esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Understanding WebAssembly
### Web Embedding  
It comes as no surprise that Wasm is designed to be utilized on the web, primarily within web browsers. However, its purpose is not limited to the web alone. The integration of Wasm with the web platform involves various aspects, such as leveraging existing web APIs, adhering to the web's security model, ensuring portability, and allowing for evolutionary development. These objectives align closely with the overarching goals of Wasm. Notably, the Wasm Minimum Viable Product (MVP) aims to maintain a security level no less stringent than that of JavaScript modules. To facilitate this integration, several key points of interaction between Wasm and the web platform have been considered:  
* **JavaScript API** A dedicated JavaScript API enables developers to compile Wasm modules, perform limited reflection on compiled modules, store and retrieve compiled modules from offline storage, instantiate modules with JavaScript imports, invoke exported functions from instantiated modules, and establish memory aliases, among other functionalities. It is important to note that these APIs may not be available in non-web environments.
* **Developer-facing display conventions** Similar to how browsers and JavaScript engines handle JavaScript artifacts and language constructs, Wasm adopts conventions for representing its constructs in a developer-friendly manner. For example, locations in Wasm binaries can be displayed similarly to JavaScript source locations, ensuring consistency across different contexts.
* **Modules** Wasm modules seamlessly integrate with the ES6 module system, facilitating interoperability between the two.
* **Names** Wasm modules utilize UTF-8 byte sequences to identify imports and exports. To align with web conventions, a mapping of export names to exports is represented as a JavaScript object, where each export is a property with a UTF-16 encoded name. On the web platform, successful transcoding of names to UTF-16 is a validation requirement for Wasm modules.
* **Security** Wasm's security model aligns with the web's same-origin policy, employing mechanisms such as cross-origin resource sharing (CORS) and subresource integrity to enable distribution through content delivery networks and support dynamic linking.
* **Future Features** When features such as Single Instruction, Multiple Data (SIMD), and garbage collection become supported, Wasm will adhere to established conventions, leveragingspecifications from existing standards such as SIMD.js and TC39 for SIMD operations and reusing backend implementations. With garbage collection support, Wasm code will be capable of referencing and accessing JavaScript objects, the DOM, and other WebIDL-defined objects.  
By taking these considerations into account, Wasm integrates seamlessly into the web platform, making use of existing infrastructure while enhancing web applications with improved capabilities and performance.
## 3 Cooperative Communication Algorithms
### Cooperative Communication
#### 3.1.2 KCN Algorithm  
When choosing the cooperative node, the source node broadcasts and sends PROB messages, and K cooperative nodes in each hop are determined by a certain algorithm. Among all cooperative nodes in each hop, one node is chosen as reference node (RN). When data is forwarded, the reference node assigns a backward delay to each cooperative node, and the skillful points with high performance will be assigned high priority. After receiving the broadcast packet, the backward delay is started by the cooperative node, and the node with the shortest countdown will continue to broadcast the packet. The comparison between KCN (k-cooperative node) and beacon-free routing is shown in Table 1.  
In KCN, under the premise of ensuring the reliability, the number of activated cooperative nodes is reduced to optimize network performance, and an ETE reliability model is established to explore the relationship between several parameters. These parameters include duty cycle, link failure rate, the average node density, the number of cooperative nodes in one hop, the hop count, and the reliability, so that the optimal parameters are set.
# Dielectric Characterization of Chinese Standard Concrete for Compressive Strength Evaluation
## 3 Experimental Results
### Dielectric Constant Modelling  
In order to characterize the measured dielectric constants of GB concrete, which have a common dispersive nature with increasing frequency, three types of mathematical regression models were considered, and will be discussed in this section.
# A Novel Localization Technique Using Luminous Flux
## 2 System Configuration Method  
The aim of this study is to investigate the impulse response of the VLC channel to exploit localization, which is done using confined luminaries. The luminaries, commonly called light-emitting diodes (LEDs), act as a transmission source for the light signal and the photodiode (PD), which collects information and acts as a receiver.
## 3 Materials and Methods
### Type of Multi-Next-Hops Selection  
The multi-next-hop approach can take various forms depending on the relevance of the participating node (Figure 4): (i) disjoint nodes, (ii) disjoint links, and (iii) non-disjoint nodes or links [44].  
