esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Digitization of the Study Cast  
A zirconia (VITA YZ HT, VITA Zahnfabrik, Bad Sackingen, Germany) cast representing the posterior teeth of the left maxillary quadrant (FDI 24-28) (Figure 1) was produced.  
It was based on the scan data of a typodont (Basic model, KaVo Dental GmbH, Biberbach, Germany). After milling, sintering, and polishing, the cast was scanned at baseline t0 using three intraoral scanners (IOS): Cerec Omnicam AC (OC) (V4.5, Dentsply Sirona Deutschland GmbH, Bensheim, Germany), Trios 3 (Tr3) (V.1.16.1.0, 3 Shape A/S, Copenhagen, Denmark) and True Definition Scanner (TD) (S1548041, 3M Deutschland GmbH, Seefeld, Germany). The use of TD required a slight dusting with titanium dioxide particles (3M High-Resolution Scanning Spray, 3M Espe Dental products St. Paul, USA) in order to create randomly distributed landmarks for the optical system applying wavefront sampling [9]. In the case of the powder-free IOS, Tr3, and OC, scans of the powdered (p) and unpowdered (non-p) cast were accomplished in order to evaluate whether the modification of the surface influenced the data. Tr3 utilizes the imaging principle of confocal microscopy and OC uses active triangulation. To obtain a highly accurate digital reference, a high-resolution non-contact white light profilometer (WLP) (CT 100 and the sensor P-CHR-6000, z-res 200 nm Cybertechnologies, Ingolstadt, Germany) was used. The sensor of CT 100 provided a vertical resolution up to 3 nm and a lateral resolution of 0.05 \(\upmu\)m [10]. Before capturing data with WLP, the model was air-abraded with 50 \(\upmu\)m aluminum oxide at 2 bar. With both acquisition systems, IOS and WLP, the partial arch was captured. The sensor's spot size was 16 \(\upmu\)m, combined with a 150 \(\times\) 150 \(\times\) 40 mm scanning area at maximum x, y, z resolution. The zirconia cast was always orientated on the WLP scanning platform in the identical occluso-apical direction (base of the zirconia cast). Simultaneously, the WLP could always capture the entire occlusal surface from the cusps to the anatomical equator. The IOS had a field of view of approximately 10 \(\times\) 13 mm at a vertical working distance of 17 mm using TD, as an example. According to the manufacturers' recommendations, a complete partial-arch scan was conducted applying the IOS.  
After all scans at t0 were accomplished, wear areas were manually simulated using a diamond-bur (\(\mathcal{O}\)1.2 mm, grit size 46 \(\upmu\)m) [11]. In total, three stages of wear (t1-t3) were created successively at tooth 26 mesio-oral and tooth 27 disto-buccal (Figure 2). At t1, t2, and t3, the cast was scanned again with the scanners mentioned above, and WLP. At each stage, the cast was scanned six times one after another by the same experienced operator with each intraoral scanning system. Before each scanning session, the scanning system  
Figure 1: The zirconia model, consisting of the teeth 24–28, is shown at baseline.  
was calibrated according to the manufacturer's recommendations. Optical profilometry (WLP) served as a reference and was carried out once per stage.
# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 2 Methods
### The Point Extraction Method Based on Line Features  
#### 2.4.1 Existing Problems and Solutions  
Corner detection can extract feature points, but it is difficult to obtain the centroid points required for localization (as shown in Figure 7a). On the other hand, edge detection is susceptible to texture interference (Figure 7b), making the extraction of points of interest challenging. Pixel-level segmentation need to classify a large number of pixels, and due to the limitations of model performance, the image segmentation results may be biased towards larger or smaller regions in certain situations. In order to clearly demonstrate the influence of segmentation results on the centroid, we adopted an exaggerated approach to illustrate it, as shown in Figure 7c. When there are obvious misclassified pixel areas within the target region, it can result in missing vertices or centroid offset. Therefore, to reduce the interference of such special cases on the results, we abandoned the approach of extracting vertices based on edges or calculating centroids based on connected regions. This improvement can ignore boundary protrusions or depressions, resulting in less impact on the corner points and centroid calculation of the lifting hole.
