esa-sceva/satcom-chunk-collection huggingface dataset:
# Is Probiotics Supplementation an Appropriate Strategy to
## 3 Results
### Quality Assessment  
Four studies [27; 28; 30; 39] had results \(\geq\) 15 points, corresponding to \"excellent\" quality, and nine [29; 31; 32; 33; 34; 35; 36; 37; 38] studies had results between 13 and 14 points, corresponding to \"very good\" quality (Table 1).  
Figure 1: Flow diagram depicting the identification and selection processes of relevant studies according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [25].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multicolumn{11}{c}{**Item**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**\%**} & \multirow{2}{*}{**Quality Score**} \\ \cline{2-13} \cline{5-13}  & **1** & & & **2** & & **3** & & **4** & **5** & & **6** & & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** & \\ \[PERSON] et al., 2019 [27] & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 15 & 93.8 & E \\ \hline [PERSON] et al., 2020 [28] & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 16 & 100 & E \\ \hline [PERSON] et al., 2016 [32] & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 13 & 81.25 & VG \\ \hline [PERSON] et al., 2019 [33] & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 14 & 87.5 & VG \\ \hline [PERSON] et al., 2019 [34] & 1 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 13 & 81.25 & VG \\ \hline [PERSON] et al., 2016 [35] & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 14 & 87.5 & VG \\ \hline [PERSON] et al., 2021 [36] & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 14 & 87.5 & VG \\ \hline [PERSON] et al., 2019 [37] & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 14 & 87.5 & VG \\ \hline [PERSON] et al., 2020 [38] & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 14 & 87.5 & VG \\ \hline [PERSON] et al., 2021 [39] & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 16 & 100 & E \\ \hline [PERSON] et al., 2014 [29] & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 14 & 87.5 & VG \\ \hline [PERSON] et al., 2021 [30] & 1 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 16 & 100 & E \\ \hline [PERSON] et al., 2019 [31] & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 14 & 87.5 & VG \\ \hline \hline \end{tabular} Abbreviations: 0 = not fulfilled criterion; 1 = fulfilled criterion; E = excellent; VG = very good; item 1; study purpose; item 2; literature review; item 3’s study design; item 4’s binding item 5’s sample description; item 6’s sample size; item 7’s ethics and consent; item 8’s validity of outcomes; item 9’s reliability of outcomes; item 10’s intervention description; item 11’s statistical significance; item 12’s statistical analysis; item 13’s clinical importance; item 14’s conclusions; item 15’s clinical implications; item 16’s study limitations.  
\end{table}
Table 1: Results of the methodological quality assessment of included studies—McMaster Critical Review Form for Quantitative Studies [26].
## 4 Quantitative descriptions of scale threshold and scale domain
### Semivariogram based method (SVM)  
The semivariogram is often used as a tool to measure the difference in property values at two sample locations as a function of the distance between these locations. It provides the mean characteristics of spatial heterogeneity at the image scale. There are three features to characterize the semivariogram: nugget, sill and range. These features can be used to characterize and quantify the spatial heterogeneity of a land surface [19]. The nugget is the discontinuity of the semivariogram at the origin. It can be used to judge if uncorrelated noise (measurement error) exists or spatial structures are smaller than the pixel size. The sill is the value that the semivariogram may reach when the distance heads toward infinity. It can be an indicator of the spatial variability of the data. The range is the distance at which it reaches a sill. It can be used to characterize the image spatial structures. [PERSON] _et al_. [62] used the semivariogram and the characteristic length calculated from the spatial autocorrelation to determine the scale of variability of the remotely sensed data. In order to account for the multiple length scales of data, [PERSON] [63] and [PERSON] _et al_. [19] proposed a linear combination of elementary variogram models to model the semivariogram. Then, the structural parameters of the semivariogram model can be generalized into a single parameter: the integral range (_A_ ). Here, its square root \(D_{c}\) is a weighted average of several range parameters and quantifies the mean length scale of data. Based on [PERSON]'s theorem, the spatial sampling frequency must be larger than \(2\,/\,D_{c}\,\), therefore, the pixel size must be smaller than \(D_{c}\,/\,2\) to retain the major part of spatial variability. Here, either \(A\) or \(D_{c}\) can be used to judge whether the geographical scale is large enough to detect the length scales of the landscape. In addition, the rate of data regularization can be used to characterize the rate of the loss of image spatial variability at a given spatial resolution. [PERSON] _et al_. [52] applied variogram modeling to evaluate the difference in spatial variability at different scales and characterize the impact of scale. Based on the analysis, an approach for selecting the spatial resolution is proposed. Subsequently, [PERSON] _et al_. [64] proposed to use multivariate, red and near infrared spectral properties to quantify the landscape spatial heterogeneity by direct and cross-variograms modeled together with the geostatistical linear model of co-regionalization. The result showed it to be more powerful than univariate variogram modeling.
