esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 2 Materials and Methods
### NDVI and SIF  
We obtained the NDVI for the study area from The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) via [[https://zenodo.org/records/8253971](https://zenodo.org/records/8253971)]([https://zenodo.org/records/8253971](https://zenodo.org/records/8253971)) (accessed on 24 March 2024), which has a spatial resolution of 1/12\({}^{\circ}\) and a temporal resolution of half a month. It is calculated based on satellite-measured surface reflectance data using NOAA's advanced very high-resolution radiometer (AVHRR) satellite data. The PKU GIMMS NDVI is based on a BPNN model specific to biomes, utilizing the GIMMS NDVI3g product and extracted from 3.6 million high-quality global NDVI samples. This approach effectively eliminates the effects of satellite orbit drift and sensor degradation. To expand the temporal coverage until 2022, it is then combined with MODIS NDVI (MOD13C1) through a pixel-level random forest fusion approach. To ensure consistency in analysis, we obtained monthly averages by taking the mean values and aggregated them at the monthly scale.  
We acquired GOSIF data from [[http://globalecology.unh.edu/data/GOSIF.html](http://globalecology.unh.edu/data/GOSIF.html)]([http://globalecology.unh.edu/data/GOSIF.html](http://globalecology.unh.edu/data/GOSIF.html)) (accessed on 24 March 2024). The GOSIF data are produced using the cubist machine learning method, incorporating MODIS EVI data and extensive reanalysis meteorological data. These datasets are available every 8 days, with a spatial resolution of 0.05\({}^{\circ}\), spanning from the year 2000 to the present [33]. We used data from the period 2018 to 2022 in our study. In contrast to previous coarser resolution SIF data obtained directly from OCO-2 observations, GOSIF offers superior spatial resolution, global continuous coverage, and an extended historical record. We are confident that utilizing the GOSIF dataset can provide a more precise representation of vegetation photosynthetic activity in the research area. Similarly, to standardize the temporal resolution of this study to a monthly scale, we obtained monthly averages using the mean value method.
## 3 Results and Discussion
### Stability of the Hydrolysate  
To evaluate the stability of the hydrolysate, 4.5 mg of inulin sample were hydrolyzed according to the optimal hydrolysis conditions. Then, the injection of hydrolysate to HPLC column was carried out at 1-h interval for 4 h. The peak area was calculated for stability analysis. Results of the stability tests of the hydrolysate are presented in Table 3. The peak areas varied from 12,456,068 to 12,736,946 mv.s within 4 h. The mean fructose concentration of the hydrolysate was 0.3995 mg/mL and the RSD was 0.78%, thus indicating that the hydrolysates obtained under the proposed hydrolysis conditions are stable in HPLC determination.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analysis** & **Concentration of Fructose (mg/mL)** & **Mean Concentration (mg/mL)** & **RSD (\%)** \\ \hline
1 & 0.4011 & & \\
2 & 0.4032 & & \\
3 & 0.3980 & & \\
4 & 0.3927 & 0.3973 & 1.11 \\
5 & 0.3971 & & \\
6 & 0.3921 & & \\ \hline \hline \end{tabular} RSD: relative standard deviation.  
\end{table}
Table 1: Precision of the combined method consisting of trifluoroacetic (TFA) hydrolysis and HPLC-ELSD analysis for inulin quantification (n = 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fructose Level (mL)** & **Recovery (\%)** & **Mean Recovery (\%)** & **RSD (\%)** \\ \hline
1.0 & 101.53 & & \\
2.0 & 99.44 & 100.27 & 1.11 \\
3.0 & 99.81 & & \\ \hline \hline \end{tabular} RSD: relative standard deviation.  
\end{table}
Table 2: Recovery values obtained for the method consisting of a combined trifluoroacetic acid (TFA) hydrolysis and HPLC-ELSD analysis for inulin quantification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time (h)** & 0 & 1 & 2 & 3 & 4 \\ \hline
**Concentration of Fructose (mg/mL)** & 0.4011 & 0.3980 & 0.3950 & 0.4005 & 0.4031 \\
**RSD (\%)** & & & 0.78 & & \\ \hline \hline \end{tabular} RSD: relative standard deviation.  
\end{table}
Table 3: Stability of method.
