esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Previous Surveys on the Application of EMAS in European Firms
Concerning the first group, there is no common agreement among scholar about the potential positive financial and competitive consequences of EMAS. Especially in early works (between 1990 and early 2000s) a greater competitiveness of EMAS firms was pointed out [15; 46; 47; 55]. On the other hand, in more recent works this seems to be a troubling point. In fact organizations are not enough satisfied in terms of competitive advantages and increased turnover [50; 51; 57]. According to the analyzed surveys, improved communication with interested parties is a major benefit of EMAS. In many cases, EMAS allows improvement of an organization's image and reputation, especially with local community and public institutions [15; 17; 51; 57; 58; 62]. Improvement of environmental performances is often reported as a main achievement of organizations implementing EMAS. The analysis of the environmental performances follows two paths. The first consists in the analysis of Environmental Indicators trend. According to [PERSON] _et al._ (2011), indicators show a slight positive effect on environmental aspects, especially in reducing waste production and water consumption [66]. Moreover, EMAS seems to be more effective in the medium-long run, because environmental performance is generally linked in a positive way with the age of the system [19; 52; 66]. Also analyzing the application of EMAS in Italian clusters (Lucca paper industry), indicators show a positive trend in environmental performances [54]. Other studies evaluate environmental performances according to organizations' perception, from the analysis of surveys responses. In general, organizations perceive a positive improvement of environmental performances. Several environmental achievements are pointed out from organizations, the most frequently related to are water consumption, waste management, recycling, energy consumption, and air pollution [15; 47; 51; 52; 57; 58; 62; 64]. In another study on European EMAS organizations, respondents underlined a positive environmental performance result, with the majority reporting significant improvement in Core Indicators, with the exception of biodiversity ([PERSON] et al., 2014). [PERSON] _et al._ (2014) realized a survey targeted to all Italian Clusters applying EMAS at a cluster level, finding that stakeholders perceive environmental performances improvement as the major positive outcome of its application ([PERSON] et al., 2014). The present work enriches the knowledge on organizations' perception of EMAS in Italy, one of the leading countries for EMAS diffusion. Thanks to the large coverage (over 500 respondents) it allows to obtain significant results, providing useful insights on firms' viewpoint to the EMAS Competent Body during the present process of revision of the Regulation. Given the recent downturn of EMAS registrations in Europe, a further added value of the work is to present figures on organizations' intention to renew or drop-out of the EMS in the future.
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 3 Results
### Validation of Methods
#### 3.1.2 Validation of Ground-Motion Prediction Equation and Constraints  
The Changing earthquake in Sichuan occurred in the western part of China; therefore, it is necessary to select a ground-motion model for western China that is suitable for this region and a ground-motion duration prediction equation based on a strong ground-motion database in China [45]. The target response spectrum and target duration ground-motion models were calculated using the response spectrum and duration, respectively, as the constraints for ground-motion synthesis of the Changing earthquake by setting the earthquake scenario. The specific forms of the selected response spectrum ground-motion model and the ground-motion duration model are shown as Equations (6) and (7).  
\[lgS_{a}(T)\ =\ C_{1}+C_{2}M_{s}+C_{4}lg[R_{rup}+C_{5}exp(C_{6}M_{s})], \tag{6}\]  
\[lnD_{5-95}\ =\ 0.1561+0.3647M_{s}+(0.4958-0.0145M_{s})ln\sqrt{R_{rup}^{2}+2.5}-0.178 4 Inv_{s}30, \tag{7}\]  
where \(Sa(T)\) is the response spectrum, \(M_{s}\) is the surface wave magnitude, \(R_{rup}\) is the rupture distance, and \(D_{5-95}\) is the ground-motion duration. \(V_{s}30\) is the shear wave velocity at 30 m below ground, representing the site conditions, and the value of our target site \(V_{s}30\) is 302 m/s.  
Figure 4a-d shows the comparison of the response spectra of the Changing earthquake records at three period points (0.01 s, 0.20 s, and 1.00 s) with the attenuation curves obtained from the corresponding ground-motion models. The figures also show the comparison of the 5-95% significant duration of the actual ground-motion data with the predicted values obtained from the duration prediction equation. The two chosen ground-motion models have the capability to predict the actual ground-motion characteristics of the region and demonstrate the validity of the selected synthetic constraints, including the target response spectrum and duration. It is important to mention that two independent GMPEs are used in this paper. However, it is more reasonable to use the generalized conditioning intensity measure (GCIM) or generalized ground-motion prediction model (GGMPM) models, which can provide realistic targets to validate the simulation.  
Figure 3: Top eight time histories and time-frequency analyses of characteristic ground motion.
