esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon Mineralization in a Soil Amended with Sewage Sludge-Derived Biochar
## 4 Conclusions  
In general, two distinct behaviors were observed in relation to the dynamics of C mineralization: (1) soil mixed with BC300, BC400, and SS increased mineralization rates, and (2) soil mixed with BC500 resembled the same C mineralization dynamics as the soil alone (control), with low CMR. The first order model was adequate to represent the dynamics of C mineralization in sewage sludge-derived biochar obtained at different pyrolysis temperatures. Pyrolysis temperature strongly influenced soil N availability. The SS and SS-derived biochars properties that mostly influenced soil C mineralization and N availability were N, C/N ratio, H, PV, and SSA. After the incubation period (127 days), BC300 and SS showed very similar behaviors; both increased soil N availability, especially in the N-NH4\({}^{+}\) form. Lower pyrolysis temperatures (up to 300 \({}^{\circ}\)C) yield biochars that are more capable of releasing soil nutrients to plants in a short-term period. In addition, our results highlight the environmental benefits of using higher pyrolysis-temperature biochars to achieve lower C-CO\({}_{2}\) emission from soils. Overall,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **PC1** & **PC2** \\ \hline pH & 0.726 & \(-\)0.662 \\ C & \(-\)0.788 & \(-\)0.609 \\ H & \(-\)0.966 & 0.248 \\ N & \(-\)0.933 & \(-\)0.348 \\ C/N & 0.986 & \(-\)0.023 \\ H/C & \(-\)0.897 & 0.436 \\ O/C & 0.868 & 0.491 \\ CCE & \(-\)0.826 & \(-\)0.188 \\ NO3\({}^{-}\) & \(-\)0.534 & 0.818 \\ NH4\({}^{+}\) & \(-\)0.626 & \(-\)0.677 \\ PV & 0.938 & \(-\)0.254 \\ SSA & 0.987 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation coefficients between the soil with sewage sludge and with biochar sewage sludge-derived biochars variables and the principal components (PC1 and PC2).  
Figure 5: Principal Component Analysis (PCA) ordination diagrams from the scores of soils with sewage sludge (SS) and with SS-derived biochars produced under different pyrolysis temperatures of 300 \({}^{\circ}\)C (BC300), 400 \({}^{\circ}\)C (BC400), and 500 \({}^{\circ}\)C (BC500). CCE: Cumulative C-CO\({}_{2}\) efflux.  
pyrolysis can increase both carbon sequestration and nutrient availability in soils, depending on the temperature adopted. Our results reinforce the idea that tradeoffs between a number of environmental benefits of biochar exist for different temperatures of pyrolysis.  
All authors contributed to the intellectual input and provided assistance in the study and manuscript preparation. Conceptualization, C.C.d.F.; formal analysis, C.C.d.F., T.N.M., A.T.d.V. and T.R.C., writing-original draft, C.C.d.F., T.R.C., T.P.L. and J.P.-F.  
This research was funded by Fundacao de Pesquisa do Distrito Federal grant number 00193.00002099/2018-65.  
The authors declare no conflict of interest.
## 7 Discussion and Analysis
### Advantages of Mel Spectrogram for Analyzing the Speech Characteristics of Autistic Children  
The experimental results presented in Table 8 show that the Mel spectrogram contains more types of speech emotional characteristics in children with autism, with distinct variations detected among different features. Using the Mel spectrogram as a model input to assess the speech features of children with autism is more advantageous.  
Our experimental findings support the theoretical advantage of using Mel spectrograms for analyzing the emotional speech characteristics of children with autism. As the literature review has indicated, the speech of children with ASD contains distinctive acoustic patterns that differ from typically developing children. The superior performance of the Mel spectrogram representation (83.47%) over MFCCs (25.72%) in our study can be attributed to its capacity to preserve the temporal dynamics and frequency distributions that carry crucial emotional information in the speech of children with autism.  
This conclusion is consistent with the findings of [PERSON] et al. [PERSON] et al. (2018) and [PERSON] et al. [PERSON] et al. (2018). MFCCs are generated from the original sound using computational processing like discrete cosine transform, and they are primarily used to describe the directionality and energy distribution information of speech samples, being particularly good at analyzing frequency domain features such as power spectral density. But these features have weak correlations with the emotions of children with autism, resulting in poor performance in autism emotion recognition studies.
