esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Work and Results  
### Experimental Work  
In this paper, an excerpt of the TIDIGITS database [27] was utilized to evaluate the performance of the proposed method in comparison to the state-of-the-art techniques. This excerpt consists of 207 speakers; each of them pronounces 11 digits twice. It was partitioned into two subsets. The training set was 37 men and 57 women, while the testing set was 56 men and 57 women. Eight different types of noise were added to the testing datasets with different SNRs from \(-5\) dB to 20 dB with a step size of 5 dB. These noises were added at the same random sequence. One of them was additive white Gaussian noise (AWGN), and the rest were AURORA environmental noise.  
Likewise, in the PNCC methods, as well as in the MFCC [28], RASTA-PLP [28], and GFCC methods, each utterance was divided by 25.6 ms overlapped frames with 10 ms shifts between frames. Then, each frame was multiplied by a Hamming window. After that, the FFT was applied with 256-bit  
Figure 3: Gammatone spectrogram of the uttered word ’one’.  
resolution. In the last stage, 13 features were obtained for each technique. After applying CMN, \(\Delta\) and \(\Delta\Delta\) features were calculated' the total number of extracted features was 39 features.  
For all types of feature extraction techniques in this paper, the Gaussian mixture model-hidden Markov model (GMM-HMM) technique was used to generate acoustic models of each word [3]. The sphinx Carnegie Mellon University (CMU) lexical dictionary was used with three Gaussian mixture components per each phoneme. All the feature extraction methods were trained with noise-free utterances, and tested with noise-free and noisy utterances. The database structure was a single word. Therefore, the language model was only one word, and in this case, the recognition accuracy was equal to the word recognition rate (WRR).
# Physiological and Biochemical Variations in Celery by Imidacloprid and Fenpyroximate  
[PERSON]  
1 State Key Laboratory for Managing Blotic and Chemical Threats to the Quality and Safety of Agro-Products, Ministry of Agriculture and Rural Affairs Key Laboratory for Pstictide Residue Detection, Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (H.H.); [EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (X.Z.)  
[PERSON]  
1 State Key Laboratory for Managing Blotic and Chemical Threats to the Quality and Safety of Agro-Products, Ministry of Agriculture and Rural Affairs Key Laboratory for Pstictide Residue Detection, Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (H.H.); [EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (X.Z.)  
[PERSON]  
1 State Key Laboratory for Managing Blotic and Chemical Threats to the Quality and Safety of Agro-Products, Ministry of Agriculture and Rural Affairs Key Laboratory for Pstictide Residue Detection, Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (H.H.); [EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (X.Z.)
Celery, a member of the apiaceae family, has long been celebrated for its unique aroma and flavor, as well as its high fiber content. Similarly, celery is widely acknowledged as a functional food that aids in the prevention, management, and treatment of chronic diseases by a wide range of consumers [13; 14]. According to a study on Labeo chrysophekadion fed celery extract, the treated fish's ability to no-specific immune response and disease struggle was improved [15]. Besides, the study revealed that celery water extract can kill viruses and prevent them from replicating [16]. Celery, on the other hand, has therapeutic actions that are highly linked to plant antioxidant status [17]. Despite this, the published research on celery physiology and biochemistry under pesticide exposure in the field remains unknown.  
Therefore, celery was used as the research plant due to the numerous characteristics of celery's popularity. Imidacoprid and fenpyroximate, which have been widely used in crop cultivation environments, were chosen as pesticide stresses. Here, the major goals of this current research were to (1) investigate the pesticide residues in celery stem and leaf at experimental periods in a field environment, (2) explore the impact of pesticides on soluble protein accumulation in celery plants, and (3) evaluate the activities of peroxidase and superoxide dismutase in celery under pesticide treatment.
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 3 Xona PULSAR(tm) Demonstration Platform  
The Xona PULSAR(tm) hardware demonstration platform (Figure 6) is based on the STMicroelectronics STA8135 GA multi-band GNSS chipset and dedicated firmware. The STA8135 GA is an automotive-grade Multi-Chip Module (MCM), combining in a single package one STA8100 GA (TeseoV receiver with embedded RF tuner) and one STA5635A (additional RF tuner) [12]. The frequency plan was configured to receive the Xona demonstration satellite signal whose center frequency is 1260 MHz, referred to here as XL together with the legacy GNSS L1/L5 bands, such that MEO dual-band multi-constellation (GPS/Galileo/BeiDou) plus LEO Xona XL at the same time could be supported.  
To enable integrated INS/GNSS positioning, the STA8135 GA is connected through an I2C or SPI interface to the ASM330 LHH module. The ASM330 LHH is a system-in package featuring a 3D digital accelerometer and a 3D digital gyroscope designed to address automotive applications. The use of inertial sensors combined with loop aiding for MEO was introduced in [10].  
