esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In order to obtain extremely high SERS enhancement, tremendous efforts for fabricating different metallic nanostructures to generate the intense electromagnetic field have been made [76; 77; 78; 79], which results in highly effective and reproducible SERS sensors [80; 81; 82; 83]. Biomolecules can be detected by using the SERS method in a label-free manner--directly interacting with SERS substrates, which results in a fingerprint SERS spectrum unique to the biomolecule [68; 84]. Alternatively, antibody/aptamer-coated SERS substrates or SERS tags can be used to capture biomolecules from complex biofluids such as blood and serum with high specificity. The SERS signals are then generated by a Raman reporter encapsulated in the SERS tags or the spectral shifts are monitored as a result of the structural deformation of the bond in the event of antibody--antigen binding [85; 86; 87]. These strategies have been implemented for the detection of relevant biomolecules like proteins, DNA, RNA, and cancer markers, as well as macromolecular structures such as bacterial cells and viruses [88; 89; 90; 91; 92; 93]. In this respect, SERS is becoming increasingly popular for the detection and analysis of exosome-like vesicles using the above-mentioned approaches. In this review, we summarize the latest achievements and ongoing research in the identification and analysis of exosome-like vesicles using the SERS method. We introduce different types of SERS substrates used for the detection of exosomes, which in most cases determine the detection efficiency and interpretation of SERS data. In addition, we discuss the methodology for detection of exosomes and interpretation of SERS results in terms of sensitivity, specificity, and performance.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Respiration Rate Extraction
#### 2.5.3 Respiration Rate Estimation  
Similarly to the calculation of the heart rate, we used a windowed Fourier transform to extract the respiration rate from the chest motion signal. Again, we divided the respiration signals into segments of 60 s and selected the respiration rate as a peak of the Fourier spectrum of each segment. However, the highest spectrum peak is not always the respiration rate; it could instead be one of the higher-order harmonics due to signal morphology. To find the correct respiration rate, we first selected the two largest peaks within the 6-48 breaths/min (RPM) range, ensuring that the smaller peak was at least half the height of the larger. Second, we determined the first harmonic through the criteria in Table 2. If a valid first harmonic was found, it was chosen as the respiration rate; otherwise, the highest peak was selected. In addition, any segments where the patient was out of view or occluded from the camera were discarded, and the remaining valid time can be found in Table 4.
# Accuracy and Wear Evaluation of the Customized Zirconia Guided Sleeves
###### Abstract  
This in vitro study investigated the accuracy and wear conditions of three drill sleeve distances (0.01, 0.02, and 0.03 mm) for 3D-guided stents in simulated clinical dental implant surgery. Fifteen sets of upper and lower partially dentulous epoxy tooling board models with four dertulous first molar sites were prepared in a Nissin Simple Manikin II and set on a dental chair. Sixty computer-aided design and computer-aided manufacturing (CAD/CAM) designed and guided stents with three drill sleeve distances were prepared in this study. The maximum height (Sz) of the wear roughness of drills, maximum deviation, and drilling time were observed. The highest maximum horizontal deviations were observed at the upper first molar (0.48 \(\pm\) 0.12 mm, \(p\) \(<\) 0.001). The highest maximum vertical and angular deviations were observed at the lower left first molar (1.08 \(\pm\) 0.35 mm and 5.61\({}^{\circ}\)\(\pm\) 1.21\({}^{\circ}\), respectively, \(p\) \(<\) 0.001). Only angular deviation significantly differed among the three drill sleeve distances (\(p\) = 0.046); the 0.03 mm distance exhibited the maximum angular deviation (3.92\({}^{\circ}\)\(\pm\) 1.87\({}^{\circ}\)). The bigger drill sleeve distance (0.03 mm) was associated with more wear roughness (8.70 \(\pm\) 2.29 \(\mu\)m) of the drills. Guided stents with varying drill-sleeve distances (0.01, 0.02, and 0.03 mm) exhibited no significant difference in preparation drilling time and abrasive wear. In practice, the optimal drill sleeve distance for single-type CAD/CAM-guided stents of dental implantation was 0.01 mm.  
d  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods
### Catalytic Tests  
Before each test, the catalyst was treated in situ under He flow (40 mL.min\({}^{-1}\)) at 200 \({}^{\circ}\)C for 1.5 h. Then, CO\({}_{2}\) purification was carried out at atmospheric pressure in a fixed-bed flow reactor containing 150 mg of catalyst (Figure 1). The catalysts were sieved in order to retain grains with diameters between 0.315 and 0.500 mm and diluted to a constant volume by SiC (exhibited no activity in CO\({}_{2}\) purification from 50 to 500 \({}^{\circ}\)C) so that all the experiments were carried out under the same conditions (GHSV = 2.24 10\({}^{4}\) h\({}^{-1}\)).  
