(B_{d}(C)\)s is the set of all motifs of \(x\), \(s_{2k}\), and \(s_{2k+1}\).