esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results of Computer Modeling  
To check the efficiency of the presented MIMO ERIP equalizer, a comparative simulation was carried out under the conditions of a stationary ionospheric shortwave multipath channel implemented in accordance with the recommendation ITU-R F.1487 [18] for the case of low-latitude quiet conditions and high-latitude quiet conditions for a system with two transmitting and two receiving antennas. The signal bandwidth was taken to be 500 kHz, the number of subcarriers was 128, and the spacing between subcarriers was 320 Hz.  
Modeling was performed for two channels' parameter combinations, corresponding to an HF low-latitude channel with quiet conditions (HFLQ) and a high-latitude channel with quiet conditions (HFHQ). The first channel model corresponds to two beams with equal average power and a delay between beams of 1 ms. Figures 7 and 8 show the dependence of the uncoded bit error ratio (BER) and the error vector amplitude (EVM) over the SNR, respectively, for the classic single-tap FBMC-OQAM ZF equalizer, MIMO ERIP equalizer, parallel multistage equalizer for the HFHQ channel model and the theoretical minimum for the case without the channel influence. Based on the presented dependencies, we can observe the close performance values of the MIMO ERIP algorithm compared to the parallel multistage equalizer, which, taking into account the lower computational complexity, makes the ERIP-based algorithm the preferred option for systems that are critical to the computational complexity of the implementation of the receiving path. The  
Figure 6: ERIP and parallel multistage computational efficiency versus number of processed symbols and subcarriers.  
performance loss at high SNR values can be explained by the imperfect calculation of the real and imaginary parts of the interference correlation coefficient due to prototype filter ambiguity function truncation by one surrounding subcarrier and three surrounding symbols.  
Figures 9 and 10 show the dependence of the uncoded BER and EVM over the SNR for the same algorithms as in the previous case but in HFLQ channel conditions. For these conditions, the channel impulse response can be represented by two beams with equal power and a delay between them of 0.5 ms. The HFLQ channel has less difficult conditions, which explains the better performance of all presented algorithms, but we can see that the difference between MIMO ERIP and parallel multistage algorithms grows, which can also be explained by the growing impact of residual self-interference.  
Figure 8: EVM performance versus SNR for HFHQ channel model.  
Figure 7: Uncoded BER performance versus SNR for HFHQ channel model.
## 4 Results and Discussion  
### Sensitivity Testing  
Four experimental survey lines (Figure 9) were laid out, each with the same length and identical shot and receiver point locations. Each survey line used the same type of geophones for data acquisition, with a receiver line spacing of 10 m. The solid black lines represent the receiver lines, each 1440 m long, with a geophone placed every 10 m. The red line represents the shot point alignment line, with a shot-receiver distance of 10 m. All four lines simultaneously recorded seismic signals, resulting in single-shot records and  
Figure 8: Spectra of seismic signals with different main frequencies (**a**—60 Hz; **b**—30 Hz) after the overtime window.  
Figure 7: Spectra corresponding to time windows with different widths.  
the corresponding signal-to-noise ratios (Figure 10). The digital geophones used were SmartSolo node geophones with a frequency response range of 0 to 413 Hz. The remaining lines used dynamic coil geophones with dominant frequencies of 60 Hz, 10 Hz, and 7 Hz, respectively.  
The sensitivities of 60 Hz, 10 Hz, and 7 Hz digital geophones used in this area were analyzed (Figure 10). The data collected using the 7 Hz geophone showed a good continuity of the target layer, a strong reflected wave energy, and a high signal-to-noise ratio. In areas characterized by high signal-to-noise ratio, near-surface absorption, and the attenuation of general areas, digital detectors with high-frequency bandwidths showed obvious advantages. However, in areas characterized by high near-surface absorption, the attenuation of intense areas, and low signal-to-noise ratio, low-frequency detectors were more advantageous.
