esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Model Construction Based on Theoretical Mechanics Methods  
### Construction of Stable Support Mechanical Model  
In order to analyze the force distribution of the TSR under stable support conditions, a mechanical model of the TSR in a stable support state is constructed based on the principle model of the temporary support robot. In this model, the force on the TSR is simplified to a plane linkage model on the neutral surface [24], as shown in Figure 5.  
Figure 4: Virtual prototype model of the temporary support robot.  
Figure 5: Force analysis of the temporary support robot in a stable support state.  
entire support 1 in the x-axis direction equals zero, and the sum of forces \(\sum F_{y1}\) on the entire support 1 in the y-axis direction equals zero. The force equilibrium equations are as follows:  
\[\left\{\begin{array}{l}\sum F_{x1}=0\\ \sum F_{y1}=0\end{array}\right.. \tag{7}\]  
From the force equilibrium equations, it can be obtained that  
\[\left\{\begin{array}{l}T_{1z}+T_{2z}-f_{1}Q_{a11}-f_{1}Q_{12}=0\\ Q_{12}-G_{1}-Q_{11}=0\end{array}\right.. \tag{8}\]  
When support 1 is accelerating, according to [PERSON]'s second law, the dynamic model of the moving cylinder pushing support 1 is as follows:  
\[T_{1}+T_{2}=f_{1}(Q_{11}+Q_{12})+\frac{m_{1}d^{2}x_{1}}{dt^{2}}, \tag{9}\]  
where \(m_{1}\) is the mass of support 1, kg; \(t\) is the time, s; \(x_{1}\) is the displacement of support 1, m; and \(f_{1}\) is the friction coefficient between support 1 and the surrounding rock.  
When support 2 is in the state of driving under pressure, assuming that it is in the state of force equilibrium, similarly, it can be known that the dynamic model of the moving cylinder pulling support 2 is as follows:  
\[\left\{\begin{array}{l}T_{1}+T_{2}=f_{2}(Q_{21}+Q_{22})+\frac{m_{2}d^{2}x_{2 }}{dt^{2}}\\ Q_{22}-G_{1}-Q_{21}=0\end{array}\right., \tag{10}\]  
where \(m_{2}\) is the mass of support 2, (kg); \(x_{2}\) is the displacement of support 2, (m); and \(f_{2}\) is the friction coefficient between support 2 and the surrounding rock.  
When the TSR is driving under pressure, there is a transition from static friction to dynamic friction between the TSR and the roof and floor. To accurately describe this friction process, this paper adopts the Stribeck friction theory [25,26] to analyze the friction coefficient between them. The model is as follows:  
\[\left\{\begin{array}{l}f_{1}=f_{d}+(f_{s}-f_{d})e^{-(v_{1}/v_{st})^{2}}\\ f_{2}=f_{d}+(f_{s}-f_{d})e^{-(v_{2}/v_{st})^{2}}\end{array}\right., \tag{11}\]  
where \(f_{d}\) is the dynamic friction coefficient between the TSR and the surrounding rock; \(f_{s}\) is the static friction coefficient between the TSR and the surrounding rock; \(v_{st}\) is the critical Stribeck velocity, (m/s); \(v_{1}\) is the moving speed of support 1, m/s; and \(v_{2}\) is the moving speed of support 2, m/s.
# RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks
## 3 The Proposed Model  
According to [33], data imbalance is one of the causes that degrades the performance of machine learning algorithms in classifications. This is due to two major causes. First, the simple accuracy as an objective function used in most classification tasks is inadequate when the classifier faces data imbalance and the second comes from the distribution of the classes where the majority class is likely to invade the territory of the minority class. In general, for the same data set, if the number of classes increases, the sub-data set of each class decreases, leading to the decrease of the generalization capability and the increase of classification errors and vice versa. The mis-classification is usually due to the classification of attacks as normal behavior, or as another attack in the same or different category.  
