esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Realization of IFC-Based 4D Construction Information Model of Prefabricated Buildings
### IFC Standard and Its Extension Mechanism  
The IFC standard's data schema is divided into four conceptual layers, from bottom to top: resource layer, core layer, interop layer, and domain layer. The quotation of information between the various layers in the IFC schema complies with strict regulations that the next layer prohibits reference to the upper layer's information. Therefore, when the IFC standard needs to be extended, it is only necessary to add information to the upper structure of the IFC standard, and new categories can be created by quoting the data from the lower layer by the upper layer without changing the overall schema of the IFC standard.  
There are three main extension mechanisms [46]: The first one is to increase the IFC entity types [47]; the second method is to instantiate IfcProxy, IfcBuildingElement [48]; the third method is to increase the property sets. A comparison of the three extension methods is shown in Table 2. The three methods have their advantages and disadvantages and are suitable for different application needs. The method based on increasing entity types has the highest operational efficiency and consumes the most time. Therefore, for choosing the extension methods of the IFC-based 4D construction management models of prefabricated buildings, this paper will avoid increasing entity types, which means that the extension process in this paper should use IfcProxy, IfcBuildingElement, and property sets more.  
Realizing the Extension of IFC-Based 4D Construction Management Information Model of Prefabricated Buildings  
Much construction management information will be obtained outside the construction site for highly industrialized characteristics of prefabricated buildings. To dynamically manage the construction information, a framework is proposed in this paper to establish an IFC-based construction management information model of prefabricated buildings. Furthermore, since all components are discretized during the prefabricated building construction process and can only correspond to one task simultaneously, this information model will center on IfcTask. In realizing the IFC-based information model's extension, this article will center on the IfcTask entities and associate all the related process information, resource information, cost information, component information, and construction schedule analysis information.
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 4 Architecture
### Integration Architecture of 5G and TSN  
3 GPP TR 23.734 [29] defines two ways of modeling 5 GS from the perspective of TSN:  
1. Link Model: The 5 GS acts as a simple link between several TSN switches, and therefore corresponds to a cable linking two TSN-capable devices.
2. Bridge Model: The 5 GS acts as a TSN bridge itself, being a node in the TSN domain, and receives configuration data from the TSN control plane.  
The Link Model is easier to implement than the Bridge Model, as it only requires a nearly constant bandwidth and no kind of traffic shaping. The link between the User Plane Function (UPF) and the UE is mapped to an Ethernet connection between two TSN devices. The corresponding PDU session within the 5 GS needs to be configured accordingly. This requires the QoS parameters allocated by the PCF to be either fixed for such kinds of connections, which may lead to inappropriate dimensioning of the values, or dynamically negotiated with the TSN control plane, which can require adaption of the TSN standards. On the other hand, the Bridge Model is more in line with the characteristics and capabilities  
Figure 1: Example of the filling out of a Transport Block for a UE.  
of the 5 GS, which is a self-organized instance connecting many ports over a wide area. For TSN itself, we use the centralized architecture model, because it corresponds to the strategy of Control and User Plane Separation (CUPS) instantiated in the Fifth Generation Core (5 GC).  
This leads to the integration architecture of 5G TSN, where the CNC is connected to a TSN Application Function (AF) in the 5 GC. At the edges of the 5 GS, i.e., behind the UE and the UPF, two Translator Functions, a Device-side Translator Function (DS-TT) and Network Translator Function (NW-TT), are deployed; these provide information about both the 5G time domain and the TSN time domain for synchronization purposes. In addition, they are meant to execute Hold/Forward Mechanisms (H/F) according to the different traffic classes in order to hide the 5G internal packet forwarding behavior from the TSN domain. The architecture regarded in this work is illustrated in Figure 2.  
The configuration process for a TSN stream starts with the communication partners sending their requirements to the CUC, which forwards the requirements of all nodes to the CNC. The CNC computes the configuration scheme for all nodes of the network and distributes it. In the case of the 5G Bridge, this configuration in the form of GCLs is received by the TSN AF, which translates them into QoS parameters interpretable by the 5 GC. According to these parameters, QoS Flows are then set up for the TSN traffic classes. This is achieved by the Policy Control Function (PCF), which provides all QoS parameters for the associated PDU Session to the Session Management Function (SMF). The SMF then configures the UPF accordingly and forwards the configuration data to the NG-RAN via the Access and Mobility Function (AMF).
