esa-sceva/satcom-chunk-collection huggingface dataset:
The study adds to the body of knowledge by demonstrating steps required to harness real-world data and develop algorithms that address bias and imbalance in datasets. Unlike previous work in the field, this study emphasises removing features that could lead to bias while maintaining model accuracy and integrity, thereby pushing the boundaries of current knowledge in this area. Additionally, the study incorporates comparative evaluations of several different models to verify the research assumptions and draw conclusions within this new domain from an application perspective. To the best of the authors' knowledge, this type of study, which exemplifies and addresses the aforementioned problems, has not yet been presented and will definitely enhance predictive models for academic advising. The research questions for the study are:  
* Which features from the dataset are the most significant when predicting student performance and qualification choice? * What is the performance of classification models when predicting student performance, qualification enrollment, and determining whether a student is better suited to enrol for mainstream or extended qualification? The remainder of the article is as follows. The literature review is presented in Section 2, followed by the methodology used for the study and the description of the evaluation techniques in Section 3. The results are shown in Section 4, and their implications are discussed in Section 5. Section 6 concludes the study by summarising key aspects and providing limitations and directions for future research.
## 2 Materials and Methods  
A search ranging from 1999 to 2020 was conducted in Ei Compendex, Inspect-IET, Web of Science, PubMed, ProQuest, and Science-Direct using the following search strategy. \"Trajectory planning\", \"upper limb\", and \"exoskeleton\" were used and combined as high-level keywords. Additionally, \"trajectory planning\" or \"motion planning\" was combined with the following keywords: \"rehabilitation\", \"humanlike motion\", \"upper extremity\", \"inverse kinematic\", and \"learning machine\". These keywords were selected after performing an initial search to ensure that there were no areas within this field that were excluded due to inadequate keyword selection. Abstracts and full texts were then collaboratively evaluated by the authors for direct relevance to the topic and scope of this study. A total of 67 papers were discovered.  
Figure 2 shows that analytic expression-based (polynomial methods, sigmoid functions, Fourier series) and computational or optimization-based methods are the most prevalent methods used for the trajectory planning of upper-limb exoskeletons. The number of studies involving trajectory planning for exoskeleton robots has increased, especially over the last three years (Figure 3). Of the papers collected involving trajectory planning, 32.07% were from 2018 to 2020 and were related to analytic expression-based and optimization-based methods. This evolution justifies the growing interest in this topic.  
Figure 1: Trajectory planning based on Cartesian motion planners.  
Figure 2: Trajectory-planning methods in the literature, 1999–2020.
# Optimal Attitude Determination for the CR200 Underwater Walking Robot
## 5 Conclusions  
The optimal posture for the biomimetic underwater walking robot CR200 to withstand strong currents was investigated using computational fluid dynamics (CFD) simulations. To enhance the robot's stability in strong currents, two primary parameters, namely, the robot's pitch angle and its distance from the seabed were identified as critical in determining its posture. Various postures, defined by combinations of pitch angle and seafloor clearance, were analyzed through CFD simulations to quantify the downforce generated by the flow around the robot toward the seabed. Based on these analyses, optimal values for pitch angle and seafloor clearance were determined, establishing the body angle and seafloor distance that maximize downforce.  
It was found that the optimal pitch angle was associated with the angle before the stall point in typical lifting bodies, which generated the maximum downward lift. Additionally, the optimal seafloor clearance corresponds to the gap where suction on the seabed surface is maximized, attributed to the Venturi effect. The posture of achieving maximum downforce, as identified in this study, will serve as a reference for future underwater robot operations.  
Future research will focus on developing underwater robots with enhanced mobility capabilities, including walking, jumping, and swimming. Achieving these advancements will require further CFD analyses and accurate predictions of fluid forces. The methodology and findings of this study are expected to provide essential foundational data for these developments.  
Conceptualization, H.T.A.; methodology, H.T.A.; software, S.P.Y., S.-H.J., and D.K.K.; validation, S.P.Y.; formal analysis, S.P.Y. and H.T.A.; investigation, S.P.Y. and H.T.A.; resources, H.T.A.; data curation, S.P.Y.; writing--original draft preparation, S.P.Y.; writing--review and editing, H.T.A.; visualization, S.P.Y.; supervision, H.T.A.; project administration, S.-y.Y., J.-B.H., H.K. and B.-H.J.; funding acquisition, B.-H.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by (a) the Korea Research Institute for the Defense Technology Planning and Advancement (KRIT)--Grant funded by the Defense Acquisition Program Administration (DAPA) (KRIT-CT-22-066), and (b) \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-003).  