## 1 Introduction
### SMOS Requirements  
ESA's SMOS mission, proposed in the framework of the Earth Explorer Opportunity Missions [16] aims at deducing soil surface moisture and ocean salinity with near global coverage every three days [17]. The mission's requirements regarding soil moisture are: the accuracy should be better than 4% volumetric moisture with a spatial resolution of 35-50 km of a single measurement. The desired accuracy of ocean salinity retrieved from a single measurement is 0.5-1.5 psu. For a 30-day average over an area of 100 km \(\times\) 100 km, the accuracy is specified to 0.1 psu, implying that brightness temperatures measured with the SMOS L-band radiometer have to be within \(\pm\)0.1 K.
# Fault Diagnosis of Universal Circuit Breakers Based on Variational Mode Decomposition and WOA-DBN
## 3 Methodology  
### Deep Belief Network  
DBN is a neural network architecture combining multiple Restricted Boltzmann Machines (RBMs) layers to form a multi-layer perceptron. The DBN is designed to extract essential features from data by progressively abstracting information from lower to higher layers. Each RBM in the DBN consists of a visible layer (\(v\)) and a hidden layer (\(h\)), with weights (\(w\)) connecting the layers. Figure 7 illustrates a DBN structural model stacked by three RBMs. Each RBM consists of a two-layer network, i.e., a visible layer (\(v\)) and a hidden layer (\(h\)). The layers are connected by weights w. The first visible layer v\({}^{1}\) serves as the initial input data and forms the first RBM (RBM1) with the first hidden layer h\({}^{1}\). The first  
Figure 6: Feature sequence for 6 fault types.  
hidden layer \(h^{1}\) then serves as the second visible layer \(v^{2}\), forming a second RBM (RBM2) with the second hidden layer \(h^{2}\). Similarly, the second hidden layer \(h^{2}\) serves as the third visible layer \(v^{3}\), forming a third RBM (RBM3) with the third hidden layer \(h^{3}\). While the layers are internally independent of each other, the data can be transformed to each other between the layers by the activation function according to the RBM learning rule. After the RBM learns the input data of the lower layer, its output results are used as inputs for the higher layer. This process keeps going layer by layer, forming a feature representation at the higher layer that is more abstract and representational than that of the lower layer.  
The DBN network accomplishes the integration of data and categories through a probabilistic generative model. It trains and optimizes the network layer by layer, generating a network structure that maximizes the probability of training data. This approach allows for the extraction of correlations between data points and facilitates the tasks of classification and identification. Neurons within the same layer operate independently, resulting in a network with solid independence, enhanced computational power, and improved training speed.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 5 Proposed Method for Privacy Preservation in Healthcare Data
### Experimental Results on the Breast Cancer Prediction Dataset
#### 5.4.3 Comparison Analysis of Laplace vs. Gaussian Mechanism  
The implementation of the Laplace mechanism using PYDP adds noise sampled from the Laplace distribution based on the privacy budget to provide results with provable privacy guarantees under differential privacy as shown in Figure 10. To generate noise, the Gaussian mechanism uses a Gaussian distribution based on the privacy budget, which also provides differentially private results but typically is used for scenarios where smoothness and sensitivity are key considerations. The Laplace mechanism [49] is generally efficient due to the simplicity of sampling from a Laplace distribution. The Laplace mechanism often provides better accuracy for discrete counting queries. In conclusion, both Laplace and Gaussian mechanisms offer differential privacy solutions with different trade-offs in accuracy, ease of implementation, and computational complexity. The choice of between them depends on the particular needs and conditions of the differential privacy application and the nature of queries being performed on the datasets. A comparison between the two mechanisms is presented the figures below.  
The Laplace mechanism is ideal for situations with bounded sensitivity. The Laplace mechanism provides noise proportionate to the function's sensitivity; hence, it might be useful when the query has a known, finite sensitivity (such as counting queries). In a healthcare dataset, using the Laplace mechanism can effectively mask the exact count while ensuring privacy; the query returns the number of patients within a specific condition as shown in Figure 11. For queries with low sensitivity, the Laplace method can offer more precise privacy guarantees, as the noise can be adjusted according to the sensitivity, resulting in less distortion in the output.  
Figure 10: Laplace mechanism.  
Figure 9: Queries with time comparison using the Breast Cancer Prediction Dataset.  
The Gaussian mechanism well-suited for scenarios with unbounded sensitivity or when dealing with aggregate statistics over large datasets. For queries that involve averages or continuous outcomes, the Gaussian mechanism might be preferable, as it can handle the potential for large deviations more gracefully. The Gaussian mechanism can manage higher sensitivity with less effort and does not require significant noise levels like the Laplace mechanism; therefore, it is advantageous for guaranteeing privacy with higher-dimensional queries.  
