esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluations
### Comparison of the Execution Time of the User Location Reset Method  
This measurement evaluates the proposed system's impact on the time required to successfully reset the user location. It measures the time taken to execute the _user location reset_ method until it produces cleaned text from the _sign image_ and resets the user location using the room coordinates retrieved from the _database matching function_. The evaluation was conducted in two scenarios, as previously described. The measurement process begins with the user positioned 1 m from the door, scanning the _Sign Image_ using _INSUS_. If the method fails to correctly reset the user's location, the user moves closer to the _sign image_ to rescan it. It was intended to produce more accurately cleaned text. This process is repeated until the correct text is successfully retrieved and the user's location is accurately reset. The results of this comparison are shown in Table 8.  
Due to _YOLOv8_'s ability to automatically isolate the _sign image_, the incorporation of _YOLOv8_ and _PaddleOCR_ achieved better performance, since it allows the _PaddleOCR_ to recognize and extract only the room number from the isolated image rather than the whole image. In addition, the approach that only utilized _PaddleOCR_ requires more time to extract accurate information as it produces inaccurate cleaned text. Thus, the user needs to move closer to the _sign image_ and prolong the execution time. Compared to the proposed approach, it can detect the _sign image_ from the input image and automatically isolate it, therefore resulting in a faster execution time.
# Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets
## 5 Discussion
### Curse of Dimensionality  
The curse of dimensionality presents a significant obstacle within LDP. LDP aims to protect privacy by injecting noise into individual data points before sharing. However, as data dimensionality grows, the volume of noise needed to obscure true values while upholding the same privacy standards increases dramatically. This results in a loss of data utility.  
Our experiments depicted in Figure 5 illustrate a noticeable trend of declining performance, as evidenced by an increased AVD, with higher \(k\) values. This highlights the trade-off between accuracy and dimensionality, underscoring the challenges associated with the curse of dimensionality. Notably, our proposed method exhibits lower AVD values, indicating superior data utility compared to LOPUB and LOCOP, especially for datasets with an AAR close to zero.
# Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments
## Materials and Methods
### Bistatic GB-SAR Focusing Algorithms  
#### 2.2.1 Range Compression  
For a single radar acquisition, microwave signal is transmitted to the target and the returning signal is received while the antennas are in a stationary position at a certain location on a rail. VNA operates in a stepped-frequency sweep mode so that the returned signal is obtained as a function of frequency \(f\),  
\[S(f)=H(f)G(f), \tag{1}\]  
where \(H(f)\) is a box function with a center frequency of \(f_{c}\) and a bandwidth of \(B_{R}\). \(G(f)\) is the scattering coefficient of a target as a function of frequency. Inverse Fourier transform of (1) results in the range-compressed signal as follows.  
\[s(t)=h(t)*g(t), \tag{2}\]  
which is represented as a convolution (\(*\)) of an impulse response \(h(t)\) and a scattering coefficient of targets \(g(t)\). In case of a point target where \(g(t)=\delta(t-t_{0})\), \(s(t)\) is a sinc function with range time resolution \(\delta t=1/B_{R}\), and equivalently, range resolution \(\delta r=c/(2B_{R})\). This comprises a conventional monostatic radar [10].  
For a bistatic case with a baseline of \(B\), the iso-range planes are ellipsoids with two focal points at the antennae locations. In a range-azimuth plane in Figure 1a, bistatic range (\(r_{B}=\sqrt{r^{2}-(B/2)^{2}}\)) is the semi-minor axis of an iso-range ellipse where monostatic range \(r\) is the semi-major axis. The range resolution of such bistatic radar can be defined by the derivative of bistatic range as follows.  
\[\delta r_{B}=\delta r\frac{1}{\sqrt{1-\left(\frac{B}{2r}\right)^{2}}} \tag{3}\]  
The bistatic range resolution is always poorer than that of monostatic case [14], and converges to the monostatic case if \(B=0\) or at far range where \(B/r\) becomes negligibly small, as shown in Figure 2. Note that this degraded range resolution effect of the bistatic  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**GB-SAR Parameter** & **Symbol** & **Value** \\ \hline Center frequency & \(f_{c}\) & 15.95 GHz \\ Bandwidth & \(B_{R}\) & 1.6 GHz \\ Range sampling number & \(N_{R}\) & 2001 \\ Nominal range resolution & \(\delta r\) & 9.4 cm \\ Antenna aperture & \(L\) & 9 cm \\ Rail scan length & \(X_{max}\) & 6 m \\ Azimuth step & \(\Delta x\) & 3 cm \\ Azimuth sampling number & \(N_{x}\) & 201 \\ Nominal azimuth resolution & \(\delta x\) & 4.5 cm \\ Baselines & B & 0.27, 0.5, 1 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters for a bistatic GB-SAR experiment.  
configuration occurs at the very near range only and becomes less than 103% of a monostatic case at a range of \(R=2\) m with \(B=1\) m. This effect would be negligible for airborne and satellite SAR operations when \(B/r\) is relatively small.