## 2 Vibration Response Analysis of Different Roadbed-Foundation Systems
### Vibration Analysis of Roadbeds on Soft Clay Foundations  
Based on the vibration response analyses in Sections 2.2 and 2.3, roadbeds on soft clay foundations can be considered to undergo overall vertical vibration. The vibration problem of the roadbed can be simplified using the lumped parameter method to the vibration problem of a circular dynamic machine foundation on an elastic half-space, as illustrated in Figure 12. This simplification facilitates the corresponding model experiments. \(Q=P_{1}\mathrm{e}^{i\omega t}\) represents the vibration load acting on the roadbed, with its amplitude \(P_{1}\) and circular frequency \(\omega=2\pi f\) selected according to the specific situation to meet the experimental requirements. Under the action of vibration load \(Q\), the interaction force between the mass  
Figure 11: Variation of displacement amplitude of a point at different cross-sections \(x\) with depth \(y\) in the soft-sandwiched system when \(f=7.2\) Hz: (**a**) \(x=10.0\) m, (**b**) \(x=34.0\) m.  
Figure 10: Variation of (**a**) vertical and (**b**) horizontal displacement of a ground surface point with horizontal distance \(x\) in the soft-sandwiched system when \(f=7.2\) Hz.  
block (roadbed) \(m\) and the half-space (foundation) is denoted by \(R_{z}\). According to the dynamic foundation half-space theory [23], this force can be expressed as follows:  
\[m\frac{\text{d}^{2}z}{\text{d}t^{2}}+R_{z}=P_{1}\text{e}^{i\omega t} \tag{1}\]  
\[R_{z}=R_{0}\text{e}^{i(\omega t-\phi_{1})} \tag{2}\]  
\[R_{0}=\frac{P_{1}}{\sqrt{\left(1+ba_{0}^{2}f_{1}\right)^{2}+\left(ba_{0}^{2}f_ {2}\right)^{2}}} \tag{3}\]  
\[\tan\phi_{1}=\frac{ba_{0}^{2}f_{2}}{1+ba_{0}^{2}f_{1}},\ b=\frac{m}{\rho r_{0} ^{3}} \tag{4}\]  
\[a_{0}=\omega r_{0}\left(\frac{\rho}{G}\right)^{1/2}=\frac{\omega r_{0}}{v_{\text {s}}} \tag{5}\]  
where \(b\) is the dimensionless mass ratio, \(a_{0}\) is the dimensionless frequency, \(m\) is the mass of the roadbed, \(\rho\) is the mass density of the soft clay, \(G\) is the shear modulus of the soft clay, \(v_{\text{s}}\) is the shear wave velocity of the soft clay, \(r_{0}\) is the radius of the mass block.  
The values of the displacement functions \(f_{1}\) and \(f_{2}\) are related to the Poisson's ratio \(\mu\) of the soft clay. These values can be found in relevant dynamic machine foundation specifications. For frequencies \(f\) between 1-100 Hz, \(R_{0}\) is approximately equal to \(P_{1}\), indicating that the amplitude of the vibration load acting directly on the soft clay layer is approximately equal to the amplitude of the vibration load acting on the roadbed.