# Removal of Phosphate from Aqueous Solution by
## 2 Materials and Methods
### Adsorption Experiment  
#### 2.4.1 Phosphate Removal Efficiency  
Optimal dosage experiment: Zeolite, biochar, and PZC 7:3 were chosen to find the optimal dosage for phosphate removal. Firstly, 0.2, 0.4, 0.6, 0.8, 1.0, and 1.2 g of the three materials were weighed and put into 50 mL polyethylene (PE) centrifuge tubes, respectively. After that, 20 mL of a 50 mg/L phosphate solution was added into the tube. Then, PE centrifuge tubes were positioned in a constant temperature oscillator at 25 \({}^{\circ}\)C and 200 rpm for 240 min. Then, PE centrifuge tubes were placed in a centrifuge (4000 rpm) for 10 min, followed by filtration of the supernatant with filter paper. The obtained supernatant was finally analyzed to determine the phosphate concentration in the solution. The optimal dosage of 0.8 g was selected for the next experiment based on the above experiment. In this study, every treatment was carried out four times to ensure accurate results.  
Optimal mixed-material adsorption experiments: Nine materials (zeolite, PZ, biochar, PZB 9:1, PZB 8:2, PZB 7:3, PZC 9:1, PZC 8:2, and PZC 7:3) were used to carry out the phosphate removal experiment. Firstly, 0.8 g of the above material was put into a 50 mL centrifuge tube, followed by adding 20 mL of a 50 mg/L phosphate solution. After the same procedure as in the above experiment, phosphate in the solution was measured. According to the results of this experiment, it was shown that PZC 7:3 had the highest phosphate removal rate compared to the biochar and zeolite composite. The next experiment was performed with zeolite, biochar, and PZC 7:3.  
2.4.2. The Influence of pH on the Adsorption of Phosphate Adsorption  
To determine the influence of pH on the adsorption performance of zeolite, biochar, and PZC 7:3, a solution of 0.1 mol/L HCl and NaOH was used for adjusting the pH of the solution to 3.0, 5.0, 7.0, 9.0, and 11.0. Thereafter, 0.8 g of zeolite, biochar, and PZC 7:3 was poured into a 50 mL centrifuge tube, followed by 20 mL of a 50 mg/L phosphate solution with a different pH. All the above experiments were placed in PE centrifuge tubes in a constant temperature oscillator at 25 \({}^{\circ}\)C and 200 rpm for 240 min. Then, the centrifuge (4000 rpm) was avaliable to measure the samples for 10 min, of which the supernatant was applied so as to determine the phosphate concentration in the solution.
# Cybersecurity for Blockchain-Based IoT Systems: A Review
## 5 Discussion
### Blockchain for IoT: Potential Applications for Smart Cities
#### 5.2.4 Supply Chain Management  
Blockchain can revolutionize supply chain management in IoT systems by providing end-to-end transparency and traceability. In a smart city context, blockchain can be used to track and verify the movement of goods, monitor environmental conditions during transportation, and ensure the authenticity and quality of products [50]. By storing supply chain data on a distributed ledger, stakeholders can have real-time visibility into the entire supply chain, mitigating risks, reducing fraud, and improving accountability.
## 5 Discussion  
### Coordinated Planning Enhances the Operational Efficiency of Megacity Transport Systems  
Figure 9 demonstrates that applying the AA-DNDP model to Chongqing's transport network significantly improves planning efficiency by optimizing the interplay among diverse infrastructure types, even within the constraints of existing planning schemes. The reason for this phenomenon is that by constructing the same amount of transportation infrastructure, different implementation sequences will affect the improvement of network efficiency. Newly established facilities not only boost service capacity but also increase interconnectivity, which is particularly critical for multimodal passenger transfers. By leveraging existing infrastructure during both design and execution, coordinated planning ensures higher alignment with real-world constraints. In addition, the service capabilities of transportation infrastructure in megacities can be effectively enhanced through the coordinate planning method.  
Figure 8: Lower limit of network efficiency variation for Chongqing’s transportation infrastructure.  
Figure 9: Median comparison of transportation infrastructure efficiency across four implementation sequences.
