esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Rating Prediction  
In a recommender system, the matrix factorization (MF) technique factorizes the user rating matrix into a user feature matrix and a product feature matrix, which are mapped into vector space \(\mathbb{R}^{K}\). The matrix inner product calculation is then used to predict the users' rating of the unrated product items. Based on the MF method [16], the user latent factor \(U\) and product latent factor \(P\), as generated from the user preference model (Section 3.5) and product evaluation model (Section 3.6), respectively, were input into the MF method to produce the predicted ratings. However, machine learning methods (e.g., CNN) require several iterations to adjust the model parameters to achieve the best prediction results. Therefore, to obtain better rating prediction results in this study, an additional layer of MF was added to the deep learning model in Sections 3.5 and 3.6 to predict the products that users may be interested in and the rating value of products. The calculation of the predicted rating is shown in Equation (15):  
\[r_{u,p}^{\sim}=Q_{p}^{T}\cdot U_{u} \tag{15}\]  
where \(r_{u,p}^{\sim}\) is the predicted score; \(U_{u}\), the user \(u\)'s latent factor; and \(Q_{p}\), the product \(p\)'s latent factor. To minimize the difference between the original and predicted ratings, this study used stochastic gradient descent (SGD) to minimize the objective function (i.e., Equation (16)) and establish the most suitable user latent factors and product latent factors. The objective of the SGD method is to identify the optimal regional solution of the function and randomly select samples to update the parameters and converge to the global minimum after several iterations in order to avoid generating regional minima:  
\[\underset{\mathbb{Q}^{+}U^{*}}{min}\sum_{u,p}\left(r_{u,p}-Q_{p}^{T}\cdot U_{ u}\right)^{2}+\lambda_{Q}\|Q_{p}\|^{2}+\lambda_{U}\|U_{u}\|^{2} \tag{16}\]  
where \(r_{u,p}\) is the actual rating; \(\lambda_{Q}\) and \(\lambda_{U}\) are regularization parameters used to avoid overfitting; \(Q_{p}\) is product \(p\)'s latent factor, and \(U_{u}\) is user \(u\)'s latent factor. The parameters in Equation (16) were updated according to the two rules [45; 46] defined in Equations (17) and (18):  
\[Q_{p} \leftarrow Q_{p}+\alpha\left(e_{u,p}U_{u}-\lambda_{Q}Q_{p}\right) \tag{17}\] \[U_{u} \leftarrow U_{u}+\alpha\left(e_{u,p}Q_{p}-\lambda_{U}U_{u}\right) \tag{18}\]  
where \(e_{u,p}=r_{u,p}-Q_{p}^{T}\cdot U_{u}\) presents the difference between the predicted rating and actual rating (i.e., the loss or error); and \(\alpha\) is the learning rate that can be selected differently for each factor matrix.
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
###### Abstract
In contrast, ZigBee has the advantage that one node is connected to 255 devices, which can be extended to up to 65,000 devices [17] and consumes little enough power to operate for several years with a low-capacity battery depending on the settings [18]. Representative technologies used for wireless communication are listed in Table 1, and the features, advantages, and disadvantages of each are also summarized. Considering the advantages and disadvantages of the ZigBee technology, which can construct a relatively wide area network at low power and cost, it was used in this study to measure the amount of vibration and dust over a wide area within an underground mine. Recently, some studies [19; 20] have used ZigBee to build wireless sensor networks (WSNs) in underground mines or tunnels. However, most studies are limited to conceptual design or communication between a few devices; moreover, there are only limited number of practically applicable systems that continuously collect data from multiple nodes while considering the battery life and data load. This study deals with practical concerns in applying ZigBee technology and discusses the problems and solutions for implementing a low-power and -cost system applicable to relatively small and poor underground mines. Digital devices capable of measuring vibration and dust were developed by combining a ZigBee module, accelerometer, and dust sensor. In addition, these devices could construct a WSN to exchange measured data. By applying the developed technology to Taeyoung EMC's Samdo Mine, we could confirm the spatial distribution and the change in vibration and dust density at multiple points in the underground mine when blasting was performed. This paper is organized into six sections. Section 1 introduces the main concepts of this study. Section 2 describes the study area in which the developed technology is applied. Sections 3 and 4 describe the Zigbee-based vibration and dust monitoring methods and application results in the study area, respectively. Section 5 discusses the challenges and methods used to solve them. Finally, conclusions are summarized in Section 6.
