esa-sceva/satcom-chunk-collection huggingface dataset:
# Delamination Localization in Multilayered CFRP Panel Based on Reconstruction of Guided Wave Modes
## 4 Experimental Investigation
The B-scan images of guided wave propagation along the length of the plate with respect to time for both defect-free and defective regions are presented in Figure 16.  
The measured distance from the receiver to the delamination is presented in Figure 15.0 The absolute error of the delamination location was calculated by considering the difference between the actual and the measured values. In the case when the delamination is located between the excitation source and the receiver, GW signals were received at distances of 280 mm, 290 mm, and 300 mm from the leading edge of the CFRP plate. In this case, the excited A\({}_{0}\) mode propagates within the delamination, further divided into converted S\({}_{0}\) and transmitted A\({}_{0}\) modes. The converted S\({}_{0}\) mode is received at the receiver as the first wave packet (\(t_{1}\)), while the transmitted A\({}_{0}\) mode is received as the second wave packet (\(t_{2}\)), as shown in Figure 15.1  
The complexity in signal interpretation arises from the varying velocities of the converted S\({}_{0}\) modes when combined with the transmitted A\({}_{0}\) mode or other modes. Estimation of delamination location using such type of overlapped modes is complex. For this reason, an analytical model was used to match the overlapped modes, and then the ToF of the converted S\({}_{0}\) mode (\(t_{2}\)) was estimated. The delamination location (\(X_{d}\)) was estimated by calculating ToF between converted S\({}_{0}\) mode and transmitted A\({}_{0}\) mode. The estimated distance from delamination to the receiver and the absolute error of the estimated delamination position are presented in Figure 15.2  
Figure 15.3 GW signal received in case delamination was after receiver point. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Receiver**} & \multicolumn{2}{c}{**Distance from**} & \multirow{2}{*}{**Propagation**} & \multirow{2}{*}{**ToF (dt) (us)**} & \multirow{2}{*}{**Estimated Distance (mm)**} & \multirow{2}{*}{**Absolute Error**} \\  & & **Delamination to** & & & \\  & & **Receiver (mm)** & & & \\ \hline
1-2 & R1 & 20 & 123.7 – 87.4 & 36.3 & 19.7 & 0.3 \\
1-3 & R2 & 10 & 114.5 – 95.6 & 18.9 & 10.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Localization of delamination when the delamination after excitation and receiver.
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 2 Antenna Design Methodology
### Effect of the Human Body on the Antenna's Behavior  
Here, the influence of the human body on the antenna's parameters is shown. For this purpose, the input impedance of the antenna in air and on the human body are compared, as shown in Figure 8. Since the proposed antenna is optimized for wearable applications, it shows good input impedance matching (close to the 50 \(\Omega\)) in all the operating frequency bands.  
However, when its behavior was observed without a human body, it did not show good impedance matching in the lower frequency bands (1.8 GHz and 2.45 GHz). Since the human body is a lossy conductor and has a high dielectric constant compared to the denim substrate, its radiation efficiency is reduced due to the loading effect of the human body [33]. As shown in Figure 9, at low frequencies (1.8 GHz and 2.45 GHz), biological tissues have high permittivity compared to at high frequency, so the antenna faces more losses, and its efficiency becomes lower than at the high frequency (5.8 GHz). In the absence of the human body, the antenna shows maximum radiation efficiencies of 98.4%, 98.7%, and 75.1% at 1.8 GHz, 2.45 GHz, and 5.8 GHz frequencies, respectively. However, in the presence of the human body, efficiencies are 62.5%, 67.9%, and 70.1%, at 1.8 GHz, 2.45 GHz, and 5.8 GHz frequencies, respectively.  
Figure 8: Comparison of antenna’s input impedance in the air (without the human body) and on the human body.  
Figure 7: Comparison of \(|\)S\({}_{11}|\) at different radii of the phantom: 25, 35, and 45 mm.