# I4.0I: A New Way to Rank How Involved a Company Is in the Industry 4.0 Era
## 2 Related Work
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Model Name [Source]**} & \multicolumn{1}{c}{**Dimensions Analyzed by the Model to**} & \multicolumn{1}{c}{**Industry 4.0 Maturity Levels**} \\  & \multicolumn{1}{c}{**Determine the Maturity Level**} & \multicolumn{1}{c}{**Industry 4.0 Maturity Levels**} \\ \hline  & - Customer segment & \\  & - Value proposition & \\  & - Channels & - Implicit \\ A Maturity Model for & - Customer relationship & - Defined \\ Business Model & - Source of income & - Validated/standardized \\ Management in Industry 4.0 [15] & - Key resources & - Analyzed \\  & - Key activities & - Optimized \\  & - Key partners & \\  & - Cost structure & \\ \hline  & & - Initial \\ Three Stage Maturity & The analyzed & - Managed \\ Model in SME’s Towards & dimensions are & - Defined \\ Industry 4.0 [16] & not well-explained & - Transform \\  & & - Detailed business model \\ \hline Maturity Model-Based & - Vertical integration & - Communication and analysis \\ Planning Of Cyber-Physical & - Horizontal integration & - Interpretation and service \\ Systems In The Machinery & - Connectivity & - Adaption and optimization \\ And Plant Engineering Industry & - Network connection & - Cooperation \\ [17] & - Security & - \\ \hline SIMMI 4.0—A Maturity & - Vertical integration & - Cross-departmental \\ Model for Classifying the & - Horizontal integration & digitization \\ Enterprise-wide IT and & - Digital product development & - Horizontal and vertical \\ Software Landscape & - Cross-sectional technology criteria & digitization \\ Focusing on Industry 4.0 [18] & - Full digitization \\  & & - Optimized full digitization \\ \hline  & - Design and engineering & - Initial \\  & - Production management & - Managed \\ DREAMY Digital REadiness & - Quality management & - Defined \\ Assessment MaturirY [19] & - Maintenance management & - Integrated and interoperable \\  & - Logistics management & - Digital-oriented \\ \hline  & - Strategy & \\  & - Leadership & - Defines five generic levels, \\ A maturity model for assessing & - Customers & where the first level defines a \\ Industry 4.0 readiness and & - Products & lack of attributes of Industry \\ maturity of manufacturing & - Operations & 4.0 and the last level is the \\ enterprises [20] & - Culture & state of the art. \\  & - People & - \\  & - Governance & \\  & - Technology & \\ \hline  & - Product development & \\  & - Technology & \\  & - Production management & \\  & - Production monitoring & \\  & - Material and inventory & The result is given by a \\ Measuring the Maturity of a & - Management of stock & number between 1 and 5, \\ Factory for Industry 4.0 [21] & - Quality Assurance & whereby, the bigger the value, \\  & - Product life cycle management & bigger is the maturity level. \\  & (PLM) & \\  & - Selection of Toyota production & \\  & system (TPS) & \\  & - Green and lean production & \\  & structure (GALP) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work overview: existing Industry 4.0 readiness and maturity models.
## 3 Experimental Results
### Steady-State Tests
Figure 12: Results of three separate runs for three tests conducted in different testing campaigns. The results slightly differ; the standard deviations of the measured U for particle flow rates of 0.666, 0.847 and 1.020 kg/s are 1.4, 6.3 and 3.3 \(W/\)m\({}^{2\text{-}\circ}\)C, respectively, which correspond to relative standard deviations of 1.6, 6.6 and 3.3%. This implies that the measured overall heat transfer coefficient is quite accurate.
###### Abstract  
Background: This study aims to analyze bone compaction and oseeedensification techniques and to investigate how cancellous bone compaction could influence primary implant stability (PS). Methods: Two different surgical protocols (bone compactors--BC; oseeedensification drills--OD) were compared by placing 20 implants into 20 fresh pig ribs for each procedure. Peak insertion torque (PIT) and peak removal torque (PRT) were investigated using an MGT-12 digital torque gauge, and implant stability quotient (ISQ) was analyzed using an Ossetl\({}^{\text{\textregistered}}\) Beacon device. Results: Analysis of our data (T-test \(p<0.05\)) evidenced no statistically significant difference between BC and OD in terms of PIT (\(p=0.33\)) or ISQ (\(p=0.97\)). The comparison of PRT values showed a statistically significant difference between BC and OD protocols (\(p=0.009\)). Conclusions: Cancellous bone compaction seems to improve PS, preserving a significant amount of bone and evenly spreading trabeculae on the entire implant site. While the PIT and ISQ values obtained are similar, the PRT values suggest different physical responses from the surrounding bone tissue. Nevertheless, a larger sample and further in vivo studies are necessary to validate the usefulness of BC protocol in several clinical settings.  