## 3 Results and Discussion
### Structural Properties
#### 3.1.2 Grazing Incidence X-Ray Diffraction  
Figure 4 shows the X-ray diffraction patterns of coated (a) and embedded films (b) before and after annealing. A well-defined main peak is observed at 28.4\({}^{\circ}\), as well as peaks with lower intensities at 47.3\({}^{\circ}\) and 56.1\({}^{\circ}\), which respectively correspond to (111), (220), and (311) Bragg peaks for silicon. These peaks match very well the PDF JCPDS #27-1402 values for cubic crystalline structure of silicon. The diffraction plane corresponds to a face centered cubic lattice as the obtained by the TEM analysis (Figures 2 and 3). These peaks indicate that the coated and embedded films contain Si-NCs with a face centered cubic lattice structure.  
As observed in Figure 4a, a wide peak centered at \(\sim\)23\({}^{\circ}\) can be seen in as-deposited samples, which shifts to a lower angle of 21.5\({}^{\circ}\) after thermal annealing for coated films with both red and blue Si-NCs. This shift has been associated with the formation of amorphous silicon oxide (_a_-SiO\({}_{2}\)) due to spinoidal decomposition [30]. Therefore, the structural units of the SOG, which can be described by the different tetrahedral units SiO\({}_{4\text{-}}\)Si\({}_{\text{x}}\) with \(n\) = 0\(-\)4, gradually transform into well-defined SiO\({}_{4}\) and Si-Si\({}_{4}\) tetrahedral structures [23; 24]. This transformation, only observed in the coated films, agrees with the TEM observation of the SOG flowing into the Si-NCs layer after thermal annealing.
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 3 Data Privatization Techniques
### Synthetic Private Data  
There were very early concepts of synthetic private data based on the statistical properties of the data, for example, by [PERSON] [21]. More recently, and with the advances in machine learning, the idea of synthetic private data came back into the focus of research. One method is to use variational autoencoders (VAEs) to generate new data based on a training dataset [22]. To preserve privacy, in this case, the learning accuracy can be limited to avoid overfitting that could reveal private information from the training data. However, the current trend is usually based on generative models, in particular, on so-called GANs, where two networks are in a game to create synthetic data that are indistinguishable from the training data [23]. In most settings, there is one network that uses training data to generate new synthetic data based on the input, and another network judges whether the data are realistic or resemble artificial data. This process is often used for image-based synthetic data generation (known as deep fakes) but can also be used for text-based or tabular data. For the GAN-based approach, the idea of using DP in the data generation process came up to enable a _real_ privacy guarantee.  
Given this foundation, we used three popular private synthetic data algorithms that are implemented in the SmartNoise Synthesizers framework [24]. Among them is the histogram-based approach Multiplicative Weights Exponential Mechanism (MWEM) by [PERSON] et al. [25]. The algorithm maintains an approximation of the data distribution. In each iteration, the worst approximated query is selected using the exponential mechanism. Subsequently, the accuracy is increased according to the multiplicative weights update rule. Through this process, a dataset can be generated that mimics the input distribution.  
DP-CTGAN, first introduced by [PERSON] et al. [11], uses the deep learning-based CTGAN model proposed by [PERSON] et al. [26] as its foundation. CTGAN generates tabular data using a generator network that can incorporate conditional distributions to avoid class imbalance problems. These generated data are then passed to a discriminator to judge the quality of the synthetic data compared to the original data. In this step, DP noise is added to privatize the results.  
Finally, PATE-CTGAN, also first outlined by [PERSON] et al. [11], likewise uses CTGAN but employs the private teacher ensemble (PATE) [27] method to split the input data into partitions. These partitioned data are used to train different teacher networks that predict labels for the data. DP noise is also added in this process to privatize the data. Then, the student networks learn with the teacher's privatized labels, resulting in synthetic data.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Discussion
Additionally, the current framework relies on a pre-defined set of visual attributes, which may not capture all possible safety hazards in real-world scenarios. Future work could explore more flexible attribute representation learning approaches to address these limitations. Future research should explore several promising directions. Integration with other sensing modalities, such as hyperspectral imaging and near-infrared spectroscopy, could enhance detection capabilities for hazards with minimal visual signatures. More flexible attribute representation learning approaches, potentially leveraging large vision-language models, could automatically discover relevant visual attributes for novel hazard types. Investigating end-to-end trainable architectures that jointly learn knowledge graph embeddings and visual feature synthesis could improve model efficiency and performance. Finally, real-world deployment studies are needed to validate the robustness of zero-shot food safety detection systems under varying lighting conditions, food presentations, and processing environments typical in industrial food safety inspection settings.
