esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Device-to-Device (D2D) is a major part of 5G that will facilitate deployments with extended coverage where devices can act as users or relays. These relays normally act as decode and forward relays (semi-intelligent devices) with limited computational and storage capabilities. However, introducing such a technology, where users can act as relays, presents a wide range of security threats, in particular, rogue relay devices or man in the middle attacks (M-I-T-M). Second, passing fewer control messages is always advisable when considering authenticity and secrecy. To mitigate M-I-T-M and to reduce communication costs, this paper presents a lightweight elliptic-ElGamal-based authentication scheme using PKI (FHEEP) in D2D communication. Pollard's rho and Baby Step, Giant Step (BSGS) methods are used to evaluate the authenticity and secrecy of our proposed scheme. The communication cost is calculated based on the comparative analysis indicating that our proposed scheme outperforms the baseline protocol. The proposed scheme can be used for any infrastructure architecture that will enhance the security of any D2D settings with better performance.  
5G; D2D; lightweight authentication scheme; elliptic curve cryptography; ElGamal; Man-in-the-middle attack Article
# A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
## 2 Related Works
UnderBagging [29] is a combination of random undersampling and a bagging process, the majority class is undersampled and a balanced training set is used to construct a bagging-based ensemble. UnderBagging reduces the imbalance ratio effectively but random undersampling may select noise samples that are unfavorable for classification. SMOTEBagging [30] combines SMOTE with bagging-based ensemble classifiers. Different from SMOTEBagging, SMOTEBoost [31] uses AdaBoost instead of bagging algorithm, which makes the classifier pay more attention to the minority samples that are hard to distinguish. RUSBoost [32] proposed by [PERSON] et al. is based on the SMOTEBoost algorithm, which uses random undersampling for the majority class to balance the dataset. In the training phase, boosting algorithm removes data samples from the majority class in each iteration, but need not to assign new weights to the data samples. Although researchers have proposed a lot of algorithms and models using resampling and ensemble learning, the oversampling and undersampling used in the algorithm are random so that increasing or removing samples have certain blindness and randomness. Generating a large number of minority samples will lead to overfitting problem, and randomly deleting majority samples will easily lose important data information. Therefore, EKR algorithm is proposed in this paper, in which the majority samples will be oversampled or undersampled according to the distribution of the minority samples. The resampling method used in this paper is not random, but after the K-means clustering for the majority class, utilizing the similarity of samples in sub-clusters and the separability of samples between sub-clusters to select the most representative samples and combining it with the minority samples to form a balanced subset, this way not only avoids the blindness of random undersampling but also reduces the influence of imbalance ratio on classification.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 2 Preliminaries  
### Assumptions  
We assume that the monitoring area is a cube denoted by \(A\), and the monitoring objects are randomly distributed events and the number of events is G. Assume that the sink node is stationary after deployment (ignoring small-scale movements), and the nodes are not restricted. The sink node and all other nodes can use the relevant positioning algorithm to know their real-time location in the monitored waters. In addition, assume that all nodes are homogeneous and have the same communication capacity, sensing range, and data-processing capability. The sensing range of a node is a spherical area with sensing radius \(r_{s}\).  
The method proposed in this paper accounts for the coverage of a given area. Here, we assume that each event \(e_{t}\) will always generate an event signal. As shown in Figure 1, each event \(e_{t}\) is surrounded by multiple nodes. If \(e_{t}\) is within the perceived range of a given node \(r_{s}\), the node will detect the event signal and transmit the measured data to the receiver through multi-hop mode.
