esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The application of Additive Manufacturing (AM) in industry 4.0
### The Applicable Materials in the AM Process
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Technique** & **Materials** & **Application** & **Advantages** & **Challenges** & **Accuracy** & **Post-Processing** & **Reference** \\ \hline Direct Energy & & Industrial purposes, & High fabrication speed, & In some cases, the & & Heat treatment, in \\ Dposition (DED) & Metals, ceramics & part repurigure, & high aspect ratios of parts, & materials are burnt due to & high laser power, the final & 100–250 μm & some cases a \\  & & implants, gluing & materials can be obtained & part accuracy is relatively & & slight deburring \\  & & & by several material nozzles & lower than SLM & & \\ \hline Selective Laser & & Industrial purposes, & biomedical complexity, a & fabrication chamber needs & & \\ Melting (SLM) & Metals, ceramics & bio-applications, & wide range of metallic and inert gas, slight metal & 50–150 μm & Heat treatment, in \\  & implants, actuators & ceramic powders, clean & evaporation in high & evaporation in high & \\  & & parts, high density & laser powers & & \\ \hline \multirow{2}{*}{Binder jeting} & Polymers, ceramics, & Industrial purposes, & High quality of the final & & \\  & metals & research, & early, high geometrical & & \\  & metals & bio-applications & accuracy, flexibility in & using bonding materials & \\ \hline \multirow{2}{*}{Metal jetting} & Polymers, plastics & Desdrop & Limitations in feedstock & & \\  & & applications, & High speed of fabrication, & material selection, low & \\  & research purposes, & high flexibility in process, & geometrical accuracy in & 5–200 μm & \\  & & bio-applications & low cost & complex parts and it is not & \\  & & & consistent & & \\ \hline \multirow{2}{*}{Sheet lamination} & Polymers, metals, and & Electronics, tissue & High speed of fabrication, & Low accuracy of the final & & Internal material \\  & ceramics & fabrication & low residual stresses & product, chance of & Depends on the & residue removal, \\  & & & low residual stresses & delamination under harsh & thickness of the & clamping in some \\  & & & thermal/mechanical conditions & sheets & cases that glue \\ \hline \multirow{2}{*}{Photo-polymerization} & Acrylonitrile & & Biomedical, & High geometrical accuracy, & Limitation in feedstock & \\  & (ABS), epoxy, & electronics, alpha & high surface quality & material selection, low & \\  & polystyrene, acrylate & prototyping & & fabrication speed & \\ \hline \multirow{4}{*}{Extrusion} & Thermoplastics such as ABS, Polykinetic acid & Visual aids, & Low geometrical accuracy, & \\  & (PLA), polyethylene, & educational models, & Simplicity, low cost, & low surface finish, only & \\  & polyber ketone, & alpha prototypes, & high speed & for polymers and & \\  & polycarbonate & & incoming models & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of different techniques of AM, materials used, and other properties of these methods.
## 4 LPWAN Technologies
### Narrowband Internet of Things  
NB-IoT is a LPWAN radio technology standard introduced by 3 GPP to enable a wide range of cellular devices and services. As a 3 GPP standard, several mobile equipment, chipsets and module manufacturers supports NB-IoT and the LPWAN standard can co-exist with 2G, 3G, and 4G networks. Furthermore, the standard benefits from distinctive mobile network features which include strong support for user identity confidentiality, authentication, and integrity. The standard enables strong connection density and they have admirable battery life. NB-IoT operate at a frequency bandwidth of 700 MHz, 800 MHz, and 900 MHz. NB-IoT is based on the characteristics of long-term evolution (LTE) protocol which enables its integration and easy deployment with LTE network. NB-IoT use Orthogonal Frequency-Division Multiple Access for downlink and Single-Carrier Frequency-Division Multiple Access [52]. With regards to the data rate allowed, NB-IoT allows up to 158.5 kbps on the UL and 106 kbps on the DL. The transmission power of NB-IoT is +23 dB and the maximum payload size for each message is 1600 bytes. [53]. Table 1 presents an analytical comparison of LPWAN technologies and their technical characteristics.
