esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
The excellent performance of geopolymer materials in immobilizing heavy metals is attributed to various solidification mechanisms, including the following:  
* Chemical adsorption: The geopolymer structure, composed of silicon-oxygen and aluminum-oxygen tetrahedra, features negatively charged aluminum-oxygen tetrahedra that adsorb metal ions. * Chemical precipitation: Lead reacts with the dissolved silicon phase to form Pb\({}_{3}\)SiO\({}_{5}\) precipitates, which are encapsulated by geopolymer gels. * Physical adsorption: Geopolymers, with their porous surfaces, adsorb molecules smaller than their pore size. * Physical encapsulation: Under alkaline activation, the aluminosilicate source dissolves, initiates gelation, and forms a network structure, encapsulating heavy metals within the cementitious material [33]. GGBFS enhances conventional concrete by improving workability, rheological properties, and reducing carbon emissions. It also increases compressive strength, provides superior abrasion resistance, and offers better protection against chloride ion and gas penetration [34]. In green concrete, GGBFS further enhances performance by improving strength, reducing void ratios, and delivering excellent mechanical properties [35]. In comparison, cement kiln dust (CKD) and slag exhibit less pronounced cementitious properties but are often used for economic reasons. Lime and lime kiln dust (LKD) are employed to adjust pH and facilitate water removal due to their high heat of hydration. Green concrete, as an alternative to conventional concrete, incorporates industrial waste residues, significantly reducing energy requirements and CO\({}_{2}\) emissions during production. By utilizing recycled materials and increasing recoverable resource use, green concrete decreases dependence on finite natural resources, contributing to a more sustainable production cycle [36]. Among the tested materials, [PERSON] et al. demonstrated that mixing ordinary Portland cement with rice husk ash (RHA) effectively remediated Pb-contaminated soil. The incorporation of RHA into the binder system significantly reduced Pb leaching, highlighting its potential in heavy metal immobilization [37]. A key concern is the long-term durability of soils treated with this process. Studies have shown that even after 17 years, stabilized soil continues to meet performance criteria [38]. For this reason, specific tests will be conducted in the future to further analyze this phenomenon. This research specifically aims to explore new mixtures and solidification and stabilization technologies to develop materials that not only meet regulatory and performance requirements but can also be reused in civil and industrial applications. This approach reduces the need for new resources and contributes to the transition towards a circular economy.
# Some Critical Insights into the Synthesis and Applications of Hydrophobic Solid Catalysts
## 4 Application of Hydrophobic Catalysts
### Sorbitoldehyd
Nevertheless, the second step is the rate determining one and to find the way to promote sorbitan conversion would reflect into a higher isosorbide yield. The hydrophobization of the surface was found to be a good strategy to boost the second reaction step. In fact, the modification of sulfonic acid mesostructured silica with -Si(CH\({}_{3}\))\({}_{3}\) groups allowed to reach 70% yield of isosorbide vs 44% obtained with the unmodified catalyst, at almost equal sorbitol conversion. This means that hydrophobization is able to strongly influence the second step, thus increasing the yield in isosorbide at the expense of sorbitan. The authors ascribe this effect to the  
Figure 15: Volcano plot for isosorbide yield vs Si/Al ratio. enhancement of the acidic strength of the catalyst. As already reported, the presence of organosilicon species on the surface promote the acid strength of sulfonic acid groups as it inhibits their interaction with surface silanols that smooths acidic properties [88]. Even higher yields in isosorbide, up to 87% can be obtained with a mesoporous polymer-based acid catalyst (P-SO\({}_{3}\)H) by virtue of its superhydrophobicity [40]. This feature allows to keep the water formed in the dehydration away from the catalyst, promoting the reaction equilibrium to move the product side. The interesting point rising from this research is that, in spite of the lower acidic content of the polymer-based catalyst, its superhydrophobicity, witnessed by a contact angle of 154\({}^{\circ}\), grants much higher selectivity to isosorbide even in the presence of water. Recently, also the use of a series of hydrophobic polymer-based solid acid catalysts (PDS) synthesized via hydrothermal synthesis method of sodium \(p\)-styrenesulfonate hydrate (SPSS) and divinylbenzene (DVB) and followed by ion exchange has been reported to promote sorbitol dehydration with very high performances [89]. The chosen examples (Table 8) show that catalyst surface modification with hydrophobic moieties in acidic systems, besides the widely studied advantage of limiting the acidic sites deactivation [10], could offer the opportunity to design an efficient catalyst on the basis of the interaction with reactants and products showing different hydrophilic/hydrophobic properties. This allows in some cases not only to improve the activity of the system but also to tune and increase the selectivity of the process.