Not applicable.  
Data availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 1 Introduction
In this context, the measurement and estimation of the spatial and angular spectral distribution of the ionizing radiation generated in high power laser-thin solid target experiments can be extremely useful. In this paper, we propose a simulation model to be used for the assessment of the radiation dose map in a TNSA regime for a high power laser-thin solid target interaction experiment. First, the energy spectral distribution of electron, proton and photon beams, have been calculated within 0-360\({}^{\circ}\) for a 5 \(\upmu\)m thick Ag target irradiated with a high-power laser pulse of about \(5\times 10^{19}\) W/cm\({}^{2}\) intensity. To this goal, the TNSA plasma expansion model has been considered in conjunction with the Geant4 Monte-Carlo radiation transport code. Then, the angular and energy spectral distributions of ionizing particles were used as input data for the calculation of electron, proton, and photon fluencies around the high power laser-thin solid target interaction point. The values of input parameters are characteristics to a high power laser-thin solid target experiment performed at the CETAL laser facility [24]. In the [PERSON]'s et al. paper [24], we reported a method for the assessment of the energy of the accelerated proton beams produced in high power laser-thin solid target experiments using a stack of CR-39 detectors. It was also stated that a compact experimental set-up which connect the target holder with the detector holder, and the laser spot optical analysis system can be useful for practical applications. For example, by using such a system, the misalignments between targets and detectors can be avoided [24]. In this paper, we introduced an updated version of the above-mentioned experimental set-up which is also suitable for estimation of laser-accelerated electron beam energies. It includes a magnetic spectrometer and a cylindrical holder for positioning the EBT3 RCF passive detectors. The calibration of the RCF was performed for both X-ray photon beams and monoenergetic electrons. The radiation dose map measured inside the interaction chamber during the high power laser-Ag thin target interaction experiments using EBT3 RCF detectors, will be presented. It will be compared with the simulated one, generated by implementing the data characteristic to angular and energy spectral distribution of electrons, protons, and photons in the geometry mesh of the experimental set-up designed using the GEANT4 standard geometry components.
## 2 Kernel Principal Component Analysis (KPCA)
#### Monitoring Metrics with KDE Control Limits  
Commonly, the KPCA-based fault recognition utilizes two statistics, the Hotelling \(T^{2}\) statistics, and the SPE or the Q statistics. The Q metric is mainly preferred for fault identification over the \(T^{2}\) graph since it is highly resilient to minor flaws. The \(T^{2}\) statistic is described by the Mahalanobis interval, while the Euclidean distance characterizes the Q statistic.  
The Hotelling \(T^{2}\) of the \(j\)th samples in the feature space used for KPCA fault exposure is calculated as:  
\[T^{2}_{j}=X^{T}\mathbb{C}^{-1}X \tag{9}\]  
where \(X=\left[z_{1,j}\,,\,\ldots,z_{q,j}\right]\) denotes the PC scores of the jth samples, q is the total of PCs retained and \(\mathbb{C}^{-1}\) signifies the inverse of the matrix of eigenvalues consequent to the retained PCs.  
The \(T^{2}\) threshold can also be anticipated from its probability density function (PDF). Kernel density estimation is a strategy for incorporating a dataset with an appropriate streamlined PDF from several random measurements. It is popularly utilized for approximating PDFs, particularly for univariate random data [39]. The KDE is relevant to GLR, \(T^{2}\), and Q statistics, since they are all univariate, though its framework exemplified by these statistics is multivariate.  
Control bounds for evaluating statistics can be computed from their corresponding PDF projections.  
\[\int_{-\infty}^{T^{2}_{\alpha}}g\Big{(}T^{2}\Big{)}dT^{2}=\alpha \tag{10}\]  
Once all scores are of Gaussian distribution, the control bound is related to a significance level, \(\alpha\), and \(T^{2}_{\alpha}\) can be originated from F-distribution as:  
\[T^{2}_{\alpha}\sim\frac{q(n-1)}{n-q}F_{q,n-q,\alpha} \tag{11}\]  
where \(F(q,n-q,a)\) is the value of the F-distribution relating to a significance level \(\alpha\), with degrees of freedom q and \(n-q\), for both the numerator and denominator.  
