esa-sceva/satcom-chunk-collection huggingface dataset:
# Simultaneous Dual-Arm Motion Planning for Minimizing Operation Time
## 3 Results and Discussion
### Computation Time  
Table 6 shows the computation time of proposed method with respect to the number of objects. Simulation was performed 100 times by giving start and goal positions at random. Specifically, the coordinates of the start and goal positions are randomly generated based on a uniform distribution within the workspace. From the result, average and standard deviation of computation time required to solve MILP were calculated. The computer applied in this simulation uses a 4.0 GHz Intel Core i7-4790K CPU, 16.0 GB of memory, and Windows 7 Professional.  
In this section, we define the conventional method as the way costs \(c_{ijk}\) are calculated from the path planned by RRT before solving MILP. It means path planning with RRT is performed with all combinations \(n!\ \times\ 2^{n}\). The computation time of RRT was calculated using the result of \(0.3\ \mathrm{s/part}\) measured in Experiment 2. In the conventional method, we referred that there are \(n!\ \times\ 2^{n}\) combinations. Therefore, we can calculate the computation time of RRT by multiplying \(0.3\) and \(n!\ \times\ 2^{n}\). For example, when \(n=6\), the computation time is about \(1.38\ \times\ 10^{4}\ \mathrm{s}\), and it increases depending on the number of objects. On the other hand, in the proposed method, since the number of times RRT is executed is \(2n+1\) at most, we can calculate the computation time of RRT by multiplying \(0.3\) and \(2n+1\). For example, when \(n=6\), the computation time is about \(2.10\ \mathrm{s}\) at most. Thus, we can estimate that total time for planning in the proposed method is about \(3.21\ \mathrm{s}\) from average time in Table 6.  
From the result, the proposed method can significantly reduce the computation time compared with the conventional method. In addition, the proposed method can complete the calculation within the real time, so it can be applied sufficiently even in a dynamic environment.
# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 2 Literature Review
While DANN outperformed a simple SVM in one-third of the experiments when it was first presented [10], it outperformed it in just one-quarter of those conducted on the ArSentD-LEV dataset. Nonetheless, both the benefits and losses of adaptation were greater and more diverse in Arabic experiments. The authors ascribed the poor performance to imbalanced data as well as to deep neural networks' failure to learn improved representation from limited data. Moreover, readers should be informed that the ArSentD-LEV dataset was used in its five-point classes (i.e., highly positive, positive, neutral, negative and highly negative), whereas the English Amazon dataset was used in a binary classification. In this paper, we employ a different dataset with standard conditions to discover the real adaptive behavior of methods on Arabic. Another study [7] applied the ALDA [44] on top of the pretrained AraBERT language model [45]. ALDA was initially developed by combining two types of methods, namely domain-adversarial learning to align source and target distributions and self-training to learn discriminative target features. To learn high-level representations, the authors utilized contextualized word embedding. The study employed two datasets for the cross-domain experiments: the ArSentD-LEV multi-domain dataset again, but with binary classes, and three different datasets combined as three domains, namely book reviews, hotel reviews and tweets. Based on the overall results, AraBERT-ALDA beat DANN and DARL. It is worth mentioning that Arabic is the official language of 28 countries and the fourth most-used language on the Internet, with around 237 million users ([[https://www.internetworldstats.com/stats/7.htm](https://www.internetworldstats.com/stats/7.htm)]([https://www.internetworldstats.com/stats/7.htm](https://www.internetworldstats.com/stats/7.htm)), accessed on 1 November 2022). There are three types of Arabic: Classical Arabic, MSA and DA [46]. Classical Arabic is the old Arabic language and the same as the language found in the holy Quran, in which most words and phrases are no longer commonly used. MSA is the unified formal Arabic among Arabic speakers and is used in Arabic schools and official news channels. DA is the language used in daily conversations, and it varies among countries and even within a single country. In contrast to MSA, DA does not adhere to strict grammtical rules, and it sometimes use words from other languages. Among dialects, many words are pronounced differently or are even unique to a single dialect. The variety of DA imposes a special challenge for Arabic NLP, in addition to other complexities caused by resource scarcity and the morphological richness of the Arabic language [47]. All these difficulties are expected to be exacerbated in domain shift scenarios, affecting even the most effective domain adaptation methods. Thus, the current study also investigates how domain adaptation methods behave when tested on both MSA and DA in standard sentiment domain adaptation settings.
