esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Dual-Radio Interface Routing Protocol for LLNs  
This section discusses the design of DRiPLOF, i.e., our own RPL OF with support for multiple radio interfaces per device.  
Firstly, exposing the cost of interfaces to RPL does not mean every interface has to be an RPL neighbor. For example, with [PERSON] et al.'s [9] PO solution, the OF is also influenced by interface metrics, but instead penalizes neighbors (i.e., neighboring physical nodes) for being unavailable over an interface. As such, RPL optimizes for interface redundancy, while the link-layer can optimize for another goal through preferred interface selection. Thus, with PO, switching preferred interfaces to a neighboring node does not automatically require a parent change. The decoupling of neighbors from interfaces also means that limiting parent changes has less impact on a network's ability to respond to local wireless distortions, which is a problem faced by [PERSON] et al. [30] and [PERSON] et al. [27], whom, unlike [PERSON] et al.'s [9] IO solution, do use a mechanism (similar to MRHOF [11]) to prevent excessive parent changes. These arguments make a strong case for PO, and so, we carried over the aforementioned principles from [PERSON] et al.'s [9; 10] Parent-Oriented Objective Function (POOF). However, POOF has several downsides (see Section 4.4), one of which is its inconsistent way of limiting parent changes. Therefore, we based our DRiPLOF on MRHOF instead of OF0 (see Section 4.3).  
The following subsections describe the provisions related to addressing and control traffic (Section 4.1), as well as semantics related to interfaces and metrics (Section 4.2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{\begin{tabular}{c} **Solution** \\ **Name** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Single** \\ **DODAG** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Stability** \\ **Mechanism** \\ \end{tabular} } & \multicolumn{3}{c}{**6 LoWPAN**} & \multicolumn{3}{c}{**Works in**} & \multirow{2}{*}{\begin{tabular}{c} **Posts in** \\ **Sloring and** \\ **Non-Storing** \\ **MOP** \({}^{\text{b}}\) \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Tested under** \\ **Jamming** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Firmware** \\ **Available** \\ \end{tabular} } & \multirow{2}{*}{**Interfaces**} \\  & & & & & & & & \\ \hline \multirow{3}{*}{[9; 10]} & MI & ✗ & ✗ & ✓ & ✓ & ✗ & ✗ & \(1\times 802.15.4\)g \\  & PO & ✓ & \(\pm\) & ✓ & ✓ & ✗ & ✗ & \(1\times\text{PLC}\) \\  & IO & ✓ & ✗ & \(\pm\) &? & ✗ & ✗ & \(1\times\text{PLC}\) \\ \hline \multirow{3}{*}{[26]} & \multirow{3}{*}{—} & ✓ &? &? &? & ✗ & ✗ & \(1\times 802.15.4\)g \\  & & & & & & & & \(1\times\text{PLC}\) \\ \hline \multirow{3}{*}{[27]} & \multirow{3}{*}{—} & ✓ & ✓ & ✗ & \(\pm\) & ✗ & ✗ & \(1\times\text{PLC}\) \\  & & & & & & & & \(1\times\text{PLC}\) \\ \hline \multirow{3}{*}{[30]} & \multirow{3}{*}{—} & ✓ & ✓ & \(\pm\) & \(\pm\) & ✗ & ✓ & \(2\times 802.15.4\)g \\  & & & & & & & & (any) \\ \hline \hline \end{tabular}  
* *
## 2 Multi-Objective Two-Level Model Construction  
The performance of the mount is influenced by various factors, including geometric parameters, stiffness, damping, and others. However, in the context of robust design for mounting systems, the optimization focus is typically placed on the main stiffness values in each direction of the mount, as they are more readily adjustable. In the case of powertrain mounts, both three-point and four-point configurations are commonly employed. In this study, we specifically consider the three-point mounting system as an example, and its optimization design is formulated as a two-objective, two-level problem, as depicted in Figure 1.  
Figure 1: A schematic diagram of the hierarchical decomposition model of the mounting system.  