# Designing an Adaptive Underwater Visible Light Communication System +
## 5 Transmitter Design
### Signal Generation
#### 5.1.1 Modulation Scheme  
For creating a payload, the message signal was modulated using pulse position modulation. PPM is a digital modulation technique in which a message signal is encoded in \(2^{M}\) possible slots, where \(M\) is the number of message bits. This modulation scheme has low transmit power and better noise performance [32]. PPM has better noise immunity because it has a constant amplitude at the expense of high bandwidth.
## 3 Results and Discussion
### Surface Roughness  
The surface roughness is an important parameter in the wear process. The surface of the samples in all cases was polished to Ra = 0.01 \(\pm\) 0.002 \(\upmu\)m, but the nitridation and the PVD coating modified it. Table 4 shows the surface roughness of the treated samples. For PVD coatings, the literature reports values between Ra = 0.09 - 0.3 \(\upmu\)m [37], thus our results correlate well with reported values.
## 3 Basic Principle of Calculating Tropospheric Zenith Delay  
### VMF Calculation of Tropospheric Delay  
#### 3.1.1 Tropospheric Delay Elevation Correction  
The following equation is used to reduce the grid ZHD to the height of the station point (site), and the horizontal location of the station point is not changed compared to the grid point. The grid point height is \(h_{g}\) and the site height is \(h_{s}\). The tropospheric delay grid elevation needs to be reduced to the station elevation.  
\[D_{H}(h_{s})=D_{H}\big{(}h_{g}\big{)}-2.277\times 10^{-3}\times\frac{g\times P \big{(}h_{g}\big{)}\times\big{(}h_{s}-h_{g}\big{)}}{R\times T\big{(}h_{g}\big{)}} \tag{1}\]  
where \(D_{H}(h_{s})\) and \(D_{H}\big{(}h_{g}\big{)}\) are the dry delay of the site and grid points, respectively; \(h_{s}\) and \(h_{g}\) are station height and grid height (unit:m); \(T\big{(}h_{g}\big{)}\) is the temperature corresponding to the grid point (unit:K); \(p\big{(}h_{g}\big{)}\) is the air pressure corresponding to the grid point (unit:Pa); temperature and pressure calculated by means of the GPT (global pressure and temperature) model or standard atmosphere; \(R\) is a constant number of gases, \(R=287.054\big{)}/\left(\text{kg}\cdot\text{K}\right)\); \(g\) is the gravity constant, \(g=9.784\) m/s\({}^{2}\). Since the ZWD caused by water vapor has the characteristics of large rate of change and randomness, the exponential attenuation function is selected to correct ZWD for the elevation [11].  
\[D_{W}(h_{s})=D_{W}\big{(}h_{g}\big{)}\times e^{-\left(h_{s}-h_{g}\right)/2000} \tag{2}\]  
where \(D_{w}(h_{s})\) is the ZWD at the reference station (unit:mm); \(D_{w}(h_{g})\) is the ZWD of the grid point (unit:mm); \(h_{s}\) is the height of the station (unit:m); \(h_{g}\) is the height of the grid point (unit:m); \(e\) is the Euler number equal to approximately 2.71828.
## 3 Result and Discussion  
### Load--Displacement Behaviour  
#### 3.1.1 Single-Cell Panels with Infill  
The load-displacement behaviour of the single-cell panels is shown along with ultimate load, and the gradual yield in Figure 5. The single cells with both types of infill material, namely SGCC and SGCC containing chopped basalt fibre, reported a gradual drop post the peak load, showing a ductile performance. The peak loads attained by all the single-cell specimens were in the range of 40 kN. However, the single cell with fibres recorded a slightly higher ultimate load-carrying capacity at an average of 42.22 kN. This signifies that the chopped basalt fibres have contributed to the load-carrying capacity of the panels by improving their ductility. The displacement is also higher at the ultimate load in a single cell with no fibres.  
Again, from the load-displacement curves, it can be seen that there were more flexural cracks in the infill panels without chopped basalt fibres, which is highlighted by the presence of several irregularities due to load drops in the plot. However, a smoother load increase due to the chopped basalt fibres was observed in the other panels. This demonstrates that chopped basalt fibres positively contribute to reducing cracks on floor slabs. The averaged results of the single-cell test specimens are tabulated in Table 3.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 6 Evaluation
### Throughput Testing and Resource Utilization under Load  
The next evaluation focused on throughput, analyzing concurrency handling capability under load. The Locust script's \"wait_time\" was set to 2-8 s to simulate peak traffic conditions, alongside increasing user numbers and spawn rates up to 40,000 users and 5000 spawn rate. Requests per second (RPS) serves as a crucial metric reflecting the system's ability to process incoming requests. Figure 9 illustrates the system's consistent RPS performance across various stress conditions. The results confirm robust performance, efficiently handling high request volumes during peak traffic periods.  