In addition, a basic calculation of the Q-statistic has been suggested [10]. For the jth samples:  
\[Q_{j}=\sum_{i=1}^{n}z_{i,j}^{2}-\sum_{i=1}^{q}z_{i,j}^{2} \tag{12}\]For testing data, an error is confirmed when the limit rate is disrupted as follows:  
\[Q_{\alpha}>C\sqrt{2\varphi_{2}}\left(1-\frac{2\varphi_{1}\varphi_{3}}{3\varphi_{2 }^{2}}\right)+1+\frac{\varphi_{2}}{\varphi_{1}^{2}}\left(1-\frac{2\varphi_{1} \varphi_{3}}{3\varphi_{2}^{2}}\right)\left(\frac{2\varphi_{1}\varphi_{3}}{3 \varphi_{2}^{2}}\right) \tag{13}\]  
where, \(\varphi_{i}=\sum_{j=l+1}^{i}\lambda_{j}^{i}\), \(i=1,2,3\)... and \(C\) is the value achieved from the normal distribution of 95% significance.  
The KDE control limit is as follows:  
\[\int_{-\infty}^{Q_{\alpha}}g(Q)dQ=\alpha \tag{14}\]
## 2 Method
### Target Operation System of Anomaly Detection  
Figure 1 shows the components of the automatic storage system targeted for anomaly detection in this study. The general function of the system is to store products scheduled for shipping; storage is automatically managed in the rack section using the shuttle vehicles shown in the figure. To put the products into and to take them out of storage, a conveyor for horizontal movement and a lifter for vertical movement are used. In many cases, the racks and conveyors in the system can be replaced immediately if one or more of their parts fail. Repairing/replacing the lifter, however, is not so easy. The lifter is essential for vertically conveying products and for distributing them to the various storage layers. It is expected that it will deteriorate more quickly than the other system components because of following reasons: Firstly, the lifter operates continuously for 24 h every day. Secondly, it frequently repeats drastic acceleration/deceleration. Thirdly, loading and unloading products on the carriage of the lifter are continuously repeated. If a problem occurs in the lifter, because of following reasons, it can have a relatively large impact on the movement of products to the shelves in each storage layer. Firstly, because the mechanical elements in the lifter are closely related and joined with one another, it often takes time to replace them in the event of a failure. Secondly, the repair of the lifter requires much time compared to that of the horizontal belt conveyor because the lifter is located in quite a narrow space surrounded by tall partition walls and, therefore, it needs to be repaired by a few people. As a consequence, effective anomaly detection for this part of the system is particularly important; if potential failures in the lifter can be predicted in advance, it is possible to minimize the economic loss due to equipment stoppage. In this study, we developed a prediction technique that can detect lifter failure in advance. As shown in Figure 1, as the lifter moves up and down, the carriage on which the product is placed moves up and down with it; in the operation of the lifter, vibration propagates to the entire body, including the steel columns supporting the lifter and other individual parts. With the proposed method, the transmitted vibration is detected by an accelerometer placed at optimal points on the body and changes in the vibration characteristics are effectively captured. Details of the method are described in the next section.  
Figure 1: Overview of the entire logistics system.
## 4 The Application of AWS Machine Learning (AML) for the Surface Finishing Process
### Amazon Web Services Machine Learning (AML)--Algorithm Perspective  
Machine learning on the AWS is classified into three main categories, i.e., (1) the machine learning application service, (2) the machine learning platform service, and (3) deep learning on AWS. The machine learning application service consists of APIs for vision services, language services, and chatbots. Some use cases include face recognition services, speed recognition, speech-to-text translations, and the building of interactive chatbots. Deep learning on AWS is dedicated to the development of artificial neural networks. In this paper, the machine learning platform service will be explored and applied to obtain the deburring data. In the second class, the AWS Machine Learning (AML) platform is essentially a Software as a Service (SaaS) solution developed by Amazon with the ability to work closely with other AWS functions in order to create machine learning models which can subsequently be utilised to fetch the required predictions. Users can choose from real-time predictions and batch predictions based on the size of the datasets.  
