esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Example
### Results of Dataset 2  
Table 6 lists the estimated parameters for the 15 models in Dataset 2. The estimated values are \(\hat{\alpha}=1.9561\), \(\hat{\beta}=0.02901\), \(\hat{\gamma}=0.04450\), and \(\hat{N}=127.2589\). Figure 4 shows the cumulative number of failures and estimates for the 15 models at each time point in Dataset 2. The black dots represent the actual cumulative number of failures in Dataset 2, and the red line represents the estimates of the software reliability models from Dataset 2. The thick black dashed and thin black dotted lines represent the 95% and 99% confidence intervals of the proposed software reliability models, respectively. The estimated value of the proposed model was the closest to the true value compared to the results of the other models, and all were within the confidence interval of the true value. Tables 7 and 8 present the results for the calculated criteria and multi-criteria based on Table 6. _MSE_, _MAE_, _PRR_, _PP_, _R\({}^{2}\)_, _Adj\({}_{-}\)R\({}^{2}\)_, _PRV_, _RMSPE_, _TS_, _PC_ and _PIC_ have the best values of 5.650, 0.040, 0.038, 2.075, 0.994, 0.993, 2.181, 2.181, 2.832, 16.818, and 95.153, respectively, whereas _AIC_ and _BIC_ have the second highest values of 88.732 and 92.715, respectively. The best results for _MCDM_, _MCDMR_, and _MCDMM_ with the 13 criteria were 0.3906, 0.0043, and 0.1410, respectively. Figure 5 is a graphical representation of the three-dimensional plots of the _MCDM_, _MCDMR_, and _MCDMM_ for the top five of the 15 models listed in Table 1 for Dataset 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**No** & **Model** & \(\hat{\alpha}\) & \(\hat{\beta}\) & \(\hat{\alpha}\) & \(\hat{\beta}\) & \(\hat{N}\) & \(\hat{\gamma}\) & \(\hat{\epsilon}\) & \(\hat{p}\) & \(\hat{q}\) & \(\hat{\alpha}\) \\ \hline
1 & GO & 130.2015 & 0.083166 & & & & & & & \\
2 & DS & 103.9842 & 0.265379 & & & & & & & \\
3 & IS & 110.8287 & 0.172063 & & 1.204647 & & & & & \\
4 & YID & 129.8017 & 0.08345 & 0.000146 & & & & & & \\
5 & YID2 & 27.23501 & 0.169462 & 0.036291 & & & & & & \\
6 & YR & 115.816 & 1.565434 & 0.017194 & 1.268592 & & & & & \\
7 & YE & 130.4047 & & 0.657408 & 0.000264 & 478.892 & & & & \\
8 & PZ & 0.253469 & 0.172063 & 1846.288 & 1.204651 & & 110.5752 & & & \\
9 & PNZ & 110.0099 & 0.172083 & 9.88 \(\times\) 10\({}^{-6}\) & 1.204507 & & & & & \\
10 & IFD & 12.22918 & 0.440572 & & & & & & & \\
11 & TC & 0.018183 & 1.109733 & 86,418.84 & 13,230.13 & 118.5624 & & & & \\
12 & TP & 90.28151 & 0.088191 & 25.76277 & 1.159252 & & & & 19.25739 & 0.95604 & 0.092387 \\
13 & 3P & 1.398567 & 0.172062 & & 0.00897 & 110.8898 & & 1997.82 & & \\
14 & Vtub & 3.761085 & 0.420911 & 7.023092 & 169.2326 & 105.9848 & & & & \\
15 & NEW & & & 1.956081 & 0.029012 & 127.2589 & 0.044502 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameter estimation of model from Dataset 2. Figure 3: Three-dimensional plot of the _MCDM_, _MCDMR_, and _MCDMM_ for Dataset 1.
# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
* Does the impact of the news and tweets on the price prediction differ under normal and market-panic conditions? Given these research questions, the primary aim of the research is to investigate whether considering news and tweets can enhance stock-price prediction accuracy. This impact is crucial for investors, financial institutions, and algorithmic trading firms, as more accurate predictions can lead to better investment decisions, reduced risks, and improved profitability. Furthermore, by examining the impact of news and tweets on stock-price prediction, the research seeks to deepen understanding of the dynamic interactions between market information, investor sentiment, and stock-price movements. This understanding can help develop more accurate prediction models that capture the complex interaction of factors influencing financial markets. Following the understanding of these impacts, this research aims to investigate the stability of these impacts under panic conditions. In other words, it aims to consider whether and how these impacts may alter during periods of market distress. To this end, we chose the COVID-19 pandemic as the case for our analysis, given its status as one of the most significant instances of market panic in recent history [32; 33; 34]. Similarly, considering the widespread utilization of LSTM and MLP models in stock-price prediction (e.g., [12; 19; 21; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]), we opted to evaluate these two methods specifically in the context of incorporating tweets and news count as predictive variables, particularly during market distress. The remainder of this paper is organized as follows. In Section 2, we review the relevant literature to identify extant knowledge gaps and highlight the contributions of our current study. Next, in Section 3, we define the problem under investigation and outline the proposed model for price prediction. Then, in Section 4, we develop the solution algorithms. Moreover, in Section 5, we analyze the results to address the research questions. Lastly, in Section 6, we offer concluding remarks and insights and suggest future avenues for research.
