esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Initial Characteristics of 'Wodarz' Apple  
The soluble solids content (SSC) represents the amount of sugars in fruit. 'Wodarz' had a relatively high content of sugars, with approximately 10.1\({}^{\circ}\) Brix (Table 1). Fructose (58.4 g L\({}^{-1}\)) and sucrose (36.0 g L\({}^{-1}\)) were the predominant sugars. In addition to these, glucose (3.9 g L\({}^{-1}\)) also contributed to approximately 98.2 g L\({}^{-1}\) of fermentable sugars. Sorbitol (about 4.6 g L\({}^{-1}\)), a sugar alcohol with a sweet taste, was also present in the 'Wodarz' apple cider must.  
The assessment of acid traits prior to fermentation revealed that the pH of 'Wodarz' apple must was approximately 3.74 (Table 2). The total acidity of apples commonly falls around 5-10 g L\({}^{-1}\); however, for 'Wodarz', it was only 2.7 g L\({}^{-1}\). The malic acid content in 'Wodarz' was 3.35 g L\({}^{-1}\), which was also lower than the common apple cultivar 'Fuiji' fruits (6-7 g L\({}^{-1}\)) [37]. The low level of acid present and the relatively high pH may warrant adjustment pre-fermentation in future research and commercial applications using 'Wodarz' apples for cider production. While the total acidity differs from the malic acid content, this is to be expected due to the variation between the availability of hydrogen ions for measurement when using meters or titration while residing in a solution compared to the total sum of all acid compounds in their associated and dissociated forms [38]. Similar differences have been observed in other work focused on apples, where the reported malic acid content differs substantially compared to total or titratable acidity measurements of acid content [39; 40].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Glucose** & **Fructose** & **Sucrose** & **Fmentable Sugars** & **Sorbitol** & \\
**(g L\({}^{-1}\))** & **(g L\({}^{-1}\))** & **(g L\({}^{-1}\))** & **(g L\({}^{-1}\))** & **(g L\({}^{-1}\))** & **SSC (\({}^{-}\)Brix)** \\ \hline
3.9 \(\pm\) 1.60 & 58.4 \(\pm\) 5.9 & 36.0 \(\pm\) 3.4 & 98.2 \(\pm\) 4.8 & 4.6 \(\pm\) 1.1 & 10.1 \(\pm\) 0.2 \\ \hline \end{tabular}
\end{table}
Table 1: Initial sugar and sorbitol characteristics of ’Wodarz’ apple cider must.  
The primary amino nitrogen content was approximately 19.5 mg L\({}^{-1}\) in musts (Table 3). In normal fermentation, assimilable nitrogen, including ammonia and amino acids, may exceed 350 mg in total when being used as a yeast nutrient to avoid sluggish fermentation [41]. Here, extra nitrogen supplementation was added to assist yeast fermentation in 'Wodarz' apple musts. The pre-fermentation total phenolics were over 14.0 AU, and the single-fruit mass of 'Wodarz' apples was approximately 121.5 g.
## 2 Related Works
### ELM-RBF  
Unlike ELM, ELM-RBF adopts RBF kernel instead of the single layer feed-forward neural network to improve model's performance [11]. The centers of clusters and impact widths of RBF kernel are initialized randomly. In addition, the output of the hidden layer is related to the specific task. For instance, in multi-label classification, the dimension of output is equal to the number of categories. In addition, the kernel function in ELM-RBF can be written as Equation (7).  
\[h_{k}(x)=h(c_{k},c_{k},x)=\exp(-\|x-c_{k}\|^{2}/(\sigma_{k}^{2})),\ k=1,2, \dots,K \tag{7}\]  
where \(x\) is the input vector. \(c_{k}\) is the center of the \(k\)th cluster in RBF kernel, and \(\sigma_{k}\) is the impact width of this cluster.  