## 2 Materials and Methods
### Dataset Establishment of Feature Parameters  
The time-domain and the frequency-domain features are extracted using a sliding window method with a certain overlap. Figure 3 shows the surface EMG data of the rectus femoris muscle in the continuous squatting and standing exercise mode. The length of the sliding window is 256 ms, and the sampling interval between the upcoming and the previous window is 50 ms. Finally, the time and frequency-domain features of the surface EMG signal are extracted.  
The time-domain and the frequency-domain features are extracted using a sliding window method, as shown in Figure 4, with some overlap; i.e., the time series of surface EMG data is framed according to the specified unit window length. According to the time-domain and the frequency-domain feature calculation Formulas (1)-(7) in Section 2.3, the time-domain and the frequency-domain feature indexes in the frame are calculated as the input of the subsequent classifier model. As the fast Fourier transform, required for the frequency-domain feature extraction, requires the length of the data raised to the power of 2, the subsequent window length is also taken to the power of 2. Here, the length of the sliding window is, primarily, selected as 256 ms, the sampling interval between the next window and the previous window is 50 ms, and the time-domain and the frequency-domain characteristics of the surface EMG signal are extracted.  
The changes in the time-domain and the frequency-domain feature parameters of the surface electromyography on rectus femoris, during the squatting-standing motion mode extracted according to the sliding window method, are shown in Figure 5. It can be seen from the figure that the first four time-domain features have a large similarity in the shape and phase of the curve, especially the root mean square and the mean absolute values. Therefore, the mean absolute value is removed from the established feature parameter set to achieve the dimensionality reduction.  
Figure 4: Sliding window method to extract the feature dataset.  
Figure 3: Surface electromyography of the rectus femoris in squatting-standing motion mode.
# Food Allergies: Immunosensors and Management
## 7 Wheat Allergy
### Immunosenor  
For patients with wheat FAs, the proteins and peptides with IgE reactivity have been investigated in non-denaturing RAST-based binding analyses. For a soluble and monomeric protein, alpha-purothionin is expressed in the prokaryotic (EcTri a 37) and the eukaryotic system (BvTri a 37). However, circular dichroism assay has demonstrated that EcTri a 37 has an unfolded protein and BvTri a 37 has a folded protein. These two proteins show comparable IgE-reactivity and the epitope mapping shows the existence of sequential IgE epitopes in the N-terminal basic thionin domain (peptide1) and in the C-terminal acidic extension domain (peptide3 and peptide4). Wheat has given rise to abnormal reactions, although the inducing factors have not been determined so far. These reactions are primarily induced by kernel proteins, including both gluten and non-gluten proteins. Among these non-gluten proteins,\(\alpha\)-amylase/trypsin inhibitors (ATI) have been included in baker's asthma and in Non-Celiac Wheat Sensitivity (NCWS). The characterizations of three transgenic lines have been obtained from the bread wheat cultivar Bobwhite silenced by RNAi in the three ATI genes, including _CM3_, _CM16_ and _0.28_.  
Responses to allergy are displayed in different ways, according to the pathways of exposure, i.e., ingestion (FA, either IgE-related or non-IgE-related), respiratory (baker's asthma), and skin contact (contact urticaria). The affronting allergens encompass the gliadins, glutenins, and other non-gluten proteins. FA of wheat is typically IgE-related, influences around -0.5% of children and mostly resolves by adulthood [93]. Symptoms are classically severe after the ingestion of wheat for several minutes and increase the risk of fatal anaphylactic response. Some rare non-IgE-related gastrointestinal situations combined with ingestion of wheat hypersensitivity have also been studied and showed eosinophilic esophaggitis and food protein-induced enterocolitis syndrome. However, the proteins of wheat correlated with these dysfunctions have not been clearly identified [94]. Wheat flour (_Trticulum aestivum_) has opportunities to be contaminated by different routes according to the Brassicaceae or during grain harvests, storage, and processing. In many cases mustards, such as _Brassica nigra_, _Brassica juncea_ and _Sinapis alba_, are most common allergenic species. Usually, in laboratories, the assays of food quality have adopted an ELISA approach to examine the presence of mustard species and this approach has shown cross-reactivity with other non-allergenic species of _Brassica napus_.
