esa-sceva/satcom-chunk-collection huggingface dataset:
# Permeability and Stiffness Assessment of Paved and Unpaved Roads with Geocomposite Drainage Layers
## 5 In Situ Permeability and Stiffness of The Test Sections
### In Situ Stiffness Assessment  
To assess the influence of the geocomposite layer on the composite stiffness of the PCC- and WMA-paved sections, two groups of FWD tests were conducted; (1) on the base layer prior to placing the geocomposite layers on the base layer and (2) on the pavement surfaces after paving, with the geocomposite layer installed at the pavement/base layer interface. The FWD tests were conducted at the same test locations on both the base and the pavement surface layers. A comparison of the two groups of test results shows the influence of the geocomposite on the composite stiffness (Figure 8).  
Figure 7: APT-measured saturated hydraulic conductivity versus depth for the surface aggregate layers of the unpaved geocomposite and control sections.  
For the PCC sections prior to paving, the foundation layer in the geocomposite section had a lower average \(E_{\text{Composite}}\) value than the control section. After placing the geocomposite layer and paving, the FWD tests conducted on the pavement surface showed similar trends, wherein the control section had a higher average \(E_{\text{Composite}}\) value than the geocomposite section. For the WMA sections, the composite modulus values in the geocomposite section were higher than in the control section for tests conducted both on the foundation layer and the pavement layer. These FWD results from the paved road test sections, although limited, suggest that the composite stiffness measurements obtained at the pavement surface were more reflective of the foundation layer support conditions beneath the geocomposite layer than of the stiffness of the geocomposite layer itself.  
FWD tests were also conducted on the unpaved sections after the spring thawing in 2015 because the spring thawing is when unpaved roads are most susceptible to moisture-related damage. Figure 8 shows that the average \(E_{\text{Composite}}\) of the geocomposite section is less than half that of the control section. However, the roadway surface performance of the geocomposite section was much better than that of the control section based on observations after a heavy rainfall and subsequent traffic, as can be clearly seen in Figure 9. The control section suffered much greater rutting and moisture retention than the geocomposite section. These field observations after the rain event indicate that the improved subsurface drainage conditions offered by the geocomposite layer can effectively prevent the surface material from becoming saturated during a heavy rain event, reduce the amount of water infiltrating into the subgrade, and thus reduce the surface damage. To further examine this hypothesis, a numerical analysis is conducted in the following section to compare the drainage performance of the geocomposite and control sections after a simulated heavy rain event.  
Figure 8: Summary of the falling weight deflectometer (FWD) test results of the PCC-paved, WMA-paved, and unpaved sections.
# Predicting the Evolution of Syntenies--An Algorithmic Review
## 5 Accounting for Gene Trees
### Evolution through Segmental Duplications and Losses
Now, given a set \(\mathcal{T}=\{T_{1},T_{2},\cdots,T_{t}\}\) of gene trees for the gene families \(\mathcal{G}=\{\Gamma_{1},\Gamma_{2},\cdots,\Gamma_{t}\}\) organized into a set \(\mathcal{X}\) of syntenies belonging to a set \(\Sigma\) of taxa, and given a species tree \(S\) for \(\Sigma\), the goal is to infer a history of segmental duplications and losses that gave rise to the extant syntenies from a unique ancestral synteny. Clearly, the problem can be subdivided into two parts:  
1. Given the set \(\mathcal{T}\) of gene trees for \(\mathcal{G}\), find a synteny tree \(T\) for \(\mathcal{X}\);
2. Given a species tree \(S\) for \(\Sigma\), find a _Super-reconciliation_\(\langle R,\vec{s},\vec{e}\rangle\) of \(T\) with \(S\), i.e., an event-labeled synteny tree which is a \"partial extension\" of \(T\), representing a valid history for \(\mathcal{X}\), of minimum _DL-distance_. Here, the DL-distance of \(\langle R,\vec{s},\vec{e}\rangle\) is the number of induced segmental duplications and losses. Notice that due to partial losses, a valid history for \(\mathcal{X}\) is not necessarily a binary tree, but rather a _partially binary tree_, i.e., a tree with each internal node having one or two children. In fact, nodes representing partial losses have a unique child (see for example Figure 5(3), left, node \(u\)). This is the reason for \"partial tree extension\" rather than tree extension, where a _partial extension_ of \(T\) is a tree \(T^{\prime}\) obtained from \(T\) by grafting edges or nodes to \(T\), where _gratting a node_ simply consists of subdividing an edge \(xy\) of \(T\), therefore creating a new node \(z\) between \(x\) and \(y\). It is important to notice that, ignoring rearrangements, an evolutionary history of duplications (only creating new gene orders, i.e., not modifying existing ones) and losses does not always exist for an arbitrary set of gene orders, and thus for an arbitrary set of syntenies \(\mathcal{X}\), regardless of the trees linking them. If this holds, the syntenies are said to be _order-consistent_. As explained in [95], this can be verified in linear time by representing the gene orders as a directed graph and verifying if it is acyclic. For example, the syntenies \(\{A_{1},A_{2},B_{1},B_{2}\}\) in Figure 5(1)-(3) are order-consistent, while the syntenies \(\{A_{3},A_{4},B_{3},B_{4}\}\) in Figure 5(4) are not order-consistent,
# Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction
## 3 System Architecture
### System Overview  
The objective of this paper was to automatically construct a Taiwan law ontology based on Taiwan's statutes. In particular, an automatic extraction mechanism was developed which extracts the definitions of legal keywords from various statutes. In this paper, we used the current Chinese law text from the Taiwan law database as the data source. The data sources of the statutes include acts, penal acts, special acts, and comprehensive acts. These acts are named according to Article two in the Central Regulation Standard Act [1]. There are 585 acts included in our data source. The number of statutes under each act is shown in Table 1.  
To construct the Taiwan law ontology, legal keywords were used as classes of this topology. The legal keywords used in this paper were obtained based on our previous work on the automatic extraction of legal keywords from Taiwan's statutes [6]. Figure 5 shows the system architecture that consists of five steps to automatically construct the Taiwan law ontology. First, the Chinese legal texts are imported to the system and stored in csv format. Second, ICTCLAS, a Chinese word segmentation tool, is used to divide Chinese sentences into meaningful units (segments). In addition, it also adds the POS tag to each unit. The word segmentation and POS tagging performed in this step are critical to the following steps, especially the accuracy of the keyword definition and class relation in the ontology. Third, examples of patterns of legal definitions in the statutes were defined by law experts first. Based on these examples, patterns of legal definitions were transformed into regular  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Type** & **Act** & **Penal Act** & **Special Act** & **Comprehensive Act** \\ \hline Laws & 353 & 0 & 216 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of laws under each type of act.  
Figure 4: The structure of the OWL languages.  
expressions to facilitate the finding of legal definitions by string matching. By searching all statutes, all pairs of (keyword, legal definition) were automatically extracted, and each pair was associated with one or more originating statutes. Fourth, with the legal keywords, their relations with acts, statutes, and legal definition, the Taiwan law ontology data model was constructed with the aim of expressing the legal knowledge. Finally, we classify the classes of the ontology using the Taiwan Six Codes structure [46] and describe the ontology using the OWL Language. After we finished the law ontology construction, we built a web platform to provide a visualized presentation of the law Ontology information, which also has a search function. In the following, we describe the technical details of each step.
## 3 Results
### JAK-STAT Signaling Pathway Was Involved in hBD3 Induction in HaCaT Cells  
When HaCaT cells were treated with KG lysates, no significant variation in signaling molecules was observed within 120 min. Rather, p38 and JNK showed a tendency to slightly decrease on phosphorylation after 60 min (Figure 3A). A change in signaling molecules was observed by extending the time to 72 h. Most signaling was down-regulated over time, but the phosphorylation of JAK 1 and JAK 2 was increased significantly (Figure 3B). In order to investigate the role of JAK1 and JAK2 in hBD3 expression, cells were pretreated with inhibitors and then KG lysates were treated. Ruxolitinib, an inhibitor of JAK1 and JAK2, decreased hBD3 mRNA expression in a dose-dependent manner (Figure 3C). Fludarabine, a STAT1 inhibitor, also decreased hBD3 mRNA level (Figure 3D). These data suggest that JAK1/2 and STAT1 are involved in the hBD3 expression in response to KG lysates in HaCaT cells. The JAK-STAT pathway is activated by IFN-\(\gamma\). Thus, we examined the effect of IFN-\(\gamma\)  
Figure 2: _L. plantarum_ KG lysates inhibited _S. aureus_ internalization by HaCaT cells. (**A**,**B**) HaCaT cells were treated with \(1\times 10^{6}\) cells/mL bacterial lysates for 72 h and infected with \(1\times 10^{6}\) cells/mL _S. aureus_ for 6 h. After removal of extracellular _S. aureus_, cells were further incubated for 24 h. (**A**) hBD3 mRNA level was examined by real-time PCR. (**B**) Intracellular _S. aureus_ was counted by spreading of cell lysates. (**C**) HaCaT cells were treated with the indicated dose of KG lysates for 72 h. After _S. aureus_ infection (as described above), intracellular _S. aureus_ was counted. (**D**) HaCaT cells were treated with hBD3+ and hBD3– CM for 72 h and the number of _S. aureus_ was measured through the above method. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\), and *** \(p<0.0001\).  
