esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of Data Analysis
#### 2.2.2 Canopy Transpiration Estimation and Evaluation  
Random Forest model can explore relationships between independent variables and dependent variables [43]. In this study, 66.7% of the SIF, Tr, and environmental factor (including PAR, Ta, VPD, SM, and Ws) observations were allocated to the training dataset (for model calibration), while the remaining 33.3% were assigned to the validation dataset (for performance evaluation). We established separate Random Forest models using the 'randomForest' package in R 4.3.3, with SIF and environmental factors at two temporal scales across different plantations as predictors and Tr as the response variable:  
\[\mathrm{Tr}\sim\mathrm{RF}(\mathrm{SIF},\mathrm{PAR},\mathrm{T_{a}},\mathrm{ VPD},\mathrm{SM},\mathrm{W_{S}}) \tag{6}\]  
The relative importance of each predictor variable in estimating Tr was quantified using 10-fold cross-validation performed on the training dataset. We ranked them by significance and then sequentially reduced each environmental factor to assess its impact on the accuracy of Tr estimation. Model evaluation was conducted on the validation dataset using the coefficient of determination (R\({}^{2}\)) and root mean squared error (RMSE) as performance indicators. The calculation formulas are as follows:  
\[\mathrm{R^{2}}=1-\frac{\sum_{i=1}^{\mathrm{N}}(\mathrm{P_{i}}-\mathrm{O_{i}} )^{2}}{\sum_{i=1}^{\mathrm{N}}(\overline{\mathrm{O}}-\mathrm{O_{i}})^{2}} \tag{7}\]  
\[\mathrm{MAE}=\frac{1}{\mathrm{N}}\mathrm{\sum_{i=1}^{\mathrm{N}}}|(\mathrm{P_ {i}}-\mathrm{O_{i}})| \tag{8}\]  
\[\mathrm{RMSE}=[\mathrm{N^{-1}}\mathrm{\sum_{i=1}^{\mathrm{N}}}(\mathrm{P_{i} }-\mathrm{O_{i}})^{2}]^{0.5} \tag{9}\]  
\(\mathrm{P_{i}}\) denotes the predicted Tr from the random forest model applied to the validation dataset; \(\mathrm{O_{i}}\) represents the observed Tr values in the validation dataset; \(\overline{\mathrm{O}}\) is average of observed values; N is the count of samples.
# 5G/B5G Internet of Things MIMO Antenna Design
###### Abstract  
The current and future wireless communication systems, WiFi, fourth generation (4G), fifth generation (5G), Beyond5G, and sixth generation (6G), are mixtures of many frequency spectrums. Thus, multi-functional common or shared aperture antenna modules, which operate at multiband frequency spectrums, are very desirable. This paper presents a multiple-input and multiple-output (MIMO) antenna design for the 5G/B5G Internet of Things (IoT). The proposed MIMO antenna is designed to operate at multiple bands, i.e., at 3.5 GHz, 3.6 GHz, and 3.7 GHz microwave Sub-6 GHz and 28 GHz mm-wave bands, by employing a single radiating aperture, which is based on a tapered slot antenna. As a proof of concept, multiple tapered slots are placed on the corner of the proposed prototype. With this configuration, multiple directive beams pointing in different directions have been achieved at both bands, which in turn provide uncorrelated channels in MIMO communication. A 3.5 dBi realized gain at 3.6 GHz and an 8 dBi realized gain at 28 GHz are achieved, showing that the proposed design is a suitable candidate for multiple wireless communication standards at Sub-6 GHz and mm-wave bands. The final MIMO structure is printed using PCB technology with an overall size of 120 \(\times\) 60 \(\times\) 10 mm\({}^{3}\), which matches the dimensions of a modern mobile phone.  