## 3 Network Model
### Network Lifetime  
The network lifetime \(t_{n}\) is defined as the number of time periods over which the information provided by the network is useful for an application. We formulate this concept based on a threshold sensitivity area \(co_{th}\), that is  
\[t_{n}=||\{t>0\in\tau:A(t)>co_{th}\}||,\quad\text{for $\tau=0,1,2,\dots$}, \tag{8}\]  
where \(||\cdot||\) is the cardinal of a set and \(\tau\) is the set of time periods.
# Development Status of Key Technologies for Optoelectronic Integrated Circuit Manufacturing
## 1 Introduction  
In 1972, the California Institute of Technology in the United States proposed optoelectronic integration [1]. With the continuous development of technology, optoelectronic integrated circuits are being considered for use in photonic technology and microelectronics technology to integrate photonic devices and electronic devices on the same substrate [2; 3; 4]. In 1979, [PERSON] and [PERSON] achieved the monolithic integration of a semiconductor laser and a field-effect transistor (FET) drive device [5]. In 1980, [PERSON] from Bell Labs realized the monolithic integration of a PIN photodiode and FET [6]. In 1982, [PERSON] et al. achieved the monolithic integration of light-emitting diodes (LEDs), detectors, waveguides, resistors, and FETs in the GaAlAs/GaAs multilayer structure [7]. Since then, optoelectronic integrated circuits have been developed rapidly, and their constituent materials, application processes, and integrated devices have changed frequently, leading to several extensive application fields (Figure 1) of national economic and social development, including network communication, computer, consumer electronics, automotive electronics, transportation, military, and medical industry [2; 8; 9; 10; 11; 12; 13; 14; 15]. With the increasing trend of optoelectronic integration, optoelectronic integrated circuits have attracted considerable research attention for extending Moore's law [16; 17; 18].  
Optoelectronic integrated circuit technology is based on optoelectronic technology and microelectronic technology and is used to control optoelectronic integrated circuits, which can control not only the direction of the current within each integrated component, but also the direction of photons to ensure that they flow in a certain direction [19]. Using GaAs, InP, and Si [20; 21; 22], novel materials and processes have been developed to fabricate light, stable, low-power-consumption, and fast-operation optoelectronic integrated circuits [23]. This study elaborates on the corresponding technological progress from the design, manufacturing, and packaging of the optoelectronic integrated circuit technology chain.
## 3 Proposed Maturity Framework
### Security Requirements  
As illustrated in Figure 1, the proposed maturity assessment model has 15 requirements. The 15 requirements followed are categorised as 'General Security Requirements'. The General Security Requirements, which are the foundation of the model, is based on cybersecurity best practices such as the CIS Controls, NIST Framework, etc. The 15 requirements were divided into 3 groups. IDENTIFY (I), PROTECT & DETECT (P), and RESPOND & RECOVER (R). It should be noted that the DETECT controls of NIST were merged into our protect & detect requirements in order to keep our model lightweight.  
Requirements \(I1\)-\(I4\) fall under Identify, Requirements \(P5\)-\(P13\) fall under Protect & Detect, whilst Requirements \(R14\)-\(R15\) fall under Respond & Recover. All the requirements of the category Identify are necessary for the facilitation of the understanding of the business and operational ecosystem of the organisation. All the requirements of Protect & Detect are necessary in order to detect incidents and protect all assets supporting the services of the organisation i.e., (people, procedures, and technologies). Lastly, all the requirements of Respond & Recover are necessary in order to respond and manage an information security incident that may have the ability to influence the provision of the services offered by the HEIs. Finally, it should be noted that some requirements do have sub-requirements (See Figure 2).