## 4 FDTD Algorithm
### Update Equations  
The FDTD method is based on a leapfrog time-stepping scheme with update equations for the electric and magnetic fields derived from discretized Maxwell's equations [9]. Below, for any discretized function \(F:\mathbb{R}^{4}\rightarrow\mathbb{R}\) representing one of the components \(E_{x}\), \(E_{y}\), \(E_{z}\), \(H_{x}\), \(H_{y}\), \(H_{z}\), we denote  
\[F^{N}(I,J,K)=F(I\cdot\Delta x,J\cdot\Delta y,K\cdot\Delta z,N\cdot\Delta t)\]  
where \(\Delta t\) is the time-step size and \(\Delta x\), \(\Delta y\), \(\Delta z\) are the spatial-step sizes along the \(x\), \(y\) and \(z\) directions, respectively.
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 4 Performance Evaluation
### Comparison with Existing Solutions
This makes our method more suitable for real-time applications requiring rapid data processing and high scalability.
# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 1 Introduction
The subsequent row depicts the results predicted by MAFE, demonstrating consistency with the sample labels. fine-grained features of the input image. This is achieved through the introduction of multi-scale and fine-grained branches. Concurrently, the region division strategy, which is based on facial feature points and the attention weight selection mechanism, enables the model to concentrate on the crucial facial regions. In the region detail feature fusion module, the method presented in this paper effectively combines crucial regional features and fine-grained features, ensuring that the fusion features both encompass crucial region information and retain the fine-grained information of the original image. The consistent feature recognition module uses con-feature loss to improve the consistency between fusion and global features, boosting expression recognition accuracy. Our method's effectiveness is confirmed by experiments on two public datasets and two occlusion test datasets, showing that it outperforms several state-of-the-art OFER methods. Our work offers a promising solution for enhancing the performance of OFER models. In particular, the contributions of this paper can be summarized as follows:  
1. A straightforward multi-scale branching PTIR-50 is devised for the purpose of extracting global information from an image. The multi-scale feature pyramid and transformer module in the PTIR-50 are capable of capturing multi-scale information from an image, along with the long-distance dependencies within the image. This results in more comprehensive and precise feature representation. Furthermore, the utilization of a region division strategy based on facial feature points and an attention weight selection mechanism enables the model to focus on crucial regions, while ignoring the occluded regions. This effectively addresses the OFER problem. 2. In order to obtain more discriminative expression features and prevent feature redundancy during the feature fusion process, a con-feature loss function is proposed for the processing of features extracted from different branches. Therefore, the global, regional, and detailed information present in the image is fully utilized for the purpose of expression recognition. 3. The experimental results demonstrate that our OFER solution attains state-of-the-art results on the Occlusion-RAFDB and Occlusion-FERPlus datasets, with 89.42% and 86.94% accuracy. Furthermore, high accuracy was attained on the original RAF-DB and FERPlus datasets, with respective rates of 92.11% and 90.15%. The remainder of this paper is organized as follows. Section 2 reviews the related work in FER and OFER. Section 3 introduces the proposed method MAFE in detail, including its overall architecture and core components. Section 4 presents the experimental setup, datasets, and evaluation metrics, followed by a thorough analysis of the results. Finally, Section 5 concludes the paper.
## 5 Element Technologies for Line Recognition
### Detecting Line Signs and Flow Arrows in a Diagram and Determining Line Types
#### 5.3.2 Changing of Line Types and Merging of Lines of the Same Types  
It is necessary to determine whether the line signs and the continuous lines overlap to change the line type. The procedure to assess the overlapping of the line signs with the continuous lines is as follows. In the first step, the distance from the center of the bounding box of the line sign is calculated for a specific continuous line. If the computed distance is shorter than the width and height of the bounding box, proceed to the next step. In the second step, it is examined whether this continuous line crosses the bounding box of the line sign or a part of the continuous line is contained in the bounding box. If this is the case, it is judged that this continuous line overlaps with the line sign, and the type of continuous line is changed according to the line sign.  