The flow of the reactant gases is composed of: 20% CO\({}_{2}\), 10% O\({}_{2}\), 0.5% CO and 0.02% NO (He as eluent gas) with a total flow of 200 mL.min\({}^{-1}\), in the temperature range 50-500 \({}^{\circ}\)C. The reaction products (CO\({}_{2}\) and N\({}_{2}\)) were analyzed on line by a gas chromatograph (GAS analyzer XXL1300), NO was analyzed with Xentra 4900C IR (infrared) analyzer (Servomex). The measurement of NO\({}_{2}\) is done indirectly using a NO\({}_{2}\) to NO converter BUNOx (Buhler Technologies) by comparison.  
The conversion, selectivity and yields of the main products are defined in the following way:  
* Conversion: \(X_{i}\) (\(\%\)) = \(\frac{n_{i}^{int}-n_{i}^{out}}{n_{i}^{int}}*100\) where \(n_{i}^{int}\) and \(n_{i}^{out}\) are number of mole of the corresponding compounds \"i\" at the inlet and the outlet of the reactor.
* Selectivity: \(S_{i}\) (\(\%\)) = \(\frac{xin_{i}}{\sum_{i}xin_{i}}*100\).
* Yield: \(Y_{i}\)(\(\%\)) = \(X_{i}*S_{i}*100\).  
In order to compare the different materials, T\({}_{50}\) and T\({}_{90}\) factors were taken into account. These factors corresponded, respectively, to the temperature at which 50% or 90% of carbon monoxide was converted. For NO reduction, the catalysts were compared according to their maximum efficiency (yield) at total conversion of CO as the first target is to fully convert CO in CO\({}_{2}\).
## 6 Concept of TACE Unsuitability  
However, TACE-resistant HCCs are also present. In 2019, Asia-Pacific Primary Liver Cancer Expert (APPLE) Meeting proposed the concept of TACE unsuitability. TACE unsuitability is defined as each one of the following three clinical conditions that prevent a survival benefit from TACE or conditions that TACE is even harmful: (i) unlikely to respond to TACE: confluent multinodular type, massive or infiltrative type, simple nodular type with extranodular growth, poorly differentiated type, intrahepatic multiple disseminated nodules, or sarcomatus changes after TACE; (ii) likely to develop TACE failure/refractoriness: up to seven criteria out nodules; and (iii) likely to become Child-Pugh B or C class after TACE: up-to-7 criteria out nodules (especially, bilobar multifocal HCCs) or modified albumin bilirubin (ALBI) grade 2b [92].  
In patients with TACE-unsuitable HCC, systemic therapy is generally recommended. However, TACE-unsuitable HCC is not always contraindicated for TACE monotherapy. Because up-to-7 criteria out tumors include various tumor conditions, like BCLC-B HCC, and some up-to-7 criteria out tumors may also be good candidates for superselective cTACE when tumors are located in limited segments of the liver [92]. Complete necrosis can also be achieved in some confluent multinodular and simple nodular type with extranodular growth HCCs when a sufficient safety margin can be embolized. A recent report indicated that age \(\geq\)75 years (\(p=0.022\)), AFP \(\geq\)200 ng/mL (\(p=0.010\)), tumor counts \(\geq\)11 (\(p=0.008\)), and heterogenous enhancement (\(p=0.024\)) were significant prognostic factors in BCLC-B HCC patients treated with superselective cTACE [93]. This indicates that TACE is also effective for some up-to-7 criteria out tumors. Therefore, the indication of TACE for unsuitable patients should be discussed according to the ability of TACE in own institution and a tailor-made treatment strategy is recommended. When TACE is performed first and the tumors may rapidly recur and/or the tumor features may change to unfavorable forms (such as hypovascular/ring-like enhancement, infiltrative tumors around the previously treated tumor that iodized oil is densely accumulated, hyperintensity on diffusion-weighted magnetic resonance imaging, and development of vascular invasion and/or lymph node metastases) following TACE, additional TACE should be abandoned and systemic therapy should be immediately stated.  