## 3 Robot Error Prediction and Accuracy Compensation Method
### Cycling Search Method for Joint Angle Compensation  
Joint angles, serving as the basic representation for describing the status of a robot in configuration space, are the most direct instructions in the programming and controlling processes of robots. Therefore, the error compensation of robot joint angles is an intuitive way to improve the robot positioning accuracy. However, while the proposed error prediction method, which utilizes the spatial similarity of robot errors, does not directly modify the parameters of the robot kinematic model, the compensated joint angles are hard to calculate through the differential equations of the kinematic model. Therefore, a joint angle compensation method based on the proposed position error prediction method that solely relies on the theoretical kinematic model is achieved, with the complete process illustrated in Figure 12. The TCP position calculated by the kinematic model from the joint angles before compensation, \(\mathbf{Q}_{target}\), serves as the target TCP position, \(\mathbf{P}_{target}\). In the iterative calculation process for joint angle compensation, the theoretical TCP position \(\mathbf{P}_{thov}\) and the predicted positioning error \(\mathbf{P}_{errorPrediction}\) corresponding to the current joint angles \(\mathbf{Q}_{current}\) are first computed, and the sum is denoted as the current predicted TCP position \(\mathbf{P}_{current}\). Subsequently, the error \(\mathbf{P}_{error}\) between \(\mathbf{P}_{target}\) and \(\mathbf{P}_{current}\) is calculated, and this error is further compensated to \(\mathbf{P}_{current}\). Finally, the inverse kinematics model is utilized to update the compensated \(\mathbf{P}_{current}\) to the joint angles \(\mathbf{Q}_{current}\). The iteration stops when the magnitude of \(\mathbf{P}_{error}\) is less than the error threshold, and \(\mathbf{Q}_{current}\) is output as the compensated joint coordinates \(\mathbf{Q}_{compensation}\).  
Figure 11: Optimal joint weights searching process based on particle filter algorithm.
## 3 Materials and Methods
### Data Processing, Chemometrics, and Machine Learning  
The data were processed using ChromaTOF(r) (ver. 5.51, LECO Corp.). The putative identification of targeted aroma-related compounds was conducted by spectral similarity library search on NIST17 and LRIs confirmation. The chromatogram alignment and non targeted peak table of all the coffee samples were generated on ChromaTOF Tile (ver. 1.01, LECO Corp.).  
The chemometric tools including \(t\)-test, one-way ANOVA, PCA, PLS-DA, HCA, and heat map were operated using R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria) and MetaboAnalyst 5.0 (Xia Lab, McGill University, Montreal, QC, Canada). Data pre-processing of normalization to sample median, log10 transformation, and autoscaling were conducted prior to applying chemometric tools. The distance measure and clustering algorithm used in the heatmap were Euclidean and Ward. D, respectively.  
The final data matrix for the non-targeted analysis included a total of 16 samples (Table 3) and 630 features. Prior to statistical analyses, the compounds' peak areas were normalized using Probabilistic Quotient Normalization (PQN) [42], log10 transformation, and autoscaling. The data were processed using ChromaTOF(r) Tile (ver. 1.01.00.0, LECO Corp.).  
In order to assess feature importance, the RF machine-learning algorithm was used to select the most discriminatory features that would help distinguish between regular and decaffeinated coffee samples. It was also used to build a classification model that enabled the prediction of the coffee type (decaf or regular) of a subset of chosen coffee samples. Briefly, RF is a classification algorithm that consists of implementing a large number of decorrelated classification trees, also called decision trees, operating as an ensemble. These trees are generated by using randomly selected subsets of features and observations. Each individual decision tree in the RF accounts for a class prediction. The class selected by the majority of the trees will express the model's prediction [30]. RF was implemented in an in-house Matlab (ver. R2019b, MathWorks, Natick, MA, USA) code using the TreeBagger function. Additionally, feature importance was assessed using the OOBPermutedVarDeltaError function.
## 3 Undifferentiated Cells (Stem Cells)
### Adult Stem Cells
#### 3.4.1 Bone Marrow Stem Cells (BM-MSCs)  
##### 3.4.1.1 General Information  
Bone marrow (BM) is a flexible spongy tissue inside the large bones where new blood cells are produced. Two types of BM including red marrow and yellow marrow have been identified. Red bone marrow is responsible for the production of red blood cells, platelets, and most white blood cells, while the rest of white blood cells develop in yellow marrow [149].  