## 1 Introduction
Object-based image analysis (OBIA) provides a suitable methodological framework for efficient landslide mapping, as well as landslide change analysis [47; 48]. By working on the object-level instead of the pixel-level, OBIA allows considering spectral, spatial, textural, morphometric and hierarchical properties for the classification of landslides [49; 50; 51]. Moreover, it is argued that using OBIA yields better classification accuracies than pixel-based classifications [51; 52; 53; 54; 55]. Several studies employed OBIA for landslide mapping and landslide change detection in Taiwan [56; 57; 58; 59; 32; 49], but none of them used time series of images for investigating the evolution and reactivation of an active large landslide. The aim of this study is to analyze the evolution of the Butangbunasi landslide in south-central Taiwan using OBIA and time series of freely available Landsat images and to investigate the potential correlation between changes in landslide area and heavy rainfall events during selected typhoon (including tropical storm) events.
# How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
## 3 Methodology
### Performance Measures  
Several performance measures have been introduced in the literature to assess the quality of the classifier. One of the most popular measures is the accuracy metric, wherein the classification domain can be defined as in Equation (9):  
\[Accuracy\ =\ \frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
where \(TP\) is the true positive, in which the positive class is predicted as positive, \(TN\) is the true negative, in which the negative class was predicted as negative, \(FP\) is the false positive, in which the negative class is incorrectly classified as positive, and \(FN\) is the false negative, in which the positive class is incorrectly classified as negative. The main drawback of the accuracy metric is that it is only robust for balanced datasets; otherwise, it can be misleading. Another metric introduced was the sensitivity metric (also known as the true positive rate, or \(TPR\)), which is defined as in Equation (10):  
\[Sensitivity\ =\ \frac{TP}{TP+FN} \tag{10}\]  
which focuses on the positive classes and how accurately they were classified. Another metric, called the _Specificity_ (also known as the true negative rate, or \(TNR\)), was introduced to focus on the negative classes, which is defined as in Equation (11):  
\[Specificity\ =\ \frac{TN}{TN+FP} \tag{11}\]  
To combine the performance of both _Sensitivity_ and \(Specificity\), a new metric called the receiver operating characteristic (ROC) curve was introduced. The ROC curve was developed during the Second World War [35]. It is a metric that is widely used to measure and visualize the classifier's ability to distinguish between two classes [36]. The curve plots the _sensitivity_ against the \((1-specificity)\). To represent the area under the ROC curve, a metric called the area under the curve (AUC) is used. The AUC of the ROC curve ranges from 0.5 to 1, 0.5 \(\leq AUC\leq 1\), where an AUC equal to 1 means that the classifier is able to distinguish between the positive and negative classes perfectly, and an AUC equal to 0.5 means that the classifier is just a random guess.
## 3 Hardware Implementation
### Hybrid ECG Classifier
#### 3.4.2 SVM Classifier  
We apply a high-performance classifier based on SVM (support vector machine) to the ECG segments that were not classified by the previous level. SVM is a pattern-recognition method developed by [PERSON] and his colleagues [43]. In this paper, we use a polynomial kernel to achieve higher accuracy because it adapts well to the ECG classification problem and has a relatively low computational complexity. The decision function and the polynomial kernel function are shown as follows:  
\[f(x)=sign(\sum_{i\in SVs}a_{i}y_{i}K(x_{i},x)+b) \tag{18}\]  
\[K(x_{i},x)=(\gamma x^{T}x_{i}+r)^{\rho} \tag{19}\]  
We perform the training phase off-line and obtain the SVM model parameters using the svmtrain function of SVMLIB [44]. Based on the results of cross-validation and the consideration of hardware implementation, we use the parameters \(\gamma=1/256\), \(\rho=2\), and r = 2 in this paper, and the total support vector number is 228.  
For hardware implementation, we have a large time margin for calculation due to the long interval between heartbeats. To reduce hardware resources, we propose the SVM classifier architecture shown in Figure 13. The data format used for computation are 32Q16. Through MATLAB simulation, converting the data format from MATLABs double type to 32Q16 for hardware implementation caused some precision loss, but it was within an acceptable range.