The result of ELM-RBF is shown as Equation (8).  
\[f(x)=\sum_{i=1}^{L}\beta_{i}\,h_{i}(x)=h(x)\beta \tag{8}\]  
where \(\beta_{i}\) is the output weight.  
The optimization target of ELM-RBF can be described as follows:  
\[H\beta=T \tag{9}\]  
where \(H\) is the output matrix of hidden layer as shown in Equation (3), and \(T\) is the output label matrix.  
We should mention that RBF function plays a significant role in the original ELM algorithm and its modified models. RBF function is one of the most common activation functions of ELM, and it is also the main kernel style of K-ELM [21]. The selection of activation function for ELM, or the design method for kernel in K-ELM [21], is critical to the performance of model. It is reasonable to believe that the modified ELM model using RBF function as the main structure, such as ELM-RBF, would be an effective algorithm [19].
## 4 Solution
### Formulation of Mixed Integer Linear Programming
#### 4.2.4 Final Formulation  
Let \(Z(\theta_{1})\) be the SP's maximum average revenue per customer from Period 1 to the end, starting at the initial state \(\theta_{1}\). Then, the equivalent mixed integer linear programming problem can be proposed as follows:  
\[\begin{split}\textbf{(MILP)}\quad Z(\theta_{1})=\max_{C_{t}\in\{0 1\}^{n}}&=\sum_{t=1}^{T}\sum_{i=1}^{m}(\theta_{i,t}+\alpha_{i,t}) \cdot[F_{i}(v_{i})+\bar{F}_{i}(v_{i})\cdot(1-w_{i})+\rho_{i}\cdot w_{i}]\cdot b _{i},\\ \text{s.t.}&\text{(A),(B),(C),(D),}\\ &\quad C_{i,t}\in\{0,1\},\quad\forall\ i,t\\ &\quad G_{t}\in\{0,1\},\quad\forall\ t\\ &\quad\theta_{0,t}\geq 0,\quad\forall\ i,t\\ &\quad\theta_{i,t}\geq 0,\quad\forall\ i,t\\ &\quad\alpha_{0,t}\geq 0,\quad\forall\ i,t\\ &\quad\beta_{i,t}\geq 0,\quad\forall\ i,t\\ \end{split}\]  
Theorem 2: _The optimal solution to (MILP) is the same as the optimal solution to (DP2), and the optimal objective values \(\textbf{Z}(\theta_{1})=\overline{\textbf{V}}_{1}(\theta_{1})\)._  
Proof: See Appendix A.  
Theorem 2 suggests that we transform the SP's dynamic programming problem into an equivalent mixed integer linear programming problem. The new problem's dimension is significantly reduced, and it can be solved efficiently by many commercial software programs, such as CPLEX and Gurobi.
## 6 Conclusions  
A field measurement study and ANN modelling were performed to evaluate the capability of R449A to be used as a lower GWP alternative to R404A in a supermarket refrigeration system. The indirect supermarket refrigeration system comprises two circuits operating at low and medium temperatures. As the operating conditions were different when using R404A and R449A in the refrigeration systems, an ANN model is employed to learn the system's behavior and make it possible to compare the performance of the two refrigerants. The experimental results were used to train some ANN models to predict the energetic performance of the system under the same operating conditions. The ANN modelling results, supported by the experimental ones, revealed that the use of R449A as an alternative to R404A improves the system's performance. The COP of the system increases by 10% and 5%, on average at LT and MT, respectively. The cooling capacity was predicted to be almost similar for both refrigerants, and the compressor discharge temperature was predicted to be higher with R449A by almost 10 K. The environmental impact analysis confirmed that R404A has a significant contribution on CO\({}_{2}\).eq emissions, and the refrigerant replacement can reduce the emissions from the supermarket refrigeration system.  
