esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Experimental Results
### Experimental Comparison  
In conclusion, the proposed approach is compared with models from other references in the literature, as shown in Tables 7 and 8. In binary classification, some studies, such as [10; 11], applied only 1D convolution to extract temporal information in EEG data, whereas [PERSON] [20] constructed 3D spatial-frequency features of DE and selected a fully convolutional residual network recognition model, and [PERSON] [27] used 3D convolutional kernels to extract and process the spatiotemporal features of EEG data. However, none of the above networks fully considered the feature information of the three dimensions, and their average accuracies were lower than the combined model of references [23; 24; 26], except for the studies where [PERSON] [20] and [PERSON] [27] used channel attention to augment the model accuracy. These findings demonstrate that attention mechanisms and consideration of multidimensional information are significant for the performance improvement of recognition systems. In this article, we employed Bi-LSTM based on LSTM to learn sequence signals for future sentiment information and used FcaNet to compensate for the shortcomings of SENet. Consequently, we achieved the best accuracy of 98.10% for binary classification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
In the context of the four-classification task, prior works such as those in [4; 5; 6] utilized wavelets to extract time-frequency features for emotion recognition using SVM and mRMR algorithms. However, these methods did not incorporate the spatial information and dynamic temporal features of EEG. [PERSON] [18] and [PERSON] [36] represented features by constructing a connectivity matrix of brain structures, followed by extracting spatial features using CNN. Similarly, PSO-BiLSTM [35] utilized DWT to decompose the signal, applied a third-order cumulant transformation to a high-dimensional space, and then reduced the dimension to eliminate redundancy. However, this approach also did not consider spatial feature learning. In contrast, the four-dimensional data used in this paper contain more information than the two- and three-dimensional data used in prior works. Furthermore, the proposed approach in this article constructs a combined network that can adapt to multidimensional features and extract spatial-frequency and temporal features. It also employs the more advanced FeaNet and fully considers information from all frame moments. These advantages make the proposed method more effective compared to those in prior examples in the literature.
## Appendix A
### Payload Sanitizing  
The next major step when integrating data into the unified management and storage system was the sanitizing step that received data packets and payloads. Broadly, this was performed for each device type based on the initially received format from the external cloud service. As shown in Figure A4, this starts by attempting to convert the data payload to a JavaScript Object Notation (JSON)--an open standard file or data format--syntax that can be easily handled by Node-RED. If not already completed, the endpoint (type of ingest) and origin (cloud platform) are added to the data flow as additional strings in the JSON syntax. Following this, redundant data and objects within the current data payload are removed (e.g., parameters which are not required for storage in the database). Finally, each payload is scanned for a device-specific ID to translate it to a system-wide ID.  
In our implementation, the system-wide ID was referred to as the Sensor-Asset Name (SAN) and translated to a device or technology specific identifier to one that identifies the type and unique number of that sensor. For example, a WiFi-based weather station could be identified by its 8-byte Media Access Control (MAC) address--which identifies a device with other devices on the same local network--and is assigned a SAN in the form of \"WTH-xxxx\", where \"WTH\" identified the device as a weather station and the four-digit number \"xxxx\" denoted the unique identifier of that particular weather station (e.g., 0001). The device to SAN ID mapping was stored as a configuration object in JSON format and read in from a file when the Node-RED flows were initialized. It should also be noted that any payload that either failed a communication to JSON, or could not have its ID decoded to a known device was discarded and was noted in the system log for future diagnoses
# A 3-D Metal-Printed Dual-Polarized Ridged Waveguide Slot Array Antenna for X-Band Applications
## 3 Antenna Design and Analysis Procedure
### V-Shaped Slot for Low Cross-Polarization  
In the dual-polarized array, the VP linear array is easily achieved by cutting longitudinal slots into the broad wall surface of the RWG. In the HP linear array, the inclined slot is a potential solution to simplify the design without using additional sub-ingredients inside the waveguide to excite the resonant slot. However, excitation for slots using inclination suffers from several disadvantages. Along with the desired longitudinally polarized electric field, the inclination also creates an undesirable vertically polarized electric field. This increases the cross-polarization level of the dual-polarized array and reduces the performance of the antenna.  