Figure 4: Illustration of the three types of hop selection according to the weight of the links; (**a**) initial graph, (**b**) with disjoint nodes, (**c**) with disjoint links, and (**d**) with non-disjoint nodes or links.  
```
_l_BN \(\leftarrow\)0; //Best neighbors set within the range of the sender node  cst \(\leftarrow\) 20%; //constant FunctionnpNexthop(destx,desty)  nexthop \(\leftarrow\)Null;  if(l_bN is empty)  createListBN(destx,desty);  end if if(l_bN is not empty)  nexthop \(\leftarrow\)l_bN(0); //assign to nexthop the first item on the list  l_bN.remove(0); //remove the first item from the list  l_bN.pushback(nexthop);/add it at the end of the same list  end if  return nexthop; End Function ProcedurecreateListBN(destx,desty)  l \(\leftarrow\) {nr1, nr2, nr3, \(\ldots\), nri} \(\in\) Range(SenderNode);  nexthop;  score, thresholdScore; if(l_bN is not empty)  l_bN \(\leftarrow\)0;  end if  minScore \(\leftarrow\) Infinity;  While(l is not empty)  nexthop = fuzzyNextHop(destx,desty,l);  if(nexthop = null)  exit while;  end if  score \(\leftarrow\) getScore(nexthop,destx,desty);  if(score \(<\) minScore)  minScore \(\leftarrow\) score;  thresholdScore \(\leftarrow\) minScore \(\leftarrow\) (1 + cst);  else if(thresholdScore \(<\) score)  exit while;  end if  end if  end if  l_removeElement(nexthop); //remove method from the list of neighbors  l_bN.pushback(nexthop); //add it to the list of best neighbors  end while End Procedure FunctionfuzzyNexthop(destx,desty,l) //here, l is the list of neighbors //that belong to the range of the sender node  minScore \(\leftarrow\) getscore(nr1,destx,desty);  nexthop \(\leftarrow\) nr1;  For each l  score \(\leftarrow\) getscore(nr1,destx,desty);  if(score \(<\) minScore)  minScore \(\leftarrow\) score;  nexthop \(\leftarrow\) nri;  End if  End for  return nexthop; End Function
```  
**Algorithm 1** Intelligent Multi-Next-Hop Algorithm of MNH-FGR
## 3 Implementation
### Analytical Solution of Lamb Waves  
The analytical estimation of the dispersion curves is performed by the Rayleigh-Lamb equations for plates considering only real values for wavenumbers \(k\). This is determined by  
Figure 3: Schematic diagram of the proposed algorithm for plotting dispersion curves by SBFEM.  
the following implicit equations: for symmetrical \(S\) Lamb waves, Equation (33), and for antisymmetric \(A\) Lamb waves, Equation (34),  
\[\frac{\tan(qh)}{q}+\frac{4k^{2}p\ \tan(ph)}{(q^{2}-k^{2})^{2}}=0\ for\ (S), \tag{33}\]  
\[q\ \tan(qh)+\frac{(q^{2}-k^{2})^{2}\tan(ph)}{4k^{2}p}=0\ for\ (A), \tag{34}\]  
where the \(p\) and \(q\) parameters are presented by the following set of equations:  
\[p=\sqrt{\frac{\omega^{2}}{C_{L}^{2}}-K^{2}}, \tag{35}\]  
\[q=\sqrt{\frac{\omega^{2}}{C_{T}^{2}}-K^{2}} \tag{36}\]  
The solutions to Equations (33) and (34) are obtained via an implicit k and \(\omega\) equation problem. Thus, the real roots are computed with a MATLAB program set up using the _findAllZeros_ function with a tolerance of \(1\times 10^{-16}\). The in-plane and out-of-plane particle motions across the plate thickness are described by the wave structures, and are obtained for symmetric and antisymmetric modes from the following equations.  
Symmetric solution:  
\[u_{x}=-2k^{2}q\ \cos(qd)\cos(py)+q(k^{2}-q^{2})\ \cos(pd)\ \sin(qy), \tag{37}\]  
\[u_{y}=-2 ikpq\ \cos(qd)\ \sin(py)-ik(k^{2}-q^{2})\ \cos(pd)\ \sin(qy) \tag{38}\]  
Antisymmetric solution:  
\[u_{x}=-2k^{2}q\ \sin(qd)\sin(py)+q(k^{2}-q^{2})\ \sin(pd)\ \sin(qy), \tag{39}\]  
\[u_{y}=-2 ikpq\ \sin(qd)\ \cos(py)+ik(k^{2}-q^{2})\ \sin(pd)\ \cos(qy). \tag{40}\]  
Figure 4 shows the direction of in-plane and out-of-plane displacement for modes A and S with respect to the plane of symmetry at the center of the plate. The amplitude along the thickness is fully described by the wave structures.