IoT; mm-wave; Sub-6 GHz; tapered slot; 5G +
Footnote †: journal: Applied Optics and Photonics  
1
Footnote 1: email: {[EMAIL_ADDRESS]
# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 4 Results and Evaluation
### Quantitative Analysis
#### 4.1.2 Swapping Evaluation  
The H-tree swap strategy effectiveness to improve the produced layout's distance preservation is evaluated in this section. By distance preservation, we refer to which degree the pairwise distance between data objects is preserved on the visual space considering the vertices' positions. This type of evaluation's most common approach is a metric stress function, such as the Kruskal stress [41]. However, such a metric evaluation does not apply to our case since the tree's topology is utilized for positioning them, which results in the edges' lengths not to be directly proportional to the distances. Therefore, we opt to use a non-metric evaluation based on the distances' ranking. To compute the distance preservation of a data object \(d_{i}\in D\), a rank \(R_{d_{i}}=\{r_{d_{1}},r_{d_{2}},\ldots,r_{d_{n}}\}\) comparing the data objects in \(D\) with \(d_{i}\) is calculated in which 1 is assigned to the most similar and \(n\) to the most dissimilar data object, where \(n\) represents the number of data objects. For the corresponding vertex \(v_{i}\), we calculate another rank \(R_{v_{i}}=\{r_{v_{1}},r_{v_{2}},\ldots,r_{v_{n}}\}\) comparing its position on the plane to the positions of the other data vertices (leaves) on the plane, assigning 1 to the closest vertex and \(n\) to the most distanced one. The distance preservation is then computed comparing both ranks using the _Spearman_ rank-order correlation coefficient [42], given by  
\[r_{s}=1-\frac{6\times\sum_{i}^{n}(r_{d_{i}}-r_{v_{i}})^{2}}{n^{3}-n}. \tag{6}\]  
\(r_{s}\) varies in the range of \([-1,+1]\), with large values indicating higher rank-order preservation or, in our case, distance preservation.  
The boxplots in Figure 4 summarize the results of our swapping strategy for each dataset. This figure compares our proposed swap with the opposite swap since it would produce the worst results in theory. The blue boxplots represent the opposite swaps, and the orange boxplots represent the proposed swaps. For all datasets, the proposed swap's average distance preservation is higher than the opposite swap. Only for the **text** dataset this improvement is not apparent. This is an expected outcome since, for this dataset, the distance distribution is highly skewed without a clear  
Figure 3: Boxplots comparing STree, NJ, and UPGMA techniques considering the four distinct datasets. STree presents similar or better neighborhood preservation if compared to more expensive techniques, with a smaller deviation, indicating a more stable structure (**a**). Also, STree produce more balanced trees potentially resulting in graph bundlings containing less distorted edges while making a better use of the visual space, reducing visual clutter (**b**).  
separation of objects that can be used for ranking. Such distance distribution is usually observed in high-dimensional sparse spaces, like bag-of-words of document collections. It is known that distance metrics are ill-defined in such spaces.
## 2 Materials and Methods
### Sample Preparation and Release Tests  
Release tests were performed in water solutions at different pH levels to evaluate the interaction strength between the carrier and Met. The release test procedure is illustrated in Figure 1 and described in detail below. In a typical experiment, 1 g of the carrier (ADS or BIO) was contacted with 50 mL of an aqueous solution at different pH levels, namely 3.0, 5.8, 8.9, by stirring at 600 rpm for 90 min at room temperature. Solid-liquid separation was performed by centrifugation at 3000 rpm for 30 min. The liquid phase, where released Met is expected to be found, was analysed by Chemical Oxygen Demand (COD) [15; 16].  
COD analyses were performed using a Spectrodirect Lovibond Instrument (2021 Tintometer GmbH, www.lovibond.com, accessed on 9 July 2021, Germany) and operated according to standard procedures [ASTM D1252-06]. Aliquots of the liquid phase were stored at \(-20\)\({}^{\circ}\)C for cell viability. Using chamber experiments were also performed and are described below.  
The residual solid phase was analysed by Fourier Transform Infra-Red (FT-IR) spectroscopy.  
FT-IR spectra were recorded in the 4000-400 cm\({}^{-1}\) range by a FT-IR Thermo Nicolet Nexus spectrometer (Thermo Electron Corporation, Madison, WI, USA) equipped with a DTGS KBr detector. FT-IR skeletal analyses of inorganic matrix, and other related modified materials were conducted on KBr pressed disks and prepared by carefully mixing the solid sample with KBr powder in an agate mortar (about 1% \(w/w\) concentration of powders). Measurements were repeated twice with 100 scans each, background air, and in the mid-IR range, i.e., 4000-400 cm\({}^{-1}\). FT-IR spectra analysis was performed considering the pH conditions with the lowest recorded release rate.