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 3 Proposed Methodology  
Figure 2 illustrates the overall architecture of the proposed approach. The main core component in our proposed system architecture is the maintenance manager. When a user query registers in the system, the maintenance manager creates a view corresponding to the query and serves the request previously issued. The aim of the maintenance manager is to determine when and what to update based on the proposed Change Metric. By utilizing the change metric, our maintenance policy effectively updates the local views. The aim of the change metric is to quantify the evolution of the Linked Data and identifies the changes occurs in the cloud. This change metric utilized the maintenance policy to establish the criteria when the maintenance jobs are executed. Moreover, queries issued by the clients are structurally similar [32]. To quantify the structural similarity between queries, we propose a bottom-up query matching approach to detect changes between triple patterns occurring  
Figure 1: Showing the life science Linked Data Web.  
in consecutive queries. The result of queries is placed in a cache (or view). When similarly structured queries arrive, the request is immediately returned from the cache module.
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 2 Basic Principle of 1D-CNN Demodulation  
The modulated BPSK signal in the time domain can be expressed as  
\[s(t)=[\Sigma_{n}a_{n}\text{g}(t-nT_{s})]\cos(\omega_{c}t+\varphi), \tag{1}\]  
where \(a_{n}=\pm A\), g(\(t\)) denotes a single rectangular pulse, \(T_{S}\) is the pulse width, and \(\omega_{c}\) is the angular frequency of the carrier. Information carried by the BPSK signal is contained in the phase shifts. Through the detection of the phase shifts, the carried information can be recovered.  
The process of 1D-CNN demodulation is shown in Figure 1. The BPSK signal is imported separately into two 1D-CNNs. The 1D-CNN1 detects phase shift from 0 to \(\pi\), and the 1D-CNN2 detects phase shift from \(\pi\) to 0. Each 1D-CNN outputs a pulse when a certain type of phase shift is met. A decision module is employed to handle the outputs of the 1D-CNNs. When the output of 1D-CNN1 is greater than the predefined threshold, the output of the decision module is converted from 1 to 0. When the output of 1D-CNN2 is greater than the predefined threshold, the output of the decision module is converted from 0 to 1.  
The two 1D-CNNs have the same structure, only the parameters of the convolution kernels and neurons are different. The structure of the 1D-CNN is shown in Figure 2, which consists of four layers: an input layer, convolution layer, hidden layer, and output layer. The input layer conveys segmented data as an input vector. The convolution layer convolutes the input vector with a convolution kernel, and the result is transported to the hidden layer. The hidden layer aims to avoid the network being trapped in the local optimum, and to make 1D-CNN convergence easier during the training process. Neurons in the hidden layer are connected to the convolution layer. The weighted results from every neuron in the hidden layer are summed in the output layer, and then imported to the decision module. The operation process of 1D-CNNs includes forward propagation and backward propagation. In the training process, the backward propagation adjusts network parameters according to the output and loss, until the loss reaches the minimum. Once the network parameters are determined, the forward propagation can deduce the result independently.
## 2 Materials and Methods
### Analysis of the Antioxidant Activity  
Antioxidant properties of obtained extracts were investigated spectrophotometrically by the DPPH method [11] and with the Folin-Ciocalteu reagent [12]. The analyses were carried out using a 1600 PC UV-VIS spectrophotometer (VWR International) in 1 cm cut-vettes. A radical scavenging activity of extracts was determined using the DPPH method with a 2,2-diphenyl-1-picrylhydrazyl radical (DPPH). For this purpose, a 0.002 mM/mLstock solution of DPPH in methanol was freshly prepared before analysis. To prepare a DPPH working solution, 3 mL of the stock solution was diluted with methanol to 50 mL in a volumetric flask, protected from light by aluminum foil. Next, to 0.5 mL of the extract, 3 mL of the DPPH working solution was added, mixed, and left in darkness for 30 min. A reference sample containing 0.5 mL of solvent was prepared analogously. All analyses were carried out in 3 repetitions, and the absorbance was measured at 517 nm. The radical scavenging activity (RSA) of maca extracts was calculated from the absorbance of the sample after 30 min incubation (A\({}_{30}\)) and the absorbance of reference sample A\({}_{0}\), using the following formula:  