Figure 5: Example plots of prediction errors for a Xona CN0 “ramp” scenario: **(a)** frequency error; **(b)** frequency rate error.  
Figure 6: TeseoV GNSS receiver and IMU: Xona application block diagram.  
The Xona observables are generated in a proprietary format based on legacy NMEA or RTCM3 MSM7 templates. External assistance commands have been extended to support the injection of Xona orbital parameters. For testing purposes, the receiver firmware can be configured also in autonomous Xona-only mode.  
The TeseoV name identifies a family of mature GNSS receiver technologies and components from STMicroelectronics that evolved through several generations over the past few decades. Firmware development, after a quick check for frequency plan feasibility, took most effort in adding Xona support to this legacy platform. In general, the existing and consolidated software architecture could be adapted to include LEO. Few outstanding algorithms required a deeper review.
# Measurement of Structural Loads Using a Novel MEMS Extrinsic Fabry-Perot Strain Sensor
## 1 Introduction
However, the above-mentioned FP cavity is fabricated in the face of the fiber or the fiber itself, which cannot ensure high smoothness, and it is difficult to increase the optical reflectivity of the surface by optical coating; thus, it is not easy to obtain a more stable interference spectrum of the interference cavity. In this study, a high sensitivity extrinsic fiber FP (EFFP) strain sensor based on MEMS technology is proposed. The surfaces of the cavity with good parallelism can effectively solve the issues that the flatness of the existing two surfaces is poor and that the FP cavity length cannot be accurately controlled. Thus, the interference fineness is high. The sensor is applied to achieve high-precision measurement of a load for a small tested structure. During the test process, the influence of temperature to the FP sensor is decoupled according to the difference in temperature sensitivity between FBG and FP sensors. Next, the finite element software Nastran is used to analyze the strain distribution of the tested structures under axial force, which provides a judgment for sensor placement. Finally, the experimental setup is described to confirm the sensor linearity to the axial force of the tested structure and ensure repeatability. This novel strain sensor has high accuracy, linearity, stability, and repeatability, and it is easy to realize miniaturization and mass production. Therefore, it can be used as a distributed strain sensor or a stable force sensor with potential industrial applications in the case of weak strain.
## 2 Materials and Methods
### MD Simulations
#### 2.3.2 Calibration  
To evaluate the performance of MM/PBSA method in predicting binding affinity of ligands with YTHDC1, we performed MM/PBSA analysis on 11 ligands of YTHDC1, which were selected from 30 YTHDC1 binders reported in a study with high-throughput protein crystallography [43]. MMFF94x [68] force field parameters were used for each ligand. We used Gaussian 09 [69] to optimize the ligands' hydrogen atoms via Hartree-Fock calculations with a 6-31G* basis set. Following this, the restrained electrostatic potential (RESP) [70] method was used to compute partial atomic charges based on calculations at the HF/6-31G* level. Each protein-ligand complex was neutralized by introducing sodium or chlorine ions as counter ions and was subsequently immersed in a cuboid box filled with TIP3P water while maintaining a 10 A distance between the solute and the faces of the box.  
MD simulations were executed using the AMBER16 package [71], with the AMBER ff14 SB force fields [72] applied to the protein. Note that the binding free energy obtained by using two different force fields (Charmm and Amber) would not differ much, as seen in study [73], which critically assessed the performance of MM/PBSA using different force fields. We used the SHAKE algorithm to constrain all hydrogen-involved covalent bonds, allowing for a 2-fs time step. Long-range electrostatic interactions were managed using the PME approach. Each system underwent two sequential minimization steps before the heating process. Initially, 4000 minimization cycles were run while restraining all heavy atoms with a spring constant of 50 kcal/(mol\(\cdot\)A2), leaving solvent and hydrogen atoms unrestricted. This was followed by an unrestrained minimization involving 2000 steepest descent cycles and 2000 conjugate gradient cycles. The entire system was subsequently heated from absolute zero to 300 K for 50 ps while employing Langevin dynamics under constant volume conditions. An equilibrium state was achieved for 400 ps at a constant 1-atm pressure, with all heavy atoms weakly restrained with 10 kcal/(mol\(\cdot\)A2) during the heating phases. The production phase employed an NPT ensemble, maintaining the system at 300 K and 1 atm pressure with a Nose-Hoover thermostat [74] and a Parrinello-Rahman barostat [64], respectively, for 200 ns of simulation.
