esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scaffold 2
#### 3.2.2 Receptor-Based Scaffold Hopping  
A total of 65 core hopping structures were obtained, out of which 9 hits were obtained with a synthesizability score >0, which also exhibited \(\geq\)8 hydrogen bonding interactions with FmIH (Table 2). The molecules that showed at least one interaction with the novel central scaffold were considered. The FmIH-hit interactions as obtained from in silico receptor-based scaffold hopping are shown in Figure 8. In all cases, the GaINAc moiety exhibited a complex network of hydrogen bonding interactions with FmIH residues, as observed for the parent reference molecules [14]. Hydrogen bonding interactions were observed between the FmIH binding pocket residues and the side-chain functional groups of the novel terminal scaffold. Additionally, electrostatic interactions were observed between ligand **52** and K132/R142. For ligands **50**, **52**, **53**, **54**, and **55** the linker moiety between Scaffold 1 and Scaffold 2 exhibited interactions with the FmIH binding pocket. The synthe  
\begin{table}
\begin{tabular}{c c c} \hline
**Ligands** & **Glide Score** & **Synth Score** \\ \hline
**01 \({}^{*}\)** & \(-\)5.710 & n/a \\ \hline
**39** & \(-\)6.506 & 1.498 \\ \hline
**40** & \(-\)6.465 & 1.000 \\ \hline
**41** & \(-\)6.402 & 2.875 \\ \hline
**42** & \  
sizability scores for all the hits obtained from receptor-based scaffold hopping of Scaffold 2 are summarized in Table 2.  
Figure 7: Matched molecular pairs obtained from in silico ligand-based Scaffold 2 hopping. Three-dimensional structures of the obtained hits. The unreplaced scaffolds are shown with green carbons and the novel scaffolds after replacement are shown with gray carbons. The two distinct binding sub-pockets in the FmIH binding region that are occupied by the hits are shown in the right bottom row panels. The orange arrows point to adjacent sub-pockets, which highlight possible exit vectors to facilitate the ligand design. The FmIH binding pocket is shown in surface representation.
## 3 Materials and Methods
### Sentiment Analysis  
Sentiment analysis is the process of identifying and analysing emotions expressed in text. Text mining can identify emotional states in large amounts of text data, such as social media messages, product reviews, and customer feedback. Sentiment analysis is a computational study of people's opinions, evaluations, attitudes, and feelings [41]. It is used in many areas, such as finance, medicine, the stock market, media and politics. It is a widely used technique in product reviews to measure consumer satisfaction with a product.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Mobile App** & **Comments** \\ \hline \multirow{6}{*}{Booking.com} & Simple, easy, convenient and useful. \\  & A perfect system makes our lives easier. \\  & Excellent, I can easily access all the information I need. \\  & I am extremely satisfied with the application, so far, all hotels have met our expectations, I recommend it. \\  & The option to show on maps has now been removed, I think it is very bad and should be brought as soon as possible. \\ \hline \multirow{6}{*}{Expedia} & I always find great deals on all my hotel and flights and have never had any issues. \\  & The programme is slow and hasn’t been working. Trying to change a flight has been a nightmare. \\ \cline{1-1}  & Most clean, best designed and easy to use application i have ever seen on android market. \\ \cline{1-1}  & I love Expedia. They save me money by rebating reductions in airfare that occur after I book. They have great customer service. \\ \cline{1-1}  & Easy booking, and rewards, customer service never gave me a hassle it’s just annoying when trying to change flights sometimes. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Examples of mobile app reviews [34; 35].  
It helps companies improve their products and services based on real and clear customer feedback [42].  
Sentiment analysis is basically a text analysis that aims to determine the emotional class (positive, negative and neutral) that the given text wants to express. There are three generally accepted types of sentiment classification methods used. These are the dictionary-based approach, machine learning and hybrid approach. In dictionary-based sentiment analysis approaches sentiment dictionaries are created by classifying words or word groups (n-grams) according to the sentiment they contain (positive, negative and neutral). According to these dictionaries, the emotional states of texts can be determined. The number of positive and negative dictionaries each text contains is examined, and the emotion is revealed by assigning it to the class with the highest number. In the supervised machine learning method, a model is trained with labelled source data. The trained model then makes a prediction for output by considering the new unlabelled input data. The hybrid approach is a model formed by the combination of the dictionary-based approach and machine learning models [17; 43].