## 5 Thermodynamic Evaluation
### Energy Analysis and Results
At the same time, in WTEU, because the RH is removed, the heat sources of SAH and PAH2 are replaced, the working fluid mass flow of PAH3 and PAH1 is adjusted, and the heat transfer temperature difference of each section of APHs is reduced, the exergy of RHs and APHs is decreased by 0.81 MW. Therefore, the total exergy loss of RHs and APHs of integrated system is reduced by 0.43 MW. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Initial Design**} & \multicolumn{2}{c}{**Proposed Design**} & \multicolumn{1}{c}{**Difference**} \\ \cline{2-6}  & **MW** & **Proportion** & **MW** & **Proportion** & **MW** \\ \hline Exergy input of CFPU & 1497.95 & 97.22\% & 1497.95 & 97.22\% & 0.00 \\ Exergy input of WTEU & 42.82 & 2.78\% & 42.82 & 2.78\% & 0.00 \\ Total exergy input & 1540.76 & 100.00\% & 1540.76 & 100.00\% & 0.00 \\ Net exergy output of CFPU & 626.59 & 40.67\% & 626.59 & 40.67\% & 0.00 \\ Net exergy output of WTEU & 8.00 & 0.52\% & 12.25 & 0.79\% & +4.25 \\ Net total exergy output & 634.59 & 41.19\% & 638.83 & 41.46\% & +4.25 \\ \hline \multicolumn{6}{c}{Exergy loss variation} \\ \hline Boiler & 720.09 & 46.74\% & 719.71 & 46.71\% & \(-\)0.38 \\ Turbine & 45.94 & 2.98\% & 46.82 & 3.04\% & +0.88 \\ RHs & 14.84 & 0.96\% & 15.22 & 0.99\% & +0.38 \\ CON & 40.60 & 2.63\% & 41.61 & 2.70\% & +1.02 \\ EG & 7.34 & 0.48\% & 7.49 & 0.49\% & +0.15 \\ UHU & 0.66 & 0.04\% & 0.51 & 0.03\% & \(-\)0.14 \\ Auxiliary power & 32.98 & 2.14\% & 32.98 & 2.14\% & 0.00 \\ Other & 8.92 & 0.58\% & 8.97 & 0.58\% & +0.05 \\ \hline Boiler & 26.20 & 1.70\% & 25.68 & 1.67\% & \(-\)0.52 \\ Turbine & 2.66 & 0.17\% & - & - & \(-\)2.66 \\ RHs + APHs & 2.33 & 0.15\% & 1.52 & 0.10\% & \(-\)0.81 \\ CON & 1.72 & 0.11\% & - & - & \(-\)1.72 \\ EG & 0.16 & 0.01\% & - & - & \(-\)0.16 \\ Auxiliary power & 1.76 & 0.11\% & 1.42 & 0.09\% & \(-\)0.34 \\ \hline Total exergy loss & 906.18 & 58.81\% & 901.93 & 58.54\% & \(-\)4.25 \\ Net total exergy efficiency & 41.19\% & & 41.46\% & 0.28\% \\ Net exergy efficiency of WTEU & 18.68\% & & 28.60\% & +9.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of the exergy analysis results between the initial and proposed designs. The total exhaust steam flow of STs falls, which contributes to the CON exergy loss reducing by 0.70 MW. 5. The exergy loss in UHU is decreased by 0.15 MW, with the heat source of UHU varying after combination. 6. There is no apparent change in the exergy loss of other components. In summary, the overall exergy destruction of systems is reduced by 4.25 MW after integration. The exergy efficiency of WTE system is increased by 9.92%, and 0.28% improvement in total exergy efficiency is achieved compared to the initial systems. Exergy analysis results show that the primary reason for efficiency growth is the exergy loss reduction in turbines and boilers, while most energy change occurs in CON through energy analysis.
## Data, Methodology and Results  
### Data and Model  
In order to avoid introducing errors of orbital simulation, this study uses the positions of GPS satellites and TDS-1 GPS-R receiver in the TDS-1 satellite data to position the specular reflection points. The static-state geoid undulation and time-varying tidal height used in the elevation correction are calculated based on the Earth Gravitational Model 2008 (EGM2008) earth gravitational model and the TPXO8 ocean tidal model, respectively.
## 3 Proposed Solution
### Implementation of Geolocation-Based Authentication and Rest API  
As the main objective of this research was to provide an extra layer of security based on the user's geolocation, GPS inputs were utilized with the assumption that every user has an active smartphone with location services enabled. The authentication system was linked with the existing financial transaction backends. The Rest API was built using ASP.NET Web API utilizing Visual Studio 2019 as the IDE. The procedures involved in the authentication API are further described in the following.  
