esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) Mag-dSPE Procedure and Its Optimization
#### 2.2.3 Optimization pH of Water Sample  
Various pH values from 3.0 to 11.0 were used to optimize the Mag-dSPE procedure, and the results were shown in Figure 10. It indicated that, with the increasing of pH values from 3.0 to 11.0, there was no significant effect on the recovery of the target analytes, and the recoveries were in the range of 70.1-101%. With the increasing of pH values from 5.0 to 7.0, the satisfactory recoveries of the 11 food additives increased slightly. Above pH 7.0, the recoveries of 11 food additives decreased with the increase in pH within the studied pH range. Above all, 7.0 was chosen as the pH value for the investigations that followed.  
Figure 10: Effect of pH on the recovery (\(n=6\)).  
Figure 9: Effect of adsorbent dosage on the recovery (\(n=6\)).
## 2 Multiprecision Hardware
### Fpga  
Notwithstanding the usefulness for neural network applications, the IEEE-754 and BF16 data formats are not necessary the best choices for any other scientific problem. In order to achieve a better optimization of computing resources for arbitrary tasks, a field-programmable gate array (FPGA) has been invented. An FPGA is an integrated circuit which can be configured for a specific functionality and/or data type as it contains an array of transistor logic elements and switchers that can be interwired in various configurations. The circuit is reconfigured using a configuration file developed on a hardware description language such as Verilog, VHDL or OpenCL. The recent Intel C++ and Fortran compilers support programming FPGA devices. Nowadays, FPGA research is an active area of computer science research [14; 29] and many hardware vendors invest their efforts into the development of high-efficiency programmable chips.
# Aggregatable Subvector Commitment with Efficient Updates
## 6 Conclusions  
In this paper, we propose a novel aggregatable subvector commitment scheme based on Newton interpolation, which can effectively support the addition of new elements. Specifically, the proposed scheme allows for the incremental updating of commitments and proofs for each position, avoiding the requirement of full recomputation, and thus, reducing the computational overhead. In addition, we employed the Karatsuba algorithm (KA) to efficiently perform large-integer multiplication, which improves the efficiency of proof aggregation and verification. However, the efficiency in updating proofs when modifying elements still needs to be improved. In future work, our research direction will further optimize the updating efficiency, which will directly contribute to the widespread deployment of vector commitment techniques in practical applications.  
Figure 2: Performance comparison. (**a**) The time required to update all the proofs against aSVC and our scheme at different vector lengths; (**b**) the time required to aggregate some of the proofs against aSVC and our scheme with different vector lengths; (**c**) the time required to verify the aggregated proofs against aSVC and our scheme with different vector lengths; and (**d**) the time required to update all proofs at different vector lengths for aSVC and our scheme after adding nodes.  
**Author Contributions:** Conceptualization, All authors; methodology, Q.X. and Y.W.; software, Q.X. and Y.W.; validation, Q.X. and Y.W.; formal analysis, Q.X. and Y.W.; investigation, all authors; resources, Q.X. and Y.W.; writing--original draft preparation, Q.X.; writing--review and editing, Y.W.; supervision, Q.X. and C.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Key Research and Development Program of China (Grant No. 2022 YFB2701500), the National Natural Science Foundation of China (Grant No. 62102313), and the Shaanxi Distinguished Youth Project (Grant No. 2022)C-47).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Enhancement of Biological Treatment Systems
### Activated Sludge and Biofilm System  
In aerobic treatment, SDB can serve as a microbial carrier to enrich beneficial communities [71]. Its surface offers attachment sites for biofilms, aiding in biomass retention within biological systems such as moving bed biofilm reactors and integrated fixed-film activated sludge. In the activated sludge system, the carbon skeleton of SDB supports larger floc growth, leading to both reduced membrane fouling and enhanced nitrogen removal [72]. Likewise, in denitrification, SDB can leach minimal amounts of organic carbon, supplementing low-C/N wastewaters and thereby enhancing nitrate removal [58]. [PERSON] et al. [73] reported that SDB acts as an electron shuttle for enhancing transfer system activity as well as improving functional microbial communities, leading to elevated denitrification performance. Additionally, SDB enhanced ammonia removal by promoting the growth of nitriifying biofilms on its surface, where the biochar created microsites with optimal oxygen gradients and pH conditions for nitrifiers [74]. Studies in other remediation contexts have noted that SDB can help microorganisms effectively resist adverse external influences, increasing overall biological process robustness [75; 76]. For instance, SDB mitigates oxytetracycline stress and its toxic by-products to the nitriifying microbiome due to the increased OTC removal with a novel biotransformation pathway [75]. In summary, these observations indicated that SDB can act as both a carrier and a mild slow-release carbon source in biological systems.