## 3 Results and Discussion
### Electrochemical Water Splitting by Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN Composite
The catalyst synthesized at 400 \({}^{\circ}\)C shows better efficiency, as compared to the product synthesized at 500 \({}^{\circ}\)C. The main reason for the better efficiency of Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN  
Figure 6: Effect of scan rate on electrochemical performance of Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN composite synthesized (**a**) at 400 \({}^{\circ}\)C, (**b**) its calibration curve; (**c**) at 500 \({}^{\circ}\)C, and (**d**) its calibration curve obtained at scan rates in 1 M KOH. composite at 400 \({}^{\circ}\)C could be better electron transfer due to the higher conductivity of metal oxides and nitrogen doped carbon, and a higher surface area. The Tafel slope for the product synthesized at 400 \({}^{\circ}\)C is 281 mV/decade and the current density is 0.599 mA/cm\({}^{2}\), as shown in Figure 7b. In addition, a current density of 10 mA/cm\({}^{2}\) was recorded at \(\eta\) (overpotential) = 396 mV. These experiments explain the good electrocatalytic response of the Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN composite toward OER. Similarly, at 500 \({}^{\circ}\)C, the overall catalytic performance is low. OER's onset potential is 1.6194, Tafel slope 261 mV/decade and current density 0.771 mA/cm\({}^{2}\), as shown in Figure 7c,d. Using Ag/AgCl as the reference electrode for electrolytes is commonplace in potentiometry and voltammetry, and their importance in the chemical laboratory is unquestionable. Ag/AgCl electrodes are usually more effective for many purposes. The electrochemical performance of Cu\({}_{2}\)O and Co\({}_{3}\)O\({}_{4}\) particles and their different composites is well documented in literature [47; 48; 49; 50; 51; 52; 53]. Similarly, the electrical conductivity of nitrogen doped carbon is also reported [54; 55; 56; 57; 58]. Incorporating a nitrogen doped carbon into an already conductive Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\) composite resulted in the formation of a versatile catalyst. The Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN composite shows low overpotential compared to several other reported materials. Two metal oxides and already conducting nitrogen doped carbon improved the catalytic water splitting performance in the 1 M KOH solution. The activity diagram of the Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN composite is shown in Figure 8.  
Figure 7: LSV curves of Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN composite synthesized at (**a**) 400 \({}^{\circ}\)C and (**c**) 500 \({}^{\circ}\)C and corresponding Tafel slops (**b**,**d**) at different scan rates, in 1 M KOH.
# Investigation of Ytterbium Incorporation in Lithium Niobate for Active Waveguide Devices
## 2 Experimental Methods  
### Sample Preparation  
For the sample fabrication, optical-grade x- and z-cut wafers of congruent LiNbO\({}_{3}\) (Yamaju Ceramics Co., LTD) were used. Pieces of \(10\times 10\times 1\) mm\({}^{3}\) were prepared by diamond saw dicing. The samples were cleaned using successive baths of acetone, isopropanol and deionized water, and an ultrasonic cleaner. Plasma cleaning in oxygen was applied as the final cleaning step prior to vacuum deposition. The samples were coated with 20 nm thick layers of Yb, with a purity of 99.9%, using electron beam evaporation. For annealing, the samples were placed in a platinum crucible in a high temperature elevator furnace for diffusion time of 30 h at different temperatures between 930 and 1130 \({}^{\circ}\)C. The heating and cooling rates were constant for all samples.  
