esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Multiobjective Optimization of Main Beam Structure Based on RSM
### Multiobjective Optimization of Main Beam Structure
#### 5.2.6 Multiobjective Optimization Solution of Main Beam  
Generally, it is difficult for multiple objective functions to obtain the optimal solution at the same time. When one optimization objective reaches the optimal value, the other optimization objectives are not optimal. Therefore, a large number of effective solutions, namely Pareto solutions [47], re obtained in the multiobjective optimization problem. The goal of multiobjective optimization is to obtain multiple Pareto solutions and select the optimal solution from the Pareto solution set according to the actual demand.  
In this paper, the multiobjective genetic algorithm [48] in the optimization module of ANSYS Workbench was used to iteratively solve the response surface model established above, and the Pareto solution set was obtained after. The multiobjective optimization design of the main beam included four design variables and five objective functions, which belonged to the multivariable multiobjective optimization problem and a multiobjective genetic algorithm was applicable.  
In order to improve material utilization and reduce the maximum equivalent stress of the main beam, the maximum equivalent stress and mass of the main beam were set as the highest priority before the iterative solution. The multiobjective genetic algorithm was selected as the solution method. The initial population size, iteration number, and other parameters were set and solved iteratively. The three groups of optimal solutions are shown in Table 13. The optimal design scheme was determined from the three groups of solutions.  
Figure 32: Fatigue life response surface.  
the overall performance of the main beam was improved and verified the rationality of the structural optimization of the main beam.
## 4 Discussion
### Towards Reliable Sound Speed Imaging  
In FWI, a model-driven approach that has been studied in sound speed imaging, the sound speed distribution is optimised so that the error between the real observed signal and the numerically simulated one is minimised [14; 15; 16; 17; 18; 19; 20]. Thus, the reliability of the solution can be assessed from the error. On the other hand, such evaluation is not available in sound speed imaging with DNNs. In addition, as seen in the experimental results, DNNs can produce false structures that look like the real thing. Therefore, sound speed imaging with DNNs for the purpose of medical examination can be very risky. To reduce this risk, it is necessary to develop methods to improve the reliability of examination, in addition to the generalization performance considerations that have been discussed in this study. There are two possible ways to do this.  
The first method is to add an out-of-distribution detection system, which assesses whether the input data to a DNN are outside the distribution of the training data [42; 43]. For example, by comparing the distribution of training data and input data in the feature space of the middle layer of the DNN, the system can tell the operator whether the prediction is reliable or not.  
The second method is to add a step to solve the forward problem, such as FWI. After prediction of the sound speed distribution with a DNN for solving the inverse problem, we acquire the observed signal for the predicted sound speed distribution. By comparing this observed signal with that of the original input, the validity of the predicted sound speed distribution can be assessed. The forward problem can be computed by numerical simulation, but real-time performance will be an obstacle, so a new DNN for solving the forward problem should be prepared.  
False structures, such as those seen in Figure 5, can be detected by the methods described above. Investigating these methods can improve the reliability of imaging.
# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 2 Materials and Methods
### Statistical Parameters for Accuracy Assessment  
Datasets are randomly subdivided into two parts: 70% of the database for model calibration and 30% for validation. The training data are used to calculate the different parameters to be estimated in the empirical and semiempirical models.  
The _Bias_, root mean square error (\(RMSE\)) and [PERSON]'s correlation (\(R\)) are considered to estimate the precision of the models.  
\[\textit{Bias}=P_{i}^{estimated}-P_{i}^{measured} \tag{8}\]\[RMSE=\sqrt{\frac{1}{N}\,\sum_{i=1}^{N}\left(P_{i}^{estimated}\,-P_{i}^{measured} \right)^{2}} \tag{9}\]  
where \(N\) is the number of data samples, \(P_{i}^{estimated}\) is the estimated value of sample \(i\), and \(P_{i}^{measured}\) is the measured value of sample \(i\).  
\[R=\frac{\sum_{i=1}^{N}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{i= 1}^{N}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{N}(y_{i}-\overline{y})^{2}}} \tag{10}\]  
where \(x_{i}\) and \(y_{i}\) are individual samples taken at points indexed with the variable \(i\).
