esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA  
Besides the basic configuration of the BOCDA shown in Figure 19a, various schemes for performance improvement are proposed and developed. Before describing examples of the setups with the schemes, an overview for the performance improvement schemes is shown here.
# Europium, Yttrium, and Indium Recovery from Electronic Wastes
## 1 Introduction
The concentrate is processed by roasting to eliminate sulfur, and a leaching process is carried out where the iron is eliminated as a residue of jarosite. In order to recover the indium, the jarosite residue is dissolved using a hot sulfuric acid solution. In this method, the indium is extracted directly from the leaching solution by solvent extraction. This technique is not suitable for residues of zinc plants with low concentrations of indium [25]. The present research analyzes the recovery of europium, yttrium, and indium from WEEE through the hydrometallurgical route. The main objective of the work consists of recovering yttrium and indium as suitable salts for their commercialization. Several works during the last years have attempted to recover yttrium and indium from electric and electronic wastes. [PERSON] et al. [26] tried to separate yttrium and indium through employing acid leaching and solvent extraction techniques. In this case, di-(2-ethylhexyl) phosphoric acid (DEHPA) was used as the extractant and kerosene was used as the diluent, achieving 99% of yttrium and indium recovery in 2 M HCl and 1 M HNO\({}_{3}\) solutions, respectively [26]. On the other hand, [PERSON] et al. [27] and [PERSON] et al. [28] performed rare earths recovery from WEEE by acidic leaching and precipitation as the main techniques. [PERSON] et al. [27] employed several acids to dissolve rare earths with a subsequent precipitation with oxal acid. This technique allowed obtaining yttrium oxalate (99% of recovery). [PERSON] et al. [28] instead attempted yttrium recovery using NaOH solutions for yttrium precipitation from acidic solutions. In this research, 95% of yttrium recovery was achieved. These research studies demonstrated that rare earths and indium extractions from WEEE are possible nowadays. Considering recent research studies in rare earths and indium extraction from WEEE, this work focused on the extraction of the mentioned elements from cathode ray tubes (CRTs) and liquid crystal displays (LCD) panels. Unlike other investigations in this area, three techniques were tested in order to obtain yttrium and indium as commercial salts; these hydrometallurgical techniques are: acidic leaching, solvent extraction, and precipitation. The novelty of this work lies in the employment of CRTs and LCD panels as rare earths and indium sources, respectively. In both studies, several acids were tested in order to achieve the greatest element dissolution. Afterwards, several extractants and different diluents were used in order to separate the desired elements from other impurities such as zinc, iron, and aluminum, among others. In contrast with other research studies, solvent extraction was implemented instead of attempting a selective precipitation of rare earths and indium, which may create products with several impurities. Finally, yttrium oxide and indium sulfate were obtained as the main products of this investigation. The results obtained show that the adequate disposal of WEEE for the recovery of metals with high added value is feasible.
## 5 Implementation and Evaluation Tests
### Ontology Evaluation
The best value is again found for the UML model. Based on the measured values, the usual ontology models appear to be weaker than the UML-oriented models from the viewpoint of property usage; these models should be extended in order to apply them as input for software or database development. With the help of the proposed measures, we can highlight the weakness of the usual ontology design approach for cases in which the resulting models cannot provide enough information for complex applications to support decision-making. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ontology** & \(m_{A}\) & \(m_{e}\) & \(m_{=}\) & \(m_{l}\) & \(m_{c}\) \\ \hline full Pizza ontology & 0.58 & 0.51 & 0.31 & 2.48 & 1.65 \\ \hline SHOE/onts/univ1.0. & 0.62 & 0.69 & 0.00 & 2.00 & 1.40 \\ \hline UML schema example & 1.10 & 0.36 & 0.00 & 2.00 & 1.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ontology metrics. Figure 4: Ontology metrics diagram. Summarizing the presented results, the main the goal of the proposed methodology is to highlight the importance of properties in ontology models and to measure the attribute-oriented quality of the prepared ontology. The key contributions cover the following elements:  
* Our analysis shows available ontology quality tests using only a simple property-based measure (number of properties);
* We argue that a high-quality, multi-purpose, and reusable ontology model can be used in many application domains, such as software development, that require an appropriate property set;
* We introduce five novel property-based quality metrics for ontology models;
* As the performed tests show, the proposed metrics are suitable for showing the key differences of different semantic models;
* The introduced metrics can be used to measure the quality level of ontology models under development. In addition to the presented benefits, the proposed methodology has limitations, which can be summarized as follows:  
* The presented method is only a measure to show the quality of the ontology, and cannot provide direct routines to perform the corrections;
* In the case of complex ontologies, the evaluation of the model may take a longer time.
# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 2 Materials and Methods
### Technology Selection
#### 2.2.1 Nodes
The Arduino Pro Mini was selected for the environment node due to its compact size, low power consumption, and cost-effectiveness--features that make it particularly well suited for battery-powered installations in rural settings. Its minimalist design, free of unnecessary components such as USB interfaces or integrated communication modules, enhances system stability for simple tasks such as environmental data acquisition. Moreover, it is fully compatible with low-cost sensors like DHT11 and shares the same programming environment as the other controllers used in the system, which simplifies maintenance and ensures architectural consistency. Its dedicated use for environmental monitoring offloads secondary tasks from the main node, contributing to a modular and scalable system design. It is also important to note that the DHT11 sensor chosen in this system is a low-cost component with limited accuracy and slower response times, particularly in high-humidity environments. In this prototype, its role was limited to providing basic environmental context rather than precise control or critical monitoring. For future implementations--especially in scenarios where environmental conditions may play a decisive role in system behavior--the use of more accurate sensors and appropriate calibration protocols would be advisable.
# Detecting Criticality by Exploring the Acoustic Activity in Terms of the \"Natural-Time\" Concept
## 1 Introduction
Moreover, it has been experimentally verified [19; 20] that the temporal evolution of the \(F\)-function provides indices that could be safely considered as pre-failure signals. In this direction, and in an attempt to detect complementary pre-failure indicators, the evolution of the \(F\)-function is considered here in terms of the Natural Time, \(\chi\), rather than in terms of conventional time (i.e., of the \"time-to-failure\" parameter). The onset of validity of the power-law of Equation (1) is indicative of the (self-organized) criticality, which governs the response of the specimens, just before catastrophic fracture. According to the influential work of [PERSON] et al. (1987) [21], various complex systems, operating far from their thermodynamic equilibrium, will be spontaneously organized under their natural evolution (and without the need of an external intervention), into a critical boundary state which resides at the transition point between the equilibrium and non-equilibrium states. During this phase transition, the appearance of power (or approximately power) laws has been extensively reported in the literature [9; 19]. Within these lines and considering that the generation of AEs while fracture approaches is a non-equilibrium phenomenon of complex dynamic systems (namely the brittle specimens under severe mechanical load), the Natural Time concept has been proven as a quite flexible alternative theoretical framework for describing the proximity of the system to criticality (i.e., to catastrophic fracture) [22; 23], which in the case of the \(F\)-function is manifested through the power-law of Equation (1). The analysis of the acoustic emission data, recorded during a series of experimental protocols with marble specimens, revealed definitely that a power law governs, again, the evolution of the \(F\)-function in terms of Natural Time during the intervals that the load-carrying capacity of the loaded element tends to be exhausted. The instant at which this power law starts governing the mechanical response of the system reflects somehow a change of the damage mechanisms which are activated within the specimen and these mechanisms that will lead to the macroscopic destruction of the specimen start prevailing. Thus, the onset of validity of this power law may be considered as the threshold beyond which the system enters into its critical stage (i.e., the stage of impending fracture) providing a pre-failure signal, which deserves to be further investigated.
## 3 Results
### Biofilm-Formation Ability  
Biofilm-formation ability is classified according to the mean optical density value. A mean of optical density value above 0.2 indicates a strong biofilm-formation ability, 0.2 \(\geq\) OD \(>\) 0.1 is considered moderate ability to form biofilm, 0.1 \(\geq\) OD \(>\) 0.05 is classified as a weak ability to form biofilm, and OD \(\leq\) 0.05 indicates negative ability [35].  
Figure 3: Cell viability of p(HEMA) hydrogel as control (C) and the drug -loaded hydrogels. Values shown represent the mean \(\pm\) SD, n = 5, **\(p\) \(<\) 0.01, and ***\(p\) \(<\) 0.0001, significant difference between formulations. The black lines marks the 80% viability where less than 80% is considered toxic.  
