esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Reading
In the context of non-laboratory level calibration using the concept of \"normal\" speech level, the obtained mean values for three different conditions of the visual stimulus are all contained in the interval of less than 3 dB. This result suggests that the level at which the subjects read a paragraph of text is reasonably stable, regardless of the (non)existence of visual stimulus. Moreover, it opens up the possibility of using a real person/reader as the source of speech that could be used for calibrating the reproduction level in a non-laboratory listening test setup. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Effect** & \(d\)**Fn** & \(d\)**Fd** & \(F\) & \(p\) & \(\eta^{2}\) & **Power** \\ \hline Visual stimulus & 1.43 & 47.07 & 30.000 & \(<\)0.001 * & 0.098 & 0.984 \\ \hline \end{tabular}
* statistically significant at the 0.05 significance level.  
\end{table} 
Table 7: The one-way ANOVA output table with effect size reported with \(\eta^{2}\) and the results of the power analysis added to the rightmost column. \begin{table}
\begin{tabular}{c c c c} \hline
**Visual Stimulus** & **No Visual Stimulus** & \
\begin{tabular}{c} **Dummy Head at** \\ **85 cm** \\ \end{tabular} &
\begin{tabular}{c} **Dummy Head at** \\ **280 cm** \\ \end{tabular} \\ \hline Mean A-weighted level (dBA) & 56.8 & 55.2 & 58.1 \\ Standard deviation (dB) & 3.1 & 3.7 & 4.2 \\ \hline \end{tabular}
\end{table} 
Table 8: Marginal means and standard deviations of the groups at three different levels of the visual stimulus in the reading part of the experiment.
## 4 Discussion
### Implications  
This research underscores the pivotal role of FDS dynamics in modulating LST evolution across Europe. The scientific and practical significance lies in three key contributions: (1) Methodological advancement--Moving beyond the traditional climate model-based analyses [59], this work pioneers an integrative approach using multi-source remote sensing data. This provides the first quantitative, continent-wide characterization of forest-LST relationships for Europe, effectively addressing the inadequate representation of localized biophysical effects in simulations. (2) Enhanced mechanistic understanding--Through multi-scale spatiotemporal analysis, this study corroborates the positive association between increased forest cover (linked to FDS recovery dynamics) and LST [16; 17; 60]. Crucially, it elucidates the underpinning biophysical mechanism, forest expansion and densification reduce surface albedo, leading to the enhanced absorption of shortwave radiation and consequent local warming [5; 61], thereby advancing the fundamental understanding of land-atmosphere interactions. (3) Guidance for sustainable management--The quantitative framework linking forest dynamics (influenced by FDS changes) to LST responses provides an empirical foundation for precision forest management. This understanding enables policymakers to better optimize forestry activities (e.g., afforestation and harvesting) by balancing the carbon sequestration objectives with biophysical climate impacts, offering vital decision support for achieving Europe's climate goals. Collectively, these findings deepen the understanding of ecosystem-climate feedback mechanisms and provide both a robust theoretical basis and practical guidance for sustainable forest management and regional climate adaptation strategies.
## 2 Materials and Methods
### Experimental Setup of Adsorption and Photocatalysis  
In order to better understand how simultaneous adsorption and photodegradation processes take place, the experiments were carried out based on the classical photocatalytic degradation models. The methylene blue (MB) degradation measurement was standardised in DIN 52980 as a typical measurement to confirm the photocatalytic activity [52]. The ternary heterostructures, were added in 50 mL of aqueous methylene blue solution (concentration of 4 mg L\({}^{-1}\)) and irradiated into a photoreactor equipped with two UV blacklight lamps (F18 W/T8 black light tubes, typically 340-400 nm, with \(\lambda_{\text{max}}\) = 365 nm, flux intensity 3 k, Philips, Bucharest, Romania), and four VIS lamps (TL-D Super 80 18 W/865 lamps, white cold light tubes, typically 400-700 nm, with \(\lambda_{\text{max}}\) = 565 nm, flux intensity 28 lx, Philips). Before illumination, to ensure the adsorption equilibrium, the heterostructures were kept in the dark in MB solution for 30 min. The adsorption experiments were performed in the same condition in the dark. At given time intervals, 5 mL of the sample was withdrawn and filtered (Millipore Millex-GN 0.2 \(\mu\)m), and the solution absorbance was spectrophotometrically measured at a maximum absorption wavelength of 665 nm (Perkin Elmer Lambda 940 spectrophotometer, Waltham, MA, USA) equipped with a quartz cell. The dye degradation efficiency was calculated as a relative percentage of dye concentration to its initial concentration.
