esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applications in Human-Robot Interaction
### Social Robotics
For this, Client/Server communication using traditional TPC/IP sockets in a local area network is used to connect Unity with Choregraphe [71] (the official visual programming environment [72] and simulation platform for Pepper and NAO social robots). The results of [68] suggest that users may prefer closer interaction with a physical robot than its virtual representation. The authors of [68] infer that this difference can be influenced by the potential discomfort that can be produced by a VR environment with technical limitations, such as a limited field of view, low visual fidelity, or a low-resolution display. Another exception is using VR environments to remotely control social robots. In this context, refs. [60; 69] have proposed ROS-based systems for controlling NAO and QTRobot.
## 5 Conclusions  
Through a series of experiments on the preparation, manufacturing and loading performance of self-compacting SFRC pervious frames, the feasibility of applying SFRC pervious frames for river revetment is finally demonstrated. The following conclusions can be drawn:  
1. The mix proportion, the workability of fresh mix, and the mechanical properties of self-compacting SFRC used for producing the pervious frames were experimentally studied with the change of the steel fiber content. The rational volume fraction of steel fiber was determined as 0.8% considering the requirement of this special application.
2. With the good workability of self-compacting SFRC, the casting process of SFRC from an inclined rod to other inclined rods, and finally to horizontal rods leads to a conducive distribution of steel fiber along the length of the rods. This improves the mechanical properties of the pervious frame subjected to tensile forces during lifting and stacking.
3. During lifting, the frame at the bottom layer is more likely to be damaged than that at the upper layer, which may be broken on the inclined rods. During stacking, the upper-layer frame is more prone to cracking and failure than the first-layer frame, but not including breaks in the rod. The frames allow six layers lifted together and nineteen layers stacked without any damage.
4. The self-compacting SFRC pervious frames have been successfully applied in a river revetment project. This is a new engineering application for self-compacting SFRC.  
Conceptualization and methodology, H.G., X.D. and X.L.; validation, H.G. and J.S.; formal analysis, H.G. and H.D.; investigation and data curation, J.S., H.D. and C.L.; writing--original draft preparation, H.D. and C.L.; writing--review and editing, H.G. and X.D.; supervision, X.L.; funding acquisition, H.G. and X.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Natural Science Foundation of Henan, grant number 212300410192; the Special Joint Research Project of Zhengzhou City and NCWU, Henan, China, grant number 2021013; and Fund of PhD students at NCWU, China, grant number HSZ021252.  
Not applicable.  
Figure 12: Batch production and dropped into the river.  
Data Availability Statement: Data is available with the first author and can be shared with anyone upon reasonable request.
## 4 Experimental Results
### Results of Experiments with Subtitles
Many answered that arranging multiple sign language interpreters for each speaker, as proposed in the basic type in step 1 for the panel discussion clip, was confusing and financially not feasible. The subjects' opinion was that it would be very good to have one sign language interpreter and individual subtitles for each speaker. A case where there is only one sign language interpreter performing while placing multiple subtitles separately for each speaker in the panel discussion, as shown in Figure 21, showed the highest preference at 62%. Most of the subjects responded that it was less confusing and easy to understand which speaker was talking when subtitles appeared for each speaker. There was one opinion that it would be nice to provide subtitles in the form of speech bubbles. In the weather forecast type, most of the subjects preferred the presence of subtitles, while 9% of the respondents preferred the clip with no subtitles. They said the reason is that subtitles are not necessary because the sign language interpretation and the reference weather report data provided can be enough, to some extent, when it comes to weather reports. In conclusion, based on the results of experiments and subjects' opinions, the role or necessity of subtitles can be identified. Therefore, subtitles are necessary and should be provided in 2-3 lines and stay longer on the screen so that DHH individuals can read them along with a sign language interpretation. In panel discussions with multiple speakers appearing, the need for individual subtitles was also found.
## 6 Experimental Procedure and Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Value**} \\ \hline
**Evaluation Metrics** & **Conamination Parameter** & **Conamination** \\  & **a = 0.2** & **Parameter a = 0.3** \\ \hline Accuracy & 0.91 & 0.91 \\ \hline Precision & 0.91 & 0.88 \\ \hline Recall & 0.95 & 1 \\ \hline F1-Score & 0.93 & 0.94 \\ \hline ROC AUC & 0.89 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Robust Covariance Ellipse evaluation metrics for gamma = 0.2 and contamination parameter = 0.2 and 0.3. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Ensemble Approach**} \\ \hline
**Evaluation Metrics** & **Majority Voting** & **Averaging** \\ \hline Accuracy & 0.94 & 0.97 \\ \hline Precision & 0.92 & 1 \\ \hline Recall & 1 & 0.95 \\ \hline F1-Score & 0.96 & 0.98 \\ \hline ROC AUC & 0.91 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ensemble approaches’ evaluation metrics. Ensemble, Max Ensemble can be employed, where the max values from each model are used. The corresponding anomaly scores are shown in Figure 20.  
