esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Optical Detection of Aminoglycosodies
## 5 Amplification Strategies
### Hybridization Chain Reaction  
The hybridization chain reaction (HCR), as one of the non-enzymatic amplification techniques, requires two hairpin species that are stable in the absence of a single-strand DNA initiator. The single-strand DNA initiator hybridizes with one of the hairpin DNA, leading to the opening of the hairpin structure. This process initiates the hybridization reaction between the two hairpin DNAs and leads to the formation of a long-chained double-helix until the hairpin DNAs are exhausted (Figure 6B). HCR has been employed in colorimetric assays to improve the detection sensitivity of AGs. [PERSON]'s group tried to integrate an Au NP-based colorimetric aptasensor with HCR-assisted amplification to improve the detection sensitivity for kanamycin. The kanamycin-aptamer was contained in a hairpin DNA sequence that had a sticky end to absorb onto the AuNPs surface. The binding of kanamycin to its aptamer exposed parts of the hairpin DNA sequence and triggered a cascade of hybridization chain reactions with the formation of numerous dsDNA, which have a weak binding ability to the Au NPs' surface. Thus, the binding caused the salt-induced aggregation of Au NPs. By monitoring the ratio of absorbance at 520 and 630 nm, the LOD for kanamycin was 0.68 uM. Although the amplification method was employed, the sensitivity is poorer than the previous colorimetric assays with amplification techniques. One possible reason may be that the integration of the aptamer in the hairpin DNA sequence may decrease the binding ability of the aptamer to the target kanamycin, thus leading to a decreased detection sensitivity [87].
## 4 Discussion
### Enamel Loss Following Debracketing  
Among the included papers, enamel damage has been reported by some authors to be caused both by debonding and clean-up procedures [5; 29; 31; 34]. [PERSON] et al. analyzed the enamel damage due to bracket removal and reported the bond failure at the interface between enamel and adhesive produces enamel loss [31]. Bond failure may occur: (1) between the bracket's surface and the adhesive; (2) within the composite; and (3) between the composite and the enamel. The last one may result in enamel substance loss of a certain amount [15] and could require direct restorations to be applied to solve the esthetic issue [37]. The enamel damage area and the volume of surface loss also depends on the size of the orthodontic bracket and on the type of material [6; 29; 34]. Using small brackets could be less detrimental since a lower force will be needed to detach them [6]. The bracket's material is also important, as reported by [PERSON] et al. [29] They compared, in fact, metal reinforced polycrystalline and clear monocrystalline ceramic brackets. The authors showed that most of the polycrystalline brackets fractured in two or more pieces when detached. The fragments remaining bonded to the tooth may cause the clean-up procedure to be more invasive, time consuming, and cumbersome. The clear monocrystalline brackets, on the other hand, resulted to be more resistant to fractures. This finding was attributed to the zirconia microspheres embedded in the bracket's base [29]. In the same study, two types of pliers were tested: the conventional Weingart pliers for the polycrystalline group and the recommended plastic debonding instrument for the monocrystalline group. The manufacturers claim that the latter allows to peel the bracket off from the tooth in one piece. This may have contributed to experiencing fewer fractures in this group [29]. In the first group, however, the use of Weingart pliers may have represented one cause of higher fracture frequency [9].  
[PERSON] et al. compared monocrystalline ceramic brackets with conventional metal brackets [34]. They found that a significantly greater demineralization volume occurredin the ceramic bracket group. Ceramic bracket debonding and consequent damage to enamel surfaces has been investigated over the years for orthodontic research. Moreover, many studies have demonstrated the negative effects of ceramic brackets on the enamel surface, supporting [PERSON]'s findings [9; 29; 38; 39]. [PERSON] et al. found that out of a mean enamel defect (post clean-up) of +/ - 0.22 mm\({}^{3}\), 0.02 mm\({}^{3}\) were lost in the debracketing phase [5]. Moreover, they reported a volume loss of 0.13 mm\({}^{3}\) in a sample with ARI score 1, a mean volume loss of 0.02 mm\({}^{3}\) when the ARI score was 2, and no volume loss when ARI score was 3, suggesting a correlation between the adhesive remnants and the enamel loss. Nevertheless, these findings must be considered carefully since this paper has been assessed with a high risk of bias.