Figure 3 depicts the electromagnetic (EM) field distributions of the two proposed slot types for HP. It can be seen that the surface EM distribution for the inclined slots cutting  
into the ridged sidewall of the RWG is similar to that on the narrow wall of a conventional waveguide, and the radiated electric field of the V-shaped slot is a combination of a pair of inclined slots on the ridged side wall of the RWG. For each certain angle of inclination (\(slot\_\theta\)), the slots have an EM field distribution \(E\) in two directions perpendicular to the coordinate axes \(E_{x}\) and \(E_{y}\) corresponding to VP and HP.  
In Figure 3a, with two linearly inclined slit arrays, the \(E_{x}\) components are suppressed due to being out of phase, while the \(E_{y}\) components are in phase, so they complement each other. Thus, the cross-polarization of a conventional inclined slot array can be suppressed by a pair of adjacent linear arrays with mirroring of the inclined slots. However, the distance (\(S_{x}\)) between the inclined slots in free space affects the performance of the cross-polarization suppression; with shorter \(S_{x}\), better cross-polarization is achieved [26]. From there, the inclined slots on the two waveguides are combined to create a V-shaped structure on a waveguide as shown in Figure 3b.  
Both sides of the V-shaped slot also have the same EM field distribution as do the separate inclined slots. The HP components are in phase, so the EM field radiation is uniformly combined, while the out-of-phase VP components are suppressed. With a shorter \(S_{x}\) distance, the canceled EM radiated field obtains a better low cross-polarization and does not need to depend on the combination of adjacent linear arrays. Thus, the V-shaped slots achieve better performance than the inclined slots in eliminating the cross-polarization component.
# Experimental Study on Mechanical Properties of Graded EPS-Steel Fiber-Reinforced Concrete
## 2 Materials and Methods
### Experimental Plan  
The design of the aggregate gradation is completed first to ensure the rationality of the selected concrete aggregates. Then, based on two sets of different regression coefficients, the mix proportions for the premixed C30, C40, and C50 grades of gravel and fine aggregate concrete are calculated. According to previous studies, improvements in the mechanical properties of concrete are most notable when the steel fiber volume fraction ranges from 1.5% to 2.25%, and the EPS (expanded polystyrene) volume fraction is between 10% and 30% [21; 22; 23]. In this study, the steel fiber and EPS volume fractions were expanded (1.5%, 1.75%, 2.25% and 15%, 25%, 35%) to set up experimental groups. EPS particles with particle sizes of 1 mm and 5 mm were selected [24], with each particle size occupying 50% of the total volume. The concrete preparation followed the guidelines outlined in the **\"Premixed Concrete\"** standard (GB/T14902-2012) [25]. The experiments were conducted according to the standards of **\"Test Methods for the Mechanical Properties of Ordinary Concrete\"** (GB/T50081-2019) [26] and **\"Test Methods for Fiber Reinforced Concrete\"** (CECS13-2009) [27].  
For the compressive strength test, the specimens were 150 mm \(\times\) 150 mm \(\times\) 150 mm in size, and a uniform, continuous load was applied until the specimen failed. For the flexural toughness test, the specimens were 150 mm \(\times\) 150 mm \(\times\) 550 mm standard beams, tested using a 50-300 kN flexural test machine, applying a two-point load at the third-point location. The loading speed was maintained at 0.05 MPa/s to 0.08 MPa/s until failure. Based on the test results, the variations in compressive and flexural strengths of concrete incorporating steel fibers and graded EPS particles were analyzed.