After changing the line type, post-processing is performed through which multiple lines that have the same type and are connected to each other are merged into one line. For example, after changing the line type, there are cases in which multiple dotted lines exist, as shown in Figure 16a. If these dotted lines are located on the same infinite line and connected to each other, they are merged into one dotted line.
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 5 Discussion of Results
### sDA, ASE, and DA\({}_{\text{avg}}\)
* In most models, achieving IES-recommended sDA levels (50-75%) necessitates exceeding the 10% ASE limit recommendation. Additionally, in London north-facing models, ASE surpasses 10%, but the UDI values suggest that excessive illuminance is confined to areas near the window. * Spatial metrics like sDA and ASE lack granularity; they overlook variations that UDI captures more precisely. Models with identical sDA (100%) and ASE values (64-66%) show major differences in daylight distribution when compared with the UDI results.
###### Abstract
[10] considered 13 indicators affecting tunnel face classification, using ensemble learning algorithms like gradient boosting regression trees (GBRT) and random forest (RF) to systematically study rock mass quality prediction. [PERSON] et al. [11] utilized TBM operational parameters and classifications obtained through HC to develop an ensemble learning model for predicting tunnel face rock mass quality using classification and regression trees and adaptive boosting (AdaBoost). Clearly, machine learning methods show promising performance, with ensemble learning algorithms demonstrating significant advantages due to their strong adaptability and robustness. Despite advancements in rock mass classification research, several shortcomings remain. For example, most studies focus on open-pit mines, tunnels, and subway projects, with relatively few studies on deep mines. As shallow mineral resources deplete, deep mining becomes necessary, requiring enhanced research on deep rock mass classification. Deep mining introduces more complex environments, making comprehensive geological surveys difficult and leading to insufficient geological information. Accurately assessing rock mass quality with limited geological data becomes a critical issue in engineering design and construction. To address these challenges, this study collected rock mass quality data from various geotechnical engineering projects, including different tunnels and underground mines, and established a multi-source underground engineering rock mass classification database. By integrating data from different regions and types of projects, the database provides comprehensive and diverse information, reducing the reliance on specific regions or types and avoiding data source limitations. Additionally, this study developed a hybrid ensemble learning model combining the zebra optimization algorithm (ZOA) and extreme gradient boosting (XGBoost) for rock mass quality classification in underground engineering. The hybrid model overcomes the limitations of single algorithms and maintains good predictive performance with incomplete data, providing a reliable and practical solution for rock mass classification in underground engineering. Section 2 introduces the principles of ensemble learning and optimization algorithms and details the construction of the hybrid model. Section 3 describes the establishment and preprocessing of the multi-source database. Section 4 explains the evaluation metrics and training process of the hybrid model. Section 5 evaluates the model's performance and validates its effectiveness through practical engineering applications. Section 6 discusses the study's limitations and future improvements. Finally, Section 7 summarizes the research and conclusions.
# Broadening of Light Coupling to Waveguide Modes in Solar Cells by Disordered Grating Textures
## 3 Results and Discussion
### Texture Analysis  
In order to prove a possible broadening of the transferred wave vector, we investigated the variety of applied disordered interface textures. A perfect periodic interface structure reveals a well-defined wave vector transfer by light diffraction into different orders. The transferred wave vector can be directly determined from the period of the grating. In the case of a disordered texture, it is necessary to apply a Fourier analysis to obtain the distribution of lateral feature sizes. Perfect periodic gratings show distinct peaks in the Fourier transform, whereas fully randomized textures result in a broad Fourier spectrum. In other words, any possible broadening of the transferred wave vector in a disordered grating texture is connected to a broader peak in the Fourier transform of the texture. Therefore, FFT is applied to our assumed disordered grating textures in order to evaluate the broadening of the transferred wave vector.  
