esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
## 2 Materials and Methods
### Description of the Experimental System for Cyclic Treatment of Gray Water  
The cyclic graywater treatment system (CGTS) is an ecological treatment system designed and constructed to use natural processes with gravel filters, soil, and vegetation (quinoa) for graywater treatment [12]. This system is appropriate for treating and reusing on-site graywater (Figure 1).  
The process consists of seven pots that have been arranged in a cascade (staircase) to ensure 3 levels of graywater treatment, allowing the irrigation of quinoa with 3 qualities of water (GWT1: graywater drained from the first treatment; GWT2: graywater drained from the second treatment; and GWT3: graywater drained from the third treatment). The plastic pots have a diameter of 32 cm and a height of 30 cm. Every pot features an aperture to let drainage water escape and a gravel bed at the bottom. A geotextile filter is placed on top of a 3 cm thick layer of gravel in each pot to filter the drained water. This thickness and the usage of geotextile to enable proper water drainage have been made possible by preliminary testing. A 25 cm thick layer of soil weighing 20 kg sits on top of this gravel layer.  
The first-stage treatment (T1) consists of four pots that received untreated graywater for preliminary filtration and partial treatment. Two more pots, representing the second treatment level (T2), are irrigated with the effluent from these pots. This is a cascade system, in which the second step benefits from the water that has already been processed in the first stage. Water from the second level is used to irrigate the single pot in the final stage, which is treated at the third level (T3).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Means Values** & **NT106.03** \\ \hline pH & 7.69 \(\pm\) 0.53 & 6.5-8.5 \\ \hline EC (dS/m) & 2.41 \(\pm\) 0.45 & 7 \\ \hline Anions (meq/L) & - & - \\ \hline HCO\({}_{3}\)\({}^{-}\) & 8.45 \(\pm\) 2.42 & \\ \hline Cl\({}^{-}\) & 11.79 \(\pm\) 2.46 & \\ \hline SO\({}_{4}\)\({}^{2-}\) & 4.65 \(\pm\) 2.12 & \\ \hline Cations (meq/L) & - & - \\ \hline Na\({}^{+}\) & 13.18 \(\pm\) 3.97 & \\ \hline K\({}^{+}\) & 0.67 \(\pm\) 0.33 & \\ \hline Ca\({}^{2+}\) & 5.96 \(\pm\) 1.34 & - \\ \hline Mg\({}^{2+}\) & 5.42 \(\pm\) 1.93 & - \\ \hline Heavy metals (mg/L) & - & - \\ \hline Cu & 0.010 \(\pm\) 0.00 & 0.5 \\ \hline Mn & 0.015 \(\pm\) 0.22 & 0.5 \\ \hline Fe & 0.066 \(\pm\) 0.03 & 5 \\ \hline Pb & 0.047 \(\pm\) 0.00 & 1 \\ \hline Ni & 0.042 \(\pm\) 0.00 & 0.2 \\ \hline Co & 0.027 \(\pm\) 0.01 & 0.1 \\ \hline Zn & 0.014 \(\pm\) 0.00 & 5 \\ \hline Cr & 0.024 & 0.1 \\ \hline Cd & 0.008 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical characteristics of irrigation raw graywater (RGW). The values are the means \(\pm\) standard deviation of 14 replications (\(n\) = 14).