# Evolution of System Embedded Optical Interconnect in Sub-Top-of-Rack Data Center Systems
## 2 Midboard Optical Transceivers  
The main advantage of midboard optical transceivers over more conventional card edge pluggable transceivers, such as the Quad Small Form-Factor Pluggable (QSFP) format [9], is that they can be placed close to the electronic signal source, such as a CPU, a Serial Attached SCSI expander switch chip or an Ethernet switch chip. This substantially reduces the electronic transmission line lengths between signal source and optical transceiver compared to edge pluggable transceivers. The resulting reduced signal integrity degradation enables energy savings at the port of the signal source by limiting or eliminating altogether the need for signal conditioning functions such as pre-emphasis or signal equalization. Furthermore, the reduced signal attenuation will allow the amplitude of the differential signal generated at the signal source to be reduced accordingly, and thus the power consumed by the signal port is also reduced. Depending on the type of signal source and the number of high-speed signal ports, this can result in a reduction in power consumption by as much as half in the signal source chip when the optical module is placed within 5 cm of the signal source chip [10].
## 4 Experiments and Results
### Parametric Analysis  
Here, we analyze the effect of four parameters on the experimental results. They are the iterations, the number of atoms, and the weight factors \(\lambda_{1}\) and \(\lambda_{2}\). The evaluation is mainly carried out from the perspective of recognition accuracy.
## 3 TXOP PSM-Based Mechanism Using Burst Transmissions
An operation example of the TXOP PSM-based mechanism is shown in Figure 1. STA\({}_{1}\) and the AP contend for access to the channel to send various data frames to each other. After a DIFS period and a random backoff time, first STA\({}_{1}\) and then the AP get access to the channel. Either STA\({}_{1}\) or the AP, i.e., the source STA, initiates an RTS/CTS handshake with the AP or STA\({}_{1}\) respectively, i.e., the destination STA, and then performs a burst transmission of various data frames. Meanwhile, other STAs, i.e., the listening STAs, set their NAVs to the duration of the burst transmission taken from the duration field of the RTS frame and go to sleep. They wake up when their NAVs expire at the end of the burst transmission. It is worth noting that the operation of the TXOP PSM-based mechanism can also support frame aggregation and block ACK in a similar way to how the TXOP PSM-based mechanism operates using burst transmissions.
## 2 Background and Related Work
### Security for SOAs in Medical Field  
[PERSON] et al. [6] present a survey on security threats for the IoMT. Based on a categorization of existing and potential cybersecurity threats, the authors also provide a categorization of security countermeasures. While this study focuses on medical devices, they point out that IoMT is based on, and therefore strongly related to, general IoT technology. The proposed system architecture of IoMT-enabled healthcare systems uses a user's terminal device such as a smartphone as a gateway for receiving and forwarding patient data over the Internet to a cloud service. Nevertheless, this approach does not directly include medical devices for hospitals such as OR table or cardiopulmonary machines and is thus focused on wearable or implantable medical devices.  
[PERSON] et al. [38] motivate a service-oriented approach by focusing on security for IoMT and provide a review of studies in this field. They emphasize that in scientific research, SOA is considered as one of the best solutions for IoT and thus consequently for IoMT. Furthermore, the authors conclude that in almost all surveyed articles, the need for more research on security for SOA in this field is stressed and that an adequate solution has not been found yet.  
[PERSON] et al. [39] present a concept based on formal interface descriptions to improve the security of medical device interconnection in an SOA. Therefore, they propose to automatically generate monitors from described interfaces that check the correct usage of an interface at runtime. An example for this is the monitoring of allowed value ranges.  
[PERSON] et al. [40] focus on challenges in securing medical device networks based on the ICE standard, which is an SOA-based middleware (see Section 2.2). Furthermore, they present a comprehensive attack and adversary model for networked medical devices and discuss the associated challenges for security measures.  
Although SOA opens up manufacturer-independent interoperability, there is a lack of concepts for service-oriented communication in the medical field to secure an entire network of medical devices as in the OR. To our awareness, current solutions consider either only the interface of a device or the communication between different devices directly. A more holistic architectural approach is a gap we aim to fill here.