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 6 Discussion  
The study systematically evaluated the performance of PSO alongside GA, GWO, and SA in the context of 5G Multi-Tier RAN planning. The meticulous consideration of parameters such as carrier frequencies, bandwidths, and distances provided a robust foundation for analysis. PSO exhibited superior performance across various metrics,  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Carrier Freq [Table 5]** & **28 GHz** & **3.6 GHz** \\ \hline \multirow{2}{*}{Using PSO for Optimization} & Final BS & 63 & 110 \\  & Final Cov & 0.9618 & 1 \\  & Final Cap & 0.9618 & 1 \\  & Final Cap & 0.8784 & 0.8819 \\ \hline \multirow{2}{*}{Using GWO for Optimization} & Final BS & 65 & 115 \\  & Final Cov & 0.9201 & 0.993 \\  & Final Cap & 0.8576 & 0.7777 \\ \hline \multirow{2}{*}{Using SA for Optimization} & Final BS & 55 & 90 \\  & Final Cov & 0.833 & 1 \\  & Final Cap & 0.7187 & 0.606 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results for different 5G Multi-Tier RAN planning scenarios using Metaheuristic Algorithms.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Carrier Freq** & **28 GHz** & **3.6 GHz** \\ \hline \multirow{3}{*}{Initial Arrangement} & Initial BS No. & 115 & 147 \\  & Initial Cov & 0.8993 & 1 \\  & Initial Cap & 0.868 & 0.927 \\ \hline \multirow{2}{*}{Before Elimination of base station} & Final BS & 115 & 147 \\  & Final Cov & 0.9131 & 1 \\  & Final Cap & 0.87152 & 0.9201 \\ \hline \multirow{2}{*}{After Elimination of base station} & Final BS & 55 & 90 \\  & Final Cov & 0.833 & 1 \\  & Final Cap & 0.7187 & 0.6006 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results for different 5G Multi-Tier RAN planning scenarios using SA.  
achieving a commendable balance between the number of base stations, coverage, and network capacity for both 28 GHz and 3.6 GHz carrier frequencies. The algorithm outshone its counterparts, GA, GWO, and SA, emerging as the most effective metaheuristic approach for optimizing 5G Multi-Tier RAN planning in the given scenario.  
Furthermore, the results highlighted the efficiency of GA in refining 5G network deployment by striking an optimal balance between reduced infrastructure and improved coverage and capacity. GWO showcased notable enhancements in coverage and capacity post-optimization, maintaining a strategic reduction in the number of base stations. Simulated Annealing demonstrated improvements in coverage and capacity despite a reduction in the number of base stations, emphasizing its effectiveness in achieving a refined deployment. The overall comparative analysis underscored the strengths of PSO and GA as the most effective algorithms for optimizing 5G Multi-Tier RAN planning, considering their ability to navigate the trade-offs between infrastructure reduction and network performance improvement.
# Machine Learning Applications for Short Reach Optical Communication
## 4 Machine Learning Applications for Indoor Optical Wireless Communication and Positioning
### Machine Learning Applications for Indoor Optical Wireless Communication System  
The typical structure of an indoor OWC system is shown in Figure 13, where both LOS signals and non-LOS signals can be used for the wireless data transmission. The background light normally comes from the sunlight or illumination lamps, and it leads to the additional noise of the received data. The limitations imposed by the channel, such as the path loss, ambient noise, inter-symbol interference and multipath propagation, normally affect the system performance. To solve these limitations and to improve the system performance, recently the use of ML technique has been proposed and studied, which has shown outstanding capability.  
The typical applications of ML in indoor OWC systems are shown in Figure 14. In indoor OWC systems, the ML technique has mainly been used in two ways: ML-based encoder/decoder and ML-based signal processor.
## 1 Introduction
### Compliance Check and Its Challenges  
Compliance checks comprise the following processes:  
1. Recording information during production in industries;
2. Auditing and checking the correctness of the information and its adherence to the rules;
3. Considering exceptional situations and making appropriate decisions;
4. Certification of products as suitable;
5. Making the outcomes available to the public in an immutable manner.  
A compliance check is mainly a one-to-one process between a producer, such as a manufacturer or farmer, and the corresponding industry regulator. However, there are many such producers and many levels of compliance regulators, e.g., governments and assessment bodies, as shown in Figure 1. The critical challenges of compliance on a large industrial scale are as follows:  
* It is impossible for a human to remember all the rules;
* Although the majority of the compliance regulations are fixed, some critical unexpected issues can appear in a lengthy process, such as in agriculture, leading to exceptional assessments;
* Conditions external to the industrial process may change, leading to minor temporary changes in the compliance expectations;
* Data are distributed among many individual sources, especially considering that data are generated by both humans and smart IoT devices. Currently, many data are paper-based, and there is a lack of digital synchronization;
* There are privacy concerns about the sharing of data among peer producers and the regulator.  
