esa-sceva/satcom-chunk-collection huggingface dataset:
## Material and Methods
### Methodology
#### 2.2.3 LCM Evaluation Testing Protocol  
The testing protocol for this study was to examine the plugging ability of the LCMs before and after exposure to fluids to understand the size degradation effect on the materials' plugging efficiency and their ability to bridge and plug fractures downhole. The research methodology started with preparing two different fluids and material mixtures. The drilling fluid used in this study was only clay-based fluids, and the first mixture was not subjected to shear. The LCM and the fluid (clay system) were mixed for one minute with a spatula to minimize forces on the particles. This mixture of the clay-based fluid was used to investigate the material's behavior without a size degradation effect. The second mixture received a high shear of 4000 RPM for up to 30 min, as per the size degradation procedure. These two mixtures were tested at test conditions of 500 psi pressure and ambient temperature. The LCM plugging performance testing investigated the fracture plugging capability with and without the size degradation effect to understand its impact on the different LCM types.  
The objective of the evaluation protocol was to determine the maximum fracture diameter that could be plugged using both the \"no shearing\" and the \"30 min shearing\" samples of each LCM. For all of the LCMs, the largest fracture diameter tested was 6 mm, after which were tested smaller diameters with an increment of 0.5 mm, simulating smaller fractures. The objective was to compare each LCM with itself in terms of the effect of shearing and mixing. The change of the maximum fracture sized plugged with and without shearing for each LCM is reported, which led to the development of the particle size selection guidelines.
## 7 3D Model Analysis for Structural Monitoring of the Aqueduct
### Analytical Longitudinal Deformation of the Composite Tied-Arch  
The longitudinal deformation of the composite tied-arch (\(\Delta L\)) in the 3D FE model is computed using the longitudinal displacements at nodes 5 and 6 specified in Figure 24, of which the positions are in line with those of the LVDTs on the a  
Figure 23: Part of the 3D structural monitoring model of the aqueduct (span #7 to #10).  
Figure 24: Analytical points in the 3D FE model correspond to monitoring locations in the real structure. (1)–(3) strains at the upstream end, top of the main arch ring, and downstream end of the main arch ring; (4) strain at the mid-span of the tension rod, (5)–(6) longitudinal displacements of the nodes at the upstream, and downstream ends of the composite tied-arch; (7)–(9) vertical displacements of the nodes at the upstream end, mid-span, and downstream end of the main arch ring.  
The model analysis indicates that a \(\pm 1\)\({}^{\circ}\)C uniform temperature change in all truss members can induce a \(\Delta L\) of \(\pm 0.46\) mm (+ stands for expansion), and, although the air temperature change \(\Delta T\) may be somewhat different from the real temperature variation inside the truss members due to some uncertain factors, such as the thermal conductivity of concrete, surface convection, and solar radiation, it can still be noted that this \(\pm 0.46\) mm in \(\pm 1\)\({}^{\circ}\)C deformation pattern is in general agreement with the regression equation specified in Figure 5, which predicts that a \(\pm 1\)\({}^{\circ}\)C air temperature change tends to produce a \(\Delta L\) of \(\pm 0.51\) mm.  
The model analysis also shows that 1 m deep water in the trough will create a \(\Delta L\) of 1.86 mm. If the temperature-effect-excluded \(\Delta L_{dx}\) specified in Figure 8 is considered as the \"monitored\" \(\Delta L\) of the composite tied-arch under only water load during operation, it is then plotted with analytical \(\Delta L\) obtained from the 3D model under the same water levels, and the result is shown in Figure 25. The correlation between this monitored and the analytical \(\Delta L\) under only water load bears a strong resemblance to that between the temperature-effect-excluded \(\Delta L_{dx}\) and \(W_{dx}\) illustrated in Figure 8, and, as mentioned earlier in Section 4.2 of this paper, the poor relationship is mainly due to the fact that this monitored \(\Delta L\) assumed under only water load is still greatly affected by air temperature, which makes it impractical for the 3D model to provide an accurate prediction on the longitudinal deformation of the tied-arch during operation.