In order to minimize these classification errors and to increase the performance of intrusion detection mechanism, we propose the hierarchical intrusion detection model illustrated in Figure 1. The main feature of the RTDITS is that uses a binary and multi-class classifier in parallel, that feed a third classifier, thus minimizing the effect of data imbalance to the final outcome. Moreover, ensemble systems of classifiers are widely used for intrusion detection in networks (maglaras2016 combining). These aim to include mutually complementary individual classifiers, which are characterized by high diversity in terms of classifier structure [34], internal parameters or classifier inputs. As stated in [35], ensemble and meta-ensemble methods show a number of advantages with regard to using a single model, i.e., they reduce the variance of the error, the bias, and the dependence on a single dataset and work well in the case of unbalanced classes.  
Our hierarchical model aims to correctly classify each attack, providing a low false alarm rate and a high detection rate. It is composed of three classifiers. The first one uses the different features of the data set as inputs, in order to classify each row as benign or malicious. The second one uses different features of the data set as inputs for classifying each row as benign or one of the different categories of attacks. The third classifier uses all the features of the initial data set in addition to the outputs of the first and the second classifier as inputs, in order to classify each row of the data set as benign or a specific type of attack. For the integration of RDTIDS system for IoT networks, we propose a three-tier fog computing architecture, in which RDTIDS system is located in the fog computing layer, as presented in Figure 2. The classifiers that are used are REP Tree [36] Jrip [37] and Forest PA [29]. Based on the values of the features, the classifier makes rules which are used in order to correctly classify the data set.
# The Impact of Information Systems Implementation in the Greek Manufacturing Enterprises
## 4 Results and Discussion
### General Characteristics
#### 4.1.2 Impact of IS Adoption  
The companies that use IS were asked to assess the impact of IS implementation in--supply, production, distribution, warehouse, and inventory--management. In addition to those variables, they assessed the impact of IS adoption in \"Customer service\" since one of the ulterior goals of management is to satisfy the customers with their products or services. A five-point Likert scale was used to determine the positivity or negativity of the effects of IS adoption using 1 as \"very negative\" and 5 as \"very positive\". The average ratings are shown in Figure 3 where it is easily visible that the impact is positive since they are all above 4. \"Inventory management\", \"Customer service\" and \"Warehouse management\" were the ones that were more positively affected by the use of IS in business. On the other hand \"Distribution management\" was less positively affected but after-all it was positively affected.  
Several studies have tried to determine the benefits of ICT into business, or in other words, whether IS positively affected the way manufacturers conduct their business. The general feeling is that IS helped companies a lot in several aspects regarding their business. The benefits that can be derived by IS adoption could be the following: better customer service, better distribution, increased productivity, better inventory control, faster delivery times, cost reduction, etc. [21]. According to authors in [14], the respondents somewhat agree that IT helped into production management while the effect on Inventory management was lower. In their study [14], the authors used a seven-point Likert scale to measure several variables that can be considered as parts of several other elements that can be appointed to different departments of management such as Production, Inventory, Customer, Supply, Distribution, and Warehouse management. The average scores in all variables were above 4 (neutral), which mean that the respondents agree that IT helped  
Figure 3: Impact of IS adoption.  
them to better support their business in several areas [14]. In [26], the authors suggest that in the supplier selection process the benefits derived from IS implementation are mostly nonfinancial and more related to management decisions. In [21], the authors among other things, exploited the benefits of ERP in the organizational performance by using a five-point Likert scale (1: \"not at all\", 5: \"a great extent\"). Among the characteristics, the respondents had to grade were: lower inventory levels, improved interaction with customers, inventory management, and supplier management/procurement. In all these characteristics, the average was between 3 and 3.5.