An additional important piece of information from the generated anomaly scores is related to the detection of conditions where the fault is more intense or detectable. It is known [4; 6] that the operating conditions of motor speed and load affect the occurrence and detectability of the fault. By using anomaly scores, we can see in which operating condition the highest anomaly score is displayed, as well as compare each operating condition with the corresponding one with a fault.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 3 Corrosion and Degradation of Structural Materials in Farm Environment
### Acid Corrosion Mechanism in Cement Structures
#### 3.4.1 Inorganic Acids
Cement additive ratios (ultra-fine fly ash (UFFA) and fly ash (FA)) impact the mass losses. The least mass losses and reduction in compressive strength were reported in 50% FA + 10% UFFA--a phenomenon that underscores the importance of supplementary cementitious material on the durability of concrete structures exposed to acidic environments. Figure 4: Impact of sulphuric acid attack on the durability of concrete specimens compared to the control [19]. “Reproduced with permission from [PERSON]; [PERSON]; [PERSON]; [PERSON]. Concr. Res; published by Elsevier, 2017.” \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Concrete Mix**} & \multicolumn{2}{c}{**7 Days (MPa)**} & \multicolumn{2}{c}{**14 Days (MPa)**} & \multicolumn{2}{c}{**28 Days (MPa)**} & \multicolumn{2}{c}{**56 Days (MPa)**} & \multicolumn{2}{c}{**90 Days (MPa)**} \\ \hline Mix 1 & OPC (control) & 29.4 & 27.6 & 24.03 & 23.47 & 21.23 \\ Mix 2 & 20\% FA + 10\% UFFA & 27.3 & 26.27 & 24.6 & 24.53 & 22.5 \\ Mix 3 & 30\% FA + 10\% UFFA & 26.23 & 25.73 & 25.53 & 25.13 & 23.4 \\ Mix 4 & 40\% FA + 10\% UFFA & 22.27 & 22.7 & 22.4 & 20.83 & 20.13 \\ Mix 5 & 50\% FA + 10\% UFFA & 20.6 & 21.67 & 21.73 & 20.73 & 20.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Loss of compressive strength in acidic environments [15]. Figure 5: (**a**) Virgin ordinary Portland cement (OPC). (**b**) Concrete microstructure following exposure to a sulphuric acid environment [15]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**3 Days (MPa)**} & \multicolumn{2}{c}{**7 Days (MPa)**} & \multicolumn{2}{c}{**14 Days (MPa)**} & \multicolumn{2}{c}{**28 Days (MPa)**} & \multicolumn{2}{c}{**56 Days (MPa)**} & \multicolumn{2}{c}{**90 Days (MPa)**} \\ \hline Mix 1 & OPC (control) & 0.52 & 2.22 & 3.64 & 4.09 & 4.17 & 4.79 \\ Mix 2 & 20\% FA + 10\% UFFA & 0.62 & 0.68 & 0.94 & 1.08 & 2.28 & 2.65 \\ Mix 3 & 30\% FA + 10\% UFFA & 0.58 & 0.62 & 1.08 & 0.89 & 2.09 & 2.23 \\ Mix 4 & 40\% FA + 10\% UFFA & 0.30 & 0.57 & 0.92 & 0.85 & 1.67 & 1.96 \\ Mix 5 & 50\% FA + 10\% UFFA & 0.31 & 0.69 & 1.09 & 1.08 & 1.74 & 2.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Concrete mass losses in acidic environments [15].
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 2 The Principle and Theory of Rainfall Measurement by CML
### Example Demonstration  
Figure 2 shows the relationship between the total attenuation of the CML and the rainfall intensity measured by RG. The simulation data come from an open access dataset [34], which contains the signal power total loss collected from six E-band (71-76 GHz and 81-86 GHz) CMLs and the rainfall intensity collected from four RGs in Prague, Czech Republic. We selected the data from one CML of the six and one RG, which is nearest to the selected link midpoint, to generate the signal level time series of 24 h (23 August 2018 19:39-24 August 2018 19:39) with a time resolution of 1 min. It can be seen that there is a clear correlation between CML power total loss and rainfall intensity, and even very low intensity rainfall can be perceived by the CML measurement, because the E-band CML is more sensitive to rainfall than other used frequency bands (roughly from 10 to 50 GHz) at present [35; 36].