## 4 Conclusions  
This study aimed to show that it is not required to present fracture in the fine grid because fluid/reservoir behavior is very well represented by a single fracture plane. We presented a workflow for modeling the effect of hydraulic fracture on the performance of a synthetic reservoir model. First, a fine grid synthetic reference model which represents a hydraulically fractured reservoir is modeled using data generated based on the information from several wells found in northern India. Two other cases are also developed using the same data but with two different resolutions, i.e., large grid blocks. By calibrating the coarse models with data obtained from the pressure transient analysis, it is shown that their performance closely matches the fine grid performance. The grid shape is a rectangle with long sides that follow the perpendicular direction of the main stress. The size of the grid can be considered normal (not refined) for a long side. Refinement is required for only the short side; however, it can also be optimized. This was especially true at later times when the flow became radial. The proposed method is effective and accurate, and the simulation run times consumed by the coarse grid models were only 1.5% of the reference fine grid models. In addition, by using the unconventional reservoir simulation together with the well test analysis, it is shown that the long-time prediction of the performance of hydraulically fractured reservoir is possible without the requirement of a fine grid model.  
J.H.L., Conceptualization, Funding, Methodology, Software, Writing--original draft preparation; B.M.N., Conceptualization, Methodology, Analysis, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to express their deepest gratitude for Universiti Teknologi PETRONAS for providing YUTP grants (015 LC0-355, 015 LC0-412), the required software license and creating the necessary working environment.  
The authors declare no conflict of interest.  
Figure 9: Bottom hole pressure for Coarse Case and Reference in respectively.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 3 Flexible Packaging and Testing of Sensors
### Flexible Packaging Method for Sensors  
If pressure sensors are directly placed on the radial artery for long-term measurements, the sensors may be affected by sweat and/or damage and cause discomfort to the wearer. Therefore, this paper proposes a flexible packaging method for MEMS pressure sensors.  
Polydimethylsiloxane (PDMS), poly-para-xylylene (parylene), polyurethane (PU), and polyimide (PI) are commonly used flexible materials. Among them, PDMS more easily bends and deforms, has stable chemical properties, is easy to process, has a low manufacturing cost, and has good biocompatibility. Its stiffness, elasticity, and other properties are close to those of human skin, so can greatly reduce discomfort during wear [12; 13]. However, PDMS also has some drawbacks. Its thermal expansion coefficient significantly differs from that of silicon, which can cause cracks at the junction of the PDMS and the silicon chip during the sensor packaging process due to temperature changes, affecting the quality of signal acquisition. Meanwhile, PDMS is porous and easily absorbs moisture during use, affecting measurement accuracy. Parylene-C, as a commonly used organic thin film coating material, also possesses desired characteristics such as biocompatibility, chemical inertness, and transparency. It has fond extensive applications in fields such as biology and medicine. Compared with PDMS, parylene exhibits better adhesion to silicon wafers and is impermeable to moisture, sweat, and other substances. It possesses excellent compactness and corrosion resistance. However, the fabrication process of parylene films is relatively complex, requiring specialized deposition equipment. For thick substrates, a prolonged deposition time is necessary. The resulting films are highly rigid and do not meet the requirements for flexible packaging.  
Based on these considerations, we first applied a layer of parylene on the sensor surface through a vapor deposition process to increase the sensor's corrosion resistance. Then, the sensor was flexibly packaged with PDMS to ensure the subject's comfort. The schematic diagram of the sensor packaging scheme is shown in Figure 4.  
Figure 4: Schematic diagram of the packaging scheme.  
Figure 3: Photo of MEMS pressure sensor.
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 3 Proposed Artificial Neural Network (ANN) Model
### Evaluation Metrics
#### 3.2.2 Root-Mean-Square Error  
There are several techniques which can be used to evaluate the performance of a regression model, but the most frequent is to compute the root mean square error (RMSE). The root-mean-square error (RMSE) is a parameter that can be used to assess the performance of a model, by determining the amount of deviation that exists between the predicted value and the observed value [41]. The unit of the error score is the same as that of the predicted value, which is an advantage of the RMSE. The root-mean-square error (RMSE) can measure the evaluation of the matrix performance, which corresponds to the predicted value of the squared (quadratic) error or loss [42]. If \(y_{i}\) is the corresponding true value and \(\hat{y}_{i}\) is the predicted value of the ith sample, the RMSE is defined as in Equation (12).  
\[\text{RMSE}=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\sum}}\left(y_{i}- \hat{y}_{i}\right)^{2}} \tag{12}\]  
where \(RMSE\) = root-mean-square error; \(m\) = total number of observations; \(y_{i}\) = actual observations time series \(y\); and \(\hat{y}_{i}\) = estimated time series \(y\).
# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 1 Introduction  
### Research Background  
Medical imaging disease classification has always been a key aspect of clinical care and related teaching activities, and ophthalmic disease recognition is an important branch of the field. According to statistics, about 2.2 billion people worldwide suffer from visual impairment, nearly half of which can be prevented by timely detection and treatment [1]. Ocular disease recognition utilizes imaging techniques such as fundus photography and optical coherence tomography (OCT) for early diagnosis and classification of ocular diseases. OCT technology captures a detailed cross-sectional view of the retina by non-invasive means. The human retina is a layer of light-sensitive tissue at the back of the eye. Incident light is converted into neural signals by receptors in the retina and processed by the visual cortex of the brain to produce images. Visual function may be impaired by a variety of pathological changes in the retina [2]. Analysis of images using optical coherence tomography (OCT) can be effective in diagnosing a variety of eye diseases including glaucoma (GLA), diabetic fundopathy (DR), and age-related macular degeneration (AMD). In recent years, with the development of deep learning techniques, research on ophthalmic disease recognition has made significant progress, and convolutional neural networks (CNNs) play a particularly important role in ophthalmic disease recognition. They can automatically learn and recognize disease features from fundus images, greatly improving theefficiency and accuracy of diagnosis. CNNs show great potential in the screening and diagnosis of common ophthalmic diseases such as diabetic retinopathy, assisting doctors in understanding the diagnostic process by providing visual feedback of lesions.  
Although deep learning shows great potential in assisted diagnosis of ophthalmic diseases, the high cost of annotation, small data volume, and uneven sample distribution of medical image data limit the training and generalization ability of the model. In addition, the differences in the characteristics of fundus images captured by different devices, as well as in people of different ages and occupations, put higher requirements on the generalization and accuracy of models. How to be able to effectively deal with these challenges is a future research direction.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 2 Related Work
### Urban Commuter Planners  
Commonly used map applications provide routing functions based on public transport. This type of route can be found in Google Maps, Bing Maps, Here Maps, and applications specifically developed for public transport, such as Citymapper. Algorithms have been developed to support multi-modal routing [17; 18], and today's systems allow routes to be calculated with transfers both between one type of transport, e.g., between different bus lines, and between different types of transport, e.g., buses and trams. The advantage of these systems is that they work in many cities around the world, and a user accustomed to the application can use them in another city. On the other hand, many cities provide applications for mobile devices that take into account specific conditions and improve the performance of generic systems. While generic systems usually do not consider users with special needs, city-specific apps are built with different assumptions. Some studies, e.g., for Paris [19] or Madrid [20], lay the foundations for commute planners for different types of users, including people with disabilities.  
The urban commuter planners focus on planning the optimal route in terms of choosing suitable buses, trams, trains, or underground. However, the issue of pedestrian navigation in areas of terminals or stations is beyond the scope of this study. While some applications include exit names, the challenge remains in finding the right one and presenting information consistent with the physical signs at the station. Due to the lack of a proper description of station exits and the lack of consideration of areas inaccessible to some people, these apps can often provide routes that confuse disabled users in complex buildings.