Additionally, resource utilization, defined as the ratio of the actual time spent by resources executing the workload, is another critical metric in the edge computing paradigm [35]. In the throughput testing under load, we also monitored the CPU uti  
Figure 9: Throughput testing in different sets of groups.  
lization to assess the overall resource utilization. We focused specifically on the 40,000 user and 5000 spawn rate scenario, and observed a peak CPU utilization of 58%. This figure indicates a balanced resource usage. While lower utilization suggests the availability of additional resources, higher average CPU utilization does not necessarily equate to more efficient orchestration [36]. Indeed, high levels of CPU utilization might indicate load accumulation on other resources due to inefficient load or resource management [37]. The relatively modest resource utilization observed in our tests could be attributed to Elixir's lightweight process model. However, it also highlights opportunities for further optimization of resource usage in future work.
## 2 DoV Estimation: Description of the Methods
### Determination of DoV from the ITALGEO2005 Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Noto**} & \multicolumn{2}{c}{**GNSS-LEV**} & \multicolumn{2}{c}{**QDaedalus**} & \multicolumn{2}{c}{**ITALGEO2005**} \\ \cline{2-7}  & \(\xi\) **(arcsec)** & \(\eta\) **(arcsec)** & \(\xi\) **(arcsec)** & \(\eta\) **(arcsec)** & \(\xi\) **(arcsec)** & \(\eta\) **(arcsec)** \\ \hline GNSS-LEV & \(-\)12.4 (0.6) & 11.1 (0.7) & & & \(-\)12.6 (2.5) & 11.7 (2.5) \\ point & \(-\)11.9 (0.4) & 11.8 (0.1) & \(-\)12.6 (2.5) & 11.4 (2.5) \\ n2 & & & \(-\)13.3 (0.3) & 11.5 (0.4) & \(-\)12.5 (2.5) & 11.5 (2.5) \\ n3 & & & \(-\)12.1 (0.1) & 11.9 (0.0) & \(-\)12.5 (2.5) & 11.7 (2.5) \\ n4 & & & \(-\)12.3 (0.1) & 12.2 (0.2) & \(-\)12.5 (2.5) & 11.7 (2.5) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Components of the DoV obtained by the three methods for the Noto site; standard deviations are given in brackets. standard deviations were deduced via a comparison with astronomical measurements performed by the Italian I.G.M. (Istituto Geografico Militare) over a set of points distributed over the country ([PERSON], private communication). However, in the future, the estimates of the DoV might be improved using the last satellite-based gravimetric missions, e.g., CHallenging Minisatellite Payload (CHAMP), Gravity Recovery and Climate Experiment (GRACE) and Gravity field and steady-state Ocean Circulation Explorer (GOCE) [43; 44; 45], thus also improving the performance of the gravimetric method that might currently be jeopardized by the use of the outdated GGM GPM98 CR. The maximum discrepancy between the three methods was found at site Medicina, where ITALGEO2005 underestimates the \(\eta\) component by 1.8 arcsec. Nevertheless, this difference is statistically not significant and does not degrade the excellent consistency of the results. Although characterized by a lower level of precision, the ITALGEO2005 DoV values should be used in absence of specific DoV measurements. Neglecting the DoV values may result in estimates characterized by a low level of accuracy. DoV values are surely important for the alignment of the tie vectors to the ITRF. Lack of information might have effects on the quality of the combined frame. In this case, the DoV values must be measured once, stored and used any time a new tie vector is determined. Finally, it is worth highlighting that the QDaedalus system not only resulted as the most precise but also as the fastest among the three methods. As stated before, the time required to complete a set of observations at each station (_i.e._, to obtain DoV component estimates) did not exceed two hours. Cost and time consumption are important parameters against which modern surveying methods must be evaluated.