The biofilm-forming ability of _S. aureus_ was similar on all dried film surfaces, whereas the biofilm-forming ability of _E. coli_ decreased on the dried films except p(HEMA) and 1:3 AMP:LVX-loaded p(HEMA) (F6) surfaces. The biofilm-forming ability for _P. aeruginosa_ increased on he p(HEMA) surface and reduced in p(HEMA) loaded with antimicrobial agents. Furthermore, the addition of LVX to the films reduced the mixed biofilm-forming ability. That is, the biofilm-forming ability of _S. aureus_ on p-HEMA (control, F1) was 0.112 \(\pm\) 0.04, AMP-loaded p(HEMA) (F2) was 0.23 \(\pm\) 0.21, LVX-loaded p(HEMA) (F3) was 0.09 \(\pm\) 0.03, 1:1 AMP:LVX-loaded p(HEMA) (F4) was 0.024 \(\pm\) 0.024, (3:1) AMP:LVX-loaded p(HEMA) (F5) was 0.009 \(\pm\) 0037, and 1:3 AMP:LVX-loaded p(HEMA) (F6) was 0.073 \(\pm\) 0.054. The biofilm-forming ability of _E. coli_ on p(HEMA) (control, F1) was 0.86 \(\pm\) 0.018, F2 was 0.041 \(\pm\) 0.06, F3 was 0.008 \(\pm\) 0.017, F4 was 0.099 \(\pm\) 0.08, F5 was 0.009 \(\pm\) 0.12, and F6 was 0.99 \(\pm\) 0.24. The biofilm-forming ability of _P. aeruginosa_ on p(HEMA) (control, F1) was 1.14 \(\pm\) 0.45, F2 was 0.046 \(\pm\) 0.057, F3 was 0.023 \(\pm\) 0.03, F4 was 0.023 \(\pm\) 0.07, F5 was 0.02 \(\pm\) 0.051, F6 was 0.24 \(\pm\) 0.13, and biofilm-forming ability of mixed biofilm on p(HEMA) (control, F1) showed overgrowth of bacteria, F2 was 2.27 \(\pm\) 0.83, F3 was 0.032 \(\pm\) 0.017, F4 was 0.032 \(\pm\) 0.016, F5 was 0.29 \(\pm\) 0.38, and F6 was 0.0026 \(\pm\) 0.116.
# Analysis of Distributed Measurement Method for Array Antenna Position
## 3 Structural Design and Method
### Strength Check
#### 3.3.1 Overload Increment in Case of Vertical Gust  
When calculating the increment of vertical gust overload of the Y12 aircraft, the formula is as follows:  
\[n=\frac{K_{g}U_{de}Va}{1.63(W_{g}/S)} \tag{35}\]  
where, \(K_{g}=\frac{0.88\mu_{g}}{5.3+\mu_{g}}\) is gust mitigation factor, \(\mu_{g}=\frac{2(W_{g}/S)}{\rho\alpha g}\) is mass ratio of aircraft, \(\overline{c}\) is average geometric chord length, \(g\) is gravitational acceleration, \(U_{de}\) is gust velocity, \(V\) is equivalent speed, \(a\) is the lift curve slope,\(W_{g}/S\) is wing loading.  
Referring to the aerodynamic performance and weight parameters of the Y12 aircraft, we can calculate that the vertical overload increment of a vertical gust is \(\Delta n_{\text{vertical gust}}=\pm 2.52\).  
It can be seen from Figure 9 that the maximum stress calculated by the plate element in the case of vertical gust overload is below 5 MPa, which is far less than the yield limit of the material, so the strength of the structure does not need to be checked.
## 4 Implementation and Result
### Future Test Scenarios  
Due to the early stage of the research, the main objective of which was to re-test the research hypothesis of deep metric learning and the validity of using cyclic model provisioning as a method of increasing efficiency and effectiveness, the current tests were conducted using simulated datasets which, while valuable for initial validation, do not fully replicate the challenges of operational scenarios. As the system develops, more advanced testing scenarios  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **Standard Model** & **Standard Model** & **Fine-Tuned** & **Fine-Tuned** \\  & **1 AM** & **3 AM** & **Model 1 AM** & **Model 3 AM** \\ \hline Accuracy & 8\% & 12\% & 28\% & 60\% \\ Precision & 3\% & 3\% & 45\% & 46\% \\ Recall & 8\% & 12\% & 28\% & 60\% \\ F1 score & 2\% & 4\% & 18\% & 46\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of values of classification metrics.  
in conditions resembling real disaster zones are planned. These tests will aim to assess the resilience of the system and provide insights into areas for further improvement.  
The following test scenarios were planned as part of the next stages of the research:  
1. Testing low-quality images: Evaluating system performance using degraded images that simulate the actual operating conditions of the system and using algorithms designed to improve the quality of the image material.
2. Disaster simulations: Conduct tests in field conditions instead of laboratory conditions to test the speed of the system.
3. Large and diverse datasets: Carry out tests of the neural identification module on different datasets covering data from people of different ages, genders, ethnicities, etc. The extended analysis of results will also include the determination of confidence intervals for selected metrics to avoid misinterpretations.
