esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater
## 1 Introduction
Due to the advantages of the convolution calculation, models based on convolutional neural network (CNN) have performed well in specific time series tasks involving multiscale patterns [21]. In [22], the CNN was combined with LSTM and GRU to compensate for their ability to abstract spatial features and thereby realize more reliable water quality predictions. [PERSON] et al. [23] considered the temporal convolutional network (TCN) an effective framework for processing sequence data since it can perform dilated causal convolution in parallel. The experimental results proved that TCN can significantly improve the simulation of total nitrogen concentrations for the next eight hours in WWTP effluent. A study on forecasting the crowd flows utilized a graph convolutional network (GCN) to capture the complicated interactive relationships among external factors and integrated it with a prediction method for exceptional accomplishment in extracting spatio-temporal dependencies [24]. Thus, it is crucial for achieving accurate predictions to select and build the most suitable model according to the various data patterns and characteristics. In this paper, a novel deep learning framework based on time scale decomposition and spatio-temporal feature extraction, which is denoted as MSTCN, is proposed to achieve more accurate predictions of the wastewater effluent indicator COD by identifying more complex time patterns in the data and realizing hierarchical feature extractions. The Fourier transform method is applied first to determine the top three main frequencies in the original data, according to which the inputs can be reconstructed into series at multiple time scales. After that, the historical dependencies and interactions between variables at each time scale can be captured by the TCN and GCN layers, respectively. The feature fusion across different time scales allows the proposed model to forecast utilizing information from various temporal dimensions and spatial interactions. Based on the actual wastewater quality data, the effectiveness and accuracy of the MSTCN model are demonstrated compared to widely applied prediction methods for time series. The aim of this study is to achieve stable and accurate predictions of the COD concentrations in water bodies after treatment. This research makes efforts to develop an effective soft-sensor model to analyze monitoring data and provide reliable predictions for managing wastewater treatment processes. This paper is organized as follows: Section 2 introduces the data collection, detailed methods, and the proposed model. In Section 3, the results of data preprocessing, parameter optimization, and experiments are shown and discussed. Finally, Section 4 presents the conclusion of this paper.
## Methods
### Relative Elevation Changes of the Radar Scattering Phase Center  
In this step, we measured relative elevation changes of the radar scattering phase center (PC) during 2011-2016. We used the TDX DEM of 06/18/2011 as reference and subtracted all subsequent DEMs from this reference TDX DEM of 2011. The location of the PC depends on the physical properties(e.g., roughness, structure and dielectric properties) of the surface and medium relative to the incident signal wavelength [28]. Hence, the seasonal change of the PC does not necessarily represent surface elevation changes, but the surface where the radar signal is reflected. Small amounts of liquid water on the glacier surface attenuate the radar signal substantially and therefore, the PC depicts the glacier surface, especially during the melt season [29]. However, fine-grained, cold snow is known as an almost transparent medium for X-band radar signals and radar penetration may reach up to several meters [30]. Also for frozen snow and firn radar penetration is known.  
We measured the relative accuracy of the DEM differencing with 190 assessment points on rocky terrain of slope \(<\)10\({}^{\circ}\). More than 80% of the DEM pairs show a root mean square error (RMSE) ranging from \(\pm\)2.62 m to \(\pm\)9.64 m. The error was mainly caused by the variable penetration depth of the X-band radar signal depending on the prevailing surface conditions (snow, no snow) as well as different heights of ambiguity (HoA) of the TDX pairs [31]. The summer comparisons reduced the error contributions (\(<\)\(\pm\)6.38 m) due to less penetration.
## 3 Vehicle Pose Estimation Method  
The comprehensive framework for vehicle pose estimation is depicted in Figure 2. The process consists of three subsystems: pixel information, point tracking, and robust orientation. The pixel information subsystem represents the extraction of information such as vehicle position and edge points from video frames. The point tracking subsystem is used to identify and track the movement of feature points on the vehicle over time. The robust orientation subsystem is used to evaluate and determine the direction of vehicle travel. The first process commences with the utilization of the convolutional neural network (MI\(-\)CNN) operating within a two-step detection paradigm, wherein it takes the initial frame (t) as input. This network serves a multi-faceted purpose: It simultaneously forecasts the positions of all vehicles along with their respective yaw angles relative to the host vehicle. Additionally, it is entrusted with the task of ascertaining the coordinates of edge points within the context of each vehicle's 2D bounding box while also evaluating their visibility. The subsequent stage unfolds in frame t + 1, whereby the local search area for each vehicle is generated via a maximum displacement prediction model, incorporating an auxiliary variable. Within this search area, a fusion of edge point similarity and Kalman filter-predicted values is employed to align with the target vehicle. This matching process is instrumental in refining the redundant variable using the outcomes obtained from the match. Following the persistent tracking of the target vehicle for a span exceeding 10 frames, vehicles devoid of inclination are enlisted to compute a sequence detailing changes in distance. This sequence is established using the bottom edge as a reference point. The stability of this sequence is evaluated by means of a Gaussian distribution, which is applied with a fixed vehicle width. Once this distribution attains a state of stability, the structural parameters of the target vehicle can be refined using the aforementioned distance sequence. Consequently, this refined information facilitates the estimation of the vehicle's pose.