It can be noticed that the Gaussian mechanism [50] is slightly more computationally intensive due to the nature of sampling from a Gaussian distribution, which involves more complex calculations. By performing time complexity analysis, it can be seen in Figure 12 and Figure 13 that the Gaussian mechanism takes slightly more time in processing queries with differential privacy.  
Figure 11: Laplace mechanism.  
Figure 12: Gaussian mechanism.  
Figure 13: Gaussian mechanism.
# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 5 Experiment Results
### Fitting the Models with LSTM
#### 5.2.1 The First Method (Univariate)  
The closing prices of the Mobily, STC, and Zain KSA stocks were fed directly into the LSTM model, which attempts to identify and comprehend patterns in the historic data. As the closing price was the only attribute used from the dataset, the model was univariate. Years of historical data were used to train the model. Table 6 and Figures  
The multivariate method, which used multiple stock prices, outperformed the univariate method and successfully predicted the stock prices for the next seven days. The multivariate LSTM models are better at predicting the stock market than their univariate counterparts as they use information from multiple sources, such as the opening, highest, and lowest prices of related stocks, which improves their predictive accuracy. Meanwhile, univariate models only use a single input variable, typically the price of the stock being predicted, which does not contain all the information necessary to make accurate predictions. Furthermore, as multivariate models are also better at capturing the complex correlations and interdependencies of the different input variables, they make significantly more accurate predictions.
## Nomenclature  
MLP Multi-layer Perceptron Network  
ANN Artificial neural network  
MSE Mean square error  
MAPE Mean Absolute Percentage Error  
\(\lambda\) Loading factor  
\(\mathbf{Y_{ob}}\) Obtained outputs  
\(\mathbf{Y_{des}}\) Desired outputs  
CPF Continuation power flow  
CP Critical point  
\(Pa\) Total real power losses  
\(Pr\) Total reactive power losses  
PCPF Parameterization continuation power flow  
p.u. Per unit
## 4 Simulation Results
### Results of FS-AQoS  
The simulation results for FS-AQoS are presented in Figure 8. We see the effect of UIVP on QoS for different degrees of reliability, by considering many scenarios with various radio interference levels and link latency values.  
Figure 8a gives the results for LL = 0.1 and RI = 0.1. We can see that in all cases, the QoS is decided above the moderate level. This is due to the fact that the communication links between vehicles have very low latency and there is no or very little interference present. However, when the interference is at harmful levels (see Figure 8b), the links that will be considered for a potential communication are mostly the ones with high reliability. The links with medium effective reliability, on the other hand, are considered only when vehicles are using a moderate number of UIVP packets. In all other cases, the QoS values are smaller than 0.5, which means that the chances of successful communications are not very high.  
In Figure 8c, we consider the scenarios with LL = 0.5 and RI = 0.5. The results show clearly the importance of UIVP in QoS. A link with effective reliability is no longer enough to have successful communication, as opposed to the scenarios in Figure 8b, where links with high ER are considered for communication regardless of the number of UIVP packets. In these conditions, acceptable QoS values are achieved only for moderate levels of UIVP.  
The same holds true for the scenarios in Figure 8d,e, with the difference that the links must have high reliability. If the link does not have high reliability, the QoS is decided under the moderate level, in any case. Comparing the results for these scenarios, we see that having no interference in reliable links with high latency (RI = 0.1, LL = 0.9) is slightly better than the other conditions (RI = 0.9 and LL = 0.5), as a vehicle being exposed under the former conditions can at least use the available communication links for applications that are not time-sensitive.  
For both high latency and high interference levels (see Figure 8f), the QoS is always decided as extremely or very low in all cases. Under these circumstances, it is impossible for vehicles to use these communication links, as performing any kind of transmission between vehicles will lead to loss of information.  
Figure 8: Simulation results for FS-AQoS.
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
## 5 Numerical Results
### BER Performance Analysis of C-JIDD  
Figure 11 depicts BER performance curves of the S-JIDD and C-JIDD with a code length of N = 256. It is obvious that the performance of the C-JIDD is superior to the traditional algorithm under the same iteration times, since the early stopping criterion can overcome the convergence error caused by decoding deviation by locking the correct codes timely. When Eb/N0 = 4 and the iteration times is 5, the BER of the S-JIDD algorithm is \(8.45\times 10^{-3}\), while that of C-JIDD is \(7.68\times 10^{-4}\).The bit error rate reduced by more than an order of magnitude. It is notable that when N = 256, the performance in three iterations of C-JIDD is better than that of the S-JIDD five iterations, because the adding of CRC termination mechanism can effectively reduce the number of decoding deviations caused by too low SNR. The gap will gradually decrease with the increase of SNR. It can also be seen from the figure that when BER = \(1.53\times 10^{-3}\) and five iterations, this paper has a 0.54 dB gain compared with the S-JIDD.  