The optimization design of the powertrain mounting system can be abstracted as a multi-objective, two-level optimization problem, and the model decomposition diagram and the formula are shown in Figure 2 and Equation (1).  
\[\left\{\begin{array}{rl}\begin{array}{rl}\text{meet}&f_{n}(x)\subset(Lb_{n},Ub_{n })\\ s.t.&h_{i}(x)=0,i=1,2,\ldots,I\\ &g_{j}(x)\leq 0,j=1,2,\ldots,J\\ &n=0,i=1,2,\ldots,N\end{array}\right.\end{array} \tag{1}\]  
where; the design variable \(x=[x_{1},x_{2},x_{3},\ldots,x_{d}]^{T}\) is a vector in the \(d\)-dimensional Euclidean space \(R^{d}\); \(f_{n}(x)\) is the objective function, where \(N\) is the number of objective functions; \(h_{i}(x)=0,g_{i}(x)\leq 0\) is the boundary condition; and \(Lb_{n}\), \(UB_{n}\) are the boundary conditions to be satisfied by the objective function.
# Using Graph Neural Networks for Social Recommendations
## 4 Experimental Evaluation  
### Dataset  
Similar to previous work, we employed the Yelp dataset [28] to evaluate our approach. The Yelp dataset is ideal, as it includes not only ratings but also additional information on businesses, reviews, check-ins, tips, and users. It contains data on various types of businesses, including restaurants, bars, and cafes. These details include their names, addresses, phone numbers, categories, ratings, operational hours, and reviews. Individual users provide a rating of 1 to 5 (5 being the best) stars and compose written evaluations for businesses. Most importantly, the dataset incorporates social network information, such as the followers of each Yelp user and the number of fans subscribed to a business. To analyze the Yelp dataset, a preprocessing step is performed by converting star ratings of 3 or higher to a rating of 1, indicating a positive sentiment towards the business, and ratings below 3 are converted to 0, indicating negative sentiment. To obtain insights from the written reviews, the _gensim_ tool and the Word2 vec [29] model are employed to generate the embedding of each word. By doing so, it becomes possible to produce a feature vector for each user by computing the mean of the trained word vectors that are correlated to their reviews. A similar process is employed to create feature vectors for every item (business). The feature vectors for both users and items serve as inputs to the model, denoted as \(X\) and \(Y\), respectively.  
Using the user followers' information, we create the user-user graph. In this graph, a link is established between user \(a\) and user \(b\) if user \(a\) follows user \(b\), and the link is assigned a weight of 1, which is used for creating the social adjacency matrix \(S\). The dataset also contains information on the items (businesses) and their categories. Businesses with at least seven common categories are considered similar, and a link is assigned between them to create a business-business (item-item) adjacency matrix \(T\). The user-user and item-item inputs \(S\) and \(T\) are respectively used for creating the graphs \(G_{5}\) and \(G_{T}\) for the neural network to capture the graph relationships.  
During the training and testing process, the dataset is filtered to exclude information that may not be reliable or useful. Users with inadequate information, such as those with less than ten reviews or ten followers, are removed from the dataset. By applying these filters, the dataset is refined to ensure that only relevant and reliable information is utilized for the analysis. Table 1 summarizes the statistics of the original Yelp dataset as well as the filtered one used in our experiments.
# Windowed Joint Detection and Decoding with IR-HARQ for Asynchronous SCMA Systems
## 3 Windowed Joint Detection and Decoding Algorithm  
### IR-HARQ Transmission Scheme  
The detailed transmission mechanism of the asynchronous IR-HARQ scheme is shown in Figure 2. To begin, the first set of information symbols for each user is encoded using RC-LDPC codes, resulting in an initial block codeword sequence. If the receiver fails to decode this initial block, parity-check blocks related to it are sent over the channel and added to the initial block codeword to create a longer one. Without loss of generality, we assume that the block length for each transmission is the same under the IR-HARQ transmission scheme in 5G. The maximum number of transmissions for the initial block, including information symbols, is limited to four (i.e., \(D_{\text{max}}=4\)). For user \(j\), \(j\in\{1,2,\ldots,J\}\), the block \(T^{d}_{j,i}\) represents the \(d\)th transmission ( \(d\leq D_{\text{max}}\)) of length \(N_{0}\) related to its initial block \(i\), including the information symbols.  
At the start, each user sends their initial block with a length of \(N_{0}\) and a code rate of \(R_{0}=k/N_{0}\). The SCMA encoder processes six blocks simultaneously and sends the resulting sequence through the channel. At the receiver, a joint detection and decoding algorithm is used. If all users successfully decode their blocks, they receive a new initial block for the next time unit. If decoding fails for some users, they receive a block of redundant parity bits corresponding to the previous initial block. Users who successfully decode the block receive a new initial block at the next time unit. This process is repeated for subsequent time units. In the provided diagram, users 1-4 decode their initial blocks with fewer than four transmissions, allowing them to move on to the second initial block. However, user 5 fails to decode their block in four transmissions, so a new initial block is transmitted on the fifth round. Similarly, user 6 fails to decode the second initial block in four transmissions, leading to the transmission of the third initial block in the sixth round.