## 2 Wear Prediction Model of DPRSs Considering Ambient Temperature
### Numerical Implementation  
The DPRS temperature rise, thermal deformation, and wear depth, shown in Figure 8, are calculated as follows:  
1. Enter the DPRS design parameters and operating parameters;
2. Calculate the thermal resistance and establish the equations for the temperature nodes;
3. Establish a thermal network model and calculate the temperature rise value of each node;
4. Solve the thermal deformation equation for the screw;
5. Given the maximum contact area of a single body, calculate the critical contact area and determine the type of contact;
6. Calculate the actual total contact area and actual contact force and determine the feasibility of the results according to [22]; if infeasible, proceed to Step (5) and regenerate the maximum contact area for a single body;
7. Combine the DPRS load distribution model, sliding distance model, and wear prediction model to predict the wear depth.  
Figure 8: Calculation flow of wear prediction.
## 6 Conclusions  
This paper proposes a novel, dual-parallelogram, passive rocking vibration isolator with QZS and demonstrated good vibration isolation performance via high-static-low-dynamic torsion stiffness. It discusses the dynamic models of different isolators and demonstrates that, through an analysis of four linear spring configuration schemes used to obtain HSLDS and QZS at the equilibrium position, the spring arrangement can be optimized and a dynamic model can be established based on the dynamic characteristics. Our experimental results verified our simulation results and confirmed the ability of this novel system to isolate vibrations.  
A rocking vibration isolation scheme with a QZS mechanism could feasibly be constructed using linear spiral springs. A dual-parallelogram passive rocking vibration isolator with QZS has the advantages of a large bearing capacity, high precision, and good vibration isolation performance. At harmonic excitations of \(A\sin(0.167\pi t)\), \(A=15^{\circ}\) and \(A\sin(0.6667\pi t)\), \(A=3^{\circ}\), the experimental results indicated that vibration could be reduced by 53% and 86%, respectively, confirming the good capacity of this system of vibration isolation. In a high-static-low-dynamic vibration isolation system, transmissibility is closely related to the excitation amplitude. At an equilibrium position of QZS, better vibration isolation performance can be achieved with a low torsion excitation. Furthermore, the influence of friction damping on ultra-low frequency vibration isolation warrants thorough investigation.  
Figure 12: Influence of spring stiffness on isolation performance. (a) Simulation output; (b) experimental output.  
Using our dynamic model of a novel, dual-parallelogram, passive rocking vibration isolator system, we were unable to simplify the mechanism to a single mass rocking around a fixed point, mainly because for most of the rods the motion comprised both translation and rotation, whereas others comprised only translation.  
This work was financially supported by the foundation of Innovative Research Groups and the Joint Funds of the National Natural Science Foundation of China (Grant Nos. 51521003 and U1613201), in part by the Science and Technology Project of Guangdong, China (Grant No. 2016A010102004), and in part by Shenzhen Research Funds (JCYJ20150529141408781 and JCYJ20150529143500954).  
**Author Contributions:** [PERSON] established the theoretical model and wrote this paper; [PERSON] analyzed the data. [PERSON] designed the experiments; [PERSON] provided some key ideas and revised the manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion
### Evaluation Metrics
#### 3.2.6 Influence of Multi-Source Data Fusion Method on Model Accuracy  
RGB images can make up for the missing texture features of pests and diseases in vegetation index features. Therefore, the method of RGB image fusion vegetation indices is adopted to further improve the precision of the model. Data-level fusion and feature-level fusion are, respectively, used in model training to explore appropriate multi-source data fusion methods. Data-level fusion refers to aligning the raw data of RGB images and vegetation indices and then concatenating them in the channel dimension to form 25-dimensional channel data for model prediction. Feature-level fusion, on the other hand, involves extracting features from RGB images and vegetation indices separately through a backbone network, concatenating these features in the channel dimension, and then sending them into a classifier for multi-label classification results. In the experiment, ResNet18 was adopted as the feature extraction network of AMMFNet. As shown in Table 12, the experimental results show that the data-level fusion is more suitable for the fusion of the RGB image and the vegetation indices. Because the vegetation indices are calculated by the meridian linearity and nonlinearity of the multispectral image, and the multispectral image is taken at the same time as the RGB image, the two positions are aligned and have a high degree of spatial consistency. Therefore, the fusion of RGB images and vegetation indices at the data level can ensure that the two data form a more consistent data expression form so as to promote the model to better capture the detailed features in the two. While the features extracted by the network are independent feature expressions of the two data types, they have become advanced semantics that can be classified. At this point, performing feature fusion between the two semantics is equivalent to introducing noise into the clear semantic information, which interferes with the original feature representation and results in classification performance that is even lower than that of single-source input.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Value** & **Subsetac/\%** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1/\%** \\ \hline \(k=5\) & **88.14** & **81.13** & **81.84** & **82.40** & **82.22** \\ \(k=10\) & 85.64 & 79.06 & 80.08 & 80.34 & 80.21 \\ \(k=20\) & 86.89 & 80.05 & 81.33 & 81.24 & 81.28 \\ \hline \hline \end{tabular} The bold content is the column optimal value.  