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 4 Simulation Studies and Performance Evaluation
### Comparison with the Conventional HHT  
Aiming at investigating the superiority of the proposed algorithm, comparison between conventional HHT and HHT-AMD in decomposing multicomponent micro-Doppler signals is provided [28]. The considered signals are \(s(t)=\exp(-j\cos(f_{1}t))+\exp(-j\cos(f_{2}t))=s1+s2\). The decompose success rate (DSR) is considered here, as a function of close-frequency ratio. The DSR represents the percentage of the successful cases decomposing close-frequency components in the all simulation tries. The close-frequency ratio is the ratio of the two close frequencies \(f_{1}\) and \(f_{2}\) (for example, the ratio of two frequencies 10 and 15 is 1.5). One-hundred Monte-Carlo-type simulations were carried out for a set of ratios 1.1-2. The DSRs of HHT-AMD were compared with those of conventional HHT, which were plotted in percentage, see Figure 8.  
From Figure 8, the DSRs of HHT-AMD were continually higher than those of HHT. There are still two main points to notice. On one hand, when the close-frequency ratios are lower than 1.5, the DSRs of HHT methods were in the low level, which means the decompose process was problematic. Meanwhile, the DSRs of HHT-AMD were obviously higher than those of HHT. On the other hand, when close-frequency ratios were higher than 1.5, the DSRs of HHT and HHT-AMD were considerable and acceptable. Consequently, there was a threshold effect at 1.5 of close-frequency ratio. Thus, HHT-AMD method showed its superiority over HHT method on decomposing close-frequency components especially when the close-frequency ratio was under 1.5.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
## 2 Materials and Proportioning Design  
### Cement Slag and Fine Aggregate  
The cement utilized in this study was Type-I produced by the Taiwan Cement Company, and the related properties of the applied cement complied with the ASTM C150 [23] specifications and are shown in Table 1. The slag was produced in the blast furnace at the China Steel Corporation in Taiwan. Both the physical properties and the chemical compounds of the slag are also shown in Table 1, and they conformed to CNS12549 specifications [24]. The fine aggregate utilized in this study conformed to the specifications of ASTM C33 [25] for concrete material.
# A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers
## 4 Simulation Results
### Correlation between Tie-Breaking Events and Waiting Times
The number of tie-breaking events (_TB# variable_) in each system (GH, GHT, LH, and LHT) and for each category are ranked in the following order:  
\[\text{TB\#GH}\text{$\sim$}=\text{TB\#Slow}_{\text{GHT}}\text{, \ \ }\text{TB\#Medium}\text{, \ \ }\text{TB\#Fast}\text{$<$}\text{TB\#Iast}\text{$<$}\text{TB\#Iast}\text{$<$}\text {TB\#I}\text  
mean processing rates with a ratio of 4:2:1 and inter-processing times of 1, 0.5, and 0.25, respectively. There are two workers in each group. The right bar in the figures shows the waiting times when the workers are homogenous (as in Table 1 in Section 4.1). The global queue shows each group's average waiting times and utilization, including the results when running with homogenous workers (GH, LH). For load 0.8, the queue size is mostly 0 (0.19); hence, the waiting times for local queues are like those in the global queue.  
For load 0.93, the queue size is mostly one, and the waiting times in the local setting are higher than in the global setting in each group. The waiting times and the queue size of the local queues using the JSQ random TB differ for each group. They are weighted proportionally to the arrival rates (2.68, 1.2, and 0.5) for the (slow, medium, and fast) group with a load of 0.93. The average wait time of the slow and medium groups of workers is much higher than that for the global queue and the local queue's homogenous setting (right bar). The queue size is empty mainly when the load is 0.8 and about two when the load is 0.93.  
Figure 3: The waiting times for GHT random TB and LHT JSQ random TB. (**a**) Load 0.8; (**b**) load 0.93.  
