esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Tests on Jacked Pile Penetration Characteristics Considering a Static Press-in Piling Machine
## 3 Test Results and Analysis
### Test Data Processing and Analysis
#### 3.1.1 Pile Axial Force Calculation  
The average strain \(\epsilon_{\rm i}\) of each section is obtained by the static strain acquisition instrument, and the axial force Ni of the pile during the pile jacking process can be obtained according to Formula (1).  
\[N_{\rm i}=E_{S}\epsilon_{\rm i}A_{S} \tag{1}\]  
where \(N_{\rm i}\) is the axial force of the pile at the \(\rm i\)-th section, \(E_{S}\) is the elastic modulus of the pile, \(\epsilon_{\rm i}\) is the strain change value measured by the strain gauge at the \(\rm i\)-th section, and \(A_{S}\) is the cross-sectional area of the pile.
## 3 Access Class Barring Configuration (ACBC) Scheme
### Least-Mean Square (LMS) Algorithm  
The LMS algorithm is widely used because of its simplicity and numerical robustness [28]. Concretely, the complexity of the LMS scales linearly with the filter length \(M\). The LMS is an application of the method of stochastic gradient descent, hence, it belongs to a different family of algorithms to that of the RLS. The internal structure of the LMS is depicted in Figure 4 and Algorithm 3 summarizes its operation.  
Figure 4: Block diagram of the LMS adaptive algorithm.  
As with the RLS, the LMS comprises a filtering and an adaptive process. An important difference between these two algorithms is that the output \(y(j)\) is compared to the desired response \(d(j)\) to obtain the error \(e(j)\) in the LMS, whereas the a priori error \(\varepsilon\) is calculated in the RLS.  
The correct selection of the rate of adjustment \(\mu\) is essential for the LMS to be stable. In particular, the value of \(\mu\) must be selected to satisfy  
\[0<\frac{1}{2}\mu\|\mathbf{u}(j)\|^{2}<1,\qquad\text{for all }\mathbf{u}(j) \tag{16}\]  
where \(\|\cdot\|\) is the Euclidean norm operator.  
Two different configurations of the LMS algorithm are considered: an adaptive line enhancer (ALE) and a \"pulling\" ALE (PALE). In the ALE, the primary input is a delayed version of \(\mathbf{u}(j)\), namely \(\mathbf{u}(j-1)\), while the desired output is set to \(d(j)=u(j)\). In the PALE, the primary input is simply \(u(j)\) and the desired output is set to \(d(j)=1\) for all \(j\). The latter configuration has a similar effect on the filter output as the one described above for the RLS configuration.
## 2 Materials and Methods
### Processing Methods
#### 2.4.3 Pixel Analysis and Zonal Histogram  
In QGIS (versions 3.10/3.16), the pixel analysis was carried out using [PERSON]'s _Add Raster Values to Points_ algorithm. This algorithm allows for the retrieval of pixel values at each point location (i.e., the archaeological sites) across multiple selected raster images. This process was applied to the archaeological site shapefiles using the annual NDWI composites, and then repeated with the 2018 monthly composites.  
The algorithm generates a new shapefile with the pixel values at the point location in all the underlying images. In the resulting shapefile, using 0 as a threshold between water and land, if a point has a pixel value below 0 in all the images, it can be assumed that the area was likely not affected by the dynamics of water retraction, while if the values are always above 0, the area was always submerged. Furthermore, if the point shows alternating values above and below 0, it can be assumed that part of the site re-emerged more than once due to the lowering of the water level. Although it can be argued that a single point is not very representative of the entire area of an archaeological site, its use can be suitable to give a first idea of the dynamics affecting an archaeological site, because it gives an unambiguous and quantifiable value. The point was usually placed at the highest point of a site, which is more prone to emerge after a change in the water level. Moreover, once a value below 0 is registered, a visual inspection of the area around the sites using satellite images can help validating the results and to assess the extent of the emersion.  