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
## 3 Numerical Methodology
### Inviscid HLLC-CICSAM Edge Flux
#### 3.5.3 Face-Flux  
Given the equation for the intermediate flux (Equation (31)), a mathematical equivalent expression for the face-flux, \(u_{ff}\), which is consistent with the proposed CICSAM discretisation, can be derived. Using an adaptation to the HLLC solver proposed by [PERSON] et al. [44], it is assumed that \(\Big{(}\alpha u_{ff}\Big{)}=\alpha_{f,C}u_{ff}\). The expression for the alpha flux hence follows as  
\[\Big{(}\alpha u_{ff}\Big{)} =\frac{1+\text{sign}(s_{*})}{2}\Big{[}\alpha_{f,C}\mathbf{u}_{L}\cdot \mathbf{n}+s^{-}\Big{(}\chi_{L}\alpha_{f,C}-\alpha_{f,C}\Big{)}\Big{]}\] \[+\frac{1-\text{sign}(s_{*})}{2}\Big{[}\alpha_{f,C}\mathbf{u}_{R}\cdot \mathbf{n}+s^{+}\Big{(}\chi_{R}\alpha_{f,C}-\alpha_{f,C}\Big{)}\Big{]}.\]  
If \(s_{*}>0\), it follows that  
\[\alpha_{f,C}\mathbf{u}_{ff} =\alpha_{f,C}\big{(}\mathbf{u}_{L}\cdot\mathbf{n}+s^{-}(\chi_{L}-1)\big{)},\] \[\implies u_{ff} =\mathbf{u}_{L}\cdot\mathbf{n}+s^{-}(\chi_{L}-1),\]  
and similarly, for \(s_{*}<0\),  
\[\alpha_{f,C}\mathbf{u}_{ff} =\alpha_{f,C}\big{(}\mathbf{u}_{R}\cdot\mathbf{n}+s^{+}(\chi_{R}-1)\big{)},\] \[\implies u_{ff} =\mathbf{u}_{R}\cdot\mathbf{n}+s^{+}(\chi_{R}-1).\]  
Therefore, for the edge \(\mathbf{t}_{l,m}\), the face-flux is  
\[u_{ff}=\bigg{(}\frac{1+\text{sign}(s_{*})}{2}[\mathbf{u}_{L,M}\cdot\mathbf{n}_{l,m}s^{ -}(\chi_{L}-1)]+\frac{1-\text{sign}(s_{*})}{2}[\mathbf{u}_{R,M}\cdot\mathbf{n}_{l,m}+s ^{+}(\chi_{R}-1)]\bigg{)}\mathcal{A}_{l,m}. \tag{33}\]  
In particular, the above definition allows for easy implementation of the VoF and HLLC solver in a manner that guarantees the contact-preserving properties.  
Finally, though the VoF CICSAM interface reconstruction is used in this work, for the purpose of comparison, the alpha face value, \(\alpha_{f,H}\), is also discretised using HLLC-MUSCL where the different states are given as  
\[\alpha_{f,H}=\begin{cases}\alpha_{L,M},&\text{if}\quad 0\leq s_{L},\\ \alpha_{*_{L}}=\chi_{L}\alpha_{L,M},&\text{if}\quad s_{L}\leq 0\leq s_{*},\\ \alpha_{*_{R}}=\chi_{R}\alpha_{R,M}&\text{if}\quad s_{*}\leq 0\leq s_{R},\\ \alpha_{R,M},&\text{if}\quad 0\geq s_{R},\end{cases} \tag{34}\]  
and the alpha flux is then computed as per Equation (31).
