esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we have proposed a CNN-based model \"Demod-CNN\" OFDM demodulation system for IRS-aided multiuser MIMO communication. In the proposed model, we designed the input layer to receive the OFDM signal by separating real and imaginary parts of the symbol. It is then classified to recognize the transmitted bits by the classification layer. The simulation results provide that the proposed Demod-CNN can outperform the conventional demodulation system. In the future, we want to expand our model for multi-IRS systems to achieve massive MIMO communication.  
Figure 5: BER performance comparison of the proposed Demod-CNN and conventional technique.  
Figure 6: SER performance comparison of the proposed Demod-CNN and conventional technique.  
**Author Contributions:** Conceptualization, M.A.S.S. and M.H.R.; methodology, M.A.S.S.; software, M.A.S.S. and M.H.R.; validation, M.A.S.S. and M.H.R.; formal analysis, M.A.S.S. and M.H.R.; investigation, M.A.S.S. and M.H.R.; resources, H.-K.S.; data curation, M.A.S.S. and M.H.R.; writing--original draft preparation, M.A.S.S. and M.H.R.; writing--review and editing, M.A.S.S., M.H.R., and H.-K.S.; visualization, M.A.S.S. and M.H.R.; supervision, H.-K.S.; project administration, H.-K.S.; and funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2018-0-01423), supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03038540).  
**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.
###### Abstract  
With the increase in engine power density, the temperature and stress carried by the cylinder head during operation also increase. The thermal engine fatigue life prediction of the cylinder head needs to consider accurate and reasonable creep-constitutive models and parameters. In view of the wide range of temperature and stress working conditions of the compacted graphite cast iron (CGI) cylinder head, the creep test of CGI under the conditions of temperature 450-550 \({}^{\circ}\)C and stress 100-300 MPa was carried out, and CGI under the conditions of wide temperature and stress was proposed to characterize a creep-constitutive model for minimum creep rate. Research indicated that under wide temperature and stress conditions, CGI was more prone to creep damage than under low load, and creep deformation was dominated by grain boundary sliding (GBS), intragranular dislocation glide (IDG), and dislocation climb (IDC). With the deformation mechanism-based true stress (DMTs) creep model, combined with the multiobjective optimization method, a creep-constitutive model of CGI was constructed, and 73% of the predicted values of the model were within twice the error range. Compared with the linear regression method, the multiobjective optimization method could still fit the accurate model parameters in the case of small samples.  
compacted graphite cast iron; wide range of temperature and stress; creep test; creep-constitutive model; minimum creep rate; multiobjective optimization Article  
Research on Creep Test of Compacted Graphite Cast Iron and Parameter Identification of Constitutive Model and Kriging
# A Novel Approach for the Definition of an Integrated Visual Quality Index for Residential Buildings
## 2 State of the Art
It could be claimed that the standards are more interested in visibility rather than visual comfort and residential buildings remain largely unregulated in this field. There is hope that the new European standard EN 17037 on daylighting could represent a shift in this trend, and in the meantime the lack is partially covered by quality certifications that give interesting suggestions for enhancing visual quality. The LEED v4 protocol [33], for instance, favors daylight access and view out. The WEEL building standard [34] is even more specific in this field and takes into consideration many visual quality related characteristics like color quality, circadian lighting design, and dimming controls. In any case, current lighting design still favors the rule of thumb or at least uses very simple methods based on static parameters like daylight factor [35]. A relatively new tool for designers is a building simulation that was created to enhance energy efficiency but nowadays can be used for predicting a large number of parameters related to human comfort. In particular, the simulation of day lit spaces has evolved both in terms of the method and in the calculation of procedures [36]. It has abandoned static evaluations towards dynamic climate-based systems by refining the predictions in terms of illuminance [26; 37], glare [38; 39], and building-user interaction [40]. For future development there is a need for uniformity of the accuracy of the inputs and of the choice of the metrics. [PERSON] et al. [37] address this topic and suggest a set of metrics considering either constancy or spatial uniformity of different comfort aspects. Given these improvements [PERSON] et al. [41] foresee the possibility of linking the field of building performance simulation with IEQ research. In this trend, IEQ models could be used not only for comparison between different environments but also for predicting user satisfaction and for evaluating the sustainability of a project. The latter is often calculated using key performance indicators (KPIs), which are drivers for the assessment of buildings under different categories, including the environmental field [42]. An interesting approach to the assessment of sustainability of a residential building is proposed by [PERSON] et al. [43], who insert indicators of indoor comfort among the environmental KPIs, highlighting the demand for a rational and robust method for IEQ evaluation. In this complex picture, an integrated VQI for residential buildings is missing. In fact, illuminance level is still largely used but it is not a good approximation of visual quality. Residential buildings are largely neglected. Moreover, simulation is an instrument than should be better linked to IEQ models. On this basis, in this paper a broader and robust definition of visual quality for residential buildings is proposed which may be objectively measured through an integrated index useful in the design phase as well as in the building evaluation through on-site monitoring.