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## 2 Theoretical Background
### Ordinary Kriging
A class of parametric and non-parametric variogram models is thus used to fit the empirical variogram. In the case of isotropy, the commonly utilized parametric variogram models include linear, exponential, spherical, and Gaussian models. The specific formulas for these parametric variograms, along with essential parameters such as nugget, sill, and range, can be found in reference [32]. In this study, the exponential variogram is utilized and written in the form  
\[\gamma^{*}(h)= \begin{cases}B_{0}+B_{1}\bigg{(}1-\exp\bigg{(}-\frac{h}{B_{2}} \bigg{)}\bigg{)}&h>0,\\ 0&h=0,\end{cases}  \tag{7}\]  
where \(B_{0}\) is the nugget value indicating the value caused by measurement errors or some small-scale variation in the regionalized variable, \(B_{1}\) is the partial sill, and \(B_{2}\) is the range. The variogram model is fitted to the empirical variograms by means of the weighted least squares approach [33]. The procedure for ordinary kriging is provided in Algorithm 1. ```
0: observed data \(Z(x_{1}),Z(x_{2}),\ldots,Z(x_{n})\) at positions \(x_{1},x_{2},\ldots,x_{n}\); target points \(x_{0i}\) for \(i=1,2,\ldots,p\)
0: estimated values \(\hat{Z}(x_{0i})\) of \(Z(x_{0i})\) at the target points \(x_{0i}\) for \(i=1,2,\ldots,p\)
1: compute the experimental variogram \(\hat{\gamma}_{CH}(\mathbf{h})\) using Equation (6)
2: fit the theoretical variogram \(\gamma^{*}(h)\)
3:for\(i=1\) to \(p\)do
4: calculate kriging weights \(\lambda\) using system (4)
5: estimated value \(\hat{Z}(x_{0i})\) at target positions \(x_{0i}\) using Equation (3)
6:endfor
```  
**Algorithm 1** Ordinary kriging algorithm
# Marine-Originated Materials and Their Potential Use in Biomedicine
## 4 Arthropod-Originated Components and Their Biomedical Applications
A vascular graft made of elastin-silk fibroins was implanted into the aorta of rats with stability and few side effects [84]. Silk can also be used in combination with hydroxyapatite for coating to improve its performance in bone tissue engineering [85]. These studies have focused on silk sericin and fibroin, particularly obtained from silk cocoons. However, the similarities between cocoon-derived silk and that obtained by crustacea allow us to think about several biomedical applications of marine-originated silk. [PERSON] et al. have shown that shrimp-like ocean crustaceans, known as tanaids, produce silk for shelter and safety, anchoring to rocks. Thus, this new type of silk could be used in environmentally friendly, water-resistant materials for fabrics; medical uniforms; and numerous other biomedical applications [86]. Table 3 presents the main biomedical applications of arthropod-originated components. In summary, arthropod-derived biomaterials are mainly used in regenerative medicine, drug delivery systems, and dentistry, possessing also antimicrobial properties. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Arthropod-Originated** & **Source** & **Application** & **Ref.** \\ \hline chitosan & crustaceans’ shells & biomedicine (tissue engineering, drug delivery systems): high solubility and active sites & [73] \\ \hline chitin & marine crab shells & biomedicine (tissue engineering, drug delivery systems, biosensoring, and water filter): several biomedical applications of chitin due to its good properties & [74, 75] \\ \hline chitin & crabs & biomedicine (denistry, wound healing, antimicrobial activity) & [78] \\ \hline calcium carbonate & crabs and shrimps & biomedicine (regenerative medicine): scaffolds or 3D templates for bone repair & [79] \\ \hline silk & arthropods & biomedicine (regenerative medicine, tissue engineering, cosmetics): biocompatibility & [76] \\ \hline silk in combination with & & biomedicine (tissue engineering, bone repair): coating in bone tissue engineering & [85] \\ \hline  & & biomedicine and environment (tissue engineering, bone repair): environmentally friendly water-resistant materials for fabrics and medical uniforms & [86] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Biomedical applications of arthropod-originated components.