The next step was to assess to what extent the sites were impacted by the water retraction, both over the long term and during the sample year of 2018. The NDWI images were reclassified so that all the pixel values between \(-1\) and 0 were counted as 0, and all the values between 0 and 1 were counted as 1. The reclassification allowed for the easy application of the _Zonal Histogram_ algorithm to count the number of 1 and 0 pixels within each polygon representing site extents. In R, the pixel analysis was carried out using the _raster_ package [76], while the _qgisprocess_ package was used for the zonal histogram analysis. This last package is still in an experimental phase, but it is available at [[https://github.com/paleolimbot/qgisprocess](https://github.com/paleolimbot/qgisprocess)]([https://github.com/paleolimbot/qgisprocess](https://github.com/paleolimbot/qgisprocess)) (last accessed on 25 January 2021).
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 4 Conclusions
The following abbreviations are used in this manuscript:  
ANN Artificial Neural Networks  
CPS Crow Pattern Search  
CS Cuckoo Search  
DE Differential Evolution  
FLC Fuzzy Logic Controller  
FOCV Fractional Open Circuit Voltage  
DE Differential Evolution  
FSCC Fractional Short Circuit Current  
GA Genetic Algorithm  
GWO Grey Wolf Optimization  
HC Hill Climbing  
IC Incremental Conductance  
IGBT Insulated-Gate Bipolar Transistor  
KF Kalman Filter  
LuT Look up Table  
MPC Model Predictive Control  
MPP Maximum Power Point  
MPPT Maximum Power Point Tracking  
P&O Perturb & Observe  
PI Proportional-Integral  
PSO Particle Swarm Optimization  
PV Photovoltaic  
PWM Pulse Width Modulation  
SFLA Shuffled Frog Leaping Algorithm  
SA Salp Swarm Algorithm  
STC Standard Test Conditions  
WO While Optimization
# New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation
## 2 Materials and Methods
### Simulation Description
#### 2.5.1 Water Details  
For the simulation, we have considered a region that entails two urban areas (UAs) with their channel to collect the water, the main channel, and one irrigation channel (IC) with the small pools (POs) in which water is treated. A total of 6 locations are considered in this simulation: 2 urban areas (UA1 and UA 2), the irrigation channel (IC 1), and the 3 pools (PO 1, PO 2, and PO 3.). In each one of these areas, a total of three nodes (a cluster) is located. The cluster comprises three nodes with LoRa interfaces. The six locations are close enough to ensure good coverage with a single LoRa gateway.  
Since we are going to simulate our results using the algorithms described above, we need to generate different values of pollution in the monitored areas. To generate these values, we have included in our simulations the movement of water from point to point and some pollution inputs generated by random numbers at different intervals. The generated pollution coming from UAs and the pollution from the IC is diluted into the channel and flows to the next station. The pollution levels are maintained along with the IC. They only decrease in the pools where biosorption material is cleaning the water. Each pool has the capacity to reduce 50% of the pollution present in the water.  
The following consideration is the probability of a false positive or a false negative due to an abnormal monitored value of one of the sensors. We have defined the chance of giving a false positive as 5% and 1% for a false negative.  
The simulation has a duration of one week in which each sensor measures the water quality once per minute. This timing is required since the arrival of pollution to the fields can transfer the problem of pollution from the water to the soil, and in this medium, the recovery is more complex. The simulation is carried out in two different periods of the year, in summer and in winter. This will affect only the energetic-related parameters.
## 3 Proposed System
### Dog Tracking
#### 3.3.2 Real-Time Dog Tracking with a Deep Association Metric (DeepDogTrack)  
Because DeepSORT is typically used to track pedestrians, and the proportions of the human body are 64 \(\times\) 128, the input must be a fixed-size image. Proportion features are extracted using a simple CNN model, and the result predicted using the Kalman filter is used as the tracking region of the object. However, the proportions of dogs are different from those of humans. To adapt DeepSORT for the tracking of dogs and improve the computational efficiency, the DeepDogTrack model takes the detected dog region as input data, and the size of the region is not fixed. To increase the depth of the model and minimize error, a deep residual network (ResNet) is used to extract the dogs' features. The DeepSORT model was retrained using the dog data-set to improve its tracking accuracy. The architecture of the proposed DeepDogSORT dog-tracking model is illustrated in Figure 5. The original and improved results are presented in Figure 6.
# Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors
## 4 Performance Results
The simulation was also performed for verifying the accuracy of the provided analytical expressions. The system included the weak to moderate atmospheric turbulence condition \(C_{n}^{2}=10^{-17}\)m\({}^{-2/3}\), \(G\) = 18. The link distance \(L\) = 800 m. The pointing error displacement standard deviation \(\sigma_{\rm s}\) = 0.05, using the optimal beam  
Figure 4: The combined channel model distribution \(f_{X}(X)\) for the case of strong atmospheric turbulence channel, the transmitter beam waist radius \(\omega_{0}\) = 0.02 m.  
waist radius \(\omega_{0}=0.02\) m. It can be seen that, compared to there being no pointing errors, the SER with pointing errors significantly decreases the system performance. On the other hand, the use of a high-order modulation scheme leads to decreasing the achieved SER. For example, at the target BER = \(10^{-3}\), the power gain of approximately 5 dB can be achieved when employing the 8-PSK scheme instead of the 16-QAM scheme. Figure 5: SER versus APD gain, \(G\), for different values of turbulence strength with a transmission link length of 800 m, an average transmitted power per symbol of \(P_{t}=-5\) dBm. Figure 6: SER versus beam waist radius, \(\omega_{0}\), for different values of turbulence strength with a transmission link length of 800 m, different values of turbulence strength and various values of the pointing error displacement standard deviation \(\sigma_{5}=0.05\), 0.07, and 0.1 (from bottom to top). Figure 8: SER versus SNR for different modulation schemes with/without pointing errors. Figure 7: SER versus link distance, \(L\), for different values of turbulence strength, various values of the pointing error displacement standard deviation \(\sigma_{\rm s}=0.05\), \(0.07\), and \(0.1\) (from bottom to top) with the optimal beam waist radius.
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 3 Results
The RIT-18 dataset's mean BF score is 98.6%, indicating exceptional boundary delineation among diverse plant species. Additionally, Table 2 depicts the evaluation metrics for the DeepLabV3+, U-Net, and SegNet networks, respectively. This table illustrates that the proposed MSCNN architecture excels across multiple datasets, achieving high global accuracy, mean accuracy, mean IoU, weighted IoU, and mean BF scores. These results affirm the robustness and versatility of the MSCNN in effectively segmenting and classifying vegetation under varying conditions and complexities. Figure 6 presents a comprehensive visual comparison of the evaluation metrics for the proposed MSCNN compared to state-of-the-art architectures such as DeepLab V3+, \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Method** & **Dataset** & **Global Accuracy** & **Mean Accuracy** & **Mean IoU** & **Weighted IoU** & **Mean BF Score** \\ \hline \multirow{3}{*}{Proposed MSCNN} & CamVid & 0.984 & 0.981 & 0.955 & 0.976 & 0.952 \\  & FloodNet & 0.973 & 0.963 & 0.941 & 0.967 & 0.954 \\  & RIT-18 & 0.969 & 0.968 & 0.939 & 0.939 & 0.986 \\ \hline \multirow{3}{*}{DeepLabV3+} & CamVid & 0.978 & 0.974 & 0.953 & 0.953 & 0.947 \\  & FloodNet & 0.962 & 0.956 & 0.944 & 0.948 & 0.953 \\  & RIT-18 & 0.963 & 0.957 & 0.922 & 0.923 & 0.961 \\ \hline \multirow{3}{*}{UNet} & CamVid & 0.952 & 0.932 & 0.947 & 0.914 & 0.925 \\  & FloodNet & 0.941 & 0.956 & 0.912 & 0.935 & 0.941 \\  & RIT-18 & 0.947 & 0.957 & 0.904 & 0.933 & 0.947 \\ \hline \multirow{3}{*}{SegNet} & CamVid & 0.884 & 0.897 & 0.921 & 0.894 & 0.942 \\  & FloodNet & 0.893 & 0.917 & 0.907 & 0.914 & 0.911 \\ \cline{1-1}  & RIT-18 & 0.914 & 0.931 & 0.917 & 0.904 & 0.924 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of the proposed MSCNN compared to the state-of-the-art methods and performed on the testing datasets. U-Net, and SegNet. This figure is essential for understanding the relative performance of these models in the context of vegetation segmentation tasks. While DeepLab V3+ shows competitive performance, especially in handling complex backgrounds due to its atrous convolution layers, it falls short