esa-sceva/satcom-chunk-collection huggingface dataset:
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
###### Abstract
The calculation of pipe-borehole wall friction force in engineering practice relies on the empirical friction coefficient which is determined experimentally rather than predicted using physical models [18]. As a significant improvement over the classical friction laws, it is now widely accepted that the friction force is proportional to the real contact area. This has been confirmed by many experiments [19; 20; 21], and much work has been conducted based on this hypothesis [19; 20; 21; 22; 23; 24]. Therefore, the understanding of friction based on the mechanics of contact [25] can provide a new idea for studying the friction force between the pipeline and borehole wall during pipeline pullback operation. Figure 1: Forces acting on a pipeline during the pullback process. Additionally, the pressure difference has a significant impact on the fluid drag force when using ASTM model. The pressure difference of the annulus is related to the annulus size, flow rate, and drilling fluid parameters [11]. Taking the pressure difference as a particular value may cause errors in calculating fluid drag force because the related parameters differ for various HDD projects. Moreover, pipelines in the borehole are more likely to contact the borehole wall than to be located at the center of the borehole [6]. The eccentric characteristics of the annulus are essential for improving the accuracy of fluid drag force. This study proposes a new theoretical model for calculating pullback loads in HDD. Based on a large number of finite element method (FEM) analysis results, formulas for the contact pressure distribution between the pipe and borehole wall and the external load-displacement relationship were derived to analyze the pipe-borehole wall contact. The pipe-borehole wall friction force was calculated based on the pipe-borehole wall contact analysis and adhesion theory without depending on the empirical friction coefficient. The fluid drag force was calculated based on the ASTM model, and the effects of the annulus size, flow rate, drilling fluid parameters, and eccentricity were considered. The proposed model, ASTM model, and PRCI model were used to analyze two HDD installations. The model predictions were compared with the field-measured results and the applicability of the proposed method was discussed.
## 2 Materials and Methods
### Testing Study Selection Process  
For the testing of the FCM-DSS model, published case studies from the literature were used. The case study selection process was conducted in three phases for inclusion/exclusion criteria. In the first phase, all retrieved titles from the searches were assessed. Titles deemed \"potentially eligible\" underwent abstract evaluation in the second phase. In the third phase, these \"potentially eligible\" references were stored in a folder for full-text evaluation to confirm their eligibility. Studies were eligible if (a) the follow-up duration met the National Institute for Health and Clinical Excellence (NICE) definition for long COVID-19 syndrome (symptoms developing or persisting \(\geq\)6 weeks post-COVID-19 onset), (b) they provided data on the prevalence of respiratory, cognitive, language symptoms, and speech and feeding disorder symptoms in confirmed COVID-19 cases, (c) they were case studies of individual patients, and (d) the patient was an adult. Excluded were articles referring to case series, those lacking detailed symptom information relevant to the domains, and studies focusing solely on acute COVID-19 (<4 weeks).  
As a preliminary step, the research team tested the proposed FCM model on four case studies that met the criteria of problems associated with persistent post-COVID symptoms related to speech and language pathology with varying severity. The cases are as follows:  
1. Case 1--A 40-year-old man who had severe COVID-19 pneumonia with a persistent, severe cough 3 months post hospital discharge. He experienced intermittent breathlessness, voice, and speech problems severely impacting his quality of life [92];
2. Case 2--A 46-year-old woman who experienced severe COVID-19, 3 months post-infection, had persistent dysphonia, intermittent dyspnea, and a severe cough. Her voice was notably affected, becoming low, monotonous, and difficult to hear [64];  
Figure 5: Fuzzy membership functions.  
Case 3--A 37-year-old woman, was more than six months post-infection and continued to experience severe post-COVID symptoms, including cognitive difficulties like memory problems and persistent fatigue that significantly impacted her ability to engage in her studies, work, social activities, and even everyday tasks like exercise and household chores [44];
4. Case 4--A 44-year-old woman who contracted COVID-19 in early 2020. She was still experiencing significant cognitive-linguistic difficulties including issues like brain fog, memory, and communication challenges 16.5 months post-infection. These ongoing symptoms severely affected her ability to work and carry out daily activities [93].  