# Experimental Study on Mechanical Properties of Graded EPS-Steel Fiber-Reinforced Concrete
## 3 Analysis of Experimental Phenomena and Results
### Flexural Strength and Failure Mechanism
As a result, the deflection value at the critical failure point C increases, indicating an enhanced ability to resist failure. The deflection values at point A for each grade of specimen are fairly concentrated, suggesting that changes in the steel fiber volume fraction have little effect on the specimen's response to tensile stress during the initial bending stage. Therefore, this change does not significantly impact the deflection values. The increase in steel fiber volume fraction leads to a reduction in the deflection value at point B. This occurs because, in the crack development stage, as the steel fiber volume fraction increases, the specimen begins to bear tensile stress earlier and enters the steel fiber-dominated working stage. Due to the excellent tensile properties of the steel fibers, the length of the BC segment increases, and the deflection value at the failure point C increases accordingly. Figure 8: Load-deflection curve of graded EPS-steel fiber-reinforced concrete. Table 5 presents the stress peak values and growth rates for concrete with different volume fractions of graded EPS and steel fiber content. It is observed that the stress peak values for all grades of graded EPS-steel fiber concrete are higher than those for the corresponding plain concrete of the same grade [31], indicating a positive enhancement effect. For an EPS volume fraction of 15%, the growth rate of the flexural strength stress peak for both fine and coarse aggregate concrete lies within the range of (2.55%, 23.32%). At an EPS volume fraction of 25%, the growth rate falls within the range of (6.23%, 26.83%), while at an EPS volume fraction of 35%, it ranges from (2.43%, 24.66%). The standard deviations for each of these intervals are 6.178, 5.524, and 6.923, respectively, with coefficients of variation of 0.413, 0.331, and 0.518. To eliminate outliers, values falling below two standard deviations were discarded for each interval (the lower limit of each interval). After this adjustment, the average growth rates for the peak stress values corresponding to different steel fiber volume fractions under each of the three EPS volume fractions were found to be 16.06%, 17.61%, and 14.35%, respectively. These average values all fall within the 95% confidence interval. From these data, it can be concluded that for every 0.25% increase in the steel fiber volume fraction, the enhancement in flexural strength is most likely to fall within the range of (14.35%, 17.61%). However, the maximum growth rate is approximately 27%, with a relatively wide distribution of growth rates. This indicates that the distribution of the steel fibers has a significant impact on the improvement of flexural performance.
# Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking
## 5 Conclusions
\begin{tabular}{l l} AoA & Angle of arrival \\ BLE & Bluetooth \\ FTM & Fine-time measurement \\ GA & Genetic algorithm \\ GNSS & Global navigation satellite system \\ IMU & Inertial measurement unit \\ IoT & Internet of Things \\ IPS & Indoor positioning system \\ LIDAR & Laser imaging detection and ranging \\ LOS & Line-of-sight \\ NFER & Near-field electromagnetic range \\ NLOS & Non-line-of-sight \\ RFID & Radio frequency identification \\ RMSE & Root mean square error \\ RSSI & Received signal strength identification \\ RTLS & Real-time locating system \\ RTT & Round-trip time \\ SDS-TWR & Symmetrical double-sided two-way ranging \\ TDoA & Time difference of arrival \\ ToA & Time of arrival \\ ToF & Time-of-flight \\ TWR & Two-way ranging \\ UAV & Unmanned aerial vehicle \\ UWB & Ultra-wideband \\ VLC & Visible light communication \\ VNV & Variable noise variance Kalman filter \\ \end{tabular}
## Materials and Methods  
### Materials  
Natural halloysite clay nanotubes (HNTs) were obtained from Yanbo Mineral Processing Factory (Shijiazhuang, China). Dopamine hydrochloride (DA, assay \(\geq\) 98.5%) and Vitamin M (VM, purity \(\geq\) 97%) were supplied by Duby Biotech Co., Ltd. (Nanjing, China). Tris(hydroxymethyl) aminomethane (Tris, 99.9% purity) was purchased from Solarbio Science & Technology Co., Ltd. (Beijing, China). Octadecylamine (ODA, AR) and sodium hexametaphosphate (SHMP, AR) were obtained from Macklin Biochemical Co., Ltd. (Shanghai, China). Other reagents including deionized water, anhydrous ethanol, acetone, benzyl alcohol, hydrochloric acid, and stearic acid etc. were purchased from Beijing Chemical Reagent Factory (Beijing, China). All of the chemical agents were used without further purification.  