compared to MSCNN in terms of mean IoU and BF scores. This suggests that while it captures broader features effectively, it may struggle with the finer details necessary for accurate vegetation classification. U-Net is known for its efficiency in biomedical image segmentation but exhibits limitations when applied to diverse environmental datasets. The figure indicates that although U-Net performs reasonably well, it does not achieve the same level of accuracy or boundary detection as MSCNN. SegNet's architecture allows for effective segmentation but often requires extensive post-processing to refine outputs. This comparison shows that while SegNet performs adequately, it does not match the precision and overall performance of MSCNN in segmenting vegetation.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 5 Experimental Results and Discussion
### Study Limitations  
The proposed study is not without limitations. While the HuBERT model was used because several robust and compressed variants have been published in the literature recently (and thus can be used as benchmarks), more recently larger models, such as WavLM (with up to 300 M parameters) [4] or Whisper (up to 1.5 B parameters) [7], have been proposed and shown to be more robust to environmental noise. As such, the findings here are to be considered a lower bound on what could be achieved with environment awareness. Future work should explore the proposed distillation recipe with these emerging models. Moreover, here we have only tested the performance of the model on noise-only and reverberation-only conditions. In realistic settings, their combined effects may be present. In such scenarios, it may be possible to utilize the SNR and RT60 predictors together and see which distortion condition is most severe. This analysis is left for future work.
## 2 Botanical Biopesticides
### Botanical Biopsticides and Synthetic Biology
#### 2.2.2 Analysis of Plant Natural Product Synthesis Pathways and Mining of Key Enzymes  
Plant natural products are diverse, and most are structurally complex. Although the precursors of many products are derived from common primary metabolic processes, the post-modifications of these products are highly diverse and specific, and most of these synthetic genes do not exist in clusters such as microorganisms, which poses a great challenge to the elucidation of synthetic pathways and the mining of key enzymes. A more general mining tool is multi-omics association analysis. Two enzymes (a UDP-glycostransferase and a P450 oxidase) involved in the biosynthesis of _breviscapine_ were identified via combined transcriptomic and genomic analysis, and a synthetic pathway was constructed in yeast [60].  
Using the available genomes and transcriptomes of _azadirachta indica_ and Brassicaceae species, [PERSON] et al. identified an oxysqualene cyclase AiTTS1, which can produce triterpene backbone fircalla-7,24-dien-38-ol in the azadirachtin biosynthetic pathway. Co-expressed cytochrome P450 enzymes from _M. azdarach_ (MaCYP71 CD2 and MaCYP71 BQ5) and _C. sinensis_ (CsCYP71 CD1 and CsCYP71 BQ4) in _S cerevisiae_, the spontaneous hemiacetal ring was formated after three oxidations [76]. For example, in the biosynthesis of the isoquinoline alkaloid thebaine, the conversion of 7 SOA ((7S)-salutaridinol-7-O-acetate) to thebaine can occur spontaneously by thebaine synthase (THS). Furthermore, since no protein catalyzing a similar reaction was previously reported, protein mass spectrometry (protein-MS) played a key role in the excavation of this enzyme [77]. [PERSON] et al. experimentally cloned the EGS gene AhEGS of _E. coli_, constructed the prokaryotic expression vector pET28a-AhEGS-BL21, transformed _E. coli_ BL21 (DE3) using recombinant pET28a-AhEGS, and produced a specific protein of about 35 kD in _E. coli_. Protein structural domain analysis showed that this product is a short-chain dehydrogenase/reductase dependent on coenzyme II (NADP), belongs to the PIP family, possesses typical characteristics of _eugenol_ synthase, and is a key enzyme for the synthesis of _eugenol_[57].  