One sample with dimensions of \(1\times 25\times 8\) mm\({}^{3}\) (x y z) from the x-cut wafer was also used to prepare Yb-doped ridge waveguides. For this, ridges were prepared in the x surface running parallel to the y-axis by precise diamond blade milling, and these samples were coated with 22 nm of Yb under symmetric angles of \(\pm 60^{\circ}\). For diffusion, the coated ridges were annealed at \(T=1125\)\({}^{\circ}\)C for \(t=216\) h. Afterwards, 85 nm of Ti were deposited under symmetric angles of \(\pm 60^{\circ}\) and in-diffused to prepare the ridge waveguides as described in [17].
## 2 Materials and Methods
The \({}^{29}\)Si experiments were performed with proton decoupling (cw (continuous wave) sequence) by applying a single pulse (\(\pi\)/2), an excitation pulse of 5 \(\upmu\)s, and a 60 s relaxation delay to obtain 10,800 scans. The \({}^{27}\)Al experiments were also performed with proton decoupling (cw sequence) by applying a single pulse (\(\pi\)/12), an excitation pulse of 1 \(\upmu\)s, and a 5 s relaxation delay to obtain 200 scans. The chemical shifts were referenced to as an external solution of tetramethylsilane (TMS) and to an external solution of 1 M of Al (NO\({}_{3}\))\({}_{3}\) for \({}^{29}\)Si and \({}^{27}\)Al, respectively. Field Emission Gun Scanning Electron Microscopy (MIRA3 FEG-SEM, Tescan, Brno, Czech Republic) was used to study the morphology of the samples synthesized with aluminum. The samples were studied by XPS (X-ray photoelectron spectroscopy, Physical Electronics PHI-750 spectrometer, Physical Electronics, Chanhassen, MN, USA) with an X-ray radiation source of Mg K\(\alpha\) (1253.6 eV) and referenced to as C _1s_ (284.8 eV) to characterize the Si/Al\({}_{\rm{TETRA}}\) ratio and the Al state at the surface. Finally, to study the textural properties of the samples, the calcined samples were pretreated at 200 \({}^{\circ}\)C under a vacuum overnight in a Micromerities Asap 2020 (Micromerities, Norcross, GA, USA) and were examined using nitrogen as a probe molecule. Ethanol dehydration: The ethanol used in this reaction was obtained from Sigma-Aldrich. An amount of 0.1 g of each catalyst was activated at 350 \({}^{\circ}\)C and at atmospheric pressure for an hour in an N\({}_{2}\) atmosphere. The catalytic tests were performed in a fixed-bed flow reactor at atmospheric pressure and at 250 \({}^{\circ}\)C. A mixture containing N\({}_{2}\) and ethanol vapor (25 mL/min) was stabilized using drag gas (N\({}_{2}\)) through a steam satuator system containing ethanol at 25 \({}^{\circ}\)C. The outlet gases were analyzed by gas chromatography (GC, Clarus 680, Perkin Elmer, Waltham, MA, USA) equipped with a flame ionization chamber (FIC) and a capillary column. The ethanol conversion [12] is defined by Equation (3):  
\[\%\text{X}(\text{EtOH})=(\text{N}(\text{EtOH}_{\text{in}})-\text{N}(\text{EtOH}_{ \text{out}}))/(\text{N}(\text{EtOH}_{\text{in}}))\times 100 \tag{3}\]  
For each product, the selectivity [12] can be calculated using Equation (4):  
\[\%\text{S}(\text{Product i})=\text{N}(\text{Product i})/(\sum\
olimits_{0}^{ \text{i}}\text{N}(\text{Product i}))\times 100 \tag{4}\]