## 3 Results
### Percentage Distributions of the Likert-Scale Items
#### 3.3.1 Organisation of the Course  
This subsection presents the percentage distribution of responses to the five items (Table 2) related to the organisation of the course (see Appendix A). These items focus on the clarity and adequacy of the course guide, the overall course structure, group sizes for collaborative activities, the duration of the course in relation to its objectives and content, and the suitability of the timetable for encouraging attendance.  
With the exception of item Q4--which was excluded from the validity analysis due to low item reliability--overall satisfaction levels across the remaining items in this dimension were notably high. The percentage of participants selecting \"Satisfactory\" or \"Very satisfactory\" (responses 4 and 5) ranged from 84.4% to 93.5%. Neutral responses (score 3) ranged from 3.9% to 10.4%, while dissatisfaction (responses 1 and 2, \"Unsatisfactory\" or \"Very unsatisfactory\") remained low, between 1.3% and 5.2%. When broken down by group, the distribution of satisfaction levels was as follows:  
Group A reported 82.1-92.9% satisfaction (responses 4 and 5), 7.1-17.9% neutral responses, and 0-3.6% dissatisfaction (responses 1 and 2).  
Group B showed a satisfaction range of 78.6-100%, with neutral responses ranging from 0% to 21.4% and dissatisfaction between 0% and 3.6%.  
Group C reported satisfaction levels between 85.7% and 90.5%, neutral responses from 0% to 4.8%, and higher levels of dissatisfaction compared to the other groups, ranging from 4.8% to 14.3%.  
These group-specific findings are largely consistent with the overall results, confirming a generally high level of satisfaction with the organisational aspects of the course across the cohort.
## 4 Partitioning Detection and Connectivity Restoration  
As stated earlier, hybrid algorithms better suit mission-critical time-sensitive applications that require a rapid recovery. The proposed PCR algorithm is hybrid in the sense it consists of two parts _i.e._, pre-failure planning and post-failure recovery.  
Figure 3: Overview of the PCR algorithm.  
Figure 3 shows the overall working of the PCR algorithm. In the pre-failure planning, PCR identifies critical/non-critical actors (primary) based on localized information and designates an appropriate backup for each critical node. The backup starts monitoring its primary and detects the failure through missing heartbeats. In the post-failure recovery, a pre-designated backup if critical notifies it's backup and moves to the location of the primary. This cascaded relocation continues until a non-critical replaces the primary. The detailed algorithm is described in the sections that follow.
## 2 Problem Description
### ADS Model
\begin{aligned} \dot{V}=&(A_{x}-g\sin\theta+d_{u}) \cos\alpha\cos\beta+\big{(}A_{y}+g\sin\phi\cos\theta+d_{v}\big{)}\sin\beta+\\ &(A_{z}+g\cos\phi\cos\theta+d_{w})\sin\alpha\cos\beta\\ \dot{\alpha}=&[(A_{z}+g\cos\phi\cos\theta+d_{w}) \cos\alpha-(A_{x}-g\sin\theta+d_{u})\sin\alpha]/V\cos\beta+\\ & q-(p\cos\alpha+r\sin\alpha)\tan\beta\\ \dot{\beta}=&[-(A_{x}-g\sin\theta+d_{u})\cos\alpha \sin\beta+\big{(}A_{y}+g\sin\phi\cos\theta+d_{v}\big{)}\cos\beta-\\ &(A_{z}+g\cos\phi\cos\theta+d_{w})\sin\alpha\sin\beta]/V+p\sin \alpha-r\cos\alpha\end{aligned} \right. \tag{7}\]  
where \(\phi\), \(\theta\), and \(\psi\) represent the roll, pitch, and yaw angle, respectively. The estimated attitude angles can be obtained through the following set of motion equations:  
\[\left\{ \begin{aligned} \dot{\phi}&=p+q\sin\phi\tan \theta+r\cos\phi\tan\theta\\ \dot{\theta}&=q\cos\phi-r\sin\phi\\ \dot{\psi}&=q\frac{\sin\phi}{\cos\theta}+r\frac{\cos\phi}{ \cos\theta}\end{aligned} \right. \tag{8}\]  
The combination of Equations (7) and (8) forms the model for the air data system. The sensors of the ADS measure the angle of attack \(\alpha\), sideslip angle \(\beta\), and airspeed \(V\). The acceleration \(A_{x}\), \(A_{y}\), \(A_{z}\) and angular velocity \(p,q,r\) can be obtained through the inertial navigation system. Figure 2 illustrates the derivation relationship of the equations above.