## 2 Materials and Methods
### Proposed Method
#### 2.2.3 Binary Auto-Encoder Based Self-Supervised Temporal Hashing  
Recently, many studies focus on the application of auto-encoder to reinforcement learning. For example, auto-encoders were adopted to automatically cluster states in a meaningful way in Montezuma's Revenge [33]. So, we apply binary auto-encoder to reduce the dimension of \(S_{b}(t)\). As shown in Figure 4 binary auto-encoder consists of an encoder layer and a decoder layer which contains two decoders named backward binary decoder and forward binary decoder, respectively.  
Figure 4: Binary auto-encoder.  
Figure 3: Process of LC-MARL.  
Encoder layer extracts the feature of inputs and output a lower dimension binary code, then decoder layer reconstructs the inputs from binary code. The loss function of auto-encoder is the mean squared error between decoder layer's outputs and inputs. After training, the encoder layer of binary auto-encoder serves as a temporal hashing function \(\Phi(S_{b}(t))\in\{1,-1\}^{k})\), which reduce the dimension of state to \(k\). So, we use hash-key output by \(\Phi(S_{b}(t))\) to maintain leniency value of state-action pair, which can be rewritten as  
\[l(S_{b}(t),\alpha_{b}(t))=1-e^{-K*T(\Phi(S_{b}(t)),\alpha_{b}(t))} \tag{21}\]  
Different from [33], state is defined as time series in D-TDD configuration task. So as illustrated in Figure 5, a novel variant of LSTM named BLSTM is adopted to generate a binary hash code in encoder layer.  
The traditional method of extracting binary features of time series is to add a binary output layer to the LSTM-based feature extraction layer. This method is more likely to lose information in the time series. Therefore, we binarize the hidden state \(h_{t}\) of LSTM and add it to recurrent calculation process. At the same time, in order to output effective binary information and avoid gradient explosion, we added a batch normalization layer to output normalized LSTM cell state information \(c_{t}\). The detailed implementation of BLSTM is given as follows:  
\[f_{t} =\sigma(W_{xf}x_{t}+U_{bf}b_{t-1}+M_{cf}\circ c_{t-1}+b_{f}) \tag{22}\] \[i_{t} =\sigma(W_{xi}x_{t}+U_{bi}b_{t-1}+M_{ci}\circ c_{t-1}+b_{i})\] (23) \[\alpha_{t} =\sigma(W_{xo}x_{t}+U_{bu}b_{t-1}+M_{co}\circ c_{t-1}+b_{o})\] (24) \[z_{t} =\phi(W_{xm}x_{t}+U_{bm}\circ b_{t-1}+b_{m})\] (25) \[c_{t} =batch_{norm}(f_{t}\circ c_{t-1}+i_{t}\circ z_{t})\] (26) \[h_{t} =\sigma_{t}\circ c_{t}\] (27) \[b_{t} =sgn(h_{t}) \tag{28}\]  
where \(\circ\), \(\sigma\) and \(\phi\) denotes the element-wise multiplication, sigmoid function, and tanh function.
## 3 Results and Discussion
### Optimization of PET Levels in Marshall Characteristics  
Regarding the purpose of the study, Table 8 shows the optimizing results for PET content as much as possible in Marshall characteristics. It is shown that the optimal PET content used was at 3.84%, with a stationary point of 0.844. It is noted that the mixing time could not affect the stability and density of the asphalt mixture since the PET is not melted but crystalized in temperature of AC-WC mixture.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** \\ \hline \multirow{3}{*}{Stability} & Residual & 1.51 & 5 & 0.3018 & \\  & Lack of Fit & 0.65 & 3 & 0.2163 & 0.50 \\  & Pure Error & 0.86 & 2 & 0.4300 & \\ \hline \multirow{3}{*}{MQ} & Residual & 0.10 & 5 & 0.0193 & \\  & Lack of Fit & 0.04 & 3 & 0.0138 & 0.50 \\  & Pure Error & 0.05 & 2 & 0.0276 & \\ \hline \multirow{3}{*}{VIM} & Residual & 0.88 & 5 & 0.1750 & \\  & Lack of Fit & 0.68 & 3 & 0.2258 & 2.29 \\  & Pure Error & 0.20 & 2 & 0.0988 & \\ \hline \multirow{3}{*}{VMA} & Residual & 1.90 & 5 & 0.3799 & \\  & Lack of Fit & 1.16 & 3 & 0.3860 & 1.04 \\  & Pure Error & 0.74 & 2 & 0.3708 & \\ \hline \multirow{3}{*}{Densitas} & Residual & 5732.75 & 5 & 1146.55 & \\  & Lack of Fit & 2046.75 & 3 & 682.25 & 0.37 \\ \cline{1-1}  & Pure Error & 3686.00 & 2 & 1843.00 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANOVA for predicting Marshall Stability based on RSM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** &
\begin{tabular}{c} **Marshall** \\ **Characteristics** \\ \end{tabular} & \multicolumn{2}{c}{**The Equation for the Results of RSM**} & **Adj. R2** \\ \hline