on hBD3 production in HaCaT cells. When HaCaT cells were treated with IFN-\(\gamma\), hBD3 mRNA was significantly increased (Figure 3E). We also observed that IFN-\(\gamma\) was increased by KG lysates in these cells (Figure 3F). These results suggest that IFN-\(\gamma\) produced in response to KG lysates stimulated the JAK-STAT pathway.
# Dynamic Response of Railway Subgrade Under Train Load and Freeze-Thaw Action
## 2 Three-Dimensional Finite Element Model of Railway Subgrade
### Model Verification and Calculation Conditions  
To validate the accuracy of the forementioned loading methodology and the developed three-dimensional finite element model, a comparative analysis of the present findings with those from extant literature is conducted. The model employed for the validation purposes comprised a 1 m-thick embankment layer overlaying a subgrade with a 6-m-depth of soft soil and a 17 m-depth of hard soil beneath the foundation. A train with an axle load of 14 tons is simulated to traverse the model at a constant velocity of 50 m/s. The accuracy of the model is assessed by examining the time-history responses of vertical displacement at a critical point, B (the center of the subgrade). Figure 4 illustrates the comparison between the vertical displacement responses computed by the finite element model (FEM) and the simulated response model reported by [PERSON] and [PERSON] [15; 20]. The abscissa in the figure denotes the distance over which the load velocity is applied, where '\(c\)' represents the speed of the moving load, and '\(t\)' signifies time.  
The results of this paper coincide well with the results of existing studies by [PERSON] and [PERSON] [15; 20], the vertical displacement change trend is the same, it indicates that the FEM has good reliability and high accuracy in calculating the dynamic response of the subgrade under the action of moving load. The consonance between the outcomes of the finite element analysis and the cited numerical simulation results attests to the reliability of the 3D finite element modeling methodology employed in this study. This validation suggests that the adopted modeling process is a dependable tool for investigating the mechanical behavior of the ballasted track-subgrade system subjected to moving train loads.  
Figure 3: Distribution of observation points A-B-C (unit: m).  
The impact of different train speeds--180, 200, and 220 km/h--is considered with the same environmental temperature, the impact of different ambient temperatures \(5\), \(-5\), and \(-15\)\({}^{\circ}\)C is considered with the same train speed, and the design of the calculation cases are shown in Table 4.
## 2 Background  
### Natural Language Processing  
To achieve human-like language processing for a variety of activities or applications, [PERSON] [18] defines NLP as a theoretically motivated set of computational approaches for evaluating and modelling naturally occurring text at one or more levels of linguistic analysis. It explores how text or speech in natural language may be read by computers and then used to perform beneficial tasks [19]. As a result of the linguistics and AI nexus [20], NLP was initially created in the 1950s. But currently, NLP derives its inspiration from a variety of disciplines, necessitating a large mental knowledge base expansion among researchers and developers. To be specific, linguistics, mathematics, computer and information sciences, electrical and electronic engineering, AI and robotics, psychology, etc., are some of the disciplines that constitute the basis of NLP [19].  