Each case symptom was characterized as not present, low severity, medium severity, or high severity by a consensus reached by the first and third authors, both speech and language therapists, and the fourth author, a general practitioner. The results for the fuzzy input concept values for the FCM-DSS are presented in Table 4.
# Accurate Image Locating by Hologram Multiplexing in Off-Axis Digital Holography Display
###### Abstract  
An approach is suggested to recover and then locate the original object image in off-axis digital holography by the multiplexing of hologram (OADHM). The recording configuration is designed by introducing one more plane wave in the interference frame to carry the information for the tilt angle of the reference wave (TARW), which is crucial for the holography recovery and the accurate locating of the reconstructed image. The intensity distribution in a hologram plane and the corresponding spatial spectrum of two sets holograms in Fourier domain are analyzed theoretically. When the specific spectra that come from the interference of the two plane waves are detected, the TARW can be calculated by the coordinates of them and then the recovered image can be retrieved by spectrum operation and located at the original place. Series numerical simulations and optical experiments have been carried out to demonstrate the availability and efficiency of the proposed design.  
image relocating; hologram multiplexing; digital holography display; numerical simulations +
Footnote †: journal: Applied  
0000-0002-4000-3322-2800][PERSON]
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 4 Proposed Method
### Local Variables for Efficient Indexing  
When performing CNNs, overlapping input data can occur, particularly during convolutional operations where filters slide over the input and create areas of overlap. This  
Figure 3: Optimize processing by splitting the weights into distinct blocks and overriding only the parts of the input that correspond to those weights.  
overlap can lead to poor performance and increased memory usage, as threads are forced to process multiple non-essential components. To avoid these inefficiencies, we restructured our approach. As shown in Figure 3, we redefined only the portion of the input that corresponds to the weights as local variables. This approach significantly reduces redundant memory accesses because the appropriate weighting and multiplication operations are performed using the redefined input values, avoiding the need to fetch duplicate data. This optimization saves memory bandwidth and reduces the amount of computation caused by overlap, as fewer unnecessary operations are performed simultaneously. Since the number of inputs and kernels used in the Dueling DQN routing scheme for low-orbit satellites is relatively small, this de-duplication of input data improves overall performance.
# Mechanical Analysis of DS in Horizontal Directional Drilling
## 3 Simulation Method and Experimental Verification
### Torque Comparison Results  
The measured torque fluctuation curve of the well (the well number is NP43-4006) is shown in Figure 6a. The numerical model under the same condition as that of the well is established through the drilling string dynamics, and the torque fluctuation curve shown in Figure 6b is obtained after calculation.  
1. The average value of measured torque was 3.29 kN\(\cdot\)m, and the average value of calculated torque was 3.37 kN\(\cdot\)m. The error between them was 2.43%.
2. The standard deviation of measured torque was 0.337 kN\(\cdot\)m, and the standard deviation of calculated torque was 0.346 kN\(\cdot\)m. The error between them was 2.67%.  
When the simulation model is established, the borehole wall is assumed as a rigid body to ensure calculation efficiency. This was not consistent with the actual situation. However, the error was less than 3%, which was an acceptable range.
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 5 ICN-Based VANET Research Opportunities
### ICN-Based VANET Naming  
Unlike the current Internet sending-receiving approach, contents in ICN are named, where names are either hierarchical or flat [43]. In ICN, the name of the content is decoupled from its location with the intention to supply it to the requesting subscriber. Hence, content retrieval follows a receiver-driven approach and therefore avoids control of subscribers over the content, as done in the IP-based architecture. In the ICN-based VANET, content may be easily discovered as compared to the IP-based VANET, because ICN does not need the original server (publisher) to be connected every time content is requested. Furthermore, content retrieval from different publishers, for instance, map from a common RSU, becomes easier through combining requests for contents with the same names. This process simplifies the process of data delivery for the incoming requests [9].  
ICN naming brings significant assistance to vehicular communications by allowing forwarding vehicles to handle contents on the basis of application requirements. Named data transmission provides ICN-based VANET with robustness to connection interruptions and hereby characterizes vehicular Internet [44].