The agricultural sector confronts a multitude of compliance challenges, primarily stemming from a complex and ever-evolving regulatory landscape [25; 26; 27]. Frequent regulatory updates necessitate constant adaptation and vigilance from regulated entities, especially small farms, which often struggle with limited resources to prioritize and meet compliance requirements [4; 9; 28; 29]. While digital technologies offer potential solutions, they also introduce new challenges, such as the digital divide [30; 31; 32], effective data management [33], non-invasive sensors [34], and cybersecurity risks [35; 36; 37; 33]. Regulators also face obstacles such as geographic diversity of the fragmented sectors [38] and resource constraints such as EU cross-compliance, which highly relies on extensive monitoring, ref. [39] hindering effective oversight and enforcement. Challenges also exist in the auditor's technical competence and field knowledge, and the potential negative impact of the common interests between certifying companies and their clients on audit quality may lead to the issue of certification reliability [40].
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 4 Case Study  
### Implementation  
To evaluate the efficiency of the proposed BuildingTree, we implement an online framework (Figure 9). The implementation is based on open standards and tools. The feedback from the user is mainly the operation or interaction between the user and the 3D models such as zoom in/out, move, turning and etc.  
**Details of the implementation.** The generalization routines are developed in Java utilizing, for example, the geometry library JTS [60] and citygml4j [61]; the development environment is Eclipse 3.4.1. The server is a PC with Inter 2.4 GHz Core2 Duo CPU, 2.39 GHz 3.25 GB RAM, and Microsoft Window XP SP3. The client is a smart phone MX2 from Meizu with an Android 4.1 operation system, Samsung 1.4 GHz CPU, and 2 GB memory. The 3D models are visualized with X3 DOM through the Firefox 21.0 browser for Android.  
The web server is Nodejs, a JavaScript-written web server using event-driven, asynchronous I/O to minimize overhead and maximize scalability.  
Nodejs supports Websocket and other HTML5 features through extension modules. It also can integrate MongoDB queries and MapReduce operations within the existing modules. These modules in Nodejs can be easily installed by _Nodejs Package Management_ (npm) functions, which makes the development much faster and easier.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 2 Background
### Demand on Ultra-Fast Stream Processing  
In previous works, the FPGA streaming architectures accept pixel streams with a throughput of one or two pixels per clock cycle (pix/cycle) [5; 7]. Due to the fast-growing bandwidth of peripherals, demand comes that FPGA should process multi-pixel blocks instead of independent pixels in each cycle. For instance,  
* **Near-storage processing:** High-bandwidth memory (HBM) stacks multiple DRAM dies to achieve a bandwidth of \(250\,\mathrm{GByte/s}\)[11]. Assuming the operating frequency of FPGA is \(250\,\mathrm{MHz}\), the max data rate of a FPGA input stream is \(1000\,\mathrm{Byte/cycle}\). For images with \(1\) byte per pixel, this translates into a pixel throughput of \(1000\,\mathrm{pix/cycle}\).
* **Near-sensor processing:** The high-resolution image sensor represents a high pixel throughput. For instance, the up-to-date CMOS sensor Sony IMX253 is capable of capturing \(68\) frames per second, with a resolution of \(4096\times 3000\)[12]. Thus, the minimum processing throughput on FPGA is \(4\,\mathrm{pix/cycle}\) (\(\lceil 4096\times 3000\times 68/250\,\mathrm{MHz}\rceil\)).
## 5 Experimental Results and Analysis
### Indicators for Model Evaluation
#### 5.1.2 Evaluation of GBDT Model  
Table 22 demonstrates that the average Accuracy was 85.58%, the average Precision was 85.02%, the average Recall was 74.32%, and the average F1 score was 663.99%. The average Loss value was 4.636, the maximum Loss value was 5.795, the minimum Loss value was 3.741, and the standard deviation of Loss value std was 0.430.  
The values of the loss function for the GBDT model, with loss values reaching an average of 4.6 per round, represent a reduction in loss values relative to the SVM model.