## 4 Data Processing
### Results I--3D Models Comparison  
A description of the data is given in Table 1. Table 2 provides more detailed information on the processing. The models were compared on various grounds, such as data collection  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Project Phase** & 1 & 2 & 3 \\ \hline
**Number of terrestrial** & & & \\
**images** & 1809 & 2858 & 1646 \\ \hline
**Number of UAV images** & 62 & 62 & 187 \\ \hline
**Excluded images** & 21 & 74 & 21 \\ \hline
**Calculation time (hours)** & 4.5 & 22 & 7.25 \\ \hline
**Number of cloud points** & 140,184,613 & 142,477,164 & 312,141,911 \\ \hline
**Standard deviation with** & & & \\
**GCPs (o)** & 26 mm & 28 mm & 17 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation parameters of the photogrammetric process from Agisoft Metashape software.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Phases** & \begin{tabular}{c} **RMSE (X)** \\ **[cm]** \\ \end{tabular} & \begin{tabular}{c} **RMSE (Y)** \\ **[cm]** \\ \end{tabular} & \begin{tabular}{c} **RMSE (Z)** \\ **[cm]** \\ \end{tabular} & \begin{tabular}{c} **RMSE** \\ **(OX) [cm]** \\ \end{tabular} & \
\begin{tabular}{c} **RMSE** \\ **(3D) [cm]** \\ \end{tabular} &
\begin{tabular}{c} **Reprojection** \\ **Error [pix]** \\ \end{tabular} \\ \hline
1 & 1.7 & 1.3 & 1.5 & 2.2 & 2.7 & 2.75 \\ \hline
2 & 1.8 & 1.8 & 1.3 & 2.5 & 2.8 & 0.69 \\ \hline
3 & 0.9 & 0.9 & 1.5 & 2.2 & 1.7 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of RMSE parameters.  
time, data processing, accuracy, and data volume. A basic comparison of the 3D models' point clouds is shown in Table 5.  
Calculations were performed on the workstation--PC with GeForce RTX 1080 Ti graphics card, 32 GB RAM, Intel i7.
## 3 Simulation Results and Discussion
### Deployment Strategy
The system parameters are chosen as \(m_{0}=10^{4}\) kg, \(m_{1}=100\) kg, \(m_{2}=500\) kg, and \(r=7100\) km, which are from [22]. The initial attitude variables are \(\theta_{1}(0)=10^{{}^{\circ}},\theta_{2}(0)=20^{{}^{\circ}},\alpha_{1}(0)=-5^{{}^ {\circ}}\), and \(\alpha_{2}(0)=-10^{{}^{\circ}}\), which are selected randomly. In order to ensure that the initial attitude is static, the initial angular velocities of attitude are set as \(\theta_{1}^{\prime}(0)=\theta_{2}^{\prime}(0)=\alpha_{1}^{\prime}(0)=\alpha_{ 2}^{\prime}(0)=0\). Figure 11 displays the time histories of the libration motions obtained with the deployment strategy. It shows that the predesigned deployment strategy effectively eliminates the out-of-plane libration motion after almost one orbit, while the in-plane libration motion exhibits a purely sinusoidal variation that persists even after the completion of deployment. To further analyze the libration motion, the phase diagrams of \(\theta_{1}-\theta_{2}\) and \(\alpha_{1}-\alpha_{2}\) are presented in Figure 12. The phase diagrams reveal that the differences in in-plane \(\theta_{1}\) and \(\theta_{2}\), as well as \(\alpha_{1}\) and \(\alpha_{2}\), tend to be very small limit cycles. Combined with the results of Figures 11 and 12, it can be concluded that the deployment strategy can effectively and stably complete the task, the libration motion in the whole process is no more than \(90^{\circ}\), and instability will not occur after deployment. Figure 11: Libration motion of two tethers. Figure 10: Predesigned motion of two tethers. (a) Deployed tether length. (b) Deployed velocity. Figure 13 presents the time history of the tension in two tethers. It can be observed that the tension in both tethers increases over the first three orbital periods and subsequently fluctuates periodically around constant values, exhibiting a very small amplitude. Therefore, it can be inferred that the libration angles attain a stable state once the deployment is complete. An important aspect that deserves attention is that the tension values in both tethers remain non-negative integers throughout the entire process, indicating the absence of slackness. In summary, the simulation results demonstrate that the predesigned deployment strategy is suitable for suppressing libration during the development of the TB-TSS and ensures that both in-plane and out-of-plane motions maintain stable limit cycles. Furthermore, although the speed of the two tethers is different, the oscillation characteristics and dynamic performance of their tension remain virtually identical.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 3 Results  
### Validation of River Monitoring Station Data  
The measured values were compared on an hourly, daily, and monthly basis with the data from Dogancay HPP I, located upstream of the manufactured river monitoring station. The river monitoring station takes two measurements per minute, and all data covering the 2023 hydrological year (1 October 2022-30 September 2023) were analyzed. During the system validation process, hourly, daily, and monthly averages were calculated for both the river monitoring station and the hydropower plant to account for the difference in measurement frequencies. While the river monitoring station produces approximately 2880 data points per day, the hydropower plant generates approximately 144 data points per day by measuring every 10 min. This approach allowed for a balanced and meaningful comparison of the systems during periods with both low- and high-rainfall regimes.