# SVM-Based Multiple Instance Classification via DC Optimization
## 2 A DC Decomposition of the SVM-Based MIL  
We tackle a binary MIL problem whose objective is to discriminate between \(m\) positive bags and \(k\) negative ones using a hyperplane  
\[H(w,b)\triangleq\{x\in\mathbb{R}^{n}\mid w^{T}x+b=0\},\]  
where \(w\in\mathbb{R}^{n}\) and \(b\in\mathbb{R}\). Indicating by \(J_{i}^{+},i=1,\ldots,m\), the index set of the instances belonging to the \(i\)th positive bag and by \(J_{i}^{-},i=1,\ldots,k\), the index set of the instances belonging to the \(i\)th negative bag, we recall that, on the basis of the standard MIL assumption, a bag is positive if, and only if, at least one of its instances is positive and it is negative vice-versa. As a consequence, while a positive bag is allowed to, possibly, straddle the hyperplane, the negative bags should lie completely on the negative side.  
More formally, indicating by \(x_{j}\in\mathbb{R}^{n}\) the \(j\)th instance of a positive or negative bag, the hyperplane \(H(w,b)\) performs a correct separation if, and only if, the following conditions hold:  
\[\left\{\begin{array}{ll}w^{T}x_{j}+b\geq 1,&\text{for at least an index $j\in J_{i}^{+}$ and for all $i=1,\ldots,m$}\\ \\ w^{T}x_{j}+b\leq-1,&\text{for all $j\in J_{i}^{-}$ and for all $i=1,\ldots,k$.}\end{array}\right.\]As a consequence (see Figures 1 and 2), a positive bag \(J_{i}^{+}\), \(i=1,\ldots,m\), is misclassified if  
\[\max_{j\in J_{i}^{+}}(w^{T}x_{j}+b)<1\]  
and a negative one \(J_{i}^{-}\), \(i=1,\ldots,k\), is misclassified if  
\[\max_{j\in J_{i}^{-}}(w^{T}x_{j}+b)>-1.\]  
Then, we come out with the following error function, already introduced in [18]:  
\[f(w,b)\triangleq\frac{1}{2}\|w\|^{2}+C\left[\sum_{i=1}^{m}\max\{0,1-\max_{j\in J _{i}^{+}}(w^{T}x_{j}+b)\}+\sum_{i=1}^{k}\max\{0,1+\max_{j\in J_{i}^{-}}(w^{T}x_ {j}+b)\}\right], \tag{1}\]  
where \(C>0\) represents the trade-off between two objectives: the maximization of the separation margin, characterizing the classical SVM [25] approach, and the minimization of the classification error.  
To minimize function \(f\), we propose a DC decomposition based on the following formula:  
\[\max\{0,1-h(y)\}=\max\{1,h(y)\}-h(y), \tag{2}\]  
Figure 1: Positive bag \(J_{i}^{+}\).  
where \(h\) is a convex function. By applying Equation (2) to our case, we can write \(f\) in the form:  
\[f(w,b)=f_{1}(w,b)-f_{2}(w,b),\]  
where  
\[f_{1}(w,b)\triangleq\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{k}\max\{0,1+\max_{j\in I_ {i}^{+}}(w^{T}x_{j}+b)\}+C\sum_{i=1}^{m}\max\{1,\max_{j\in I_{i}^{+}}(w^{T}x_{ j}+b)\}\]  
and  
\[f_{2}(w,b)\triangleq C\sum_{i=1}^{m}\max_{j\in I_{i}^{+}}(w^{T}x_{j}+b)\]  
are convex functions. Hence, we come up with the following nonconvex nonsmooth optimization problem, DC-MIL,  
\[\min_{w,b}[f_{1}(w,b)-f_{2}(w,b)]. \tag{3}\]
# The Use of an Exact Algorithm within a Tabu Search Maximum Clique Algorithm
## 2 Exact Algorithms with a Coloring Bound  
Exact algorithms for the maximum clique problem are surveyed in [4]. The basis for many improved algorithms is the algorithm of [PERSON] and [PERSON] [14], and this is also the basis for the exact and pseudoexact algorithms used in HTS [2]. Within HTS, this algorithm works with a current clique \(F\) and a potential expansion set \(S=N(F)\) of vertices adjacent to all vertices of \(F\). Vertices of \(S\) are selected in turn and removed from \(S\). The new potential expansion set \(S^{\prime}\) is computed and the selected vertex is added to \(F\) to create a set \(F^{\prime}\). If the potential expansion set is empty, and a new best clique is obtained, the clique is assigned to a global variable \(Best\). Otherwise, subject to a pruning condition, the algorithm is applied recursively to the new potential expansion set \(S^{\prime}\). The usual condition is that pruning of the search tree takes place unless \(|F^{\prime}|+|S^{\prime}|\) is greater than the size of the best clique found so far in the exact algorithm. Within HTS, extra pruning is used involving an external lower bound arising from the best clique found so far in the metaheuristic. Pseudoexact search uses a probable upper bound for pruning derived from an evaluation of typical subproblems solved for the instance within HTS.  