## 1 Introduction
Although time-distributed convolutions already (partially) capture temporal dependencies, our proposal provides CMU layers [11] between the decoder and encoder in order to strengthen temporal consistency without losing the advantage offered by the use of convolutions in learning spatial patterns. As a matter of fact, compared with the more traditional LSTM [12] or GRU [13], CMU is designed to handle spatial information over time using convolutional layers natively, and it also allows time dependency to be modeled without using the state-to-state transition (used instead by LSTM and GRU). Finally, in the decoder component is in charge of building the prediction from the information processed by the encoder and CMUs stage. The decoder includes two attention mechanisms, namely _Temporal Attention_ and _Spatial Attention_, to identify and reinforce the information that predominantly influences the prediction. The main contributions of this paper can be summarized as follows:  
1. We propose a novel autoencoder-based architecture that outperforms the state-of-the-art for the flow prediction problem. To the best of our knowledge, STREED-Net is the first autoencoder architecture that combines the use of time-distributed convolutional blocks with residual connections, a CMUs and two different attention mechanisms. Moreover, unlike other state-of-the-art models [4; 6; 7; 8], focuses only on recent time dependencies (closeness), that is it only relies on a small number of time periods preceding the one to be predicted. This results in fewer hyperparameters to tuned. 2. The impact of the most important components of the architecture on prediction is assessed through an ablation study and discussed. In particular, the study demonstrates the ability of the two different attention blocks to capture and harness important underlying temporal and spatial information. 3. Finally, this work presents a methodologically sound comparative assessment against the best models from the literature on real-life case studies. The analyses consider different error measures, the number of trainable parameters, and a complexity indicator (number of FLOPs). Results indicate that outperforms the considered state-of-the-art approaches using a relatively reduced number of parameters and FLOPs. The rest of this paper is organized as follows. In Section 2, the literature on techniques used in flow and traffic prediction is analyzed. Section 3 defines the flow prediction problem in urban areas, while in Section 4, the core deep learning techniques exploited in this work and the proposed framework are described in detail. In Section 5 data and results of experiments are presented and analyzed. Finally, the conclusions and recommendations for future work are discussed in Section 6.
## 2 Literature Review
### Financial Data Prediction Using LSTM in Finance
#### 2.1.3 Hybrid LSTM Architectures Incorporating Signal Decomposition and CNN  
A model combining wavelet transform, Attention mechanism, and LSTM neural networks has been proposed for predicting stock opening prices with high accuracy [5]. The experimental results on S&P 500 and DJIA datasets demonstrated excellent performance, marked by high coefficients of determination and low mean square errors. This model outperformed existing models such as LSTM, Gated Recurrent Units (GRUs), and wavelet-transformed LSTM models. Future plans include incorporating stock-related news and fundamental information to further enhance stability and prediction accuracy.  
A hybrid model combining a new version of EMD with Akima spline interpolation and an LSTM network has been introduced to handle complexities and nonlinearity in stock market forecasting [14]. It outperforms other machine learning models in predicting the daily closing prices of the KSE-100 index. Performance evaluation using RMSE, MAE,and MAPE confirms the model's effectiveness for stock market prediction. Future research will extend this approach to stock markets in other countries and various nonstationary time-series data. This hybrid model proves highly useful for predicting complex financial time series data.  
A CNN-Bidirectional LSTM (Bi-LSTM)-Attention-based model has been proposed to address the challenges of stock price prediction, including high frequency, nonlinearity, and long memory [21]. This model significantly enhances prediction accuracy, outperforming other models, particularly for the CSI300 index and 12 other stock market indices. Despite its superior performance, the model has limitations due to various financial factors. Future research may focus on improving accuracy by integrating multi-source heterogeneous information and combining the latest models.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 6 Evaluation
### Discussion  
The development of the CPS hub and evaluations were done in a systematic step-by-step manner, which has proven to be a very powerful approach. The evaluations indicate that the capabilities of the CPS hub to manage presence, trust process, and publish/subscribe type of information sharing between heterogeneous resources/services of multiple stakeholders are functionally sufficient to operate in the considered evaluation cases. The CPS hub enabled interactions of very heterogeneous physical resources, such as, for example, smart watches (Polar), hunting dog collars (Tracker), street lamps (Valopaa), EEG measurement device (Bittium), electric vehicles (simulated), charging stations (simulated), buildings (simulated), simulation models, and multiple servers of companies, so that ownership was taken into concern. However, the evaluation related to energy flexibility case was limited in the sense that only simulated physical resources were used in a limited scenario. Especially the scalability, real-time and streams-based operation, application of physical DERs, aggregation, virtual power plants, and interaction within day ahead/intraday energy markets are seen to be issues for future research. In addition, the evaluations of the CPS hub demonstrated potential methods for including the policies of the owners for trustable interactions between CPS resources. However, combining the policies of the owners, service providers, and OEMs in the physical resources, communications and information levels in dynamic contexts requires future research actions. On the basis of the evaluations, it is envisioned that the constructed CPS hub contributes a set of very essential technical enablers for future smart CPS systems and creates a strong basis for such future research.