## 4 IoT Protocols
### Infrastructure Protocols
#### 4.3.2 Internet Protocols  
Internet protocols consist of a transport layer, routing protocols, and network-layer protocols.  
* **Internet Protocol v6 (IPv6):** It is a 128-bit addressable protocol that provides improved remote access and large-scale IoT device management [63]. It ensures the security, scalability, and connectivity of IoT ecosystems. Thus, it is the perfect solution for real-time IoT deployment.
* **IPv6 over Low Power Wireless Personal Area Network (6 LoWPAN):** This is an adaption layer for IPv6 over IEEE 802.15.4 links [64]. It operates only in the 2.4 GHz frequency range with a 250 kbps transfer rate. It is a network encapsulation protocol.
* **Routing Protocol for Low Power and Lossy Networks (RPL):** It was developed by the IETF ROLL working group. It is ideal for N to 1 links (meters reading) [65]. It is a proactive protocol that is susceptible to packet loss. It is implemented in Contiki OS for usage on microcontrollers and sensor nodes.
* **User Datagram Protocol (UDP):** This connection-less and lightweight communication protocol focuses on low latency communication, rather than reliability, which is the desirable characteristic of IoT communication [66].
# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 4 Ethical Considerations
### Non-Maleficence: Privacy, Security and \"Capability Caution\"
#### 4.2.2 Data Tracking  
Here, there are concerns related to the privacy of the people whose data is being used, especially when the data is linked from different sources such as self-reporting mobile apps or trackers, or CCTV video, making it easier to identify the people involved. Many countries relied on an extrapolation of infection-control and public-health measures to contain the COVID-19 pandemic. These have ranged from extreme quarantine measures in China to detailed contact tracing in South Korea to enforced stay-at-home policies and travel restrictions [51; 68]. Such measures have been variously implemented as COVID-19 contact tracing has revealed ethical trade-offs between public health and privacy. For instance, in South Korea, there were human rights concerns that the excessive disclosure of private information could cause people with symptoms to avoid testing [68].  
Bluetooth privacy-presenting, anonymous, voluntary opt-in apps have been championed by human rights and data protection groups and supported by the WHO [69]. The decentralized privacy-preserving proximity tracing (DP-3T) protocol developed by several European academic institutions was among those developed in response to the COVID-19 pandemic to safely facilitate digital contact tracing of infected individuals at the population level [70]. These were intended to offer a comprehensive set of technical and legal safeguards against the potential misuse of personal data. However, a majority of individuals have not downloaded national digital contact tracing apps [70]. According to the Future Society report [59], public hesitancy querying the purpose of COVID-19 contact tracing apps has also emerged, caused by mistrust in public authorities and by fears around the establishment of continuing forms of government surveillance. Additional means of tracking and screening such as thermal image scanning are among the potential privacy breaches and creep of mass surveillance [71].  
What should the ethically acceptable trade-offs be between stopping the spread of disease (a public good) and protecting a person's privacy from potential breaches (a private good)? This is a sensitive issue.
# Ultra-High-Cycle Fatigue Life Prediction of Metallic Materials Based on Machine Learning
## 4 Model Evaluation and Comparison
### Evaluation of Model Accuracy and Stability  
The dataset was divided into two different proportions to investigate the impact of training dataset size on the performance of the machine learning models. The cases with 80% and 60% of the total training data were labeled as Case 80-20 (67 data for training and 17 data for testing) and Case 60-40 (50 data for training and 34 data for testing), respectively. It should be emphasized that the database was randomly disrupted before data partitioning to ensure the consistency of the two partitioning methods. To evaluate the models accurately, each model was trained and predicted ten times, and subsequently the best prediction was selected to illustrate prediction accuracy. Figure 6 compares the predicted and experimental fatigue life values for the two data division cases (the solid black lines indicate that the predicted and experimental values are equal, and the two black dashed lines are threefold error bands).  
It is evident that the prediction accuracy of both models was improved greatly as the proportion of training data increased, implying that the size of training data significantly affected the accuracy of the models. The GB model predicted more accurately than the RF model. Both models had a conservative tendency for the prediction of 10\({}^{7}\)-10\({}^{9}\) lifetimes, indicating that the predicted lifetimes were smaller than the experimental ones; however,  
Figure 6: Predicted fatigue life values for Case 60-40 ((**A**) GB and (**B**) RF) and Case 80-20 ((**C**) GB and (**D**) RF).  
for the prediction of lifetimes below 10\({}^{7}\) cycles, the predicted values were larger, and these two prediction tendencies were more obvious in the RF model. The GB model took three times longer consumption time than the RF model, and it happened because the RF model used parallel generation, whereas the GB model adopted serial generation. In addition, the RF model predicted less volatility in the results. For example, during the fatigue life prediction of TC11 under 570 MPa, the variance of the ten predicted fatigue life values for the RF and GB models was 0.46 and 3.86, respectively. The RF model emphasizes stable prediction to ensure control, whereas the GB model is more suitable for scenarios where prediction accuracy is the primary goal.