Conceptualization, M.G., A.M.-B., P.M.; methodology, M.G., A.M.-B., B.E.B.; formal analysis, M.G.; investigation, M.G., A.M.-B.; resources, M.G., A.M.-B., B.E.B.; data curation, M.G., P.M., J.R.; writing--original draft preparation, M.G.; writing--review and editing, A.M.-B., B.E.B.; visualization, M.G.; supervision, R.K.; funding acquisition, R.K. All authors have read and agreed to the published version of the manuscript.  
[PERSON] acknowledges the financial support of the Swedish Energy Agency and the Swedish Refrigeration Cooperation Foundation, KYS. Additionally, [PERSON] acknowledges R+D+i project PID2020-117865 RB-I00 and grant IJC2019-038997-I, both funded by MCIN/AEI/10.13039/501100011033.  
Figure 13: Equivalent CO\({}_{2}\) emissions of the MT system (emission factor (kgCO\({}_{2}\).eq kWh\({}^{-1}\)) used in this study from ref. [26] Sweden: 0.012; Brazil: 0.074; Canada: 0.0130; Germany: 0.378; Japan: 0.506; Poland: 0.791).
## 2 Materials and Methods
### Algorithm
#### 2.1.1 Movement Detection
The \(T_{areanorm}\) condition corresponds to a peak that exceeds approximately three times the SD of the examined part of the MSD (of the length _wise_). **Step 3:** For each sample point (_t_) a value of \"0\" or \"1\" is assigned to a vector \(\xi_{acc}(t)\), depending on the SD and the thresholds. To obtain only one vector from the three acceleration components, the individual \(\xi_{acc,t}(t)\) are combined by a logic OR operation (Equation (3)):  
\[\xi_{acc,i}(t)= \begin{cases}1,\text{if }MSD_{acc,i}(t)>T_{acc,i}(t)\\ 0,\text{if }MSD_{acc,i}(t)\leq T_{acc,i}(t)\end{cases} \text{ for }i=x,y,z \tag{2}\]  
\[\xi_{acc}(t)=\ \xi_{acc,x}(t)\ V\ \xi_{acc,y}(t)\ V\ \xi_{acc,z}(t) \tag{3}\]  
Figure 2: Example of noise detection (**left column: a,c,e**) and artifact detection (**right column: b,d,f**) in [O\({}_{2}\)Hb] data (sampling rate: 35 Hz). Top row (**a,b**): original signal. Middle row (**c,d**): moving standard deviation (MSD) of the input signal. Bottom row: quantiles from 0 to 1 in steps of 0.01 of the corresponding MSD values. “A” is the area between the quantile distribution (D) and the straight line (L) connecting the first and the last quantile. Anorm is A normalized, _i.e._, the distribution was divided by its maximum value. On the bottom right (**f**), the Zack triangle method is displayed which leads to a threshold of approximately 0.4 \(\upmu\)M. For a detailed description, please refer to the text. Small inserts in the top (**b**) and middle right (**d**) panel depict the artifact on a magnified time scale (\(\sim\)14.3 s). **Step 4:** If the length of a movement segment is shorter than \(L_{min}\), \(\zeta_{acc}\) is set to \"0\" again at the corresponding time points. Correspondingly, if the gap between two movement segments is shorter than \(L_{min}\), this gap is also set to be a movement. This reduces false positive detections due to the Zack triangle algorithm, which often sets the threshold very conservatively, _i.e._, slightly higher than the noise level. It is statistically possible that the MSD of the noise also exceeds the threshold but then only for a very short period. This behavior can be controlled by adjusting \(L_{min}\). The value of \(L_{min}\) used was 2 s \(\times\)_fs_. The final output of the movement detection process is the vector \(\zeta_{acc}(t)\) which contains values of \"1\" at samples assigned to be a MA.
## 5 Results and Discussion
### The Method  
The experiment has been based on the Arion platform scenario of use.  