A major improvement arises from the use of a vertex coloring bound.  
**Definition 1**.: _A vertex coloring of a graph \(G\) is an assignment of colors to the vertices of \(G\) such that adjacent vertices are assigned different colors._Definition 2: The chromatic number of a graph \(G\) is the minimum number of colors in a vertex coloring.  
The following proposition is well known:  
Proposition 1: The chromatic number of a graph \(G\) is an upper bound for the number of vertices in a maximum clique.  
Proof: Every vertex of the clique must be assigned a different color.  
The colors can be represented as positive integers \(1,2,\ldots\). Thus, any upper bound \(k\) for the chromatic number of the subgraph induced by \(S^{\prime}\) (obtained from a coloring of \(S^{\prime}\) with \(k\) colors) can replace \(|S^{\prime}|\) in the pruning to reduce the size of the search, sometimes dramatically. Then, options to be considered here are (i) the best coloring algorithm to use, which must be very fast as the exact algorithm is used many times in hybrid tabu search, (ii) whether to locally color each subgraph induced by \(S^{\prime}\), which may be expensive, or to color the initial subproblem and use the colors inherited by the subgraph induced by \(S^{\prime}\), or a combination of the two, (iii) whether to re-order the vertices of \(S^{\prime}\) in non-increasing order of color within the exact solver.
## 2 Computed Tomography and Aortic Valve
### CT Assessment of the Aortic Valve for Transcatheter Procedures
#### 2.3.1 Peripheral Access  
Femoral access is the preferred access for TAVI. MDCT imaging identifies patients with complex vasculature anatomies, which may impact the decision to use an alternative vascular approach (i.e., transapical, transaxillary, or direct aortic) [31]. Vessel size and diameters, degree of calcification, vessel tortuosity, and high-risk features can be properly identified by CT through multiple projections (Figure 2a,b).  
In a prospective study of 130 patients undergoing TAVI, [PERSON] et al. found that vascular complications and 30-day mortality were associated with a sheath-to-femoral artery ratio (SFAR) of 1.05 or higher [32]. Later, [PERSON] et al. proposed a change in this cut-off value, believing that this was too strict, providing high sensitivity but poor specificity, and 1.12 may be more appropriate (Table 1) [33].  
Interestingly, the most recent valve delivery systems have a size ranging between 14 and 20 Fr, depending on the specific prosthesis valve type and size. While some years ago, a minimal lumen of femoral arteries of 6.0-6.5 mm was required, currently, with reduced delivery profiles, TAVI might also be indicated in patients with peripheral vessels as small as 5.0 mm [34].
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 1 Introduction
### Entry Points for Attacks to 5G Networks  
#### 1.1.1. Compromised Mobile Device  
The first possible entry point is compromised or malicious User Equipment (UE). This is a significant threat to mobile networks, serving as both targets and enablers for attacks. Malware spread through application downloads is a common method of compromising devices [7; 8].  
Compromised device attacks are also influenced by the behavior of UE users. Risky actions, such as downloading from unofficial application stores [9], connecting to unknown Bluetooth [10] and WiFi [11] devices, scanning harmful QR codes (phishing), or receiving malicious messages via SMS or communication apps [12; 13], can lead to attacks on mobile devices. Mobile botnets, facilitated by malware like Trojan horses, are emerging as major threats, allowing the remote control of compromised devices. Denial of Service (DoS) or Distributed Denial of Service (DDoS) attacks on mobile networks can be launched through malware on user equipment, targeting specific network components [14; 15]. Furthermore,  
Figure 1: (a) SA vs. (b) NSA architecture of 5G New Radio (NR) network with possible vector attacks.  
malicious applications can compromise devices through permission grants, and threats from the internet, phishing, and Man-In-The-Middle (MITM) attacks also pose risks.
## 2 Materials and Methods
### Prediction of Chlorophylll Content Using the R Value and G Value
#### 3.2.1 R and G Value Distribution  
The frequency histogram of R and G values of _Astragalus_ seed batch showed that the R values of most seeds were between 60-90 and the G values of most seeds were between 55-70, but the R and G values of some seeds were not within this range (Figure 1). This showed that the R and G values of seed batches were not uniform, which may be due to the difference in chlorophylll content between different seeds.  
