esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Comparison with State-of-the-Art Methods  
In this section, we compare our proposed model with related work from recent years. The selected baseline approach is based on the four public datasets in Table 2. Firstly, our model outperforms SVM because earlier machine learning methods relied heavily on hand-crafted features, which limited their accuracy. Additionally, our model outperforms CNN and LSTM, which only consider either temporal contextual relevance or spatial relevance. Secondly, our model is more accurate than the DeepConvLSTM and  
Figure 5: Distribution of sample categories across training, validation, and test sets in the four benchmark datasets, as well as the proportion of the overall number of samples accounted for by each category. The ratio of training, validation, and test sets is approximately 80:10:10%.  
DeepConvLSTM + Attention models because our HMC structure effectively captures finer-grained information about tiny action processes. Furthermore, our model is more accurate than recently proposed methods such as ConvAE, AttnSense, and Self-Attention that incorporate attention mechanism. It reflects that our model outperforms most existing models and illustrates the effectiveness of our hierarchical architecture for feature extraction. In addition, Table 3 is categorized by network type, which are traditional models, LSTM-based, and attention mechanism-based models, and, finally, our proposed model.  
As shown in Table 3, the recognition performance of HMA Conv-LSTM significantly outperforms the other baselines. Despite USC-HAD being a challenging dataset, our model performs better than other models such as DeepConvLSTM (0.38) and AttnSense (0.49). Additionally, our proposed model outperforms other models which are based on attention mechanisms. On the PAMAP2 dataset, our model achieves better results (0.91) than DeepConvLSTM + Attention (0.88), LSTM + Continuous Attention (0.90), and AttnSense (0.89). For the Skoda dataset, our model achieves high performance and outperforms other well-performing models such as AttnSense (0.93) and LSTM + Continuous Attention (0.94). In addition, compared to other methods, our model performs well on the Opportunity dataset (0.68), which contains complex actions. Due to the short duration of some of these mid-level gestures, the hierarchical architecture does not improve the current results much. However, when considering more complex and confusing gestures, the effect of our model is evident.  
In conclusion, our proposed model outperforms other baseline methods on all datasets except the Opportunity dataset. It demonstrates the effectiveness and contribution of our model. The evaluation results further show that our proposed HMA Conv-LSTM can effectively obtain both temporal context information and spatial information from sensor sequence data. It can also recognize some subtle action processes with fine-grained detail, ultimately achieving good results.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Risk Parameters for the Environment Outside the Cableway
#### 2.3.5 Transportation and Building Construction  
Certain locations, due to their proximity to major traffic arteries, subway lines, or building construction areas, have increased mechanical increases, which can easily cause structural damage to the cable pathway and increase the risk of interruption or damage to the cable sheath.
# Cognitive Radio Network Technology for IoT-Enabled Devices +
## 3 Cognitive Radio Network Based on IoT
### Reasons for Utilizing Cognitive Radio Network in the IoT  
The deployment of IoT networks is difficult due to a number of challenges, including a limited communication range, resource scarcity, interference problems, and Reconfigurability. The application of cognitive radio has the potential to improve all of these challenges, as well as others, including heterogeneity, reconfigurability, and automaticity. The utilization of Cognitive Radio in the Internet of Things is probably driven by the requirement to overcome these issues. It is difficult to allot frequency bands to the ever-increasing number of Internet of Things devices because there is a limited amount of the spectrum available. By enabling more frequency reuse, CR provides a solution to this challenge. The vast majority of technologies that are a part of the internet of things, such as RFID and IEEE 802.15.4 (ZigBee), make use of the same oversubscribed ISM and UHF frequency bands. This results in interference. Therefore, it is to be anticipated that interference will occur when multiple devices use these frequencies.  
