esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 3 Indirect Type Electrochemical HbA1c Sensors  
Indirect type electrochemical HbA1c sensors work based on the measurement of FV or FVH, which is a form of enzymatic determination. According to the type of enzymes, indirect sensors are divided into FAO type, FPOX type, and MIC type sensors. These enzymes usually need to be processed with nanotechnology or imprinting technology to immobilize them on the electrode surface. The enzymatic determination of HbA1c entails the following three steps [78; 79; 80; 81; 82]:  
1. Enzymatic hydrolysis: HbA1c is proteolytically decomposed, and its \(\beta\)-chain glycated nitrogen terminus is hydrolyzed to produce FV or FVH.
2. Enzymatic catalysis: FV and FVH are oxidized to produce H\({}_{2}\)O\({}_{2}\) by FAO and FPOX. In addition, in indirect MIC type sensors, FV usually participates in a redox reaction with other electronic mediators and does not produce H\({}_{2}\)O\({}_{2}\).
3. H\({}_{2}\)O\({}_{2}\) determination: The produced H\({}_{2}\)O\({}_{2}\) can be determined electrochemically, and the results are further used to determine the content of HbA1c in samples. The process can be described by steps (1)-(4), as follows. \[\text{HbA1c}\xrightarrow{\text{protease}}\text{FV or FVH}+\text{amino acid}\] (1) \[\text{FV}+\text{O}_{2}+\text{H}_{2}\text{O}\xrightarrow{\text{FAO}}\text{H}_{2 }\text{O}_{2}+\text{Valine}+\text{D}-\text{Glucosome}\] (2) \[\text{FVH}+\text{O}_{2}+\text{H}_{2}\text{O}\xrightarrow{\text{FPOX}}\text{H}_{2 }\text{O}_{2}+\text{Valine histidine}+\text{D}-\text{Glucosome}\] (3) \[\text{H}_{2}\text{O}_{2}\to 2\text{H}^{+}+\text{O}_{2}+\text{H}_{2} \text{O}\] (4)
# New Challenges in (Bio)Analytical Sample Treatment Procedures for Clinical Applications
## 3 Sample Preparation Techniques for Diagnostic Purposes
### Dispersive Liquid-Liquid Microextraction (DLLME)  
Dispersive liquid-liquid microextraction (DLLME) is a type of sample preparation based on the liquid phase microextraction (LPME) technique in which the target analytes are extracted from an aqueous solution with small volumes of extraction solvent. In DLLME, the dispersion procedure increases the extraction kinetics by enlarging the exposure surface between the sample and the extractant, the emulsion was centrifuged, and the extractant was isolated. This technique showed several advantages, such as easy operation, low sample volume, and rapid processing with high recovery without the necessity of any appliance [2].  
[PERSON] et al. validate an easy method of sample preparation with a high efficiency based on ultrasound-assisted ionic liquid dispersive liquid-liquid microextraction (UA-IL-DLLME) for simultaneous determination of neurotransmitters (NTs), such as \(\gamma\)-aminobutyric acid (GABA), acetylcholine (Ach), and glutamic acid (Glu) in mild cognitive impairment, mild dementia, and moderate dementia patients' urine samples. The analysis of clinical samples showed that some NTs presented significant differences in different dementia stages. This method showed many advantages, such as selectivity, stability, sensitivity, and simple procedure of sample preparation. Thanks to these characteristics, the validated strategy of preparation and analysis might be used also with different types of biological samples [47]. Table 2 represents a summary of microextraction techniques used for diagnostic purposes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Target analytes** & **Matrix** & **Extraction Technique** & \multicolumn{2}{c}{**Analytical Characteristics**} & **Ref.** \\ \hline Dopamine (DA), & & DA: LOD 0.59 \(\mu\)g L\({}^{-1}\), LOQ 0.81 \(\mu\)g L\({}^{-1}\) & \\ Serotonin (5-HT), & Human urine & SPME-GC-QqQ-MS & 5-HT: LOD 0.38 \(\mu\)g L\({}^{-1}\), LOQ 0.74 \(\mu\)g L\({}^{-1}\) & [40] \\ Norepinephrine (NE) & & & NE: LOD 13.5 \(\mu\)g L\({}^{-1}\), LOQ 21.3 \(\mu\)g L\({}^{-1}\) & \\ Homovanillin acid (HVA), & & & HVA: LOD 1.3 \(\mu\)g L\({}^{-1}\), LOQ 2.7 \(\mu\)g L\({}^{-1}\) & \\ vanylmandic acid & & & VMA: LOD 0.046 \(\mu\)g L\({}^{-1}\), LOQ 0.063 \(\mu\)g L\({}^{-1}\) & \\