## 3 Results
The results show that the use of an IVR headset generated significant differences between the groups in favor of the EG in the following domains: stigma, cognition, body discomfort, and total score for the PDQ-39 questionnaire (University of Oxford, Oxford, UK). Furthermore, \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**EG (MOOTOD® + IVR)**} & \multicolumn{2}{c}{**CG (MOOTOD®)**} \\  & **(n = 30)** & **(n = 22)** & \\
**Variable** & **Average \(\pm\) SD** & **Average \(\pm\) SD** & **Student’s t Test Results** \\ \hline \multirow{7}{*}{Pre-intervention} & Strength of lower train & & & \\ Five sit to stand (s) & 14.30 \(\pm\) 5.42 & 13.08 \(\pm\) 3.00 & t = 0.901; \(p\) = 0.186 \\ \cline{2-5}  & Dynamic balance & & & \\ Time up and go (s) & 14.08 \(\pm\) 18.28 & 12.46 \(\pm\) 9.19 & t = 0.359; \(p\) = 0.361 \\ Time up and go + & 17.64 \(\pm\) 26.02 & 14.51 \(\pm\) 11.95 & t = 0.495; \(p\) = 0.312 \\ \cline{2-5}  & Tinettic scale & & & \\ Balance (pts) & 12.89 \(\pm\) 3.54 & 14.80 \(\pm\) 1.77 & t = \(-\)2.143; \(p\) = 0.019 \\ Gait (pts) & 9.47 \(\pm\) 1.87 & 10.20 \(\pm\) 2.55 & t = \(-\)1.011; \(p\) = 0.159 \\ Total (pts) & 22.37 \(\pm\) 4.88 & 25.00 \(\pm\) 3.87 & t = \(-\)1.872; \(p\) = 0.035 \\ \hline \multirow{7}{*}{Post-intervention} & Strength of lower train & & & \\ Five sit to stand (s) & 13.81 \(\pm\) 6.68 & 13.18 \(\pm\) 4.54 & t = 0.336; \(p\) = 0.369 \\ \cline{2-5}  & Dynamic balance & & & \\ Time up and go (s) & 10.16 \(\pm\) 6.42 & 13.70 \(\pm\) 4.51 & t = \(-\)1.589; \(p\) = 0.040 \\ Time up and go + & 11.59 \(\pm\) 9.27 & 14.57 \(\pm\) 12.52 & t = \(-\)1.828; \(p\) = 0.206 \\ cognitive task (s) & & & & \\ \cline{2-5}  & Tinettic scale & & & \\ Balance (pts) & 15.00 \(\pm\) 2.53 & 15.33 \(\pm\) 1.59 & t = \(-\)0.465; \(p\) = 0.322 \\ Gait (pts) & 11.37 \(\pm\) 1.40 & 9.75 \(\pm\) 2.93 & t = 2.540; \(p\) = 0.007 \\ Total (pts) & 26.37 \(\pm\) 3.54 & 25.53 \(\pm\) 3.64 & t = 0.736; \(p\) = 0.233 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of the effects of intervention programs (MOOTOD® + IVR; MOTOD®) on functional parameters. the analysis of symptomatology also showed that the use of an IVR headset (Meta Quest II (Oculus VR, Menlo Park, CA, USA)) led to significant improvements in all of the dimensions composing the MDS-UPDRS scale. Table 4 shows the 2 \(\times\) 2 analysis (moment; program) in relation to the functional parameters, quality of life, and disease symptomatology. Significant differences are shown in several functional parameters (dynamic balance, balance, and gait), in the stigma dimension of quality of life (PDQ-39), and in parts III (motor) and IV (motor complications) of the [PERSON]'s symptomatology scale (MDS-UPDRS). Finally, Figure 3 shows indicators
## 3 Results
### Gene Co-Expression Networks  
A co-expression network is evident from significant (p < 0.05) and strong (p < 0.01) correlations between gene pairs. For each strain, the best network (Figure 5A-C) only shows nodes (genes) with strong (r > 0.8) correlations and high significance (_P_ < 0.05 or \(P\) < 0.01). For _L. rhamnus_ R0011, the network is composed of the co-expression of 23 genes belonging to a number of functional groups (Fig. 6A). The genes (nodes) forming the network are interconnected by the gene coding for dipeptidyl aminopeptidase (_R0011_04490_) with BC = 0.49 and degree = 6. In the central part of the network, genes linked to groups G3 and G4 are surrounded by genes belonging to G1 and G2 groups. Most are positively correlated, but _groES_ is negatively correlated with _galE_ and _ackA_. Gene _cysK_ was expressed together with genes of the G2 group and at the same time with the gene (_R0011_04490_). A complementary network indicated that the expression of rhamn isomerase (_rhoA_) and carboxymuconolactone decarboxylase (_pcaC_) are strongly correlated.  