CR solves this problem by allowing for interference-free spectrum access on demand. Because of the restrictions placed on them by the ISM's unlicensed bands, wireless technologies can only communicate over short distances. Buying licensing to access a frequency spectrum that guarantees long-distance connectivity is expensive and unnecessary. With CR, you do not have to spend money on a license, and you may take advantage of empty frequencies to have long-distance conversations whenever you want. In order to store and analyze the data they produce, IoT items must establish connections with one another and send that data to a large number of servers (Cloud servers).  
To fix this problem, CR is the best option. Many different approaches can be taken to solving heterogeneity issues in IoT applications. To handle this diversity, new forms of communication should be developed that allow for self-discovery, self-organization, and self-management of their respective environments [26]. Among these paradigms, CR is a viable option for dealing with heterogeneity problems. The flexibility to change configurations and operate autonomously is another factor. In fact, it is anticipated that smart things will be able to alter themselves automatically. This means that things should be able to scan their surroundings, identify nearby companions, and reorganize themselves accordingly. In this case, CR is shown to be an effective strategy. When everything is considered, it is clear that cognitive radio holds great potential as an IoT enabler.
## 1 Introduction
### The Problem of Determining the I-V Curve Maximum Power Point (MPP)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Model** & \(R_{s}\) **(\(\Omega\))** & \(R_{\text{sh}}\) **(\(\Omega\))** & \(I_{\text{pp}}\) **(A)** & \(I_{\text{OI}}\) **(A)** & \(a_{1}\) & \(I_{\text{02}}\) **(A)** & \(a_{2}\) & \(\xi\) \\ \hline
1D & - & - & 1.0300 & \(1.55\times 10^{-4 instantaneously in order to reach the voltage level corresponding to the maximum output power. The problem lays in the reduced accuracy obtained when calculating that voltage level, due to the complexity of the equations used for estimating the equivalent circuit parameters. Additionally, another problem shown by the offline methods is the absence of feedback to know whether the prediction is correct. On the other hand, feedback is available in online methods (e.g., perturb and observe methods). As a consequence, using these methods the maximum power point (MPP) of the _I-V_ curve is continuously searched for, and the operating voltage accordingly corrected. However, online methods also have some drawbacks, as large periods can be required to estimate the MPP voltage level. Finally, there are also hybrid solutions, such as combining two different methods, each one from one of the aforementioned categories. The offline method is used to get a quick approximation of the MPP, whereas the online method is used to improve the result.
## 2 Materials and Methods
### Microcapsule Characterization
#### 2.3.3 Scanning Electron Microscopy (SEM) Analysis  
A JSM-6610 LV microscope (JEOL, Tokyo, Japan) was used to observe the FS, FG, SS, and SG microstructures. Samples were attached to the table with conductive double-sided tape and gold sprayed on the surface (Bal-Tec Sputter Coater SCD 050, New York, NY, USA). The SEM was performed at 10 kV with a working distance of 9.5-9.7 mm and magnifications of 800-900\(\times\).
# Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
## 3 Our Proposed Work
### Recovery Mode  
When a \(S\) is unable to find lower pressure node then all proposed algorithms (Improved Hydrocast, Co-Hydrocast, and Co-Improved Hydrocast) follow the same void node recovery mechanism. Let a \(S\) has to transmit a data packet and it has no neighbor node in the transmission range. It wastes a lot of energy, therefore to avoid this problem, backward transmission mechanism is adopted to find a path to the final destination. The procedure is discussed below:  
Figure 6 shows case 1, when a source node \(S\) has only void nodes as lower pressure nodes as neighbors. n1, n2 and n3 are the neighbors of \(S\). Both \(n1\) and \(n2\) have lower pressure level than S, and both are void nodes. \(n3\) has higher pressure level than \(S\) and is not a void node. So \(S\) selects \(n3\) as a forwarder and transmits the data packet to it. This backward transmission is changed to greedy forwarding as soon as a lower pressure level neighbor node is found that it is not in a void region. The backward transmission starts when there is no neighbor node which is computed according to Equation (1) and \(\theta\) varies from 180 to 360 degree to find out a lower level neighbor that is not a void node as shown in Figure 6. It ends when n3 finds a neighbor (Sub n1) that has a lower pressure level neighbor than itself, and is not a void node. In this way loops are avoided during the packet forwarding process.  
