esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Assembly Technology of Dense Small Screws for Flat Panel Parts
## 3 Experiment  
In this study, a specialized experimental platform was constructed to carry out the necessary investigations, as illustrated in Figure 20. The essential equipment procured and their respective characteristics are detailed as follows:  
* Robotic Arm: Repeated positioning accuracy of 0.02 mm and maximum payload capacity of 9 kg.
* CMOS Camera: 10 million pixels, resolution of 3664 \(\times\) 2748, pixel size of 1.67 \(\upmu\)m, and frame rate of 8 FPS.
* Laser Displacement Sensor: Accuracy of 0.07 mm.
* Aluminum Experimental Board: Dimensions of 500 \(\times\) 200 mm, with threaded holes ranging from M2-M5 sizes uniformly distributed.  
The practicality and applicability of the proposed methodology were substantiated via a successful assembly of screws on the aforementioned experimental board, as demonstrated in Figure 21.  
Figure 19: Fréchet distance between experimental and template curves.  
Figure 20: Automatic screw assembly experimental platform.  
First, the camera is positioned above the parts to be assembled and it captures images and identifies the threaded holes. Subsequently, the affine transformation matrix is applied to calculate the displacement (unit: mm) of the camera in the workpiece coordinate system, as shown in Figure 22.  
Approximate positioning is implemented according to the solution distance. After completing the movement of the manipulator, photographs are captured to measure the diameter of the threaded hole. According to the measurement results, the screws are auctioned by the screw-arranging machine. Then, the screws are set for precise positioning, as shown in Figure 23.  
Figure 21: Test plate assembly.  
Figure 23: Precise positioning after screw suction.  
Figure 22: Identification and positioning of threaded holes.  
When precise positioning is achieved, the control system sends a signal to guide the offset movement of the manipulator. After the movement is completed, the sliding table cylinder is extended, the screw is aligned with the threaded hole, and the screw-locking device performs the assembly action, as shown in Figure 24.  
After installing a screw, the assembly quality assessment method is implemented to determine whether the screw was successfully locked. If locking is successful, the assembly of the next screw is initiated; otherwise, the screw-locking device reverses the installation of the screw to mark the coordinates of the threaded hole.  
The experimental results are shown in Figure 25. The system can achieve high-precision positioning and assembly for the M2, M3, M3.5, M4, and M5 screws.  
The accuracy of visual positioning is shown in Figure 26. Generally, a visual positioning accuracy of less than 0.05 mm can be achieved after two precise positioning maneuvers, thus satisfying the requirements.  
Figure 24: Screw assembly.  
Figure 25: Assembly effect of countersunk screws.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Experimental Demonstrations
#### 3.2.2 Satellite Rotation Compensation  
The characterization of CAI contrast dependency relative to, independently, platform rotation or the EA's proof-mass rotation was studied in detail. We report here the main results.  
Figure 16: Retrieval error contributors with scale factor and instrument noise contributors, considering a _Case 2_ 1D-hybridized instrument (**a**) and the corresponding LOS projection (**b**).
## 10 Security in WMSN
We foresee that for WMSNs asymmetric cryptography will be the chosen approach--over symmetric cryptography--since it makes a lot easier to solve several security problems related to eavesdropping and compromised nodes, and as mentioned before at least some of the nodes will have the required processing power to perform the signature and verification operations. Watermarking has also been proposed as a way to provide data integrity. Nevertheless, watermarking solutions might be vulnerable to attacks from entities that know how the watermarks are done. In addition, watermarking alone does not solve data authentication. Therefore, we do not foresee watermarking solutions becoming a mainstream approach, but a marginal solution for very specific problems. Moreover, in order to preserve battery and to save bandwidth, many WMSNs will use some sort of data aggregation. We consider that security and aggregation schemes cannot be devised separately. Therefore, new security schemes will have to be both energy-aware and designed in together with the aggregation scheme.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 6 Performance Evaluation  
In this section, we show how our tool maintains coverage and improves network lifetime. We use the following metrics in the evaluation:  
1. The global quality of coverage (QoC) over time shows the overall percentage of area in a floor that is covered by active sensor nodes. In this case, we only take into account rooms that have sensors.