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
## 3 Illustrative Example
### Constrained Control
\[U_{min}=0;\ U_{max}=2.05 \tag{73}\]  
The sets of time constants considered are then given as  
\[T_{c}=\{0.02,0.06\};\ T_{f}=\{T_{c},5T_{c},10T_{c}\} \tag{74}\]  
The corresponding controllers will be denoted as ARC0-1, ARC0-5, ARC0-10, and DOB0-1, DOB0-5, and DOB0-10. For the selected values (74), together with the control constraints (73), the set points are limited in the interval \(w\in[0.5,1]\). The shape-related performance measures in Figure 15 show that the requirement for ideal shapes of the input and output signals of the process leads to more constrained responses up to the need to use \(T_{f}=10T_{c}\). This finding complements the analysis of disturbance responses presented in [5]. It showed that the increased time constant \(T_{f}\) in saturated control leads to non-overshooting ARC0 responses while ensuring reconstruction of the disturbance around the steady state. The control constraints prolong the initial saturated phase of the transient responses. Therefore, the time constant \(T_{f}\) must be increased based on the increase in the length of the saturation phase compared to the stabilizing controller without constraints. The value of \(T_{c}\) no longer describes the time constant of the constrained responses in the closed loop and must be increased to a more realistic value. Figure 14: Setpoint step responses of one-tank hydro system with a variable cross-section (63) with constrained 2 DoF gain-scheduling ARC0 and DOB0 controllers, pre-filters (44) and (51) and control signal constraints (73); \(T_{c}=0.02\) (left); and \(T_{c}=0.06\) (right); \(T_{f}=T_{c}\), \(T_{f}=5T_{c}\) and \(T_{c}=10T_{f}\). If you work with a sufficiently large value \(T_{f}\), the homogeneity of the shape achieved in step responses also applies to situations where the control does not have saturation. Again, it is obvious that the simplified ARC0 cannot replace the more complex DOB0 for all controller parameter settings, but there are also settings where both types are almost equivalent. Such comparisons are of immense importance for explaining the historical status of both types of controller and for other model-based solutions.
## 2 Materials and Methods
### RT-qPCR and ELISA  
hG-MSCs were seeded onto each type of surface at an initial density of 3 \(\times\) 10\({}^{4}\)/well in 200 \(\upmu\)L of DMEM. After 3, 7, and 14 days of incubation, hG-MSCs were lysed and mRNA was transcribed into cDNA using the following TaqMan gene expression assays (Applied Biosystems, Foster City, CA, USA): COL1A1 (Hs00164004_m1), VEGF-A (Hs00900055_m1), TGF-\(\upbeta\)1 (Hs00998133_m1), ACTA2 (Hs00909449_m1), FAK (Hs00169444_m1), ITG-\(\upbeta\)1 (Hs01127536_m1), FGF-2 (Hs00266645_m1), IL-8 (Hs00174103_m1), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH, Hs99999905_m1). Reverse transcription reaction was performed with the following conditions: 37 \({}^{\circ}\)C for 1 h and 95 \({}^{\circ}\)C for 5 min using Primus 96 advanced thermocycler (Peq/Lab/VWR, Darmstadt, Germany). Quantitative analysis of the gene expression was carried out by using a QuantStudio 3 device (Applied Biosystems, Foster City, CA, USA). All the samples were heated at 95 \({}^{\circ}\)C for 10 min and then followed with 50 cycles, with the denaturation of each process under 95 \({}^{\circ}\)C for 15 s and annealing/extension at 60 \({}^{\circ}\)C for 1 min. The expression results of the target gene were generated based on 2\({}^{-\Delta\Delta\text{CI}}\) calculations with a control sample based on a previous study [38]. GAPDH was used as a reference gene. hG-MSCs seeded on TCP and incubated for 3 days served as a control.  