# Non-Mechanical Beam Steering with Polarization Gratings: A Review
## 2 Non-Mechanical Beam Steering with PGs
### Optical Properties of PG-Based Non-Mechanical Steering Systems
#### 2.2.3 Wavefront Quality  
Many beam steering applications of interest require high wavefront quality in the steered beam. This is particularly true for many coherent lidar and passive imaging applications. In a typical PG steering assembly, wavefront error can arise from the quality of the grating patterns, the uniformity of the LC cells, the flatness of the glass substrates, and stress-induced aberrations caused during assembly. Despite these varied potential sources of aberration, diffraction-limited performance is easily achieved in PG steering assemblies in real manufacturing environments. Figure 4a,b show wavefront measurements of a 10 cm clear aperture PG and LC handedness switch, respectively, measured at 633 nm by a Shack-Hartmann wavefront sensor. In both cases, the glass substrates used were inexpensive 2-mm thick Corning Eagle XG display glass.  
Although diffraction-limited wavefront can be achieved with PG steering, it is important to note that steering a beam waist through a fixed planar aperture results in a foreshortening of the beam in the axis of steering (Figure 5). This is a geometric effect akin to the foreshortening that occurs when deflecting light with a prism at normal incidence to the entrance face. When using a single prism (or grating), this effect can be minimized by orienting the prism at the angle of minimum deviation. This is not possible with a PG steering assembly due to the varying output angles, and the resulting anamorphic magnification results in asymmetric divergence at increased steering angles. This is a trait shared by Risley prism [41] and planar phased array [42] (RF and optical) steering systems as well and for the same reasons. It should be noted that this effect does not introduce a wavefront error, but any wavefront variation will be magnified by the foreshortening, which for example can cause a spherical wavefront to become more cylindrical.  
Figure 4: Measured wavefront error for (**a**) a 10 cm clear aperture PG and a (**b**) 10 cm clear aperture nematic LC variable retarder, both designed for 1550 nm operation. Measurements taken by Shack-Hartmann wavefront sensor at (**a**) 532 nm and (**b**) 633 nm test wavelengths and scaled to 1550 nm design wavelength.  
Figure 3: Efficiency of an example PG beam steering (**a**) single stage and (**b**) 5-stage device. In (**a**), a 30-mm clear aperture nematic ECB cell is mated to a PG steering 1550 nm light to \(\pm\) 3.5\({}^{\circ}\). In (**b**), a 50-mm clear aperture beam steering assembly was built to steer 905 nm light over \(\pm\) 37\({}^{\circ}\) in approximately 3\({}^{\circ}\) steps.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**Mean Rank**} & \multirow{2}{*}{**Mann–Whitney U**} & \multirow{2}{*}{**Z**} & \multirow{2}{*}{\(p\)**} & \multirow{2}{*}{\(r\)**} \\ \cline{2-2} \cline{5-6}  & **Group 1** & & & & & \\ \multicolumn{1}{c}{} & **n = 20** & & & & \\ \hline Average duration & 15.25 & 16.00 & 95.00 & \(-0.22\) & 0.83 & \(-0.04\) \\ Total Time Interest duration & 15.90 & 16.20 & 103.00 & \(-0.09\) & 0.93 & \(-0.02\) \\ Total recording duration & 15.29 & 17.50 & 90.00 & \(-0.63\) & 0.53 & \(-0.11\) \\ Fixation point X & 15.62 & 16.80 & 97.00 & \(-0.34\) & 0.74 & \(-0.06\) \\ Fixation point Y & 15.87 & 16.70 & 98.00 & \(-0.30\) & 0.77 & \(-0.05\) \\ Average pupil diameter & 15.24 & 17.60 & 89.00 & \(-0.68\) & 0.50 & \(-0.12\) \\ Saccade direction & 14.90 & 18.30 & 82.00 & \(-0.97\) & 0.33 & \(-0.18\) \\ Saccade average velocity & 16.48 & 15.00 & 95.00 & \(-0.42\) & 0.67 & \(-0.08\) \\ Saccade peak velocity & 16.43 & 15.10 & 96.00 & \(-0.38\) & 0.70 & \(-0.07\) \\ Saccade amplitude & 15.67 & 16.70 & 98.00 & \(-0.30\) & 0.77 & \(-0.05\) \\ GSR amplitude & 16.14 & 15.70 & 102.00 & \(-0.14\) & 0.88 & \(-0.03\) \\ SCR count & 15.36 & 17.35 & 91.50 & \(-0.64\) & 0.52 & \(-0.12\) \\ Score of multiple-choice test & 13.79 & 20.65 & 58.50 & \(-1.98\) & 0.05 & \(-0.36\) \\ Project execution score & 13.90 & 20.40 & 61.00 & \(-1.92\) & 0.05 & \(-0.35\) \\ Project presentation score & 16.26 & 15.45 & 99.50 & \(-0.25\) & 0.81 & \(-0.05\) \\ Satisfaction with learning & 14.93 & 18.25 & 82.50 & \(-1.05\) & 0.30 & \(-0.19\) \\ Satisfaction with virtual lab & 14.02 & 20.15 & 63.50 & \(-1.87\) & 0.06 & \(-0.34\) \\ Anxiety lab use & 13.19 & 21.90 & 46.00 & \(-2.52\) & 0.01 \({}^{*}\) & \(-0.46\) \\ \hline \hline \end{tabular}  