# Experiment on Flexural Fatigue Performance of Precast Bridge Deck Joints with Loop Connections
## 4 Experiment Results and Analysis
### Crack Propagation and Failure Mode  
The crack distribution and failure mode of the contrast specimen B3-A after the static loading is shown in Figure 6a. The crack distribution and failure mode of the fatigue specimens after the \(200\times 10^{4}\) fatigue-loading cycles are presented in Figure 6b-d.  
For the specimen SF1, when the specimen is destroyed the crack is distributed on both sides of one of the interfaces between the joint segment and the precast segment. The crack at the position of the interface surface runs through the cross section of the specimen, and the crack height is more than \(1/2\) of the height of the cross section. In the joint section, there are two cracks in the lower edge of the concrete, but the cracks do not run through the whole section.  
By comparing the specimens B3-A and SF1, it can be learnt that the failure modes of the two specimens are basically similar with different loading methods. The failure modes of the two specimens are as follows: vertical cracks along the interface between the joint segment and precast segment appear in the tension zone, and the concrete compression failure appear in the compression zone.  
For the specimen SF2, the cracks are mainly distributed in the joint segment and the interface, and the cracks run through the whole section. Due to the low thickness of the specimen SF2, the height of the cracks can quickly exceed \(1/2\) of the specimen height at a low load level.  
For the specimen SF3, the cracks on the bottom of the specimen are mainly concentrated in the double-overlapping zone. There are no obvious cracks in the other zone of the joint segment. And, for the precast segment in the pure flexural section, the crack length at the lower edge of the specimen is short, and there is no obvious penetrating crack.  
By comparing the three fatigue specimens, it can be seen that the smaller the width of joint is, the less the concrete crack appears in the joint. During the fatigue-loading process, the concrete first cracks at the interface of joint segment and precast segment, and the position on both sides of the center line with a distance of 50 mm. With the increase of the static load level, the height of cracks at these positions increases continuously. However, it no longer propagates when the cracks reach a certain height. The cracks begin to develop along the longitudinal direction of the specimen, and the crack morphology is presented as Y-shaped or T-shaped. In addition, it can also be learnt from the specimens SF2 and SF3 that the joint with loop connections usually crack at the overlapping positions. It is similar to the test results of the related flexural specimens [26; 27; 28].
# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 4 Experiments
### Experiment of Depth Mask  
Depth maps represent the distance between objects and the camera in the scene, so the depth values can vary significantly in different scenarios. However, the relative depth values within the object region tend to remain relatively consistent and are related to the object's size. Therefore, intuitively, separating the background from the depth map and normalizing the depth within the object's region should enhance the utility of the depth map.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Hardware} & CPU & Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30 GHz \\ \cline{2-3}  & GPU & NVIDIA GeForce RTX 4090 \\ \hline \multirow{4}{*}{Training} & Batch size & 12 \\ \cline{2-3}  & Optimizer & Adam \\ \cline{2-3}  & Learning rate & \(2.0\times 10^{-4}\) \\ \cline{2-3}  & Weight decay & \(5\times 10^{-4}\) \\ \cline{2-3}  & Milestones & \(20,30,35\) \\ \cline{2-3}  & Decay rate & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters. The processors used for the experiments and the parameters employed during training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\
\begin{tabular}{c} **Number** \\ **Templates** \\ \end{tabular} } & \multicolumn{3}{c}{**Mean VSD Recall**} \\ \cline{3-5}  & & **Seen Obj. 1–18** & **Unseen Obj. 19–30** & **Total** \\ \hline \hline
\begin{tabular}{cTo further explore the value of depth features, an experimental group was established by introducing masks into depth images to segment and isolate the depth data containing only object regions. These masks used were derived from the T-LESS dataset. The segmented depth images underwent the data preprocessing operations outlined in Section 4.1, ultimately scaling the depth values to fit within the range of 0 to 255.  