The levels of VEGF-A, TGF-81, and IL-8 proteins in the conditioned media were determined after 3, 7, and 14 days of incubation using a human VEGF-A kit (Cat. Nr. BMS277-2, ThermoFisher Scientific, Waltham, MA, USA), a human TGF-81 kit (Cat. Nr. CEK1332, Cohesion Biosciences, London, UK), and a human IL-8 ELISA kit (Cat. Nr. 88-8086, ThermoFisher Scientific, Waltham, MA, USA), respectively, according to the manufacturer's protocols, and using a Synergy HTX multi-mode reader (BioTek Instruments, Winooski, VT, USA).
## 6 Performance Analysis
### Battery Life of Sensor Node  
The calculating battery life of the sensor node is a necessary element in the hardware prototype. Here, we assumed the processing power of 15 mW for 5 ms during sensing value and power consumption at the sleep is 10 uW. SF 7 with 125 kHz BW and 2 dB Tx power are considered due to its optimal energy consumption during transmission. We subdivided the evaluation of battery life into three scenarios based on payload. Figure 12 presents the distinct battery life scenarios, where the battery life is represented as a time to live (TTL). In our study, we need to sense only the level measurement data, so, here, we considered a payload of 22 bytes, 25 bytes, and 28 bytes for analyzing the battery life at distinct periodicity. Here, periodicity is considered in terms of minutes likely, 15 min, 30 min, 45 min, and 60 min. The battery life of the sensor node is about months, and three distinct battery capacities are considered likely 260 mAh, 1000 mAh, and 2000 mAh Li-ion batteries. If the periodicity of sending the data from the sensor to the receiver end is 60 min, the battery life is long, and, in the case where the periodicity of sending the data of sensor  
Figure 11: Link budget at distinct transmitter power (Tx).  
is 15 min, the battery life is shorter. It is also observed that the battery life is the same for the (i) 260 mAh and 1000 mAh at a payload of 22 bytes and 25 bytes (ii) 1000 mAh at a payload of 22 bytes and 25 bytes.
## 2 Materials and Methods
### Tests on a Real Building
#### 2.1.3 Building Vibration Measurements  
In the process of FEM modelling of the building, numerous simplifications are used that affect the reliability of the results of calculations. Vibration measurements were used to verify the correctness of the numerical model of the building. Tests were carried out on the structure that consisted of forcing vibrations on ceilings and measuring vertical accelerations. A total of 54 measurements were performed at various locations in the building, which allowed for identifying floor vibration frequencies. Verification of the correctness of the model of the building was described in [49].
## 4 Experimental Design and Setup
### Datasets
#### 4.1.1 Dependent Variable: Change-Prone  
As already mentioned, Change_Prone is used in this study as dependent variable to reflect that a change (insertion, removal, or edit) has been performed on a given class for a version of the system. Table 5 summarizes the number of true and false values for the variable, which demonstrates the imbalance between the two values. We categorized this difference between values into small, medium, and large according to the percentage of true values in our datasets, where values less than 1% correspond to a large difference, values less than 2% are a medium difference, and values out of these ranges are a small difference. However, it should be pointed out that all the percentages of true values in Table 4 are very small, ranging from 0.69% to 5.28%.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **MIN** & **MAX** & **Standard Deviation** & **Average** & **Median** \\ \hline antlr4 & 0 & 8 & 0.70 & 0.13 & 0 \\ \hline junit & 0 & 3 & 0.21 & 0.02 & 0 \\ \hline MapDB & 0 & 2 & 0.13 & 0.01 & 0 \\ \hline mcMMO & 0 & 2 & 0.18 & 0.02 & 0 \\ \hline mct & 0 & 5 & 0.18 & 0.01 & 0 \\ \hline oryx & 0 & 6 & 0.35 & 0.05 & 0 \\ \hline titan & 0 & 3 & 0.13 & 0.01 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of Refact_Sum.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset Name** & **\# Instances** & **\# True Value** & **\% True Value** & **\# False Value** & **\% False Value** & **Category of Difference between True and False** \\ \hline antlr4 & 436 & 23 & 5.28\% & 413 & 94.72\% & Small \\ \hline junit & 657 & 9 & 1.37\% & 648 & 98.63\% & Medium \\ \hline MapDB & 439 & 4 & 0.91\% & 435 & 99.09\% & Large \\ \hline mcMMO & 301 & 4 & 1.33\% & 297 & 98.67\% & Medium \\ \hline mct & 2162 & 15 & 0.69\% & 2147 & 99.31\% & Large \\ \hline oryx & 536 & 15 & 2.80\% & 521 & 97.20\% & Small \\ \hline titan & 1486 & 13 & 0.87\% & 1473 & 99.13\% & Large \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of true and false values in the change-proneness attribute.