## 1 Introduction
In [4], the existence and uniqueness criteria and the closed-form solution of the convolution-type linear VFIDEs of the following form were investigated:  
\[\sum_{i=0}^{n}a_{i}u^{(n-i)}(x) + \sum_{i=0}^{n}\int_{0}^{x}k_{i}(x-t)u^{(n-i)}(t)dt \tag{1}\] \[- \sum_{j=1}^{m}\int_{0}^{b}k_{j}(x,t)\sum_{i=0}^{n}a_{i}u^{(n-i)} (t)dt=f(x),\]  
subject to the boundary conditions  
\[\Phi_{i}(u)=0,\quad i=1,2,\ldots,n, \tag{2}\]  
where \(a_{i}\), \(i=0,1,\ldots,n\) (\(a_{0}\
eq 0\)) and \(b>0\) are given real constants; \(k_{i}(x)\), \(i=0,1,\ldots,n\), \(k_{j}(x,t)\), \(j=1,2,\ldots,m\), and \(f(x)\) are given continuous functions; \(u(x)\) is the unknown function assumed \(n\) times continuously differentiable; and \(u^{(i)}(x)=\frac{df\,u}{dx^{i}}\) and \(\Phi_{i}\), \(i=1,2,\ldots,n\), are given linear bounded functionals. This technique was used to solve the second-order VFIDEs encountered in the bending analysis of elastic Euler-Bernoulli beams in [PERSON]'s two-phase nonlocal integral model in the case of a distributed transverse load and boundary conditions for a simply supported beam, cantilever beam, clamped pinned beam and clamped beam [4; 34]. In this article, we extend the technique to Volterra-Fredholm integro-differential equation in the Hammerstein form  
\[\sum_{i=0}^{n}a_{i}u^{(n-i)}(x) - \sum_{i=0}^{n}\int_{a}^{x}k_{i}(x,t)u^{(n-i)}(t)dt \tag{3}\] \[- \sum_{j=1}^{m}\int_{a}^{b}q_{j}(x,t)\varphi_{j}\Big{(}t,u(t),u^{ \prime}(t),\ldots,u^{(n)}(t)\Big{)}dt=f(x),\]  
under the general nonlocal boundary conditions\[\Phi_{i}(u)-\sum_{j=1}^{l}\
u_{ij}\Theta_{j}(u)=0,\quad i=1,2,\ldots,n, \tag{4}\]  
where \(a_{i}\), \(i=0,1,\ldots,n\), (\(a_{0}\
eq 0\)), \(a\) and \(b\) are given real constants; \(k_{i}(x,t)\), \(i=0,1,\ldots,n\), \(q_{j}(x,t)\), \(\varphi_{j}(\cdot)\), \(j=1,2,\ldots,m\), and \(f(x)\) are given continuous functions; \(u(x)\) is the unknown function assumed \(n\) times continuously differentiable with \(u^{(i)}(x)=\frac{d^{i}u}{dx^{i}}\); \(\Phi_{i}\), \(i=1,2,\ldots,n\), are some special, linear bounded functionals; \(\Theta_{j}\), \(j=1,2,\ldots,l\), are given general linear bounded functionals involving values at fixed points and definite integrals of \(u^{(i-1)}(x)\), \(i=1,2,\ldots,n\), in the interval \([a,b]\); and \(\
u_{ij}\), \(i=1,2,\ldots,n\), \(j=1,2,\ldots,l\), are real constants. The outline of the paper is as follows: In Section 2, a direct operator method for solving nonlocal boundary value problems for nonlinear VFIDEs is presented. In Section 3, existence and uniqueness criteria and a solution formula for linear VFIDEs are given. Section 4 deals with the inversion of linear Volterra integro-differential operators (VIDEs) of convolution type and the solution of the corresponding VFIDEs. The proposed methods are tested by solving several examples in Section 5. Finally, conclusions and future directions are discussed in Section 6.
## 5 Proposed Model
### Mobility Movement and Localization Process
#### 5.3.1 Procedure in Unknown Node's Side  
1. UNs will be deployed randomly.
2. Each node will communicate with its neighbours' node that are located within its communication range, collecting their information and adding them to its neighbours table was shown above in Table 3.
