esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Building  
Machine learning (ML) predictive models were developed using Orange Data Mining and Python package (version 3.8). Since our study was conducted using a single PET/CT scanner and a uniform acquisition and reconstruction protocol across all patients, no feature harmonization techniques were needed.  
To address class imbalance in both the tumor grade and best clinical response (BCR) datasets, SMOTE (Synthetic Minority Oversampling Technique) was applied. Separate ML models were built for each target (tumor grade and BCR) and for each imaging modality (PET and CT) using robustly selected features. Two classifiers were employed: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). Each dataset--PET and CT for both BCR and tumor grade--was independently split into a training cohort (70% of the data) and an internal validation cohort (30%). A 10-Fold cross-validation (10-Fold CV) was performed on the training sets, and performance metrics were averaged across the folds. The remaining 30% of each dataset was used for internal validation. Model performance was evaluated using several metrics, including the Area Under the Receiver Operating Characteristic Curve (AUC), classification accuracy (CA), precision (PRE), sensitivity (SEN),specificity (SPE), and true-positive (TP) and true-negative (TN) counts. The Receiver Operating Characteristic (ROC) analysis was performed, and the subsequent ROC plots were built. In total, four different ML models were trained and validated, namely, BCR_CT, BCR_PET, GRADE_CT, and GRADE_PET.  
A schematic flowchart that illustrates the full machine learning pipeline, including data preprocessing, feature selection, model training, and evaluation, is depicted in the Supplementary Material (Figure S2).
# Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs
## 1 Introduction
Specifically, LSTM focuses on the temporal relations between nodes of the same type, such as users or items, and RSA combines multiple sandwich networks and gated self-attention networks, where each sandwich network includes convolutional neural networks (CNNs) [21] and linear rectification functions. Further, RSA can effectively reduce the problems of information loss and overfitting to enhance the ability to learn relationships among nodes of the same type. In addition, we introduce a deep perception self-attention (DPSA) model with max pooling and L\({}_{2}\)-normalization to process meta-paths. Compared with the previous methods, it can effectively capture the temporal relations among different nodes in meta-paths. DPSA is a novel self-attention network. Unlike traditional self-attention models, it adopts deep convolutional neural networks (DCNNs) with different convolutional kernels and a residual network (ResNet) to obtain the query, key, and value. In DPSA, the DCNNs with different convolutional kernels can learn from multiple perspectives, thereby improving the performance of the model. ResNet can effectively address the problems of vanishing and exploding gradients, thereby enhancing the model's learning capability. Their combination can enhance the perceptual ability for different nodes in meta-paths [22]. Finally, we adopt the collaborative attention model to connect the learning results of meta-paths, users, and items to obtain the interactions between nodes and meta-paths. The contributions of our work are summarized as follows:  
1. We propose a system for the temporal enhancement of top-N recommendations on HNNs called TMRec. It can improve the performance of recommender systems by learning the temporal relations of nodes and meta-paths. 2. We adopt the architectures of LSTM and RSA for embedding the learning of users and items, which can highlight the temporal relations and interactions among users or items. This can also enhance the ability to learn heterogeneous information from these nodes. 3. We use DPSA with max pooling and L\({}_{2}\)-normalization to learn the interactions among users and items in meta-paths. Additionally, collaborative attention is introduced to obtain the interactions of meta-paths, users, and items. 4. Extensive experiments are conducted on four public datasets of recommender systems. The results demonstrate that the performance of the proposed TMRec model is largely improved compared with other SOTA models. The structure of this paper is organized as follows: In Section 2, we introduce related work on recommender systems, heterogeneous information networks, and network embedding. In Section 3, we introduce the definition of a heterogeneous information network and the main notations used. In Section 4, we explain the framework of the proposed TMRec in detail. In Section 5, we present the experiments conducted and analyze their results, which demonstrate the effectiveness of our model. Section 6 includes the conclusion of our study.
## 5 Conclusions  
In this paper, we investigated a best relay selection-based cooperative UWVLC system considering both horizontal and vertical propagation. We considered the effects of both path loss and optical turbulence, and assumed constant turbulence for horizontal links and variable depth-dependent turbulence for vertical links. The turbulence was assumed to be weak and modeled using a log-normal distribution. We derived the closed-form expressions for the outage probability and ergodic capacity. We conducted a detailed simulation study and presented numerical results comparing the performance of vertical and horizontal links for varying numbers of relays and no-relay cases. It is shown that by using multiple parallel relays and the best relay selection strategy, this results in significant performance gains as compared to the no-relay case. Results reveal that the horizontal link always outperforms the vertical link irrespective of the number of relays used. To obtain similar performance to that of the horizontal link, the vertical link requires one additional relay as compared to the horizontal link for \(\mathcal{M}>3\). It is further demonstrated that the performance of the horizontal communication system improves with depth.  