# Smart Gateway for Healthcare Networks Based on Beam Steering Technology
###### Abstract
The angle of the incoming wave is estimated by using signal processing techniques, such as the multiple signal classification (MUSIC) algorithm and the estimation of signal parameters via the rotational invariance technique (ESPRIT) algorithm [22; 23]. Stable communication is realized by generating a  
Figure 1: Conceptual illustration of a smart home. beam in the direction estimated. However, it is known that the estimation accuracy of these methods depends on the signal-to-noise ratio (SNR), resulting in a difficulty with estimating in hostile environments. In our previous study, to realize the position for ground-based vehicle applications, an angle-of-arrival (AOA) antenna system that utilizes the radio-frequency (RF)-based interferometric monopulse technique was proposed [24; 25]. The proposed AOA antenna has a high degree of accuracy regardless of the K-factor, which expresses the ratio of the direct wave power to the scattered wave power in a LOS propagation environment. However, the proposed antenna in this case was composed of nine elements to achieve a full-azimuth beamforming multiple-input multiple-output (MIMO) function. For a small antenna with a reduced number of elements that does not require high-capacity communication, an AOA estimation of the incoming wave and beamforming in the desired direction has not been considered. This paper presents a smart gateway system that includes the AOA estimation and the full-azimuth beam steering functions for a small circular antenna array. The proposed antenna was redesigned to increase the radiation gain and to improve the estimation accuracy of the incoming wave based on previous observations. The fabricated antenna was assessed for its estimation accuracy of incoming wave using over-the-air (OTA) testing in Rice propagation environments with a two-dimensional fading emulator. Moreover, the ability of the beam steering in the proposed antenna was evaluated by propagation experiments using a human phantom model in an indoor environment. The remainder of this paper is organized as follows: in Section 2 the estimation method and beamforming for the proposed antenna are described; Section 3 shows the directivity measurements made in an anechoic chamber; Section 4 shows the verification of the estimation accuracy using an OTA apparatus; Section 5 shows the experimental results of the beam steering function in an indoor environment; and Section 6 concludes the paper.
# Freshness Monitoring of Packaged Vegetables
## 8 Conclusions  
In this article, we review the current trends in smart packaging systems. The smart packaging systems provide potential solutions to the conventional packaging problems such as food loss and contamination. For packaged vegetables, the most important factors to monitor as a part of freshness assessment of vegetables are the ratio of oxygen and carbon dioxide inside the package, ethylene response, pathogen presence, and temperature, which has a huge effect on pathogens growth rate, chlorophyll content, and humidity. Tracking these parameters can be done through intelligent systems such as sensors (optical, bio, gas, and humidity), indicators (TTI, gas and humidity, and freshness), or by data carriers (RFID tags and barcodes). Adjusting these parameters can be done using active packaging such as using scavenging objects (oxygen or carbon dioxide scavengers, moisture absorbers), or releasing objects (oxygen or carbon dioxide emitters, antioxidants, antimicrobials). However, the challenges facing this industry in terms of complexity of legislations and regulation, cost, privacy concerns, and sustainability of the systems used are inhibiting mass-production of such smart systems. Therefore, collaboration between the researchers, industries, regulatory agencies, and the consumers will allow for further development of smart packaging systems.  