## 1 Introduction
The lower the coefficient of friction, the less shear is transmitted. However, in order to provide sufficient resistance to wind load and avoid unnecessary vibrations under small earthquakes, a fairly high value of frictional coefficient is required. The most popular sliding system that applies low frictional interfaces to reduce the transmission of shear force to the isolated structure includes Friction Pendulum Bearings (FPB). At this point, it should be mentioned that base isolation is recognized to be most effective for structures with a fixed-base fundamental period of 1.0 s or less (e.g., low-rise and medium-rise buildings), and preferably erected on stiff and stable soils. Moreover, it should also be noted that the application of a seismic isolation system assumes that the structure will behave mainly in an elastic range, which may consequently result in a sub-exploitation of the building materials capacity. Therefore, this approach requires design procedures (specified, e.g., in building design codes and standards) that are slightly different from those traditionally utilized for the fixed-base buildings. Nevertheless, base isolators are widely used in practice in many earthquake-prone regions of the world and their effectiveness in protecting important structures during seismic events has been already confirmed (see, for example, [33; 34]). However, the past few decades have witnessed tremendous progress in material engineering. Because of the impressive range of useful and exceptional properties, new materials may play an essential and ubiquitous role in many fields of science, including also earthquake engineering. The recent development of polymeric materials has resulted in an increased number of new applications and modifications of the existing ones. Therefore, the present paper aims to verify experimentally the effectiveness of a prototype base isolation system made of the Polymeric Bearings (PBs), which are constructed with the use of a specially prepared polymeric material with improved damping properties. In order to achieve the above-mentioned aim of the current study, the following specific objectives have been established:  
1. Perform an extensive material testing to determine the basic mechanical properties of the polymer used to construct the PBs;2. Conduct the Dynamic Mechanical Analysis to evaluate the viscoelastic characteristics of the analyzed polymer over a wide range of temperatures for different excitation frequencies;
3. Design and construct a prototype base isolation bearing with the use of a specially prepared polymeric material;
4. Prepare two experimental models with different dynamic characteristics;
5. Investigate the dynamic behaviour of the structure models of both fixed-base and base-isolated during sine sweep testing;
6. Conduct an extensive shaking table testing to investigate the dynamic behaviour of the tested models with and without the PBs under various ground motions.
# On the Relationship between Self-Admitted Technical Debt Removals and Technical Debt Measures
## 3 Study Setup
### Research Questions  
This paper reports on the effects of removing the SATD, on the objective measurement of technical debt, and other metrics of the main source code, in open source software projects. The study is based on the following research questions:  
**RQ1:**_To what extent do self-admitted technical debt removals actually lead to lower technical debt value?_  
This research question aims to understand if self-admitted technical debt removals correspond to lower values of the technical debt measure. To perform the assessment, a fine-grained analysis has been performed, collecting and analyzing the data of open-source software systems, commit per commit, and class per class. Descriptive statistics and graphs have been used to support the analysis.  
**RQ2:**_To what extent do self-admitted technical debt removals lead to lower Chidamber and Kemerer metrics values?_The goal of this research question is to look for empirical evidence to support the hypothesis about the positive impact of SATD removals on software quality metrics. In particular, the study focused on the OO metrics with the objective of understanding if their values improve when SATD removals occur.
# Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
## 4 Digital Twins: Enabling Interoperability in Manufacturing  
Smart manufacturing approaches informed by continuous data monitoring will enable companies to tailor production runs to better match consumer stipulations [5]. The following are some examples of the manufacturing use cases accomplished by the upcoming Industrial IoT and edge computing solutions:  
* Predictive maintenance and equipment safety--e.g., a pump installed with edge computing capabilities can determine if an established threshold has been surpassed, using basic analytics, and shut itself in milliseconds. Applying an edge computing device to perform this function will result in zero decision latency without any requirement for internet connectivity.
* Production flow monitoring and optimisation--to compile the data on a local gateway and send overall equipment effectiveness (OEE) patterns and alerts to the operational staff, edge computing can do near real-time analytics across a variety of data obtained from sensors installed within the floor.