## 3 Experimental Results and Discussion
### Results of the Channel-Based Evaluation Procedure
#### 3.1.2 Results of the Channel Reduction Procedure  
In this section, we present the results obtained for our proposed approach using the channel reduction procedure described in Section 2.7. Table 2 shows the average classification accuracies computed over all subjects for each of the 14 iterations of the channel reduction procedure. Specifically, at the first iteration, eliminating the channel \(FC_{6}\) provides an average classification accuracy of 84.33%, which is higher than the average classification accuracies obtained when any of the other 13 EEG channels is eliminated. In the second iteration, the additional elimination of the channel \(T_{8}\) provides an average classification accuracy of 83.76%, which is higher than the average classification accuracies obtained when any of the remaining 12 EEG channels is eliminated. Moreover, in the subsequent iterations of the channel reduction procedure, the average classification accuracy is drastically decreasing after each iteration. Finally, at the last iteration, an average classification accuracy of 65.9% is achieved using the channel \(F_{7}\).  
Figure 4 indicates that the average classification accuracy obtained using all the EEG channels, which is equal to 84.6%, is higher than the average classification accuracies obtained after eliminating any of the EEG channels, where these accuracies are reported in Table 2. This can be attributed to several factors, including (1) the volume conductor effect [36], which describes the fact that the electrical potentials generated at a particular small region of the brain are spatially propagated to different regions of the brain. Consequently, these electrical potentials are recorded by the sparsely distributed electrodes on the scalp [6; 23; 36; 37]. (2) The pain perception mechanism involves the activation of several regions of the brain, such as the frontal, prefrontal, temporal, parietal, and occipital regions [14; 38; 39; 40]. Therefore, eliminating any EEG channel implies that the brain activities produced within the region covered by the eliminated channel might not be captured during the analysis. This can justify the observed decrease in the classification accuracies obtained during the channel reduction evaluation procedure. In light of the results presented in this section, we have utilized all the EEG channels to perform the feature-based evaluation procedure, which is presented in Section 3.2, and the performance comparison with the other previous approaches, which is described in Section 3.3.
## 5 Performance monitoring  
Elements of performance monitoring may or may not be available depending on both the application and the network infrastructure. Essentially this process involves observing the network to determine the performance in terms of evaluation metrics. If these metrics are available they can be fed back into the solution data database for refined modelling. Should the  
Figure 2: Functional block diagram showing the component relationships for the framework.  
deployment involve debugging infrastructure or maintain node time synchronisation over the network many of these metrics such as packet reliability, latency, energy efficiency and route stability can be monitored.