# Continuous Wavelet Transform and CNN for Fault Detection in a Helical Gearbox
## 4 Proposed Defect Detection Models
### Proposed Architecture of 2D-CNN-Based Defect Detection Models  
To achieve the research goals RG1 and RG2, four 2D-CNN-based defect detection models are proposed. These models share the same architecture depicted in Figure 8 (using the pictograms in MATLAB), with appropriate values for p, r, s, t, u, and v. The architecture contains: (1) the _image input layer_; (2) the feature extraction component composed of four blocks, each with _convolution-normalization-activation-pooling_ layers; (3) the _flatten layer_ representing the feature vector; and (4) the classification block.  
The models Mcwt_x, Mcwt_y, and Mcwt_z have the input value \(p=34\), and then the following values are obtained: r = 12, s = 4, t = 2, u = 1, and v = 384. The input grayscale image is of size [34, 7710], and the vector used further in classification contains v = 384 features. The model Mcwt_xyz uses as input a grayscale image of size [\(p=102,7710\)], obtained by concatenating (on the height dimension) the CWT-based representations of the signals measured on the three individual axes. During the feature extraction process performed in the four blocks r = 34, s = 12, t = 4, and u = 2 are obtained, and v = 768 features are extracted for use in classification.  
In all four blocks, 16 filters (kernels) are applied in the 2D convolutional operations, followed by batch normalization (the values are re-centered and re-scaled based on the statistics within each mini-batch). The activation function is the rectified linear units (ReLU(x) = _max_{0,x}) function. _Max-pooling_ is applied in the downsampling operation, with a sliding window of [3, 3] and a stride of [3, 3], since it captures the local features of signals and presents the overall trend of signal changes. Due to the large width size (7710) of the input image, in Block 1 and Block 2, we proposed kernels of sizes [7, 7] and [5, 7], respectively, to extract more global signal patterns, combined with a stride of [1, 2] to reduce the width dimension of the image. In Block 3 and Block 4, the convolutional kernels are of size [3, 7] and the stride is of size [1, 1].  
Figure 7: Methodology.
## 2 Constraints and Challenges of Agricultural Sprayer UAV
### Liquid Load and Sloshing  
As is well known, the agricultural sprayer UAV carries a liquid tank, and that liquid tank has three possible situations: (a) the liquid level continuously decreases during flight; (b) vehicle flight movement changes the angle of the vehicle as well as the tank; and (c) the tank liquid create slosh on changing direction. The first situation is that the decreasing level of liquid continuously changes the tank's center of gravity.  
Second, the flight's activity changes the tank's angle, which also changes the center of gravity of the liquid tank. [PERSON], [PERSON], [PERSON], and [PERSON] [75] have shown how a tilted tank changes the center of gravity, which is shown in Figure 4. The third situation, which is the liquid sloshing, is related to the liquid level, tank tilt angle, and velocity [76; 77; 78].  
Figure 3: (a) Selected field from satellite data; (b) the generated plan for the field [70].  
In Figure 5, the sloshing impact inside a tank has been shown with a 30% filling rate which was experimented by [PERSON] [79]. He did the experiment specifically for sprayer UAV's liquid sloshing and has shown that the inner horizontal and vertical grille can effectively reduce tank liquid sloshing and the oscillation of the sprayer UAV's tank. In the literature, several works have been conducted to reduce big liquid tankers' slosh [80; 81; 82; 83; 84].
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 5 Summary and Outlook  
In this paper, by integrating SLAM positioning and map information, combined with the local stability and non-divergence of lane line positioning, and the globality and continuity of SLAM, a prior map of lane lines based on directed graphs is established to solve the problem of unmanned vehicle positioning. There is a lack of correlation between local positioning and global positioning, and the positioning results are not robust enough, especially in the case of GNSS failure, providing good local positioning information to ensure the realization of the navigation function. While preventing the divergence of global information, the positioning application of unmanned vehicles have a certain contribution. At the same time, the positioning method described in this paper can be applied to more scenes, such as semantic map information, the image information of known positioning, etc. By establishing observation equations and judging and adjusting one's own position, the accuracy of positioning can be increased further. The method proposed in this paper has practical application significance in universal map construction and localization. However, it is highly dependent on the observability of road information, and the fusion effect is limited when the road is a pure linear motion, which needs further research.  