# Enhancing Antifragile Performance of Manufacturing Systems through Predictive Maintenance
## 3 Predictive Maintenance in Self-Improving Manufacturing Systems
They investigate the combination of DTs and the IoT and conclude that it is of high importance to take into consideration three aspects: the data, model, and service when a DT is in an application. In the aforementioned context, the achievement of PdM objectives is based on three pillars: (i) data gathering and preprocessing; (ii) the integration of maintenance knowledge in rigorous mathematical models; and (iii) maintenance planning based on RUL calculations. We insist that preprocessing should not be neglected, as it is crucial in establishing trust in the data in the process of transitioning from potential information to consistent real information. Only in this way can the condition of the machine be predicted as a result of the simulation on virtual models without stopping the operation of the machines. The great advantage of PdM is the ability to make decisions in the shortest time without reaching the preset intervals for performing the revision. Even if, at first glance, the success of a PdM program depends on the performance of the predictive algorithm, in reality, the challenge is to determine the optimal moment in which to apply the interventional decision. It should be noted that the way in which an antifragile system is designed, by increasing its resilience in difficult conditions, is fully in line with multiobjective optimization because all the essential criteria pursued by optimal control (efficiency of asset management, robust behaviour, and minimum intervention time) are complementary and nonconflicting. For modern manufacturing systems, the tendency is to achieve integrated planning (IP) that synchronizes manufacturing planning and maintenance planning with predictive maintenance capabilities. We already mentioned in the introduction the differences between PdM and PvM. Recall that PvM ensures robustness and PdM ensures resilience, but neither of them successfully cope with uncertainties. Although they use real-time collected data for prediction, PdM algorithms allow decisions based on finding specific patterns in historical data and cannot correctly interpret sudden and unexpected changes in context. It requires an additional ability to adapt to these changes, and we see this possibility through learning techniques, thus reaching AfM. AfM respects all of the PdM characteristics but adds the ability to adapt to unexpected changes in the operating environment, as it is also context-aware. Therefore, a system that has AfM can be called an antifragile self-improving system (ASIS).
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 9 Discussion
### Performance Comparison
#### 9.3.3 Receiver Sensitivity and Data Rate  
The RAD-2400-IFS offers a receiver sensitivity of \(-93\) dBm at a data rate of 250 kbps, marking the lowest possible sensitivity at the highest data rate achievable by the Radioline series. This indicates that the Radioline could potentially reach higher sensitivity at reduced data rates. Nonetheless, the 250-kbps rate adequately meets the current application's needs. In comparison, the SCALANCE wireless modules exhibit a receiver sensitivity of \(-90\) dBm, which is the highest sensitivity comparable to the lowest that Trusted Wireless can offer. At this level of sensitivity, SCALANCE supports a significantly higher transmission rate of 15 Mbps, far surpassing that provided by Trusted Wireless. Although SCALANCE is capable of higher data rates, achieving these would necessitate a reduction in sensitivity, which may prove challenging given the extensive coverage area of the water station.
## 3 Results and Discussion
### Performance of the Soil Nail System
#### 3.2.1 Inclinometer  
Inclinometers are commonly used to monitor horizontal deformations in soil nail systems [31]. These instruments enable precise long-term monitoring of borehole positions. The setup of an inclinometer consists of an electrical cable with graduated depth markings, a handheld computer for data display, a plastic casing tube with four longitudinal grooves, and a probe with guide wheels to maintain rotational stability.  
Measurements are taken at predefined depth intervals, typically 0.50 m, with the slope angle (\(\psi\)) recorded at each increment.  
The total displacement (\(\sum\) L sin \(\psi\)) at the top of the hole is calculated as described by [PERSON] [32] (Figure 12). To ensure accuracy, readings are verified by rotating the probe 180\({}^{\circ}\) and allowing time for an oscillatory equilibrium to be reached.  
Lower slope angles measured via an inclinometer indicate greater wall performance [29].  