The combination of an antiangiogenic and TACE is a promising treatment option for TACE-unsuitable HCC because pre-administration of an antiangiogenic can enhance the therapeutic effects of TACE considered to be facilitated by promoting vascular normalization and improving the distribution of iodized oil mixed with chemotherapeutics [94; 95]. Therefore, TACE or hepatic arterial infusion chemotherapy should be promptly performed for patients with antiangiogenic-refractory HCC, if possible.
## 5 Discussion
### The Epistemic Uncertainty of Selecting One Year of Data as a Basis  
In the process of time window selection, one year of vessel traffic accident data is selected as a basis to calculate the confidence interval and to split the uncertainty time series of the vessel traffic accident frequency. As there is no a priori knowledge of selecting one year of data as a basis, this choice will introduce epistemic uncertainties. In order to discuss the possible effects of selecting one year of data as a basis on the time window selection, two years or three years of data are taken as the basis for comparative purposes. According to Equation (1), the confidence intervals of the vessel traffic accident frequency for every two years from the years 2001 to 2016 can be estimated for the confidence value 0.95 and can be arranged to form the uncertain time series, which is shown in Figure 5a. Similarly, the uncertain time series can be formed based on the confidence intervals calculated by every three years of the accident data from the years 2002 to 2016, which is shown in Figure 5b. In Figure 5a,b accident frequencies are represented by diamond icons and are connected by a line, while the limits of the confidence interval are shown and connected by dotted lines.  
From the comparison of the uncertainty time series in Figures 2 and 5, we can find that the longer the time interval of data selected as the calculation basis, the lower the volatility of the uncertain time series. According to Section 3, the uncertain time series in Figure 5a can be split into two segmentations, which are the years 2001-2006 and 2007-2016. The latest segmentation, 2007-2016, is considered as the statistical time window. Similarly, the uncertain time series in Figure 5b can also be split into two segmentations, which are the years 2002-2007 and 2008-2016. The latest segmentation, 2008-2016, is taken as the statistical time window. In Section 4, the segmentation, 2006-2016, is taken as the statistical time window when one year of vessel traffic accident data is selected as a basis in the process  
Figure 4: Uncertain time series of fatalities per accident.  
of time window selection. By comparison, the length of the time window selected by taking one year of data as a basis is longest. This means that more vessel traffic accident data can be included, which are useful for statistical analysis and uncertainty quantification.
## 6 Testing and Measuring Performance: Analysis of PSO, GWO, and Hybrid PSO-GWO Methods
### Results  
#### 6.4.1 Fitness Value vs. Iterations  
The chart below shows the evolution of fitness values across iterations for the PSO, GWO, and hybrid PSO-GWO methods. Each line represents the average fitness score of the swarm or pack for each replicate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Street1 Priority** & **Street2 Priority** & **Street3 Priority** & **Street4 Priority** & **Best Objective Function Value** \\ \hline
3.912 & 1.5572 & 1.9302 & 1.2084 & 0.99999 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation of hybrid PSO-GWO.  
The PSO method starts with a fit value of approximately \(-0.7\) and increases continuously until it reaches a value of approximately \(-0.2\). This indicates a rapid improvement in the quality of the solution. The GWO method starts at \(-0.5\) and shows a significant difference every time it is used, eventually settling at \(-0.4\). This shows that GWO can explore a wide range of information but is intelligently integrated. The PSO-GWO hybrid method combines the initial convergence speed of PSO with the higher search capabilities of GWO. The fit value of the hybrid system initially follows a similar path to PSO but benefits from GWO analysis, finally obtaining a fit value of around \(-0.35\), indicating a consistent improvement process. Figure 3 shows the fitness values vs. iterations for the three methods.