Although the full details on AML implementations are not publicly available, the service documentation and API are able to provide sufficient information on its functionality. These detail that the machine learning performed on the service is based on the linear regression and logistic regression algorithms. Both algorithms are less complicated than other machine learning-based regression methods, e.g., artificial neural methods (ANN) and support vector regression (SVR) methods. However, these algorithms are suitable for binary, classification, and regression machine learning problems in industries with less complex datasets.  
In addition, AML uses two types of algorithms--e.g., (1) the linear regression algorithm and (2) the logistic regression algorithm--to produce three types of machine learning models. The linear regression algorithm consists of squared loss functions, where the stochastic gradient descent is used for the logistic regression models. These are basic standard algorithms and are able to be efficiently applied to machine learning problems in the industry [13]. Binary classification models utilise the logistic regression algorithm which consists of a logistic loss function and a stochastic gradient descent. Multiclass classification models utilize the multinomial logistic regression algorithm which consists of a multinomial logistic loss function and a stochastic gradient descent.  
These models are selected depending on the problem that the user is looking to solve. If the user wants to predict an output value from a set of input values, the linear regression model should be used. On the other hand, if the user seeks to classify the inputs values into two outcomes/categories, the binary classification model should be used. If the user is looking to classify the inputs into a range of outcomes/categories, the multiclass classification model should be chosen.
## 3 Results and Discussion
### Determination of Idoquinolines  
The content of iodquinolines in the potato tubers biofortified with the control, KIO\({}_{3}\), 8-Hydroxy-7-iodo-5-quinolinesulfonic acid, and 5-Chloro-7-iodo-8-quininol treatments is presented in Figure 1B,C. There was a statistically significant higher accumulation of 8-OH-7-I-5 QSA (24.48 \(\mu\)g kg\({}^{-1}\) d.w.) in potato tubers biofortified with this form of iodine compared to the control (2.98 \(\mu\)g kg\({}^{-1}\) d.w.). There were no statistically significant differences in the amount of accumulated iodine in the 8-OH-7-I-5 QSA form in the other enrichment types (KIO\({}_{3}\), 5-Cl-7-I-8-Q) compared to the control. Therefore, the biofortification of potatoes with 8-OH-7-I-5 QSA showed the highest accumulation of this form of iodine in the tuber.  
When analysing the iodine accumulation of 5-Cl-7-I-8-Q in biofortified potato tubers, the content of the 5-Cl-7-I-8-Q form of iodine (15.94 \(\mu\)g kg\({}^{-1}\) d.w.) was higher compared to the control (5.68 \(\mu\)g kg\({}^{-1}\) d.w.); this difference was statistically significant. A statistically significant decrease in the accumulation of 5-Cl-7-I-8-Q in potato tubers biofortified with the KIO\({}_{3}\) form (1.45 \(\mu\)g kg\({}^{-1}\) d.w.) and the 8-OH-7-I-5 QSA (0.91 \(\mu\)g kg\({}^{-1}\) d.w.) treatment was observed compared to the control (5.68 \(\mu\)g kg\({}^{-1}\) d.w.) As a result, potato biofortification with 5-Cl-7-I-8-Q showed the highest accumulation of this form of iodine in the potato tuber.  
According to our study, both iodoquinolines (8-OH-7-I-5 QSA and 5-Cl-7-I-8-Q) are endogenous compounds. Unfortunately, the metabolic pathway is not known at this time. Our next research objective will be to know and describe the metabolic pathways of the endogenous iodoquinoline compounds that occur in the potato tuber. The amounts of endogenous compounds detected are small in the potato tubers, but they are there. What is more, research conducted by the team at the University of Agriculture in Krakow on another vegetable, kale roots, also found the presence of both iodoquinolines. Small peaks were observed in the kale roots, demonstrating that endogenous compounds were detected.