# Experimental Simulation of Thunderstorm Profiles in an Atmospheric Boundary Layer Wind Tunnel
## 1 Introduction
The objective of this paper is to propose the use of a passive device to reproduce the wind speed profile of thunderstorm downbursts in an ABL wind tunnel. For this purpose, a specially designed grid was used in addition to traditional devices in order to replicate thunderstorm mean wind speed and turbulence profiles, hence avoiding active devices. The temporal evolution of the thunderstorm is not considered, and attention is focused only on the vertical mean wind speed profile corresponding to the worst-case scenario with respect to the structural response (i.e., during the velocity ramp-up, at the end of the passage of the primary vortex, e.g., [10]). This study represents the first stage of a research project aimed at comparing wind loads and cladding pressures generated on tall buildings of heights in the range of 100 m to 300 m, by synoptic and thunderstorm phenomena. At first, a literature review about the available experimental techniques for thunderstorm downburst simulation and their properties in terms of mean profile and turbulence characteristics is provided in Section 2. Then, Section 3 presents the experimental setup adopted. The results and main observations during the tests are discussed in Section 4, in terms of mean velocity and turbulence properties. Finally, the conclusions of the work are given in Section 5.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 3 WUR Performance Metrics and Requirements
System Impact: Cellular IoT uses licensed spectra that are costly for service providers. The introduction of WURs should co-exist with legacy signals and cause minimum system overhead to the base stations: this includes time and frequency resource elements (REs) allocated for the WUS, and the impact of WUS on system capacity and base station energy consumption.
## 1 Introduction
Yet, existing studies mainly focus on simply utilizing topography, vegetation indices, and meteorological data as model inputs for GPP estimation. They ignore the process of GPP synthesis and lack the influence of plant physiological activation [28]. Ecosystems constantly adjust plant growth to cope with the changing environment, causing seasonal variations and the formation of transitional periods known as phenology [29]. Changes like earlier leaf growth and delayed crop activity could affect the seasonal climate and CO\({}_{2}\) absorption [30]. Thus, phenology greatly affects ecosystem productivity [31] and is vital for carbon fixation and photosynthesis. At the leaf scale of a crop, chlorophyll (Chl) content per unit leaf area is closely related to the photosynthetic rate [32; 33; 34]. A previous study established a close link between GPP and Chl [35]. However, obtaining significant observed chlorophyll data is challenging [36]. Owing to the close correlation between leaf Chl content and leaf nitrogen content per unit leaf area (i.e., specific leaf nitrogen (SLN)) [37; 38], the leaf photosynthetic rate is also strongly associated with SLN [39; 40; 41]. SLN changes with different phenological stages [42], and the maximum allowable SLN is a function of the phenological stage [43; 44; 45]. Therefore, it is of great significance to consider the maximum allowable SLN and plant phenology as factors potentially affecting the leaf photosynthetic rate in ML methods. This approach is expected to enhance the estimation of maize GPP via ML methods from a physiological aspect by integrating SLN and phenology. In this study, widely used meteorological data (i.e., solar shortwave radiation (SSR) and air temperature (Tair)) and a satellite vegetation index (i.e., NDVI) were selected to compose the control group of the input combination. The vegetation index is highly correlated with GPP [46]. GPP is directly controlled by SSR [47]. Air temperature (Tair) affects the carbon absorption of vegetation [48]. For comparison, input combinations including SLN and maize phenology (represented by the normalized maize phenology (NMP)) alone or simultaneously were created. The purpose of this study is to first verify the importance of SLN and NMP, via RF methods, in improving maize GPP estimation with different input combinations. Subsequently, the optimal input combination including SLN or NMP will be applied to validate and compare three other ML methods' performance (i.e., the SVM, CNN, and ELM methods). This study attempts to determine the importance of SLN and NMP in improving GPP estimation via ML methods and provide a reference for similar research.
## 4 Result and Discussion  
The information collected from Coimbra Hospital (at the university center) in Portugal is taken from the UCI repository. The missing values are replaced with the mean and mode method in the first phase. Here, a mean value is used for the quantitative attributes, and the mode method is applied for the qualitative attributes. Accuracy and F1-score are chosen as basic metrics. This proposed work consists of three stages: First, it selects the most significant representative features based on the embedded and wrapper methods. Following that, four machine-learning algorithms, namely, LR, k-NN, DT, and RF, are employed for classifying HCC. Finally, a comparative study is done based on the smaller number of selected features with the highest accuracy. Figure 4 shows the accuracy and F1-score obtained after filling in the missing values using the mean and mode method. The result showed that compared with other classification algorithms, RF gives a greater accuracy value and a higher F1-score value, of 82.26% and 81.36%, respectively, using the parameter values of n_estimators = 100, criterion = \"entropy\", and random_state = 35. Figure 5 depicts the result obtained after replacing the missing values using k-NN. The experimental results indicate that RF gives a greater accuracy value and a higher F1-score value, of 85.48% and 85.71%, respectively, using the parameters of n_estimators = 100, criterion = \"entropy\", and random_state = 35. Compared with the two missing-value-replacement methods, the k-NN method provides a more accurate result without using the feature-selection technique. Similarly, the RF method gives a higher accuracy. Figure 4: Mean and mode methods without using feature selection. The feature-selection method is used to increase accuracy. Table 2 shows the features chosen by employing mean- and model-based missing-value replacement with LASSO regularization. When employing this strategy, 28 features are chosen. The machine-learning algorithm receives these selected features as input. RF produces a greater accuracy value and a higher F1-score value, of 95.16% and 94.74%, respectively, when employing parameter values of n estimators = 100, criterion = \"entropy,\" and random state = 35, as shown in Figure 6.  