Based on the distribution of R and G values, the seed batch was divided into five groups. The boundaries of the five sub samples were set as R values of 60, 70, 80 and 90 respectively; The G values were 55, 60, 65 and 70, respectively. The color difference between the seeds of the five subsamples was visible to the naked eye (Figure 7). The higher the R and G value, the lighter the seed coat color. The color difference features that chlorophylll may be located outside the seed coat.
## 3 Results and Discussion
### Growth Inhibition Assay with Lenna minor  
The toxicity test with \(L\). _minor_ for the RE was carried out after a pH adjustment to between 6 and 7, since at pH values of 3.07 (initial pH of the effluent), there was a total inhibition of growth and development for all the concentrations tested.  
For the RE sample and after the pH adjustment, significant differences in growth rate of \(L\). _minor_ were observed (Figure 2), both for the data based on french number (F\({}_{5,17}\) = 69.76, \(p\) < 0.05) and for the dry weight (F\({}_{5,17}\) = 25.82, \(p\) < 0.05). NOEC and LOEC values of 51.0% and 71.4%, respectively, were recorded for the growth rate based on the french number and dry weight. Regarding the samples with TE2, the growth rate based on the french number was also significantly inhibited (F\({}_{6,20}\) = 303.25, \(p\) < 0.05), with a NOEC and LOEC of 68.3% and 75.1%, respectively (Figure 2a). Regarding the growth rate based on the dry biomass, significant differences between the control and test dilutions were observed (F\({}_{6,20}\) = 152.42, \(p\) < 0.05) with NOEC and LOEC values of 62.1% and 68.3%, respectively (Figure 2b). The EC\({}_{50}\) value based on the number of frenchs was higher for the RE (95.73%) than for the TE2 (82.92%), which indicates that the treated effluent was slightly more toxic than the raw effluent for \(L\). _minor_. A similar result was reported by [PERSON] et al. [3] in textile effluents from different phases of the textile processing.  
Figure 1: Average growth rate (day\({}^{-1}\)) of \(R\). _subcapitata_ exposed to different dilutions of the effluent. Error bars represent the standard deviation of the mean (n = 3); asterisks indicate statistically significant differences from the control ([PERSON]’s test \(p\) < 0.05).  
Comparing the values obtained in this study with those reported by [PERSON] et al. [5], it was verified that the real effluent tested in this study was more toxic to the aquatic plant _L. minor_ than the artificial effluent, since the authors could not calculate an EC\({}_{50}\) for the artificial effluent. The same was observed in the study of [PERSON] et al. [3]. The same study also revealed that the toxicity of textile effluents varied with the stage of the manufacturing process where it was generated, with the most hazardous steps being the fixation of dyes, the first wash and the bleaching process [3]. As observed in our study for the samples RE and TE2 (personal observation), the colonies of _L. minor_ exposed to the effluents from the different stages in the study of [PERSON] et al. [3] showed, in the highest concentrations, fragmentation of the colonies, reduction in size and thickness of the fronds and loss of roots. Besides these effects, [PERSON] et al. [3] also reported chlorosis in the fronds exposed to the effluent from the sodium silicate process, and accumulation of dyes on the frond and roots.
# Laser Technology in Photonic Applications for Space
## 6 Conclusions  
In this review, we have discussed the main types of laser technologies suitable for space applications, with a focus on the laser devices already deployed in space. The main applications were described, together with the most relevant devices deployed during past space missions. These applications include remote sensing (LIDAR), spectroscopy, quantum technologies including atomic clocks and gravitational wave sensors, and communications. Fiber optic sensing, optopyrotechnics, and finally integrated solid state gyroscopes have also been discussed. Future missions and devices have then been mentioned. Finally, technical specifications requirements have been reviewed, before discussing some of the difficulties, drawbacks, and advantages of such devices and technologies.  
This review illustrates the general conservative attitude towards the use of laser technologies in space application, since most of the devices currently deployed in space are semiconductor laser diodes or use an Nd:YAG crystal as the active medium. This can be explained by the cost of space missions and by the difficulties for posterior maintenance. These two factors make it more efficient for a new laser device to be based on a validated design from a past mission or to rely on industrial commercial devices, as demonstrated by ESA's three-fold approach to space qualification of laser devices [5]:  
* assessment of space suitability of commercially available laser products through functional and environmental testing,
* selection of the most suited component and collaboration with its manufacturer for improvement towards space qualification,
* formal space qualification of the resulting device.  
Nevertheless, novel laser sources and amplifiers (such as EDFA) are appearing in cases where their novelty brings important improvement compared to the state-of-the-art, or when they enable new applications, as is the case with atomic clocks. Such new laser systems are worth the complex, expensive, and time-consuming space qualification process when they provide a high added-value compared to existing space qualified laser technology.  