1 & Stability & \(19.15+0.63\text{A}-0.85\text{B}+0.26\text{C}+0.88\text{AB}+0.04\text{AC}-0.15 \text{BC}-1.26\text{A}^{2}-0.56\text{B}^{2}-0.23\text{C}^{2}\) & 0.7975 \\ \hline
2 & MQ & \(4.79+0.16\text{A}-0.21\text{B}+0.06\text{C}+0.22\text{AB}+0.0075\text{AC}-0.04 \text{BC}-0.32\text{A}^{2}-0.14\text{B}^{2}-0.06\text{C}^{2}\) & 0.7921 \\ \hline
3 & VIM & \(3.73+0.84\text{A}-0.09\text{B}+0.17\text{C}+0.74\text{AB}+0.42\text{AC}-0.04 \text{BC}-0.32\text{A}^{2}-0.14\text{B}^{2}-0.06\text{C}^{2}\) & 0.7956 \\ \hline
4 & VMA & \(3.73+0.84\text{A}-0.09\text{B}+0.17\text{C}+0.74\text{AB}+0.42\text{AC}-0.04 \text{BC}-0.32\text{A}^{2}-0.14\text{B}^{2}-0.06\text{C}^{2}\) & 0.3241 \\ \hline
5 & Density & \(2270-1.25\text{A}-4.88\text{B}+78.88\text{C}-2.50\text{AB}+12.50\text{AC}-12. 25\text{BC}-12.8\text{A}^{2}+17.38\text{B}^{2}+30.87\text{C}^{2}\) & 0.7424 \\ \hline \hline \end{tabular} Note: The coefficients A, B, C refer to the linear response. AB, AC and BC are interactions between independent variables. A\({}^{2}\), B\({}^{2}\) and C\({}^{2}\) is a quadratic response involved in the process.  
\end{table}
Table 7: Equations for predicting Marshall Stability based on RSM.
# Rationale for the Combined Use of Biological Processes and AOPs in Wastewater Treatment Tasks
## 1 Introduction
The use of a strong oxidizing agent such as ferrates (VI)--which are among the most powerful existing oxidizers and, due to their action, decompose many toxic chemicals into low-toxicity products, as well as causing the death of microorganisms due to their disinfecting action--should be singled out separately. In an acidic environment, the potential of FeO\({}_{4}\)[2]- ions is higher than that of ozone, and is the highest among the currently used compounds [24]. At the same time, even the AOP technologies used must be an integration of two or more processes. Furthermore, nanotechnology can be applied in wastewater separation, bioremediation, and disinfection [25]. [PERSON] et al. studied vacuum filtering and mixed-polymer e-spinning membranes made from Fe-containing nanomaterials to remove Cd\({}^{2+}\) ions from neutral aqueous solution [26]. In a study by [PERSON] et al., Ca-Fe oxide pellets were studied as a potential sorption material for established wetlands. However, the weaknesses of this material for sorption filter systems were noted: mass loss caused by rapid dissolution of gypsum, increased sulfate and calcium concentrations in the water, and rapidly changing hydraulic conductivity [27]. In a study by [PERSON] et al., wastewater from a cosmetic factory was treated with a combined light/Fe\({}^{0}\)/H\({}_{2}\)O\({}_{2}\) process, followed by biological treatment. The light/Fe\({}^{0}\)/H\({}_{2}\)O\({}_{2}\) process with doses of 1000/2280 mg/L Fe\({}^{0}\)/H\({}_{2}\)O\({}_{2}\) and a process time of 120 min resulted in 70% removal of COD. The chemically treated wastewater was subjected to biological treatment, resulting in an overall COD removal of up to 97.7%. Untreated cosmetic wastewater subjected to biological treatment caused serious changes in the microbial community structure, resulting in a significant decrease in the removal efficiency of organic carbon, nitrogen, and phosphorus [28]. The results indicate the feasibility of further research on the combination of AOP processes with bioremediation. Thus, the problem of wastewater treatment must be considered comprehensively, combining different technological solutions with a combination of treatment methods to achieve the most sustainable operation of treatment plants. This study aims to form a unified concept of the integrated use of different methods of wastewater treatment to form a stable technological system under the influence of toxic factors such as the intake of antibiotics and surfactants. The set objectives of the study are:  
* To study the wastewater treatment of closed-loop water supply installations (CLWSI) from nitrogen compounds using AOPs;
* To research the effects of antibiotics and surfactants on the activity of biological processes;
* To develop an integrated model for combining biological processes and AOPs in wastewater treatment.
