esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation Analysis and Results
### Microscopic Numerical Simulation
#### 4.1.2 Microscopic Numerical Analysis in the Vehicle Braking Scenario  
Initial state: all vehicles traveled at a constant initial speed, and the positions of Car 1 to Car 5 were 150 m, 120 m, 90 m, 60 m, and 30 m, respectively. Braking process: at time t = 0, the leading vehicle (Car 1) started to speed down with an intial deceleration \(-\)3 m/s\({}^{2}\) that decreased in magnitude to 0 until coming to a complete stop. Following the brake of Car 1, Car 2 to Car 5 decelerated one after another by the extended IDM model proposed in this paper. And record and plot the acceleration changes of the five vehicles at 0.1 s intervals. Figure 6a-f shows the acceleration variations of the five vehicles over time in each type of Car 5 in the braking scenario.  
Figure 5: The variations of vehicle acceleration over time with Car 5 being an RV (with each of four driver types) or an ACC or a CACC in the starting scenario.  
In the braking process, the difference between the maximum and minimum deceleration of Car 5 being an RV with each of four driver types is \(0.6558\) m/s\({}^{2}\), \(0.5181\) m/s\({}^{2}\), \(0.5804\) m/s\({}^{2}\), and \(0.7390\) m/s\({}^{2}\), respectively. It can be concluded from Figure 6 that the difference is smallest for the Type II driver and largest for the Type IV driver. The reason is that the Type II driver tends to be more skilled in driving and more steadily responsive to the braking of the front vehicle compared with the extraordinarily cautious Type IV driver. The difference between the maximum and minimum deceleration of Car 5 as an ACC vehicle is \(0.5737\) m/s\({}^{2}\), slightly lower than that with a standard driver; the difference is \(0.4540\) m/s\({}^{2}\) for Car 5 as a CACC vehicle, \(21.78\%\) lower than that for Car 5 as an RV with a standard driver, and \(20.86\%\) lower than that for Car 5 as an ACC vehicle. This means that when the fleet is disturbed by braking, in the case of Car 5 as a CACC vehicle other than an RV or an ACC vehicle, the fluctuation of speed can be reduced to a great extent.
## 6 Result and Discussion
### Food Classification  
Our method attained a high recognition rate for five food types (chips, cookies, nuts, pizza, and water) when 30 samples (1.5 s) were used. Our method is compared to the most similar published method [10, 11, 13, 19] in Figure 8, which clearly demonstrates that our method performs better than the previous method in recognizing all food types. Random forest, k-NN, and SVM are used to assess the effectiveness and efficiency of the two algorithms. The performance of the classifiers is evaluated based on precision, recall and the f-measure. The previous methods [10, 11] exhibited a moderate recognition rate because of their large sample window. We think that large windows average out short-duration swallowing events, and STFT cannot produce distinct statistical features.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Description** & **Performance** & **Limitation** \\ \hline A wearable system presented in [10, 11] to detect neck skin movement caused by ingestion. Nutrition intake monitoring system [19] consisting of a necklace similar to [10, 11] estimated meal volumes. The authors improved wearable system of [10, 11], and [19] by the addition of an accelerometer to decrease detection of false positive swallows [13]. The authors compared the performance of a microphone and piezoelectric sensor for swallow detection [3], when used separately. Our proposed method based on piezoelectric sensor recognized suitable count of food classes by exploiting an accurate flexible sensor with better data processing technique & Their method achieved maximum accuracy of 90\% for a small number of categories. & Binary classification \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance comparison between the proposed study and previous related studies.  
All three classifiers exhibited the highest classification for water over foods as water shares no common ingestion pattern with other food. Thus, the users' ingestion patterns for water were totally different, as can be seen in its generated waveform in Figure 4. The classifiers attained lower performance for cookies and pizza as instances belonging to these  
Figure 8: Food recognition performance: (**a**) Food recognition performance of the proposed method; (**b**) Food recognition performance of the previous method.  
foods were misclassified to each other. The misclassified or incorrectly classified foods may have common textural characteristics causing their incorrect classification.