## 2 Local Path Generation Based on Spatial Perception
### Dynamic Control Point Determination
#### 2.1.1 Acquisition of Global Path Points  
The local path's derivation is intrinsically linked to the global path. Hence, acquiring global path points becomes pivotal for establishing control points within the local obstacle avoidance trajectory. The procedure to shape the global path for the intelligent driving sightseeing vehicle unfolds in this manner: In specific contexts, the vehicle's perception system processes data from an array of heterogeneous sensors. This fusion enhances both environmental perception accuracy and the quality of intelligent decision-making. This approach harnesses an ensemble of sensors, including LiDAR, cameras, and GNSS, among others, to glean real-time information on vehicle position, obstacle locales, and road specifics. Such data is then synergized to formulate high-fidelity maps. Subsequently, a secluded road is designated as the reference trajectory, with the sightseeing vehicle navigating its centerline at subdued speeds. Throughout this journey, the GNSS/GPS high-precision positioning module captures real-time coordinates of the road's central path. The data amassed is fed into the Prescan 2019.2.0 software to devise a structured roadway. The Prescan software is capable of generating a comprehensive global map by utilizing the collected data. Subsequently, an integrated simulation is executed, intertwining Simulink and Prescan functionalities. Within the Prescan simulation interface, the sightseeing vehicle maintains a steady velocity along the road's median, archiving its positional coordinates. These coordinates are subsequently relayed to Matlab, facilitating the fitting process and the computation of the road's curvature.  
The road centerline path, once fitted, is delineated by a series of discrete spatial points spaced 0.2 m apart. Each of these points is defined by its positional coordinates (X, Y) and accompanying curvature (\(\omega\)). To elevate the precision of the local obstacle avoidance trajectory, the outset of each operational cycle pinpoints the point nearest to the vehicle's center as the reference path's commencement. Consequently, the subsequent 150 points from this origin form the reference trajectory. This reference trajectory, constituted by X(i), Y(i), and \(\omega\)(i) for i ranging from 1 to 150, undergoes consistent updates in line with the vehicle's advancement.
# Pseudorange Measurement Method Based on AIS Signals
## 3 Detective Feature of Timestamp
### Differential Characteristics of Timestamps  
The derivative of the ideal Gaussian baseband waveform rising edge function \(a(t)\) (which is defined by the error function) is in accord with the characteristics of the Gaussian function.  
\[f(t)=\frac{d}{dt}a(t)=\frac{d}{dt}erf(kt)=\frac{k}{\sqrt{\pi}}e^{-k^{2}t^{2}}=h (t) \tag{15}\]  
where the peak point of the differential results corresponds to the bit-0-phase of the digital signal.  
To discuss the influence of adjacent symbols on the bit-0-phase differential characteristic, the result of the differential operation on the digital message sequence (defined by Equation (9)) is shown below.  
\[f_{G}(t)=\frac{d}{dt}O_{G}(t)\,=\,\sum_{m=0}^{N}\left[(-1)^{m}\times h(t-mT)\right] \tag{16}\]  
According to the characteristics of Gaussian function, it is known that the inter-symbol interference is more significant when the interval of the symbol sequence is smaller. Therefore, we still use the unit periodic sequence in which bit 0 and bit 1 alternate as the object of analysis. In Equation (16), let \(T=1\) and \(m\in\{0,1,2,\cdots N\}\), where \(N\) is the number of bits. Under such conditions, the influence of inter-symbol interference on the timestamp differential property when the parameter \(BT\) takes different values is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Bit Number** & **1** & **2** & **\(\cdot\cdot\cdot\cdot\cdot\)** & **55** & **56** \\ \hline Bit-0-Phase amplitude error & 1.268 \(\times\) 10\({}^{-3}\) & 7.826 \(\times\) 10\({}^{-10}\) & 0 & 7.826 \(\times\) 10\({}^{-10}\) & \(-1.268\) \(\times\) 10\({}^{-3}\) \\ \hline \end{tabular} \({}^{1}\) The package length \(N=56\).  
\end{table}
Table 1: Relationship between bit-0-phase amplitude error and bit number in AIS packet. \({}^{1}\)It can be seen from Figure 6 that, when \(BT\) is greater than 0.2, the influence of inter-symbol interference on the timestamp differential property can be ignored, and when \(BT\) is greater, the differential peak of bit-0-phase is sharper.