## 3 UWB Signal Propagation Properties
### UWB Signal Generation  
Before introducing the UWB signal, a Gaussian pulse signal is introduced because of its advantages in terms of time response, frequency response, and signal-to-noise ratio [40]. Its one-dimensional form is  
\[f(t)=\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{t^{2}}{2\sigma^{2}}} \tag{2}\]  
where \(\sigma^{2}\) denotes the variance of the Gaussian pulse signal. To simplify Equation (1), we set \(\alpha^{2}=4\pi\sigma^{2}\). Then, the time-domain expression of the Gaussian pulse is  
\[f(t)=\frac{\sqrt{2}}{\alpha}e^{-\frac{2\pi t^{2}}{\alpha^{2}}}=\pm A_{p}e^{- \frac{2\pi t^{2}}{\alpha^{2}}} \tag{3}\]  
where \(\alpha\) denotes the forming factor of the Gaussian pulse. By tuning the forming factor \(\alpha\), narrow pulses of different pulse widths can be obtained. The larger the value of \(\alpha\), the wider the pulse width, and the narrower the corresponding spectrum. Reducing the value of \(\alpha\) compresses the pulse width, thus expanding the bandwidth of the transmission signal. The Gaussian pulse waveform with amplitude normalization is shown in Figure 1.  
Figure 1: UWB signal transmitting simulation.  
In the UWB positioning system, to transmit the signal, the second or higher derivative of the Gaussian pulses is typically adopted in the signal transmission block diagram shown in Figure 2. The second-order derivation is  
\[\frac{d^{2}f(t)}{d^{2}}=\frac{4\sqrt{2}\pi t}{\alpha^{3}}\bigg{(}1-\frac{4\pi t^{ 2}}{\alpha^{2}}\bigg{)}e^{\frac{2\pi t^{2}}{\alpha^{2}}} \tag{4}\]
## 2 Related Work on Diacritics Restoration
### Classical Approaches
#### 2.1.3 Translation-Based Approaches  
Sometimes the diacritization problem is formulated as the machine translation problem, but instead of translating from the source language to the target, the undiacritized text is \" translated\" into the diacritized text. However, such a translation problem is less complex due to a simpler (one-to-one) alignment and decoding.  
The phrase-based Statistical Machine Translation (SMT) system has been successfully applied to restore diacritics in the Algiers dialectal texts of the Arabic language [22]. This system uses the Moses (Open Source Toolkit for SMT) engine with the default settings, such as the bidirectional phrase and lexical translation probabilities, the distortion model with seven features, a word and phrase penalty, and a language model.  
The SMT-based method was also applied to Hungarian texts [23]. Similar to [22], Moses was used with the default configuration settings (except for the translation model that contained only unigrams, and the language model with \(n\) up to 5), monotone decoding, and without the alignment step. However, SMT alone was not enough to solve their task: the agglutinative morphology of the Hungarian language results in plenty of word forms that are unseen by the system with the restricted vocabulary. To handle this, a morphological analyzer was incorporated into the system. It generates candidates for unseen words that are later fed into the Moses decoder. The probability of each candidate was estimated from the corpus with a linear regression model considering its lemma frequency, the number of productively applied compounding, the number of productively applied derivational affixes, and the frequency of the inflectional suffix sequence returned by the analysis.  
Despite the problem to be solved in [24] being formulated as a word-to-word translation problem, this is not the typical case with SMT. The authors investigated two approaches that only required monolingual corpora. Their lexicon-based approach (applying the most frequent translation observed from the training data) was outperformed by the corpus-based approach (combining information about the probability of translation and the probability of observing a translation in the given context, via a simple log-linear model). This research is interesting for several reasons. First of all, the effectiveness of the method is proven in several languages, i.e., Croatian, Serbian, and Slovenian. Similarly, the diacritics are restored on both standard and non-standard (Web data) texts. Moreover, the authors also performed cross-lingual experiments by training their model on one language and testing it on another. The cross-lingual experiments revealed that the Croatian and Serbian languages can benefit from each other (training/testing in both directions), whereas the model trained on the Slovenian language was not effective for Croatian or Serbian.