\end{table}
Table 11: Classification results of the model guided by the RF-ML algorithm under different \(k\) values.
## 4 Case Study
### Average Response Time  
On the other hand, we would like to show how slicing can be beneficial in reducing the time required to respond to the varying tasks demanded by users. To achieve this, we assume that the probability density function (PDF) of the requested tasks is Poisson distributed with a parameter \(\lambda\), which represents the average frequency of requests for each VU. The expected time value required for the slices to reconfigure to a new state for the requested services is reported, showing how much time is needed between consecutive requests to deploy a slice. Figure 8 shows this variable versus the number of slices for five different values of \(\lambda\) in requests per second. This figure leads us to conclude that the more slices, the lower the reconfiguration frequency, and five slices are enough for the reconfigurability to go to zero.  
Figure 7: User satisfaction with respect to average latency versus the number of slices for different number of VUs.
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 5 Simulation Analysis
### Simulation Analysis with Fluctuating Loads  
The islanded microgrid in forest is generally vulnerable to stochastic fluctuating loads, such as electrical machinery with characteristic of frequent start-up and shut-down, especially when the micro-gas turbine of the biomass unit has encountered fault or energy exhaustion. The following simulation presents the above extreme condition, with corresponding figures shown in Figure 14. In Figure 14a, the dashed lines and solid lines represent the simulation results when the basic method of sectional coordinated control and the improved method of hybrid complementary energy storage control based on prediction model were adopted, respectively. As is shown in the figures, when the basic method was utilized, although the energy storage system responded quickly, the fluctuation of DC voltage was large and the lowest point of voltage was below 0.95 pu. When the improved method was adopted, the hybrid energy storage system could rapidly increase the output power according to the voltage change rate, so as to provide rapid power support for the system. At the same time, the wind power system released its rotational kinetic energy and provided an intermittent energy supply to smooth the power peak. With the joint efforts of the battery, the capacitor, and the wind  
Figure 13: Performance of each unit in the DC microgrid under fault condition in the biomass unit: (**a**) The solid lines were obtained by using the improved method of hybrid complementary energy storage based on prediction and the dotted lines were obtained utilizing the basic coordinated control; (**b**) The solid lines were obtained by using the improved method based on PI control and the dotted lines were obtained utilizing the basic coordinated control.  
turbine, the lowest point of DC voltage was raised to about 0.97 pu, and the quality of the DC power supply was greatly improved.  
When the hybrid energy control (with only battery and supercapacitor) based on traditional PI regulation was adopted, as shown in the solid lines in Figure 14b, the output power of the hybrid energy storage unit increased slightly compared with the basic control, and the corresponding fluctuation amplitude of DC voltage was between the value using the basic control and the one utilizing the proposed method.
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Digital Terrain Model Generation
#### 4.3.4 Extension  
The following extensions could be easily performed, where currently only the first one is supported by our implementation. The second and third options are very generic and independent of the proposed MSD DTM generation method, such that they are not evaluated here.  
* Apply a peak filter after the filtering step to get rid of small, mostly incorrect, regions. This idea is similar to the filtering of disparity maps in image matching.
* Since, taking the minimal value of a distribution is not robust to outliers, the \(n\)th percentile could be taken instead. Then the algorithm becomes robust toward outliers in the input DSM.
* Usage of more appropriate hole filling techniques by, for instance, using Kringing [124], anisotropic diffusion [125], total variation [98] or total generalized variation [126].