## 3 Model Verification and Discussion
### Model Verification of Spiral Bevel Gears Considering Assembly Error  
For validating the correctness of the proposed method in this paper, geometry parameters and machining parameters of the example SBGs [31] which are shown in Tables 1 and 2 are computed by the proposed method.  
On the rotating projection of the gear tooth surface, the contact trajectories of the example SBGs obtained by the presented method under different assembly errors are compared in Figure 7. In addition, the comparisons of contact trajectories due to assembly errors in [PERSON]'s paper [16] are displayed in Figure 8. It is obvious that the variation trend of \(\Delta\)AP, \(\Delta\)AG, and \(\Delta\)E is the same. The variation trend of \(\Delta\Sigma\) is different because the direction of \(\Delta\Sigma\) between this paper and [PERSON]'s work is opposite. Additionally, comparing the amounts of movement in the trajectories is challenging due to the differences in SBG pairs. The variation trend can also be verified by the publications of [PERSON] [19] and [PERSON] [13]. It should be noted that the direction of \(\Delta\)E in those two literatures is opposite to ours.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Pinion** & **Gear** \\ \hline Tooth number & 26 & 31 \\ Module (mm) & 8.654 & \\ Shaft angle (\({}^{\circ}\)) & 90 & \\ Mean spiral angle (\({}^{\circ}\)) & 35 & \\ Face width (mm) & 57 & \\ Mean cone distance (mm) & 146.57 & \\ Hand of spiral & Left & Right \\ Pitch angles (\({}^{\circ}\)) & 39.98 & 50.01 \\ Root angles (\({}^{\circ}\)) & 37.13 & 46.17 \\ Face angles (\({}^{\circ}\)) & 43.83 & 52.87 \\ Addendum (mm) & 8.27 & 6.44 \\ Dedendum (mm) & 8.47 & 10.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic geometry parameters of the example gear pair.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**Pinion**} & \multicolumn{2}{c}{**Gear**} \\ \cline{2-5}  & **Concave** & **Convex** & **Concave** & **Convex** \\ \hline Cutter point radius (mm) & 108.58 & 112.05 & 116.45 & 112.15 \\ Pressure angle (\({}^{\circ}\)) & \(-\)17.50 & 22.50 & \(-\)18.50 & 21.50 \\ Root filled radius (mm) & 1.10 & 1.10 & 2.50 & 2.50 \\ Machine center to back (mm) & \(-\)4.34 & 6.90 & 0 & 0 \\ Sliding base (mm) & 2.14 & \(-\)4.68 & 1.46 & 1.46 \\ Blank offset (mm) & \(-\)2.50 & 2.5 & 0 & 0 \\ Radial distance (mm) & 116.73 & 127.5 & 123.81 & 123.81 \\ Machine root angle (\({}^{\circ}\)) & 37.38 & 37.38 & 46.17 & 46.17 \\ Cradle angle (\({}^{\circ}\)) & \(-\)51.07 & \(-\)53.00 & 49.13 & 49.13 \\ Velocity ratio & 1.50 & 1.62 & 1.30 & 1.30 \\ Modified Roll Coefficient C & 0.0197 & \(-\)0.0231 & 0 & 0 \\ Modified Roll Coefficient D & \(-\)0.0172 & 0.0477 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Machining parameters of the example gear pair.
## 4 Relationship between Force and Displacement  
This section presents a model that describes the relationship between the force applied to an actuator and displacement, which is also necessary for force control. The relationship is written as follows:  
\[F_{\text{out}}=F-K_{c}x-F_{r}(x), \tag{26}\]  
where \(x\), \(F_{\text{out}}\), \(F\), \(K_{c}\), and \(F_{r}(x)\) denote the displacement of the actuator, the output force, the driving force by the working fluid, the spring coefficient of the reinforcement coil, and the elastic force of the rubber material, respectively. \(F_{r}(x)\) is usually a nonlinear function since the strain of the rubber cylinder is large.  