## 2 Materials and Methods
### Instrumentation  
Full details about the PEF system used are given in our previous studies [23; 26]. In brief, a high-voltage current generator (Leybold, LD Didactic GmbH, Hurth, Germany), a digital oscilloscope, a function/arbitrary waveform generator, and two stainless steel plates (10 cm long, 10 cm high) with a 1 cm Teflon layer between them, for insulation used. The pulse duration used to process the sample was 1000 \(\upmu\)s, the pulse frequency was 1 Hz, the electric field strength was 0.85 kV cm\({}^{-1}\), and a square waveform was employed. Furthermore, the maximum delay was set at 20 ns.  
The absorbance measurements were recorded on a Shimadzu spectrophotometer (UV-1700, Shimadzu Europa GmbH, Duisburg, Germany).  
A Finnigan AQA mass spectrometer (San Jose, CA, USA), a P4000 pump, and a UV6000 LP diode array detector were used for the chromatographic separation and detection of the compounds. The stationary phase was a Phenomenex Luna C18 column (5 \(\upmu\)m, 4.6 mm \(\times\) 250 mm) (Phenomenex Inc., Torrance, CA, USA), which was placed in an oven, set at 40 \({}^{\circ}\)C. The mobile phase consisted of (A) water containing 0.5% \(v/v\) formic acid and (B) a mixture of acetonitrile: water (6:4) containing 0.5% \(v/v\) formic acid. The following gradient elution program was employed for the separation of the compounds: 5% B to 40% B in 40 min, then to 50% B in 10 min, and finally to 70% B in 10 min and kept constant for 10 more min. The flow rate of the mobile phase was 1 mL min\({}^{-1}\). The injection volume was 20 \(\upmu\)L, and injections were made using a rheodyne injector. The spectra were recorded between 220 and 360 nm. The electrospray ionization (ESI), in a positive ion mode, was used to acquire the mass spectra.  
An Agilent Technologies (Santa Clara, CA, USA) gas chromatograph model 7890A, equipped with a mass detector (5975C), and a capillary column Agilent J&W DB-1 (30 m \(\times\) 320 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m) (Agilent Technologies Inc., Santa Clara, CA, USA) was used for the analysis of the volatile compounds. Helium (flow rate 1.5 mL min\({}^{-1}\)) was used as carrier gas. The injector was set at 240 \({}^{\circ}\)C. The samples were inserted in the column using a splitless mode. The temperature program was as follows: 40 \({}^{\circ}\)C for 5 min, increased to 140 \({}^{\circ}\)C with a rate of 2 \({}^{\circ}\)C per min, and, finally, increased to 240 \({}^{\circ}\)C with a rate of 10 \({}^{\circ}\)C per min. The volatile compounds were identified by comparing their mass spectra with data from the integrated NIST 11 library (National Institute of Standards and Technology, Gaithersburg, MD, USA). The peaks were assigned when the similarity was above 80%. The percentages were expressed as mean values from the duplicate analyses.
## 5 Results and Discussion
### Simulation Capability  
All four parametric double S-shaped models are capable of simulating a seasonal signal when the number of data points is large enough to individually document the growth and senescence phases. Each of those models relies on seven adjustable parameters to match the empirical evidence, so eight measurements are required at a minimum before attempting to invert the model against the data for a particular season. It is generally easy to document the evolution of the phenology during the dry season, i.e., when the vegetation senesces or becomes dormant. The difficulty arises when persistent cloudiness generates long gaps in the remote sensing measurements during the rainy season, precisely when the vegetation grows. Figure 9 exhibits the number of FAPAR values available per year over the target of interest for the entire record available.  
As expected, there are fewer observations during the rainy season (growth phase). Whenever too few FAPAR values are present during the growth (more rarely senescence) phases, the inversion process fails. It is interesting to note that the various models exhibit different sensitivities to the presence of those  
Figure 9: Boxplots summarizing, for the entire MISR-HR FAPAR record, the number of observations available per year to fit the model during (**a**) entire vegetative seasons, (**b**) the growth phase, and (**c**) the senescence phase. The top and bottom limits of the boxes show the quartiles, the horizontal line in the middle of those boxes and the crosses indicate the median and the average values, respectively, while the ‘T’ shaped extensions document the range of values (minimum and maximum).  
data gaps. Figure 10 summarizes the number of successful inversions for each of the four models in the target area.  