**Step 1:** The API receives an authentication request from the bank with the ATM location.  
**Step 4:** The authentication API fetches data from the user's authenticator mobile app regarding the user's current location.  
**Step 5:** IF ATM location and users' current location match? Then  
IF Request amount < withdrawal limit Then  
Go to Step 6.  
Else  
Go to Step 7.  
**Step 6:** Approve the transaction.  
**Step 7:** Deny the transaction.  
**Step 8:** Send the status (approval or denial) of the transaction to the bank.
## 2 Materials and Methods  
### Materials  
The 4-n-nonylphenol analytical standard (4-n-NP, CAS: 104-40-5, purity \(>\) 98%) was provided by Sigma-Aldrich (Madrid, Spain). Approximately 90% of NPs from the degradation of NPEs are 4-NP isomers, and 4-n-NP was selected for the present research. The radiolabeled compound ring, 14C-4-n-NP (36 mCi-mmol\({}^{-1}\), purity 99.9%, and radiochemical purity 100%) was obtained from the Institute of Isotopes (Budapest, Hungary). Hydroxypropyl-\(\beta\)-cyclodextrintrim (HPBCD, purity 97%) was supplied by Cyclolab (Budapest, Hungary). All other chemicals used were of analytical grade, obtained from Sigma-Aldrich, and used as supplied without further purification. A fresh sewage sludge sample from a WWTP in Seville (southwest Spain) was supplied by Emasesa. It was dried by freezing and lyophilization, crushed, and homogenized before use, and its characteristics are shown in Table S1 (Supporting Information). The nutrients solution (NS) used in biodegradation assays was composed of the mineral salt medium (MSM) and micronutrients (mg/L, 75.0 MnCl\({}_{2}\) 4H\({}_{2}\)O; 37.5 FeSO\({}_{4}\) 7H\({}_{2}\)O; 25.0 SnCl\({}_{2}\) 2H\({}_{2}\)O; 12.5 ZnSO\({}_{4}\) 7H\({}_{2}\)O; 12.5 Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)-18H\({}_{2}\)O; 12.5 NiCl\({}_{2}\) 6H\({}_{2}\)O; 12.5 CoCl\({}_{2}\) 2H\({}_{2}\)O; 10.0 CaSO\({}_{4}\) 2H\({}_{2}\)O; 3.75 KBr; 3.75 KCl; 2.5 LiCl). The MSM contained principal and essential trace elements (per liter of deionized water): 0.5 g of KH\({}_{2}\)PO\({}_{4}\), 0.5 g of K\({}_{2}\)HPO\({}_{4}\), 0.01 g of NaCl, 0.2 g of MgCl\({}_{2}\) 6H\({}_{2}\)O, 0.02 g of CaCl\({}_{2}\), 1 g of (NH\({}_{4}\))SO\({}_{4}\), 0.339 mg of MnSO\({}_{4}\), 0.428 mg of ZnSO\({}_{4}\), 0.347 mg of (NH\({}_{4}\))6 Mo\({}_{7}\)O\({}_{24}\) 4H\({}_{2}\)O, 0.4 mg of CoCl\({}_{2}\) 6H\({}_{2}\)O, 5 mg of FeSO\({}_{4}\) 7H\({}_{2}\)O, 0.2 mg of CuSO\({}_{4}\) 5H\({}_{2}\)O, and 10 mg of EDTA. The pH was adjusted to 7.3 \(\pm\) 1 with NaOH. Luria-Bertani (LB) broth contained (g L\({}^{-1}\)) 10.0 tryptone, 5.0 yeast extract, and 10.0 NaCl ([PERSON]\({}^{\text{TM}}\), Fisher Scientific, Madrid, Spain).  
