esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 1 Introduction
### Contributions  
In this paper, we analyze mobile network traffic collected from a network in Portugal over a half-month period. The available data include the number of megabytes (MB) sent and received by various applications during each hour. Our goal is to forecast the total traffic behavior separately for downlink and uplink using fast processing statistical models.  
The main contributions of our study are as follows:  
* We analyze the dataset of real network traffic from a mobile operator in Portugal using fast processing statistical models, namely SARIMA, and Holt-Winters, which have not been applied to this data before.
* We demonstrate that the SARIMA model is more appropriate for forecasting download traffic, while the Holt-Winters model is better suited for forecasting upload traffic, showing appropriate errors in the considered dataset.
* Since statistical models are suitable for fast and precise forecasting of mobile network traffic, they can be implemented in cellular operators' solutions without a significant increase in cost.  
The rest of the paper is organized as follows. Section 2 provides a description of the dataset and illustrates the traffic behavior, both in total and by various applications. In Section 3, we discuss the SARIMA and Holt-Winters models, along with the necessary preliminary checks. Section 4 outlines the metrics used for evaluating the models and presents forecasts for both download and upload traffic. Conclusions are drawn in Section 5.
## 2 Background and Literature Study
### IoMT Transforming Healthcare  
The vulnerabilities of the National Health Service (NHS) were illuminated during the chaos of the COVID-19 pandemic. The chaos revealed a system drastically underprepared to deal with surges of in-patient visits. The IoMT could and has reshaped the healthcare industry, bringing it forward into a new age of patient medical care. As stated in [17; 18], the IoMT has been a significant contributor to developing and enhancing the infrastructure of hospitals and the way that medical professionals can provide care for patients. The IoMT allows for continuous real-time monitoring and tracking of patients; due to its lightweight form, this can be achieved with minimal discomfort to patients. In addition, applications of the IoMT with wireless communication allow the patient to freely move around and maintain mobility. Advancements in remote patient monitoring also make it possible for detailed and accurate data on patients to be gathered while they are in their own homes. The IoMT has the added benefits of allowing for decreased hospital bills due to its affordability, scalability, and ease of adoption, leading to overall cost savings.
## 4 Case Study of Stainless Steel Columns with Cold-Formed RHS and SHS  
A total of 21 stainless steel columns with cold-formed RHS and SHS are selected among the tested specimens reported in the literature [21; 28; 29; 37; 38; 39]. The studied columns have cross-sectional slenderness (\(\lambda_{\mathrm{I}}\)) higher than 0.776. This is to ensure that the columns undergo cross-sectional local buckling reduction before they reach the ultimate compressive strength. According to the work of [24], the nominal compressive strength of a column with RHS or SHS is determined by min{P\({}_{\mathrm{ne}}\), P\({}_{\mathrm{nl}}\)}, where P\({}_{\mathrm{ne}}\) and P\({}_{\mathrm{nl}}\) are the nominal global buckling strength and local buckling strength, respectively. The interaction between global and local buckling depends on \(\lambda_{\mathrm{I}}\) and is determined by  
\[\mathrm{When}\ \lambda_{I}\leq 0.776\ \mathrm{P}_{\mathrm{nl}}=\mathrm{P}_{ \mathrm{ne}}, \tag{10}\] \[\mathrm{When}\ \lambda_{I}>0.776\ \mathrm{P}_{\mathrm{nl}}=\lambda_{ \mathrm{I}}^{-0.8}-0.15\lambda_{\mathrm{I}}^{-1.6}. \tag{11}\]  
A plot of P\({}_{\mathrm{nl}}\)/P\({}_{\mathrm{ne}}\) versus \(\lambda_{\mathrm{I}}\) is shown in Figure 9. It should be pointed out that, although Equations (10) and (11) are developed based on carbon steel members, the two equations are applicable to stainless steel members with cold-formed RHS and SHS and give an accurate prediction [40; 41].  
Figure 8: Generated 3D model. (**a**) Surface with random \(\omega\) and half-sine edges; (**b**) member with random \(\omega\) and half-sine edges.  
Figure 9: The interaction between global and local buckling strength.  