primary implant stability; oseeedensification; bone compaction; resonance frequency analysis; insertion torque +
Footnote †: journal: Applied Sci. 2020, 10, 8623; doi:10.3390/app10238623
## 2 Results
### Direct Functionalization of PLA Backbone: Towards PLA-Based Graft Copolymers
#### 2.2.1 PLA-\(g\)-Maleic Anhydride (PLA-\(g\)-MA)
By reactive extrusion of the bio-based poly(glycerol succinate-co-maleate) (PGSMA) with PLA at 150-180 \({}^{\circ}\)C in the presence of a free radical initiator, a PLA-g-PGSMA graft copolymer was obtained that acts as an interfacial compatibilizer due to the double bond of the maleate unit in PGSMA [56]. The tensile strength of PLA/PGSMA blends was improved by almost 400% compared to that of pure PLA. This increase was caused by (i) the in situ formation of PLA-g-PGSMA graft copolymers and (ii) the crosslinking of PGSMA within the PLA matrix, which act as interfacial compatibilizers. Two-dimensional NMR and FTIR confirmed the formation of PLA-g-PGSMA, but the substitution degree on the PLA backbone was not evaluated. It is important to note that this work was performed with very-low-molecular-weight PGSMA (Mn < 1200 Da). PLA-g-MA is also used to compatibilize PLA with thermoplastic polyurethane elastomers (TPU) and thermoplastic polyester elastomers (TPE). Charpy impact, toughness and fracture toughness of brittle PLA were improved when the blends were compatibilized by addition of PLA-g-MA without adversely damaging effects on other mechanical and thermal properties of the PLA blends [57]. Other PLA-Based Blends  
PLA-g-MA serves as reactive compatibilizer for other polymer blends by reacting the anhydride functions on the second constituent of the blend. Examples include polyamides 11 and 12, where anhydride function reacts in the amine groups [58; 59]. The ductility and impact strength of the compatibilized blends were increased by a factor two compared to non-compatibilized blends. A crosslinking agent (trimethylolpropane trimethacrylate) was also used to enhance the impact strength of the PLA-g-MA-containing blend. PLA/Polyamide6 (PA6) blends were compatibilized with PLA-g-IA (IA = taconic anhydride), a compound similar to PLA-g-MA [60]. It was reported that IA can react with amine and amide functions of PA6. Unfortunately, a weak compatibility effect was obtained, likely due to the low concentration of IA moieties grafted onto PLA backbone (0.4 wt.%). PLA-g-IA is also a compatibilizer of PLA/Novatein (a protein-based thermoplastic) as shown in the SEM micrographs of Figure 13[61]. Compatibilizing effects of PLA-g-MA on the properties of PLA/Soy Protein concentrate (SPC) blends were demonstrated by a 19% increase in tensile strength compared to the non-compatibilized blend [62]. Finally, PLA-g-MA also has the ability to provide some compatibility to PLA/mineral mixtures such as PLA/carbon nanotubes [22], PLA/Titanium oxide [63], PLA/halloysite [64], PLA/hydroxyapatite [65], PLA/talc [66], PLA polyhedral oligomeric silsesquioxane (POSS) [67]. Figure 13: SEM micrographs of PLA/novatein 90/10 blends (**a**) uncompatibilized, (**b**) compatibilized (adapted from [PERSON] et al. [61], copyright Wiley-VCH GmbH. Reproduced with permission).