* Supply chain optimisation--any industrial facility's supply chain processes must be optimised, which calls for the use of optimisation algorithms and data analytics that can quickly adjust supply-chain goals inside business systems such as ERP, SCM, etc.
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 5 Applications, Use Cases, and Cost Analysis  
The deployment of a cluster of commodity hardware provides an affordable and scalable solution to run various workloads, including hosted services, machine learning experiments, and big data processing. This section highlights the primary applications and use cases supported by the cluster, which demonstrate its versatility and resource efficiency. In addition, a cost analysis will be conducted to evaluate the economic benefits of utilizing commodity hardware, emphasizing its cost-effectiveness compared to traditional high-end computing solutions.  
We implemented our proposed architecture in a research laboratory at the University of Patras equipped with 16 personal computers (PCs), each powered by an Intel(r) Core(tm) i7-10700 CPU @ 2.90 GHz processor, except for two systems that are equipped with an intel i7-3770K processor at 2.50 GHz and one that uses an Intel(r) Core(tm) i7-4770 @ 3.40 GHz. Fifteen of these PCs are configured with 16 GB of memory, and one has 24 GB. In terms of storage, the systems are equipped with 512 GB Solid State Drives (SSDs). Detailed specifications are shown in Table 1. Additionally, the three systems with different processors feature RAM and storage components of varying speeds and manufacturers, allowing us to evaluate CommC's performance under heterogeneous hardware conditions. The first version of our system is publicly available on GitHub and can be accessed using the GitHub link, accompanied by a detailed installation tutorial and troubleshooting documentation.
## 3 Methodology  
Figure 5 depicts the technical route of this study. Firstly, relevant data in the Zayu area were collected and organized to form a factor dataset. Then, from the landslide dataset in the Zayu area, landslide factors required for this study were selected, and collinearity diagnosis was conducted to screen out the landslide evaluation factors suitable for susceptibility assessment. The factors were normalized and standardized as the input set.  
This study employed the Inception model for landslide susceptibility and the \"April 25, 2010, Tibet Zayu heavy rain\" landslide risk assessment. The BPNN, ResNet, CNN, and VGG-16 models were introduced for comparative analysis [31]. The fitting effects of these models in this scenario were evaluated, and the prediction results of each model were assessed for accuracy. SHAP was used for model interpretation further to understand the decision-making process and logic of the models.
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 3 System Modeling
### Formulation of the Problem  
A user can establish a connection with a device when the user is located within the communication range of the respective device, and the required computation does not exceed the available computational capacity of the device, while satisfying the delay and energy-consumption constraints (i.e., the maximum delay limit \(d_{\text{max}}\) and the UAV battery capacity limit \(E_{\text{max}}\)). The establishment of a connection between a device and a user can be represented as follows:  
\[X_{n}=\begin{cases}1,&\text{if }L\leq r_{\text{max}}\text{ and }a_{n}\leq a_{\text{ava}}\text{ and }d_{\text{total}}(t)\leq d_{\text{max}}\text{ and }E_{m,n}(t)\leq E_{\text{max}}\\ 0,&\text{else}\end{cases} \tag{30}\]  
To evaluate the coverage of the overall network deployment, a parameter \(Q\) is defined to quantify the coverage performance of the entire system as follows:  
\[Q=\frac{1}{N}\sum_{n=1}^{N}X_{n} \tag{31}\]In order to optimize mobile communication network coverage and ensure efficient collaboration between satellites and terrestrial base stations for comprehensive communication support for the maritime user community, this study designs a dynamic deployment strategy for UAVs. This strategy aims to provide reliable and immediate communication support for users in emergency situations or for those who cannot be covered by conventional networks. Based on a comprehensive consideration of the computational resources of each communication node, the coverage connectivity and communication delay for mobile users, and the battery capacity of the UAV, the objective is to minimize the total system cost and maximize user communication coverage. The optimization problem is modeled as follows:  
\[\begin{split} P1&\max\sum_{n=1}^{N}X_{n}-\lambda \left[\sum_{m=1}^{M}\left(E_{\text{fly},m,n}(t)+E_{\text{hover},m,n}(t)\right) +\sum_{n=1}^{N}d_{\text{total}}(t)\right]\\ \text{s.t.}& C1:L\leq r_{\text{max}},\\ & C2:d_{\text{total}}(t)\leq d_{\text{max}},\\ & C3:E_{\text{fly,m,n}}(t)+E_{\text{hover},\text{m,n}}(t)\leq E_{ \text{max}},\\ & C4:a_{n}\leq a_{\text{ava}},\\ & C5:X_{n}\in\{0,1\},\,\forall n\end{split} \tag{32}\]
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Iceberg