The horizontal displacement values measured at IM1 (Section 1-1\({}^{\prime}\)) and IM2 (Section 2-2\({}^{\prime}\)) inclinometers are shown in Figure 13. The maximum displacement at IM1  
Figure 11: Longitudinal section along the profile of the soil nail support system.  
Figure 12: A view of the soil-nailed support system after the completion of the construction phase (PT refers to pull-out test location, and IM refers to inclinometer location).  
was 2.30 mm, while that at IM2 was 3.90 mm. These values are significantly lower than those predicted by the Mohr-Coulomb model, demonstrating that the actual soil nail system provides a higher level of safety than initially estimated. This result indicates that the current design of soil-nailed walls provides a higher level of safety than the minimum required for the project, as the actual horizontal displacement values are lower than those predicted by the Mohr-Coulomb model during the design phase. Consequently, implementation costs may be reduced without compromising stability. The initial design and field displacement measurements for Section 1-1 and Section 2-2 are summarized in Table 5.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 3 Result and Discussion
### Channel Capacity and Outage Probability  
Another focus of this section was on the performance metrics of channel capacity and outage probability within ultra-massive MIMO systems. Equation (19) was used for processing and comparing the channel capacity, as presented in Figures 9 and 10.  
Figure 8: The computational times for ELMx and CNN-SD.  
Figure 9: The channel capacity performance of ELMx and CNN-SD in ultra-massive MIMO systems.  
Meanwhile, for determining the outage probability, Equations (20) and (21) were utilized, as demonstrated in Figure 10. The test result for channel capacity vs. SNR shows that the machine learning algorithm ELM, RELM, and ORELM techniques provided a lower channel capacity than the CNN-SD techniques. In Figure 9, the CNN-SD at 10 SNRdB had a capacity 200 bps/Hz more than the ELMx-based techniques. It is apparent that the ultra-massive MIMO channel is highly likely to achieve high capacity.At a 90% probability level, the capacities stand at 105 bps/Hz for ELM, 105 bps/Hz for RELM, and 116 bps/Hz for ORELM. Consequently, this demonstrates a significant likelihood of high channel capacity when compared to ELM, RELM, and ORELM. The CNN-SD, with a capacity of 128 bps/Hz, exhibited excellent performance in line with the method, as depicted in Figure 10.
## 3 Results and Discussion
### Evaluation of Button Sensor Antenna: Simulated Results
#### 3.1.4 Effects of Fabric  
A layer of fabric might normally be under or over the BSA. In order to take the effect of fabric into account, various garments were considered, and the antenna was simulated while the fabric layer was underneath and over the antenna and covering it. These scenarios are illustrated in Figure 13. The distance between the surface of the antenna and the fabric was chosen to be 1 mm. A fabric thickness of 5 mm was considered. Various typical materials were considered in the simulations [63]. The reflection coefficient (S\({}_{11}\)) for the cases where the fabric was on top of the BSA is shown in Figure 14. Figure 15 depicts the reflection coefficient (S\({}_{11}\)) for the cases that the fabric was placed under the BSA. In both cases, the system continues to function effectively and efficiently within the specified operating frequency bands, demonstrating the proposed design's robustness.  
Figure 14: Reflection coefficient for the antenna covered by various types of fabric. (Textile fabrics relative permittivity (\(\varepsilon_{\mathrm{r}}\)): Cordura/Lycra: 1.50, denim: 1.47, fleece: 1.02, cotton: 1.60, and PTFE: 2.1).  
Figure 13: Button sensor antenna with the fabric placed (**a**) under the antenna, and (**b**) over the antenna (fabric thickness t: 5 mm).