## 5 Conclusions  
This paper presents a system for measuring pointing errors based on a biaxial inclinometer and an environment control box, which enables the measurement of alidade tilt caused by track unevenness and thermal gradients.  
The analysis of the installation angle error for the inclinometer indicates that a slight deviation from the preset angle does not result in significant measurement errors. The implementation of an environment control box effectively reduces system noise and improves zero-point stability, enabling accurate data filtering without loss of real-time information, and has been proven through a stability test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EL Direction** & **AZ Direction** & **Total** \\ \hline Full time before compensation & 31.4\({}^{\prime\prime}\) & 19.3\({}^{\prime\prime}\) & 36.8\({}^{\prime\prime}\) \\ Full time after compensation & 9.8\({}^{\prime\prime}\) & 6.7\({}^{\prime\prime}\) & 11.9\({}^{\prime\prime}\) \\ Before compensation (14:00–17:00) & 64.2\({}^{\prime\prime}\) & 22.3\({}^{\prime\prime}\) & 67.9\({}^{\prime\prime}\) \\ After compensation (14:00–17:00) & 17.1\({}^{\prime\prime}\) & 7.7\({}^{\prime\prime}\) & 18.7\({}^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The RMS value of pointing errors before and after compensation.  
Figure 17: The comparison of pointing errors between these two astronomical observations: Pointing errors in AZ and EL direction.  
The inclinometer measurement system demonstrates the impact of track unevenness and thermal gradients on the antenna alidade, with its accuracy validated by data obtained from observation and pointing measurements of Pulsar 1800 + 7828. Compared to the method of calculating structural deformation to derive pointing errors through measuring temperature gradients by sensors, this method is more direct and accurate, with less data computation and faster speed, and can be easily deployed on the antenna. Through further improvements, this is the most likely way to achieve real-time online compensation for pointing errors. The implementation of this system can significantly enhance daytime antenna pointing precision.  
Conceptualization, Q.X.; methodology, Q.X.; validation, F.X., H.W. and L.Y.; formal analysis, F.X., H.W. and L.Y.; investigation, Q.X.; resources, Q.X.; writing--original draft, L.Y.; writing--review and editing, Q.X. and F.X. All authors have read and agreed to the published version of the manuscript.  
This work is sponsored by the Natural Science Foundation of Xinjiang Autonomous Region (No. 2021D01B11), the National Key Research and Development Program of China (No. 2021 YFC20203601), the Youth Innovation Promotion Association, CAS (No. Y202019), the Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments, budgeted from the Ministry of Finance of China (MOF) and administrated by the Chinese Academy of Sciences (CAS), and the Scientific Instrument Developing Project of the Chinese Academy of Sciences (No. PTYQ2022 YZZD01).  
Not applicable.  
The authors declare no conflict of interest.
# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 4 Experimental Results and Analysis
### Time and Performance Analysis: ODIR vs. OIIR
In contrast, the OIIR employed a single-stage method that simultaneously predicted objects and their interactions as an HOI, and thus, reduced the inference time to 0.51 milliseconds per sample. The average F1-score obtained in this case was 60%, which was 14% lower than that achieved with the ODIR. This was because OIIR classified a set of 54 classes as a whole, whereas ODIR had a lower number of classes owing to a two-step classification. As a result, it was easier to distinguish between different object categories and corresponding interactions within an object, as was the case with the ODIR. Generally, it was easier to train and classify the interactions within an object instead of recognizing the interactions globally. Thus, in the case of joint HOI labels, there was a high probability of inter-class similarity between different classes, which eventually led to misclassification, which reduced the system performance. The overall results achieved for the proposed scheme demonstrated the efficiency of ODIR over OIIR, particularly in scenarios where the correctness of output labels was crucial. However, OIIR can be useful in scenarios that require quick decision making, and thus, is better suited for real-time applications, where speed is crucial. In general, there should be a trade-off between the accuracy (or F1-score) and time complexity of the decision making. The proposed HOIR approach is based on only time-domain features; thus, it is computationally efficient when compared with the state-of-the-art deep learning models due to its simpler mathematical operations for extracting features, resulting in linear time complexity. In contrast, deep learning includes complex operations, nonlinear activations, and repeated backpropagations, which results in extra computational overhead.