When N = 1024, the BER performance comparison is shown in Figure 12. Similar to the performance comparison above, the BER of the C-JIDD is lower than that of the original S-JIDD algorithm under the same conditions. When Eb/N0 = 3 and three iterations, the BER of the S-JIDD algorithm is 1.74 \(\times\) 10\({}^{-3}\), while that of the C-JIDD is \(4.01\times 10^{-4}\). It is also notable that the BER in three iterations of the proposed C-JIDD is lower than the BER of the S-JIDD in five iterations. Similarly, it can be seen from the figure that when BER = \(5.98\times 10^{-4}\) and five iterations, this paper has a 0.32 dB gain compared with the traditional S-JIDD.  
Figure 11: BER performance comparison of C-JIDD with \(N=1024\).  
Figure 10: BER performance comparison of PI-JDD with \(N=1024\).
###### Abstract  
The aim of the present study is the evaluation of a novel potentially probiotic _Lactobacillus paracasei_ SP5, previously isolated from dairy products, as a starter culture of white primed cheese production, either free or immobilized on a traditional food, \"trahanas\", in order to provide protection to the starter culture and a prebiotic effect. All produced cheeses were compared with cheese manufactured by renin enzyme. Several parameters that affect the acceptability, quality, and shelf life of white brined cheeses were investigated, including microbial populations, physicochemical characteristics, and cheese volatiles through 70 days of ripening and storage. White brined cheese production by free or immobilized _L. paracasei_ SP5 resulted in significantly higher acidity (over 0.8 g of lactic acid/100 g of cheese at the 70 th day of ripening) and significantly reduced counts (around 50%) of coliforms, yeasts, and fungi compared to cheese produced with no starter culture. The use of the freeze-dried novel starter culture, either free or immobilized, improved the aromatic profile of cheeseses as was proven through a GC-MS analysis. In addition, it should be underlined that the application of the novel strain led to white brined cheese with improved overall quality and sensory characteristics. The results indicate the potential industrial use of freeze-dried _L. paracasei_ SP5 as a starter culture for the production of good-quality functional white brined cheeses.  
white brined cheese; cheese quality; ripening; prebiotic; probiotic; aromatic profile Article
# Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Ref.** & **Algorithm** & **Application** & **Main Idea** & **Contribution** \\ \hline
[17] & Amplitude comparison monopulse DOA estimation & Radar tracking & Analytic performance analysis & Computationally inexpensive performance analysis \\ \hline
[18] & Conventional beamforming (Bartlett beamforming) algorithm & FMCW automotive radar & Motion compensated Bartlett beamforming induced phase into account \\ \hline
[19] & MVDR beamforming-based DOA algorithm & DOA of underwater acoustic sources & Signal self-cancellation MVDR algorithm for underwater acoustic source localization \\ \hline
[20] & New DOA estimation algorithm based on Complex Watson mixture model and time-frequency selection & Acoustic source localization & Isolation of target speaker by time-frequency selection & Performance improvement of DOA estimation of target speaker \\ \hline
[21] & DOA estimation with acoustic vector sensor (AVS): Preprocessing for target speech enhancement & Enhancement of target speech & Estimated DOA-based isolation of target speech in time-frequency distribution for enhancement \\ \hline
[22] & Array signal processing-based DOA estimation algorithms using array antennas and DOA estimation using single directional antenna & AOA-based localization & Not applicable [Review paper] & Not applicable [Review paper] \\ \hline
[23] & Rician beamforming for array design [Not beamforming-based DOA estimation algorithm] & Ultrasonic imaging & Rician beamforming-based array design for ultrasonic imaging using convex optimization & Improved despeckle in ultrasonic imaging via beamforming-based array design \\ \hline
[24] & TDMA-beamforming for array design [Not beamforming-based DOA estimation algorithm] & Interference suppression suppression in communication system & Interference suppression via spatial filtering avoidance \\ \hline
[25] & Hybrid beamforming for array design [Not beamforming-based DOA estimation algorithm] & Massive MIMO system & Optimal combination of variable phase shifters and constant phase shifters & Improvement in beamforming performance \\ \hline
[26] & MVDR beamforming for array design [Not beamforming-based DOA estimation algorithm] & Bird audio enhancement & Beamforming method based on a local average consensus network for surveillance algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithms, applications, main ideas and contributions of the recently published works on direction-of-arrival (DOA) estimation and beamforming.