## 3 Results and Discussion
### Active Control Analysis
#### 3.2.2 Establishment of Control Rules  
Firstly, the relationship between amplitude ratio and control parameters under different excitation frequencies, is obtained through numerical experiments, as shown in Figure 14.  
\[\left\{\begin{array}{l}\mathcal{g}_{P(10\text{rad}/\text{s})}=1.253\times 10^{ 4}\alpha+1.219\times 10^{4}\\ \mathcal{g}_{P(20\text{rad}/\text{s})}=6261\alpha+6141\\ \mathcal{g}_{P(30\text{rad}/\text{s})}=4204\alpha+3877\end{array}\right. \tag{26}\]  
\[\left\{\begin{array}{l}\mathcal{g}_{I(10\text{rad}/\text{s})}=1.251\times 1 0^{5}\alpha+9.505\times 10^{4}\\ \mathcal{g}_{I(20\text{rad}/\text{s})}=1.252\times 10^{5}\alpha+1.252\times 1 0^{5}\\ \mathcal{g}_{I(30\text{rad}/\text{s})}=1.255\times 10^{5}\alpha+1.755\times 1 0^{5}\end{array}\right. \tag{27}\]  
Secondly, the relationship between the excitation amplitude ratio and the peak value of the output light angle response is analyzed, as shown in Figure 15a.  
Thirdly, combined with the previous two steps, the corresponding relationship between the peak value of output light angle response and velocity feedback gain and displacement feedback gain under the excitation of 10 rad/s, 20 rad/s, and 30 rad/s is established, and the accurate active control rules are constructed, as shown in Figure 15b.c.  
According to the external excitation frequency and the peak value of output light angle response, the corresponding control parameters in the active control rules are automatically queried. The algorithm calculates the driving actuator to realize the active control of the Optomechanical coupling system.  
Figure 14: The relationship between control parameters and amplitude ratio: (**a**) The relationship between velocity feedback gain and amplitude ratio; (**b**) the relationship between displacement feedback gain and amplitude ratio. By fitting the experimental data, the relationship between control parameters and the amplitude ratio is obtained as follows.
# Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters
## 1 Introduction
Moreover, several factors are linked to household water consumption, as studies conducted over the past decade indicate that the number of residents in a household influences water consumption [28; 29; 30; 31]. Several studies have specifically examined the effects of calendar factors on the water consumption of households [32; 33; 34; 35; 36]. The study city managed water consumption by reading water meters at each household every two months. To obtain more updates on residential water demand, the district with the provision of smart water metering for electricity has installed smart water meters at all households; hence, the metering values are more updated than the 2-month data. This study analyzes variations in water consumption through households, consumers, and periods in which smart water meters are used and influence water consumption patterns. In addition, it provides useful information for water utilities, and they can be more understandable in terms of residential water consumption patterns. This research focuses on the impact of water consumption within single households, with a particular focus on how smart water meters are integrated into the daily lives of households, how residents interact with these meters, and their impact on water consumption services at the district scale. Therefore, the integration of smart water meter data into distribution systems provides valuable evidence for advancing digital water technologies and improving consumption management. In addition, these findings may contribute to enhancing resource management by distributing schedules around peak household consumption to support water supply planning. The primary objective of this study is to evaluate water consumption on the basis of smart water meter data to understand household water consumption behavior.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
###### Abstract
This research was conducted in 2020 in the (Iranian) region and, to achieve the purpose of researching data and images of time series, Sentinel 1 and Sentinel 2 were used to answer the following questions with Tarom help of the obtained results: (i) how can acceptable classification accuracy be achieved given the changes in plant growth during the growing season? (ii) What is the contribution of each data set used in this study in estimating the research objectives? (iii) How can the accuracy of the information be measured for acceptable classification?