In recent years, with the rapid development of DNA synthesis, sequencing, and analysis technologies, we have been able to resolve and express increasingly complex biosynthetic pathways. In 2018, scientists successfully completed the heterologous synthesis, in yeast, of noscapine, an anticancer compound consisting of 25 enzymes [78]. _Allicin_ is a novel insecticide with a wide range of applications in agriculture. [PERSON] et al. completed the genome sequencing of _garlic_ in 2020 [79] and screened the most likely several allicinases [80]; however, glutathione and methacrylic acid-binding key enzymes were not reported, and host selection for heterologous expression of _allicin_ remains a challenge [81].
###### Abstract  
Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R\({}^{2}\) = 0.80, RMSE = 2.66 gC/m\({}^{2}\)/day and R\({}^{2}\) = 0.53, RMSE = 2.12 gC/m\({}^{2}\)/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (\(\epsilon_{\text{max}}\) or LUE\({}_{\text{max}}\)), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.  
terrestrial ecosystem; MODIS GPP product; calibration; arid region; oasis-desert ecosystem 201911225; doi:10.3390/rs11030225
## 3 Methodology and Cooperative Game Theory Models
### Listing Cooperative Game Strategies and Computing Their Returns
#### 3.2.2 Computing the Returns for the Game Strategies  
With respect to the nature of cooperative games, player rewards are either minimized costs or maximized benefits. This research basically builds the provided cases and associated models with regard to the benefits provided to the players. In order to build a strong and sustainable grand coalition, an incrementally increasing coalition reward/bonus scheme needs to occur alongside the increasing size of the coalition. Otherwise, forming a grand coalition becomes technically impossible without an external subsidy. In other words, the individual and/or sub-coalition returns for some cases may entail higher returns to their associated players compared to the grand coalition allocations. In those cases, no core solution at all is present.  
In the case study, all return/bonus schemes are designed to encourage more extensive coalitions, ending with the grand coalition by means of the highest benefit multiplier. The ultimate goal is to measure the geographic advantages/returns of players along with their sizes/scales rather than to measure their actual empirical parametric benefits, which are mostly unavailable.  
In order to establish the core constraints, the benefits for the large number of coalition strategies need to be computed. The benefit calculations can be modeled in two different ways:  
* _Discrete incremental bonus calculation:_ This is based on discrete reward thresholds that define the level that triggers bonus rewards as a multiplier. When any valid coalition size reaches the threshold level, an associated bonus multiplier is applied to the benefit calculation. These types of bonus schemes have been extensively used in various urban development/regeneration projects in Istanbul in the form of floor area ratio (FAR) bonuses.
* _Continuous incremental bonus calculation:_ This is based on the continuous reward function defined in Equation (10), where \(A_{i}\) is the size of player \(i\), \(\sum_{i\in S}A_{i}\) is the sum of the sizes of all players in the sub-coalition \(S\), \(k\) and \(\alpha\) are the critical parameters determining the incremental return based on coalition size in \(\sum_{i\in S}A_{i}\), and \(b_{S}\) is the sub-coalition benefit/return of the subgroup \(S\). This reward function has been designed to compute the coalition benefits of multinational systems. In this paper, the selected multinational system is the European Union geography, whose spatial economic cooperation occurs in the form of free trade as the source of incremental returns. \[b_{S}\ =\ k\Big{(}\sum_{i\in S}A_{i}\Big{)}^{\alpha}\] (10)  
Equation (10) can be remodeled in order to incorporate additional parameters such as distance decays. One advantage of the continuous reward function is that the relations can be later adjusted to any other mathematical forms that take the distance between masses into consideration, such as gravity models.