3. When MA arrives at each node, the node will exchange its table with the MA.
4. When three different locations are received by each UN, it will be able to calculate its own location.  
The flowchart in Figure 8 presents the localization procedure done on the UN's side.  
Figure 7: The system scheme of FLPPL.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 4 Categorization of Bridgeless Boost Converters
### Pseudo Totem-Pole  
Figure 16 depicts the pseudo totem-pole converter. This converter has two AC/DC boost rectifiers (one for each grid semi-cycle) [9; 135; 136]. In the positive semi-cycle, the first boost converter (formed by \(L_{1}\), \(Q_{1}\), and \(D_{1}\)) operates due to the polarization of \(D_{4}\). In the negative semi-cycle, the second boost converter (formed by \(L_{2}\), \(Q_{2}\), and \(D_{2}\)) operates due to the polarization of \(D_{3}\). This topology needs independent signals for controlling both switches [36]. Additionally, it also requires coupling inductors for EMI mitigation and snubber circuits for switching losses' reduction [137; 138]; therefore, this topology is not commonly used since a similar performance can be obtained with other BLBC topologies (with less number of components or control complexity).  
Figure 16: Interleaved boost converter: (**a**) totem pole topology; (**b**) GaN totem-pole.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 4 Experimental Setup and Results
### Supporting Findings and Conclusions
#### 4.6.1 Creation and Processing of Datasets  
This article provides a clear delineation of the methodology involved in gathering Turkish financial tweets, the manual tagging of sentiments and the creation of both binary and multi-class datasets. The pre-processing phase of tweets is meticulously detailed, encompassing actions such as removing superfluous elements, transforming text to lowercase and rectifying spelling errors through the utilization of the ITU Turkish NLP Web Service API.
# Development of a Maintenance and Spare Parts Distribution Model for Increasing Aircraft Efficiency
## 4 Simulation Model Results
### System Dynamics Model Definition
#### 4.1.6 Maintenance Program Sub-Model  
Reactive maintenance and preventive maintenance are considered in the maintenance program sub-model, which considers one spare part for which the intervals for planned maintenance activities are determined. The intervals can be fixed or variable. Based on this, the maintenance activities are performed and the unplanned maintenance activities are derived from them.  
For each component, depending on its ABC classification or the criticality analysis, the maintenance programs can be influenced to reach the target levels for each individual component or spare part.
## Empirical Analysis
### Reliability and Validity Analysis  
Although many questionnaire items in this study were drawn from the existing literature on perceived value, the exploration of the new concept related to entertainment platforms necessitated a rigorous examination of reliability and validity. The results in Table 2 show a robust assessment of these measures. \begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **Category** & **Frequency** & **Percentage** \\ \hline \multirow{2}{*}{Gender} & male & 175 & 53.0 \\ \cline{2-4}  & female & 155 & 47.0 \\ \hline \multirow{4}{*}{Age} & below 20–29 & 37 & 11.2 \\ \cline{2-4}  & 20–29 & 98 & 29.7 \\ \cline{2-4}  & 30–39 & 91 & 27.6 \\ \cline{2-4}  & 40–49 & 59 & 17.8 \\ \cline{2-4}  & over 50 & 45 & 13.6 \\ \hline \multirow{4}{*}{Occupation} & employed & 145 & 43.9 \\ \cline{2-4}  & self-employed & 45 & 13.6 \\ \cline{2-4}  & student & 107 & 32.4 \\ \cline{2-4}  & others & 33 & 10.0 \\ \hline \multirow{4}{*}{Education level} & high school or below & 97 & 29.4 \\ \cline{2-4}  & bachelor’s degree & 179 & 54.2 \\ \cline{2-4}  & postgraduate degree & 54 & 16.4 \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristics of respondents. Reliability, gauged