These segmented depth images were utilized throughout both the training and testing phases of the network. The evaluation outcomes of this experiment are presented in Table 3, where the suffix'masked' denotes the introduction of masks. The comparative experiment's results align with our hypothesis: undisturbed object depth information contributes to enhancing pose estimation accuracy, as it contains purer geometric features of the object surfaces without background interference. However, this enhancement comes at a cost, as it requires additional instance segmentation masks from the upstream task.
## 9 Critical Assessment and Discussion  
The present study aims to emphasize the gaps in our knowledge on the following: (i) the use of squalene synthase and farnesyl diphosphate synthase as potential target proteins for the discovery of new anti-_Trypanosomatidae_ treatments; and (ii) the issues thus far not suitably explored.  
As already discussed, a total of forty compounds were reported to inhibit squalene synthase. Additionally, forty compounds (40) were found to inhibit the activity of farnesyl diphosphate synthase. Notably, among the inhibitors of squalene synthase, eight compounds (3, 22, 23, and 29-33) were found to be active against _Leishman_ spp., whereas five compounds (22-26) exhibited anti-_T. cruzi_ activity. For the inhibitors of farnesyl diphosphate synthase, nineteen compounds (41, 43, 45, and 49; 52 and 53; 29-32, 54-57, 33, 34, 59-61) displayed anti-_Trypanosoma cruzi_ activity.  
However, the following research gaps were noted: (i) lack of appropriate controls (negative and positive) in experiments; (ii) in most of the papers, the enzymatic assays were not followed by phenotypic screening of the compounds for anti-Trypanosomatid activity; (iii) there was a lack of data regarding the cytotoxicity of bioactive compounds against human mammalian cells; and (iv) noteworthy, data regarding enzymatic assays and phenotypic screening in vivo are scarce.  
As already discussed, reverse pharmacology, or target-based drug discovery, has been used to identify lead compounds against the parasites responsible for leishmaniasis and trypanosomiasis [62]. However, new drug discovery strategies, such as the dereplication technique, can be used to identify ubiquitous natural products or even known compounds that are potentially active against the _Trypanosomatidae_. As already discussed, the increase in partnerships between the public and private sectors, and the fulfillment of concerns regarding research funds might sustain anti-_Trypanosomatidae_ drug discovery [63]. Although rational design of multitargeting agents is extremely complex in polypharmacology modeling, this concept could be useful in drug discovery as it involves the interaction of drug molecules with multiple targets, which may interfere with a single or multiple disease pathways [80]. In addition to these activities, toxicity studies and the pharmacokinetics of the most promising compounds should be investigated. Phenotypic screening of inhibitors of squalene synthase and farnesyl diphosphate synthase should be performed for anti-trypanosomatid activity against _Leishman_ and _Trypanosoma_ species to verify the activity on the parasite. In vitro and in vivo toxicity studies of the bioactive compounds should be carried out to evaluate their selectivity.
## 4 Optimization Techniques and Procedures
### TOPSIS Method for Multi-Objective Optimization  
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a method used for converting multiple objectives into a single objective, which is consequently applied in optimization techniques for decision making [44; 45]. TOPSIS is based on choosing the alternative with the shortest geometric distance from the positive ideal solution and the longest geometric distance from the negative ideal solution. By implementing the TOPSIS method, multiple objectives are converted into a single objective value. Algorithm 1 shows the the pseudo-code for TOPSIS method.  