## 3 Experiments  
In this paper, to verify the proposed micro-wear monitoring method for the tool in the machining of The milling experiment was carried out on a five-axis Demag CNC machining center (model: DMU80T); the linear displacement error was 0.001 mm, and the maximum spindle rotation concentricity was less than 0.02 mm, which met the requirements of fine milling. The workpiece is a square groove with a wall thickness of 5 mm, the inner wall size is 70 mm, and the groove depth is 10 mm, including 4 corners, which are corner I, corner II, corner III and corner IV in order of processing. The machining radius of each corner is 8 mm. The workpiece is directly fixed on the dynamometer, and the dynamometer is fixed on the machine tool through a vise. The workpiece material is selected from the most widely used titanium alloy TC4 in the industry. Table 6 is the material parameter attribute table. Due to the rapid wear and tear of titanium alloy materials on the tool, this experiment uses a special alloy tool for titanium alloy, model UTH0804. Table 7 shows the detailed parameters of the tool.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Material Composition**} & **Thermal Conductivity** & **Tensile Strength** & **Yield Strength** & **Elastic Modulus** & **Hardness (HB)** \\  & **(W**\(\cdot\)**m\({}^{-1}\cdot\)**K\({}^{-1}\))** & **(MPa)** & **(MPa)** & **(MPa)** & **(GPa)** & **(HB)** \\ \hline Ti-6 Al-4V & 8.37 & 993 & 830 & 114 & 36 \\ \end{tabular}
\end{table}
Table 6: Physical and mechanical properties of TC4 at ambient temperature.  
Figure 5: Multi-sensor information synchronous acquisition metal cutting condition monitoring platform. (**a**) Demaji five axis machining center; (**b**) Sensor layout; (**c**) Key phase sensor; (**d**) Multi-sensor signal synchronous acquisition instrument; (**e**) charge amplifier; (**f**) Collect the host.  
In this experiment, the spindle speed of the corner milling is 5000 r/min, the converted cutting speed is 125.6 m/min, the axial depth of cut is 4 mm, the radial depth of cut is 0.1 mm, the feed per tooth is 0.02 mm/tooth, and the milling method is down milling. The starting point of the path is the straight line between corner I and corner IV (the position corresponding to the acoustic emission sensor AE1 in Figure 5), and the tool moves in a counterclockwise direction. The four workpieces shown in Figure 5 are machined continuously with the same milling cutter. After each machining, the flank wear values of the four teeth were measured, as shown in Table 8. According to the definition in ISO8688-2, the average value of the flank wear band is taken as worn VB, and the maximum value of the wear band is taken as worn \(VB_{max}\). According to the standard, when \(VB\) reaches or exceeds 0.3 mm or \(VB_{max}\) reaches or exceeds 0.5 mm, the tool life limit is considered to be reached.
# 6G Visible Providing Advanced Weather Services for Autonomous Driving
## 7 Results
### V2X Communications
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Connection Points**} & **Mean Throughput** & **Min 95\% Confidence** & **Max 95\% Confidence** \\  & **(Mbits/s)** & **Interval** & **Interval** \\ \hline
43.0 & 4.378 & 4.304 & 4.453 \\
82.0 & 4.1299 & 3.995 & 4.265 \\
62.0 & 4.227 & 4.094 & 4.3597 \\
90.0 & 4.284 & 4.213 & 4.3547 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Throughput in ITS-G5 tests, each representing one test drive lap. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Connection Points**} & **Mean Latency (ms)** & **Min 95\% Confidence** & **Max 95\% Confidence** \\  & & **Interval** & **Interval** \\ \hline
130.0 & 4.874 & 4.771 & 4.977 \\
585.0 & 6.294 & 3.439 & 9.149 \\
584.0 & 4.8584 & 4.783 & 4.933 \\
577.0 & 6.2674 & 3.562 & 8.973 \\
586.0 & 4.896 & 4.799 & 4.993 \\
578.0 & 7.937 & 3.687 & 12.187 \\
642.0 & 4.771 & 4.743 & 4.800 \\
607.0 & 4.772 & 4.737 & 4.808 \\
104.0 & 4.723 & 4.675 & 4.772 \\
790.0 & 4.767 & 4.740 & 4.793 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Latency in ITS-G5 tests, each representing one test drive lap. Figure 10: ITS-G5 (**left**) and C-V2X (**right**) ranges in test measurements. As stated above, the capacity of all evaluated networks was adequate for the service data delivery of route planning and road weather services. However, if we consider the idea of collecting continuous LiDAR and/or camera data from autonomous vehicles, generally, every communication system would fail to provide enough capacity. Even our relatively simple LiDAR imaging presented in Figure 2 requires an 18 Mbps uplink rate to collect from a vehicle or roadside. And we are talking about just one vehicle. This kind of data collection from a traffic entity of multiple vehicles can only be realized by (1) packing/pre-processing the data in the vehicle before sending or (2) capturing only a burst of data, e.g., when passing a road weather station entity with high-capacity local networking.