Conceptualization, R.S., V.B. and M.A.-H.; data curation, R.S.; formal analysis, R.S., Y.N.T. and K.K.G.; investigation, R.S. and K.K.G.; methodology, R.S. and K.K.G.; resources, R.S. and K.K.G.; software, R.S.; supervision, Y.N.T. and V.B.; validation, Y.N.T., V.B. and M.A.-H.; visualization, R.S. and K.K.G.; writing--original draft, R.S., K.K.G. and Y.N.T.; writing--review and editing, R.S., K.K.G., Y.N.T., V.B. and M.A.-H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing not applicable.  
This work is partially funded by the Centre for Wireless Communications, University of Oulu, Finland, and the United Arab Emirates University, Al-Ain, UAE.  
The authors declare no conflict of interest.  
Figure 5: Ergodic capacity (\(C_{e}\)) versus average SNR (\(\psi_{\circ}\)) db. \(\mathcal{M}\) and SD represent number of relays and direct non-relayed links from source to destination, respectively.
# Vegetarian \"Sausages\" with the Addition of Grape Flour
## 1 Introduction  
In developed countries, households produce 42% food waste; in the food industry, it is 39%; in catering establishments, 14%; and 5% of food waste is produced by retail and distribution. The main goal of eco-innovation is the so-called zero-waste economy, where waste is used as a raw material, which is further processed to create new products [1].  
[PERSON] et al. (2018) stated in their work that modern food processing involves a significant production of byproducts, including grape seeds, which are rich in biologically active substances [2]. The transformation of vegetable waste into value-added products was discussed by [PERSON] et al. (2003). These authors present several options for using grape pomace. The authors mentioned the use of grape marc as a matrix for antioxidants, flavors, dyes, emulsifiers and a source of fiber and vitamins [3]. The wine industry, which consumes approximately 75% of the world's grape production, produces large quantities of grape marc as a byproduct. The annual production of grape marc in countries such as Italy, France, and Spain is up to 1200 tonnes of grape marc per year. Therefore, it is important in terms of sustainability and waste reduction to create a new product from this byproduct [4].  
Grape seeds, out of which grape pomace is mainly produced, make up only 5% of the weight of the fruit, but 60-70% of the total polyphenol content is concentrated in them [5, 6].  
Another nutritional benefit of grape pomace is their fiber, mineral and antioxidant content [7; 8; 9]. Antibacterial activity is also well documented for polyphenols [9; 10], which, however, contributes to phytotoxicity and thus limits the use of grape marc in agriculture [11]. The most common functions associated with grape pomace products are their use as antioxidants, followed by their use as fortifying, coloring, and antimicrobial agents. These products have mainly been applied to the preparation of meat and fish products and, to a lesser extent, cereal products [7].  
On the other side, the excessive intake of processed meat products is associated with health problems, such as cardiovascular diseases, diabetes, and various types of cancer [12; 13; 14]. Because of this, the WHO's International Agency for Research on Cancer [15] recommends reducing the consumption of processed meat. Vegan sausages, where meat is replaced by vegetable protein, may be a suitable alternative to reduce the intake of processed meat.  
Due to advantageous nutritional properties of grape byproducts, including grape seed flour, and their potential application as fortification elements, there are many scientific studies that included byproducts accumulated during grape processing [16; 17; 18; 19; 20].  
The aim of the study was to experimentally produce vegetarian sausages without the application of heat treatment and with the use of grape flour as a grape byproduct, to evaluate the influence of the addition of grape flour on the chemical, physical, and sensory properties of an alternative type of sausage.