Eight different specific NP-degrading bacterial strains, which were previously isolated from sewage sludge through the enrichment culture technique with an NP and deposited in the NCBI GenBank with their corresponding accession numbers [36], were used in this study: _Aeromonas salmonicida_ CN1, _Alcaligenes faecalis_ CN8, _Bacillus safensis_ CN12, _Enterobacter bugandensis_ CN13, _Pseudomonas putida_ CN14, _Pseudochromochrom vacuum saccharolyticum_ CN15, _Orthochromatum anthropi_ CN16, and _Shewanella putrefaciens_ CN17. These bacterial strains remained stored in MicrobankTM cryovials in the LB medium mixed with a 40% glycerol solution (50/50 \(v/v\)) and were kept at \(-\)80 \({}^{\circ}\)C.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 2 Binaural Recording and Rendering
### Binaural Recording and HRTF Measurement
#### 2.1.2 Test Signal and Post-Processing  
Various test signals with corresponding signal processing methods have been used for HRTF measurement, such as exponential swept-sine, maximum length sequences (MLS), Golay codes, etc. Post-processing is necessary to remove the responses of the measurement system as well as reflections from the measurement apparatus or reverberation for measurements performed in a semi-anechoic chamber. The swept-sine based method with appropriate time windowing has been shown effective to remove reflections [36]; in addition, a common practice is to divide the measured transfer function by the spatially averaged mean response, i.e., the Common Transfer Function, which is irrelevant to direction, in order to extract the so-called Directional Transfer Function [1]. Another major focus is to improve the measurement efficiency, in order to minimize the recording time but without reducing the spatial resolution. More specifically, the proposed approaches include the measurement via the  
Figure 1: Binaural recording packages. (**a**) dummy head: Neumann KU-100; (**b**) head-and-torso simulator (HATS): Brüel Kjaer 4128D/C; (**c**) binaural microphones: 3 Dio Free Space.  
reciprocity method [37], the multiple exponential sweep method [25], the continuous measurement method [38], and the HRTF acquisition with unconstrained movements of human subjects [39].  
An ongoing project, the \"Club Fitz\", was initiated in 2004 with the aim to compare HRTF databases from laboratories across the world [27]. The database consists of physical measurements and numerical simulations performed on the same dummy head (Neumann KU-100), where HRTFs are converted to the widely-adopted open standard SOFA file format [40]. Investigations on 12 different HRTFs from 10 laboratories showed an observation of large ITD variations (up to 235 \(\upmu\)s) and spectral magnitude variations (up to 12.5-23 dB) among these databases. This further demonstrates the profound impact of measurement systems and signal processing techniques on HRTF data acquisition.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 2 Materials and Methods
[23] improved long short-term memory (LSTM) using a genetic algorithm (GA), in which the GA is utilized to optimize the LSTM hyperparameters. [PERSON] et al. [24] proposed a deep learning approach for cellular traffic forecasting with deep neural networks (DNNs) to model cellular traffic. Traffic volume data are treated as tensors, similar to images, which are then fed to a convolutional neural network. [PERSON] et al. [25] proposed the GRU neural network traffic prediction algorithm based on transfer learning. The proposed method combines transfer learning to solve the problem of insufficient online traffic data. The particle filter online training algorithm is adopted to reduce the computation complexity. The above network traffic forecasting methods are comprehensively compared in terms of computation complexity and forecasting accuracy, as shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Forecasting** & **Scheme** & **Computation** & **Forecasting** & **Characteristics** \\
**Method** & **Scheme** & **Complexity** & **Accuracy** & **Characteristics** \\ \hline \multirow{6}{*}{Statistics-based schemes} & ARIMA [1] (2019) & Low & Normal & A variety of parameters need to be set based on experience. \\  & Markov model [16] (2018) & Normal & Normal & Network traffic is converted into an MMDP/D/I/K queued model. \\  & ARMA [17] (2015) & Low & Low & Parameters to be set; ARMA(2,1) to reduce computation complexity. \\  & Surrogate model [18] & Normal & Normal & Traffic prediction time-independent ground traffic distribution. \\  & & SSA-AWELM [20] (2020) & High & High & Decompose raw traffic into three parts and predict separately. \\  & RNN-GRU [10] (2019) & High & High & Combines RNN and GRU to forecast network traffic. \\ Neural network schemes & GCN-GAN [21] (2020) & High & Normal & GCN extracts complex properties; GAN models data structures. \\  & LA-ResNet [22] (2020) & High & High & Attention mechanism. \\  & GA-LSTM [23] (2023) & Normal & Normal & Optimize the hyperparameters of LSTM via GA. \\  & DNN [24] (2020) & High & Normal & Treat traffic volume data as a tensor. \\  & GRU [25] (2021) & Normal & High & Reduce computation complexity via a particle filter online training algorithm. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of network traffic forecasting methods. To address the above issues, this paper decomposes satellite network traffic with self-similarity into multi-order IMFs with short-range correlation using EMD. The ARIMA, improved using an adaptive fixed-order search operator, is utilized to forecast IMFs with low computation complexity. IGWO is utilized to optimize the hyperparameters of a BPNN, which forecasts the residuals of the EMD-ARIMA. With the enhancement of the global optimization capability of the BPNN, the proposed EMD-ARIMA-BPNN hybrid model can fully realize the potential of mining the linear and non-linear laws of satellite network traffic, hence improving the forecasting accuracy.