## 6 Conclusions  
This study presented Sentinel-1 multi-temporal data for mapping smallholder maize farms' spatial distribution and estimated production areas. The two-stage image fusion approach was adopted. The SVM and Xgboost machine learning algorithms were applied. The results revealed that most smallholder farms in our study area are distributed in the south eastern part of Makhuduthamaga. The algorithms provided comparable statistical evaluation results. However, [PERSON]'s test showed that the results from the two algorithms were statistically different from each other. The SVM and Xgboost algorithms estimated maize production areas to be 7073.558 \(\pm\) 0.01 ha and 7303.32 \(\pm\) 0.180 ha, respectively, for the region. The classified areas for selected farms compared favorably with the measured areas in the field and the SVM classifier had a better fit (R = 0.89) in comparison with the Xgboost algorithm (R = 0.84). The SVM algorithm seems to have generally performed better than the Xgboost algorithm. The use of multi-temporal Sentinel-1 with a two-stage image fusion approach proved to be effective in mapping smallholder farms. This framework can be used to support the SDGs and to provide spatial agricultural information to inform policy design and implementation by local government. Different seasons and different crop types should be tested using this approach, including extraction of phase and amplitude data from multi-temporal Sentinel-1 data. Multi-sensor data fusion should be explored to improve the mapping of smallholder farms in the future.  
[PERSON]-[PERSON] conceptualized and developed the original draft of the manuscript. [PERSON] revised the manuscript, supervised, and provided financial resources for the project. [PERSON] was involved in data analysis and reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript. The authors would like to thank the three anonymous reviewers for their valuable comments that improved the original manuscript. This research was funded by the Agricultural Research Council, University of Pretoria and National Research Foundation (grant number: SFH170524232697 and TTK200221506319). The authors declare no conflict of interest.
## 3 The Smart Home Setup
### Data-Driven Orientation  
As highlighted in the Introduction, knowledge-driven approaches encounter significant challenges due to the necessity of constructing complex libraries of Activities of Daily Living (ADLs) by human experts, rendering them intricate to implement in larger-scale projects [16]. Moreover, these models often lack the flexibility to adapt to the diverse profiles and behaviors of individual residents [10]. Recognizing these limitations, a substantial portion of the research community, including our team, has shifted towards data-driven approaches [21]. However, in our specific context, the utilization of existing machine learning algorithms poses challenges. Straightforward application is not feasible, and even the adoption of unsupervised methods, which hold considerable promise for our objectives, presents significant hurdles. Furthermore, the deployment of smart home environments generates vast quantities of data. For instance, within the LIARA smart home, binary sensors alone produce approximately one million raw data entries per day, and when RFID tracking is incorporated, this figure increases by up to 5 million, depending on the number of objects monitored.  
Figure 1: The smart home prototype used to collect the datasets. Unused sensors are hidden. Several electromagnetic contacts were omitted for clarity.  
While numerous methodologies exist for handling large datasets, the unique characteristics of smart home data present distinct challenges. The majority of collected data often lack meaningful events, as most of the time, the home environment remains inactive. Even during periods of activity, only a fraction of the data capture relevant information pertaining to ADLs. Thus, the challenge lies in identifying and extracting the pertinent knowledge embedded within this vast and largely inconsequential dataset. Consequently, the raw data corresponding to ADLs are similar to the raw data corresponding to idle time (or no activity ongoing). This property makes it difficult to automatically extract interesting frequent patterns, since such patterns are, in fact, not frequent at all. Researchers often partially address this issue by transforming the data into an event-based representation, storing only the significant changes in states [8]. The main advantage of the method is that the dataset is easy to understand and process, and the risk of losing information is limited for simple sensors (e.g., binary sensors). However, the story unfolds differently for analog sensors or other complex technologies such as ultrasound, laser range scanner, and RFID. Defining events is harder since the value of these sensors varies with every reading. In that case, the threshold must be set, and it increases the risk of losing information [16]. The activity recognition method presented in this paper uses only RFID and the binary sensors of the smart home (electromagnetic contacts, passive infrared, tactile mat, light detection, and flow sensors). RFID is therefore the only technology causing real challenges in the data collection phase. Figure 2 shows a sample of the binary sensors' dataset.