# Arm Swing Movements during Walking as an Early Predictor of Multiple Sclerosis Progression
###### Abstract  
Persons with Multiple Sclerosis (pwMS) are at a high risk of falling, with abnormal gait pattern. The upper limbs play an important role in postural control and gait stability. The presence of arm swing changes during walking in pwMS, especially in the early period, may be an indicator of balance problems. The current study aimed to assess arm swing during walking in early MS. A total of 18 pwMS were evaluated in two time points. The first time was after their first (stable) diagnosis (pre-evaluation) and the second time was 3 months after the pre-evaluation. In addition, 10 healthy controls were evaluated once. Arm swing analysis during walking, using video recording, was applied to both groups. Additionally, the MS group performed the Two-Minute Walk Test, Timed Up and Go, and Timed 25-Foot Walk Test. The pwMS showed similar joint angles at both the first and second evaluations. Only the elbow ROM value on the least affected side was lower in pwMS than healthy controls at the second evaluation (\(p=0.027\)). The early MS patients showed altered arm swing pattern. As walking speed and mobility scores did not change over time, the decrease in elbow amplitude over a 3-month period indicates that the arm swing may present a pattern resulting from MS-specific disorders rather than being a compensatory mechanism in walking. From the earliest stages of the disease, variations in arm swing movements during walking may be considered as a disease progression-predictor for MS.  
upper extremity; gait; multiple sclerosis; arm swing; early stage of multiple sclerosis +
Footnote †: journal: Applied Sciences
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 2 Theoretical Background  
### Stacked Sparse Autoencoder  
Autoencoder (AE) is a three-layer unsupervised neural network model, which is composed of an input layer, a hidden layer and an output layer, as shown in Figure 1. The core principle of AE is to make the output \(y\) equal to the input \(x\) and find a brief representation of the deep law of the original data through the encoding and decoding transformation of the hidden layer. The reconstruction error \(E_{AE}\) of AE, whose input layer is \(m\)-dimensional, is expressed as:  
\[E_{AE}=\frac{1}{m}\sum_{i=1}^{m}\|x(i)-y(i)\|^{2} \tag{1}\]  
Different from traditional autoencoders, the Sparse Auto Encoder (SAE) network adds KL divergence during the training process to limit the sparseness of the network to enhance the performance of the autoencoder [31]. Most of the nodes in the hidden layer are restricted by sparseness to suppress their activity, and only a small number of nodes are activated to explore the potential characteristics of the data. \(H_{j}(x_{j})\) indicates the activation level of the \(j\)-th neuron in the hidden layer, and the average activation level \(\hat{\rho}_{j}\) of \(m\) neurons in the hidden layer can be expressed as:  
\[\hat{\rho}_{j}=\frac{1}{m}\sum_{i=1}^{m}\left[H_{j}(x_{i})\right] \tag{2}\]  
In order to better optimize the average activation degree, KL divergence was added to the SAE model. The KL divergence formula is as follows:  
\[KL(\rho\parallel\hat{\rho}_{j})=\rho\log\frac{\rho}{\hat{\rho}_{j}}+(1-\rho) \log\frac{1-\rho}{1-\hat{\rho}_{j}} \tag{3}\]  
Then the loss function \(E_{SAE}\) of traditional SAE is:  
\[E_{SAE}=\frac{1}{m}\sum_{i=1}^{m}\left\|x(i)-y(i)\right\|^{2}+\beta\sum_{j=1}^ {n}KL(\rho\parallel\hat{\rho}_{j}) \tag{4}\]  
Figure 1: The structure diagram of AE model.  
where \(m\) is the dimension of the input data, \(n\) is the number of neurons in the hidden layer and \(\beta\) is the sparse penalty coefficient.  
The Stacked Sparse Autoencoder (SSAE) connects multiple SAE structures by stacking, making full use of the characteristics of SAE to remove high-dimensional interference and extract low-dimensional features, enhancing the robustness of the autoencoder network, as shown in Figure 2[32; 33].