## 3 Numerical Results and Complexity Analysis
### Simulation Setup  
The main 3 GPP LTE-based PHY layer transmission of NB-IoT systems in MATLAB [27] was used for the link level simulation, and the bandwidth of the NBIoT system was selected as 180 kHz and 900 MHz carrier bandwidth respectively, i.e., single-tone or multi-tone transmission. Multi-tone transmission is an optional numerology that uses the same 15 kHz LTE SCS with 3, 6, 12 as the acceptable number of tones. However, for single tone transmission, 3.75 kHz and 15 kHz are satisfactory SCS options. For multiple tone transmission, RU programming assigns 1 ms for 12 tones, 2 ms for 6 tones, 4 ms for 3 tones, 8 ms for individual tones, 4 ms for 3 ms, 2 ms for 6 tones and 1 ms for 12 tones, respectively. Note that 15 kHz SCS is similar to LTE and provides better performance. When single tone transmission is used, a new radio frame structure is defined from a time domain perspective.  
We considered the uplink single-tone NPUSCH transmission with \(\Delta f_{n}=15\) kHz and \(\Delta f_{n}=3.75\) kHz with \(N_{s}=1.92\) MHz as the sampling rate, \(M=128\) as DFT size and \(N_{cp}=35\). The channel imposed six taps over wireless multipath fading plus AWGN. For channel coding, the LTE Turbo code with code rates of \(1/2\) and \(1/3\) were used and the equalizer was implemented using a soft-output MAP algorithm. However, MAP equalization was implemented by the BCJR algorithm with the number of ISI channel taps \(L=6\). A TBs length of 1000 bits (corresponds to \(N_{RU}=4\) at \(MCS=12\) in Table 2.2 of [28] was scheduled on NPUSCH which was mapped to QPSK constellation using Gray coding. The proposed ML-based channel estimation technique was performed using two NDMRS symbols per subframe whose parameter was set to \(\epsilon=0.211\).
## 3 Results and Discussion
### X-ray Photoelectron Spectroscopy (XPS) Surface Characterization
#### 3.2.1 X-ray Photoelectron Spectroscopy (XPS) Spectra Analysis
Each bond has binding energy of 284.80 eV, 287.26 eV, 290.55 eV and 293.27 eV, with an AC% of 6.99%, 2.32%, 6.78%, 1.12%. For the chemical state of O 1s, it is produced by the C=O bond/Si-O bond, and the bonding energy is 532.60 eV with an AC% of 41.76%. The chemical state of Si 2p is produced by the Si-O bond, and the bonding energy is 103.00 eV with an AC% of 20.22%. For the chemical state of F 1s, it is produced by the C-F bond, and the bonding energy is 687.76 eV with an AC% of 20.80%. The total AC% of these chemical states is 100%. Figure 4: Induced secondary electron image (SXI) of an area of 50 \(\upmu\)m by 50 \(\upmu\)m. Two points are selected for further analysis. Point 1 is on one silicon dioxide pattern, and Point 2 is on one FDTS SAM film pattern. Figure 5: (a) Atomic concentration (AC%) of four chemical states (C 1s, O 1s, F 1s, and Si 2p) when the X-ray beam size changed from 10 μm to 200 μm. X-ray photoelectron spectroscopy (XPS) spectra of Point 1 and Point 2 with the X-ray beam changed from 10 μm to 200 μm: (**b**) 10 μm, (**c**) 20 μm, (**d**) 50 μm, (**e**) 100 μm, and (**f**) 200 μm.
# Phase Portraits of Families VII and VIII of the Quadratic Systems
## 4 Proof of Statement (\(b\)) Theorem 1
### The Infinite Equilibrium Points in Chart \(U_{1}\)  
System (3) in the local \(U_{1}\) chart can be expressed as follows:  
\[\dot{u}=\ell+mu+av+nu^{2}+(b-1)uv+dv^{2},\qquad\dot{v}=-v^{2}, \tag{7}\]  
Assuming \(n\
eq 0\), the infinite equilibrium points are  
\[P_{\pm}=\left(0,\frac{-m\pm\sqrt{m^{2}-4\ell n}}{2n}\right),\]  
if \(m^{2}-4\ell n>0\). If \(m^{2}-4\ell n=0\), then \(P_{+}=P_{-}=P=(0,-m/(2n))\). The eigenvalues of the Jacobian matrix at \(P_{\pm}\) are \(0\) and \(\pm\sqrt{m^{2}-4\ell n}\)). By Theorem 2.19 of [8], we obtain that \(P_{\pm}\) are semi-hyperbolic saddle-nodes. The Jacobian matrix at \(P\) is  
\[\left(\begin{array}{cc}0&\frac{2 an-bm+m}{2n}\\ 0&0\end{array}\right).\]  
If \(2 an+(1-b)m\
eq 0\), then \(P\) is a nilpotent equilibrium point, and by Theorem 3.5 of [8], is a saddle-node. If \(2 an+(1-b)m=0\), then \(P\) is degenerate. If we translate the equilibrium point \(P\) to the origin, it becomes a homogeneous quadratic system; the phase portraits have been classified by Date in [14]. It follows that if \(b^{2}-4 dn\geq 0\), we obtain that the local phase portrait at \(P\) on the Poincare sphere is formed by two hyperbolic sectors separated by two parabolic ones, and infinity separates the two hyperbolic sectors, which have one separatrix at infinity. If \(b^{2}-4 dn<0\), then the local phase portrait at \(P\) is a node, unstable if \(n<0\), and stable if \(n>0\).  