2. The increased lifetime of a network is the ratio of the lifetime with a redundant node algorithm to when all nodes are active. The lifetime is the time until the global QoC cannot satisfy the local coverage threshold.
3. The percentage of active nodes over time and/or when the simulation finished.
4. The percentage of dead nodes over time and/or when the simulation finished.
5. The percentage of disconnected nodes when the simulation finished.  
The results presented in this paper are based on one simulation run each, as we use trace files from the datasets, and so, the sensor samples do not change. The topologies of the networks are also fixed, based on the connectivity calculation using UDGM and DGRM in Section 3.  
We will firstly present the simulation results for the one-room deployment, _i.e._, the data centre room at the Informatics Institute at Federal Fluminense University [20]. The TMON deployment is very dense, and we expect to get a very good result in lifetime improvement. Secondly, we will present the results for multiple-room deployments, _i.e._, the ground floor and first floor of the Nimbus building at Cork Institute of Technology and the second floor and third floor of the Strata Conference building in New York [21]. In these multiple-room deployments, even though the coverage redundancy among the deployed sensor nodes is smaller, we still expect to get improvement in network lifetime as some rooms have more than one sensor node. Finally, we will show the results of lifetime prediction if we use the real energy storage in AA batteries.
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 4 Qualitative Analysis
### Construction Phase
#### 4.3.3 Logistics Management  
Logistics management involves adept planning, coordination, and optimization within supply chain and logistics management contexts. This is achieved through the utilization of integer programming models, optimization algorithms, BIM, VR, and simulation techniques. The primary aim is to ensure the seamless operation of the logistics chain and optimal resource utilization.  
Mathematical models have been used to reduce logistic costs within industrialized construction settings. [PERSON] et al. [74] developed an integer programming model to minimize total logistic costs, encompassing trailer rental, fuel consumption, and labor expenditures. [PERSON] et al. [75] introduced an optimization model focused on minimizing both the transport and on-site storage costs of prefabricated modules. It is noteworthy that the effectiveness of the model is most pronounced in the transport of rectangular modules and may exhibit diminished efficacy for irregularly shaped counterparts.  
The assessment of logistic costs should extend beyond direct economic outlays to consider external costs, including environmental burdens not directly borne by the involved parties. These external costs may encompass greenhouse gas emissions, air pollution, and traffic congestion, among others. However, the accurate estimation of external costs necessitates comprehensive and precise data, such as vehicle or tonne-kilometer metrics. Consequently, [PERSON] et al. [76] devised an integrated impact assessment framework for construction logistics flows, facilitating the evaluation of economic and environmental impacts associated with various logistics solutions.  
Conceptual models and VR systems also play pivotal roles in enhancing component logistic planning and inventory management. [PERSON] et al. [77] proposed a conceptual model that integrated an information-sharing platform, dynamic resource management, and optimization system to ensure on-time coordination among stakeholders. In a similar vein, [PERSON] et al. [78] utilized VR simulation in conjunction with BIM to visualize material flows and inventories, thereby facilitating the identification of materials and components suitable for reintegration into the supply chain at the end of their useful life.  
Furthermore, discrete event simulation can be employed for impact assessments and strategic decision-making in sustainable supply chain management. [PERSON] et al. [79] compared construction durations, costs, and greenhouse gas emission metrics under different supply chain strategies, based on the varying production and delivery capabilities of component suppliers. Meanwhile, [PERSON] et al. [80] developed an intuitionistic fuzzy large group decision-making model for selecting component suppliers. This model considers criterion weights, decision maker weights, and the inherent fuzziness in decision-making, thereby addressing diverse needs for selecting appropriate suppliers.