NLP was created to facilitate user tasks and meet the demand for natural language computer interaction [21]. Since its inception in the 1950s, NLP research has focusedon topics such as machine translation, question answering, information extraction, topic modelling, information retrieval, and opinion mining more recently [22]. Over the past 20 years, NLP has developed into a fascinating field of scientific study and practical technology. Its application can serve a variety of purposes: human-human communication can be facilitated by machine translation (MT), human-machine interaction can be facilitated by conversational agents, or both humans and machines can gain from the massive amount of human language content that is currently available online by examining and learning from it [23]. NLP is particularly useful for people or children who do not have the time to learn new specific languages or become proficient in them because it is impossible to force users to learn the machine-specific language if they want to communicate with the computer [24].  
With the rapid development of NLP, a series of algorithms and models came into people's eyesight. For instance, by comparing the relative frequency of terms in a given document with the inverse proportion of that word in the entire document corpus, Term Frequency-Inverse Document Frequency (TF-IDF) analyses the relevance of a word in a given document [25], which is viewed as a classical method in the field. Other NLP techniques that are often applied include topic modelling [26], text classification [27], and text sentiment analysis [28]. Support Vector Machine (SVM) is another conventional algorithm that can be created as a classifier [29; 30]. Different from TF-IDF, SVM learns by example to assign labels to objects to accomplish the task [31]. Word2 Vec distinguishes itself and demonstrates its advantage when working with a large dataset. It creates a vocabulary from a training text corpus, learns the vector representation of each word, and computes the cosine distance between each word. The original feature dimension is projected into a new lower dimension by grouping similar words based on their distances, reducing the time and challenge of classification [32].
###### Abstract  
This study aimed to investigate the transfer accuracy (trueness and precision) of three different intraoral scanning families using different hardware and software versions over the last decade from 2012 to 2021, compared to a conventional impression. Therefore, an implant master model with a reference cube was digitized and served as a reference dataset. Digital impressions of all three scanning families (True definition, TRIOS, CEREC) were recorded (\(n=10\) per group), and conventional implant impressions were taken (\(n=10\)). The conventional models were digitized, and all models (conventional and digital) were measured. Therefore, it was possible to obtain the deviations between the master model and the scans or conventional models in terms of absolute three-dimensional (3D) deviations, deviations in rotation, and angulation. The results for deviations between the older and newer scanning systems were analyzed using pairwise comparisons (\(p<0.05\); SPSS 26). The absolute 3D deviations increased with increasing scan path length, particularly for the older hardware and software versions (old vs. new (MW \(\pm\) SD) True Definition: 355 \(\pm\) 62 \(\mu\)m vs. 483 \(\pm\) 110 \(\mu\)m; TRIOS: 574 \(\pm\) 274 \(\mu\)m vs. 258 \(\pm\) 100 \(\mu\)m; and CEREC: 1356 \(\pm\) 1023 \(\mu\)m vs. 110 \(\pm\) 49 \(\mu\)m). This was also true for deviations in rotation and angulation. The conventional impression showed an advantage only regarding the absolute 3D deviation compared to the older systems. Based on the data of the present study, the accuracy of intraoral scanners is decisively related to hardware and software; though, newer systems or software do not necessarily warrant improvement. Nevertheless, to achieve high transfer accuracy, regular updating of digital systems is recommended. The challenge of increasing errors with increasing scan paths is overcome in the most recent systems. The combination of two different scanning principles in a single device seems to be beneficial.  
Keywords:dental implants; digital dentistry; dental impression technique; dimensional measurement accuracy; intraoral scanner +
Footnote †: journal: Applied Sciences
# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
## 1 Introduction
Two different types of software (one online, the CSRS-PPP [34], and one open-source, RTKLIB 2.4.3 b33 [35]) and two different test sites have been considered to make the analyses as general as possible. The positioning solutions have been computed to verify that the improvements in the ZTD estimations do not negatively affect the coordinate estimations, because both have been estimated from the same observations. The remainder of this paper is organized as follows. Section 2.1 describes the ZTD estimation from GNSS observations, while Section 2.2 presents the case studies. Section 3 highlights the results of ZTD estimations regarding both smartphones and geodetic receivers. Section 4 provides the discussion on the research outcomes, and Section 5 reports the conclusions and gives some ideas for future research activities and investigations.
# Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
## 4 Proposed Algorithm  
The rank is calculated based on the predict optimal cost matrix in the proposed algorithm, \(P_{j}\) denotes the processor \(P_{j}\), and \(t_{i}\) denotes the task \(t_{i}\). The rank calculation formula is given by  
\[\text{POCM}_{POC}(\text{t}\,,\,p_{k})=\overline{CT(t_{i},P_{k})}+\max_{t_{m} \in succ(t_{i})}\left\{\min_{P_{\beta}\in P}[\text{POCM}_{POC}\big{(}\text{t}_{ \text{m}}\,,\,P_{\beta}\big{)}+CT\big{(}\text{t}_{\text{m}}\,,\,P_{\beta} \big{)}+CT\big{(}\text{t}_{i}\,,\,P_{\beta}\big{)}+\overline{TC_{i,k}}] \right\} \tag{11}\]  
Here, \(TC_{i,j}\) = 0 when \(P_{k}=P_{\beta}\), and the value of \(\text{POCM}_{POC}\) for the exit task is \(\text{POCM}(\text{t}_{\text{exit}}\,,\,p_{k})=\,\,\,CT(\text{t}_{\text{exit}} \,,\,P_{k})\).  
The terms \(\overline{CT(t_{i},P_{k})}\) and \(CT\big{(}\text{t}_{\text{m}}\,,\,P_{\beta}\big{)}\) are of great importance. The computation time denoted by \(\overline{CT(t_{i},P_{k})}\) defines the task importance of the current process on a processor \(P_{k}\), and \(CT\big{(}\text{t}_{\text{m}}\,,\,P_{\beta}\big{)}\) defines the task importance of the successor node in the critical path. The aim of the algorithm is to allocate the task \(t_{i}\) to the better processor, so that the makespan and the reduced cost is achieved. The existing algorithms have not considered the effect of task \(t_{i}\) successors, but they just found the least EFT for a task \(t_{i}\) and chose the processor accordingly.  
The proposed method is shown in Algorithm 1. The algorithm has two stages. In phase one, prioritize that task; and in phase two, select the processor. In the prioritizing phase the tasks are ordered depending on the rank calculation in the reducing order. Rank is calculated as follows.  
\[Rank_{POC}(t_{i})=\frac{\sum_{m=1}^{P}POCM(t_{i},\,\,p_{k})}{P} \tag{12}\]  
In the processor selection phase, two queues are maintained, namely the task queue and the ready queue. Initially, the task queue has all the tasks. Depending on the rank of the tasks, they move the task to the ready queue and calculate the earliest finish time of the processor and the VM cost at its earliest. Choose the processor with minimum EFT and cost. This process is repetitively done for all the tasks in the queue. Finally, the tasks with higher priority are mapped to the appropriate processor. The focus of this technique is to diminish the makespan and cost within the deadline and minimum cost.
# In Vitro Preliminary Evaluation of Bacterial Attachment on Grooved and Smooth Healing Abutments
)1 Department of Oral Biology, School of Dental Medicine, Tel Aviv University, Tel Aviv 6997801, Israel; [PERSON]RESS] (H.Z. ); [PERSON]ADDRESS]. enhances inflammatory reactions in the surrounding gingiva and alveolar mucosa as well as around teeth [13]. Inflammatory mediators are enhanced in inflamed tissues, thus promoting angiogenesis [14] and pro-inflammatory cytokines [15]. Therefore, this inflammatory process within the soft tissue around healing abutments is also likely to affect implant osseous integration. _Fusobacterium nucleatum_ and _Porphyromonas gingivalis_ are well known perio-pathogenic bacteria. _Fusobacterium nucleatum_ belongs to the _Bacteroidaceae_ family. It is a Gram-negative anaerobic species of the phylum _Fusobacteria_, numerically dominant in dental plaque biofilms, and important in biofilm ecology and human infectious diseases [16]. _Porphyromonas gingivalis_ is a Gram-negative anaerobic bacterium [17]. Infection by _Porphyromonas gingivalis_ could modulate host immune responses and ultimately destroy the balance between the normal cell cycle and apoptosis, thereby leading to periodontal tissue destruction [18; 19]. Most healing abutments are made of titanium alloys [20]. However, the influence that certain alterations, such as laser markings or the machining of depth grooves on the abutment surface, may have on bacterial adhesion has not been evaluated. This preliminary in vitro study aimed to evaluate the bacterial attachment of two strains onto titanium alloy healing abutments, from the same manufacturer, differing only in their surface macro-morphology; while one was CNC (Computer Numerical Control) machine-grooved, the other was completely smooth.