## 4 Results and Discussion
### Spectral Characteristics
#### 4.1.2 Sudan Dye-Adulterated Red Chili  
The reflectance spectra of red chili adulterated with oil mixed with Sudan dye is similar to pure red chili with a decrease in reflectance values as shown in Figure 13. However, it can be observed that the rise in the reflectance values of red chili from blue wavelengths begins after 510 nm, while in Sudan-adulterated red chili this increase begins after 550 nm as shown in Figure 13.  
Figure 13 shows that with the addition of Sudan dye in oil the spectral reflectance in the range from 650 to 680 nm matches the oil mixed red chili, while Sudan dye causes a shift in the derivative spectrum from 510 nm to 550 nm. As discussed above in Section 4.1.1, quantification of adulterants cannot be ensured due to limitations in penetration depth and surface scanning.
## 4 Experiments
### Metrics  
Hop Latency  
It is the duration needed to broadcast the packet from one node to another node.  
\[l_{n}=MAC\_{n}\times Exp\_data_{n} \tag{9}\]  
where  
\[MAC\_{l_{n}}=\left(Exp\_bff\_slot_{n}\times\left(1+\frac{b}{n\_rate_{n}} \right)+Exp\_slot_{n}\right)\times Exp\_effort_{n} \tag{10}\]  
\[\left(\frac{b}{n}\right)^{f}_{\_rate_{n}}=\frac{b\_slot_{n}+b\_slot\_occp_{f}}{ n\_slot_{n}-b\_slot\_occp_{f}} \tag{11}\]  
In Equation (10), \(l_{n}\) is the 1-hop latency of \(n^{th}\) node, \(MAC\_{l_{n}}\) is the MAC access latency of a packet forwarded by \(n\) and \(Exp\_data_{n}\) is the predicted quantity of packets in the MAC queue of \(n\)at a constant interval.  
In Equation (11), \(Exp\_bff\_slot_{n}\) is the predicted quantity of backoff interval slots of \(n\)at a constant interval, \(\frac{b}{n\_rate_{n}}\) is the engaged/free channel ratio found by \(n\)at a given interval, \(Exp\_slot_{n}\) is the predicted quantity of interval slots required for \(n\) for broadcasting the packet at a given interval and \(Exp\_effort_{n}\) is the predicted number of transmission efforts  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Region of Simulation & 100 \(\times\) 100 m\({}^{2}\) \\ \hline Total nodes considered & 1000 \\ \hline Media Access Control (MAC) & IEEE 802.11 \\ \hline Channel & Wireless \\ \hline Antennas & Single-way \\ \hline Propagation method & Two-way \\ \hline Bandwidth & 300 Kbps \\ \hline Data Header & 20 bytes \\ \hline Payload & 256 Bytes \\ \hline Factor of multiplication & 2.4 \\ \hline Queue & 128 bits \\ \hline Type of traffic & CBR \\ \hline Refreshing rate & 8 \\ \hline Length of Simulation & 100 S \\ \hline Size of Packet & \(\mathit{lnai}\) & 64 Bytes \\ \hline \multirow{2}{*}{\(\mathit{Query}\)} & \(\mathit{20\ bytes}\) \\ \cline{2-3}  & \(\mathit{Reply}\) & 150 bytes \\ \hline \multicolumn{3}{c}{\(\mathit{identified}_{\mathit{path}}\)} & 10 Bytes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental Setup.  
by \(n\)at a given interval. In Equation (6), \(\left(\frac{b}{n}\right)^{f}_{\_rate_{n}}\) is either the channel ratio of a free or an engaged node, and it is determined by n for a period of the desired interval. The desired traffic/packet (\(f\)) agreed, \(b\_slot_{n}\) is the count of intervals that are engaged, \(n\_slot_{n}\) depict the count of intervals that are free.
# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
## 1 Introduction
The accuracy of multispectral prediction models based on machine learning algorithms (neural networks, support vector machines, random forests, and decision trees) was evaluated from the surface reflectance extracted from Planet images that was obtained during the main phenological stages of a corn crop. It is worth emphasizing that our research presents a solution for precision agriculture and decision-making in the agricultural management of corn. Even at the predicted stages of ear reproduction, the spectral estimation models showed the possibility of an accurate large-scale monitoring of yield in commercial plots. Therefore, the advantages of using the models presented are as follows:  
* Possibility of remote monitoring of yield;
* Prediction of places of high and low productivity in the first reproductive stages of maize;
* Provision of a database on culture within the scope of digital agriculture;
* Mapping of the geospatial distribution of crop production for later decision-making. Finally, this work presents a possibility of monitoring with the exclusive use of remote data, as opposed to the current spectral models, which are mostly hybrids.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 3 Signal Model and Imaging Algorithm
### Signal Model of the Sat-2
Finally, the solution with the minimum weighted energy is chosen as the scene estimation.