**Author Contributions:** The manuscript was written through contributions from all authors. [PERSON] conceived of the idea, and together with [PERSON], [PERSON], [PERSON] and [PERSON], defined the detailed contents of the paper. [PERSON] wrote the first draft of the article which was critiqued by all authors. The article was then revised by [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by NSERC Green Electronics Network (GreEN, Grant No. 508526-7), a Discovery Grant from the Natural Science and Engineering Research Council (NSERC) of Canada of Canada and the Canada Research Chair program.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effect of Different Bonding Materials on Flip-Chip LED Filament Properties
###### Abstract  
This article researches the effect of Sn-based solder alloys on flip-chip light-emitting diode LED (FC-LED) filament properties. SEM images, shearing force, steady-state voltage, blue light luminous flux, and junction temperature were examined to demonstrate the difference between two types of FC-LED filaments welded with two soldiers. The microstructure surface of Sn90 Sb10 filament solder joints was smoother and had fewer voids and cracks compared with that of SAC0307 filament solder joints, which indicates that the Sn90 Sb10 filaments had a higher shearing force than the SAC0307 filaments; moreover, the average shearing force was beyond 200 gf (standard shearing force). The steady-state voltage and junction temperature of the Sn90 Sb10 solder-welded FC-LED filament were lower, and the Sn90 Sb10 filament had a relatively higher blue light luminous flux. If high reliability of the solder joints and better photoelectric properties of the filaments are required, this Sn90 Sb10 solder is the best bonding material for FC-LED filament welding.  
FC-LED filament; lead-free solder; photoelectric properties; reliability +
Footnote †: journal: Applied Sciences
## 2 Formation of the Model of Radiation Thermometry
### The Hybrid Model of Radiation Thermometry  
Authors have further developed the hybrid model of radiation thermometry, permitting mathematically correct use of spectral input parameters in the inverse model.  
The hybrid model permits the use of spectral input parameters, improving traceability of the scalar inverse model. As radiation thermometer sensors operate on the principle of absorption, only the total radiance is measured, whereas the spectral distribution of this radiance is unknown. Despite spectral data loss in the sensor, traceable measurements are enabled in the hybrid model, which accounts for the missing data by means of predicting spectral components. However, the traceability is limited to systems with fully known influential spectral parameters and spectral radiances, including uncertainties.  
The hybrid model operates the same way as the scalar model from Figure 3; however, here, the instrumental parameters account for spectral distributions in the inverse model using the effective influence parameters. The effective parameters (e.g., \(i\epsilon\)) are calculated as scalar equivalents of corresponding spectral parameters (e.g., \(\epsilon(\lambda)\)) according to the hybrid model method for effective parameter calculation (7)-(10) for a given temperature [11]. Their values correspond to the spectral parameter's effective attenuation of the radiance component in magnitude, as exhibited at the sensor.  
\[i\epsilon_{\text{eff}}\Big{(}T_{obj}\Big{)}=\frac{\int M_{BB}\Big{(}T_{obj}, \lambda\Big{)}\;\epsilon(\lambda)\;S(\lambda)\;d\lambda}{\int M_{BB}\Big{(}T_{ obj},\lambda\Big{)}\;S(\lambda)\;d\lambda} \tag{7}\]  
\[i\rho_{eff}(T_{env})=\frac{\int M_{BB}(T_{env},\lambda)\;\rho(\lambda)\;S( \lambda)\;d\lambda}{\int M_{BB}(T_{env},\lambda)\;S(\lambda)\;d\lambda} \tag{8}\]  
\[i\tau_{eff}\Big{(}M_{obj},\tau(\lambda)\Big{)}=\frac{\int\Big{(}M_{BB}\big{(} T_{obj},\lambda\big{)}\;\epsilon(\lambda)+M_{BB}(T_{env},\lambda)\;\rho( \lambda)\Big{)}\;\tau(\lambda)\;S(\lambda)\;d\lambda}{\int\Big{(}M_{BB}\big{(} T_{obj},\lambda\big{)}\;\epsilon(\lambda)+M_{BB}(T_{env},\lambda)\;\rho( \lambda)\Big{)}\;S(\lambda)\;d\lambda} \tag{9}\]  
\[i\epsilon_{\text{eff,atm}}(T_{atm})=\frac{\int M_{BB}(T_{atm},\lambda)\; \epsilon_{atm}(\lambda)\;S(\lambda)\;d\lambda}{\int M_{BB}(T_{atm},\lambda)\;S( \lambda)\;d\lambda} \tag{10}\]  
Considering mathematically correct performance, the hybrid model is used for uncertainty simulations, permitting traceable calculation with spectral input parameters, as well as permitting scalar input values in case of grey body systems.