## 3 Methods
### Random Forest Classification
#### 3.5.2 Random Forest Classifier Based on GEE  
Due to the large number of input features and samples, this study classifies the input features by calling the random forest classifier _ee.Classifier.SmileRandomForest_ on a high-performance GEE, where the _ee.Classifier.SmileRandomForest_ classifier contains the following parameters:  
1. **NumberOfTrees**: The number of decision trees to create.
2. **VariablesPerSplit**: The number of variables per split. Unless otherwise specified, the square root of the number of variables was used.
3. **MinLeafPopulation**: Only creates nodes whose training set contains at least this many points; default:1.
4. **BagFraction**: The fraction of input to the bag per tree.
5. **MaxNodes**: The maximum number of leaf nodes in each tree. If unspecified, it defaults to no limit.
6. **Seed**: The randomization seed.  
Algorithm 1 describes a specific algorithm for RF classification based on GEE.  
```
Input: The randomly generated samples in Section 3.1; The combination of features InputBands; The number of decision trees Number of Trees. Original image composed of InputBands Output: Classification results; four types of accuracy evaluation indicators for OA, kappa, UA and PA
1. Train_points = 70% samples; // Division of the training set
2. Test_points = 30% samples; // Division of the test set
3. Train_label = [Train_points.label]; // Get the vector of label values for the training set
4. Test_label = [Test_points.label]; // Get the vector of label values for the test set
5. for each InputBands do:
6. Train RFclassifier = RandomForest(Number of Tree, Train_label, InputBands); // Training RFclassifier
7. Result = RFclassifier(image, InputBands); // Classify with classifier and get results
8. RFclassifier (Test_label).errorMatrix; // Get Confusion Matrix
9. Calculation Accuracy with errorMatrix; // Calculate OA, kappa, UA and PA
10 endfor
```  
**Algorithm 1** Random forest classification based on GEE  
To consider both the accuracy and computational efficiency of the random forest algorithm and to prevent the algorithm from overfitting, this study conducted a control experiment based on RGB Bands, and the number of decision trees with high overall accuracy was determined as the number of optimal decision trees between 10 and 200. The experimental results showed that when the number of decision trees was 50 or 70, the overall accuracy (OA) obtained by RF classification was higher. The experimental results are described in Figure 8. The number of decision trees in the classifier was set to 70, and the other parameters were set to their default values.
# Phase-Sensitive 2D Motion Estimators Using Frequency Spectra of Ultrasonic Echoes
###### Abstract  
Recently, high-frame-rate ultrasound has been extensively studied for measurement of tissue dynamics, such as pulsations of the carotid artery and heart. Motion estimators are very important for such measurements of tissue dynamics. In high-frame-rate ultrasound, the tissue displacement between frames becomes very small owing to the high temporal resolution. Under such conditions, the speckle tracking method requires high levels of interpolation to estimate such a small displacement. A phase-sensitive motion estimator is feasible because it does not suffer from the aliasing effect by such a small displacement and does not require interpolation to estimate a sub-sample displacement. In the present study, two phase-sensitive 2D motion estimators, namely, paired 1D motion estimators and 2D motion estimator with shifted cross spectra, were developed. Phase-sensitive motion estimators using frequency spectra of ultrasonic echoes have already been proposed in previous studies. However, such methods had not taken into account the ambiguity of the frequency of each component of the spectrum. We have proposed a method, which estimates the mean frequency of each component of the spectrum, and the proposed method was validated by a phantom experiment. The experimental results showed that the bias errors in the estimated motion velocities of the phantom were less than or equal to (11.5% in lateral, 2.0% in axial) by the proposed 1D paired motion estimators and (3.0%, 2.0%) by the proposed 2D motion estimators, both of which were significantly smaller than (14.0%, 3.0%) of the conventional phase-sensitive 2D motion estimator.  