5-hydroxyindoacetic acid & & & 5-HIAA: LOD 24.3 \(\mu\)g L\({}^{-1}\), & \\ (5-HIAA) & & & & LOQ 49.6 \(\mu\)g L\({}^{-1}\) & \\ VOCs & Human Breathe & MESI-GC & & LOD Acetone 0.4 \(\mu\)g/L & \\  & & & & LOD Ethanol 0.5 \(\mu\)g/L & \\ F2-isoprostanes & Human plasma & & SPE-GC-MS & LOD (plasma) 0.037 ng/mL & \\  & Urine & & & LOD (urine) 0.007 ng/mg & \\  & LTB4 & Human urine & MEPS-UHPLC-PDA & LOD 0.37 ng/mL & \\  & & & & LOQ 1.22 ng/mL & \\ Metanephrine (MN) & & & & NM LOD 12.4 pg/mL & \\ Normetanephrine (NMN) & & & MEPS-HILIC-MS/MS & NMN LOD 12.3 pg/mL & \\ \hline \end{tabular}
\end{table}
Table 2: Microextraction techniques used for diagnostic purposes.
## 4 Simulations, Results and Discussion
### Equivalence Among Complex Wald, Rao Test and GIRT for Linear Model  
When the unknown parameter is present in the covariance matrix, the structure of the FIM does not attain the special form that allows one to use the Reduced Complex Wald Test and the Reduced Complex Rao Test [19]. One such case is the problem of testing the bandedness of the covariance matrix discussed above. As a counter example, in this section, we show the equivalence between the Complex Rao Test and the Complex Wald Test for an important case of general practical interest--the complex Gaussian linear model. A large number of signal processing, detection and estimation problems like radar signal processing and communications can be represented by a linear model, and hence it is of practical importance to discuss the topic.
# Computer-Generated Holography Methods for Data Page Reconstruction Using Phase-Only Medium
## 3 Methods for Synthesizing Phase Fourier CGH of Data Page  
The impulse response of an optoelectronic coherent system can be formed by modulation of different types. Phase modulation has the following advantages:  
1. Holograms implemented with the phase medium can have high diffraction efficiency values [46];
2. The most perspective SLMs with high spatial resolution and short response time (for example, ferroelectric SLM [28]) are purely phase devices;
3. Phase holograms can be realized using different holographic materials such as photopolymers and photo-thermo-refractive glasses or printed as surface reflective or transmissive holograms.  
The first step in the Fourier CGH synthesis process is calculating the object beam model in the hologram plane [29]. Direct calculation using a point-cloud model is a standard in the field of 3D objects and 3D scene visualization [47]. However, this approach requires either a lot of calculations or an extension of system random access memory (RAM). In the case of plane objects, such as holographic data storage pages, implementing scalar diffraction theory integral transformations using fast Fourier transformation algorithms can significantly speed up the calculation process. In the case of Fourier CGH object beam model can be obtained using a simple equation:  
\[A_{O}(\xi,\eta)=\mathcal{F}^{-1}[a_{O}(x,y)](\xi,\eta) \tag{3}\]  
where \((x,y)\) are the coordinates in the data page plane, \((\xi,\eta)\) are the coordinates in the hologram plane, which is the Fraunhofer plane for the \((x,y)\) plane, \(a_{O}(x,y)\)--data page model function, \(A_{O}(\xi,\eta)\)--object beam model function, \(\mathcal{F}^{-1}[](\xi,\eta)\)--Fourier transformation operator from the \((x,y)\) to \((\xi,\eta)\) plane. In the common case \(a_{O}(x,y)\) and \(A_{O}(\xi,\eta)\) are complex functions of spatial coordinates. While the object beam function is modeled numerically, there is no problem with object model complex function implementation. The main problem is the encoding of object beam function in the hologram plane \(A_{O}(\xi,\eta)\) onto the phase-only fringe pattern function.  