## 4 Experimental Results
We analyzed the effect of TSR data by adding or removing TSR data while constructing the proposed model. Table 4 shows the difference between the models with and without TSR. Based on the results shown in Table 4, we conducted an ANOVA test and calculated the effect size. The results of the ANOVA test showed that there was no statistically significant difference (\(F(1,\;6)=0.42\), \(p\)-value = 0.54). [PERSON]'s d was approximately 0.4596 \(\cong\) 0.5. This effect size is approximately medium. The results of the ANOVA test based on RMSE also indicated no statistically significant difference (\(F(1,6)=1.18\), \(p-value=0.317\)). However, unlike the result of MAE, the effect size for RMSE, such as [PERSON]'s d, was 0.771. This effect size is medium. Although there was no statistically significant difference between the presence and absence of TSR, practical significance, such as [PERSON]'s d, presented that the effect was sufficient to be meaningful in the real world. Therefore, using TSR can improve the performance of the model.
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 2 Materials and Methods
### Why Is User-User Filtering More Suitable?  
Unlike item-based filtering, which focuses on individual tools or treatments, user-user filtering considers broader environmental and contextual factors influencing agricultural success, such as climate, soil type, and pest pressures.  
Farmers operating under similar conditions are more likely to benefit from shared strategies. For instance, if a farmer with loamy soil in a temperate climate grows tomatoes successfully, the system will recommend similar practices to other farmers with matching conditions.  
Moreover, user-user filtering allows dynamic adaptation to changing environments, whereas item-based filtering assumes static product effectiveness. For personalised agricultural recommendations that consider environmental variability, user-user filtering is the most suitable method [13; 19; 20].
## 4 Discussion
### Limitations of the Proposed AMARA Approach and the Validation  
Apart from the positive evaluation of AMARA the following limitations of AMARA and our approach to validate AMARA must be considered. (1) AMARA also relies (as MARA and ABAMAR) on a choice of specific parameter values that have to be chosen manually. Although we minimized this need, AMARA uses nine parameter values, which, however, can all be set for a whole study instead of having to be set then for each measurement individually. This limits the adaptive nature of the algorithm to data of a specific set-up. The parameters have to be modified if measurements with a different experimental setup are processed. (2) For the validation of AMARA, we used the information on artifacts based on four humans scoring the artifacts manually. A better solution would be to use a non-subjective gold standard; however, such a gold standard does not yet exist. A solution could be tosimulate artifacts. Then the true values would be known, but this requires making several assumptions about MAs, which may not apply to real data.
# Cosine-Based Embedding for Completing Lightweight Schematic Knowledge in DL-Lite\({}_{core}\)+
## 3 Preliminary
### DL-Lite\({}_{core}\)  
DL-Lite\({}_{core}\) is the core language for DL-Lite [26]. It is the lightweight language of Description Logics that represents the domain of interest via concepts denoting the set of instances and binary relations between instances. For the syntax of DL-Lite\({}_{core}\), the concepts and relations are defined as follows:  
\[\begin{array}{l}(1)\;B::=A\;\middle|\exists Q,\\ (3)\;C::=B\;\middle|\
eg B,\end{array}\qquad\qquad\begin{array}{l}(2)\;Q::=P \;\middle|P^{-},\\ (4)\;R::=Q\;\middle|\
eg Q,\end{array}\]  
where the symbols \(A\) and \(B\) denote an atomic concept and a basic one, \(P\) and \(Q\) represent an atomic relation and a basic one, and \(C\) and \(R\) denote the general concept and role, respectively.  
The forms of axiom in DL-Lite\({}_{core}\) can be asserted as follows: (1) the inclusion axiom of concepts is denoted by \(B\sqsubseteq C\); (2) the membership axiom of concept is denoted by \(A(a)\), where \(a\) is an individual; and (3) the membership axiom of relation is denoted by \(P(a,b)\), where \(a\), \(b\) are two instances.  