Details of the studied columns are shown in Table 2. Namely, b1, b2, and t are the depth, width, and thickness of the hollow cross-section, respectively; R is external radius of the round corner; L is the length of the column; \(\lambda_{\rm c}\) and \(\lambda_{\rm l}\) are member slenderness and cross-sectional slenderness, respectively; \(\omega_{\rm g}\) is the amplitude of global member imperfection (out-of-straightness). \(\omega_{\rm g}\) is not reported for some cases of stub columns (\(\lambda_{\rm c}\leq 0.2\)), while the shape of \(\omega_{\rm g}\) is adopted as a half-sine wave for other columns.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 2 Related Works
### Aspect Sentiment Classification
[4] proposed the LCF-BERT model, which is the first local context-focused mechanism to focus more on contextual words using the context-feature dynamic mask (CDM) or context-feature dynamic weighting (CDW). This demonstrates the importance of local context for predicting aspect sentiment. This study uses one of the context-feature dynamic mask strategies. [PERSON] et al. [5] propose the LCFS-ASC model based on the LCF-BERT model, which uses dependent syntactic analysis to determine the local context and addresses the use of token-based semantic relative distance to cover negative words or exclude emotive words. [PERSON] et al. [22] proposed the LCF-ATEPC model based on LCF-BERT, which can perform aspect term extraction and aspect sentiment classification on Chinese reviews. Later, [PERSON] et al. [23] argued that using syntactic dependency trees would take up more resources, so they proposed the LSA mechanism to learn the aspect sentiment dependency in sentiment clusters and constructed a differential weighting strategy to enhance the importance of sentiment dependency. The above model learns local text information, focuses on fine-grained information, fuses fine-grained information with coarse-grained information, and even uses syntactic relations to determine local text information, which is more accurate. However, the local text length varies and should be adaptively adjusted, otherwise using a fixed length will result in errors, which can be followed in Section 3.2.2. Based on previous studies, we propose adaptive semantic relative distance as a starting point for our research.
# Machine and Deep Learning Applied to Predict Metabolic Syndrome without a Blood Screening
## 2 Materials and Methods
### Methods  
For the study, we applied three machine learning algorithms, RF, C4.5, and deep learning. We conducted experiments for each sex. We choose these machine learning algorithms because of their excellent results in many applications and because each one uses a different approach in a classification task [18; 19; 36]. We also performed feature selection to search for simpler and better models. The experiments were conducted using R programming language [37]. We aimed to predict the MetS without using biochemical variables.  
Initially, the parameters of the biochemical evaluation, as well as the anthropometric factors: waist circumference, systolic and diastolic blood pressure, glucose, high-density lipoprotein cholesterol (HDL), and triglycerides, in order to identify and classify participants with MetS based on the definition that was established by the NCEP ATP III. Once the participants have been classified as positive and negative concerning the MetS, the input dataset for the learning algorithms is generated, being composed of categories related to clinical and anthropometric parameters and habits, like alcohol consumption, smoking, physical activity, dietary, and quality of sleep.  
The performance of the models was evaluated according to accuracy, balanced accuracy, PPV, and NPV. Figure 1 shows a block diagram of the prediction model and describes the general methodology applied. As a first step, the instances of the data set were classified using the biochemical evaluation data and the clinical and anthropometric parameters, according to the NCEP ATP III criteria. Subsequently, we separated men and women to obtain the most important variables for each sex.  
We applied RF to identify and evaluate the variable importance by category, as well as Pearson Correlation Coefficient (PCC) and chi-square, to obtain the most important variables when considering all of the categories together.  
We created and evaluated predictive models using the ML algorithms for different subgroups that formed based on the variables obtained. From these models, we selected the most relevant features for men and women.  
In all experiments, we performed 30 independent runs by algorithm, which is a typical number that is used in the literature for fair comparisons among experiments [38; 39]. Subsequently, we calculated the mean and standard deviation for each metric.
# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
## 3 Methods
### Accuracy Evaluation Index  
After Kriging interpolation and fusing XCO\({}_{2}\) using the TC algorithm, it was necessary to evaluate the accuracy of the fusion result by comparing the fused XCO\({}_{2}\) value with the measured value of TCCON. The smaller the difference, the better the fusion result. Three indexes, \(R^{2}\), mean absolute error (MAE), and root mean square error (RMSE), were selected to evaluate the accuracy of the result. The determination coefficient \(R^{2}\) can be used to evaluate the consistency between the fusion result and the measured value. The value range of goodness of fit \(R^{2}\) is from 0 to 1. The larger the \(R^{2}\) value, the better the fitting between the XCO\({}_{2}\) fusion result and the TCCON measured value. The formula is as follows.  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}{{{{(y_{i}-\overline{y})}^{2}}-\sum\limits_{i=1} ^{n}{{{{(y_{i}-\hat{y})}^{2}}}}}}}{\sum\limits_{i=1}^{n}{{{{(y_{i}-\overline{y} )}^{2}}}}} \tag{12}\]  
The MAE is the average value of the absolute value of the deviation between the estimated value and the real value. The calculation formula is shown in (13). The weight of the difference of all sub items on the average value is equal, which can reflect the actual situation of the difference between the estimated value and the real value. The smaller the MAE, the smaller the deviation between the estimated value and the measured value of TCCON, and the higher the fusion accuracy.  
\[{\rm MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}{{{{\left|{y_{i}-\hat{y_{i}}}\right|}}}}} \tag{13}\]  
The RMSE is the square root of the ratio of the square sum of the deviation between the estimated value and the real value to the number of observations n. The calculation formula is shown in (14). RMSE measures the deviation between the estimated value and the real value. The smaller the RMSE, the smaller the deviation between the estimated value and measured value of TCCON, and the higher the fusion accuracy.  
\[{\rm RMSE}=\left({\frac{1}{n}{\sum\limits_{i=1}^{n}{{{{\left({y_{i}-\hat{y_{i} }}\right)}^{2}}}}}}\right)^{\frac{1}{2}} \tag{14}\]  
In (12)-(14), \(y_{i}\) is the fused XCO\({}_{2}\) value of multi-source datasets, \(\hat{y_{i}}\) is the measured value of TCCON.
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 3 Proposed Indoor Positioning Method
### Post-Processing  
The post-processing step involves a LOR (location outlier removal) and is conducted using a moving average filter (MAF).  
The estimated location coordinates must exist only in the valid region formed by the APs and must not exist outside the valid region, such as outside of a building, on a wall, or in an impassable region. The possible valid region for positioning is inside the triangle where the AP is installed, as shown in the green region of Figure 9. A blue arc in Figure 9 means a maximum reception distance from AP3 after applying OR. Although the inaccuracy of trilateration positioning is reduced by removing RSSI outliers in the OR step, there is still the possibility of positioning outside the valid region, as shown in the hatched region of Figure 9. To prevent positioning outside of this valid region, LOR is applied. LOR removes the estimated location coordinate if it is outside the valid region.  
Despite correcting the unstable one-dimensional RSSI through the RSSI filtering process, there are still instabilities when calculating two-dimensional coordinates through the mathematical operations of trilateration. To enhance positioning accuracy and improve precision, filtering for the two-dimensional coordinates is necessary. The MAF is commonly adopted to improve precision when noise exists in the data for tracking moving objects [35]. MAF calculates the average of the collected data within a fixed window size. Since the MAF operates using a sliding window approach, where the window moves one step at a time, it can calculate positioning results from collected signals without any loss in quantity. Figure 10 illustrates the MAF process. In Figure 10a, during the initial step where fewer  
Figure 8: An example of modified proximity positioning in the vicinity of AP1.  
Figure 9: An example illustrating outlier and valid regions.  
data points are collected than the window size, the positioning results are obtained by averaging the collected coordinates. Subsequently, in a sliding window structure as shown in Figure 10b, the coordinates are averaged over the window size to determine the positioning results, and this process repeats. In this paper, the window size is set to 5. Utilizing MAF allows for the correction of abrupt changes in estimated positions, thereby enhancing both the accuracy and precision of the positioning results.