## 3 Results and Discussion
### Influence of Oils on the Desensitization Rate  
The Inhibition of the EOs on AMPAR activity shed light on the possibility of their impact on other biophysical gating properties of AMPARs such as desensitization (Figure 2). The average desensitization time of GluA1, GluA2, GluA1/2, and GluA2/3 were as follows; \(2.9\pm 0.1\) ms, \(2.4\pm 0.1\) ms, \(5.1\pm 0.3\) ms, and \(2.4\pm 0.2\) ms denoting an average rate (\(\tau\) = 1/ms) of \(0.34\) ms\({}^{-1}\), \(0.42\) ms\({}^{-1}\), \(0.20\) ms\({}^{-1}\), and \(0.42\) ms\({}^{-1}\), respectively Figure 2A. A decrease in desensitization rate was in GluA1, GluA2 homologous AMPARs and GluA1/2 and GluA2/3 Heterologous AMPARs upon treating the cells with  
Figure 1: Effect of essential oils and compounds on the amplitude of the whole-cell generated by various AMPAR subunits. (**A**). Amplitude generated from glutamate alone (black), glutamate + Citronellol (white) and from glutamate + Geraniol (grey). (**B**). Pair of representative whole-cell current traces from GluA1 channels (most significant) expressed in HEK-293 cells in the absence (left) and presence (right) of Citronellol oil. The recording was conducted upon 500 ms application of 10 mM glutamate to whole-cell recording from HEK293 cells expressing various AMPARs subunits.  
either EOs. Hence for Citronellol oil desensitization increased to \(4.3\pm 0.4\) ms, \(2.8\pm 0.2\)ms, \(5.7\pm 0.3\) ms, and \(2.7\pm 0.3\) ms, which read a decrease in the desensitization rates to \(0.23\) ms\({}^{-1}\), \(0.36\) ms\({}^{-1}\), \(0.18\) ms\({}^{-1}\), and \(0.37\) ms\({}^{-1}\), respectively Figure 2A. Likewise, for Geraniol oil, the desensitization reduced to \(3.6\pm 0.3\) ms, \(2.6\pm 0.2\) ms, \(5.4\pm 0.3\) ms, and \(2.6\pm 0.4\) ms denoting a decrease in the rate of desensitization to equal; \(0.28\) ms\({}^{-1}\), \(0.38\) ms\({}^{-1}\), \(0.18\) ms\({}^{-1}\), and \(0.38\) ms\({}^{-1}\), respectively. Figure 2B showcases the whole-cell current traces from GluA1 channels (most significant) expressed in HEK293 cells in the absence (left) and presence (right) of Citronellol oil.
## 3 Experiments
### Ablation Study  
As shown in Table 1,we conducted ablation experiments to verify the performance effectiveness of the innovations proposed in this paper. We conducted two sets of experiments on the KITTI validation set for our methods, Experiments 1 and 2. Experiment 1 used only the DepthNet network for depth completion, while Experiment 2 introduced the DepthNet with ASFF-ED module. The experimental results show that all four performance metrics improve with the ASFF-ED module. This indicates that the error in the prediction results is reduced, which fully demonstrates the effectiveness of our method. Consideringthe characteristics of the KITTI dataset, the AFF-ED module can make full use of the information of different size depth maps to obtain more accurate dense depth maps. These findings further validate the feasibility and superiority of the method in this paper in practical applications. Through these ablation studies, we validate the effectiveness of our method in improving performance and provide strong support for further research.
## 4 Discussion
### Effect of Smartphone Use on Static Posture  
#### 4.1.1 Effect of Smartphone Use on Cervical Spine Posture  
Seven studies have investigated the effects of smartphone use on cervical spine posture [5; 11; 12; 13; 14; 15; 16; 17]. Prolonged smartphone use has been shown to significantly increase cervical flexion angles, thereby elevating the risk of posture-related cervical issues [15; 17]. For example, using a smartphone while seated without backrest support increased cervical head flexion by approximately 2\({}^{\circ}\)[5]. Similarly, individuals who used smartphones for more than 4 h per day exhibited forward head posture angles that were, on average, 9\({}^{\circ}\) greater than those who used them for less than 4 h [14]. Collectively, these findings indicate that smartphone use shifts the head position anteriorly, increases cervical flexion, raises muscular loading around the cervical spine, and slows cervical nerve conduction velocity [14]. Additional studies have shown that smartphone usage exceeding 3 h daily contributes to greater cervical flexion angles, increasing biomechanical load and elevating the risk of cervical intervertebral disk herniation and degeneration [44; 45].