Two major types of stem cells can be found in BM including hemopoietic (which can produce blood cells) and stromal cells (which can produce fat, cartilage, and bone). Bone marrow stromal stem cells (BMSSCs) are postnatal stem cells, which are also known as BM-MSCs. BM-MSCs were initially identified and characterized from bone marrow by [PERSON] et al. in the 1980s through an in vitro culture and transplantation in laboratory animals [150; 151; 152]. These cells comprise only 0.01% of the total nucleated cells in the bone marrow. Therefore, they are usually concentrated to use in the next steps of experiments. Human BM-MSCs are routinely harvested by a simple method, i.e., aspiration from the iliac crest in humans [153]. However, the isolation procedure of these cells is different in other animals. For example, the aspiration of tibia and femur has been standardized to isolate BM-MSCs from mouse and rabbit laboratory animals [154; 155]. BM-MSCs as the most universal source of MSCs are identified through some criteria including the formation of clonogenic adherent cell clusters, fibrobl morphology, and the potential to differentiate into all the three lineages including ectoderm [156; 157], mesoderm [158; 159], and endoderm [160; 161]. To characterize BM-MSCs, a number of positive and negative surface antigens have been recommended by the International Society for Cellular Therapy [162]. These surface markers are used to purify BM-MSCs by fluorescence-activated cell sorting (FACS) as well as the magnetic-activated cell sorting (MACS). In addition, the use of Percoll gradient centrifugation and adherence to tissue-culture treated plastics are proposed as more simple techniques to purify BM-MSCs [163]. BM-MSCs can be used as undifferentiated or fully differentiated cells for TE applications. Undifferentiated forms of BM-MSCs are obtained through the culture in defined serum-free media or pre-screened fetal bovine serum. Fully differentiated cells can be obtained through physical, chemical, and mechanical approaches exerted on BM-MSCs [164; 165; 166]. These cells are used for orthopedic applications in vitro and in vivo. The reasons are summarized as the rapid proliferation, induction of osteogenesis, angiogenesis, and easy differentiation into osteogenic lineages [167; 168]. As a result, more research has been conducted to treat various bone disorders using BM-MSCs or BM-MSCs-seeded constructs [169].
## 4 Conclusions  
Deacetylated chitosan was prepared by NaOH solution, and the chitosan was modified with chlorosulfonic acid and octaldehyde to produce the amphoteric chitosan. The sodium acrylates copolymer was successfully replaced by the amphoteric chitosan as an emulsifier in the processes of emulsion production, and the moisture retention, emulsification condition, and bacterial growth were examined through spatial moisture measurement, photometric spectrometry, and the spread plate method. A slightly smaller dose of amphoteric chitosan than of sodium acrylates copolymer yielded the same transmittance, indicating that the same emulsification effect could be achieved with a smaller dose of the amphoteric chitosan. Since the moisture retention of the amphoteric chitosan emulsion was better, the spatial humidity of the modified chitosan emulsion was lower than that of the sodium acrylates copolymer emulsion. The number of bacteria was lower when the emulsion using amphoteric chitosan was used than when sodium acrylates copolymer was used because of the inherent antibacterial property of chitosan. The stability tests showed no separation of the oil and water phases within one hour for either emulsifier, as determined from observations of the separation of oil and water phases in the diluted emulsion, and the stability of the modified chitosan was better than that of the sodium acrylates copolymer emulsion. Therefore, the advantages, including the smaller dose, the better moisture retention, the lower number of bacteria, and the better stability for using the amphoteric chitosan, make it a successful replacement for sodium acrylates copolymer as an emulsifier in the preparation of emulsion.  
Conceptualization: H.-T.W. and C.-C.C.; Experimental runs and analysis: H.-W.C. and C.-C.C.; Writing: H.-W.C. and H.-T.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 3 Experimental Analysis  
### Evaluation Metrics  
In the experimental analysis of this paper, the accuracy of the model predictions is measured using three metrics: RMSE, MAE, and R\({}^{2}\). The calculation formula for RMSE is presented in Equation (7):  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left({Y_{i}-\hat{Y}_{i }}\right)^{2}}}, \tag{7}\]  
where \(Y_{i}\) represents the actual value of the data, \(\hat{Y}_{i}\) represents the predicted value output by the model, and \(n\) denotes the total number of prediction samples.  