In case 2, a \(S\) selects a higher pressure level neighbor as the next hop forwarder if it has no lower pressure level neighbors then backward transmission ends as soon as a lower pressure level neighbor is found. In this way, average throughput of the network is increased.
## 3 Mapping of Land Cover and Physiological or Structural Variables
### Physiological Variables
#### 3.2.4. Phenology Cycle  
Phenology of evergreen tropical forests is controlled by the emergence of new leaves, their maturity and shedding of old leaves [136]. However, the magnitude and variations in photosynthesis seasonality are less prominent compared to phenological cycles in other ecosystems [51]. The seasonality of the tropical forest significantly influences the carbon stock and GPP [137]. In the old-growth Amazonia tropical forests, [PERSON] et al. [138] identified nine distinct phenological zones indicating GPP seasonal cycles, by combining monthly MODIS LAI/EVI, AMSR-E vegetation optical depth (VOP), GRACE terrestrial water storage (TWS), Clouds and the Earth's Radiant Energy System (CERES) Photosynthetically active radiation (PAR), and Tropical Rainfall Measurement Mission (TRMM) precipitation measurements. Thus, knowing phenological characteristics, the timing of green-up and senescing, and changes in leaf area and density over the growing season are critical while assimilating and interpolating carbon stock measurements in tropical forests [47; 139]. Many satellite-based phenology products, such as from AVHHR, MODIS, SPOT vegetation, Medium-Resolution Imaging Spectrometer (MERIS), with varying spatial resolutions (8 to 100 m), have been developed by the scientists which are now an imperative part of the carbon cycle and stock measurements in the tropics and elsewhere in the world [37; 51; 87]. Recently, continental-scale Land Surface Phenology (LSP) has been produced by combining time series of images Landsat 8 (30 m) and Sentinel-2 (10 m), which can capture finer details of variation in phenology and AGB across various biomes [140].
# Powdered Foods: Structure, Processing, and Challenges: A Review
## 4 Instant Properties  
For the evaluation of powdered products, a fundamental attribute to be considered is their 'instant' properties, referring to the ability of these products to disperse and dissolve in liquids. These properties are related to the formation of agglomerates and their behavior, indicating how easily the powdered product can be reconstituted in a liquid [10].  
As for instant properties, the desirable characteristics of powdered products can be categorized into four dissolution parameters: wettability, sinkability, dispersibility, and solubility. These properties represent the steps involved in the reconstitution of powdered products [10; 37; 38]. Figure 3 presents dissolution properties and how to improve their characteristics.  
Wettability is defined as the ability of a liquid to penetrate a porous agglomerate, replacing the gaseous phase with water on the surface of the powder. The efficiency of this process is affected by particle size (directly proportional) and the degree of polarity on the surface (hydrophilic compounds have greater wettability) [10; 37]. To enhance wettability properties, one approach is the use of additives with surfactant or emulsifying properties, providing greater control over the surface and interfacial tension between solids and liquids [39].  
Sinkability is the process described by the ability of agglomerates to sink below the surface of water, evaluated in terms of how fast the process occurs. This property strictly depends on the physical characteristics of the particle, namely size, density, porosity, and particle size distribution. In general, larger particles with higher density exhibit better sinkability, but particles with high porosity (low density) also demonstrate a high degree of sinkability due to the diffusion process within the particle [10].  
Dispersibility is defined by the ability of a powder to disperse in water, which occurs with reduced efficiency when there is formation of agglomerates and increased efficiency with enhanced sinkability. Efficiency is reduced when agglomerates form because the dispersibility process involves breaking down particles to increase the surface area between the powder and liquid. Lastly, solubility is the ability of particles to dissolve in liquid, resulting in the complete disappearance of the solute in the solvent, which mainly depends on the chemical composition of the powder and its physical state [10; 37].  