The next step in the CGH obtaining process is diffraction pattern synthesis, the process aimed at converting the object beam complex model onto the fringe pattern model with the a priori specified modulation characteristic of a holographic medium. Within the variety of methods for calculating the phase Fourier CGH fringe pattern, the bipolar intensity and double-phase coding methods are among the fastest. Further, the mathematical descriptions of these methods' implementation are presented.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions
### Fast Learning Process in Highly Dynamic D2D Networks  
The implementation of ML-based algorithms requires fast speed learning, especially for fast-moving devices. Additionally, suitable models for high channel dynamicity should be learned and updated. However, it is not always achievable to model in practice, which forms a bottleneck in D2D communication networks, especially for high-speed trains (HST) and vehicles. In mmWave D2D, due to the inherent complexity of the BT algorithms, proposing an optimal/sub-optimal ML-based algorithm with fast inference time is a challenging task, especially if we take into consideration different D2D use cases in Figure 1.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 6 Simulation Results Discussion
### BER Performance  
As shown in Figure 10, compared with the conventional OTFS system, our proposed transmission mechanism will not reduce the bit-error rate of the legal receiver, [PERSON]. Compared with the traditional OFDM system, under the same signal-to-noise ratio, the proposed transmission mechanism can significantly increase the bit-error rate difference between [PERSON] and [PERSON]. With the increase in the signal-to-noise ratio, the more obvious the bit-error rate difference becomes, and the more obvious the superiority of our proposed transmission mechanism becomes.  
As shown in Figure 11, because the illegal eavesdropper, [PERSON], cannot extract the channel information between [PERSON] and [PERSON], it cannot extract the correct secret key and thus cannot crack the matrix replacement process and cannot perform correct demodulation, so the bit-error rate is close to 0.5. However, due to the reciprocity of the channel, the legitimate receiver, [PERSON], can extract the correct secret key and demodulate correctly, so the transmission mechanism we propose will not affect [PERSON]'s bit-error rate. For the traditional OTFS system, since the eavesdropper, [PERSON], knows the modulation matrix, the bit-error rate is much lower than 0.5 through correct demodulation. In the scheme using the chaotic algorithm as the key generation algorithm, there is only one source of the initial value, so the key is not sensitive enough to the channel change; though the bit-error rate is still high, it is less than 0.5. To sum up, our proposed transport mechanism has superior security performance.  
Figure 11: BER for legal receiver and illegal eavesdropper.  
Figure 10: BER for different schemes.
## 4 Results and Discussion
### Detector-Level Experiment Results  
#### 4.2.1 Detector-Level Bench Stability Experiment Results  
A detector-level bench stability experiment was performed first to verify the temperature control effect for key components and the stability of the detector radiation response. In this sub-experiment, the circulating water-cooling temperature was set to \(-15\)\({}^{\circ}\)C, the temperature controller of the detector was set to \(-65\)\({}^{\circ}\)C to suppress the interference of dark current and background radiation response as much as possible, the integral time of the digital source table was set to 50 ms, and the light source was continuously observed for approximately 7 min. After dark count deduction and averaging, 55 effective response currents of the detector were obtained according to the rotation sequence of the  
Figure 11: Results of G\({}_{\text{ins}}\)(T). This instrument-level experiment determined that the responsivity was reduced by approximately 1.3% when the PSAC operated on orbit compared to the laboratory-based radiometric test, with around 35 \({}^{\circ}\)C PST difference.  
chopper. The experimental results are shown in Figure 12. The response current fluctuation was approximately 0.19%, and the temperature fluctuations of the filter, heat sink, and heat exchanger were less than 0.02 \({}^{\circ}\)C. The stability results suggest that the design of the detector-level experiment was reasonable, the temperature of each component was stable, and the electromagnetic interference in the experimental environment was effectively shielded. Thus, this bench provided a good experimental basis for conducting independent filter transmittance temperature experiments and independent detector responsivity temperature dependence experiments.
# Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis
## 3 Methodology
### Model Architecture  
This section primarily introduces the overall architecture of the DMMSA model to facilitate the reader's understanding. The overall architecture of the DMMSA model is depicted in Figure 3.  
Figure 3: The overall framework of the DMMSA model.  