# Monitoring of Atmospheric Corrosion of Aircraft Alloy AA2024 by Acoustic Emission Measurements
## 2 Experimental Investigation
### Acoustic Emission Monitoring  
The AE method is a passive SHM method, i.e., no actuators are used for the excitation of the structure. The AE method evaluates the transient elastic waves triggered by the time-discrete introduction or release of mechanical energy. The introduction of energy can result from, e.g., the turbulence of the surrounding medium. However, AE mostly refers to the sudden release of elastic energy, e.g., due to crack propagation. For the latter, already existing damage, such as a non-growing crack or non-active corrosion, cannot be detected [32; 33].  
AE testing has a long history in NDT applications for the temporary investigation of structures; thus, such commercially available sensors are often bulky and have been applied to and removed from many different structures. However, the sensing elements in such sensors are typically piezoelectric transducers that measure the pressure applied to the sensor, i.e, they are predominantly sensitive to out-of-plane wave motion [32; 34; 35]. In an SHM concept, the sensors should ideally be small, lightweight, inexpensive, and unobtrusive or even integrated into the structure. Furthermore, the sensors are attached permanently and should not be removed after a testing routine, as typically done in NDT. Therefore, PWASs are commonly used for the AE sensing of structural damage [33; 35; 36; 37; 38].  
The first free radial resonance frequency of the used PWAS was 194 kHz. Due to the typically thin adhesive bonding of PWASs to the structure, PWASs are highly sensitive and measure both in-plane (predominantly caused by symmetric wave mode) and out-of-plane  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Salt** & **Formula** & **Purity** & **Supplier** & **RH at 20 °C (\%)** & **RH at 25 °C (\%)** \\ \hline Magnesium chloride & MgCl\({}_{2}\cdot 6\,\mathrm{H}_{2}\)O & p.a. * & Merck & 33.0 & 32.5 \\
\begin{tabular}{c} Magnesium nitrate \\ hexhydrate \\ \end{tabular} & Mg(NO\({}_{3}\))\({}_{2}\cdot 6\,\mathrm{H}_{2}\)O & p.a. * & [PERSON] & 55.0 & 53.0 \\ Sodium chloride & NaCl & \(\geq\)99.5\% & Pharmasal & 76.0 & 75.5 \\ Potassium chloride & KCl & p.a. * & [PERSON] & 85.0 & 85.0 \\ Sodium sulfate decahydrate & Na\({}_{2}\)SO\({}_{4}\cdot 10\,\mathrm{H}_{2}\)O & p.a. * & [PERSON] & 93.0 & 93.0 \\ \hline \hline \end{tabular}  
* pro analysi.  
\end{table}
Table 1: Salts required for the experiments described below, their purity and supplier, as well as their equilibrium relative humidity (RH) at 20 °C and 25 °C when used for the saturated salt solutions [31].  
Figure 2: Aluminum specimen with applied and contacted PWAS: (**a**) top side and (**b**) bottom side with applied and contacted PWAS.  
(predominantly caused by antisymmetric wave mode) wave motion [36]. This sensing mechanism results in a very good frequency response over a wide frequency range, which makes PWASs competitive with leading commercial broadband AE sensors [35; 39].
# Macro-Micro-Coupled Simulations of Dilute Viscoelastic Fluids
## 2 Governing Equations
### Constitutive Equations for Extra Stress
#### 2.2.2 Macro-Constitutive Equations--Closure Models  
In this section, we introduce macroscopic closures for the two dumbbell models discussed in Section 2.2.1. We introduce these closures here and use them in Sections 3.1 and 3.2 to compare their results with those of their corresponding microscopic dumbbell model. The main objective is to show how much information is lost at the microscale using closure approximations. These shortcomings of the macroscopic closures highlight the importance of micro-macro models to properly capture the underlying dynamics that govern the flow in complex fluids. Recall that the extra stress is found as a function of the second moment of the end-to-end vector, \(\mathbf{Q}\). From the equation of motion of a dumbbell, Equation (5), the following evolution equation for the second moment of the end-to-end vector \(\mathbf{Q}\) can be derived:  
\[\big{(}\langle\tilde{\mathbf{Q}}\tilde{\mathbf{Q}}\rangle\big{)}_{(1)}+\frac{4 }{\xi}\langle F(\tilde{\mathbf{Q}})\tilde{\mathbf{Q}}\rangle=\frac{4k_{B}T}{ \xi}\mathbf{I}, \tag{14}\]  
where \((\cdot)_{(1)}\) is the upper convected derivative, defined as  
\[(\cdot)_{(1)}=\frac{\partial(\cdot)}{\partial t}+\mathbf{u}\cdot\mathbf{\
abla}( \cdot)-(\cdot)\cdot(\mathbf{\
abla}\mathbf{u})-(\mathbf{\
abla}\mathbf{u})^{\mathsf{ T}}\cdot(\cdot). \tag{15}\]