Superhydrophobic Surface Modification of the Halloysite Clay Nanotubes (HNTs) by Polydopamine (PDA) Coating and Octadecylamine (ODA) Grifting  
The pristine HNTs were purified by dispersing into 2 mM hydrochloric acid solution (60 \({}^{\circ}\)C, 2 h) with energetic magnetic stirring to eradicate Fe impurities, followed by thoroughly washing, filtering, drying, and grinding, which was named as pure HNTs (P-HNTs). The catechol surface modified HNTs were obtained by submerging the white P-HNTs powders into dopamine aqueous solution (2.0 mg/mL) and then adding Tris buffer to alter pH to 8.5 with gently stirring for 12 h at room temperature (RT). After that, the black PDA decorating HNTs (PDA-HNTs) were obtained through centrifugation, completely rinsed with distilled water and vacuum dried at 60 \({}^{\circ}\)C. Next, the PDA-HNTs were immersed into ODA ethanol solution (10 mM) under mildly stirring at RT for 12 h, and then purified with anhydrous ethanol several times through centrifuge washing and vacuum dried at 60 \({}^{\circ}\)C. Finally, the hydrophobic ODA grifting HNTs (ODA-HNTs) were prepared. Furthermore, to increase the roughness of the PDA coatings, the dopamine aqueous solution was treated with VM (1 mg/mL) and was stirred for 12 h at 60 \({}^{\circ}\)C before use. After cooling to RT, the P-HNTs powders were soaked in this VM treatment dopamine solution, followed by the same modification process described above to obtain the VM assisted PDA surface modified HNTs and prepare the resultant superhydrophobic HNTs, which was labeled as VM/PDA-HNTs and VM/ODA-HNTs, respectively. The intermediate and final HNTs based on different preparation conditions were listed in Table 1.
## 2 Methods and Details of the Technical Assessment
### The Aspects of the Analysis
#### 2.2.2 Data Processing  
With the help of the EG database, the negative and positive power divergence for every 15 min and the summarized daily amount of the energy of these time periods were determined between the real and forecasted data (from the perspective of the PV system operators) for a 1000-MW PV system for the period of 30/09/2014-30/09/2019. The purpose of this was to show the magnitudes of the power divergences and their percentage distributions in the examined 5-year period. In addition, the daily sizes of the negative and positive balancing energy requirements were also determined from the above-mentioned database. This information showed the occurrences of the daily balancing energy requirement amounts and their percentage distributions in the examined period. Knowing these, for example, provides information about scaling an inverter performance and the energy storage capacity for the battery on a probability basis.  
Figure 4: Explanation of the interpretation of real, day-ahead (**a**) and most recent (**b**) PV power by a transmission system operators (TSO) and a PV system operator based on real data of a 1000-MWp PV system [82].  
A second modeling approach has also been developed for the period of 30/09/2014-30/09/2019. With the help of Elia's real and forecasted PV data, several energy storage capacity sizes (nominal net storage capacity) from the value of 10 to 10,000 MWh were simulated in connection with a 1000-MW PV system grid balancing reduction potential. In this case, the batteries were charged only by PV power when a positive power divergence occurred (from the perspective of the PV system operators) between the real and the day-ahead/intraday forecast within a quarter of an hour. In contrast, the batteries were discharged when a negative power divergence occurred between the real and the day-ahead/intraday PV forecast within a 15-min period. Maximum charging and discharging times of 2 h were considered. At the beginning of the 5-year simulation, the battery charge level was 100%. However, in order to understand the logic, the day 12/07/2019 was separately simulated (Section 3.2), and the battery charge level started from 0% in this case. The results were determined on the basis of the average of the 5-year examined period of TSO regulations and battery usages. Therefore, the energy storage capacity change (physical amortization) after charge/discharge in the case of Li-ion technology was not considered. The power consumption of the air conditioner was provided by the network to reach the optimal temperature range of the batteries. The goal was always to try to keep the day-ahead or most recent (intraday) PV forecast. In the case of a discharged or fully charged battery, the balancing energy was provided by the grid.
## 5 Conclusions  
In this paper, a joint decoding (JD) framework is proposed that reduces the redundancies present between video frames at the decoder and reduces the computational burden on the encoder. The proposed scheme makes use of an efficient registration and fusion method to generate side information that helps in the reconstruction of final frame sequences. The simulation results are based on different arrangements with different subrates and GoP sizes (3, 5, 8). The results show the effect of different GoP sizes on the reconstruction quality i.e., smaller GoP = 3 provides improved reconstruction quality compared to larger ones. The comprehensive experimental analysis proves that the proposed JD-TV performs notably better than the independent BCS-TV-AL3 scheme at different subrates for various video sequences having low, moderate and high motion contents. In addition, the proposed scheme outperforms the conventional CS video reconstruction schemes at lower subrates. Further, when compared with conventional video codecs (DISCOVER, H-263, H264) the proposed framework shows notable performance at various bitrates. Further, the coding efficiency in terms of encoding and decoding time of the proposed and conventional video codes are also compared.  