# Analysis of Electromagnetic Field Characteristics of Wave Glider
## 2 Analysis of Motion Characteristics of Wave Glider
### Structure and Motion Characteristics of Wave Gliders
\(\mathbf{i},\mathbf{j},\mathbf{k}\) is defined as the unit vector in the \(x,y,z\) axis direction. Figure 2: Principles of wave glider motion. The white arrow indicates the direction of movement, \(a\) indicates fluctuation amplitude. The geomagnetic field is a stable magnetic field, and its three components at the moving position are expressed as:  
\[\mathbf{F}=F[(\cos\gamma\cos\theta)\mathbf{i}-(\cos\gamma\sin\theta)\mathbf{j}+(\sin\gamma) \mathbf{k}] \tag{1}\]  
Assuming the glider's motion speed is:  
\[\mathbf{V}=-a\omega(i\cdot i+\mathbf{k})\textit{exp}(i\omega t-irx-rz) \tag{2}\]  
In the formula, \(\omega\) is the angular frequency; \(r\) is the angular wave number, \(r=\omega^{2}/g\), where \(g\) is the gravitational acceleration; \(i^{2}=-1\). The attitude change of a wave glider is a slow process, and the azimuth angle change is very small in the short term. Therefore, the angle between the measured electrode direction and the magnetic north pole can be regarded as a constant value in the short term. To simplify the calculation, assuming that the \(x\)-axis direction of the electric field is consistent with the direction of motion, there is a correlation between \(\alpha_{2}\) and the velocity \(v_{x}\) along the \(x\)-axis direction and the vertical velocity \(v_{z}\), which can be expressed as follows:  
\[\alpha_{2}(k)=f(v_{x},v_{z})=\sum_{n=0}^{N-1}\left[c_{n}v_{x}(k-n)+c_{n}^{ \prime}v_{x}(k-n)\right] \tag{3}\]  
In the formula: \(k\) represents the discrete time; \(c_{n}\), \(c_{n}^{\prime}\) is \(\alpha_{2}\) and \(v_{x}\), respectively. The coefficient between \(v_{z}\); \(N\) is the duration that needs to be accumulated. According to [PERSON]'s law of electromagnetic induction, the three components of the electric field \(\mathbf{E}=-\mathbf{V}\times(\mathbf{F}+\mathbf{H})\) can be obtained, where \(\mathbf{H}\) is the wave-induced magnetic field. Since \(\mathbf{F}\gg\mathbf{H}\), this equation can be simplified as follows:  
\[\mathbf{E}=-\mathbf{V}\times\mathbf{F} \tag{4}\]
## 4 Numerical Simulation Method
### The Mathematical Method  
The simulation parameters were based on actual experimental values of the gas flow standard device: the medium was air with the pressure 360 kPa, while the air was treated as the incompressible flow [25; 31]. The air density was 4.2 kg/m\({}^{3}\), and viscosity was 1.81 \(\times\) 10\({}^{-5}\) kg/(m\(\cdot\)s). The five flow rate points selected in experiments of SN device were 2.9, 5.6, 7.6, 10.7, and 15.1 m/s, while they were 1, 5, 10, and 15 m/s, respectively with the Reynolds numbers 2.3 \(\times\) 10\({}^{4}\), 1.1 \(\times\) 10\({}^{5}\), 2.3 \(\times\) 10\({}^{5}\), and 3.5 \(\times\) 10\({}^{5}\) corresponding to the four flow points in simulation. So, all the conditions of flow were within the turbulence region.  
Figure 11: Diagram of plate flow conditioner.  
Figure 12: The grid in the vicinity of the FC and boundary layer. (**a**) The boundary layer grid; (**b**) The greatly increased grid of FC; (**c**) Grid at the connection region near FC.  
The governing equations were represented by the conservation of mass and momentum, which were averaged using the well-known procedure introduced by [PERSON] (1964). The Reynolds averaged Navier-Stokes \(k\)-\(\varepsilon\) turbulence model was one of the most widely used models in the turbulence simulation due to its high stability, economy, and accuracy. Compared with the standard \(k\)-\(\varepsilon\) model, the Renormalization-group (RNG) \(k\)-\(\varepsilon\) model could better respond to the influence of transient flow and streamline bending, and the model had better performance for a low Reynolds number and near-wall flow. So, the RNG \(k\)-\(\varepsilon\) turbulence model was adopted. Since steady-state incompressible flow was considered in this work, the RANS equations could be written as:  
\[\Rightarrow\] mass conservation \[\
abla\cdot\mathrm{U}=0 \tag{6}\]  
\[\Rightarrow\] momentum conservation \[\
abla\cdot(\mathrm{U}\otimes\mathrm{U})=-\frac{1}{\rho}\
abla p+\
abla\cdot 2 \mu\mathrm{D}-\
abla\cdot\mathrm{R} \tag{7}\]  
where \(\mathrm{U}=[\mathrm{U},\mathrm{V},\mathrm{W}]\) was the average velocity vector, \(\mathrm{D}=\frac{1}{2}\Big{(}\
abla\mathrm{U}+\
abla\mathrm{U}^{T}\Big{)}\), and \(\mathrm{R}=(\overline{\upmu\otimes\upmu})\) represented the well-known turbulent or Reynolds stress tensor, due to fluctuating velocities \(\upmu=[\upmu,\upupsilon,\upupsilon,\upupsilon]\).  