## 3 User Study
### Procedures  
For the experiment, the subjects were divided into the remote training group and the face-to-face training group. The face-to-face group and remote group were each comprised of four teams of six as shown in Figure 6. The four instructors were assigned to teach one session to the face-to-face group and one to the remote group. Both groups used a mannequin equipped with a feedback device, and the students belonging to the remote training group received their mannequins by mail. The instructors and students used their own smartphones. The students in the remote group installed the remote app, while those in the face-to-face group installed the AoK app. For the remote training group, all students were given a mobile device stand to reduce limitations in posture evaluation that may come from using the front camera alone. Before the start of the session, a silhouette-like ghost image was displayed as a guide for students to place their cameras in the same position and angle.  
The two groups were trained under the general BLS program, which lasted for an hour excluding artificial respiration due to the pandemic. The students in the remote group studied theories while looking at the instructor's face on their smartphone screens. Both groups followed directions given by instructors for the hands-on practice on the mannequin. Feedback data was displayed only to instructors, and student evaluation was carried out for two minutes. After evaluation, the instructors began debriefing, and held a Q&A session. At the end of the experiment, the subjects were asked to complete and submit the survey (Figure 7).
# Towards Adaptive Gamification: A Method Using Dynamic Player Profile and a Case Study
## 3 Runtime Method for Adaptive Gamification
### Adaptive Method  
Our method relies on the fact that the participant's inner player type (or real player type, \(RPT\)) may evolve slightly (i.e., is dynamic) during the experience. We therefore assume that this inner profile at the start of the experience, \(RPT_{0}\), can be approximated by an initial player type, \(PR^{(0)}\), using known and validated questionnaires [20]. Furthermore, we assume that the users' behavior--interactions with and opinions of game elements--reveal their real player type, \(RPT^{t}\). Thus, we will take into account this behavior to approximate a different \(PR^{(t)}\) during the experience. To recompute the player type in each iteration \(t\), our algorithm takes into account the current \(PR^{(t)}\) as well as the interactions that users made with the different game elements and how they rated them.  
Once the type of initial player is defined, our method iteratively updates the player profile of the user at time \(t\), \(PR^{(t)}\), and thus calculates the utility of showing one or another game element to the user, \(ge_{i}^{(t)}\). Each iteration consists of the three steps depicted in Figure 2 (considering the definitions introduced in Section 3.1). The steps are: (1) obtain \(PR^{(t+1)}\); (2) score the utility of showing a game element to a user at a specific time \(t+1\) (denoted by \(U^{(t+1)}\)); and (3) select which game element to activate based on the assigned scores.  
In the first step, we compute the new player type ratings, \(PR^{(t+1)}\):  
\[PR^{(t+1)}=(1-\epsilon)\ PR^{(t)}+\epsilon\ (M^{+}\cdot S^{(t)}) \tag{8}\]  
where  
\(PR^{(t)}\): the player type of the user at time \(t\);  
\(S^{(t)}\): the interaction indexes;  
\(\epsilon\): to avoid extreme fluctuations between \(PR^{(t)}\) and \(PR^{(t+1)}\), where \(0<\epsilon<1\)--the value of this parameter should be tuned experimentally;  
\(M^{+}\): the Moore-Penrose pseudoinverse matrix of \(M\), needed in order to interpret \(S^{(t)}\) and \(PR^{(t)}\) in the same space.  
In the second step, once we calculated the new player profile, we compute the utilities as indicated in the top right-hand side of Figure 1, using the matrix \(M\) defined in Equation (5):  
\[U^{(t+1)}=M\cdot PR^{(t+1)} \tag{9}\]  
Finally, in the third step, we select the next game element to display considering the \(i\)th component of \(\frac{U^{(t)}}{\|U^{(t)}\|_{1}}\) as the probability of choosing the \(i\)th game element using a weighted random choice (\(\|\cdot\|_{1}\) is the \(\ell_{1}\)-norm).