#### 2.1.2 SAR  
In SAR data, icebergs are the sum of volume and surface scattering mechanisms [37]. The scattering intensity reflects the characteristics of icebergs and, thus, it can be employed to estimate the physical parameters of icebergs (e.g., size, roughness, and freeboard). Due to the scattering properties of icebergs, they appear brighter in comparison with the darker backgrounds of SI and open water. This contrast enabled the researchers to utilize SAR data for iceberg studies. In this regard, great effort was made to employ SAR data for iceberg studies by generating mosaic datasets and developing various Machine Learning (ML) algorithms [25; 38; 39; 40]. For instance, [PERSON] et al. (1998) utilized over 3000 individual Radasat-1 C-band data, acquired between September and October of 1997, to construct the Antarctic near-coastal zone mosaic dataset. Moreover, [PERSON] et al. (2016) developed a CNN algorithm to discriminate between ships and icebergs in high-resolution TerraSAR-X images. The CNN algorithm was employed to solve the existing challenges of the algorithm of the constant false alarm rate [41; 42]. The achieved high Precision, Recall, and F1-score criteria demonstrated the capability of the CNN algorithm for ship and iceberg discrimination in SAR images. Furthermore, [PERSON] et al. (2019b) developed an adaptive ML algorithm to improve the automatic detection capability of icebergs in SAR images. The proposed approach was comprised of three concepts of superpixel segmentation, ensemble learning, and incremental learning applied to two SAR mosaic datasets. The low average false positive rate of 2.3 and the high average classification accuracy of 97.5% confirmed the robustness of the proposed method for iceberg detection.  
Icebergs are generally observed as brighter than the surrounding backgrounds (i.e., open water) in SAR images. The main challenge of identifying icebergs is when ships have existed in an SAR image. Since some radar signals like L-band can penetrate icebergs there are lower possibilities for corner reflector backscattering returns, while this happens more for ships. In particular, the HH polarization is preferred over VV for only iceberg detection in open water, while the HV polarization proved its higher potential for iceberg-ship discrimination [43].
###### Abstract  
This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July-August (JA) database as the best months to predict corn and soybean yields. Six different AI models for crop yield prediction are tested in this research. Then, a comprehensive and objective comparison is conducted between the AI models. Particularly for the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio. In terms of mean absolute error (MAE), our DNN model with the JA database was approximately 21-33% and 17-22% more accurate for corn and soybean yields, respectively, than the other five AI models. This indicates that corn and soybean yields for a given year can be forecasted in advance, at the beginning of September, approximately a month or more ahead of harvesting time. A combination of the optimized DNN model and spatial statistical methods should be investigated in future work, to mitigate partly clustered errors in some regions.  
crop yield; artificial intelligence; satellite product; meteorological dataset +
Footnote †: journal: Geo-Informat
## 4 Results
### Helix Angle Directions and Structural Analysis Setup  
In designing, a helix angle is generally applied to increase face contact ratios for qualified performances in the aspects of strengths and vibrations [29]. For defining a helix angle of gears, an amount of the angle and its direction must be specified together. Generally, both the angle and its direction affect thrust loads acting to bearings. While gear performances are affected by an amount of the helix angle at the gear-mesh level, amounts of thrust loads are systematically determined by both specifications of the helix angle at the transmission level. However, determining the amount of the helix angle is related to defining gear geometry profiles of a planetary gear set. For this reason, this study is confined to analyzing the influences of the helix angle directions of planetary gear sets on bearing power losses.  
For a gear and pinion which are in an external gear mesh, once a helix angle direction of the gear is machined in one of left- or right-hand, then the direction of the pinion mating with it is determined in the other direction accordingly. However, for the case of an internal gear mesh, pinion and internal (or ring) gears have the same direction of a helix angle. Hence, for a planetary gear set including sun, ring and planet gears, as the helix angle direction of one of the gears is selected, the others are determined accordingly. If a helix angle direction of a sun gear is decided in left-hand, both planet and ring gears have right-hand angle. As a helix angle direction of each planetary gear set has two options of left- or right-hand, the transmission model shown in Figure 4, containing 4 decks of single-planet planetary gear sets, has 16 combinations of helix angle directions. Furthermore, this transmission model produces 9 speeds of 8 forward and 1 reverse drives by actuating frictional devices. For the entire combinations of helix angle directions, structural analyses of 144 cases are executed in total, and input torque of 550 \(Nm\) and rotational speed of 3000 \(RPM\) are applied to the input shaft for the analysis setting. MASTA, which is a commercial software developed by Smart Manufacturing Technology, is used for the modeling and structural analysis. For bearing load analysis, preload conditions on bearings are not considered in the simulations.