* \(p<0.05\). Note: \(r=z/\sqrt{8}\) where N is the total number of participants [36]. The interpretation of effect sizes is as follows: \(r>0.3\) small effect, \(r=0.3\)-0.5 medium effect and \(r<0.5\) large effect. Group 1 = undergraduate students in Occupational Therapy; Group 2 = undergraduate students in health engineering. One experimental death occurred in Group 1.  
\end{table} 
Table 4: Mann–Whitney U test for differences in degree type. Figure 4: Example of gaze point for members of Group 1 vs. Group 2. Figure 5: Example of heat map for members of Group 1 vs. Group 2. In Group 1 (health science students), 10 students chose to visualise the virtual laboratory with voice (50%) and 10 without voice (50%). In Group 2 (biomedical engineering students), six students visualised the virtual laboratory (60%) with voice and four without voice (40%).
## 1 Introduction  
Technological advancement has always benefitted the medical field, especially in the enhancement of the medical environment. Recent amelioration in algorithmic science allows us to simulate and solve many'real system' problems, previously inaccessible due to practical limitations such as including unknown dynamics and large or continuous state, action, and parameter spaces in data poor environments [1; 2]. 'Noise' is one of the real system problems currently accessible through this method.  
Computer aided design and computer-aided manufacturing (CAD/CAM) milling has proven to be advantageous in various fields, through improved product design, higher productivity, better utilization, and stronger quality control [3]. Advanced CAD/CAM technologies have actualized precise and patient-customized prosthesis in the field of dentistry [4]. However, the 3D CAD/CAM milling machines work as a source of disruptive noise, as they add quite a cacophony to the dental environment. This has been acknowledged as an even greater problem [5; 6].  
It is evident that stress and disruption causing external sounds in the dental environment should be regulated [7]. Currently, there are limited amount of research on noise reduction in dental clinics, and a serious lack of research on modification and qualitative aspects of noise in dental environments.  
Our previous study [8] demonstrated that noise reduction of CAD/CAM dental milling machines can be done through an active vibration control (AVC) method with tactile transducers.  
Earlier studies accomplished noise reduction through active noise control methods [9]. Moreover, a recent study identified'residual noise', after noise reduction, as an important key in increased auditory comfortability, and the consumer's emotional quality [10].  
Previous literature by Genuit reported a reduction in unwanted noise through technical development [11]. However, they considered the complete removal of such noise from machines, as impossible. It is notable that [PERSON] et al. suggested that any kind of noise can be used as music as a consequence of more adaptation of noises using technologies [12].  
The size of the noise felt is not solely determined by the sound pressure level, and thence it is necessary to focus on changing the quality in terms of auditory characteristics [13]. A product's noise is an important factor for consideration in relation to user's convenience. This all comes down to a conclusion that attenuation of noises, as well as converting noises generated from CAD/CAM milling machines into more subjectively suitable sounds, is a crucial factor for the well-being of the dental environment.  