# Current Status of HbA1c Biosensors
## 2 HbA1c Biosensors
### Affinity Biosensors Based on Boronic Acid Derivatives
#### 2.1.1 Biosensors Based on Ferrocene-Boronic Acid (FcBA)  
FcBA plays a dual role in the determination of HbA1c. First, the boronic acid portion of the molecule interacts with HbA1c. Second, the ferrocene portion is a redox-active label, therefore, it can be utilized for both target recognition and signal production. By using a pyrolytic graphite electrode (PGE), that is coated with zirconium dioxide (ZrO2) nanoparticles (NP)/didodecyddimethyl-ammonium bromide (DDAB) interface, [PERSON] et al first immobilized HBs on the electrode, and then incubated the protein-bound electrode in FcBA solution. The binding of the FcBA to HbA1c generates a clear redox peak monitored by cyclic voltammetry, and the peak current of the bound FcBA increases linearly with HbA1c levels in the range of 6.8-14% [24]. However, the adsorption of HBs onto ZrO2/DDAB interface is time-consuming and the statistical deviation of the method is relatively high by a comparison with the results measured by the reference method. Further improvement is required in terms of the response time and precision for the clinical application.  
[PERSON] and coworkers tested various HBs-adsorbent materials including surfactants, a strong cation propidium modifier, an organophosphorous compound and Hp as well. The deoxycholic acid (DOCA)-modified surface showed the best overall performance based on three parameters: loading capacity of the modifier, reproducibility of the piezoelectric sensor and the direct Hb reduction. The total Hb content deposited on the surface was monitored using a quartz crystal microbalance (QCM), while the HbA1c concentration was obtained by subsequent voltammetric detection of FcBA coupled on the sensor surface, in which the signal is proportional to the HbA1c concentration [25]. Due to the heat-denaturation of the Hb pretreatment and a pH-dependent high standard deviation value of the method, currently, this is still a HbA1c-assay concept.  
Recently, quite a few of boronic acid-based biosensors have been developed for the capture and determination of HbA1c including 3-aminophenylboronic acid (APBA), thiophene-3-boronic acid (T3 BA), and formylphenylboronic acid (FPBA), as summarized in Table 2. In order to attach boronic acids to the electrode surface, one must modify them with cross-linking agents. For example, the electrodes can be modified with the cysteamine self-assembled monolayer (SAM), followed by glutaraldehyde and then APBA [26], to create the final boronate affinity layer (Figure 1C). There are other functions of cross-linking agents such as the ERGO-PQQ, ESM-glutaraldehyde, carboxy-EG6-undceanthiol and pTTBA-Au NPs used for the same purpose.  
Figure 1: Schematic illustration of construction of boronic acid-based HbA1c sensors by using (**A**) FcBA; (**B**) T3 BA; and (**C**) APBA or FPBA for HbA1c recognition.
## 3 Calibration of the Measurement System
### Identification of the Slide Error Motion  
During the measurement procedure, the \(Z\)-slide of the motion platform is used to drive the measuring gauge to move along the \(Z\)-axis, while the \(Y\)-slide of the motion platform is used to drive the high aspect ratio aspheric workpiece to move along the \(Y\)-axis to keep the measuring gauge in contact with the inner profile of the workpiece. Thus, the motion errors of the \(Z\)-slide and the \(Y\)-slide would influence the measurement accuracy [13]. To ensure measurement accuracy, it is necessary to evaluate and compensate for the motion errors of the \(Z\)-slide and the \(Y\)-slide.  
The identification procedure of the slide motion errors is expressed in Figure 9. For evaluating the motion error of the \(Z\)-slide, as expressed in Figure 9a,b, the lateral gauge mounted on the \(Z\)-slide and a standard gauge block mounted on the \(Y\)-slide are applied. Firstly, the lateral gauge is moved to perform a circular motion on the right side of the standard gauge block and the peak-to-valley output of the lateral gauge is evaluated. The tilt stage mounted under the standard gauge block is then adjusted to keep the peak-to-valley output within 100 nm to make sure that the right side of the standard gauge block is parallel to the \(X\)Z-plane of the motion platform. After that, the lateral gauge is moved along the \(Z\)-axis to measure the right side of the standard gauge block. During this measurement procedure, only the \(Z\)-slide moves and the variation of the lateral gauge output represents the motion error of the \(Z\)-slide of the motion platform. Similarly, by applying the axial gauge mounted on the \(Z\)-slide and the standard gauge block mounted on the \(Y\)-slide, the axial gauge is controlled to perform a circular motion on the upper side of the standard gauge block to make sure that the upper side of the gauge block is parallel to the \(XY\)-plane of the motion platform. Then, only the \(Y\)-slide moves and the axial gauge probe is moved along the \(Y\)-axis on the upper side of the standard gauge block to evaluate the motion error of the \(Y\)-slide of the platform. The motion errors of the \(Z\)-slide and the \(Y\)-slide can then be compensated by the measurement of the workpiece inner profile to improve measurement accuracy. It should be noted that the surface flatness of the gauge block is nominally on the order of 30 nm, which is good enough for being used in this method to identify the motion error of the slides that is in the order of 1 micrometer.  