**Definition 1** ([42] (Ontology)).: _Let \(\mathcal{L}\) be a logical language from Description Logics. An ontology denoted by \(O=\langle\mathcal{T},\mathcal{A}\rangle\) consists of a TBox \(\mathcal{T}\) and an ABox \(\mathcal{A}\), where \(\mathcal{T}\) is a set of concept inclusion axioms that is also called schematic knowledge, and \(\mathcal{A}\) is a set of membership axioms about concepts and roles. The forms of all the axioms in \(O\) are constrained by the syntax of \(\mathcal{L}\)._  
For the lightweight schematic knowledge of KG, its logical axioms are mainly asserted based on \(subclassOf\) axioms and \(disjointWith\) axioms. According to the previous study [27], the authors demonstrated that all the axioms from TBox in DL-Lite\({}_{core}\) could be completely reduced to axioms with \(subclassOf\) and \(disjointWith\) relations using the transformational rules based on a directed graph. Hence, our proposed model can encode all the axioms from TBox expressed in DL-Lite\({}_{core}\).  
Unless specified otherwise, we assume that all the axioms of lightweight schematic knowledge in the subsequent sections are expressed in DL-Lite\({}_{core}\), and we do not differentiate the TBox in DL-Lite\({}_{core}\) ontology and lightweight schematic knowledge. For convenience, we divide the axioms in TBox of DL-Lite\({}_{core}\) into two sets, denoted by \(\mathcal{T}=\{(C_{i},subclassOf,C_{j})\}\cup\{(C_{i},disjointWith,C_{j})\}\), where \(C_{i}\) and \(C_{j}\) are two general concepts. Notice that the axioms are also one special kind of triples. For schematic knowledge embedding, the encoded objects of models are head concepts, and the tail ones are defined by the syntax of DL-Lite\({}_{core}\), rather than entities without semantics.  
Next, we give the definition of schematic knowledge embedding and formulate its properties for preserving the transitivity and symmetry of relations in DL-Lite\({}_{core}\).
# Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks
## 3 The CORAL Algorithm
### Online Meta-Learning
It is sensible to introduce some restrictions to make the matrix easily invertible. In our evaluation, we use a diagonal matrix for \(\Lambda_{0}\), which we found to work well in our experiments. ```
0:\(\Lambda_{0},\overline{K}_{0},\Sigma_{c}\) and \(data:\{(x_{t},y_{t})\}_{t=\tau-n-m+1}^{\tau},x_{\tau+1}\)
1. Store \(\{(x_{t},y_{t})\}_{t=\tau-n-m+1}^{\tau}\) as \(\{(x_{t},y_{t})\}_{t=1}^{n+m}\)
2. Store \(x_{\tau+1}\) as \(x_{n+m+1}\)
3. \(Q_{0}\leftarrow\Lambda_{0}\overline{K}_{0}\)
4. Compute \(\Lambda_{0}^{-1}\)
5.for\(t=1\ldots n+m\)do
6.\(Q_{t}\gets Q_{t-1}+\phi(x_{t})y_{t}^{T}\)
7.\(\Lambda_{t}^{-1}\leftarrow\Lambda_{t-1}^{-1}-\frac{(\Lambda_{t-1}^{-1}\phi(x _{t}))(\Lambda_{t-1}^{-1}\phi(x_{t}))^{T}}{1+\phi(x_{t})^{T}\Lambda_{t-1}^{-1} \phi(x_{t})}\)
8.if\(t>n\)then
9.\(Q_{t}=Q_{t-1}-\phi(x_{t-n})y_{t-n}^{T}\)
10.\(\Lambda_{t}^{-1}\leftarrow\Lambda_{t}^{-1}-\frac{(\Lambda_{t}^{-1}\phi(x_{t- n}))(\Lambda_{t}^{-1}\phi(x_{t-n}))^{T}}{-1+\phi(x_{t-n})^{T}\Lambda_{t}^{-1} \phi(x_{t-n})}\)
11.\(\overline{K}_{t}\leftarrow\Lambda_{t}^{-1}Q_{t}\)
12.\(\overline{y}_{t+1}\leftarrow\overline{K}_{t}^{T}\phi(x_{t+1})\)
13.\(\Sigma_{t+1}\leftarrow(1+\phi(x_{t+1})^{T}\Lambda_{t}^{-1}\phi(x_{t+1}))\Sigma_{c}\)
14. Accumulate gradients of \(L\) with respect to \(\Lambda_{0},\overline{K}_{0}\)
15.endif
16.endfor
17. Update \(\Lambda_{0},\overline{K}_{0}\) (apply gradients)
18. return \(\overline{y}_{n+2:n+m+1},\Sigma_{n+2:n+m+1}\) as \(\overline{y}_{\tau-n-m+2:\tau+1},\Sigma_{\tau-n-m+2:\tau+1}\)