## 4 Experimental Results  
This section presents and discusses the experimental results. Specifically, the base models with different feature combinations are compared to the three backbone models. Informer, MLR, and SVR have been chosen as the comparison methods in this study due to their outstanding performance and broad application in long-time series prediction, linear regression, and nonlinear regression tasks. Additionally, the prediction results of the stacking ensemble model are compared with those of the 12 base models to evaluate the effects of different feature combinations and the ensemble method.
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 4 Experiments
### Experimental Setup
#### 4.1.2 Implementation Details  
All videos are encoded by HM 16.22 under the LDP configuration at four different quantization parameters (QP) (i.e., 22, 27, 32, 37) on an Intel ( R ) Core ( TM ) i7-9700K CPU @ 3.60 GHz processor. We train four models from scratch for the four QPs independently. The Adam optimizer [58] with \(\beta_{1}~{}=~{}0.9\), \(\beta_{2}~{}=~{}0.999\), and \(\varepsilon~{}=~{}10^{-8}\) is adopted for the training of the proposed CMGNet. We set the batch size to 32 and randomly crop the input frame into patches in 128 \(\times\) 128. The learning rate is set to \(10^{-4}\) thorough out the training process. We train the luma component and chroma component separately. In the CMGP block, there are three downsampling and upsampling layers with 32 channels followed by ReLU activation for the luma component, while there are two for the chroma component. The number of output channels is equal to 64. We select an eight-layer convolution with residual learning in the reconstruction module for the luma component and that with four layers for the chroma component. All layers have 48 convolution filters followed by ReLU activation. The generated frame is added at the first position of the original reference frame list, and original reference frames are moved backward in sequence. For the evaluation of the performance, we adopt the Bjontegaard Deltas [59] for the rate (i.e., BD-rate,) and the Bjontegaard Deltas for the quality measure (i.e., BD-PSNR) in the YCbCr space to evaluate the proposed coding optimization algorithm, and use the Peak Signal-to-Noise Ratio (PSNR) in the YCbCr space and Structural Similarity Index (SSIM) in the RGB space to evaluate the proposed CMGNet.
## 3 Frequency Model Set Up
\[\Delta w\left(s\right)=\frac{-\Delta P_{0}\left(s\right)\prod\limits_{i=1}^{N} \left(1+sT_{i}\right)}{\left(2 Hs+D\right)\prod\limits_{i=1}^{N}\left(1+sT_{i} \right)+\sum\limits_{j=1}^{N}\left[\frac{K_{mi}}{R_{j}}\left(1+F_{j}T_{j}s \right)\prod\limits_{i=1,j\
eq j}^{N}\left(1+sT_{i}\right)\right]} \tag{6}\]  
\[\Delta w(s)=\Delta P_{0}\sum\limits_{i=1}^{N+1}\frac{A_{i}}{p_{i}}\left(\frac {1}{s}-\frac{1}{s-p_{i}}\right) \tag{7}\]  
\[\Delta w\left(t\right)=\Delta P_{0}\sum\limits_{i=1}^{N+1}\frac{A_{i}}{p_{i}} \left(1-e^{p_{i}t}\right)\left(U\left(t\right)\right) \tag{8}\]  
In (6)-(8), \(\Delta P_{0}\) is the initial demand-generation imbalance or system disturbance which can be a generator loss, sudden huge demand increase/decrease, or transmission line tripping. \(R\) is the governor system regulation gain, \(D\) is the system damping coefficient, \(F\) is a fraction of the total power generated by the HP turbine, \(T\) is a time constant and \(K_{mi}\) is the effective gain constant that represent the generator share of \(i\)th unit. The subscript, \(i\), corresponds to the generator number from 1 to \(N\). The above obtained SFR model-based WAMS technologies can have several implementations in modern and future smart power systems. It can be used in load frequency control (LFC) studies which study the behavior due to load fluctuations. In this regard, this model is useful for optimal tuning of the LFC controllers' parameters. Likewise, it can be adopted for frequency stability analysis of large power systems and can be equipped with other types of governor-turbine systems for investigating their effects on frequency stability. The model is also applicable for designing and testing the frequency protection schemes such as UFLS techniques.