Keywords:phase-sensitive motion estimator; frequency spectrum; cross-spectra; high-frame-rate ultrasound +
Footnote †: journal: Applied
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 4 The HO-FIM Architecture  
The architecture of HO-FIM and its basic concept and design rationale are presented first. Then, the problems for the design of a control algorithm for the HO-FIM architecture are formulated.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 1 Introduction  
Cisco visual networking index predicts that by 2021 global mobile data traffic will increase seven-fold, with the number of devices connected to IP networks increased to three times more of the global population [1].Similarly, according to the Ericsson mobility report, there were approximately 0.7 billion IoT devices connected with cellular networks in 2017. Due to overwhelming interest by industries and standardization of cellular IoT technologies by the 3 rd Generation Partnership Project (3 GPP), connectivity is expected to reach 3.5 billion IoT cellular devices in 2023 [2]. This huge usage of wireless devices in mobile networks, the ratification of Device to Device (D2D) communications,and IoT allow us to be remotely connected to everyday objects. However, the problem of these wireless devices is the limited battery life. Consequently, most of the efficient protocols for wireless communications will be inoperative if batteries are exhausted.  
Over the past few years, wireless communications have made significant advancements in technology--the high bandwidth of millimeter (mm) waves in the 5G network, internet connectivity of smart objects in IoT, and improved energy harvesting techniques. These advancements in technology are gradually leading us towards one of the most distinct phases of wireless communications--the \"Backscatter Communications for next-generation 5G wireless,\" which aims to render communications battery-free in emerging wireless network devices.
## 4 Experiments and Results
### Results and Analysis  
To verify the advancement of our proposed model and explore the role of different modules, we chose short-chunk CNN [44; 45], CBAM [45], BiLSTM + Attention [33; 46], ADFF [27], and the latest SCMA [28] for comparison. Moreover, to verify the effectiveness of the combination of FFA and BiGRU in our method, we also constructed two other models called FFA + BiLSTM and CBAM + BiGRU as baselines. The details of the baseline models are given as follows:  
Short-chunk CNN: this comprises seven-layer CNNs with residual connections, and the output is obtained through max pooling, followed by two fully connected layers with ReLU activations for the classification.  
CBAM: a convolutional block attention module that includes a channel attention map and a spatial attention map.  
BiLSTM + Attention: a combination of a bidirectional LSTM and a self-attention module.  
ADFF: an attention-based deep feature fusion approach that sequentially uses an adapted VGGNet as the spatial feature learning module and a squeeze-and-excitation (SE) attention-based module as the temporal feature learning module.  
SCMA: this is a short-chunk CNN with multi-head self-attention.  
Table 2 summarizes the accuracy, precision, and AUC performance of the proposed model and the other seven baselines. The proposed model outperforms all the baseline models with an accuracy of 69.8%, a precision of 70.3%, and an AUC score of 0.91. It suggests that our model can effectively capture more relevant information from the features for emotion recognition tasks. Moreover, the proposed model also outperforms the FFA+ BiLSTM and CBAM + BiGRU models, which indicates that the combination of FFA and the BiGRU in the proposed model is effective for extracting high-level emotion-related information.  