```  
**Algorithm 1** One cycle of the CORAL algorithm.
# Forecasting the Impacts of Prescribed Fires
## 2 Methodology
### Air Quality and PB Impact Modeling  
We have expanded our air quality forecasting system, HiRes (2016), to forecast the impacts of various types of emission sources for dynamic management purposes ([PERSON], 2016). Using the sensitivities calculated by the decoupled direct method (DDM) ([PERSON], 2016) available in the Community Multiscale Air Quality (CMAQ) model (version 5.0.2), the impacts of PB emissions to the forecast ozone and PM\({}_{2.5}\) concentrations are calculated. The new system, HiRes2, which consists of the Weather Research and Forecasting model (WRF) (version 3.6) and CMAQ, has been operational since 1 January 2015. DDM requires a well-defined emission source for each sensitivity that it calculates, and additional sensitivity calculations significantly increase the computational burden. We treat all PB emissions in Georgia as a single source and calculate a single DDMsensitivity to statewide PB. Additional DDM sensitivity calculations are required to determine the impacts of PB emissions from a specific fire district or county within the state.  
The air quality and burn impact forecasts are reported daily via a website [25]. Figure 1 shows the PM\({}_{2.5}\) and PB impact forecasts for 9 March 2016 as an example of a high burn activity day. Exceedance of the 35 \(\upmu\)g m\({}^{-3}\) daily average PM\({}_{2.5}\) standard is expected for the purple colored grid cells in Figure 1a. The PB impacts shown in Figure 1b are responsible for the exceedances. With this information, the amount of area permitted to be burned can be restricted to avoid the forecast exceedances. The website also contains numerical information for maximum PM\({}_{2.5}\) concentrations and PB impacts in each county along with the forecast burn areas.
## 3 Applications of Multimodal Fluorescent-Magnetic Nanoparticles  
Fluorescent-magnetic nanoparticles have found many biological applications such as multimodal bioimaging agents, diagnostic tools, drug delivery and hyperthermia/photodynamic therapy systems.
## 4 Results and Discussion
### Preprocessing Stage
Of investigated studies, 34 used image composite methods, such as best available pixel (BAP), to remove atmospheric effects and cloud/cloud-shadows and to choose the best observation per pixel. Figure 16: The trend in the use of different preprocessing strategies over time. Other studies simply picked the median value or maximum of spectral indices (e.g., NDVI) of each pixel to create synthetic seasonal or annual images.
# Mechanical Strength Study of a Cranial Implant Using Computational Tools
###### Abstract  
The human head is sometimes subjected to impact loads that lead to skull fracture or other injuries that require the removal of part of the skull, which is called craniectomy. Consequently, the removed portion is replaced using autologous bone or alloplastic material. The aim of this work is to develop a cranial implant to fulfil a defect created on the skull and then study its mechanical performance by integrating it on a human head finite element model. The material chosen for the implant was PEEK, a thermoplastic polymer that has been recently used in cranioplasty. A6 numerical model head coupled with an implant was subjected to analysis to evaluate two parameters: the number of fixation screws that enhance the performance and ensure the structural integrity of the implant, and the implant's capacity to protect the brain compared to the integral skull. The main findings point to the fact that, among all tested configurations of screws, the model with eight screws presents better performance when considering the von Mises stress field and the displacement field on the interface between the implant and the skull. Additionally, under the specific analyzed conditions, it is observable that the model with the implant offers more efficient brain protection when compared with the model with the integral skull.  