# A Comparative Study of Signal Processing Methods for Contactless Geodetic Monitoring
## 1 Introduction
### The Purpose of Study  
The monitoring of displacements and deformations is necessary primarily for the protection of lives and property and to determine the condition of structures. There are many methods for monitoring displacements, and the purpose of our work is to upgrade already known methods and perform synchronization and comparison of results obtained with different methods. In this work, we used a robotic total setting, which with an additional program, increased the possibility of capturing data from 9 to 26 measurements per second. The program allows different combinations of measurements depending on what we need. A novelty in the paper is the analysis, comparison, and synchronization of the obtained measurements with emphasis on the results of RTS in determining the dynamic response of the structure.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 6 Strategies for Eradication of Biofilms
### Nanoparticles
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Agent**} & \multirow{2}{*}{**Target Microbe**} & \multicolumn{2}{c}{**Expected Mode of**} & \multirow{2}{*}{**Reference**} \\  & & **Anti-Biofilm Action** & \\ \hline
**Bacteriophages** & & & \\ \hline vB\_SauM\_ME18 and & \(S\). aureus & - & [199] \\ vB\_SauM\_ME126 & & & \\ \hline _Pseudomonas_ Phage & \(P\). aeruginosa & - & [183] \\ \hline pSp-J and pSp-S & _Staphylococcal_ spp. & - & [253] \\ \hline Bacteriophage K & \(S\). aureus & - & [184] \\ \hline Bacteriophage cocktail & \(P\). aeruginosa & - & [254] \\ \hline Bacteriophage cocktail & \(P\). mirabilis & - & [194] \\ \hline vB\_PmiS-TH & \(P\). mirabilis & - & [185] \\ \hline vB\_EcoS-Golestan & \(E\). coli & - & [186] \\ \hline PhiS1 & \(P\). aeruginosa & - & [187] \\ \hline PhiE2005-A & \(P\). aeruginosa & - & [191] \\ \hline Lytic bacteriophages cocktail & \(P\). mirabilis, \(E\). coli & - & [190] \\ \hline Lytic bacteriophage & \(K\). pneumoniae & - & [192] \\ \hline Staphylococcus bacteriophage K & \(S\). epidermidis & - & [193] \\ \hline
**Antimicrobial Lipids (AMLs)** & & & \\ \hline Glycerol monolaurate & Haemophilus influenzae, S. aureus & Disruption & [255] \\ \hline Alginate oligosaccharide & \(P\). aeruginosa & of biofilm matrix & [256] \\ \hline
**Antimicrobial Peptides (AMPs)** & & & \\ \hline AMP 1018 & \(S\). mutans & & [216] \\ \hline LL-37 & \(E\). coli & Disruption of biofilm cell & [207] \\ \hline DJK-5 & \(P\). aeruginosa & & [219] \\ \hline DJK-6 & \(P\). aeruginosa & & [219] \\ \hline
**Nanoparticles (NPs)** & & & \\ \hline Ciprofloxacin-mesoporous silica & \(S\). enterica & - & [236] \\ \hline Metformin-silver NPs & \(S\). aureus & - & [257] \\ \hline Thymol nano-emulsion & Salmonella spp. & - & [134] \\ \hline Gold nanoparticles (GNPs) & \(E\). coli, S. aureus, K. pneumonia & - & [258] \\ \hline SeNPs & \(P\). mirabilis & - & [259] \\ \hline ZnO:MgO NPs & \(P\). mirabilis & & [260] \\ \hline Ambotericin B-PEG-ZnO & \(C\). albicans & - & [220] \\ \hline Nigella sativa-Zn NPs & \(E\). coli & Antagonize QS system & [261] \\ \hline Punica granatum - AgNPs & \(E\). coli, P. aeruginosa, P. mirabilis, S. aureus & - & [262] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
**Funding:** This research received no external funding. This research has been funded by the Scientific Research Deanship at the University of Ha'il, Saudi Arabia through project number MDR-22 007. **Conflicts of Interest:** The authors declare no conflict of interest.