# Detection of High-Temperature Gas Leaks in Pipelines Using Schlieren Visualization
## 3 Experimental Results and Discussion
Consequently, in distinguishing the differences between actual case to current experiments, the outflow velocity determined by the cross-sectional area of the leakage hole is considered the most critical variable rather than the shape of the leakage hole. To obtain more precise observations of the leaks from the pipe, further considerations can be made regarding the Schlieren optical system and image processing. The optical system utilized in this experiment, the single-mirror off-axis Schlieren system, plays a crucial role. Its simplicity, employing a single reflective mirror, makes it well-suited for monitoring leaks in pipes situated in less accessible locations. The system's capability to detect leaks if the leaking flow has a temperature difference of approximately 10 \({}^{\circ}\)C or more from the surrounding air, provided there are no external heat sources, underscores the significance of the Schlieren optical system in the experiment. Figure 7: Calculation of \(I_{E}\) depending on temperature of leaking air inside the pipe covered by insulation and aluminum jacket with a gap. Suppose pipes are situated in more expansive areas or less accessible locations. In that case, it may be advantageous to consider a background-oriented Schlieren (BOS) system, which does not require mirrors. In contrast to traditional Schlieren optical systems, which position mirrors and imaging devices around the subject, BOS implements Schlieren flow visualization using only image recording from a camera positioned on one side of the subject. The subject is positioned against a background with a random pattern, and flow fields are calculated by extracting x- and y-directional flow motion through brightness changes in background pixels, thereby providing more detailed flow information [11; 12]. Pipes that transport high-temperature fluids, which are prone to leakage, are typically insulated and encased in jackets. In the event of a leak caused by a crack or perforation, the temperature differential between the leaking gas and the surrounding environment is likely to diminish considerably as the gas traverses the insulation and jacket. These factors have the potential to interfere with natural convection on the pipe covering surface due to surrounding heat sources, thereby making it more challenging to ascertain the presence of a leak. To enhance leak sensitivity through Schlieren flow visualization, it may be beneficial to employ flow field calculations using pixel-by-pixel analysis, as applied in BOS image processing. By calculating velocity fields, it may be possible to distinguish between intermittent or random rising flows due to natural convection and consistent flows due to leaks [17]. Additionally, the potential of combining Schlieren flow visualization image processing with artificial intelligence and big data learning is expected to significantly improve accuracy and sensitivity in leak detection and assessment, providing reassurance and confidence in the system's capabilities.
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.7 Different Batch Size \(K\) Analysis  
If the batch size is denoted as \(k\), we select \(K\) samples at each iteration and compute the corresponding parameter adjustment values. The average value of these adjustment values is then utilized as the final adjustment value to update the parameters of the social network. Increasing the batch size \(K\) can lead to faster processing speed in our proposed model, as well as reduced training fluctuations, thereby facilitating convergence. By ensuring a consistent sample size at each iteration, stable adjustment values are obtained, benefiting all samples. Conversely, if the batch size \(K\) is small, such as \(K=1\), the resulting adjustment value may exhibit random properties. While it may be the optimal adjustment method for a specific sample, it may not adapt well to other samples. Consequently, the training process will exhibit continuous fluctuations, making it challenging for our proposed model to converge. Typically, this parameter is set between 1 and 40 in conventional models. For detailed experimental results, please refer to Figure (b)b.  
The analysis of Figure (b)b reveals that the previous batch size \(K\) was too small, rendering our proposed model ineffective due to insufficient data for learning. As the batch size increases, our proposed model achieves optimal performance at \(K=20\). However, when \(K<5\), the model training fails to converge due to the small batch size. In comparison to other models, the LSTM model exhibits a gradual decrease in the \(F1\) score with an increase in batch size. On the other hand, the Bi-LSTM model performs better as it considers more information. The overall performance of the CNN+BiLSTM model is comparable to our proposed model. Nevertheless, in general, our proposed model outperforms other models.
# Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
## 5 Experimental Results  
### Sleep Dataset  
We used the Sleep-EDFx dataset in Physionet [32] to evaluate the performance of the proposed method. The dataset included PSG recordings at a sampling rate of 100 Hz and each record consists of EEG (from Fpz-Cz and Pz-Oz electrode locations), EOG (horizontal and vertical), chin electromyography (EMG), and event markers. We only used the Fpz-Cz EEG and the horizontal EOG channels in our research. The hypnograms (sleep stages for each 30 s epoch) were manually labeled by somnologists, followed by the Rechtschaffen and Kales rule [33]. Each stage was mapped to each sleep stage. The sleep stages included Wake, R(REM), S1, S2, S3, S4, MOVEMENT (movement time), and UNKNOWN (not scored). According to the American Academy of Sleep Medicine (AASM) standard, the S3 and S4 stages were merged into a single stage: SWS (Slow-Wave Sleep) [5]. Wake, MOVEMENT, and UNKNOWN were excluded.  
The distribution of sleep stages in the Sleep-EDF database might be biased. Hence, the number of W and S2 stages were much larger than other stages. The deep learning algorithms cannot handle the class imbalance problem. To address this problem, the dataset was sampled to nearly reach a balanced number of sleep stages in each class. Because of the imbalance in the classes, we applied balanced sampling. For example, a total of 16,000 experimental data were randomly sampled from the dataset, consisting of 2400 (15%) for S1, 7200 (45%) for S2, 3520 (22%) for SWS, and 2880 (18%) for R. The experimental dataset was split randomly into a training, a validation, and a testing set with respective proportions (0.6, 0.2, 0.2). The sizes of the training set, validation set, and testing set were 9600, 3200, and 3200, respectively. Therefore, there was no dependency on the training, validation, and testing sets.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 6 Conclusions  
In this work, a scheduling scheme for the uplink of MU-MIMO systems using \(A\) antennas and the ZF multi-user detector in the receiver was proposed and evaluated over a Rician fading channel in the presence of imperfect channel estimation. With the proposed scheme, the system selects \(U\) users from a group of \(U_{t}>U\) users, who transmit simultaneously on the same radio resource so that the SNR is maximized. The computational complexity of the proposed scheme was determined in terms of the number of complex operations, and its performance was evaluated in terms of the OP and the BER.  
It was determined that as \(U_{t}\) increases, the OP and the BER decrease, which implies that the proposed scheme improves the performance of MU-MIMO systems. Furthermore, it was observed that the system diversity increases when \(U_{t}\) increases. Thus, the proposed scheme along with the ZF detector eliminates interference and increases system diversity even when it is fully loaded, that is, when \(A=U\). The results also show that the proposed scheme mitigates the undesirable effects produced by channel estimation errors, and this mitigation is greater as \(U_{t}\) increases. Finally, since the proposed scheme allows maximization of the SNR of the users, it also allows an increase in their achievable rate,  
Figure 11: SOP as a function of \(E_{b}/N_{0}\), parameterized by \(A_{E}\) and \(U_{t}\), considering \(M=4\), \(A=3\), \(U=3\), \(e=0.1\), \(c=0.05\), and \(R=1\) bps in a Rician fading channel with \(K=2\).  
which in a scenario in the presence of an eavesdropper, allows a reduction of the SOP of the MU-MIMO system.  
In this work, the ZF detector was considered due to its low computational complexity and good performance. However, there are other multiuser detectors on which the proposed scheme can also be applied. Therefore, this is an option for future work. In addition, there are other more general distributions to model the fading phenomenon, such as \(\alpha-\mu\) or \(\kappa-\mu\) distributions, that are adequate for propagation environments in millimeter wave scenarios [54]. Thus, the analysis of the proposed scheme in these channels is also an alternative for future research.  
Conceptualization, H.C. and N.O.; methodology, H.C. and N.O.; software, H.C., S.C., P.S., E.R. and F.A.; validation, H.C., N.O. and F.A.; formal analysis, H.C., S.C., P.S. and E.R.; investigation, H.C., N.O., S.C., P.S., E.R. and F.A.; data curation, H.C. and N.O.; writing--original draft preparation, H.C., N.O., S.C., P.S., E.R. and F.A.; writing--review and editing, H.C., N.O., S.C., P. S., E.R. and F.A.; visualization, H.C. and N.O.; supervision, H.C.; project administration, N.O.; funding acquisition, N.O. All authors have read and agreed to the published version of the manuscript.  