Driverless positioning technology is developing rapidly, and more sensors and algorithms are being proposed. Sensors such as millimeter wave radar, ultrasonic radar and infrared are all important in driverless positioning technology. SLAM systems are one of the important methods of multi-source sensor fusion, and we believe that the combined application of deep learning technology and SLAM will be an important method for future driverless capability.  
Conceptualization, S.L.; Formal analysis, Y.Z.; Methodology, [PERSON] and C.S.; Project administration, H.C.; Software, C.S.; Writing--original draft, Z.L.; Writing--review & editing, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D Projects of the Science & Technology Department of Sichuan Province of China under Grant 2021 YFG0070.  
The authors declare no conflict of interest.
# Variable Curvature Displays: Optical Designs and Applications for VR/AR/MR Headsets
## 7 Conclusions  
We propose a concept, a design algorithm and an example design of projection system with a variable curvature display and show its performance advantages and physical feasibility.  
The design algorithm uses Seidel aberration theory to find the initial approximation and a multi-configuration two-step numerical optimisation to finalise the design. It was demonstrated that application of this algorithm allows designing a projection system with \(F/6.2\) aperture and \(90^{\circ}\) field of view, which is able to re-focus the projected image from infinity to \(1.5\) m. The energy concentration in a point source image is higher by a factor of \(1.54\) in comparison with a system with flat focal surface. A preliminary modelling shows that a silicon-based display can be curved to the calculated focal surface radius with the breakage safety factor of \(4.3^{\times}\).  
The developed optical system, as well as the corresponding design and optimisation approach, can help to improve image quality of the future VR/AR headsets and implement an operational mode with re-focusing. However, the prospective application area is much wider and can include, e.g., the design of multimedia projectors and laser scanning systems.  
Conceptualisation, E.M. and T.B.; Data curation, E.M. and I.G.; Formal analysis, E.M.; Funding acquisition, E.H.; Investigation, E.M.; Methodology, E.M., T.B., and K.J.; Resources, T.B. and K.J.; Software, E.M. and I.G.; Supervision, E.H.; Validation, T.B. and K.J.; Visualisation, E.M.; Writing--original draft, E.M.; and Writing--review and editing, E.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Research council through the H2020-ERC-STG-2015-67877 ICARUS program. The authors would like to thank their colleague [PERSON] for correction of the manuscript and useful discussions.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Determination of the Counts of Selected Bacterial Groups by the Culture-Dependent Method
The lower counts of _Enterococcus_ and _Enterobacteriaceae_ in autumn cheeses could also indicate that hygiene standards are strictly observed by dairy employees. Yeast counts were below log 2 cfu/g in spring chees, and they were significantly lower in autumn cheeses (log 2.82 cfu/g). The observed yeast counts were below the values given by other authors in Swiss-type cheese (3.48 cfu/g) [29], Gouda-type cheese (3.22-50 cfu/g) [29; 31], Chihuahua cheese (5 cfu/g) [34], and Serrano cheese (4-4.5 cfu/g) [30]. Similar results were reported by [PERSON] et al. [34]; in their study, yeast counts were 50% higher in cheeses produced in autumn. Different results were reported by [PERSON] et al., [30]. Higher yeast counts in autumn cheeses probably resulted from the low microbiological quality of brine or cheese contamination during packaging. Autumn chees are made from the milk of pasture-grazed cows, which is why _Clostridium_ counts in autumn cheeses should have been lower than in spring cheeses. Despite the above, _Clostridium_ counts were approximately 10% (\(p>0.05\)) higher in autumn cheeses than in spring chees. In a \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Bacteria**} & \multicolumn{2}{c}{**Season**} \\ \cline{2-3}  & **Autumn** & **Spring** \\ \hline _Lactococcus_ & 6.55 \(\pm\) 0.25  & 7.16 \(\pm\) 0.33  \\ _Leuconostoc_ & 4.60 \(\pm\) 0.99 & 4.25 \(\pm\) 0.86 \\ _Propionibacterium_ & 5.64 \(\pm\) 0.53 & 5.85 \(\pm\) 0.12 \\ _Lactobacillus_ & 5.97 \(\pm\) 0.55 & 6.64 \(\pm\) 0.51 \\ Enterobacteriaceaeaceae & 2.50 \(\pm\) 0.55  & 2.00 \(\pm\) 0.01  \\ _Enterococcus_ & 3.63 \(\pm\) 0.36  \\ _Staphylococcus_ & 3.00 \(\pm\) 0.24 & 2.70 \(\pm\) 0.79 \\ _Clostridium_ & 4.32 \(\pm\) 0.40 & 3.87 \(\pm\) 1.15 \\ _Bacillus_ & 3.35 \(\pm\) 0.66  \\ \multicolumn{1}{c}{Yeasts} & 2.83 \(\pm\) 0.26  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Counts of selected microbial groups in Edam cheeses determined by the culture-dependent method. study by [PERSON] et al. [35], _Clostridium_ counts were also approximately 9% higher in autumn than in spring cheeses. In recent years, many milk producers have been increasing the proportions of silage in the diets of lactating cows to maximize milk yields and to allow for greater flexibility in managing cattle diets. Such practices contribute to the accumulation of _Clostridium_ spores in the bovine digestive tract. Bacterial spores can be transferred to milk during milking, especially if tests are not properly cleaned [35]. Lower _Clostridium_ counts in autumn cheeses probably result from higher levels of bacteriocin synthesis by LAB (mainly: _Lactococcus_, _Propionibacterium_, and _Lactobacillus_) [36].
# A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics
## 3 The I-TROPHYTS Framework
### Technological Components  
The I-TROPHYTS framework provides the technologies needed to enable the IoT robotic environment depicted in Figure 0(a). Its components, which span across the IoT, knowledge modeling, and robotics domains, are organized into four distinct layers, as shown in Figure 0(b).  
The layers are detailed as follows:  
* _Sensing and Connectivity Layer_. This layer is responsible for collecting IoT data from patients, via sensors embedded in wearable devices, and transmitting the data towards the ES, via wireless Machine to Machine (M2M) links.
* _Edge Processing Layer_. This layer temporarily stores and aggregates, on the ES, raw time-series data from each patient, including IMU data and vital signs. It extracts second-layer information relevant to the physiotherapy session, such as automatic  
Figure 1: Framework and architecture of I-TROPHYTS.  
identification of exercises within the motor routine, 3D tracking of each sensor position, and statistical features derived from vital signs. As future work, we plan to offload some of these tasks to the extreme edge, specifically to IoT wearable devices, to reduce the amount of raw data transferred from them, thus improving both privacy and energy efficiency, in line with the edge computing approach.
* _Knowledge Modeling Layer_. This layer is responsible for modeling and organizing information related to the physiotherapy domain, enabling better data management and supporting semi-automatic decision-making by the robot. The knowledge includes: (_i_) second-level information generated by the Edge Processing Layer, which provides quantitative data on the current states of the session and individual patients; and (_ii_) domain-specific rules and constraints, such as how a physiotherapist would assess the correct execution of an exercise or respond to partial failures. We plan to develop semantic ontologies, specific to the physiotherapy domain [26], to model these rules, as discussed further in Section 6.
* _Reasoning and Actuation Layer_. This layer uses the dynamic state of the session (provided by second-layer IoT information from the knowledge modeling layer) and the static set of domain rules and constraints to plan the next actions of the I-TROPHYS framework. We distinguish between two types of outputs: (_i_) robotic actuation, which controls the physical mobility of the humanoid robot, including decisions about the next exercise in the sequence and precise control of the robotic joints; and (_ii_) interaction with the human supervisor, which involves recording the session data to a remote storage, detecting alert conditions and notifying medical supervisors, if necessary.