## 3 Methodology
### Dual-Branch Low-Band Speech Enhancement Network LB-Net
#### 3.1.1 Encoder and Decoder  
The network architecture of MLB-Net is shown in Figure 5. The encoder is composed of a DenseNet [23] sandwiched between two convolution layers. The first convolution layer  
Figure 4: The architecture of TS-CGANet.  
is used to extend the channel of input characteristics to 64, and the second convolution layer is used for down-sampling to reduce complexity. DenseNet contains four dense connected convolution blocks, and the dilation factors of each block are set to {1,2,4,8}, which can effectively expand the receptive field and extract features at different levels. After all the convolution layers are in the encoder, there is an instance normalization [24] and a parameter ReLU (PReLU) activation [25]. The feature map of encoder output is \(D\in\mathbb{R}^{\mathbb{R}\times T\times F\prime\times C}\), where \(F^{\prime}=F/2\) and \(C=64\). The decoder consists of a dilated DenseNet and then uses a subpixel convolution layer to up-sample the frequency dimension back to \(F\). Finally, a convolution layer is used to obtain the final output.  
The network architecture of CLB-Net is shown in Figure 6. The encoder and decoder structure of CLB-Net is the same as that of MLB-Net. Since the input of CLB-Net is complex, it is necessary to replace the convolution in CLB-Net with the corresponding complex convolution. Each complex convolution contains two convolution layers. For the input \(D=[D_{r};D_{i}]\), the complex convolution calculation formulas are as follows:  
\[D_{r}^{out}=D_{r}W_{r}-D_{i}W_{i} \tag{6}\]  
\[D_{i}^{out}=D_{i}W_{r}+D_{r}W_{i} \tag{7}\]  
\[D^{out}=\left[D_{r}^{out};D_{i}^{out}\right] \tag{8}\]  
where \(W_{r}\) and \(W_{i}\) represent the learnable parameter matrixes of the convolution layers. \(D^{out}\) is the feature mapping of the output of the complex convolution. The feature map of encoder output is \(D^{enc}\in\mathbb{R}^{\mathbb{R}\times T\times F\prime\times C\times 2}\). The last dimension of the feature mapping represents the real part and the imaginary part.
# An Integrated Software for Computing Mechanical Properties of Crystalline Material by Means of XRD
## 3 Results and Discussion
### Determination of Crystalline Grain Size  
The well-known Scherrer formula below is used to calculate a grain size \(t\)[1]:  
\[t=\frac{K\lambda}{B\cos\theta_{B}} \tag{32}\]  
where \(K\) is a constant referring to the cell geometry of lattice, where \(K\) = 0.94 for the cubic lattice; \(\lambda\) is X-ray wave length; \(B\) is the half-width of diffraction line; and \(\theta_{\text{B}}\) is the Bragg angle of a peak position.  
The experimental conditions for determining grain size of commercial synthetic zeolite ZSM-5 used in environment treatment are listed in Table 8. Table 9 represents the grain sizes of zeolite crystals, determined from XRD and observed using SEM techniques. It is shown that the XRD technique gives accurate results, in comparison to the SEM method.  
\begin{table}
\begin{tabular}{c c} \hline
**Diffraction Method** & \(\mathbf{\Omega}\)**-Type, Fixed-\(\mathbf{\psi}\) and \(\mathbf{\psi}_{\mathbf{0}}\)** \\ \hline Scanning range & 40–96\({}^{\circ}\) \\ Step size & 0.03\({}^{\circ}\) \\ Preset time & 2.5 s \\ X-ray characteristic & CuK\(\alpha\) \\ Filter & Ni foil \\ Voltage and current & 20 kV and 10 mA \\ \hline \end{tabular}
\end{table}
Table 6: XRD conditions in thickness measurement of nickel-coating layer.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Samples** & **MS** & **EC** & **XRD** \\ \hline
1 & 2.00 & 1.94 \(\pm\) 0.13 & 2.02 \(\pm\) 0.49 \\
2 & 2.25 & 2.38 \(\pm\) 0.17 & 2.34 \(\pm\) 0.50 \\
3 & 2.75 & 2.63 \(\pm\) 0.22 & 2.99 \(\pm\) 0.49 \\
4 & 3.25 & 3.14 \(\pm\) 0.35 & 2.96 \(\pm\) 0.45 \\
5 & 3.50 & 3.35 \(\pm\) 0.21 & 3.40 \(\pm\) 0.43 \\ \hline \end{tabular}
\end{table}
Table 7: Thickness for nickel-plating layers with MS, ED, and XRD techniques (\(\upmu\)m).  