```
1: Read alternate and objectives matrix--\(O_{ij}\) with weights (\(W_{j}\)) and types of objectives (\(ot_{j}\))
2: For each alternative (i = 1, 2, 3, \(\ldots\), m) and objective (j = 1, 2, 3, \(\ldots\), n)
3: Compute Normalized value of \(O_{ij}\) using \(N_{ij}=\frac{O_{ij}}{\sqrt{\sum_{i=1}^{n}O_{ij}^{2}}}\)
4: Calculate Performance matrix (\(A_{ij}\)) using \(A_{ij}=N_{ij}*W_{j}\)
5: End
6: For each objective (j = 1, 2, 3, \(\ldots\), n)
7: Determine positive ideal (\(P_{i}\)) and negative ideal solution (\(M_{j}\))
8: For minimization objective--\(P_{j}=\min\limits_{1<i<m}\left(A_{ij}\right)\) and \(M_{j}=\max\limits_{1<i<m}\left(A_{ij}\right)\)
9: For maximization objective--\(P_{j}=\max\limits_{1<i<m}\left(A_{ij}\right)\) and \(M_{j}=\min\limits_{1<i<m}\left(A_{ij}\right)\)
10: End
11: For each alternative (i = 1, 2, 3, \(\ldots\), m)
12: Calculated Ideal (\(SP_{i}\)) and negative ideal separation (\(SM_{i}\))
13: Determine relative closeness (\(R_{i}\))
14: End
15: Rank alternatives w.r.t. \(R_{i}\) in descending order
```  
**Algorithm 1** Pseudo-code for TOPSIS.
## 1 Introduction
Using the aforementioned methods and data, it is possible to predict the yield of different crops in different areas in order to guide government policies that prioritize food security, especially the decisions on the export and import of food products, and also boost the agricultural economy by helping farmers and agriculture-related businesses make more informed decisions about crops
# Optimal Prefix Free Codes with Partial Sorting +
## 5 Discussion
### Relation to Previous Work
#### 5.1.3. Applicability of Refined Results on Deferred Data Structures  
[PERSON] et al.'s analysis (2013) of the complexity of the deferred data structure is in function of the total number \(q\) of queries and operators, while [PERSON] et al. (2015) analyzed the complexity of an offline version in function of the size of the gaps between the positions of the queries. [PERSON] et al. (2014) combined the three results into a single deferred data structure for multisets which supports the operators rank and select in amortized time proportional to the entropy of the distribution of the sizes of the gaps between the positions of the queries.  
At first view, one could hope to generalize the refined entropy analysis (introduced by [PERSON] et al. (2015) on the static version and applied by [PERSON] et al. (2014) to the online version) of multisets deferred data structures supporting rank and select to the computational complexity of optimal prefix free codes: a complexity proportional to the entropy of the distribution of codelengths in the output would nicely match the lower bound of \(\Omega(k(1+\mathcal{H}(n_{1},\ldots,n_{k})))\) suggested by information theory, where the output contains \(n_{i}\) codes of length \(l_{i}\), for some integer vector \((l_{1},\ldots,l_{k})\) of distinct codelengths and some integer \(k\) measuring the number of distinct codelengths. Our current analysis does not yield such a result: the gap lengths in the list of weights between the position hit by the queries generated by the GDM algorithm are not as regular as \((n_{1},\ldots,n_{k})\), so that the entropy of such gaps seems unrelated to the entropy of the number \(n_{i}\) of codes of a given length \(l_{i}\) for each \(i\in[1..k]\).