The scenario of using the Arion platform starts with a teacher who schedules a Learning session (an online version of a classroom lesson) on the IOLAOS platform. After the Learning Session is concluded, the Learning Analytics material generated by the IOLAOS platform, like screen or camera video and chat messages, are stored in the IOLAOS common data space [47]. The teacher's goal is to assess his/her decisions regarding the learning preferences assigned to the students who participated in the Learning session. To achieve this goal, the teacher joins the Arion platform to evaluate the learning process through the synchronization utility that the platform provides, utilizing the learning analytics materials stored by the IOLAOS platform during the learning session.  
As described in Section 4, the experiment was conducted at the premises of the NILE laboratory of the Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece [[https://nile.hmu.gr/](https://nile.hmu.gr/)]([https://nile.hmu.gr/](https://nile.hmu.gr/)) and was involving seven participants (four male undergraduate students and three male postgraduate students). Before the experiment, there were four full laboratory academic hour trial sessions where the participants became acquainted with the learning objects. The experiment duration was a full laboratory academic hour which is 45 min. The experiment was divided into two sessions: (a) the learning session  
Figure 12: Available Notes Modal.  
mainly supported by the IOLAOS platform and (b) the evaluation session supported by the ARION platform.
## 4 Numerical Results
### Minimizing Total Weighted Tardiness  
In practical use cases, due dates are usually assigned for work orders. Therefore, in other to consider this feature, the developed algorithm is tested with another performance objective which is minimizing the total weighted tardiness defined as:  
\[\sum w_{i}T_{i} \tag{6}\]  
where \(w_{i}\) is the weightage of job \(i\). \(T_{i}=\max\{0,C_{i}-d_{i}\}\) is tardiness of job \(i\), \(C_{i}\) and \(d_{i}\) are completion time and due date respectively. In this case study, the weightage of all jobs is set to be equal to each other. The input data of machines, operations and processing time is obtained from the minimizing make-span case. The due date is set based on due date tightness factor which is also fixed value for all jobs [21].  
Figure 6: Tuning Number of Ants parameter, \(A\), for a use-case of 60 operations and fixed value of \(Q=9500\): (**a**) \(A=15\), (**b**) \(A=30\) and (**c**) \(A=45\).  
Similar to minimizing the make span, three cases with variable number of operations: small (30 operations), medium (60 operations) and large (100 operations) are used to test the developed algorithm. The tuning parameters are the same as previous simulation, which are number of ants in the colony \(A\) and parameter \(Q\).  
The convergence graphs of the problems with different numbers of operations and the parameters adjusted are presented in Figure 8. When we increase the number of operations, different combinations of \(A\) and \(Q\) are needed to ensure converged solutions. Small value of \(A=30\) and \(Q=1200\) are sufficient for small number of operations. However, for larger size problem, the number of ants \(A\) and \(Q\) should be increased. Large value of \(A\) requires more computing time; therefore, for the largest number of operations, increasing \(Q\) is preferred to speed up the running time.
## 5 Discussion
### Limitations and Future Directions
#### 5.6.3 Application in Other Domains  
Although the MS-RRBR framework is specifically designed for identifying RRBs in children with autism, it may also be applicable to other behavioral disorders or clinical fields. Future research should explore the applicability of the MS-RRBR framework in identifying behaviors associated with attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), or other neurodevelopmental disorders. Furthermore, this framework could be utilized in broader clinical applications, such as monitoring patient behaviors within healthcare settings or supporting early intervention programs. Exploring these applications will not only expand the practicality of the MS-RRBR framework but also aid in developing more comprehensive and versatile AI tools for behavioral analysis.
