esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
A total of 35 patients undergoing orthodontic treatment for palatally impacted canines were recruited for this study. The test and control groups included 17 patients each. During the observation period, six patients decided not to undergo a second CBCT, and for another patient a second CBCT was not requested because the canine had already erupted (Figure 4).  
A total of 27 patients (mean age: 14.4 \(\pm\) 1.2 years; 13 M and 14 F) were analyzed in the present study; 13 patients (7 M, 6 F) formed the control group, while 14 patients (7 M, 7 F) were in the test group. Twenty canines in the control group and fifteen canines in the test group were included in the analysis of both apex displacement and cusp displacement. Timespan, apex displacement, and speed are given as medians with interquartile ranges, while cusp displacement is given as the mean (standard deviation). Timespan, displacement, and speed values with a summary of the used tests are reported in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Control Group** & **Test Group (mTPA)** & \\  & **(13 Patients,** & **(14 Patients,** & _p_**-Value** \\  & **20 Teeth)** & **15 Teeth)** & \\ \hline Timespan (days) & 85.50 [73.00, 99.00] & 130.00 [108.00, 189.00] & \textless{}0.001 \\ \hline
**Apex** & & & \\ \hline Displacement (mm) & 3.09 [2.07, 4.70] & 3.32 [1.75, 4.54] & 0.868 \\ \hline Speed (mm/day) & 0.03 [0.01, 0.04] & 0.03 [0.02, 0.05] & 0.507 \\ \hline Monthly Speed (mm/month) & 1.05 [0.74, 1.64] & 0.72 [0.27, 1.30] & 0.205 \\ \hline
**Cusp** & & & \\ \hline Displacement (mm) & 5.71 (3.10) & 5.31 (3.50) & 0.722 \\ \hline Speed (mm/day) & 0.06 [0.03, 0.09] & 0.04 [0.03, 0.06] & 0.210 \\ \hline Monthly Speed (mm/month) & 1.89 [1.04, 2.84] & 1.08 [0.81, 1.91] & 0.210 \\ \hline \end{tabular}
\end{table}
Table 1: Median or mean of displacements in each group; _p_-value: Mann–Whitney U test or Student’s _t_-test. mTPA: miniscrew with transpalatal arch.  
The median monthly apex speed was 1.05 mm in the control group (IR [0.74, 1.64]) and 0.72 mm in the test group (IR [0.27, 1.30]). The median monthly cusp displacement was 1.89 mm in the control group (IR [1.04, 2.84]) and 1.08 mm in the test group (IR [0.81, 1.91]). No statistically significant differences in the reported speeds were found (Figure 5).  
Root length changes were tested on a sample of 58 adjacent teeth; 42 teeth were adjacent to an impacted canine (control group) and 16 teeth were adjacent to a non-impacted canine (test group); 29 teeth of the sample were second incisors, while 29 were first bicuspids. Teeth in the test group showed a median root length change of 0.01 mm, while teeth in the control group showed a median root length change of \(-\)0.01 mm; 50% of teeth adjacent to an impacted canine underwent a negative root length change, which was less than 1 mm in the majority of cases, but no significant differences were found in root length changes between the test group and the control group.  
No significant differences were found in root length changes between lateral incisors and first bicuspids. No miniscrew failures were observed.  
Figure 4: Flowchart of the study.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 1 Introduction
The increase in produced data volume has gradually led to the development of Big Data, Cloud and Edge Computing. Some of the key issues with the large volumes of data have been their management in traditional data warehouses and knowledge acquisition using data mining and statistical analysis techniques [9]. The solution to this problem was the development of Big Data, a data analysis methodology that allows the storage and analysis of a big volume of data using a new generation of technologies and architecture [10]. In Big Data technology, each node is independent and self-sufficient. In contrast, the Cloud solution provides more computing power by distributing individual tasks to multiple Cloud devices [10]. The data are stored on the cloud, which can be accessed by a user from any location without the need to know the specific location of the data storage [11]. There are many benefits to using Cloud technology such as its virtual infrastructure, access for any device via the Internet and scalability. According to [10], Big Data and Cloud computing complement each other  
Figure 1: Comparison of total vehicle data production by autonomy level according to [8]. and their combination provides many benefits that neither technology would be able to provide on its own. Many of the tasks performed by IoT devices are time sensitive, and the use of the Cloud is not a suitable solution for them. The data must be uploaded to centralized servers and the results are sent back to the IoT devices after making the necessary calculations. These results are often needed in less time than they can be provided with Cloud computing. The solution to this problem may be the use of Edge computing. It allows processing of data closer to the device and end-user, thus also reducing the time it takes to transfer data. Thanks to distributed deployment, balanced network traffic is ensured and traffic peaks are avoided. This also makes latency and energy consumption lower. However, unlike the Cloud, Edge computing devices have less storage capacity and, therefore, they would not be able to store large amounts of data. If we need to ensure low latency and unlimited storage capacity, a combination of Edge computing and the Cloud is the ideal option [12]. As well as the inclusion of autonomous vehicles in normal traffic with the ability to communicate with the surroundings, the design of the processing architecture and the way data are stored in real time, are currently a major challenge for the entire Smart Mobility concept.