cranial implants; finite element method; PEEK; cranioplasty +
Footnote †: journal: Medical Imaging
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 3 Graph Databases
### Nebula Graph  
Nebula Graph was released by Vesoft Inc. as a native database specialized in storing various graph connections and retrieving information from them. It also stores nodes and edges in graphs with properties and labels. As a query language, it uses nGQL (nebula graph query language), similar to SQL, and designed for programmers and ordinary users. Nebula Graph supports C++, Java, Python, and Go programming languages.  
The main features of Nebula Graph are as follows [18]:  
* Ability to host graphs with hundreds of billions of nodes and trillions of edges;
* Fast queries with a millisecond latency.  
As main advantages, we can highlight the following:  
* Due to its three-part architecture, it brings benefits such as high availability and cost-effectiveness by providing a higher resource utilization rate;
* As an SSD-based product, as compared to HDD and large memory products, it is more suitable for future hardware trends and easier to achieve balanced read and write.  
However, it also has some limitations [18]:  
* If we back up a part of a particular graph in cluster A, the backup files cannot be restored in another cluster B;
* It takes up a relatively large amount of disk space.  
Nebula Graph Studio is used for data representation. This is a browser visualization tool that provides an interactive user experience. It also allows manipulation of the data schema, import of data, and execution of nGQL statements for possible retrieval. Figure 2 shows the data representation in Nebula Graph Studio.
## 2 Background
### Requirements for the Calibration Data Management and Data Formats
#### 2.3.3 Requirements for Data Integrity  
One of the topics where improvements are pursued by investing in digitalization in the pharmaceutical industry is data integrity. The basis for the data integrity regulation compliance is typically referred to as the ALCOA+ principle [60; 61], the definition of which is presented in Table 1.  
Regulative requirements for data integrity in manufacturing are either defined as a part of the GMPs and GLPs or in separate regulations such as the US Food and Drug Administration (FDA) 21 Code of Federal Regulations (CFR) Title 21 Part 11 [62] or Medicines & Healthcare Products Regulatory Agency (MHRA) Data Integrity Guidance and Definitions [63]. An example of a de-facto standard on data integrity is the ISPE GAMP Guide: Records & Data Integrity [12], where data integrity requirements defined in the regulations are approached in a more practical way.  
Data integrity is also covered, although to a lesser extent, in the ISO 17025 Section 7.11, where requirements for the data management systems used by calibration laboratories for collecting, processing, recording, reporting storing, or retrieving data [48] are defined. However, as the requirements for the calibration certificates focus on the content that must  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Symbol** & **Meaning** & **Definition** \\ \hline A & Attributable & It must be clear when an action was taken and by whom, where the data came from, e.g., the systems, devices, or instruments used. \\ \hline L & Legible and intelligible & The data and records must be comprehensible regardless of the record types. \\ \hline C & Contemporaneous & Recordings must take place at the time of the action or observation and a timestamp must be included. \\ \hline O & Original & If a record is copied, the original record must be distinguishable, and the copies must be verified as “true copies”. Especially for electronic records, all relevant metadata must also be considered. \\ \hline A & Accurate & All instruments used for collecting data must be accurate and controlled, e.g., calibrated, and any corrections or changes that need to be made in the records must be documented as amendments. \\ \hline \multirow{5}{*}{+} & Available & A record must be accessible and available for audits over its lifetime. \\  & Complete & All relevant data and metadata must be included in the records and must not be omitted or deleted. \\  & Consistent & The timestamps in the records of actions and observations must be consistent with order of the steps in the operation procedure and be based on a common time reference. \\  & Enduring & The media used to store the records must be authorized and robust to ensure the preservation of the record over its lifetime. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The definition of the ALCOA+ principle [60; 61].  
be included, regardless of whether the certificate is a physical or electronic document, the standard does not specify detailed requirements or methods for securing a digital file.