## 4 HUVEC Identification
### Cellular Markers
#### Other Markers  
ABH antigens.EC contain ABH antigens specific to the donor's blood type tissue, while connective tissue and smooth muscle lack this antigen system. The agglutinin of a plant called _Ulex europaeus_ is used to identify these antigens [11]. HUVEC express the H antigen, whereas, smooth muscle cells and fibroblasts lack this antigen system [28].  
Dil-conjugated acetylated low-density lipoproteins (Dil-Ac-LDL).In this assay, cells are incubated in the presence of acetylated low-density lipoproteins (Ac-LDL), where uptake is monitored by fluorescence microscopy. This technique can be used to verify the presence of endothelium, but not of vascular smooth muscle cells. This marker is constitutive, and can be modulated in vitro, depending on confluence state of the culture [11; 49]. The lipoproteins are labeled with a fluorescent probe containing the compound 1,1'-dioctadecyl-3,3,3',3'-tetramethyl-indocarbocyanine perchlorate or Dil, a highly lipophilic molecule that can be incorporated non-covalently in lipoproteins. Once a lipoprotein containing Dil is assimilated by a cell, the probe accumulates in lysosomal membranes [102]. The advantage of using LDL metabolism as a marker is that viable EC can be labeled, without the need to fix or permeabilize these cells [93].  
_Prostacyclin release (PGI2)_. Prostaglandins are products of the arachidonic acid metabolic pathway, synthesized by various tissues, including vascular EC. PGI2 is a potent vasodilator that relaxes the vascular tone of smooth muscle in arteriolar terminals [103]. Prostaglandins are involved in acute inflammation binding G proteins [104]. The production and release of PGI2 in HUVEC culture supernatant can be measured by commercial radioimmunoassay kits, such as 6-keto-prostaglandin F1\(\alpha\) (6-keto PGF1\(\alpha\)) [49].  
_Tube-formation assay_. The Matrigel basement membrane is a solubilized extract of the extracellular matrix of Engelbreth-Holm-Swarm (EHS) sarcoma [105]. It has two main components: Laminin and type IV collagen, but also contains proteoglycans with heparan sulfate chains, entactin (nidogen-1) and some other minor components. The Matrigel is used to form a substrate or a three-dimensional gel, in which the EC form tubules that simulate the microanatomic cellular relationships that are generated in vivo [67]. This assay is used to assess the angiogenic capacity of the EC by the formation of tubular structures using different microscopy techniques, such as digital microscopy in low power fields [53], fluorescence microscopy [93], inverted microscopy [106; 107], and reverse phase contrast microscopy [108].
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 4 Experiments
### Algorithm Robustness Evaluation
#### 4.4.1 Evaluation of Resistance to Local Nonlinear Distortions  
To evaluate our method's ability to resist local nonlinear scale distortions, we generated test point sets with varying levels of local distortion from 0% to 50% in 5% increments for 76 template point sets. The distortion percentage is relative to the horizontal dimension of the image. For each category, 100 sets of test point sets were generated with randomly positioned distortions at each specified distortion level. To realistically simulate the localization error of object detection algorithms, each point in the target point set was randomly offset by up to 20 pixels in both horizontal and vertical directions. Local nonlinear scale distortions were simulated by applying specific scale offsets to random horizontal positions and using cubic polynomial interpolation for local interpolation. Since the point sets of the same carriage type under four movement modes have a unique spatial relationship, their recognition accuracies are essentially the same. Therefore, we calculated the average recognition accuracy by carriage type when reporting accuracy statistics. Figure 6 shows the recognition accuracy of our method under different distortion scales. As expected, the matching accuracy generally decreases with increasing distortion levels. However, the recognition accuracy remains unaffected in the 0% to 10% distortion range. At a 15% distortion level, minor changes in recognition accuracy are observed for some categories. When the distortion level exceeds 25%, the recognition accuracy significantly decreases, but even at a 50% distortion level, the recognition accuracy for all categories remains above 69%. These results demonstrate the excellent robustness of our method in resisting local nonlinear scale distortions in line-scan images of train carriages.