## 3 Pathogenesis of AD  
AD was first mentioned by [PERSON] at a conference in Germany in 1906 as a \"distinctive extreme cerebral cortex disease phase.\" Recently, AD is believed to be a persistent or progressive condition, marked by diminished cognitive performance beyond what may be considered the result of normal aging, influencing memory, orientation, thinking, awareness, listening, judgment, and vocabulary [48]. Over a hundred years have passed since the very first pathophysiological dimensions of AD were mentioned. Over the last decades, developments in molecular biology have been critical to the interpretation of  
Figure 1: Biosynthesis of WA. Abbreviations: ACT: acetyltransferase; HMGS: hydroxymethylglutaryl CoA synthase; HMG-CoA: 3-hydroxy-3-methylglutaryl-co enzyme; HMGR: 3-hydroxy-3-methylglutaryl-coenzyme A reductase; MVAK: mex-onate kinase; IPP: 3-isopentenyl pyrophosphate; GPPS: geranyl pyrophosphate synthase; FPPS: farnesyl diphosphate synthase; SQS: squalene synthase; SQE: squalene epoxidase; CAS: cycloartenol synthase; SMT: sterol methyl transferase; ODM: obtusifoliol-14-demethylase; DOXP: deoxy xylulose pathway; MEP: methyl erythreitol pathway; DXS: 1-deoxy-D-xylulose-5-phosphate synthase; DXR: 1-deoxy-D-xylulose-5-phosphate reductase; WA: withaferin A.  
the molecular pathways behind AD. Therefore, it is widely understood that the molecular pathogenesis of AD is complex and includes a variety of theories or explanations where several different variables interrelate [49; 50]. Nevertheless, neither of these postulates alone is capable of clarifying the whole pathology component, and further research is required [2].  
Areas including the original factors of the disorder, the abnormal development of A\(\beta\) and the pathways through which it disrupts nicotinic acetylcholine receptors and neurons, and the association between cholinergic disorders and AD cognitive impairment are still not well known [48]. In this context, new findings that lead to the explanation and interaction of AD pathogenic mechanisms are important because they may allow the development of new treatment methods of the disease where current drug therapy cannot inhibit its incidence and growth [2]. Neurobiological pathways underpinning AD have been a key factor in pathological interpretation [51]; specifically, the most popular found alteration can be clarified by the cholinergic hypothesis, the amyloid peptides hypothesis, the tau protein hypothesis, the metal imbalance hypothesis, and the presence of oxidative stress (OS) [2; 52].
## 2 Materials and Methods  
### Scaffold Design  
Solidworks was used to design a standard tessellation language (STL) file of scaffold solid form that incorporated both the PCL outer skeleton and the cell-embedded bioink part. The PCL skeleton design was composed of a square base of 8 \(\times\) 8 mm\({}^{2}\) with a thickness of two layers (0.4 mm) and four vertical pillars at the corners with a height of 3 mm. The scaffold was designed as a 7.2 mm \(\times\) 7.2 mm \(\times\) 3 mm cube. The designed STL file was then processed in Slic3r [20] to generate the gcode file used by the bioprinter (BIO-X bioprinter, Cellink, Sweden). Briefly, the parameters used in Slic3r were as follows: infill pattern, rectilinear; infill density, 100% for PCL frame design and 10% for scaffold design; infill angle, 90\({}^{\circ}\); solid infill threshold area, 10; layer height, 0.2 mm; nozzle diameter, 0.2 mm. Two types of scaffolds were designed: a scaffold corresponding to a homogeneous cell density with one zone of 15 layers and a scaffold with three different zones corresponding to the three different cell densities. The dimensions of the zones were chosen to mimic the dimensions of the three different zones in the human articular cartilage. The top zone had a thickness of three layers (0.6 mm), the middle zone had a thickness of seven layers (1.4 mm), and the bottom zone had a thickness of five layers (1 mm), resulting in a total of 15 layers per scaffold.
## 1 Introduction  
The food, cosmetic, pharmaceutical, paper, and textile industries produce approximately 800,000 tons of dyes annually, and 20% end up as effluents that can be discharged into water bodies, creating persistence, bioaccumulation, and toxicity [1]. Most dyes present very stable molecular structures which are resistant to chemical degradation [2]. Likewise, they present xenobiotic properties that affect biological processes such as the interruption of photosynthesis in aquatic flora [3], mutagenic diseases in organisms present in bodies of water, and carcinogenic affectations in humans [4, 5].  