## 2 Materials and Methods
### Validation Methods  
#### 2.5.1 Meteorological Validation  
The meteorological validation of the WRF model in the MAGDA project was conducted using the MODE (Method for Object-Based Diagnostic Evaluation) technique, an object-oriented verification method designed to assess spatial agreement between forecasted and observed precipitation fields. This approach provides diagnostic insights that are more directly interpretable than traditional grid-point verification techniques, making it particularly useful for evaluating high-resolution numerical weather prediction (NWP) models [65; 66; 67].  
MODE identifies precipitation objects in both forecast and observation fields, characterizing their attributes such as area, centroid position, orientation, intersection, and intensity ratio [68]. Key spatial attributes include rainfall extent characteristics such as area ratio and intersection area, and spatial relationships such as centroid distance and angle difference. These descriptors quantify the shape, orientation, and spatial alignment of forecasted versus observed features. The primary statistical indices used for evaluation were the Frequency Bias Index (FBIAS), the Probability of Detection (POD), the Critical Success Index (CSI), and the False Alarm Rate (FAR) [67].  
The assimilation analysis of 2 mT, ZTD, and reflectivity was summarized using a boxplot approach to assess improvements at each assimilation cycle across different variables. Boxplots provide a visual representation of the distribution of bias and standard deviation, offering a statistical summary to evaluate how assimilation impacts model accuracy by examining changes in central tendency, variability, and outliers. The boxplot includes the first quartile (Q1), third quartile (Q3), and interquartile range (IQR = Q3 - Q1). The whiskers extend based on 1.5 - IQR, with the actual length depending on data distribution. The upper whisker reaches the largest value within Q3 + 1.5 - IQR, and the lower whisker extends to the smallest value within Q1 - 1.5 - IQR. Outliers are represented as dots beyond this range.  
This method effectively evaluates the impact of data assimilation on forecast uncertainty: a reduction in the spread of the box or whiskers indicates decreased variability, and a shift in the median toward zero suggests improved bias. Outliers highlight specific cases where assimilation has a localized effect, either correcting or amplifying forecast errors.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### PHY Layer Challenges
#### 7.1.3 Local or Cooperative Sensing  
Local sensing is conducted by a single node in order to decide whether PU signals are present or absent in any band of interest. On the other hand, cooperative sensing involves multiple end nodes conducting SS and collectively deciding whether PU signals are present or absent [138]. This decision is often taken by a central coordinator. There are different approaches for cooperative sensing including linear combination of local sensing from individual nodes [139], \(k-out-of-N\) approach wherein a certain number of participating nodes is required to conclude on a certain outcome [140], the \(And\)-method in which any node that declares a band free (null hypothesis) would make the coordinator to declare the band as free[141]. Cooperative sensing is theoretically more effective than local sensing as it alleviates the issues of shadowing and fading due to the collaborative efforts from different nodes [138]. However, cooperative sensing introduces complexities, such as large messaging overhead between nodes and gateways, and poor energy efficiency across nodes. Local sensing on the other hand is limited by errors due to shadowing and fading. Thus, it is essential to develop new methods to address these limitations for effective CR-LPWAN deployment.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 3 Results
### Performance Comparison with other Algorithms
#### 3.3.2 Evaluation of GNSS Signals Algorithm  
This algorithm also divided the detection status into three types, which was similar to the definition of a fuzzy intermediate state of SatProbe. Hence, we modified the mean and variance of the Gaussian distribution in the emission matrix of the algorithm according to the statistical data of the features extracted by the algorithm from the training set. Furthermore, we adjusted the transition probabilities of HMM. We evaluated the accuracy of the algorithm on four datasets. As shown in Figure 17, the accuracy of this algorithm in Dataset_2 (0.8778) and Dataset_4 (0.8774) is nearly 0.10 lower than that in Dataset_1 (0.9753) and Dataset_3 (0.9798). Similarly, the IO detection accuracy of IO transition regions is lower than indoor and outdoor scenarios.  