# Multiscale Analysis for Improving Texture Classification
## 5 Discussion
The best result for each magnification is shown in boldface. * The accuracy performance of the BiT, GLCM, and Haralick descriptors on the Outex dataset was not too different, and they all presented an accuracy above 95%. Still, the BiT and Haralick descriptors outperformed the GLCM descriptor with nearly 4% in terms of accuracy using the LDA classifier, the lowest accuracy among all the classifiers. However, since TiO's performance was roughly equivalent to that of the BiT and Haralick descriptors for the Outex dataset, such a combination was not necessary to this extent. * Along the same line, the accuracy performance of the BiT and Haralick descriptors on the KTH-TIPS dataset was similar, regardless of the classifier. The GLCM descriptor, nonetheless, showed a difference of about 11% (with its lowest accuracy) and 7% (with its highest accuracy) compared with the BiT and Haralick descriptors. However, the difference was insignificant when comparing the best descriptor, BiT, with TiO. Therefore, for the KTH-TIPS dataset, such a combination brought a slight improvement. * Unlike the Outex and KTH-TIPS datasets, the presented concatenation played a significant role in the classification performance of the ALOT dataset. First, the best performance of all the datasets was obtained with the LDA classifier. The highest accuracy performance with TiO was 98.48%, outperforming the BiT, GLCM, and Haralick descriptors by nearly 20%, 32%, and 14%, respectively. The lowest difference, 14%, was still a significant improvement. Such a classification performance is promising as it is above several state-of-the-art deep methods on the ALOT dataset. The ALOT dataset is challenging due to different variations in images regarding viewing angle, illumination angle, and color. * Furthermore, the combination of BiT, GLCM, and Haralick descriptors provided a state-of-the-art classification performance on HIs. Notwithstanding, one states that an effective feature selection may be necessary to derive subsets of relevant features from each descriptor such that each dataset can be characterized to the maximum extent with as minimum a feature dimension as possible. Finally, it is worth mentioning that the experimental protocol with a train-test split and 10-fold cross-validation provided similar results.
## 5 Discussion  
During the tests without the ECC technique implemented, the lowest BER was obtained in the case of 256 OFDM subcarriers with the subcarrier spacing of 19.53 Hz and symbol duration equal to 51.20 ms. Such a subcarrier spacing is smaller than the coherence bandwidth \(B_{C}\) measured at the threshold level of 0.9 of the maximum value of the SFCF of the channel. In the 64 subcarriers case, in which the BER is almost twice as high as in the case of 256 subcarriers, the subcarrier spacing is larger than the \(B_{C}\) measured at the threshold levels of 0.7 and 0.9, but not 0.5. Thus, the threshold level of 0.5 of the maximum value of the SFCF seems to be too low as the indicator of the strong correlation of frequency components of the channel's transfer function, and thus it shouldn't be used to choose the subcarrier spacing in UAC OFDM system.  
In the case of 512 subcarriers, a BER was significantly higher than for other subcarriers configurations. Such a number of subcarriers located in the 5 kHz band at a sampling rate of \(f_{s}=200\) kHz may cause that DFT used for OFDM symbols processing has insufficient resolution. Moreover, although the subcarrier spacing equal to 9.77 Hz is greater than each of the three Doppler spread values shown in Table 1, the difference is too small and the Doppler shift may affect the OFDM signal spectrum causing the ICI. Thus, it seems that it is worth using Doppler spread measure that achieves the highest values, i.e., the maximum spread at the threshold level of \(-20\) dB relative to the maximum value of Doppler Power Spectral Density of the channel. The subcarrier spacing should be much larger than the maximum Doppler spread calculated this way.  
In each of the OFDM configurations, the OFDM symbol duration was longer than the maximum value of the delay spread measured at the threshold level of \(-10\) dB relative to the maximum value of the Power Delay Profile. In the case of 64 subcarriers, the OFDM symbol duration was no longer than the maximum value measured at the threshold level of \(-20\) dB and no longer than the rms value of the delay spread of the channel. In the case of 256 subcarriers, for which the lowest BER was achieved, the symbol duration was longer than the maximum delay spread measured at the threshold level of \(-20\) dB. Thus it seems it is worth using the latter measure of time dispersion of the UAC channel in the process of designing the physical layer of the OFDM data transmission system. In all OFDM subcarrier configurations, the OFDM symbol duration was much shorter than the coherence time of the channel.  