# Analysis of Vertical Stiffness Characteristics Based on Spoke Shape of Non-Pneumatic Tire
###### Abstract  
Recently, research regarding non-pneumatic tires that are resistant to punctures has been actively conducted, and the spoke structure design of non-pneumatic tires has been found to be a crucial factor. This study aimed to analyze the vertical stiffness characteristics of a non-pneumatic tire based on the shape of the spoke under the application of a vertical load. The three-dimensional model of a commercial non-pneumatic tire was obtained from the manufacturer (Kumho Tire Co., Inc., Gwangju, Korea), and the vertical stiffness characteristics of the three tire models with modified spoke shapes were compared and analyzed based on a reference tire model. Results show that the vertical stiffness of the filled applied model is most appropriate. Furthermore, the vertical stiffness characteristics of the analyzed tire models indicate that if fillets with a minimum size are applied to the spokes, the stability of the non-pneumatic tire is expected to improve.  
airless tire; non-pneumatic tire; spoke shape; branch-shaped spoke; vertical stiffness Article Analysis of Vertical Stiffness Characteristics Based on Spoke Shape of Non-Pneumatic Tire  
1  
[PERSON]; [PERSON]; [PERSON]; [PERSON] Analysis of Vertical Stiffness Characteristics Based on Spoke Shape of Non-Pneumatic Tire. _Appl. Sci._**2021**, _11_, 2369. [[https://doi.org/10.3390/app11052369](https://doi.org/10.3390/app11052369)]([https://doi.org/10.3390/app11052369](https://doi.org/10.3390/app11052369))  
Accademic Editor: [PERSON]  
Received: 24 January 2021  
Accepted: 2 March 2021  
Published: 7 March 2021  
**Published:**  
rectangular structure [4]. [PERSON] et al. investigated the dynamic and static behaviors of a non-pneumatic tire with honeycomb-structured spokes according to the angle of spokes [5].  
of the tire was 355.60 mm, which is comparable or equivalent to 14-inch wheels. Light-duty vehicles, such as KIA RAY and GM Chevrolet Spark, use this type of tire. The outer diameter and width of the tire were 554 mm and 130 mm, respectively. The thickness of the shear beam was 17 mm, and the thickness of the inner band in contact with the wheel was 10 mm. There are three rows of grooves on the outer surface of the tire, which represent a simplified model of the tread. The illustration on the left in Figure 3 shows the spokes of the tree branch structure at a scale of 2:1.  
Between the two spokes, the radius of the outer arc of the long part was 80.38 mm, and the radius of the inner arc was 70.24 mm. In addition, the arc was refracted once such that it was in contact with the shear beam, and the radius of this arc was 36.90 mm. Between the two spokes, the radius of the outer arc of the short part was 105.69 mm, and the radius was then refracted to 97.75 mm. The arc of the part in contact with the inner band had a radius of 23.75 mm, and the radius of the inner arc of the short part was 95.33 mm. The tree branch structure comprised 40 spokes, and the optimized results of numerous studies conducted by Kumho Tire Co., Inc. were used as the detailed dimensions.
## 4 Borehole Resistivity Monitoring of Overburden
### Monitoring Scheme  
Based on the analysis of the geological conditions of the working face, the observation system is arranged near point Y12 of the auxiliary transportation roadway of the working face to observe the dynamic change process of the \"three zones\" of the overburden after the gradual advance of the working face and to determine the height of the caving zone and the water-flowing fractured zone. The layout diagram of the observation system is shown in Figure 6. The three boreholes are located at the same position, which is the auxiliary transportation roadway of the adjacent working face. The position is 620 m away from the working face, which can make it easier to observe the whole overburden process, from failure to stability. The final position of borehole #1 is located at the edge of the working face, and the final positions of borehole #2 and borehole #3 are located inside the working face, which can facilitate accurate detection of the height of the water-flowing fractured zone with a saddle-shaped fracture.  
The installation of the working face overburden resistivity monitoring system began on 4 March 2018, and all of the systems were installed and grouted until 11 March 2018. After the installation of the whole monitoring system, the cable in the borehole was tested first. After the cement slurry in the borehole was fully consolidated and stable, the background value was collected, which provided an effective reference for later monitoring data. The resistivity data acquisition in the whole monitoring period is shown in Figure 7.  
Figure 6: Schematic diagram of observation system layout.  
Figure 7: Monitoring cycle data collection and recording.