by Cronbach's \(\alpha\), demonstrated strong internal consistency for all constructs, with values surpassing 0.778, which was well above the acceptable threshold of 0.60, as established by [PERSON] [64]. This result confirmed the reliability of the measurement items. Confirmatory factor analysis was employed to assess convergent validity. The results revealed that all items had factor loadings exceeding 0.608, thus surpassing the minimum threshold of 0.60. In addition, the composite reliability (CR) values ranged from 0.840 to 0.961, exceeding the recommended threshold of 0.70. The average variance extracted (AVE) for each construct ranged from 0.639 to 0.861, surpassing the threshold of 0.50. These metrics collectively underscored the robust convergent validity of the measures. The measurement model's indices indicated a commendable fit with the data (\(\chi^{2}=435.371\); d.f. = 194; \(\chi^{2}\)/d.f. = 2.244; comparative fit index (CFI) = 0.952, Tucker-Lewis index (TLI) = 0.942, root mean square error of approximation (RMSEA) = 0.061, root mean square residual (RMR) = 0.061, normed fit index (NFI) = 0.917; goodness-of-fit (GFI) = 0.913, adjusted goodness-of-fit (AGFI) = 0.881). The \(\chi^{2}\)/d.f. ratio fell below the recommended level of 3.00, affirming the adequacy of the proposed model. [PERSON] et al. [65] observed that many studies commonly use CFI, TLI, and RMSEA alongside chi-square tests to assess model fit. Typically, if these indices meet the recommended thresholds, the model is considered appropriate. In this study, following the criteria established by [PERSON] and [PERSON] [66] and [PERSON] et al. [67], CFI, TLI, NFI, and GFI all exceeded the cutoff value of 0.90. Additionally, RMSEA and RMR were below their respective thresholds of 0.08 and
## 5 Experiments and Analyses
### Video Delivery Experiment
#### 5.1.2 Ratio of the Three Types of Requests: 1:1:1 on the SPA-WP  
In the second part, 200 type-\(i\) requests, \(i=1\), 2, and 3, were sent to Resource Broker. We recorded how many requests of type-i were assigned to node j and the average turnaround time of node \(j,j=1\), \(2...6\). This time, the SPA algorithm with preemption (SPA-WP for short) was invoked. The 600 submitted requests were identical. Each requested a 10 MB video program. We evenly assigned them to three different request types, _i.e._, 200 for each type. Due to the requirement of identifying the type of a request, type-1, type-2 and type-3 requests asked for transferring the video data in DVD, mp4 and flv formats, respectively, to the request nodes.  
The experimental results are illustrated in Table 14 (see the X portion of data values in X/Y format which represents the values produced by the SPA-WP/Random), in which the percentages of the requests assigned to different nodes (see percentage-of-requests column) are not significantly different from those listed in Table 11. The turnaround time of the type-1 requests (see column Type-1) was the shortest among the three types, since the priority with which type-1 requests were executed by level-1 resource nodes was the highest, and up to 95 (\(=(96+94)\)/200)% of this type of requests were executed by level-1 resource nodes. The turnaround times of type-2 and type-3 requests were longer because they were preempted up to 46 and 69 times respectively (see No.-of-preemption row), and 40.5 (\(=(36+32+8+5)\)/200)% of type-2 requests and 41.5 (\(=(44+22+9+8)\)/200)% of type-3 requests were executed by the four low level nodes, _i.e._, nodes 2, 3, 5 and 6. Also, the average turnaround times of the level-1 resource nodes, _i.e._, 157.47 s on node 1 and 157.36 s on node 4, were shorter than those of the other two levels' resource nodes, _i.e._, 168.90, 169.03, 167.46 and 168.77 s. The average turnaround time of the 600 requests was 164.83 s.  
Figure 10: CPU loads of the SPA-NP and Random algorithms (150 th–1500 th s are not shown since they are similar to the shown portion).