\begin{table}
\begin{tabular}{c c} \hline
**Diffraction Method** & \(\mathbf{\Omega}\)**-Type, Fixed-\(\mathbf{\psi}\)** \\ \hline Scanning range & 10–50\({}^{\circ}\) \\ Step size & 0.03\({}^{\circ}\) \\ Preset time & 3 s \\ Voltage and current & 40 kV and 20 mA \\ \hline \end{tabular}
\end{table}
Table 8: XRD conditions for thickness measurement of nickel-plating layer.  
\begin{table}
\begin{tabular}{c c} \hline
**Specimens** & **XRD** & **SEM** \\ \hline
1 & 3.06 \(\pm\) 0.09 & 2.5 \\
2 & 3.64 \(\pm\) 0.12 & 1 to 8 \\ \hline \end{tabular}
\end{table}
Table 9: Grain size of zeolite crystals using XRD and SEM techniques (in \(\upmu\)m).
## 5 Implementation
### Infrastructure  
Decentralized data monetization implementation is deployed in the cloud infrastructure represented in Figure 9. As performed earlier for data certification Ethereum-based use-case, we utilize the same TAS cloud infrastructure. The Substrate smart contract pallet containing data monetization logic and the developed middleware are packaged as docker containers. These containers are then deployed on the cloud infrastructure using the Kubernetes orchestration system as pods with necessary extensible data volume, processing, and memory allocations. The client deployed on a separate pod submits a transaction representing Vehicle_OEM or RADAR_OEM to a round-robin-based Nginx load-balancer, which is then redistributed uniformly to the blockchain nodes for validation and execution. The health metrics of the blockchain nodes regarding processor and memory load are stored in the form of the time-series database Prometheus. It is persisted at a pre-determined frequency, then represented graphically in the form of Grafana dashboards deployed in separate pods. The middleware containers of RADAR_OEM and Vehicle_OEM are deployed running on an Apache Tomcat application server. All the internal networking and forwarding among the pods is managed at the Kubernetes level, along with service discovery and load balancing.  
Figure 8: OEM Middleware Data Monetization UML Model.
# Autonomous Manipulator of a Mobile Robot Based on a Vision System
## 1 Introduction
Another system incorporated into the unmanned ground vehicle (UGV) is the autonomous manipulator control system. A vehicle with a total weight of 50 kg was designed, constructed, and programmed by the IMPULS student research group affiliated with the Department of Automation and Robotics of the Kielce University of Technology in Poland. The four-wheeled vehicle is equipped with a specifically developed six-degree-of-freedom manipulator and a wireless communication and control system. The vehicle navigation and manipulator operation are controlled by STM microprocessors programmed, for instance, to solve the inverse kinematics of the manipulator. The IMPULS robot project involved (a) developing vision system algorithms to determine the position of elements on the operator panel in relation to the end-effector, (b) installing the vision system hardware, i.e., a camera and a minicomputer, (c) analyzing the manipulator positioning accuracy, (d) assessing the marker recognition accuracy, and (e) determining the accuracy of the autonomous positioning of the end-effector relative to the markers. Section 2 of this article describes the design and control of the manipulator; it also includes the algorithm for the vision system. Section 3 provides the research results, which are discussed in Section 4. The last section outlines the key conclusions drawn, as well as proposals for the further development of the robotic system. The improved manipulator is expected to be able to work alongside humans as a collaborative robot, performing simple tasks such as the preparation of beverages in the catering services sector or performing pick-and-place operations along with object orientation detection in the manufacturing sector.