The features obtained for the k-NN using LASSO regularization are shown in Table 2. When this approach is used, 25 features are obtained. These pre-selected features are fed into the machine-learning algorithms. When using parameter values (n estimators = 50, criterion = \"entropy\", random state = 60), RF algorithm provides a greater accuracy value and a higher F1-score value, of 94.11% and 93.33%, respectively, as shown in Figure 7.  
The features chosen for mean and mode based on l2 regularization are shown in Table 2. Here, 17 attributes are chosen. These selected features are fed into the machine-learning algorithm. When parameter values (metric ='manhattan', n neighbors = 1, weights = 'distance') are used, k-NN provides a greater accuracy value and a higher F1-score value, of 91.67% and 90.91%, respectively, as shown in Figure 8.
## 2 Materials and Methods
### Overview of Research Methodology  
The three-step framework for discussing the potential of monitoring NPP dynamic changes with SIF under drought events in this study is shown in Figure 2. Firstly, we examine the consistency between SIF and NPP in terms of numerical spatial distribution, spatiotemporal correlations, and dynamic trends. Second, we quantify the relationship between SIF and NPP by constructing the SIF/NPP ratio index, and we analyze and discuss the changes in the relationship between SIF and NPP from the perspectives of different drought events and drought levels. Finally, we discuss the sensitivity of SIF to the environmental parameters of the inverted NPP. Our study explores the potential of SIF to monitor the dynamic changes in NPP induced by drought through the above framework and provides a theoretical basis for understanding the relationship between SIF and NPP as well as monitoring the dynamic changes in NPP induced by drought.
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
###### Abstract  
Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 \(\upmu\)m CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity.  
EEG; lossless compression; VLSI architecture; wireless body area network +
Footnote †: journal: Applied Soft. 2018
## 2 Related Works
### Semantic Interpretation  
There are many evidences that feature maps generated by successive convolutional layers of a CNN correspond with some semantically important parts of the analyzed images [3]. The identification of relationships between these parts and feature maps is not, however, a simple task. First of all, CNNs were always treated as trainable black boxes (similarly to other neural networks) and while designing their architecture - no attention was paid to how the intermediate outputs can be interpreted. The resulting feature maps are usually blurred and it is really hard to understand the relation between them and the content of the input images. Moreover, in classic CNN architectures (pooling layers and no padding) the size of the feature maps is reduced in consecutive layers. This leads to further problems with identifying the precise location of semantically important regions.  
In the literature there can be found several techniques trying to reveal the aforementioned relationships. In [4], first the peaks of the feature maps are mapped onto visual (receptive) fields within the input image. There their correspondence with known semantic parts is checked. As more than one feature map may be connected with a given part, a genetic algorithm is then applied to find the most appropriate subset of the feature maps from all the convolutional layers. In [5], instead of the layer outputs, their gradients maps, calculated with backpropagation algorithm, are used to find activation centers. In [16], the authors introduce class activation mapping (CAM), which can be used for identification and visualization of discriminative image regions, as well as for weakly supervised object localization. In that approach, a CNN network must be trained to classify images (supervised learning) and typical fully-connected layers are replaced by global average pooling (GAP) followed by a fully connected soft-max layer. The CAM for a given class can be found as a linear combination of the final feature maps generated by convolutional layers with weights corresponding to a specific network output. As the size of the class activation map is equal to the size of the final feature maps, it must be upsampled to be comparable with the input image. Finally, in [6], the authors assume that the top layers of a network correspond to the bigger parts or whole objects, while the lower layers reflect smaller parts which are building blocks for the more complex ones. They propose a method that is able to automatically discover a graph describing these relationships. It should be noted, that all these methods, although interesting, are quite complex. Moreover, they try to find correspondence with known parts (supervised process), which need not be optimal in every application.