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
The MIEX(r) GOLD resin is a second-generation product of IXOM. Compared with MIEX(r) DOC, the advantageous features of MIEX(r) GOLD resin include increased surface area for greater adsorption capacity, larger porosity for easy access to the inner area and functional groups, and less steric or physical hindrance to enable the removal of both low (\(<\)5000 Da) and high molecular weight dissolved organic carbons [21]. It is expected that the new MIEX(r) GOLD resin would have better performance on PFAS removal. In this study, we examine the removal efficiency of MIEX(r) GOLD resin for six selected PFASs, including perfluorobutanoic acid (PFBA), perfluorobutanesulfonic acid (PFBS), perfluorohexanoic acid (PFHxA), perfluorohexanesulfonic acid (PFHxS), perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS). The interference of natural organic matter (NOM) (tannic acid and humic acid) and inorganic matter (sulfates and nitrates) on the removal capacity of PFAS compounds is then studied in detail. In addition, this study also explores the possible conditions (organic solvent and inorganic salt) to regenerate the PFAS-adsorbed resin. The regeneration study is important as it could reduce the PFAS remediation cost by reuse of the resin; it also provides insights into the understanding of the PFAS adsorption mechanism.
# Self-Recognition Grasping Operation with a Vision-Based Redundant Manipulator System
## 4 Simulation and Experiments
### Feasible Solutions Based on the ISPF Method  
By the experiments in Section 4.1, the position of target object and grasping orientation angle were obtained. The pose of the end-effector, namely, \([0.78\text{m},-0.10\text{m},0.40\text{m},-1.57\text{rad},-0.17\text{rad},-1.57 \text{rad}]\), could be obtained according to Equation (1). With the method of ISPF, kinematics and transformation matrices between joint coordinates were calculated by the DH parameters listed in Table 1. Then, solutions for successful grasping could be obtained by changing the value of phase angle \(\varphi\) in \([0,2\pi]\). In Figure 6, four representative solutions (obtained at \(\varphi=0^{\circ}\), \(\varphi=90^{\circ}\), \(\varphi=180^{\circ}\), and \(\varphi=260^{\circ}\)) are shown to achieve grasping operation.  
From Figure 6, all four configurations of the redundant manipulator could successfully achieve grasping operation. Different joint angles correspond to different joint positions and manipulator configurations. By varying \(\varphi\), which varies in \([0,2\pi]\), at an interval of \(20^{\circ}\), \(18\) groups of inverse solutions were calculated, and the corresponding configurations are shown in Figure 7. Figure 7a is a general view of the solutions, and Figure 7b is viewed from the Y-Z plane. It clearly shows that the possible positions of \(P_{2}\) form a circle. When \(\varphi\) varies, all the feasible solutions can be obtained and represented, as shown in Figure 7a. This verifies with the method of ISPF, where the manipulator can efficiently calculate the entire feasible solution set. Besides, the shape formed by the possible solutions confirms the validation of manipulator simplification and the locus circle application outlined in Section 2.
## 4 Performance Evaluation
### Transmission Success Ratio According to the Number of Mobile Devices  
Figure 14 presents the performance evaluation results when the number of devices moving in linear, non-linear, and random patterns is increased to 6, 7, 8, 9, and 10, respectively. In the case of [21], similar to the experimental results in Section 4.3, the transmission success ratio decreases as the number of mobile devices increases. This is because, as previously explained, the conventional neighbor discovery process does not respond adequately to mobility. In addition, it hinders the connectivity of devices that should communicate with the gateway through mobile devices. In the case of [21], similar to the experimental results in Section 4.3, a high transmission success ratio is maintained even as the number of mobile devices increases. However, as will be discussed in detail in Section 4.5, this mobility support comes at the cost of highly inefficient resource allocation. Lastly, the proposed scheme also achieves a high transmission success ratio by predicting movement paths and allocating resources based on mobility pattern classification. However, as the number of mobile devices increases, the possibility of errors in pattern classification and prediction also increases, resulting in slightly lower performance compared to [15]. Nevertheless, it uses fewer resources than [15], which allocates a large amount of resources to all mobile devices.  
Figure 13: Packet delivery ratio versus movement speed has been analyzed based on [15; 21] and proposed scheme.