We use a simple nonlinear elastic model as described in the following. Firstly, it is assumed that the Poisson's ratio of the rubber is 0.5, which implies that the volume of the rubber cylinder is constant. Under this assumption, elongation of the actuator results in the reduction of the cross-sectional area. We calculated the instantaneous cross-sectional area and the corresponding spring coefficient at the specific length, then calculated its integral to obtain the elastic force.  
From the constant volume assumption, we obtain:  
\[\pi({r_{o}}^{2}-{r_{i}}^{2})(1+\varepsilon_{l})=\pi({r_{o}}^{2}-{r_{i0}}^{2}), \tag{27}\]  
where \(r_{o}\), \(r_{i}\), and \(r_{i0}\) are the outer diameter, inner diameter, and the inner diameter at the initial state, of the rubber cylinder, respectively. The left-hand side and the right-hand side are the volume after deformation and that before deformation, respectively. The force-displacement relationship is instantaneously linear, and it is written as:  
\[dF_{r}=K_{r}(x)dx, \tag{28}\]  
where \(K_{r}(x)\) is the instantaneous spring constant. This is just a differential form of [PERSON]'s law. The constant can be written as follows:  
\[K_{r}(x) =EA_{r}\frac{1}{l} \tag{29}\] \[=E\frac{A_{r0}}{1+\varepsilon_{l}}\frac{1}{l_{0}+x}\] (30) \[=\frac{EA_{r0}l_{0}}{(l_{0}+x)^{2}}. \tag{31}\]  
Here, \(l_{0}\) are the length and the natural length of the actuator, respectively, \(A_{r}=\pi({r_{o}}^{2}-{r_{i}}^{2})\) and \(A_{r0}=\pi({r_{o}}^{2}-{r_{i0}}^{2})\).  
Figure 5: Relationship between \(\alpha\) and \(\eta\).  
Calculating the integrals yields:  
\[F_{r}=-\frac{EA_{r0}l_{0}}{l_{0}+x}+C_{3}. \tag{32}\]  
Given that the boundary condition is that \(F_{r}=0\) at \(x=0\), the constant is \(C_{3}=\frac{EA_{r0}}{l_{0}+x}\). Finally, we get:  
\[F_{r}=EA_{r0}\left(1-\frac{1}{1+x/l_{0}}\right). \tag{33}\]  
Figure 6 shows the relationship between \(x\) and \(F_{r}\). The preceding equations assume a linear stress-strain relationship. For large deformation of rubber materials, a nonlinear model based on polymer molecule chains [29] is useful.
###### Abstract  
In recent years, the development of low-cost GNSS sensors allowed monitoring in a continuous way movement related to natural processes like landslides with increasing accuracy and limited efforts. In this work, we present the first results of an experimental low-cost GNSS continuous monitoring applied to an unstable slope affecting the Madonna del Sasso Sanctuary (NW Italy). The courtyard of Sanctuary is built on two unstable blocks delimited by a high cliff. Previous studies and non-continuous monitoring showed that blocks suffer a seasonal cycle of thermal expansion and a long-term trend to downslope a few millimeters (2/3) per year. The presence of a continuous monitoring solution could be an essential help to better understand the kinematics of unstable slope. Continuous monitoring could help to forecast a possible paroxysmal phase that could end with a failure of the unstable area. The first year of experimental measurements shows a millimetric accuracy of low-cost GNSS, and the long-term trend is in agreement with other monitoring data. We also propose a methodological approach that considers the use of semi-automatized procedures for the identification of anomalous trends and a risk communication strategy. Pro and cons of the proposed methodology are also discussed.  
low-cost GNSS; warning threshold; unstable slope; cultural heritage +
Footnote †: journal: Sensors
## 5 Conclusions  
In this paper, a method for removing non-uniform band noise in the hyperspectral imaging system is proposed in order to solve the noise problem in hyperspectral brain tumor images collected in real scenes. The proposed strategy of extracting edge information from a dark background image can the calculate correction factor more accurately and reduce the fixed-mode noise effectively. At the same time, the bilateral filter embedded least squares filter (BLF-WLS) algorithm proposed in this paper can remove the fringe noise and retain the image details well, avoiding the blur and detail loss that may be caused by the traditional denoising method. The experimental results show that the proposed method can reduce the non-uniform noise to a certain extent and maintain the image features well. By using subjective and objective evaluation indices, and comparing it with other denoising techniques, the experimental results show that the proposed method has certain advantages in the denoising effect and image detail retention. Despite the long runtime of the method, future work will focus on optimizing the smoothing algorithm to improve computational efficiency, while there are plans to extend the technique to more hyperspectral brain tumor images to further validate its effect and universality.  