# Design and Research of Photonic Reservoir Computing for Optical Channel Equalization
## 1 Introduction
These structures are designed using the spatial-node scheme, where DCs serve as nodes and long waveguides provide delays between nodes. The input and output positions of the reservoir are designed on the outermost nodes, significantly reducing waveguide crossings compared to PhRC structures using internal nodes for input and output, thus minimizing noise and insertion loss. The input and reservoir layers are implemented based on silicon-on-insulator (SOI) technology, while the readout layer is realized in the electrical domain. Based on these four different reservoir layouts, we optimize and discuss parameters such as the number of nodes, DC splitting ratio, waveguide delay line length, and the number of input and output ports. Simulation results indicate that the number of nodes is basically proportional to the equalization capability of PhRC, but the optimal equalization result may not be achieved at the maximum possible number of nodes and needs to be optimized according to the equalization difficulty. When the \(2\times 2\) DC device splits light, achieving approximately equal output power from both output ports generally leads to better equalization results. Discussion on waveguide delay line lengths reveals an inverse relationship between waveguide delay line length and equalization performance. Although variations in the number of reservoir input ports have little impact on equalization effectiveness, an increase in the number of output ports enhances equalization performance. After parameter optimization, we compare the four layouts under isolated dispersion noise and ASE noise conditions and find that PhRC is more suitable for equalizing dispersion noise. We also investigate the impact of mixed noise in the channel and signal speed on PhRC equalization, discovering that all four layouts exhibit equalization effects at a transmission distance of 30 km and a signal speed of 50 Gb/s. Since most computations in our proposed PhRC equalizer are performed in the optical domain, it can be directly placed before the receiver to alleviate computational pressure. The design of its structure can also evolve into an all-optical structure in the future, which can further improve the equalization speed and bandwidth of optical signals.
## 4 Extending M2M Transformations with NLP
### Implementing Extended M2M Transformation Metamodel in a CASE Tool  
The extended metamodel was implemented in the MagicDraw CASE tool (Figure 3) by extending the previously introduced implementation of the M2M Transformations profile in [14]. At its core, the original solution consists of the native MagicDraw's DSL Customization profile and our developed M2M Transformations profile. In the DSL Customization profile, native _ChragAndDropSpecification_* stereotype is extended with _ChragAndDropSpecificationExtension_* specialization. A model element mapping pattern is realized by a custom stereotype _MappingPattern_* and implemented as a structured class, holding two parts: A source part (represented using _*Source_* property) and a target part (represented using _*Target_* property). These parts contain one or more SMCTs and TMCTs, respectively, which are interconnected using mapping connectors (_MappingConnector_). These mappings are implemented as UML connectors defined by the _*MappingConnector_* stereotype; connectors may be defined between SMCT elements, TMCT elements, or both elements. In the first two cases, the connectors represent the structure of the underlying source and target metamodels, respectively, while the latter case defines mappings between source and target model elements. For more details on the model-driven specification of M2M transformation patterns, refer to the original paper [14]. Generally, a complete M2M transformation model is composed of a customization class, a D&D action specification, and an optional transformation pattern specification. A customization class relates to one or more D&D action specifications. A D&D action specification is to be executed by the DSL engine upon dragging a certain _sourceElement_ specified in that specification and dropping it onto the target element _customizationTarget_ specified in the customization class. The customization class will not define any target element if the target is a diagram of the target model itself, that is, the source element is dragged and dropped directly onto the diagram instead of dropping it onto some specific element in the target diagram. The target diagram is specified using the property _targetDiagram_. Further, Table 1 describes extensions required to enable NLP-based conditional processing. This is enabled by introducing the stereotype _*TransformationPatternSpecification_*, which enables selective processing of one or more patterns, along with two of its specializations, and the stereotype _*ConditionedMappingSpecification_*.
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 3 Structure of the ARIBC
### ARIBC Setup Phase  
The establishment of an ARIBC instance requires setting up a central trusted coordinator, that is responsible for generating the private keys of the users using their public identifiers (_IDs_). This coordinator, who is also the operator of the ARIBC instance, is referred to as the ARIBC Key Management Server (ARIBC-KMS); this is the dominant and most sensitive entity in an IBC scheme and should be trusted a priori by all entities that interact with the ARIBC. Setting up the ARIBC-KMS entails:  
* defining a security parameter to be used for calculating a number of system parameters, henceforth referred to as Public Parameters (PP), which will be made public;
* selecting a random master secret, henceforth referred to as Master (Server) Secret key (KSAK), which will be kept private;
* computing the ARIBC public key, henceforth referred to as Master (Server) Public key (_KPAK_), which will be made public;
* selecting the hash functions to be used; and
* publishing _PP_ and _KPAK_.