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 2 Related Work on Point Cloud Attribute Compression
### Point Cloud Reorganization and Clustering
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**3D Geometry Location in X Y Z Axis**} & \multicolumn{2}{c}{**Hilbert Code**} \\ \hline
0 & 0 & 0 & (0 0 0) \\
0 & 1 & 0 & (0 1 1) \\
0 & 1 & 1 & (0 1 0) \\
1 & 0 & 0 & (1 1 1) \\
1 & 1 & 0 & (1 0 0) \\
1 & 1 & 1 & (1 0 1) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The mapping rules from the locations in the 3D cube to the Hilbert code. Figure 2: The proposed Hilbert-based point reorder operation. (**a**) An example of the fitted Hilbert filling curve in a \(2\times 2\times 2\) 3D cube. The numbers from 0 to 7 represent the Hilbert-based order of points in the 3D cube. (**b**) An example of original points reordered by the traditional Morton code and our Hilbert code. prediction units in subsequent processing. Specifically, suppose there are \(m\) resorted points in the current point cloud to be assigned into \(k\) clusters. In that case, the desired number of points \(n\) for each cluster is computed as \(n=\text{floor}\big{(}\frac{m}{k}\big{)}\), where the floor function \(\text{floor}(\cdot)\) provides the nearest integer down of the value \(\frac{m}{k}\). Let \(p=(p_{1},\ldots,p_{m})\) denote the resorted points following a Hilbert space-fitting curve of the point cloud. With the uniform clustering method, the resorted point sets \((p_{1},\ldots,p_{n})\) are assigned to the first cluster, and the resorted point sets \((p_{i},\ldots,p_{i\cdot n})\) are assigned to the \(i\)th cluster, where \(i=1,\ldots,(k-1)\). The last cluster contains the resorted point sets \((p_{n(k-1)+1},\ldots,p_{m})\). An important property of the proposed Hilbert-based point cloud reorganization is that two consecutive resorted points on the curve differ in their coordinate value in only one dimension, while the coordinate values in other dimensions remain unchanged. This ensures spatial coherence among all resorted points. Leveraging this property, the proposed Hilbert-based uniform clustering method not only generates a series of point cloud subsets with a uniform number of points but also provides a compact representation and avoids density jumps observed in other point cloud organizations like the Morton-based methods.
# Error Analysis and Calibration Improvement of the Imaging Section in a Mueller Matrix Microscope
## 2 Materials and Methods  
### Experimental Setup  
As shown in Figure 1, we set up a polarized Mueller matrix microscope in our laboratory by integrating a polarization state generator (PSG) and a polarization state analyzer (PSA) into a commercial microscope structure. A light-emitting diode (LED) with a center wavelength of 633 nm and bandwidth of 10 nm was used as the light source. A lens was placed in front of the light source to collimate the diverging light. Both the PSG and PSA contain a fixed linear polarizer (GCL-050003, Daheng Optics, Beijing, China) and a rotating quarter-wave plate (WPQ10 ME-633, Thorlabs, Newton, NJ, USA). An electric rotary platform (KPRM1E/M, Thorlabs, Newton, NJ, USA) with angular control precision of 1 arc sec was used to control the angular position of the quarter-wave plate. A 14-bit charge-coupled device (CCD) sensor (Retiga R6, QImaging, Tucson, AZ, USA) was used as the light-intensity detector.  
The light emitted by the light source passes through the collimating lens, PSG, measured sample, commercial objective lenses (marked as low polarization or even nonpolarizing [23]), and PSA before being detected by the CCD camera. One common measurement scheme consists of 30 different intensities detected by the CCD; at each step, the wave plate in the PSG is rotated by 6\({}^{\lx@math@degree}\), and the wave plate in the PSA is rotated by 30\({}^{\lx@math@degree}\)[11]. This measurement scheme has a condition number of the instrument matrix of about 20, which is a minimum value [19], and the measurement scheme was adopted for our instrument. Finally, the Mueller matrix elements were computed from the measured intensities.  
Figure 1: (**a**) Photograph and (**b**) schematic of the Mueller matrix microscope. Here, CCD: charge-coupled device, LED: light-emitting diode, PSA: polarization state analyzer, and PSG: polarization state generator.