Figure 2 shows selected interface topographies and their corresponding FFT images for the case of the perfect periodic grating, the disordered texture assuming a position disorder with \(\sigma_{p}=22\) nm, and the disordered texture with an introduced radius disorder of \(\sigma_{r}=32\) nm. As expected, the FFT image of the perfect periodic grating structure only consists of distinct peaks at integer multiples of the reciprocal grating period. This reflects the well-defined wave vector transfer when a periodic texture is applied to a wave guiding slab. For both disordered textures, a diffusive signature is visible in the FFT images. For the structure with radius disorder (Figure 2c), there is only a weak diffusive background visible in the FFT (Figure 2f) where the peaks related to the diffraction orders are still dominating. In contrast, the diffusive part strongly dominates the FFT in the case of an applied position disorder (Figure 2e). Note that the applied standard deviations cannot be directly compared as their boundaries are different. It is therefore concluded that position disorder influences the wave vector transfer much more strongly than radius disorder.
## 2 Materials and Methods
### Experimental Setup  
A lightweight inertial measurement unit (ICM-20601, InvenSense, San Jose, CA, USA, mass 4 g), combining a tri-axial accelerometer (measurement range \(\pm\)353 m/s/s) and a tri-axial gyroscope (measurement range \(\pm\)4000 deg/s), was attached to the shaved skin at the medial aspect and mid-distance between the malleolus and the plateau of the runner's right tibia [25]. An elastic strap was used to stabilize the sensor to prevent excessive movements due to its own weight. Additionally, the same sensor type was affixed to the heel cap of the right shoe. All sensors operated synchronously and data were sampled at 3750 Hz by a data logger, secured in a waist belt. The data logger and PC were connected via a 10-m cable. To confirm FS pattern of the runners, running was controlled visually using high-speed video (Exilim EX-F1, Casio, Tokyo, Japan, 300 Hz). Ground reaction force measurements were simultaneously recorded using a force plate (9287 BA, 0.6 \(\times\) 0.9 m, 3750 Hz, Kistler, Winterthur, Switzerland), which was synchronised with the inertial sensors. Data post-processing was done using MATLAB R2016b (MathWorksTM, Natick, MA, USA).
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 5 Implementations in Graphics Processing Unit (GPU)
### Proposed CUDA Block and Thread Mapping Strategies
#### 5.2.3 Shell-Block Mapping (SBM)  
Shell-Block Mapping (SBM) assigns GPU threads based on Algorithm 3. More specifically, one CUDA block is assigned to one of the shell-based ERI, in which CUDA threads in each CUDA block compute the Basis-ERI by the parallel algorithm shown in Algorithm 2. To show the basic idea behind the thread assignment in SBM, Figure 9 shows a parallel thread assignment of CUDA blocks and CUDA threads to the shell-based ERI computation in SBM. The reader may note the relevant roles of Algorithms 2 and 3 in the construction of the components. As described in Section 4.1, when computing the GTO-ERIs within four shells, performance can be improved by efficiently reusing previously calculated values of the Boys function. By capitalizing on this notion, the CUDA threads in each CUDA block in line 2 of Algorithm 3 compute all the values of the Boys function required for the GTO-ERI calculation in advance. Afterward, the computed values are reused in subsequent GTO-ERI calculations to avoid redundant computation. Different from BBM and BTM, the resulting value of the calculation for each CUDA block is accumulated, as shown in line 4 of Algorithm 3, implying the possibility of accessing similar memory addresses at the same time, thus inducing the requirement for exclusive memory access. CUDA provides the _atomicAdd_ function that enables the accumulative computations in memory exclusively [46].  
Figure 8: Basic idea of the parallel thread assignment of CUDA blocks and CUDA threads to the Basis-ERI computation in BTM.  
Therefore, the proposed implementation capitalizes on the _atomicAdd_ function to realize parallel memory access. As such, SBM offers the advantage of significantly reducing the number of Boys function evaluations compared to BBM, yet it incurs increased memory access operations.
## 5 5G: The Broadband \"Game Changer\"  
The outgoing 4 th generation long-term evolution (4G LTE) system supports data transfer speeds of up to 100 Mbps downlink [6]. Its extension, the 4G+ (LTE-A) system supports rates of up to 300 Mbps, and by using small cells to provide additional broadband coverage we have been able to achieve downlink rates of up to 500 Mbps in the microwave frequencies [6].  