In the single-modality feature extraction stage, we employ the pre-trained \(BERT\) model to extract text features. \(BERT\) is a pre-trained language model based on the transformer architecture. During pre-training, it leverages a vast amount of unsupervised text data to learn rich linguistic knowledge and semantic information. Therefore, compared to models such as RNN, LSTM, and GRU, it can encode text more effectively. For processing visual and audio modality data, we use the transformer model as the visual encoder and audio encoder in DMMSA. The overall formula is as follows:  
\[T=BERT(I_{t}) \tag{3}\]  
\[V=Transformer_{v}(I_{v}) \tag{4}\]  
\[A=Transformer_{a}(I_{a}) \tag{5}\]  
where \(T\), \(V\), and \(A\) represent the text, visual, and audio features extracted by the encoders, respectively. \(Transformer_{v}()\) represents the encoder for visual information, and \(Transformer_{a}\) represents the encoder for audio information.  
Next, we input the features from different modalities into the feature decomposition module to obtain modality-similar features and modality-dissimilar features. The feature decomposition module consists of six parallel project modules, each of which includes a fully connected layer with a Tanh activation function and layer normalization [5]. The formulas are as follows:  
\[T_{s}=\sigma(Linear_{s}^{T}(T)) \tag{6}\]  
\[T_{d}=\sigma(Linear_{d}^{T}(T)) \tag{7}\]  
\[V_{s}=\sigma(Linear_{s}^{V}(V)) \tag{8}\]  
\[V_{d}=\sigma(Linear_{d}^{V}(V)) \tag{9}\]  
\[A_{s}=\sigma(Linear_{s}^{A}(A)) \tag{10}\]  
\[A_{d}=\sigma(Linear_{d}^{A}(A)) \tag{11}\]  
where \(T_{s}\), \(V_{s}\), and \(A_{s}\) represent the modality-similar features extracted by the mapping modules; \(T_{d}\), \(V_{d}\), and \(A_{d}\) represent the modality-dissimilar features; \(\sigma\) represents the Tanh activation function; and \(Linear\) represents the fully connected layer. The feature decomposition module decomposes features from different modalities into modality-similar and modality-dissimilar features. This is attributed to the incorporation of two tasks, single-modality sentiment classification and contrastive learning, in the training process, which helps the model effectively perform feature decomposition. The details are discussed in the section on multi-task learning.  
Finally, we concatenate the extracted modality-similar and modality-dissimilar features and input them into a multi-layer perceptron (MLP) network to obtain the final sentiment prediction result. The formula is as follows:  
\[y^{*}=MLP([T_{s};T_{d};A_{s};A_{d};V_{s};V_{d}]) \tag{12}\]  
where \(y^{*}\) represents the predicted result and \";\" represents concatenation.
## 3 Research Methodology
### Dataset Generation
#### 3.1.2 Network Testbed for Dataset Generation
During the packet's storage phase, the router conducts an examination of the packet header to detect any alterations made to the number of \"Flow Duration\" and \"forward packets\". Packet forwarding occurs after the router has conducted header inspection and made a decision regarding the appropriate forwarding path. At this stage, the router has two options: it can either forward the packet in its original form to the intended destination, or it can undertake any required actions for packets that have been modified. The router has the capability to record the results of its inspection and any actions it has taken in relation to altered packets, with the purpose of conducting subsequent analysis or auditing. This particular scenario holds potential value within the realm of network security, specifically in the areas of intrusion detection and the monitoring of potentially suspicious activities. That is why machine learning is crucial in order to utilize the functions capable of recognizing potential modifications to headers and implementing suitable measures to safeguard the integrity and security of the data being transmitted.
# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 3 Results
### Geographical Homogeneity  
Following the assessment of temporal stability, an assessment of geographical stability was carried out, again at fixed wind speed conditions (U10 \(\sim\)7 m/s). The analysis presented here considers a single transmitter-receiver pair, but similar results were found using other configurations. Figure 3 top shows a global map of NBRCS reported by the bistatic radar consisting of SVN70 (GPS transmitter) and FM03 (CyGNSS receiver). A well-defined geographical pattern of NBRCS is found around the globe, repeating every 90 degrees in longitude, which cannot be directly attributed to an origin of geophysical nature. A map of the average incidence angle (the vertical angle between the signal path and the line perpendicular to the surface at the specular point) between the transmitter and receiver reveals a similar pattern to NBRCS (Figure 3 bottom). Similar longitudinal NBRCS patterns were found in other transmitter-receiver pairs, albeit varying in magnitude and location. Given the strong correlation between the spatial patterns of NBRCS (Figure 3 top) and the corresponding incidence angle (Figure 3 bottom), it can be speculated that most of the observed NBRCS spatial inhomogeneity can be attributed to the residual dependence of NBRCS on geometry. Thus, further investigation of the relationship between NBRCS and geometry was conducted.  