* **Upper convected Maxwell model:** If the spring is linear (Hookean), where \(F(\mathbf{Q})=H\mathbf{Q}\), the resulting constitute equation obtained from Equation (14) is known as the upper convected Maxwell (UCM) model \[\tilde{\mathbf{\tau}}=nH\langle\tilde{\mathbf{Q}}\tilde{\mathbf{Q}} \rangle-nk_{B}T\mathbf{I},\] (16a) \[\big{(}\langle\tilde{\mathbf{Q}}\tilde{\mathbf{Q}}\rangle\big{)}_{(1)}+ \frac{4H}{\xi}\big{\langle}\tilde{\mathbf{Q}}\tilde{\mathbf{Q}}\big{\rangle}= \frac{4k_{B}T}{\xi}\mathbf{I}.\] (16b)Using the same scaling as before leads to the following non-dimensional constitutive equations:  
\[\mathbf{\tau}=\left\langle\mathbf{Q}\mathbf{Q}\right\rangle-\mathbf{I}, \tag{17a}\] \[De(\left\langle\mathbf{Q}\mathbf{Q}\right\rangle)_{(1)}+\left\langle\mathbf{Q} \mathbf{Q}\right\rangle=\mathbf{I}. \tag{17b}\]
* **FENE-P model** In a FENE model, with the spring law given by Equation (12), the evolution equation, Equation (14), becomes \[\left(\left\langle\mathbf{\tilde{Q}}\mathbf{\tilde{Q}}\right\rangle\right)_{(1 )}+\frac{4H}{\zeta}\left\langle\frac{H\mathbf{\tilde{Q}}\mathbf{\tilde{Q}}}{1-( \tilde{Q}/Q_{\text{max}})^{2}}\right\rangle=\frac{4k_{B}T}{\zeta}\mathbf{I}.\] (18) Different FENE models can be formulated based on the approximation used to calculate the second term in Equation (18).
# Deep Learning in the Biomedical Applications: Recent and Future Status
## 4 Omics
### Around the Genome
#### 4.1.2 Gene Expression  
Gene expression is a biological process divided in three steps: the transcription, the RNA processing and the translation. The transcription creates an RNA molecule (called precursor messenger RNA (pre-mRNA)) that is essentially a copy of the DNA in the gene being transcribed. Then, RNA processing modifies the pre-mRNA into a new RNA molecule called messenger RNA(mRNA). Translation creates a protein molecule (an amino-acid chain) by reading the three-letter (codes) in the mRNA sequence [59]. The deep learning techniques are applied in gene expression according to two orientations: the alternative splicing field and the prediction of gene expression.  
* **Alternative splicing**: Alternative splicing is a process whereby the exons of a primary transcript may be connected in different ways during pre-mRNA splicing [120]. The objective is to build a model that can predict the outcome of AS from sequence information in different cellular contexts [120; 121; 122].
* **Gene expression prediction**: Gene expression refers to the process of producing a protein from sequence information encoded in DNA [123]. Predicting the gene expression from histone modification can be formulated as a binary classification where the outputs represent the gene expression level (high or low) [124; 125; 126].  
Figure 8: Around the genome.
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 5 Discussions and Conclusions
### Conclusions  
In this paper, a real-time driver activity recognition technique based on the LRCN model was proposed, which detects the driver activities in diverse day- and nighttime scenarios. Our designed model combines the TimeDistributed CNN with LSTM to utilize spatial, temporal, and spatio-temporal information. In contrast to most existing works that use a single input frame that could get confused with a sequence of frames of different driver classes, a sequential input frame was applied using both RGB and IR images to recognize the driver's activity throughout the entire day.  
Moreover, the proposed model was benchmarked against existing state-of-the-art approaches. The experiments revealed that the synergistic interplay between TimeDistributed CNN and LSTM models significantly contributes to maximizing the performance of the model. Additionally, the binary classifier, which determines whether the driver is in a non-distracted or distracted condition, achieved a promising state-of-the-art result, thereby showcasing the applicability of our approach in practical settings. With its efficient and cost-effective properties, this approach can be readily integrated into the design of ADAS to provide real-time warnings to drivers, thereby reducing potential risks. In future work, we will emphasize advancing the recognition algorithm and expanding the study to encompass a broader range of driver activities, aiming to enhance the adaptability and generalization of the model across diverse environmental conditions.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14177985/s1](https://www.mdpi.com/article/10.3390/app14177985/s1)]([https://www.mdpi.com/article/10.3390/app14177985/s1](https://www.mdpi.com/article/10.3390/app14177985/s1)), Video S1: Real-Time-Driver-Activity-Recognition-Daytime.mp4; Video S2: Real-Time-Driver-Activity-Recognition-Nighttime.mp4.  