For _L. rhamnus_ ATCC 9595, the relationship between the target genes formed more than one significant network (Figure 5B). A bigger network composed of 16 genes from groups G3, G4 and G5 all linked to the _wzm_ gene (G2), which is also connected to G1 genes such as _ackA_. Gene _alhpC_ (BC = 0.345 and degree = 10) seems to be an important node between two sub-networks. The other smaller networks show EPS operon genes (G2) principally associated with G1-type genes. An interaction between genes coding _wzd_ (G2) and _cysK_ (G3) was also observed.  
For _L. rhamnus_ strain RW-9595M, two distinct networks were obtained (Figure 5C). The first of these, formed by 14 genes from groups G1 and G2, represent the co-expression of gene _cysK_ (G3) with _lacD_ (G1) or G2-type genes such as _welE_ and _rmlD_. Genes coding for _ackA_ (BC = 0.54 and degree = 5), _wzb_ (BC = 0.54 and degree = 4) and _rmlD_ (BC = 0.38 and degree = 4) connect the parts of the network and are important genes because of their centrality. The second network represents the correlation between 16 genes, all belonging to groups G3, G4 and G5. In this network, as was also observed for _L. rhamnus_ strain ATCC 9595, the gene coding for _alhpC_ (BC = 0.297 and degree = 11) appeared as a node with high connectivity. Genes _metC_ and _metA_ are correlated with genes linked to cell membrane composition. The analysis of the networks (Figure 5) displays the common co-expression of genes such as _wzb-lacT_, _rmlA_ with _galR_, and/or with _bgaC_ for the three strains of _L. rhamnus_.
## 3 Numerical Modelling
### Case Study 1--Flat Plate  
In this case study, a flat-sheet metal plate is considered; the dimensions of the sheet metal are 800 mm \(\times\) 600 mm \(\times\) 1 mm in the x, y, and z axes, respectively. Primary plane clamps are placed at the long edge of the workpiece. The finite element model of the flat plate is shown in Figure 5. Two subcases were considered in this case study. In subcase 1, 20 N load is applied at (600, 450, 0); in subcase 2, 30 N load is applied at (660, 510, 0) in \(Z\)-direction (downward).
## 2 Materials and Methods
### Methodology  
Combined with the spectral characteristics of the water body in the study area, this study builds and optimizes the TSS retrieval model based on field data and quasi-synchronous satellite imageries. By comparing and verifying different TSS retrieval models, comprehensively considering the accuracy errors and applicability of various models, the optimal TSS retrieval model is obtained. Finally, on the basis of the optimal TSS model, we analyzed the spatial and temporal changes in the TSS concentration using the remote sensing data and evaluated the dynamic trends of multi-time series TSS concentration with the help of the Markov model.
## 8 Use Cases and Applications
### Search and Rescue  
Drones and autonomous ground vehicles represent significant potential in rescue operations, particularly in rural and remote areas where the location of the person in need of help is unknown. Time is a critical factor in such situations, and the deployment of drones and ground vehicles can significantly reduce rescue time [179]. In the case of natural disasters such as avalanches, floods, fires, or contamination, these autonomous systems can move more efficiently than search teams. Equipped with various sensors, including those for radioactivity, they play a vital role in the search and scanning of an area.  
For instance, a sensor for radioactivity on these autonomous systems can prevent further complications that may arise without their intervention. However, challenges can arise in navigation, as the position of the drone or ground vehicle may not always be accurate, thereby complicating the search action significantly [180]. In another scenario, such as the loss of a child in an amusement park, drones and ground vehicles using cameras to identify people may raise privacy concerns. By addressing these challenges, it becomes possible to utilize drones and ground vehicles efficiently in the search for individuals in need of assistance.