## 4 Accident Control Strategy
### Classification of the Accident and the Potential Damage Analysis
#### 4.1.2 Level 2 Accident  
An SCWO system is operated in a high-temperature and high-pressure environment. The maintenance of high pressure in the system is of great importance because the loss of pressure of the high-temperature fluids means they can drastically vaporize. The SCWO system is series-connected; therefore, the blockage or leakage at any place can affect the whole system. A Level 2 accident refers to an accident with limited impact on the normal operation of the system. If a Level 2 accident happens, the normal operation of the system can be interrupted, and it cannot be resumed by simply replacing some components. For instance, the opening of a relief valve from a high-temperature and high-pressure facility is a Level 2 accident, because the relief valve re-seats and the pressure of the whole system can be established again after discharging some of the feedstock. Some action should be taken to minimize secondary damage to the facilities that is caused by a Level 2 accident, and the whole system should be shut down immediately.
## 2 Materials and Methods
### Statistical Analysis  
The participants' characteristics were presented using descriptive statistics and individual data points. Normality was tested using Shapiro-Wilk tests. Given the small sample size and as outcomes were not normally distributed, median values and interquartile ranges (IQR) were determined. Differences in the ROM parameters between the children with HIVE and their TD peers were investigated using non-parametric Mann-Whitney U-tests. The joint angle trajectories of the two cohorts were investigated using the open-source 1-dimensional statistical parametric mapping method (1 DSPM) in MATLAB (R2017b, Mathworks Inc., Natick, MA, USA). This is an open-source statistical method that allows analysis of continuous biomechanical data (such as joint angles) across an entire time series rather than at discrete points, enabling the identification of statistically significant differences between conditions or groups throughout the complete movement cycle. Herewith, the non-parametric method was applied to compare the joint angle trajectories (vM0.1, www.spmld.org: download non-parametric toolbox), and median and IQR of the joint angle trajectories were calculated and presented in figures (0-100% gait cycle). Significance was set at \(p<0.05\) for all statistical analyses.
# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 5 Collaborative Learning Technologies  
Machine learning and AI have infiltrated various research and application fields since the Renaissance caused by backpropagation algorithms in the 1980s. AI technology with machine learning methods provides more intelligent solutions to improve the efficiency of IoV. With the development of the field of AI, many excellent machine learning methods have been derived, and also applied to the IoV for intelligent decision-making, which can be optimized for different related problems and needs. For example, unsupervised learning, supervised learning, deep reinforcement learning, and federated learning are used to solve specific problems such as path planning, communication routing protocol, task offloading, load balancing, vehicle blockchain, network attack prevention, and resource management based on traffic prediction.  
Federated learning is a distributed machine learning method. Under the guidance of a central server, multiple clients use local data to train a model, then aggregate the model parameters of each client in the central server to integrate the knowledge, and then distribute it to each client. Since only intermediate model parameters are sent to the server instead of raw local user data, this is a way to obtain global knowledge under the premise of protecting user privacy.  
Ensemble learning completes the learning task by constructing and combining multiple learners; that is, combining multiple weakly supervised models in order to obtain a better and more comprehensive strongly supervised model. The underlying idea of ensemble learning is that even if a weak classifier makes an incorrect prediction, other weak classifiers can correct the error back.  
As shown in Figure 6, both federated learning and ensemble learning are distributed learning methods. They are all parallel learning models. However, the difference is that federated learning has the same model regardless of client or server, while ensemble learning has different models. Note that the ensemble learning uses multiple models to generate a better decision, where the data distributed to different models could be different or the same.  
In the following, we provide a detailed discussion of the key learning approaches for collaborative intelligence in IoV, including:  
* Federated learning.
* Ensemble learning.
* Supervised learning.
* Reinforcement learning.