This work received the support of Universidad de Las Americas (UDLA), Ecuador, as part of the research project ERT.NOG.22.01, and the support of the Sao Paulo Research Foundation (FAPESP) under Grant 2021/03923-9.  
Not applicable.
## 2 Materials and Methods
### Theoretical Considerations  
#### 2.2.1 Formulation of the Problem  
One method to analyze the interference resistance of communication systems is to sum up at the reception, over the signal modulated with modulation signal \(x(t)\), a white Gaussian noise, uncorrelated, with zero mean value \(z(t)\)[21; 22; 23; 24]. In this sense, the improvement factor of the demodulator (IFD) is defined in Equation (1) as the ratio between the SNR (signal-to-noise ratio) at the demodulator output and the SNR (\(P_{\textit{Simp}}\)/\(P_{\textit{Zinp}}\)) at the demodulator input:  
\[\rho=(P_{\textit{Sout}}/P_{\textit{Zout}})\ /\left(P_{\textit{Simp}}/P_{\textit{Zinp}} \right), \tag{1}\]  
Here, \(P_{\textit{Sout}}\) and \(P_{\textit{Zout}}\) represent the signal power and the noise power, respectively, at the demodulator output, whereas \(P_{\textit{Simp}}\) and \(P_{\textit{Zinp}}\) represent the signal power and the noise power, respectively, at the demodulator input. The quantities are noted on the schematic diagram illustrated in Figure 2.  
The demodulator input noise power \(P_{\textit{Zinp}}\) is considered equal to the output noise at the band-pass filter (BPF) with bandwidth \(B\) and the central frequency, \(fc\), i.e., a narrow-band noise that can be written as Equation (2):  
\[z(t)=n_{1}(t)\cos\omega_{c}t-n_{2}(t)\sin\omega_{c}t, \tag{2}\]  
where \(\omega_{c}\) is the central angular frequency, \(t\) is the time, and \(n_{1}(t)\) and \(n_{2}(t)\) are white, Gaussian, uncorrelated, low frequency noises with zero mean value, (\(\overline{n_{1}(t)}=0\), \(\overline{n_{2}(t)}=0\)). If we denote by \(N_{0}\) the power spectral densities of \(n_{1}(t)\) and \(n_{2}(t)\), the power of the noise at the input to the demodulator \(P_{\textit{Zinp}}\) can be calculated with Equation (3):  
\[P_{\textit{Zinp}}=\frac{1}{2\pi}\int\limits_{\omega_{c}-\pi B}^{\omega_{c}+ \pi B}\!\!N_{0}d\omega=N_{0}B, \tag{3}\]  
The signal and noise power at the demodulator output \(P_{\textit{Sout}}\) and \(P_{\textit{Zout}}\), respectively, are evaluated according to the ideal low-pass filter (LPF) with a transfer factor equal to the unit in the passband and the cut-off frequency equal to the maximum modulation frequency (\(f_{\textit{mM}}\)). Additionally, in this analysis there is considered that the amplitude of the signal is higher in relation to the noise for most of the time (with a maximum probability).
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 3 Results
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles
#### 3.2.5 Zeta Potential Analysis  
The zeta potential is a physical feature displayed by any particle in a suspension that predicts the particles' long-term stability, with the pH of the ZnO solution being 12. For _U. lactuca_ ZnO-NPs, the zeta potential analysis revealed a net charge of \(-42.1\) mV, a conductivity of 2.06 mS/cm, and a standard deviation of 4.10 mV. Additionally, the analysis demonstrated a conductivity of 1.92 mS/cm, a net charge of \(-25\) mV, and a standard deviation of 4.10 mV for the _U. intestinalis_ ZnO-NPs, as well as a conductivity of 4.10 mS/cm, a net charge of \(-37\) mV, and a standard deviation of 4.84 mV for the _S. muticum_ ZnO-NPs. These values can be characterized as high-quality potentials with high levels of particle stability because they fall within the known range of \(-30\) mV to \(+30\) mV (Figure 6).