The residual dependence of NBRCS on the incidence angle was examined (Figure 4) for each individual CyGNSS unit and averaged across all GPS transmitters. Overall, as found above, the consistency between different CyGNSS units is good, with similar sensitivity to viewing geometry. However, NBRCS variability with incidence angle exceeds 2 dB, for all CyGNSS receivers. The NBRCS relationship shows a large drop at incidences beyond 50\({}^{\circ}\) and a smaller drop is observed in the range of 5\({}^{\circ}\)-15\({}^{\circ}\).  
Finally, the dependence on the azimuth angle (the angle, in the horizontal plane, of the specular point relative to the receiver's path of travel) is assessed, with results (Figure 5) showing a non-negligible residual effect in NBRCS of up to \(\sim\)1 dB for all FMs. In light of these findings, it can be concluded that the observed geographical NBRCS biases can be primarily attributed to residual sensitivity to both azimuth and incidence angle.
## 3 Results  
Table 1 shows between-group and between-time-point differences. A significantly (\(p<0.05\)) greater performance was found in both groups (i.e., group effect) in NDR (\(n^{2}=0.41\), \(p=0.001\)), curve sprinting to the right (CSR) (\(n^{2}=0.12\), \(p=0.049\)) and to the left (CSL) (\(n^{2}=0.43\), \(p<0.001\)), and the V-cut test (\(n^{2}=0.24\), \(p=0.004\)). A significantly (\(p<0.05\)) greater performance was found at all time-points (i.e., time effect) in 10 m (\(n^{2}=0.56\), \(p<0.001\)), CSR (\(n^{2}=0.74\), \(p<0.001\)), CSL (\(n^{2}=0.52\), \(p<0.001\)), and V-cut (\(n^{2}=0.57\), \(p<0.001\)). A significantly greater performance (\(p<0.05\)) was found in both groups at different time-points (i.e., group x time interaction) in 10 m, CSR, CSL, and V-cut, while NDR and NDR only reported significance (\(p<0.05\)) in group A. Specifically, post-hoc comparisons showed significant differences (\(p<0.05\)) between time-point 1 and 2 and between time-points 1 and 3 in all variables except for NDL (1 v 3) in group A. Group B achieved significant differences (\(p<0.05\)) between time-points 1 and 3 and 2 and 3 in 10 m (\(p=0.010\) and 0.009), CSR (\(p<0.001\)), CSL (\(p=0.002\) and 0.010), and V-cut (\(p<0.001\)).  
A significant between-group difference (\(p<0.05\)) (i.e., time x group interaction) was reported at time-point 1 in NDR (\(n^{2}=0.14\)), CSR (\(n^{2}=0.13\)), CSL (\(n^{2}=0.43\)), and V-cut (\(n^{2}=0.24\)) in favor of group B, except for NDR. As such, NDR showed a significant difference at time-point 2 (\(p<0.001\), \(n^{2}=0.35\)) in favor of group A. On the other hand, group B showed a significantly better performance in CSR (\(p<0.013\), \(n^{2}=0.19\)), CSL (\(p<0.047\), \(n^{2}=0.13\)), and V-cut (\(p<0.001\), \(n^{2}=0.46\)) at time-point 3.  
Small to moderate relationships (r = \(-0.23\) to 0.45) were found between those relative changes in the navicular drop test (both sides) and the multidirectional sprinting test changes. When those relationships were individually grouped and analyzed, navicular drop improvements in both the right and left were largely (r = 0.53 to 0.54) related to curve sprinting to the left side enhancements in group A between the pre- and mid-test.
## 3 Proposed Coordinated Control Method
### Strategy 2: Dynamic Offset Adjustment Strategy
#### 3.3.1 Determining Offset Adjustment  
First, based on the signal phase status of the downstream intersection's coordinated phase (westbound through) at the end of each cycle of the upstream intersection, estimate the number of queued vehicles for westbound through traffic in the transition section for the current cycle among the merging traffic streams of westbound through, northbound left turn, and southbound right turn. Second, considering the arrival of traffic from the upstream intersection in the next cycle for the westbound through merging direction, determine the adjustment offset for the next operational cycle of both intersections. Finally, calculate the difference between the offsets before and after adjustment to obtain the adjustment amount. The steps of this adjustment are mathematically presented in Appendix A.