The developed scheme can be used in real-time transport safety applications such as driver warning systems for motorbikes or cars, or driver drowsiness detection. Similarly, it can be applied for real-time visual patient monitoring, MRI, patient care and remote sensing.  
Conceptualization: M.E.; Methodology: M.E.; Software: M.E. and S.H.A.; Validation: M.E., S.H.A., and K.R.; Formal analysis: M.E., S.S.A.A.; Investigation: M.E. and S.H.A.; Resources: M.E. and K.R.; Data curation: M.E. and S.H.A.; Writing--original draft preparation: M.E.; Writing--review and editing: M.E. and S.S.A.A.; Visualization: M.E., K.R. and S.S.A.A.; Supervision: S.H.A. and K.R.; Project administration: M.E. and S.H.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The author would like to acknowledge the technical and administrative support of Sunway University, Malaysia, Iqra University, Pakistan and Universiti Teknologi PETRONAS, Malaysia. This research is partially supported by Iqra University, Pakistan and Ministry of Education, Malaysia under the Higher Institute Centre of Excellence (HICoE) awarded to Center for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Malaysia. The authors declare no conflict of interest.
## 2 Materials and Methods
### Positional Encoding (PE)  
PE is an essential technique in Transformers that incorporates information about the position of input sequences. Unlike RNNs, which process sequence data in an ordered fashion, Transformers, with their AM, process sequences in a parallel fashion, which means that the position of elements within the sequence is not implicit in the model [43]. To address this limitation, PE is introduced, which adds positional information to the input vectors to the attention layer. This encoding is performed using trigonometric functions (sine and cosine) to generate a unique vector for each position, which makes it easier for the model to capture the positional relationship of the elements in the sequence.  
Equations (13) and (14) are used in this process.  
\[PE_{(pos,2l)}=\sin\Big{(}\frac{pos}{10000^{2l/d_{\text{model}}}}\Big{)} \tag{13}\]  
Figure 6: Schematic diagram of the MA mechanism.  
\[PE_{(pos,2i+1)}=\cos\Bigl{(}\frac{pos}{10000^{2i/d_{model}}}\Bigr{)} \tag{14}\]  
where _pos_ is the position in the sequence, \(i\) is the dimension, and _dmodel_ is the size of the feature vector.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Experimental Result and Analysis
#### 4.5.5 Inversion Results of Dynamic Flame Parameters  
Utilizing the obtained values of the flame height, width, and area parameters from the previous section, the dynamic flame parameters (rates of change in flame height, width, and area) are calculated. The rates of change are computed at 1-s intervals and and visualized in Figure 15. Positive rates of change indicate gradual increases in each static flame parameter, signifying flame spread in the vertical and horizontal directions. A rate of change of 0 indicates relatively stable flame shapes and complete burning. Conversely, negative values indicate a decrease in flame regions. According to Figure 15, some observations are made as follows.  
Figure 14: Flame height on an FRNC N2 XCH cable pre-heated to T = 191 \({}^{\circ}\)C.  
Figure 13: The static flame parameters of a flame in five videos.  
During the initial stage of the fire, the flame spreads rapidly, with the spreading rate gradually decreasing before reaching the violent burning stage. In the intense burning stage, the rates of change remain close to 0 or stable. As the flame decays, the rate of change becomes negative, resulting in the contraction of the flame height, width, and area. [PERSON] et al. studied the evolution of ammonia/air flame fronts and laminar flame parameters influenced by buoyancy [33]. The red and blue curves in Figure 16 depict the rate of increase for the flame in the height and width directions over time during the early and midterm stages of flame propagation. After ignition, the flame exhibits vertical expansion, with the flame generally growing at a higher rate in height than in width. After a certain time, the flame reaches a smooth burning state. Although the figure does not display the rates of change during the flame extinction phase, it is reasonable to infer that the flame intensity gradually decreases during the later stages of combustion. These findings further support the potential of Flame-SeaFormer in utilizing fire images to explore flame development, specifically focusing on the analysis of evolving flame patterns. Clearly, the flame parameters calculated from the Flame-SeaFormer model are reasonably plausible.