Implementing the ECC technique allowed obtaining the BER less than \(10^{-4}\) in the case of 64, 128, and 256 subcarriers. Only in the case of 256 subcarriers, it was possible using both types of ECC: BCH codes and Reed-Solomon codes. The highest transmission rate with the BER less than \(10^{-4}\) was obtained using Reed-Solomon code and it was equal to 653.63 bps. Thus, applying the ECC reduces more than twice the transmission rate compared to the configuration without ECC, but with a significant gain on reliability.
# Limitations of Using DAQ Cards for Absolute Vibration Measurement in Hydraulic Machines
## 2 Vibration Measurement, Specificity of Vibration Measurement in Hydraulic Machines, Standards  
Vibrational motion can be described by three mutually dependent quantities: vibration acceleration, velocity, or displacement. Relations (1)-(3) present the relationship between these quantities.  
\[a=\frac{dv}{dt}=\frac{d^{2}s}{dt^{2}}, \tag{1}\]  
\[v=\frac{ds}{dt}=\int a\;dt, \tag{2}\]  
\[s=\int v\;dt=\iint a\;dt, \tag{3}\]  
The above dependencies indicate that knowledge of any of the quantities characterizing vibrations allows for the determination of others. However, in practice, the choice of the analyzed one depends on the rotational speed of the tested machine/characteristic frequencies of the element.  
Typically, the rms value (4) for vibration velocity and the peak-to-peak value (5) for vibration displacement are used as estimators of absolute vibrations.  
\[x_{RMS}=\sqrt{\frac{1}{T}\int x^{2}(t)dt}, \tag{4}\]\[x_{p-p}=\max(x(t))-\min(x(t)) \tag{5}\]  
In measurement practice, one can also encounter other estimators, e.g., kurtosis, crest factor, cepstrum, spectral, or acceleration envelope analysis [30; 31; 32; 33; 34].  
The choice of measured values, estimators, and analyses depends on the purpose of the measurements and the specifics of the machines, devices, or elements being tested. The selection of appropriate measuring equipment should depend on the same factors.  
Vibration measurement in hydraulic machinery is distinguished by specific characteristics, primarily due to the relatively low rotational speeds and slowly changing phenomena occurring in them, which affect the observed vibroacoustic effects (e.g., part load vortices). In the case of water machines, the requirements for vibration measurements can be found, among others, in the standards [35] (shaft vibrations), [36] (absolute vibrations), and [37]. This standard somehow combines and replaces the standards [35; 36]. However, despite this, measurements are still performed based on older standards, especially in relation to existing objects. Therefore, the recommendations of standards [36; 37] will be outlined below in relation to absolute vibrations of non-rotating elements.
## 3 DA-Based Turbo Equalization
### Decoding  
After processing by decision device, the signal \(\mathfrak{s}^{a,m}(n)\) is mapped into the output probabilities on the possible symbols. For each \(s\) belonging to the symbol alphabet \(\mathcal{S}\), the output probabilities can be computed as  
\[p_{\mathrm{out}}(s^{a,m}(n)=s)=\frac{p[\mathfrak{s}^{a,m}(n)|s^{a,m}(n)=s; \mathfrak{F}^{a,m}(n)\,]p_{\mathrm{in}}[s^{a,m}(n)=s]}{\sum\limits_{s\in \mathcal{S}}p[\mathfrak{s}^{a,m}(n)|s^{a,m}(n)=s;\mathfrak{F}^{a,m}(n)\,]p_{ \mathrm{in}}[s^{a,m}(n)=s]} \tag{21}\]  
The output probabilities are sequentially processed by the soft demapper and deinterleaver, and LLRs of coded bits for the decoder input can be obtained,  
\[\widehat{\Lambda}_{\mathrm{in}}(c^{a,m}(n))=\log\frac{p_{\mathrm{in}}(c^{a,m} (n)=1)}{p_{\mathrm{in}}(c^{a,m}(n)=0)} \tag{22}\]  
To ensure that only extrinsic information is transferred, LLRs of coded bits \(\widehat{\Lambda}_{\mathrm{in}}(c^{a,m}(n))\) are passed to the decoder after subtracting the last output LLRs of the decoder \(\Lambda_{\mathrm{out}}(c^{a,m}(n))\). In the first iteration, the last output LLRs of the decoder is set to 0.