# Interval Feature Transformation for Time Series Classification Using Perceptually Important Points
## 3 Interval Feature Transformation
### Discriminative Feature Vector Selection  
#### 3.2.1 Internal Feature Vector  
Interval features are calculated from a time series interval, e.g., \"the interval between time 10 and time 30\". Many types of features over a time interval can be considered, but one may prefer to define simple and interpretable features such as the mean and standard deviation [12]. Previous studies have shown that interval features are effective for the TSC problem [12; 20; 21; 22; 23].  
In this paper, we adopt six statistical merits, the mean value \(\mu\), standard deviation \(\sigma\),the sum of first difference absolute value (\(d\)), interquartile range (\(IQR\)), skewness (\(SKEW\)) and kurtosis (\(KIRT\)). The first difference absolute value can reflect sensitively the contiguous data of quality indexes in the time sequence; however, the directional fluctuation is not considered. Skewness and kurtosis contain information on the shape of the distribution of the time series data. More precisely, \(SKEW\) characterizes the degree of asymmetry of values around the mean value. \(KURT\) measures the relative peakedness or flatness of the value distribution relative to a normal distribution. Suppose we have an interval between time \(p\) and time \(q\) (\(p<q\)), the interval features can be calculated by Equations (1)-(6) as shown below:  
\[\mu=\frac{\sum_{i=p}^{q}t_{i}}{q-p} \tag{1}\]  
\[\sigma=\sqrt{\frac{\sum_{i=p}^{q}(t_{i}-u)^{2}}{q-p}} \tag{2}\]  
\[d=\underset{i=1}{\overset{n-1}{\sum}}\big{|}t_{i+1}-t_{i}\big{|} \tag{3}\]  
\[IQR=Q_{3}-Q_{1} \tag{4}\]  
\[SKEW=\frac{\sum_{i=p}^{q}(t_{i}-u)^{3}}{(q-p)\sigma^{3}} \tag{5}\]  
\[KURT=\frac{\sum_{i=p}^{q}(t_{i}-u)^{4}}{(q-p)\sigma^{4}}-3 \tag{6}\]  
In this paper, we define a group of interval features with the above six statistical merits, \(f_{j}(j=1,2,3,\cdots\omega)=\left\langle\mu_{j},\,\sigma_{j},\,d_{j},IQR_{j}, SKEW_{j},KURT_{j}\right\rangle\) as an interval feature vector. In this way, every segment is replaced by the feature vector \(f\). The original time series \(\left\langle t_{1},t_{2},t_{3},\cdots t_{m}\right\rangle\) can be represented by a set of feature vectors \(\left\langle f_{1},\,f_{2},\,f_{3},\cdots t_{o}\right\rangle\).  
Figure 2: Influence of parameter \(\tau\) selection for data set OliveOil, which is \(\tau=1\) (**a**), \(\tau=25\) (**b**) and \(\tau=50\) (**c**), respectively.
## 5 Conclusions  
As a result of the research work, the main hypothesis was supported based on the following facts:  
* Thanks to a new conceptual model for capacity planning able to make decisions for reduction or increase of production capacity, the viability of a company can be assured. As a result, it proves the need for such a system as a standard tool for managers in the future in order to increase the efficiency and adaptability of manufacturing organizations.
* The viable system model provides the necessary structure to determine the interrelationships among areas and parameters that allow decisions to be made regarding capacity in an autonomous process based on selected control limits.
* The simulation of an OEM plant using the developed conceptual model presents better results compared with currently available structures regarding how to deal with customer demand volatility.  
The final goal is to transfer this research method to real production systems, where it could be applied in particular cases as a tool to assist managers by centralizing all data related to a topic in a short period of time, enabling the simulation of what-if scenarios, and speeding up decision-making processes as a unique selling proposition (USP) of manufacturing organizations.  
The results show the benefits of capacity management based on a digital-twin approach in which top management can make decisions in a short period of time. However, the question of how it can be made possible in large organizations involving multiple plants and departments remains to be answered.  
Future research could develop a virtual model of a manufacturing organization by including other decision-making factors and determine how these decisions and approaches could be realized in the real world with current technologies and systems.  