It can be seen that the hyperbolic tangent model offers the most frequent opportunities to successfully simulate a vegetative season, as well as that the sine model is the most sensitive to the presence of gaps in the data, i.e., to the temporal distribution of empirical data throughout the season.
## 1 Introduction
### Self-Healing Networks  
As mentioned, the number of published articles in self-healing networks is less than in other fields. There are several main reasons for this. First, it is difficult to specify the common faults and the corresponding causes in these networks, due to the network dynamics and their dependence on the behavior and services used by the users. When different experts face a specific fault, they may have different opinions. On the other hand, there is no valid document in the literature in this field to correlate the fault and the cause of the fault in the network. The second reason is the complexity and difficulty of simulating faults and modeling the network mode in simulators. In addition, it is almost impossible to have an acceptable quantity of faults during the lifetime of a network. Due to the reasons mentioned, less investigative research has been conducted in this area than in other areas.  
As was mentioned before, this research aimed to solve various challenges in the field of cellular networks' fault detection, to categorize different types of faults according to varying values of performance evaluation indicators, and to investigate the impact of each fault on these indicators. In other words, our research was conducted in the framework of self-healing networks. In general, the nature of a cellular network's components and systems is not immune to faults and failures. In the old methods, faults were detected by activating alarms. When remote methods could not solve the faults, radio network engineers were sent to the site to identify the fault. As expected, this process is very time consuming, and sometimes it takes more than a week for the system to return to its normal status [10].  
On the other hand, some problems cannot be traced through alarms and are only identified if a complaint is reported to the operator by the customer. The future of these systems is improving with the use of self-healing methods. In general, self-healing methods address the process of remote fault detection, identify the cause of the fault, and, finally, set up the fault compensation system, or repair activities, to minimize the effects of the fault on the equipment and network components. For this purpose, in this section, we first present the available views and methods in the fault detection field and in the diagnosis of causes of faults in cellular networks. The logical flow of self-healing systems is given in [10]. This flow consists of three logical parts: identifying the fault, analyzing the cause of the fault, and, finally, setting up the fault compensation system. In the following, we briefly review the studies conducted, according to the categories presented in this study.
# Registered Keyword Searchable Encryption Based on SM9
## 2 Preliminaries  
### Bilinear Maps  
**Definition 1** (Bilinear Maps).: \(G_{1}\) _and \(G_{2}\) exist as two additive cyclic groups of prime order \(N\), the generator of whom are \(P_{1}\) and \(P_{2}\), respectively. Have \(G_{T}\) be a multiplicative cyclic group; there exists a homomorphic map \(\phi\) from \(G_{2}\) to \(G_{1}\), such that \(\phi(G_{2})=G_{1}\). Have an admissible bilinear map \(\hat{\epsilon}:G_{1}\times G_{2}\to G_{T}\), and satisfy the following conditions:_  
* _Bilinearity: for all_ \(P\in G_{1}\)_,_ \(Q\in G_{2}\)_, and all_ \(a,b\in\mathbb{Z}_{N}^{*}\)_,_ \(\hat{\epsilon}(aP_{1},bP_{2})=\hat{\epsilon}(P_{1},P_{2})^{ab}\)_._
* _Non-degeneracy:_ \(\hat{\epsilon}(P_{1},P_{2})\
eq 1_{G_{T}}\)_._
* _Computability: for all_ \(P\in G_{1}\) _and_ \(Q\in G_{2}\)_,_ \(\hat{\epsilon}(P,Q)\) _can be computed efficiently._
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 2 System Overview
#### 2.1.2 The Analog-to-Digital Converter  
Considering the sampling rate and the sampling precision, the AD7768-4 chip (Analog Devices, Inc., Norwood, MA, USA) is chosen. Developed by the Analog Device company, the AD7768-4 is widely used in underwater acoustic applications because of its high performance. The AD7768-4 is integrated with four identical and simultaneous channels. The sampling frequency can be configured as 32 kSPS, 64 kSPS, 128 kSPS, and 256 kSPS, respectively. The dynamic range is 108 dB, and the sampling precision is 24 bits, which are able to measure the tiny variations of acoustic signals. The highest power consumption of each channel is 51.5 mW, which is suitable for long-time signal acquisition. In our system, the first channel is used, and the remaining channels are reserved for further use and working in sleep mode. The serial peripheral interface (SPI) is used to set the parameters, and the multichannel audio serial port (MCASP) is used for digital signal transmission.