Figure 8: Schematic of tilt alignment of the high aspect ratio aspheric workpiece. (**a**) Schematic of marking the farthest point; (**b**) tilt alignment of the high aspect ratio aspheric workpiece.
## 3 Design and Operation of Microgrids
### Optimization Techniques for Microgrids
_Genetic Algorithms_: Genetic algorithms are a type of evolutionary algorithm used to optimize the operation of a microgrid. They involve creating a population of potential solutions and iteratively improving them through a process of selection, mutation, and crossover [100]. _Artificial Neural Networks_: Artificial neural networks (ANNs) are used in MGs to optimize the operation of the system by learning from historical data. ANNs are trained on data from the microgrid to develop a model of the system's behavior [2; 94], which can then be used to predict future performance and optimize the operation of the system. _Particle Swarm Optimization_: Particle swarm optimization (PSO) is an optimization technique inspired by the collective behavior of swarms of birds or insects [16; 18]. It involves creating a population of potential solutions that \"swarm\" around the search space, with each individual adjusting its position based on its own experience and the experience of other individuals. _Reinforcement Learning_: Reinforcement learning is a machine learning technique used to optimize the operation of a microgrid by learning through trial and error [102; 112]. The microgrid is modeled as an agent interacting with an environment, with the objective of maximizing a reward signal. The agent learns from experience, adjusting its actions based on the feedback it receives. _Multiobjective Optimization_: Multiobjective optimization is used in MGs to optimize multiple conflicting objectives simultaneously. The optimization problem is formulated as a multiobjective function, and the solution space is searched to identify the optimal trade-off between the objectives [113; 114; 115]. This section discussed a selection of commonly used optimization techniques in MGs. The optimal choice of optimization technique depends on the specific requirements of the microgrid and the nature of the optimization problem being addressed. For instance, if the goal is to minimize costs, economic dispatch or mixed-integer linear programming may be a suitable choice, whereas if the goal is to optimize performance under uncertain conditions, stochastic optimization or reinforcement learning may be more appropriate. Ultimately, the choice of optimization technique should be guided by a thorough understanding of the microgrid's characteristics and objectives.
## 2 Theoretical Background
### E-Commerce  
Digital transformation along with the widespread adoption of the internet and mobile technologies has resulted in the creation of global markets where buyers and sellers transact for goods and services using different physical and virtual network architectures for offering, creating, and delivering value [4]. E-commerce refers to the sale and purchase of goods and services over the internet, with the transfer of money and data to complete transactions [5]. E-commerce platforms, in addition to selling products, facilitate the discovery of product information, which allows price comparison and decision-making about a purchase and the seller [6].  
A company's business model represents its core logic and strategic choices for creating and capturing value within a value network [7]. The e-commerce model includes commercial transactions between buyers and sellers; the main flows of products, services, information, and money; and the main benefits to participants.  
There are several e-commerce business models, and more are being created every day [5]; however, despite the abundance of models, it is possible to identify the main models that have been developed for e-commerce. According to [PERSON] [8], all the major e-commerce business models fall under three key categories: B2B, business-to-business; B2C, business-to-consumer; and C2C, consumer-to-consumer. In B2B, the businesses transact with other businesses. In B2C, business transactions are carried out with individual consumers and include purchasing of retail goods, travel, and other types of online services and content. C2C provides a way for consumers to sell to each other, with the help of an online platform. According to [PERSON] [5], the most discussed type of e-commerce is B2C commerce.  
However, a lack of trust is particularly relevant in B2C and C2C online business models, where there is no prior relationship between the participants, as is usually the case in the B2B e-commerce model or in traditional commerce, where the buyer may know the participants (buyer and seller). Over the past few years, recommendation models and reputation rating rankings have been used to address this problem, helping the user, usually a buyer or seller, to minimize transaction risk. In general, all online marketplaces provide recommendation and reputation data, typically in the form of a score, about the user or product, in order to gain the trust of users.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 9 Challenges and Future Directions
### Areas to Explore  
#### 9.2.1 New Protocols for BackCom  
Recently, a battery-free cellphone was shown to operate on the principle of BackCom to achieve voice calls [8]. This battery-free cellphone design did not consider analog to digital conversion in the handset, and no digital processing module was incorporated. The analog voice signals were directly sent to (few meters distant) a base station for signal processing. This work motivates us to think about future network models for 5G that can support BackCom at the cellphone while considering  
Figure 49: Open problems in BackCom.  
all signal processing at base stations or cloud radio access networks (CRAN). This seems possible in the near future by utilizing massive MIMO and coordinated multi-point with joint transmission (CoMP-JT) to establish cell-free communications between BackCom devices [225]. The nearest AP in cell-free structures can support passive user equipment to harvest power and then transmit data using BackCom. Moreover, battery-free cellular network protocols for BackCom-enabled 5G devices can be suggested to establish effective communication in mission-critical communication without the need of the battery.