Conceptualization, S.G.-G.; data curation, S.G.-G.; formal analysis, S.G.-G.; Investigation, S.G.-G.; methodology, S.G.-G. and J.R.; resources, S.G.-G.; software, S.G.-G.; supervision, M.G.-G. and J.R.; validation, S.G.-G.; writing--original draft, S.G.-G.  
This research received no external funding.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Simulation** & **Demand Scenario 4** & **Key Performance** & **VSM Simulation** & **Nonviable** \\
**Level** & **Demand Scenario 4** & **Indicator (KPI)** & **Model** & **Simulation Model** \\ \hline \multirow{8}{*}{Plant level} & & Profits (million euros) & 935 & 842 \\  & & Total production (units) & 617.390 & 565.968 \\  & - & Launch curve until t & Capacity utilization (\%) & 88.2 & 94.3 \\  & - & Seasonal demand for the new model at & Maximum production & 700 & 600 \\  & & & capacity (units/day) & 97.9 & 91.8 \\  & & & Customer order lead time & 44.2 & 88.2 \\  & & stationary for the 2 nd model at 300 & Operational savings & 12.5 & 0.0 \\  & & & investment value (million & 30.0 & 0.0 \\  & & & return on investment & 20.4 & - \\  & & & (million euros) & 20.4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results for demand scenario 4, seasonal scenario, at the plant level. VSM, viable system model.
# Navigation in GEO, HEO, and Lunar Trajectory Using Multi-GNSS Sidelobe Signals
## 4 Discussion  
Our results show that space users benefit from multi-GNSS considerably, especially for users that are far away from the Earth. Therefore, developing interoperable multi-GNSS is important. Different from terrestrial uses, GNSS antenna patterns play an important role in spacecraft positioning since sidelobe signals are mostly received. Until now, only antenna patterns of GPS Block IIR, IIR-M, IIF, and IIA were published. Research regarding BDS-3 and Galileo antenna patterns can be found in [9; 30]; however, the data are unavailable. Great progress could be made once these data are published. More accurate simulation results can be provided and better suggestions regarding spaceborne receiver design could be proposed. In addition to these global positioning systems, navigation performances could be further improved once regional satellite navigation systems, such as the quasi-zenith satellite system (QZSS) from Japan, navigation with Indian constellation (NavIC) from India, and Korea Positioning System (KPS) from South Korea are used. These abundant signals will help with GNSS-based spacecraft positioning at even further distances such as halo orbits near the Earth-moon Lagrange points, contributing to deep space exploration.
## 5 Conclusions  
To mitigate the limitations of the vector MMC control and traditional VSG-MMC control, this study investigated the feasibility of a novel TD3-based adaptive control strategy for optimizing the MMC-VSG system. To be more specific, this approach can effectively address the challenges posed by external disturbances and parametric uncertainties. Our proposed TD3-VSG prototype facilitates stable and rapid learning and reimburses system model indeterminacy, thereby improving the system robustness and performance against disturbance.  
In addition to suppressing active power oscillation and primary frequency modulation, we developed a preliminary definition of the TD3-VSG algorithm and established its general framework. After training, the algorithm enhanced the system's transient performance and operational stability, while also improving the voltage amplitude performance and system constancy relative to other control strategies.  
Experimental results based on the MMC demonstrated the benefits of our proposed solution, which shows promise for fulfilling control tasks. However, due to inevitable discrepancies between emulation models and real-time dynamics, the consistency and attenuation of TD3-VSG in power electronics remain as research directions that require further attention for industrial implementation.  
Potential future studies on this subject will include extending the reward functions for various parameters, such as line loss, harmonic distortion rate, and submodule capacitance balance. Additionally, a multi-terminal MMC project can also be established to emulate a high-voltage direct current (HVDC) system.  
Figure 17: THD for output current \(i_{ij}\) with mismatch L and R. **(a)** TD3-VSG. **(b)** DDPG-VSG. **(c)** Fixed VSG. **(d)** Vector MMC.  
**Author Contributions:** Writing--original draft, M.Y.; Writing--review & editing, M.C.L.; Supervision, X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China under Grant 2017 YFF0210600.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.