Conceptualization, T.K. and Y.S.; methodology, T.K. and Y.S.; software, T.K. and Y.S.; validation, T.K., Y.S., K.-W.S., S.L. and T.P.; formal analysis, T.K. and Y.S.; resources, T.K., Y.S., K.-W.S., S.L. and T.P; data curation, T.K. and Y.S.; writing--original draft preparation, T.K.; writing--review and editing, T.K., Y.S. and T.P.; visualization, T.K. and Y.S.; supervision, K.-W.S. and T.P.; project administration, T.P.; funding acquisition, T.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the MOTIE (Ministry of Trade, Industry & Energy), Republic of Korea, under the Technology Innovation Program (Project No. 10077617), and in part by the MSIT (Ministry of Science and ICT), Republic of Korea, under the Grand ICT Research Center Support Program (IITP-2020-0-101741).  
Not applicable.  
The datasets presented in this article are not readily available due to further ongoing studies and privacy restrictions.  
The authors declare no conflicts of interest.
# Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems
## 5 Discussion
### Spectral Inconsistencies
Spectral changes of unknown small targets will have more consequences on an algorithm's performance because these routines tend to key off the spectral shape. The well-known (solar-like spectrum) ability to predict the radiometric signal from convex mirrors lends itself to the only method that we know of for understanding spectral impacts linked to the HS orthorectification process.
# Contact Lenses as Drug Delivery System for Glaucoma: A Review
## 2 Drug Delivery Systems with Contact Lenses
### Colloidal Nanoparticles  
The development of nanomaterials permitted the encapsulation of drugs inside colloidal nanoparticles, such as liposomes, micelles, microemulsions, and polymeric-based nanoparticles. Nanoparticles (between 10 and 100 nm) represent a barrier against metabolic degradation while drugsare released onto the ocular surface [117]. In soft contact lenses, functionalized nanoparticles can be incorporated during the polymerization reaction, by soaking, or being immobilized to the contact lens surface through chemical bonds (Figure 1d) [64]. Of all these methods, the soaking one is probably the most limited because it depends on the size relationship between nanoparticles and the porosity of hydrogels. Concerning the efficacy of functionalized contact lenses with nanoparticles, their drug release is sustained over a longer period compared to nanoparticles in solution [64; 117].  
Over the last decade, a large number of studies used different nanoparticles to incorporate ophthalmic drugs, such as antibiotics [118], antihistamines [119], immunosuppressive [120], corticosteroids [76], or glaucoma medication [121], into contact lenses. As examples of animal experimentation, different studies showed drug release for seven days with voriconazole-loaded chitosan nanoparticles in mice [122], 10 days with ketotifen-loaded silica shell nanoparticles in rabbits [119], and 14 days with cyclosporine A-loaded polymeric nanoparticles in rabbits [120].
## 3 Proposed LVRT Control System  
In this section, the overall LVRT control algorithm is described. The overall control structure is illustrated in Figure 2 including GSC, RSC, and ESS controls.The proposed method demonstrates that when the ESS is connected to a WPS, a coordinated form of control between the two is required for an efficient operation of the combined system. When the ESS SoC is low, the LVRT can be achieved by using just the ESS alone and when the WP produces low power at low wind speeds, the WT can reduce its power without increasing the dc link voltage significantly. However, the ESS can be used to achieve different objectives. Thus, it is not efficient to conserve the ESS capacity by limiting its SoC only during uncertain grid events. Moreover, when the WP produces large power at high wind speeds and the WPS operates near the rated rotor speed, it is not possible to achieve successful LVRT operation using the WPS alone when the rotor speed is greater than the speed limit. Thus, the ESS LVRT operation is required in order to help the WPS during LVRT. An ESS plays a very important role in many WT operations, especially at the time of a grid fault. Because the capacity of an ESS is limited and involves high installation costs, it is not cost-effective to limit the SoC range during normal operation; further, it is important to control the ESS using proper information from the WT operation. In the case of LVRT, the grid codes specify the fixed duration for which a WPS should be connected, and the ESS capacity required for LVRT operation can be obtained from current WT operation information. We analyzed the relationship between the ESS capacity required and WPS operation status and proposed a coordinated LVRT control method.