To further check the performance of the proposed model for each class of samples in the dataset, the confusion matrix of accuracies is provided in Figure 5. It shows that the accuracies of the HVHA and LVLA categories are relatively high, while those of the HVLA and LVHA categories are relatively low. This coincides with our intuition since music with extremely high or low emotional intensity is easier to distinguish, and those with moderate emotional intensity are prone to be misclassified.
## 3 Multi Objective Optimization of SFD Parameters  
### Initial Scheme and Experimental Design of SFD  
SFD can effectively reduces the vibration of the rotor system and restrain the nonlinearity caused by the misalignment coupling fault, in this paper, a variable-structured SFD is designed, which is shown in Figure 5, the SFD oil film gap \(C_{\text{S}}\) and elastic support stiffness \(K_{\text{a}}\) are changed by adjusting the  
Figure 4: The main process of NSGA-II.  
Figure 3: Latin Hypercube Design. (**a**) Random Latin hypercube and (**b**) optimal Latin hypercube.  
relative position of moving ring and length of elastic rod. The oil viscosity \(\mu_{\text{s}}\) also can be changed with the help of Magnetorheological Fluid. According to the structural characteristics of SFD, to realize the optimization of SFD structure parameters, and taking the SFD oil film gap \(C_{\text{S}}\), elastic support stiffness \(K_{\text{a}}\) and oil viscosity \(\mu_{\text{s}}\) as variables, the changing range of various parameters are shown in Table 1.  
First, according to the changing range of the above mentioned variables and the optimal Latin hypercube design, the design matrix table of each parameter of SFD is obtained, and a total of 80 groups is obtained, while the design matrix of the SFD parameters is shown in Table 2.  
Second, according to the optimized parameters of SFD in the design matrix of above experiment, a dynamic model of the SFD rotor rolling bearing system under three states is established: normal, misalignment fault, and the misalignment-rubbing coupling fault, while the model equations of motion are solved using the fourth-order Runge-Kutta method. The model parameters of the rotor shaft, SFD, ball bearing, rub-impact, and spline coupling are listed in Appendix A. Finally, the design parameters of SFD in Table 1 are used as inputs, while the calculated frequency amplitude is used as output, introducing into RBF neural network model, so as to establish an approximate model of SFD design parameters on the system frequency amplitude. Meanwhile, 10 sets of design parameters are selected and calculated by using the optimal Latin hypercube method, and the calculated results and prediction results of an approximate model are compared.
## 2 Results
### Identification of Volatiles
#### 2.1.1. Concentrations of Isothiocyanates and Nitriles in Raw and Cooked _Brassica_ Vegetables
The changes in isothi cyanates and nitriles induced by cooking are presented for selected cultivars in Figure 3. It illustrates the general decrease in nitriles caused by cooking and also the different behavior of isothi cyanates in these cultivars. Initially, research on glucosinolate degradation products was mainly focused on their toxic, antinutritive and goitrogen properties. More recently, attention has shifted to investigations concerning their beneficial effects against various diseases. Most studies are focused on different _Brassica_ vegetables and the detection of glucosinolates, which are biologically non-active molecules. Still, a higher glucosinolate content does not always guarantee an increment of desirable isothi cyanates after tissue mastication. The formation of beneficial isothi cyanates depends on a variety of factors, such as the activity of myrosinase and nitrile specific proteins or domestic processing. It is worth mentioning that even if glucosinolates are not degraded, their consumption is beneficial, since they can be hydrolyzed by a healthy intestinal microbiome [26]. Studies focusing on the health-promoting effects of isothi cyanates are numerous; however, they are mostly focused on sulforaphane [27], allyl isothi cyanate [21] and benzyl isothi cyanate [28], while as presented the qualitative diversity of isothi cyanates is considerable. Figure 3: Proportions between isothi cyanates and nitriles in selected raw and cooked _Brassica_ vegetables. The results are the mean of three replicates and the error bars show standard deviation. Amounts were expressed in total (sum of) peak areas.