Figure 3: Desirable characteristics for reconstitution processes.
## 3 Results
### Prediction of Hydrogen (H\({}_{2}\)) Gas Levels
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Optimal**} & \multicolumn{4}{c}{**Suboptimal**} \\ \cline{2-9}  & **RMSE** & **RAE (\%)** & **MAPE (\%)** & **Exec. (s)** & **RMSE** & **RAE (\%)** & **MAPE (\%)** & **Exec. (s)** \\ \hline RF & 3.717 & 8.258 & 9.378 & 79.05 & 7.951 & 18.687 & 21.065 & 48.18 \\ XGBoost & 2.774 & 6.471 & 7.542 & 37.50 & 5.707 & 13.230 & 16.290 & 32.08 \\ LightGBM & 3.329 & 8.062 & 9.582 & 26.90 & 11.453 & 27.069 & 34.179 & 16.68 \\ ElasticNet & 7.256 & 17.975 & 20.556 & 24.39 & 12.695 & 30.808 & 35.267 & 28.24 \\ AdaBoost & 3.144 & 7.975 & 9.551 & 47.46 & 9.709 & 24.406 & 29.441 & 30.22 \\ GBR & 3.248 & 7.122 & 8.170 & 58.79 & 5.736 & 13.928 & 17.470 & 38.64 \\ KNN & 2.870 & 6.835 & 8.120 & 16.98 & 13.323 & 33.220 & 42.218 & 15.01 \\ DT & 4.851 & 10.316 & 12.334 & 19.21 & 9.949 & 24.914 & 29.033 & 18.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test performances of H\({}_{2}\) level prediction under optimal and suboptimal conditions. Figure 6: Linear and nonlinear relationships between H\({}_{2}\) output and input variables. The results of the \(t\)-test analysis (t-statistic = 3.13, \(p\)-value = 0.0166) reveal a statistically significant difference in the models' optimal and suboptimal prediction performance (\(p\) < 0.05). Moreover, [PERSON]'s d value (1.1067) confirms the strong effect of hyperparameter tuning on model performance.
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
###### Abstract  
Ambisonics is a spatial audio technique appropriate for dynamic binaural rendering due to its sound field rotation and transformation capabilities, which has made it popular for virtual reality applications. An issue with low-order Ambisonics is that interaural level differences (ILDs) are often reproduced with lower values when compared to head-related impulse responses (HRIRs), which reduces lateralization and spaciousness. This paper introduces a method of Ambisonic ILD Optimization (AIO), a pre-processing technique to bring the ILDs produced by virtual loudspeaker binaural Ambisonic rendering closer to those of HRIRs. AIO is evaluated objectively for Ambisonic orders up to fifth order versus a reference dataset of HRIRs for all locations on the sphere via estimated ILD and spectral difference, and perceptually through listening tests using both simple and complex scenes. Results conclude AIO produces an overall improvement for all tested orders of Ambisonics, though the benefits are greatest at first and second order.  
Ambisonics; binaural Ambisonic rendering; interaural level difference; virtual loudspeaker; binaural synthesis +
Footnote †: journal: Applied
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 4 Discussion
### CorvisST Airflow Pressure  
In comparison to previous studies [21; 22; 23; 24], the maximum pressure produced by the CorvisST was lower in both magnitude of pressure and duration. Regarding the temporal profile, the values determined in the present study showed a sharper change than suggested previously. Significant differences in the procedures and components used between studies are likely to contribute to the variation in results observed in the literature. [PERSON], [PERSON] [21] also used a piezoresistive pressure sensor to investigate the airflow pressure. The preliminary investigation into the variability of the sensors revealed that peripheral air streams around the differential pressure sensor tube opening had a significant influence on the measured value, leading to an overestimation of the recorded value.  