Next, we evaluated the IO transition delay on Dataset_2 and Dataset_4. Figure 18 showed the cumulative distribution probability of time delay on Dataset_2 and Dataset_4. To achieve more than 80% cumulative probability of indoor to outdoor transition on Dataset_2 and Dataset_4, the IO  
Figure 16: The cumulative distribution probability of transition delay using SatProbe algorithm on Dataset_2 and Dataset_4.  
Figure 17: The accuracy of indoor and outdoor detection using algorithm [22] on four datasets.  
transition delay must be greater than 7 s and 4 s, respectively. To achieve more than 80% cumulative probability of outdoor to indoor transition on Dataset_2 and Dataset_4, the IO transition delay must be greater than 12 s and 10 s, respectively. The transition efficiency of the algorithm [22] is better than that of SatProbe. However, 12 s is still unacceptable.
## 2 Flow Field State Detection Technology for Multiphase Sink Vortex
### Advances in the Multiphase Vortex-Induced Vibration Technology
(**b**) Time-domain vibration signal. Reproduced with the permission of Elsevier. (Reprinted with permission from Ref. [42], 2014, Elsevier). Figure 3: Recognition of vortex sucking slag based on SA–ANN algorithm. (**a**) Algorithm implementation. (**b**) Vibration signal distortion feature. (Reprinted with permission from Ref. [41], 2013, IEEE). Vortex-induced vibration detection technology offers several benefits in terms of investment cost, product quality, detection accuracy, and service life: (1) the vibration detection technique can assess and realize the previous vortex slag condition during the vortex generation process by using the amplitude difference of vibration signals; (2) the vibration sensor does not have to be installed inside the high-temperature flow control system; however, it can be located in a low-temperature region away from the industrial environment to ensure that the detection system's service life is reliably assured and has no negative influence on typical industrial productivity; (3) the vortex-induced vibration detection platform has a basic design, is simple to install and maintain, and does not require changes to existing industrial production equipment; and (4) the cost of producing the equipment is minimal, and it is simple to popularize. While the vibration detection approach has had a lot of success, there are still a few issues that need to be addressed in real-world engineering applications: (1) When the sliding door is opened, the flow control equipment is set to low-flow, reducing the fluid level. Impurities cannot be recognized if a vortex does not develop. (2) The fluid impact force is restricted and susceptible to external noise influences, resulting in erroneous judgments by the fluid-induced vibration system. Its detection rate and consistency need to be improved. (3) A nonlinear pulse with unpredictability characterizes the fluid impact signal. Many experimental data and field practical experience must be used to identify the signal's characteristics. As a result, in order to satisfy the actual industrial applications, the detection software must spend a long time debugging to meet the premise of system identification real-time performance. The primary problem is a scarcity of comprehensive studies on the multiphase sink vortex's transport characteristics and fluid-induced vibration dynamics. Further investigation on how to solve the issues above is required. As a result, the technical theories for multiphase vortex-induced vibration detection technology should be further developed
## 4 Discussion
### Comparison with FaceReader Results  
The results show that the proposed system achieves high accuracy. The SAE helped reduce the complexity of the data processing, reducing the data dimensionality. The SAE automatically selects the appropriate RSP signal features for classification through training, thereby enhancing the overall system performance.  
Under normal lighting conditions, the accuracy of our method is comparable to that of the FaceReader. However, under poor lighting conditions, the accuracy of the FaceReader decreased significantly owing to its reliance on clear facial images, which could not be obtained. In contrast, our method maintained consistent accuracy regardless of lighting conditions, as visual data are unnecessary.  