In order to improve this problem, we propose a noise improvement method through sound source characterization and sound control technology. Our study focuses on objective criteria that can make a rasping sound into a sound pleasant to the ear. We also focus on sound enhancement through tone analysis of instruments and structural understanding of harmonics. Our approach is expected to contribute the noise quality improvement process as real-time sound quality enhancement systems.
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 4 Performances Evaluations
### The Experimental Setup  
We chose some more typical cache placement strategies to compare with our scheme, such as LCE, LCD [24], ProbCache [25], CL4M [12]. For the caching strategies used for comparison, we chose LRU as the cache replacement algorithm. In our experiment scenario, to verify the scalability of PSBC in different network topologies, we chose the following real-world network topologies: GEANT (European academic network), WIDE (Japanese academic network), and GARR (Italian academic network). The topologies are shown in Figure 3. In the three topologies, nodes of degree 1 were set as receivers.  
According to our optimization objectives, we used two performance metrics (KPIs), i.e., cache hit ratio and average access latency, to measure the performance of the strategies. Cache hit ratio measures the portion of content requests served by a cache, and average access latency measures the delay taken to deliver content.  
The selection of experimental parameters is shown in Table 2. In our simulation, we set the amount of content in the network to 3 \(\times\) 10\({}^{5}\), which is a reasonable amount of content in network topology and can be used to simulate real scenes. We set the number of warm-up requests to 3 \(\times\) 10\({}^{5}\) and the number of requests tested to 6 \(\times\) 10\({}^{5}\). We assumed that the user's requests follow the Poisson distribution at a rate of 10 requests per second. The cache size for each node is the same. Cache size ratio reflects the total network cache size as a percentage of the total content, and we set its range to [0.001, 0.002, 0.004, 0.01] for simulation. We assume the content requests follow the Zipf popularity distribution, and the parameter skewness (\(\alpha\)) of Zipf distribution is set to the range [0.2, 0.4, 0.6, 0.8, 1.0].
## 3 Data and Methodology
### Methodology
#### 3.2.3 Spatial Statistical Analysis  
Hot spot analysis was proposed to calculate the degree of aggregation of the deformation points [39]. For a detection location, \(i\), within a given range of distance, the ratio of the sum of the attribute values of the features at adjacent locations to the sum of the attribute values of the features at all locations is used to calculate whether \(i\) and the surrounding adjacent location features are high-value aggregations or low-value aggregations. The equation is as follows:  
\[G_{i}(d)=\frac{\sum_{j=1}^{n}w_{ij}(d)x_{j}}{\sum_{j=1}^{n}x_{j}}(j\
eq i) \tag{3}\]  
where \(w_{ij}(d)\) represents the spatial weight matrix of the topological relationship between the elements \(i\) and \(j\); \(\sum_{j=1}^{n}w_{ij}(d)x_{j}\) represents the sum of the attribute values of all \(x_{j}\) elements within the distance \(d\) of the point (excluding \(x_{i}\)); and \(\sum_{j=1}^{n}x_{j}\) is the comprehensive attribute value of all \(x_{j}\) elements (not including \(x_{i}\)).  
After hot spot analysis, we could obtain the G_Bin and Z-score of each element. Here, G_Bin indicated the confidence interval, which was used to obtain statistically significant points. Z-score is expressed as a multiple of the standard deviation and was used for kernel density analysis.  
Kernel density analysis was used to calculate the density of the elements in a neighborhood around the elements to determine the degree of aggregation of the point elements or polyline elements in each grid cell. In analysis of the point elements, it is assumed that a smooth, curved surface is fitted over each point. The surface value is highest at the position  
Figure 5: Flowchart of the visibility analysis of the study area. Note—\(\theta\) is the average incident angle of the Sentinel-1A images in Table 1.  
of the point and gradually decreases with an increase in the distance to the point. The volume of the space enclosed by the surface and the plane below is equal to the value of the population field of this point. If the value of the population field is specified as NONE, the volume is 1. The density of each output raster cell is the sum of the values of all core surfaces superimposed on the center of the raster cell.  
The functional equation of the kernel density analysis is as follows:  
\[f(x)=\frac{1}{nh}\sum K\bigg{(}\frac{x-x_{i}}{h}\bigg{)} \tag{4}\]  
where \(f(x)\) is the probability density function; \(h\) is the window size; \(n\) is the number of deformation points; and \(K(x)\) is the kernel function.