Consequently, there has been a trend towards protecting the ecosystem and effluent treatment with less environmental impact in recent years. Methods such as contaminant adsorption are preferred since they are simple, low-cost, highly efficient, and recyclable [6]. In dye removal, both organic and inorganic materials can be used. For example, activated carbon is a conventional adsorbent used due to its excellent adsorption performance [7]. However, its application has been limited by its high operating cost and complex regeneration process [8].  
Consequently, low-cost adsorbent alternatives and high adsorption capacities have been developed, which can be used in voluminous quantities with minimal effects of damage to nature [9; 10]. Therefore, special attention has been paid to the various bio-adsorbents, such as fungi, bacterial biomass, agricultural solid waste, and biopolymers [11]. Among which, researchers have recently focused on the study of biopolymers such as chitosan (CS), which are polysaccharides extracted from non-living organisms that are characterized by being biodegradable, biocompatible, and easy to modify chemically; in addition, it has low production costs and good properties as an adsorbent for removing dyes thanks to its active use of adsorption groups (-NH\({}_{2}\) and -OH). However, it presents high solubility only in acidic media, low mechanical resistance, and low surface area, limiting its use in water treatment [12]. Fortunately, these limitations can be overcome by chemically cross-linking CS with agents such as glutaraldehyde (GLA) and nanofillers such as graphene oxide (GO) and titanium dioxide (TiO\({}_{2}\)) nanoparticles, which improve the adsorption properties of CS [13; 14; 15].  
CS-GO-TiO\({}_{2}\)-GLA beads can present advantages such as improving the mechanical resistance by the GO [16] and a better surface area by adding TiO\({}_{2}\) nanoparticles [17]. In addition, GO can provide adequate thermal stability in the environment and offer additional mechanical stability to the processed composite due to its high strength [16]. To the best of our knowledge, CS-GO-TiO\({}_{2}\)-GLA beads for removing an anionic dye have not been reported. Therefore, the objective of this work consisted of elaborating a hybrid material from CS-GO-TiO\({}_{2}\)-GLA composites and the removal of the anionic dye FD&C Red 40 as a green environmentally and economic adsorption process.
# Reverse Time Migration Imaging Using SH Shear Wave Data
## 4 Real Data Application
### RTM Results of P and SH Shear Wave  
The reverse time migration of P-wave and SH shear wave data were carried out, respectively; there are 1519 shots and 8472 detection points in this survey, and the same acquisition geometry was adopted for both the P- and SH shear wave. The eighth-order finite difference scheme with a variable grid was adopted for P-wave reverse time migration for seismic wave simulation, the imaging condition is the cross-correlation imaging condition, and we use Laplace filtering to remove the low-frequency noise of the imaging. The SH shear wave reverse time migration was realized on a regular grid with the sixth-order CCD (Formula (3)) wave field simulation method adopted, an interval of 2 m for the grid size in the z-direction and an interval of 5 m for the grid size in the x-direction were adopted, and Laplace filtering was adopted to remove the low-frequency noise of the imaging to ensure stability and suppress alias frequencies in the wave field continuation process. Figure 10 shows the pre-stack depth migration images of the P-wave and SH shear wave. It can be seen in the image that there is no clear reflector for the P-wave in the area of CMP (common middle point) 1200-2500, which is due to the gas cloud in the area; the P-wave is significantly absorbed and has difficulty in passing through the area and reflecting to the surface when the compression wave goes through the area, but the SH shear wave can propagate normally in the area because it propagates through the rock frame and is not  
Figure 8: V\({}_{\rm S}\)/V\({}_{\rm P}\) ratio in the survey.  
Figure 9: Velocity modeling flowchart.  
affected by the gas. Therefore, SH shear wave reverse time migration can clearly image through this area and has obvious imaging advantages in this area over P-wave.