The RANS \(k\)-\(\varepsilon\) model was found to give satisfactory results for the simulation of low-level disturbance and some flow conditioners (1963, 1975, 1976).
## 2 Data and Method
### Methods
#### Dynamic Cluster K-Means Method  
To delineate rain areas over the Tibetan Plateau with a sparse rain gauge network, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [22] attempted to delineate rain/no rain areas with the TBs measured by TMI using an unsupervised classification method, the dynamic cluster K-means (termed as Kmean, hereafter). In this study, the performance of this method was assessed as a comparison of the new rain area delineation method proposed by this study. In the Kmean method, several categories were first defined, and the centers were calculated by averaging the values of similar samples. Then, the distance between a sample and a category center was calculated. For each category, the square sum of the distance between this category and the neighboring samples is called the classification error. Subsequently, the Kmean method attempts to make the classification error as small as possible by assigning each sample to the nearest category. In this step, the distances between a sample \(i\) and all category centers were first calculated, and then, the sample \(i\) was assigned to the category with the smallest distance. At the same time, the category center is re-calculated. This process iterated until the smallest classification error was obtained. The advantage of this method is that ground observations are not necessary. According to [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [22], the TBs at 19.4, 21.3, 37.0 and 85.5 GHz were used as the inputs of the Kmean method to delineate rain/no rain areas. In this study, the Kmean method was conducted using the \"kmeans\" toolkit provide by the MATLAB 2009a software.
# Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery
## 3 Stimuli-Responsive Polymeric Nanosystems
### Exogenous Stimuli-Responsive Polymeric Nanosystems
#### 3.2.3 Ultrasound-Responsive Polymeric Nanosystems  
Owing to its deep tissue penetration, noninvasiveness and good biocompatibility at low frequency, ultrasound (US) has emerging as a promising tool to trigger the drug release of nanocarriers with high spatiotemporal selectivity [161]. Great efforts have been made to develop biocompatible polymers that undergo degradation upon US treatment [162]. Recently, the concept of mechanophores-functionalities offers novel paths to prepare stress-responsive materials by incorporating the functional moieties into polymer materials [163; 164; 165], which leads to the study of US-responsive mechanolabile moieties [165]. For example, the copolymer PEO\(-\)THPMA derived from 2-tetrahydropyranyl methacrylate (THPMA) can be hydrolyzed upon treated with the high-intensity focused US (HIFU) [166]. Based on the property of this copolymer, [PERSON] et al. reported the HIFU-responsive nanosystems constructed from THPMA based amphiphilic block copolymer [167]. In addition, [PERSON] et al. reported ultrasound and redox-responsive copolymer micelles by functionalizing pluronic type copolymer with ester and disulfide bonds to impart the copolymer with UV response [168]. The copolymer micelles demonstrate a slow redox-triggered release and fast HIFU-triggered release property, and the cleavage sites of the two cases are deferent.
## 3 Results and Discussion
### Effect of Steeping the Barley Grains on DON Degradation by ACP
Hence, we believe that the reduction in degradation rate in steeped barley compared to the unsteeped grains could be due to the changes in surface properties of the steeped grains. In 10 min drying of the barley grains inoculated with DON in pure ACN, the solvent was completely evaporated. However, due to the higher boiling point of water compared to ACN, the water in ACN/Water (20:80 v/v) was not evaporated, but the ACN could be evaporated completely within 10 min. This means that the 10 min drying step would have allowed the ACN to evaporate, but some amount of water was still there. Consequently, we observed significantly (\(p<0.05\)) higher degradation rates of DON in treatment E compared to B, due to the presence of water. Comparison of the treatment C (with 100% ACN, 0% water) with treatment F (20 % ACN, 80% water) suggests the greater impact of ACN compared to water on the degradation of DON by ACP. The considerable effect of ACN on DON degradation under ACP could be observed by comparing treatments B and C. The degradation rate of DON increased from 36.3 to 66.3% by skipping the drying step and hence the presence of ACN. A greater degradation rate of DON in treatment F (no drying step) was observed in comparison to E (10 min drying), since the amounts of water and ACN were higher in treatment F, which contributed to the higher degradation of DON. Overall, the results of this experiment suggest the importance of drying step and hence the presence of water and ACN on the surface of the barley grains to increase the degradation rate by ACP; it seems that water contributes to the formation of greater amounts of ROS.