###### Abstract  
Background: Nowadays, to improve animal production sustainably, the zootechnical sector is exploring novel, functional ingredients, such as seaweed. This study investigated the functional properties of _Fucus vesicolusus_ and their persistence after simulated digestion. Methods: _F. vesicolusus_ was nutritionally characterized (AOAC methods) and digested in vitro through the INFOGEST protocol. The polyphenol, flavonoid, and phlorotannin contents of the samples were analyzed through colorimetric assays. The antioxidant properties were evaluated using ABTS assay and the growth inhibition capacity against _Escherichia coli_ using the microdilution method. The cytotoxic activity and anti-inflammatory properties were evaluated on mouse peritoneal macrophages using crystal violet assay and the gene expression of IL-1\(\beta\), IL-6, TNF-\(\alpha\), and iNOS. Results: _F. vesicolusus_ demonstrated high levels of dietary fiber (47.36%) and protein (13.99%). Significant levels of polyphenols (6428.98 \(\upmu\)g TAE/g), flavonoids (5171.31 \(\upmu\)g CE/g), and phlorotannins (2.10 mg PGE/g) were detected. These bioactive compounds allowed for strong antioxidant activity (85.96% ABTS+ scavenging) and _E. coli_ growth inhibition (17%). Simulated digestion minimally impacted the content of bioactive compounds and their associated functional properties. _F. vesicolusus_ exhibited a protective effect against oxidative stress in macrophages, downregulated proinflammatory dormancy cytokines (IL-1\(\beta\), IL-6, and TNF-\(\alpha\)). Conclusions: These findings support the potential of _F. vesicolusus_ as a functional feed ingredient for livestock, maintaining its beneficial properties even after digestion.  
**Keywords:**_Fucus vesiculuss_; bioactive compounds; antioxidant; antimicrobial; anti-inflammatory; peritoneal macrophages; functional ingredients
## 2 Results and Discussion
### Phenolic Compounds Profile and Quantification  
The qualitative and quantitative profile of phenolic compounds present in the BE apple extracts is of major interest for valorizing their potential since the type and the structure of present phenolics may predetermine extract's antioxidant and other activities that can be responsible for health claims. The BE apple antioxidant extracts were analyzed by mass spectrometry for a more complete characterization of phenolic compounds profile (Table 2). As a result, five different chemical classes of phenolic compounds, including hydroxcinamic acids, flavanols, flavonols, hydroxybenzoic acids and dihydrohalcones, were detected. Thus, a total of 23 compounds were identified on the most promising BE apple extracts (from MAE and CET), while in CE and UAE only eight phenolic compounds were identified. The main phenolic acids detected belong to hydroxcinamic acids group, of which caffeoylquinic acids (CQAs) with a molecular ion [M-H]- at _m/z_ 353, and coumaroylquinic acid (CoQAs) with a molecular ion [M-H]- at _m/z_ 337, were widely distributed in apples, in particular in the BE apple variety [3]. The three main compounds belonging to CQAs were 3-caffeoylquinic acid (neochlorogenic acid), 5-caffeoylquinic acid (chlorogenic acid) and 4-caffeoylquinic acid (cryptochlorogenic acid). The molecular ion [M-H]- of CQAs was consistent with their molecular formula of C\({}_{16}\)H\({}_{18}\)O\({}_{9}\) and since those molecules can suffer esterification at different positions of quinic acid moiety, different isomers are detected. Regarding the CoQAs, three different isomers (3-CoQAs, 4-CoQAs and 5-CoQAs) were found in BE apple extracts, showing the typical fragments of the p-comamic acid _m/z_ 163 and 119 and the loss of quinic acid (_m/z_ 191). Same CQAs and CoQAs derivatives were already reported in other works where apple extracts were characterized [1], particularly, [PERSON] et al. [3] who detected 5-CQAs, 4-CQAs, 5-CoQAs and 4-CoQAs in BE apple dried powder. Among the other group of phenolic acids, hydroxybenzoic acids, only one compound with an _m/z_ 315 was detected, and in the MS/MS fragments, it produced a base peak at _m/z_ 153 linked to a loss of sugar with 162 Da, corresponding to protocatechuric acid aglycone. This is the first time that protocatechuric acid was identified in BE apple to the best of our knowledge.
## 4 Discussion
### Technical and Economical Perspective  
A standard computer with the following characteristics was used to implement simulations: 64-bit operating system, Intel core i7-9750H CPU @ 2.60 GHz, 16 GB RAM, 22.8 GB virtual memory, 256 SSD, and 1 TB HD. The average simulation time was 126 s per simulation and Comsol Multiphysics software was used. The average amounts of physical and virtual memory used to handle simulations were 6.77 GB and 9.60 GB, respectively.  
A technical challenge to integrating the proposed methodology into equipment used in the clinical practice could be the data conditioning required to adapt the thermal image files provided by the screening instrument to the required format, which could involve several processing stages. Another consideration is the great amount of data to be stored and processed. High-resolution IR thermometry and MR imaging generate large image files that can be difficult to manage when processed.  
The proposed model can be considered a medium-cost approach compared with other technologies. Assuming the availability of thermographic instrumentation, the main investments required to implement it would be (i) the required software, (ii) a suitable workstation to run the required code, and (iii) a storage device with the required capacity. These costs can amount to several thousand dollars. The main budget constrain could be instrumentation acquisition in cases that require it, which would represent a budget of hundreds of thousands of dollars.