Hence, when the queue is mostly empty, and the load is at 0.8, the average waiting times for GH, GHT, LH, and LHT are ranked in the following order:  
\[\text{W}_{\text{LHT}}^{\text{Fast}}\sim<\text{W}_{\text{GH}}\text{ and }\text{W}_{\text{GH}}\ \ \sim=\text{W}_{\text{GHT}}^{\text{Slow}},\ \text{W}_{\text{GHT}}^{\text{Medium}}\text{ and }\text{W}_{\text{GHT}}^{\text{Fast}}\ \ \sim=\text{W}_{\text{LH}}<\text{W}_{\text{LHT}}^{\text{Medium}}<\ \text{W}_{\text{LHT}}^{\text{Slow}}\]  
When the load is 0.93, and the queue size is two on average, the average waiting times for GH, GHT, LH, and LHT are ranked in the following order:  
\[\text{W}_{\text{LHT}}^{\text{Fast}}<\text{W}_{\text{GH}}\text{ and }\text{W}_{\text{GH}}\ \ \sim=\text{W}_{\text{GHT}}^{\text{Slow}},\ \text{W}_{\text{GHT}}^{\text{Medium}}\text{ and }\text{W}_{\text{GHT}}^{\text{Fast}}\ \ \sim<\ \text{W}_{\text{LH}}<\ \text{W}_{\text{LHT}}^{\text{Medium}}<\ \text{W}_{\text{LHT}}^{\text{Slow}}\]  
The amount of time you must wait is dependent on the number of TB events that are presented earlier. When you use a simple JSQ, along with random TB in a local queue setup, the wait times for tasks assigned to slow and medium workers can be excessively long. As a result, it is important to consider alternative routing policies to ensure a balance between the waiting times for both slow and fast workers.
_Article_  
**Tele-Operated Bilateral Control of Hydraulic Manipulator Using a Robust Controller Based on the Sensorless Estimated Reaction Force**  
**[PERSON], [PERSON], [PERSON], [PERSON]\({}^{\copyright}\) and [PERSON] * School of Mechanical Engineering, Pusan National University, Busanadehak-ro 63 beon-gil, Jangjeon- dong Geumjeong-gu, Busan 46241, Korea; [EMAIL_ADDRESS] (K.D.K.); [EMAIL_ADDRESS] (S.J.A.); [EMAIL_ADDRESS] (H.K.); [EMAIL_ADDRESS] (J.W.)**  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-10-3861-2688  
Received: 29 March 2019; Accepted: 9 May 2019; Published: 15 May 2019  
**Featured Application: Applicable for higher order degree of freedom (DOF) manipulator used for dismantling in nuclear power plant (NPP).**  
**Abstract: In nuclear power plants (NPP), dismantling is the most technically involved process during their life time. During the dismantling process, public safety must be ensured. In crisis situations, a remotely controlled robot system is needed for the dismantling of NPP. Therefore, in this research, a bilateral tele-operation system is proposed to tackle these emergency conditions. Transparency can be improved by using force and position signal in the control strategy. In some applications, force cannot be determine directly using physical sensors. In this work, a novel tele-operated bilateral control strategy is proposed to estimate the reaction force of 3-degree-of-freedom (DOF) master and hydraulic slave manipulators without the use of a sensor. The control strategy is developed by using sliding mode control with sliding perturbation observer (SMCSPO). The sliding perturbation observer (SPO) estimates the reaction force at the end effector and second link without using sensors. The sliding mode control (SMC) is used as a tele-operated bilateral controller for the robust position tracking and control of the slave device. The impedance model is used to differentiate between the applied force (force exerted by operator) and the reaction force due to the remote environment. Different experiments were performed to verify the proposed strategy. The results indicate that the slave manipulator exactly follows the trajectory of the master device. A camera is used to take visual feedback of the workspace for safety purpose. This technique can also be applied for higher-order DOF manipulators in NPP.**  
**Keywords: SMCSPO; hydraulic manipulator; tele-operation; bilateral control; master-slave; estimated reaction force; position tracking; visual feedback**
# Farm Monitoring System with Drones and Optical Camera Communication
## 2 Related Work
### Sensor-Based Monitoring in Agriculture  
Sensor-based monitoring is considered one of the most important technologies for smart agriculture [21]. [PERSON] et al. [22] developed a real-time soil health monitoring system based on a Long-Range Wide-Area Network (LoRaWAN). In this system, data collected from soil sensors are transmitted to a cloud platform via the LoRaWAN. [PERSON] et al. [23] proposed a real-time temperature monitoring and frost prevention control system. This system, based on Z-BEE wireless sensor networks, automatically monitors temperature to prevent frost damage to tea plants. [PERSON] et al. [24] introduced an energy-efficient and secure IoT-based wireless sensor network framework for smart agriculture. Their framework provided stable network performance between agricultural sensors and ensured secure data transmission. [PERSON] et al. [25] evaluated the performance of data collection and monitoring systems in agriculture using Narrowband Internet of Things (NB-IoT) technology. They analyzed performance metrics such as data accuracy, communication latency, and battery consumption. [PERSON] et al. [26] proposed an environmental monitoring system for microfarms. In this system, environmental data collected from sensors are transmitted to a cloud server via a Wi-Fi network.