The calculation formula for MAE is presented in Equation (8):  
\[\text{MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left|{Y_{i}-\hat{Y}_{i}}\right|}. \tag{8}\]  
The coefficient of determination, denoted as R\({}^{2}\), is an important metric for assessing the goodness of fit of a time series forecasting model. It indicates the proportion of variance between the predicted values and the actual observed values. The specific calculation method for R\({}^{2}\) is presented in Equation (9):  
\[\text{R}^{2}=1-\frac{\sum\limits_{i=1}^{n}{\left({Y_{i}-\hat{Y}_{i}}\right)^{2 }}}{\sum\limits_{i=1}^{n}{\left({Y_{i}-\overline{Y}_{i}}\right)^{2}}}, \tag{9}\]  
where \(\overline{Y}_{i}\) represents the mean value.  
Figure 12: Learning process of nonlinear unit functions.
# Absorption Enhancement in Hyperbolic Metamaterials by Means of Magnetic Plasma
###### Abstract  
The main features of surface plasmon polaritons (SPPs) that can propagate in a metamaterial-magnetic plasma structure are studied from theoretical perspectives. Both the conventional and imaginary parts of the dispersion relation of SPPs are demonstrated considering transverse magnetic (TM) polarization. We examine and discuss the influence of the external magnetic field. The results demonstrate that this factor dramatically alters the nature of SPPs. It is concluded that the positions and propagation lengths of SPPs can be engineered. Moreover, we present an approach allowing for an absorption enhancement that is a pivotal factor in antenna design. A unified insight into the practical methods aiming to attain hyperbolic dispersion by means of nanostructured and nanowire metamaterials is demonstrated.  
metamaterial; hyperbolic; absorption Article
## 2 Related Work
### Feature Extraction  
Common disfluent speech features in existing speech disfluency detection methods include the Mel frequency cepstral coefficient (MFCC) [16; 17; 18; 19; 20; 21; 22; 23; 24], linear predictive coding (LPC) [25; 26; 27], the linear prediction cepstral coefficient (LPCC) [20; 28], perceptual linear prediction (PLP) [23; 27], and spectrograms [29; 30; 31]. In recent years, wav2 vec features have shown strong performance in multiple speech tasks, such as speech recognition, speech emotion recognition, and mispronunciation detection [32; 33; 34]. As an emerging speech feature extraction method, wav2 vec2 is capable of learning richer and more abstract speech representations and has also been widely used in speech disfluency detection [35; 36; 37; 38; 39].
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
###### Abstract  
This paper investigates the use of drive-by train measurements for railway track monitoring. An in-service Irish Rail train was instrumented while using accelerometers and a global positioning system. The measurements were taken over two months and the train bogie accelerations from 60 passes on the Dublin-Belfast line were used for this study. A 6 km section of the line is the particular focus, where the maintenance measurements from a Track Recording Vehicle (TRV) were available. The Hilbert transform is used to obtain the instantaneous amplitudes of the acceleration signals. A new representation of the signal is proposed to show the signal energy level as a function of train location. It is shown that the forward speed of the train has a significant influence on the energy level of the signals. Therefore, a two-step speed correction is applied to the data. First, data from passes with forward speed below a certain limit are removed from the data set. Subsequently, a scaling factor is defined for the remaining signals and the energy levels of those signals are scaled while using online speed measurements. The scaled amplitudes are compared with the TRV data. It is shown that the energy levels of the signals match the TRV measurements very well.  