**Author Contributions**: conceptualization, D.G. and P.R.-P.; investigation, D.G. and P.R.-P.; data curation, D.G. and P.R.-P.; writing--original draft preparation, D.G. and P.R.-P.; writing--review and editing, D.G. and P.R.-P.; visualization, D.G. and P.R.-P.; supervision, D.G. and P.R.-P.; project administration, D.G. and P.R.-P.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors wish to acknowledge [PERSON], [PERSON] and Monocrom S.L. in Vilanova I la Geltru, as well as the Lotus and [PERSON] who have, in different ways, initiated and strengthened this collaboration. Also, [PERSON] for Figure 1 and others published elsewhere, and finally some colleagues who helped us improve this review: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. Finally, to [PERSON] and Star Wars.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation
## 5 Experiments
### Baselines  
We compare the proposed model with the following baselines to demonstrate the effectiveness of KGET. The baselines are divided into two types: KG-free method and KG-aware methods. The KG-free method is a CF-based method (BPRMF), and KG-aware methods include embedding-based (CKE), path-based (RippleNet) and hybrid-based (KGCN, KGNN, KGAT, CKAN).  
BPRMF [25]. The method belongs to a CF model, which uses matrix factorization for item recommendation from implicit feedback, and directly optimizes for ranking by proposed Bayesian personalized ranking from implicit feedback.  
CKE [16]. The model is an embedding-based method, which learns the latent representations in collaborative filtering, and extracts items' semantic representations with structural, textual and visual information by leveraging the heterogeneous information in the knowledge graph.  
RippleNet [21]. The model is a path-based method with a knowledge embedding-based method, which thinks of the preference propagation as ripples in water, and automatically discovers the path between users and items that they might prefer.  
KGCN [24]. The method is a non-spectral GCN approach in the knowledge graph, which extends the receptive field to multiple hops for capturing high-order structural information and users' preference, and user-relation scores are calculated as neighbor weights.  
KGNN [26]. The method proposes knowledge-aware graph neural networks with label smoothness regularization, which transforms a heterogeneous KG into a user-personalized weighted graph that characterizes user's preferences, and then uses a supervised fashion to train the edge weights. To avoid overfitting, the technique for regularization of edge weights is designed to generalize to unobserved interactions.  
KGAT [27]. The method combines a user-item collaboration graph and knowledge graph into a collaborative knowledge graph, which explicitly models the high-order relations. The knowledge-aware attention mechanism is adopted to learn the weights of neighbor nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Last.FM** & **Book-Crossing** \\ \hline \#user & 1863 & 5275 \\ \#item & 1024 & 957 \\ \#interactions & 13,729 & 11,615 \\ \hline \#entities & 9366 & 77,903 \\ \#relations & 60 & 25 \\ \#KG triples & 15,518 & 151,500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic statistics for datasets.  
CKAN [23]. The method applies a heterogeneous propagation strategy to explicitly encode collaborative signals and knowledge associations, and a knowledge-aware attention mechanism is employed to discriminate the importance of neighbors.
# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 2 Materials and Methods
### Dataset  
#### 2.2.1 Subject Eligibility and Sequence Information  
Subjects at the UCSF Orthopedic Institute between 14 June 2021 and 21 June 2022 were scanned with an imaging protocol that included 3D fat-suppressed CUBE acquisitions (n = 816). There were no exclusion criteria placed on patients for inclusion in the eventual K2S dataset, and patients were scanned in accordance with all pertinent guidelines, including approval from the UCSF Institutional Review Board (Human Research Protection Program), obtaining informed consent from all study participants. The 3D fat-suppressed CUBE sequence was selected for K2S, as 3D sequences have higher SNR compared to 2D imaging, allowing for higher resolution acquisitions that can be reformatted into multiple planes for subsequent research objectives. Scans were performed on a GE Discovery MR750 3T Scanner using an 18-channel knee transmit/receive coil. The full-length acquisition time of the sequence was 4 min and 58 s. Complete acquisition parameters are listed in Table 1.  
Figure 1: Overview of steps involved in human-in-the-loop training of models to generate ground truth bone and cartilage segmentations, and the process for radiologist approval of final 300 segmentations to be included in K2S dataset. The K2S challenge was for participants to segment knee bones and cartilage from 8\(\times\) undersampled k-space, with the training set released on 15 April, the test set released on 6 July, and the submission deadline on 21 July.