## 2 DAB DC/DC Resonant Converter TPS Close Loop Control Scheme
### Mathematical Formulation of DAB TPS Control Method
#### 2.2.2 Resonant Circuit Load Resistance Regulation with Rectifier Phase Shift Control
\begin{split}\delta=phase\left(\frac{V_{ac\_inv}}{V_{ac\_rcrc}} \right)\\ =phase\left(\left(R1*Z_{ac}-(Z_{c2})^{2}\right)\frac{Z_{11}}{Z_{ac}Z_{ c2}Z_{c2}}\right)\\ =phase\left(\left(R1*R_{ac}-\frac{1}{(\frac{Z_{11}}{Z_{c2}Z_{c2}})}\right)^{2} \right)\frac{i\omega_{\text{zz}}L_{1p}*\omega_{\text{zz}}C_{2p}}{j\omega_{\text{zz }}L_{1m}R_{ac}}\right)\\ =phase\left(\left(R1*R_{ac}+\left(\frac{1}{\omega_{\text{zz}}C_{2p}}\right)^{2 }\right)\frac{i\omega_{\text{zz}}L_{1p}*\omega_{\text{zz}}C_{2p}}{L_{m}R_{ac} }\right)=90^{\circ}\end{split}  \tag{35}\]  
Equation (35) proves that the rectifier voltage phase should lag the inverter voltage phase by \(90^{\circ}\) for DAB LCC resonant converter to operate at unity power factor. DAB resonant circuit topology affects the inverter-to-rectifier phase shift angle \(\delta\) setting for inverter and rectifier unity power factor operation. For other DAB circuit topologies, the inverter-to-rectifier phase shift angle for unity power factor operation needs to be derived from the equivalent resonant circuit model.
# An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs
## 5 Results and Analysis for Performance Evaluation
### Memory Overhead  
The node of each member holds its public key g-1 and a private key, which is a group size g. For a 64-bit key, if a group consists of 15 soldiers, then each device will have \(15\times 8=120\) stored bytes for public keys of 14 soldiers, as well as their private keys. Likewise, when a key has 128 bits, then each device holds 240 bytes. Its group size effect on the requirements for the maintenance of individual nodes is shown in Figure 3(a). The results show that increasing the number of bands also enhances the requirement of memory for a device.  
For a key with a 64-bits size, if a group consists of 20 soldiers, then every soldier has \(8\times 20=160\) store bytes for 19 public keys and a private key. If the network size increases and the bandwidth remains the same, the storage requirement on a single node will remain the same. Figure 3(b) illustrates where the size of the network fluctuates from 200 to 2,000 soldiers with a constant number of 20 soldiers. The 128-bit key size results are also presented, where every soldier has \(20\times 16=320\) stored bytes, the public keys of 19 soldiers, and their private keys.
## 3 Results and Analysis
### Influence Analysis of Various Factors
#### 3.3.4 Lateral Stirrup Ratio  
The effect of the lateral stirrup ratio is similar to that of the cover thickness, which can effectively prevent the lateral deformation of the RAC and delay the cracking time. The equations are as follows.  
\[\overline{\tau_{s}} =(2.934\rho_{\text{ss}}-0.206)f_{t} \tag{12}\] \[\overline{\tau_{u}} =(0.269\rho_{\text{ss}}-0.770)f_{t}\] (13) \[\overline{\tau_{r}} =(1.556\rho_{\text{ss}}-0.172)f_{t} \tag{14}\]  
It can be seen from Figure 8d that the average characteristic bond strength increases with the increase of the lateral stirrup ratio, and the average initial bond strength increases obviously. The effect of increasing the ultimate bond strength is poor, indicating that the increase of the lateral stirrup ratio can effectively delay the appearance of the initial crack and increase the cracking load, but the effect on improving the average ultimate bond strength is not significant.
## 5 Simulation Experiment and Analysis
With the increasing of threshold value, the filtering effect of redundant information is more obvious, especially when the node group radius is 10 m. If the node group radius is larger, the number of nodes will increase, the coincidence rate will be higher than threshold, so that the redundant nodes cannot be filtered out. If the node group radius is smaller, the number of nodes in a group will be 1, there is no need to filter. So in the simulation experiment, node group radius is 10 m, and node coincidence threshold is 1.75.