According to the literature [44], using manually selected features for prediction achieves a higher accuracy than directly using segmented signal waveforms. Compared with our method, the manually selected features resulted in a higher accuracy, validating the findings in the literature. Additionally, the results of our method are close to those obtained using manual feature selection. The manual selection of signal features requires experts with extensive experience and knowledge, which increases the deployment complexity and cost of the entire system. In contrast, our method demonstrates the power of machine-learning in this field, with broad potential for application. The automation and efficiency of machine-learning models make them suitable for emotion recognition tasks in various environments, including those with challenging conditions, such as poor lighting.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 3 Results
### Within-Field Variability
In all vintages and for both VIs, the CI computed in vineyards planted in a flat terrain was higher than those planted on the slope side (14.2% and 12.7%, respectively, for NDVI and NDRE). Finally, the boxplots in Figure 20c,d portray the CI grouped according to the vineyard's training systems. The VS-trained vineyards showed, on average, the lowest CI, while the AS recorded the highest CI (13.6% and 17.7%, respectively, for NDVI and NDRE). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **VI** & **Direction** & **Location** & **Topography** & **Training** & **Vintage** \\ \hline Sill & NDVI & * & NS & NS & NS & NS \\ Nugget & NDVI & *** & *** & ** & *** & *** \\ Range & NDVI & ** & NS & NS & NS & NS \\ CI & NDVI & *** & NS & *** & *** & NS \\ Sill & NDRE & * & NS & NS & NS & NS \\ Nugget & NDRE & *** & *** & ** & *** & *** \\ Range & NDRE & ** & NS & NS & NS & NS \\ CI & NDRE & *** & NS & *** & *** & NS \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Outputs were retrieved from the multifactorial ANOVA performed on the categorical features (factors) and the semivariogram-specific indices (dependent variables). Sill, nugget, and range are the indices extracted from the experimental semivariogram of the average pixel values owned by the same vineyard per vintage *** indicates a \(p\) value \(<\) 0.001, *** indicates a \(p\) value \(<\) 0.01, *** indicates a \(p\) value \(<\) 0.05, while “NS” indicates nonsignificant. Figure 18: The boxplots describe the seasonal range (m) retrieved by the experimental semivariogram for all vineyards through the six-year monitoring according to the cardinal direction classes used in this paper. Boxplot (**a**) refers to NDVI and boxplot (**b**) refers to NDRE.
## 3 Materials and Methods
### Analytical Validation  
Based on current international guidelines of the European Medicines Agency (EMA) and Food and Drug Administration (FDA), the in-tube SPME-LC-MS/MS method was validated.  
Using linear regression of the ratio between the peak area of the target drugs and the IS (Y-axis) peak area versus the nominal drug concentrations (X-axis, ng mL\({}^{-1}\)), the calibration curves were generated.  
The lower limit of quantitation (LLOQ) is the lowest concentration in the calibration curve that can be quantitatively determined with suitable precision and accuracy.  
Accuracy was evaluated from relative squared error (RSE) values. Precision was estimated from the CV values of the analyses of the blank plasma samples spiked with drugs at five different concentrations (\(n=5\)), namely LLOQ, low quality control (QC), medium QC, high QC, and upper limit of quantitation (ULOQ).  
Matrix effects were investigated by using eight lots of blank plasma obtained from different sources spiked with drugs at low QC and high QC concentrations. The matrix factor (MF) was evaluated for each matrix lot, by comparing the drugs responses in the presence of matrix with those in the absence of matrix. The IS normalized MF was also calculated by dividing the drug MF by the IS MF. The CV of the IS-normalized MF calculated from the eight matrix sources should not exceed 15%.  
The carry-over should be evaluated by analyzing a blank sample following the highest concentration calibration standard. The response in the blank sample obtained after measurement of the highest concentration standard should not be greater than 20% of the analyte response at the LLOQ and 5% of the response of internal standard.