[PERSON], [PERSON] [22; 23] used a hot wire anemometer to assess the airflow velocity. However, the tenability of relating airflow velocities to pressure values is not comparable to this study given that the wire of the hot-wire anemometer is placed perpendicular to the  
Figure 10: Graphical analysis of the comparison between the CorvisST (blue) and ORA (green) airflow pressure over time with the respective 95%CI error bands (n = 10). The time of respective maximum is highlighted (vertical dotted lines). The differences (CorvisST -ORA) are shown in the diagram below.  
airflow direction: Integration of measurements across the wire length (1-3 mm) can lead to an overestimation of the maximal stagnation pressure.  
Numerical simulations are limited by the assumptions used during modelling. As such, in their simulation model, [PERSON], [PERSON] [24] assumed the velocity at the nozzle to be 80 m/s; e.g., the dynamic stagnation pressure of the airflow was 52.1 mmHg. It may be that the investigators used the values published by [PERSON] [15] describing the prototype of the non-contact tonometer, with an airflow velocity within the range of 83.3 to 111.1 m/s. The values reported by [PERSON] [15] appear to be underestimated; the studies found the velocity of the airflow at the nozzle to be much higher by over 100 m/s [23], 115 m/s [21] and 99.5 m/s in the present study. Using a smaller value leads to an underestimation of the pressure applied to the cornea.
# Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting
## 1 Introduction
Accordingly, we also evaluated the intra- and inter-expert volume estimation difference in MR images and compared these values with our system's volume estimations and expert estimations on AUS images. The results show that our system achieved the volume estimate difference values of human experts. Our novel data set consists of both transverse and sagittal AUS samples from 305 patients. Of these patients, 75 had corresponding MR images from both the transverse and sagittal planes. These AUS and MR images were annotated by several experts during medical treatments. Two experts marked 251 AUS and 73 MR samples at two marking sessions in our experiments. As one of the contributions of our work, this data set is opened to the academic community. We expect this data set to be particularly useful, as, to our knowledge,  
Figure 1: Comparison of AUS and TRUS images of the prostate from transverse plane. While AUS images have lower SNR and contain other anatomical structures, TRUS images have higher SNR, and the prostate is the only anatomical structure contained. there is no AUS data set with corresponding MR markings. Supplementary material for this study is added to [[https://github.com/nurbalbayrak/prostate_volume_estimation](https://github.com/nurbalbayrak/prostate_volume_estimation)]([https://github.com/nurbalbayrak/prostate_volume_estimation](https://github.com/nurbalbayrak/prostate_volume_estimation)) (accessed on 23 January 2022). The rest of this article is organized as follows: Previous work on prostate-segmentation methods on US images is briefly given in Section 2. The proposed method on prostate volume estimation is explained in Section 3. Experiments and results are given in Section 4. The final conclusions and discussions are presented in Section 5.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 4 Simulation Results and Comparative Analysis
### Transient Response Comparison  
Figure 13 shows the transient response of \(d\)-axis and \(q\)-axis currents for different current control techniques, along with the percent of overshoot and response time (\(t_{r}\)), which is the time taken by the response signal to increase from 10-90% of the final value. For the PI controller, there was a significant overshoot of 40%, and the response time was the slowest among all the controllers. The PR controller was able to achieve a lower overshoot of 8% with a slightly faster response time, while the overshoot was negligible at 1% with the hysteresis controller. The MPC controller, on the other hand, was able to achieve both of these objectives of a low overshoot and fast response time (about 50% reduction in response time), but at the cost of a higher average switching frequency about three-times higher than the PI and PR controller. The supplementary ADP was able to reduce the overshoot to 26% from the 40% value of the PI controller with a similar response time. However, the main advantage with this approach is that there was no increase in the switching frequency as in the case of MPC. Moreover, the constant frequency with ADP controller means the harmonics in the current are predictable and can be filtered out easily.  
Figure 13: Comparison of \(d\)-axis and \(q\)-axis current responses. (**a**) PI controller. (**b**) PR controller. (**c**) Hysteresis controller. (**d**) MPC. (**e**) Supplementary ADP controller; O.S.: Overshoot.