Conceptualization, J.Y.; Software, J.Y. and M.Q.; Validation, J.Y. and C.T.; Formal analysis, C.T.; Investigation, J.Y.; Data curation, Y.W.; Writing--original draft, J.Y.; Writing--review & editing, J.Y. and C.T.; Supervision, J.D.; Project administration, Z.Z. and B.H.; Funding acquisition, Z.Z. and B.H. All authors have read and agreed to the published version of the manuscript.  
This study was granted by the project of \"Research on automatic hyperspectral pathology diagnosis technology\" (Project No. Y855W11213) supported by the Key Laboratory Foundation of Chinese Academy of Sciences, the project of \"Research on microscopic hyperspectral imaging technology\" (Project No. Y839S11) supported by the Key Laboratory of Biomedical Spectroscopy of Xi'an,and the Open Research Fund for development of high-end scientific instruments and core components of the Center for Shared Technologies and Facilities, XIOPM, CAS (Project No. SJZ1-202406004).  
Not applicable.  
**Data Availability Statement:** The data will be made available by the corresponding author upon reasonable request.  
The authors declare that they have no conflicts of interest.
# Near-Infrared Quartz-Enhanced Photoacoustic Sensor for H\({}_{2}\)S Detection in Biogas
## 3 Optimization and Performance of the H\({}_{2}\)S Sensor
The QEPAS signal rapidly rose as the current modulation depth increased and reached its maximum at 20 mA, which corresponds to a wavelength modulation depth of 0.33 cm\({}^{-1}\). Different H\({}_{2}\)S concentrations in the range 0.5-50 ppm were generated by using a gas mixer generator, starting from a certified 50 ppm H\({}_{2}\)S in N\({}_{2}\) mixture and by using N\({}_{2}\) as the diluting gas. The peak values of the _2f_ scans were recorded at different H\({}_{2}\)S concentrations. For each concentration, the low-frequency ramp for wavelength scanning was repeated 50 times, and the peak values were averaged and plotted in Figure 7 at different H\({}_{2}\)S concentrations. The best linear fit returns an _R_-square value of ~0.998, assessing the linearity of the sensor system response to H\({}_{2}\)S concentration levels. The intercept of the linear fit is 2.65 \(\mu\)V, comparable with 1\(\sigma\) noise level measured previously. Thus, a minimum detection limit (1\(\sigma\) value) of 330 ppb was estimated, corresponding to a normalized noise equivalent absorption (NNEA) coefficient is 3.15 \(\times\) 10\({}^{-9}\) W\(\cdot\)cm\({}^{-1}\)/(Hz)\({}^{1/2}\), with a laser power of 1.6 W.  
The high power QEPAS-based H\({}_{2}\)S sensor was employed for measuring H\({}_{2}\)S in a biogas sample. Biogas utilization has undergone great development in China as it is very important for resolving the country energy problem in rural areas. An anaerobic ferment digester is the key component of household biogas digester in which human waste, livestock manure, and organic waste can be converted into biogas. CH\({}_{4}\) and CO\({}_{2}\) are the main ingredients of the biogas. However, H\({}_{2}\)S is usually contained in biogas. The H\({}_{2}\)S concentration can reach 200 ppm for the biogas which is converted via livestock manure. As H\({}_{2}\)S is dangerous to human health, detecting the H\({}_{2}\)S concentration in biogas is very important. A sample bag was used to collect the biogas sample from a household biogas digester which is built in a livestock farm. The biogas was pumped into the detection unit by a pump. The gas flow and the pressure in the gas enclosure were set at 150 sccm and 760 Torr, respectively. A full 2/ H\({}_{2}\)S QEPAS spectrum was obtained when the laser wavelength was scanned from 6320.2 cm\({}^{-1}\) to 6321.1 cm\({}^{-1}\), as shown in Figure 8, at the best operating conditions. Based on the QEPAS sensor response curve in Figure 7, the estimated H\({}_{2}\)S concentration of the sample biogas is 78.2 ppm. Figure 2:1 QEPAS signal as a function of H\({}_{2}\)S concentration (\(\bullet\)). Red solid line is the best fit of the experimental data. The data shown in this Figure was obtained at high optical power (1.6 W), atmospheric pressure and room temperature while the tubes were equipped at y = 1.4 mm.