The implementation of fifth generation (5G) cellular systems heralds the move towards more sophisticated and smarter technology [16]. 5G licensing started in the second half of 2020 and covers the microwave frequencies of 700 MHz, 3.5 GHz for urban environments (cities), and 28 GHz [5]. A bandwidth up to 100 MHz is allocated for urban topologies and for the 3.5-3.6 GHz microwave band [5]. Thus, it is possible--and in many cases, essential--to establish smaller cells than the 4G infrastructure, with a radius of 100 m or even smaller depending on the coverage scenario in question. The criterion for cell deployment is the minimum broadband per device (Mbps per device), which indirectly includes user density (device density) [5].  
5G deployment allows for transmission rates ranging from 250 Mbps per device up to peak rates of 1 Gbps per device [3]. At such broadband speeds, all multimedia applications that constitute the 5G use cases can be supported, as well as AR/VR applications [17]. Smart city platforms can be implemented with a very low network latency, since both the transmission delay (due to the high transmission rate) and the propagation delay (due to the smaller cells and, therefore, smaller propagation distances) will have significantly lower values [18].  
The deployment of 5G in relation to smart city strategic designs and implementation will exploit all the advantages of this smaller cell, higher bandwidth, and higher spectral efficiency technology, with a focus on broadband connectivity per device, thus addressing one of the main network-related challenges of smart cities: user density in a topology with high density of obstacles and shadowing effects. The densification of 5G base stations resolves the smart city's need for more antenna systems per unit area (base station density) [19].  
5G can be described as a software-defined network architecture that can be dynamically deployed to provide the essential level of control for a given application. It is also possible to dynamically allocate resources based on network slicing to serve private customers and implement private use cases. This will facilitate novel business use cases. This potential impacts positively on ad hoc networks in general and on smart city scenarios in particular [20].  
The role of 5G as an enabler of smart city technology is studied in the next section.
# Metal Nanozymes: New Horizons in Cellular Homeostasis Regulation
## 2 A Short History of Nanozymes
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nanozymes** & **Substrate** & **K\({}_{\text{m}}\) (mM)** & **Vmax (M \(\times\) s\({}^{-1}\))** & **Ref.** \\ \hline \multirow{2}{*}{V\({}_{2}\)O\({}_{5}\)} & TMB & 0.738 & \(1.85\times 10^{-5}\) & [21] \\  & H\({}_{2}\)O\({}_{2}\) & 0.232 & \(1.29\times 10^{-5}\) & [21] \\ \hline \multirow{2}{*}{Fe\({}_{3}\)O\({}_{4}\) MNPs} & TMB & 0.434 & \(10.00\times 10^{-8}\) & \\  & H\({}_{2}\)O\({}_{2}\) & 154 & \(9.78\times 10^{-8}\) & [21] \\ \hline \multirow{2}{*}{MOF(Co/2 Fe0)} & TMB & 0.25 & \(0.38\times 10^{-7}\) & \\  & H\({}_{2}\)O\({}_{2}\) & 4.22 & \(0.49\times 10^{-7}\) & [20] \\ \hline AuNPs & glucose & 0.41 & \(0.10\times 10^{-6}\) & [22] \\ \hline ZnO & starch & 0.022 & \(1.81\times 10^{-6}\) & [23] \\ \hline GOx-Fe\({}^{\theta}\)@EM-A & H\({}_{2}\)O\({}_{2}\) & 29.59 & \(3.81\times 10^{-8}\) & [24] \\ \hline \hline TMB\(-\)3,3,5,5-tetramethylbenzidine, K\({}_{\text{m}}\)—[PERSON]–[PERSON] constant, V\({}_{\text{max}}\)—maximum rate. & & \\ \end{tabular}
\end{table} 
Table 1: Comparison of the Km and Vmax of selected nanozymes.