## 2 Materials and Methods  
### Selection of Material and Manufacturing  
To automate the process of raster orientation analysis, this study uses components prepared on an industrial-scale FFF machine (Essentium HSE 180-HT, Pflugerville, TX, USA) with a nozzle diameter of 0.8 mm. The parts have a planar dimension of 76.2 mm \(\times\) 76.2 mm (3 inches \(\times\) 3 inches) and a height of 6.35 mm (0.25 inches). Simplify 3D (Cincinnati, OH, USA) software version 4.0 was used to slice the CAD model, with the pertinent infill parameters shown in Table 1, specifically the orientation \(\theta_{i}\) of each of the individual layers. All layers were set to a layer height of 0.35 mm and 100% infill. The print speed was set to 100 mm/s, and the nozzle temperature was set to 340 \({}^{\circ}\)C. This study used three materials: polycyclohexylenedimethylene terephthalate glycol (PCTG), carbon fiber-filled polyethylene terephthalate (PET-CF), and high-temperature-grade nylon (HTN) manufactured by Esenstium. Nine coupons were manufactured across the three materials, categorized into three groups (A, B, or C) based on their raster orientation layup, as shown in Table 1. The orientations were selected to mitigate warping while avoiding internal reflections from repeated layers. The material properties of the three inspected materials are presented in Table 2. These properties have been sourced from the manufacturer's technical datasheet [31; 32; 33], along with the as-deposited density of the printed components.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample Group**} & \multicolumn{2}{c}{**Print Orientation \(\theta_{i}\) (in Degrees)**} \\  & & \([\theta_{1},\theta_{2},\ldots,\theta_{18}]\) \\ \hline A & [0,90,10,100,20,110,30,120,40,130,50,140,60,150,70,160,80,170] \\ B & [0,90,10,100,20,110,30,120,40,130,50,140,60,150,70,160,80,170] \\ C & [0,90,45,135,20,110,70,160,50,140,85,175,25,115,10,100,110,180] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Layer orientation parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Density (g/cc\({}^{3}\))**} & \multicolumn{2}{c}{**As-Printed Density (g/cc\({}^{3}\))**} & \multicolumn{2}{c}{**Modulus of Elasticity (GPa)**} & \multirow{2}{*}{**Chemical Formula**} \\ \hline PCTG & 1.23 & 1.17 & 1.81 & \((\text{C}_{10}\text{H}_{8}\text{O}_{4})_{\text{n}}\) \\ HTN & 1.20 & 1.15 & 3.18 & \((\text{C}_{6}\text{H}_{11}\text{NO})_{\text{n}}\) \\ PET-CF & 1.40 & 1.32 & 9.38 & \((\text{C}_{10}\text{H}_{8}\text{O}_{4})_{\text{n}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties.
# A New Bloom Filter Architecture
## 4 Bloom Filter Analysis
### Indeterminable Probability  
When querying the \(p\)-BF, if all of the \(k\) cell values of an input are _conflicts_, then the output face of the input cannot be identified, which is the _indeterminable_ case. The _indeterminable_ probability should be handled separately depending on whether or not the input is included in programming set \(S\). In other words, the _indeterminable_ probability (\(P(I_{t})\)) is  
\[P(I_{t})=P(S)P(I|S)+P(S^{c})P(I|S^{c}). \tag{5}\]  
\(P(I|S)\) is the probability that all hash indexes for an input among \(n^{\prime}\) elements of a specific face set included in programming set \(S\) have conflict values. \(P(I|S^{c})\) is the probability that all of the hash indexes for an input not included in programming set \(S\) have conflict values.  
In order to obtain \(P(I|S)\), in querying an input among \(n^{\prime}\) elements of a specific face set included in programming set \(S\), let \(p_{ic}\) be the probability that the cell with the conflict value is indicated by a hash index of the input, which means the probability that at least one of the hash indexes for \((N-n^{\prime})\) elements not included in the specific face set also programmed the cell. Then, \(p_{ic}\) is as follows:  
\[p_{ic}=\Big{(}1-(1-\frac{1}{m})^{k(N-n^{\prime})}\Big{)}. \tag{6}\]  
In order for the input to be _indeterminable_, every hash index should indicate the conflict cell. Hence, \(P(I|S)=p_{ic}^{k}\).  
In order to obtain \(P(I|S^{c})\), in querying an input not included in programming set \(S\), let \(p_{oc}\) be the probability that the cell with the conflict value is selected by a hash index for the input. The \(p_{oc}\) is the same as the probability that the selected cell has the conflict value, and hence, from Labels (3) and (4),  
\[p_{oc}=\Big{(}1-(1-\frac{1}{m})^{kr^{\prime}}\Big{)}\sum_{i=1}^{L-1}\Big{(}1-( 1-\frac{1}{m})^{k(N-in^{\prime})}\Big{)}. \tag{7}\]  
Hence, \(P(I|S^{c})=p_{oc}^{k}\).  