\[\text{RSA [\%]}=100\ (\text{A}_{0}-\text{A}_{30})/\text{A}_{0} \tag{1}\]  
The results of DPPH radical scavenging activity measurements were also expressed as Trolox Equivalents Antioxidant Capacity (TEAC). For this purpose, the RSA values of various concentrations of Trolox (Acros Organics, Geel, Belgium) standard solutions were determined, and a calibration curve of RSA [%] vs. C\({}_{\text{Trolox [\mu M/L]}}\) was prepared. The obtained linear regression equation in the range of linearity (concentrations: 0.01-0.030 uM/L) was used for the calculation of TEAC [uM/L] for maca extracts obtained in various conditions. To determine the content of phenolic compounds extractable by ethanol and water in specific conditions, total phenolic content analysis was performed using the Folin-Ciocalteu (F-C) method. For this purpose, 0.5 mL of the extract, 0.5 mL of a Folin-Ciocalteu reagent (Chempur, Piekary Slaskie, Poland), and 1.5 mL of freshly prepared sodium carbonate solution (C = 200 mg/mL) were placed in a 25-mL volumetric flask, filled up to the mark with demineralized water, mixed and left for blue color development. After 30 min, the absorbance at 760 nm was measured for the extract samples and the reference samples (in 3 repetitions). The total phenolic content (TPC) was expressed as mg of Gallic Acid Equivalent per 1 L of the extract [mg GAE/L]. To create a calibration curve, gallic acid (Sigma-Aldrich) was used as a standard, and the analyses of standard solutions in a concentration range of 10-500 mg/L using the same procedure were performed.
## 3 Theoretical Formulation of Turbulent Gas Flow Modeling
### Computational Domain and Boundary Condition  
Figure 4 shows the computational domain which consists of a nozzle and a buffer region. The buffer region consists of a cut kerr channel and a free stream region extending 50 mm below the bottom surface of the cutting plate. Figure 4c shows the tetrahedral mesh grid of the computational domain. An element size of 0.30 mm was chosen for the inner walls of the nozzle, nozzle tip, and the cut kerr walls. The buffer zone under the lower surface plate was meshed to an element size of 1.00 mm and the openings (outlets) of the CFD model were meshed to 6.00 mm. The total number of mesh elements present was 866,070.  
The initial and boundary conditions for velocity (**u**), pressure (\(p\)), temperature (\(T\)), and turbulence variables (\(k\) and \(\epsilon\)) over the inlet, outlet, and walls were defined. All the boundaries at the openings of the computational domain were set as outlets. Figure 5 shows all the boundary layers as colored patches. The inlet and outlets are modeled as pressure inlet and pressure outlet, respectively. The inlet and outlet temperature were fixed to 25 \({}^{\circ}\)C and gauge inlet and outlet pressure were fixed to 6 atm and 0 atm, respectively. These values were chosen to reflect the experimental conditions. In compressible flows, isentropic relations for an ideal gas are applied to indicate total pressure, static pressure, and velocity at a pressure inlet boundary. No slip boundary condition was implemented in the walls.  
The governing equation was solved using ANSYS FLUENT v19.0. The computations utilized a pressure-based iterative coupled algorithm for discretizing the convective transport terms. This algorithm solves the momentum and pressure-based continuity equations together. The full implicit coupling is achieved through an implicit discretization of pressure gradient terms in the momentum equations, and an implicit discretization of the face mass flux, including pressure dissipation terms. A compressible form of the Navier-Stokes equation along with the previously discussed turbulent models discretized by the second  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Coefficients** & **Standard \(k\)–\(\epsilon\)** & **RNG \(k\)–\(\epsilon\)** & **Realizable \(k\)–\(\epsilon\)** & \(k\)–\(\omega\)** \\ \hline \(C_{\mu}\) & 1.44 & 0.0845 & Calculated & - \\ \(C_{1k}\) & 1.92 & 1.42 & Calculated & - \\ \(C_{2k}\) & 1.92 & 1.68 & 1.9 & - \\ \(\sigma_{k}\) & 1 & 0.7194 & 1 & 2 \\ \(\sigma_{\epsilon}\) & 1.3 & 0.7194 & 1.2 & - \\order upwind for the momentum, energy, and turbulence equations were used to simulate the phenomenon of flow pattern along the cut kerf slot and around the top and bottom surfaces of the cutting plate. The number of iterations was selected to be 10,000, but in these computations, all the models converged in under 7000 iterations. The convergence criteria were chosen as \(10^{-4}\) for continuity, velocity, \(k\), \(\epsilon\), and \(\omega\); and \(10^{-6}\) for energy equations.