Keywords:railway track; drive-by; Hilbert amplitude; energy; monitoring; train +
Footnote †: journal: Applied Sciences
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
###### Abstract  
An unmanned free-swimming submersible (UFSS) is designed to perform certain tasks in water without interposing humans. The vehicle's control is achieved by integrating mathematical (analog) and non-mathematical (embedded) controllers. The main goal of integrated controllers is to overcome the environmental disturbances and noise of the sensor data. These disturbances, as well as the noise data, are generated during steering, diving, and speed control. The amplitude of disturbances and noise varies with the depth and intensity of water waves. This article presents a robust hybrid linear quadratic regulator (HLQR) controller for UFSS. The presented controller targets the desired state of the UFSS in the presence of a disturbing environment. The hybrid approach is achieved by employing: (1) two linear quadratic regulators or controllers and (2) a mathematical structure of the Riccati equation. Consequently, the proposed HLQR controller is integrated into the UFSS system to evaluate the response in terms of settling time, rise time, overshoot, and steady-state error. Furthermore, the robustness of the HLQR is investigated by considering the feedback to step response and hydrodynamic disturbances. The implementation results reveal that the proposed controller outperforms state of the art controllers, such as proportional-integral-derivative and lead-compensator controllers.  
linear quadratic regulator; lead-compensator; Riccati model; proportional-integral-derivative +
Footnote †: journal: Applied  
The UUVs or unmanned free-swimming submersible (UFSS) can be classified into two categories: (1) remotely operated vehicle (ROV) and (2) autonomous underwater vehicle (AUV) [3]. The prior is more useful when someone working in a shipyard to control and manage the vehicle. The latter is more convenient as compared to the prior because of its independent nature (or) autonomous behavior and ability to make smart decisions quickly. It is important to worth mentioning that the response of a UFSS to the control system is not as efficient as required. Similarly, its underwater response is not ideal (e.g., disturbance, and noise) [4]. Therefore, simple controllers have been introduced to improve the response of a UFSS in the presence of hydrodynamic disturbances [4; 5; 6; 7; 8; 9]. These controllers assist the UFSS to take efficient actions when required, and, therefore, contribute towards the smooth operation [4]. Furthermore, they calibrate the parameters and observe the best response of the controller. Moreover, they assist the equipment to make smarter moves and lessen the error in their decisions [4; 5; 6; 7; 8; 9].
## 3 Port COBie Schema for Digital Data-Based Facility Management
### Development of Port COBie Schema  
#### 3.3.1 Background References for Developing Port COBie Schema  
In this study, a BIM-based COBie data standard suitable for the port field is developed through the processes of COBie standard analysis, maintenance data analysis, development methodology establishment, COBie data model development, and COBie data verification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System Name** & **Use Purpose** & **COBie Data Processing** & **Usage Phase** \\ \hline dRoufus & To generate COBie data &
\begin{tabular}{c} COBie data from spreadsheet were extracted \\ from BIM tool, then are imported to the system. \\ IFC is extracted from BIM tool, then it is \\ imported to the system, COBie data are \\ generated using the system. \\ COBie data formed spreadsheet were extracted \\ from BIM tool, then are imported to the system. \\ IFC is extracted from BIM tool, then it is \\ imported to the system, COBie data are \\ generated using the system. \\ COBie data are entered using Plug-in, then a \\ spreadsheet is extracted. \\ C\&IM,  
COBie standard analysis is an essential process for developing the port COBie schema. Maintenance research has been consistently performed in the construction field, and there is sufficient research related to COBie. The basis for COBie data inputting into the BIM environment has also been prepared. However, very little research has been performed upon facility management in the port sector, when compared with the construction field; furthermore, no maintenance exchange standard has been developed for COBie. Therefore, to develop the port COBie schema, we analyzed the existing literature and conventional systems developed for maintenance. The first reference for developing the port COBie schema was the original COBie schema (Version 2.4) used in the existing building facilities. The second and third references were the object breakdown structure (OBS) and property breakdown structure (PBS) of port facilities (Figure 5). In the case of the existing COBie schema, since it is an international standard maintenance data exchange standard format in the field of architecture, based on it, it was intended to aim for an international standard. In addition, the OBS and PBS of the port facility were applied to the port COBie schema, which means what the purpose of port COBie schema was based on BIM. The port facility's OBS follows the international standard ISO12006-3. The following figure shows the composition and structure of the port object classification system applied to the development of the port COBie schema. The port facility's PBS was referred to in order to define which properties are required in each sheet of the port COBie schema.