Based on Labels (5)-(7), the _indeterminable_ probability  
\[P(I_{t})=P(S)\cdot p_{ic}^{k}+P(S^{c})\cdot p_{oc}^{k}. \tag{8}\]
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 4 Proposed Scheme
### Energy Consumption Model  
Consider that \(\mathbf{d}\) is the transmission distance in the network field, \(\mathbf{b}\) represents the total size of the packet in bits, \(\mathbf{S}_{b}\) stand for the bit duration and \(\mathbf{E}_{disp}\) refers to the radio dissipation. Thus,energy consumption in transmitting \(b\) bits packet within the transmission distance \(d\) by one sensor node can be calculated using the equation in [27].  
\[\mathbf{E}_{trans}(\mathbf{b},\mathbf{d})=(\mathbf{b}*\mathbf{E}_{disp})+(\mathbf{b}*\mathbf{S}_{b}). \tag{1}\]  
The energy is also consumed by the sensor node when the packet is given reception. The energy consumed during the reception of the packet can be calculated by the equation in [27].  
\[\mathbf{E}_{rcv}(\mathbf{b},\mathbf{d})=\mathbf{b}*\mathbf{E}_{disp}. \tag{2}\]  
Thus, the residual energy \(\mathbf{E}_{r}\) of any sensor node can be obtained as follows:  
\[\mathbf{E}_{r}=\mathbf{E}_{Total}-(\mathbf{E}_{rcv}+\mathbf{E}_{trans}), \tag{3}\]  
where \(\mathbf{E}_{Total}\) refers to the initial energy level of the sensor node.
# Roco Proteins: GTPases with a Baroque Structure and Mechanism
## 4 Physiological Functions of Roco Proteins
### Roco Proteins from Vertebrates
It has been reported consistently that LRRK2-mediated phosphorylation negatively regulates Rab signaling. PD-mutated LRRK2 was found to cause a delay in late endosomal trafficking and epidermal growth factor receptor degradation by a decrease in Rab7 activity [79]. Moreover, [PERSON] et al. discovered that LRRK2 also has an inhibitory effect on \(\alpha\)-synuclein clearance by microglia possibly by decreasing the number of Rab5-positive early endosomes [85]. Finally, it was shown for Rab8a, Rab10, and Rab12 that upon phosphorylation these Rabs bind Rab interacting lysosomal protein like 1 and 2 (RILPL1 and 2), key proteins for ciliogenesis [81]. The phosphorylation of numerous Rab proteins by LRRK2, in combination with their large variety in subcellular localization and the capacity of Rab proteins to insert in subcellular membranes via their lipid group, has led to the emerging theme that LRRK2 recruitment to various membrane structures in cells is controlled by Rab GTPases [64]. It was demonstrated that Rab29 controls a proportion of membrane-bound LRRK2. Moreover, it was shown that Rab29 recruits LRRK2 to the trans-Golgi network and stimulates its kinase activity [83; 84]. Another recent study by [PERSON] et al. also demonstrated that Rab29 plays a crucial role in recruiting LRRK2 to lysosomes upon stress-induced lysosomal overload. This leads to phosphorylation of Rab8a and Rab10 by LRRK2, causing an attenuation of stress-induced lysosomal enlargement and a promotion of lysosomal secretion, respectively [86]. However, several aspects of these Rab-mediated mechanisms remain to be clarified [64]. Recently, LRRK2 has been closely linked to a function in the immune system. LRRK2 is expressed upon activation of microglia [65; 87; 88]. Knockdown of LRRK2 results in an attenuated microglial-induced inflammatory cytokine production, while in contrast PD patients show higher levels of activated microglia and an accompanying elevated cytokine response [65; 87; 88; 89]. LRRK2 is also expressed in different cell types of the peripheral immune system and plays a regulatory role in both innate and adaptive immunity [65; 90]. An increased expression of LRRK2 was detected in monocytes and macrophages upon stimulation with interferon gamma [65; 90; 91; 92; 93]. In dendritic cells, LRRK2 plays a regulatory role in calcium signaling, which is important for the immune function of these cells [65; 93; 94]. Furthermore, LRRK2 is responsible for the development and maintenance of the B-cell population [65; 92; 95]. Up to date, two major inflammatory pathways have been linked to LRRK2: the toll-like receptor-mediated and lectin-1 mediated pathways [65; 96]. These pathways are responsible for the host immune response against both pathogen-associated and danger-associated self-originating molecules. It is clear that LRRK2 regulates the production of inflammatory cytokines. However, details concerning the exact working mechanism remain to be elucidated [65].