## 1 Introduction  
The utilization of modern high-power laser systems in laser-arc hybrid welding and laser welding with filler wire processes can provide new tools and techniques for efficiency improvement in heavy-section welding. For example, efficiency enhancement via reduced manufacturing time can be achieved if thicker sections can be welded using deep single-pass welding. Single-pass welding tends to result in less weld distortion and thus less time-consuming post-weld finishing is required [1; 2; 3; 4; 5]. When considering welding of thick section joints an autogenous laser beam welding is not always practically feasible and that is why filler metal addition to the welding process is needed. Filler metal can be introduced to the laser process for example, via an arc (laser-arc hybrid) as a molten form or using a cold-wire [6; 7; 8; 9] or pre-heated wire feeding [10; 11]. Also pre-set filler metal foil inserts [12; 13]inside the joint has been used. The need for the use of filler addition can be associated with the joint fit-up tolerance requirements of the welded parts or for example, metallurgical and mechanical properties required for the produced weldments. Although offering clear benefits, the use of filler material in thick section laser or laser-arc hybrid welding applications faces a number of challenges. Results from previous investigations have indicated that in thick section joints with closed or narrow square butt configurations, the filler metal proportion in the weld metal is mainly limited to the surface part of the weld cross-section and filler metal mixing in the root area of the weld metal is minor or negligible [14; 15; 16; 17]. Depending on the application and the combination of base metal and filler metal used, inhomogeneous filler metal mixing can have an adverse effect on weld metal microstructural homogeneity and weld metal phases. In turn, the non-optimal microstructure can cause, for example, loss of corrosion resistance, reduction in ductility properties or enhanced susceptibility to weld solidification cracking [13; 18; 19; 20; 21].  
The aim of this study is to investigate filler metal mixing behaviour in 10 mm thick joints of austenitic stainless steel in single-pass laser-arc hybrid and laser cold-wire welding processes. The objective was to examine the effect of groove geometry and filler wire feeding orientation on filler metal mixing in terms of the intensity and homogeneity of mixing throughout the weld cross-section from the weld surface to the root. The experimental work aimed to gain practical knowledge and provide answers to the following research questions: Can filler metal mixing at the root portion of the weld be enhanced by widening the root gap of an I-groove weld or by using a suitable V-groove? Can any difference be found between the results of using leading or trailing wire feeding, that is, when the filler wire is introduced to the melt pool in front of or behind the laser beam with respect to the welding direction? Is there any noticeable difference in filler metal mixing behaviour between laser-arc hybrid and laser cold-wire welding?
## 4 Discussion
The differential expression of miR-205 in the estrogen sensitive and non-sensitive ovarian cell lines is another important aspect of our study. Similar phenomenon was reported previously in endometrial cancer where the expression of miR-205 and miR-200s proved to be associated with ER status [17]. However, miR-205 expression does not seem to be mediated by ER\(\alpha\), in contrast to miR200s [11]. The role of miR-205 in estrogen response might be explained by the fact that it regulates ESSRG (Estrogen related receptor \(\gamma\)), which was confirmed in endometrial cells [30]. Furthermore, the overexpression of miR-205 resulted in higher E-cadherin level in colon cancer cells [31]. A similar phenomenon might exist in ovarian cancer cells, as well, since miR-205 expression is associated with E-cadherin expression, according to our previous studies [11]. Although the cell-free expression of let-7f-5p also proved to be high in the ovarian cell cultures, no significant difference was observed in its expression between the healthy and malignant plasma samples. A possible explanation might be that the expression of let-7f is mostly down-regulated in cancer [29; 32; 33; 34; 35; 36]. It is also important to mention that the expression of let-7f-5p proved to be ER\(\alpha\) dependent in our study and was up-regulated in response to E2 exposure. These results are in good agreement with previous studies [37; 38]. The role of let-7f in estrogen response, however, needs to be further characterized. MiR-483 was found to be elevated in prostate, easophageal, nasopharyngeal, adrenocortical, oral, hepatocellular, and adrenocortical cancer [39; 40; 41; 42; 43; 44; 45]. Fewer studies are available about miR-483-5p expression in ovarian cancer, where the diagnostic parameters of this miRNA did not prove to be well enough for being a good biomarker candidate [29; 46]. This miRNA is considered to be important in Polycystic Ovary Syndrome instead [47]. The expression of miR-483-5p proved to be low in both of the ovarian cell lines tested, and no significant difference was observed in the expression of miR-483-5p comparing the blood samples of patients and healthy individuals. These results are in good accordance with the bioinformatics analysis of miR-483-5p targets that did not result in any clusters related to the development of cancer. These data suggest that the otherwise well-detectable expression of miR-483-5p in plasma samples might not related to the expression of this miRNA in ovarian cells.