## 4 Experimental Results
### Experiment Results and Analysis
#### 4.3.5 Confusion Matrix and Analysis  
We can summarize and visualize the performance of the classification algorithm through the confusion matrix. The number on the diagonal represents the correct prediction, and the others are the wrong prediction. The higher the percentage value shown diagonally, the more accurate the algorithm. This metric can reflect the tolerance degree of the algorithm to easily distinguish confused data.  
In this experiment, two datasets, TREC-6 and THUCNews, were selected for visual analysis, and five or six methods were used for comparison on each dataset (Figures 6 and 7). Figure 6 shows the accuracy of different methods on various categories of data, which are extracted from the confusion matrix calculation process. In Figure 7, if the diagonal color is darker, then the prediction accuracy of the corresponding category is higher. The actual label is on the \(X\)-axis, and the prediction label is on the \(Y\)-axis. Among all of the methods, our model (Figures 6 and 7f) shows strong classification ability because it mines the key information relations in sentences and the word group inline relations to learn the special distinguishing information in the text.  
Figure 5: Ablation studies on the hierarchical deeper attention module and the FFCon module. We conducted experiments on two datasets: (i) the _TREC-6 dataset_ and (ii) the _AG News dataset_. The experimental results on four different types of collocation combinations demonstrate the importance of the two modules. APTrans(i): _our model with the two modules_; APTrans(ii): _APTrans without hierarchical deeper attention_; APTrans(iii): _APTrans without FFCon_; and APTrans(iv): _APTrans without the two modules_.  
Figure 6: Visualization on TERC-6 dataset. Line chart for four different types of methods. ABBR: _abbreviation_. ENTY: _entity_. DESC: _description and abstract concept_. HUM: _human being_. LOC: _location_. NUM: _numeric value_.  
Figure 7: Visualization on THUCNews dataset. Confusion matrix for six different types of news. (**a**) BigBird. (**b**) DeBERTa. (**c**) CPT-2. (**d**) RoBERTa. (**e**) ERNIE. (**f**) APTrans (ours). Ent.: _entertainment_. Edu.: _education_. Tech.: _technology_.
## 3 Animal Health Monitoring
### Mental Health Monitoring
Figure 4 illustrates an architecture that is capable of monitoring the physical and mental health parameters of the dairy cattle with sensor more and vision more. The sensor more attached to the body of the dairy cattle provides information related to body temperature, position (lying, standing), and heart rate. The sensor for muscle strength and behavior analysis can only be used during critical conditions and rare conditions. All the sensors are interfaced with the controller of the sensor more. In this architecture, a long-range radio module is preferred as it is the communication protocol that is capable of transmitting to long-range up to 10 km with low power consumption. Every individual sensor more of dairy cattle is identified with an ID provided in the controller through programming. Based upon ID, the edge gateway identifies the identity of the sensor more and processes the data. An edge gateway is powered with edge computing and a pre-trained ML model, where it analyzes the received data and provides insights from it. In case, if it identifies that the particular dairy cattle are unwell, then it immediately transmits the information to the farmer/user on the cloud server. The vision more is placed inside the cattle shelter, before the cattle and the visuals captured are transmitted to the edge gateway for analyzing the physical and mental behavior of the cattle. The computing of the data is processed at the edge gateway, and it sends the insights or data related to the condition of dairy cattle on a cloud server. To enhance the computing power of the edge gateway, it is powered with a co-processor and is charged with a dual power supply. Transfer learning and domain adaption approaches can be employed in collaboration with deep neural networks at the edge gateway to improve cross-domain image recognition generalisation [60; 61]. Furthermore, transfer learning ensures that deep-learned features become more domain-invariant when carrying discriminative representations for cross  
domain visual recognition. Edge gateway also enhances the latency and provides insights in real-time within a minimum duration.
## 2 Materials and Methods
### Data
#### 2.2.2 Validation Data  
Very High Resolution (VHR) RapidEye data were utilized to validate the Landsat-derived coastline results. This commercial satellite constellation is operated by the company Planet and was launched in August 2008. It consists of five identical satellites capturing images of Earth's land surface every 5.5 days over mid-latitude regions. The RapidEye Earth-imaging System (REIS) provides imagery in five spectral bands (Blue, Red, Green, Red Edge, and NIR) with a spatial resolution of 6.5 m at nadir, which is resampled to 5 m [57]. For this analysis, orthorectified level 3A products were used. The data are provided radiometrically and geometrically corrected. RapidEye provides data in 25 by 25 km tiles [58]. All utilized tiles are listed in Table 1, including the Landsat scene that was used for comparison. The RapidEye mosaic was the only available VHR image for which a temporally matching cloud-free Landsat image existed. The close acquisition times are important to avoid the influence of tidal and wave attack on the land-water boundary. The RapidEye mosaic covers an area of about 3000 km\({}^{2}\) around Da Nang in central Vietnam with a shoreline length of approx. 250 km.
# Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation) +
###### Abstract  
The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin-destination (OD) matrix that describes the time dependencies of travel patterns; this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alternatives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin-destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process.  
OD estimation; bi-level optimization; derivative-free optimization; stochastic perturbation stochastic approximation (SPSA); nonlinear optimization; structural similarity +
Footnote †: This paper is an extended version of our conference paper: [PERSON]; [PERSON]; [PERSON] Application of data driven approach to dynamic origin-destination matrix estimation (DODME). In Proceedings of the Conference ISTDM2021, online, 21-24 June 2021.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
###### Abstract  
Wireless Sensor Networks (WSNs) are vulnerable to faults because of their deployment in unpredictable and hazardous environments. This makes WSN prone to failures such as software, hardware, and communication failures. Due to the sensor's limited resources and diverse deployment fields, fault detection in WSNs has become a daunting task. To solve this problem, Support Vector Machine (SVM), Convolutional Neural Network (CNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Probabilistic Neural Network (PNN) classifiers are used for classification of gain, offset, spike, data loss, out of bounds, and stuck-at faults at the sensor level. Out of six faults, two of them are induced in the datasets, i.e., spike and data loss faults. The results are compared on the basis of their Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC), and F1-score. In this paper, a comparative analysis is performed among the classifiers mentioned previously on real-world datasets. Simulations show that the RF algorithm secures a better fault detection rate than the rest of the classifiers.  
WSNs; fault detection; machine learning; random forest; support vector machine; convolutional neural network +
Footnote †: journal:  
_Article_
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 1 Introduction
### Microalgae in Cosmetics and Cosmeeticals  
Cosmetics may be defined as any substance or mixture placed in contact with the skin or outer sparts of the human body such as the epidermis, hair, nails, lips, external genital organs, teeth and mucous membranes of the oral cavity that can clean them, perfume them, change their appearance, protect them and keep them in good condition or reduce body odors [113]. Cosmeeticals are cosmetic products with biologically active ingredients aimed at having medical or drug-like benefits [114]. Microalgae and bioactive components extracted from microalgae are used in cosmetics as antioxidants, free-radical collectors, stress protectors, immune system boosters, odor maskers, make-up pigments, sunscreen protectors and anti-ageing agents. The different effects of active ingredients extracted from microalgae include blemish prevention, damaged skin reparation, seborrhea improvement, inflammation process inhibition, acceleration of the healing process and skin moisture maintenance [115; 116]. Examples of some microalgae species that are in cosmetics and have potential for use in cosmeceuticals are summarized in Table 8.  
Microalgae are found in several products for personal skin care. For example, the company Soliance uses whole _Arthospira_ sp., and the peptide sequence LVMH, derived from _Chlorella_ sp., is used in personal skin care products. Furthermore, the company Solazyme uses alguronic acid in its anti-aging skin products. Soliance also uses the alga _Skeletonema costatum_ in hydrating skin products and uses _Dysmorphococcus_ globosus in products muted to have anti-inflammatory effects [16]. Due to their unique cellular composition and content of PUFAs, including DHA, EPA, vitamins and folic acid, microalgae are also of great interest in the field of thalassotherapy. Thalassotherapy is a modern procedure working with seaweed and marine elements including microalgae, mud, sand or plankton for therapeutic and preventive health care purposes [119].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Species** & **Observed Effects** & **References** \\ \hline \multirow{2}{*}{_ArthInospira maxima_} & Skin protection & [116] \\  & Skin regeneration & \\ \hline \multirow{2}{*}{_Arthospira platensis_} & Wrinkle formation prevention & [116] \\  & Early skin aging prevention & [117] \\ \hline \multirow{2}{*}{_Chlorella vulgaris_} & Support collagen repair mechanism & [117] \\  & Sunscreen protection & [117] \\ \hline \multirow{4}{*}{_Nannochloropsis gaditana_} & Decreased oxidative stress in human & \\  & dermal fibroblasts & \\ \cline{1-1}  & Skin protection & \\ \cline{1-1}  & Skin hydration & \\ \hline \multirow{2}{*}{_Nannochloropsis_ sp.} & Tanning cosmetics & [117] \\ \end{tabular}
\end{table}
Table 8: Microalgae with potential use in cosmetics or cosmeceuticals.