## 2 Materials and Experimental Methods  
### Materials  
The main constituent materials investigated for geopolymer concrete manufacture in this study included low-calcium fly ash (class F), FCC residue, alkali liquid, and fine and coarse aggregates. Low-calcium fly ash was provided by Formosa Dong Nai Thermal Power Plant (Dong Nai, Vietnam). Coarse FCC was obtained from Petro Vietnam-Dung Quat (Vietnam). The densities and particle size ranges for the low-calcium fly ash and FCC residue were 2.5 and 0.88 g/cm\({}^{3}\), and 5-10 and 18-100 \(\upmu\)m, respectively. Coarse FCC residue was initially milled and dried at 100 \({}^{\circ}\)C for 24 h to obtain finer powder fractions (18-100 \(\upmu\)m) and promote higher levels of reactivity [16]. Representative microscopic images of the fly ash and FCC residue particles are provided in Figure 1, which clearly highlights the disparity in particle sizes. Apparent from this figure is particle shape and texture, which for the FCC residue is marginally more angular and rougher relative to the spherical, smooth fly ash particles. In contrast to these physical differences, both materials had comparable SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) contents, as presented in Table 1.  
The alkali liquid (AL) used in this study consisted of sodium silicates (8.37% Na\({}_{2}\)O, 27.63% SiO\({}_{2}\), and 64% H\({}_{2}\)O by mass) and sodium hydroxide (NaOH, concentration of 10 M) at a ratio of 1:1 by mass, which was purchased from VMC Group (Vietnam). Local sand with a density of 2.65 g/cm\({}^{3}\) and crushed rock with a maximum size of 20 mm and density of 2.70 g/cm\({}^{3}\) were used as fine and coarse aggregates, respectively.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 5 IoT in 5G Enabling Technologies
### Advanced Modulation and Coding  
This technological group involves advanced modulation (i.e., frequency and quadrature amplitude modulation (FQAM)) and advanced channel coding schemes.  
**Advanced modulation schemes** group includes FQAM, Amplitude and Phase Shift Keying (APSK), Unitary Space-Time Modulation (USTM), Spatial Modulation (SM), Wave Modulation(WAM), and Orthogonal Time Frequency and Space (OTFS). The FOAM is a combination of frequency shift keying (FSK) and quadrature shift keying (QAM) [124; 125; 126]. This modulation can achieve a higher transmission rate for cell edge users reducing interference at cell edge [52; 234; 235; 236]. PQAM also improves energy efficiency, which makes it adequate for MTC devices with stringent energy consumption requirements [124]. Due to performance advantages in terms of Frame Error Rate (FER) [53; 236], FQAM is an ideal candidate for services with high coverage and reliability requirements [237]. APSK is another modulation which draws a lot of attention. It is shown that its main performance gain (i.e., to achieve a channel capacity very close to Shannon's) relies on advanced channel coding and demodulation algorithms [237]. This adds more complexity at the APSK transmitter and receiver. Another modulation, which does not need Channel State Information (CSI) to enable high throughput, is called USTM [237]. USTM and its extension (see [238]) is very useful for 5G services with high mobility [127] or latency and reliability constraints [239]. Other modulation schemes involve SM [128], and proprietary WAM and OTFS [237]. On the basis of the foregoing, researches have analyzed and compared many modulation forms, but still have not found a practical guide on how to choose modulation in any of the 5G use cases.  
**Advanced channel coding** techniques are used for correcting the communication errors caused by noise, interference, and poor signal strength [129]. Authors in [54; 129; 130] compared turbo, low density parity check (LDPC) and polar codes in decoders in contrast to 5G requirements: (1) maturity; (2) throughput and latency; (3) error correction capability; (4) flexibility; (5) computation complexity; (6) interconnect complexity; (7) high-performance flexible implementation complexity; and (8) backward compatibility. This comparison showed that turbo codes hold the greatest promise for offering high performance throughputs, latencies and error correction capabilities, as well as high degrees of flexibility at the lowest implementation complexity [129] in most 5G use cases. However, further work is needed to implement a decoder based on this code followed by detail analysis.