Assume that \(n=0\). Then the unique infinite equilibrium point in the local \(U_{1}\) chart is \(P=(-l/m,0)\), and the eigenvalues of the Jacobian matrix of system (7) at \(P\) are \(0\) and \(m\). If \(m\
eq 0\), from Theorem 2.19 of [8], \(P\) is a semi-hyperbolic saddle-node. If \(m=0\), there are no infinite equilibrium points in the local \(U_{1}\) chart.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 4 Experimentation Methodology
### Analysis  
The analysis methodology follows the flow shown in Figure 4. The first step is to configure the environments as described in the above sections. These configurations indicate to the simulated nodes the use of _tshark_, the interfaces to sniff in, and the filters to be applied during the capture of packets.  
After preparing the environment to collect results, the emulation can start. While the emulation is running, the analysis process has some tasks to take care of. The main task, of course, is to capture packets and save them in a pcap format. This task can be carried out in two ways: simply capturing all the traffic on every interface, or cleverly collecting the packets needed for the post-processing stage, which is called _live filtering_.  
Live filtering is a crucial step in light of the scale that experiments may reach. Remember that there may be thousands of network nodes in a massive-scale data center, which translates into tens of thousands of interfaces to capture traffic (e.g., in a topology with 1280 nodes there are 40,960 interfaces). Without proper live filtering, every single capture can achieve a significant size. This adds two main drawbacks: (i) an additional storage requirement for the environments and (ii) an increase in the time required to carry out the post-processing step. The live filtering process faces these drawbacks using the dissectors provided by _tshark_ for BGP, ISIS, and Openfabric. For the RIFT protocol, the dissector presented in [67] is used.  
Finally, the post-processing is driven by a program that takes as input all the capture files (pcaps) and the information about the emulation and produces miscellaneous statistical output such as control packets per interface or the total number of BGP update packets.  
Figure 3: A fat-tree build with GNS3.
## 1 Introduction
Although these studies provide many bases for revealing the spatiotemporal effects of soft soil pit deformation, they mostly focus solely on the spatial or temporal effects of soft soil pit excavation without considering the interaction between the temporal and spatial effects, and there is seldom research on the spatiotemporal effects of pit engineering deformation in mucky soft soil areas considering seepage conditions. Mucky clay, a typical type of soft soil, has a higher water content, stronger rheological properties, lower permeability, and obvious time-dependent characteristics. These unique properties lead to complex issues related to the spatiotemporal effects of deep excavations. In the subway stations of Suzhou Metro Line S1, unevenly thick flow plastic mucky clay is distributed. The uneven thickness of the flow plastic mucky soils may cause non-uniform settlement during excavation. The time-dependent nature of creep deformation means that it does not occur instantaneously but rather accumulates over time, potentially intensifying during various construction stages, which continuously threatens the stability of the retaining structures and excavations. Therefore, time-dependent deformation characteristics should be carefully considered during excavation, particularly in deep excavation projects in soft soils and high-water-level areas. Given the time-dependent nature of creep deformation, precise control is crucial, as the deformation caused by these construction stages can pose serious risks to the stability of the excavation and supporting structures [32,33]. In previous studies, the retaining structure and surface settlement deformation induced by the creep of soft soil and spatial effect of the excavation sequence were not fully considered. With the Huaxi Park Station pit project of the Suzhou Metro Line S1 as a background, a three-dimensional numerical model was established where the creep characteristics of mucky layers and the spatiotemporal deformation induced by excavation under seepage conditions were considered. The spatiotemporal effects of mucky soft soil, including retaining structure deformation, surface settlement, and the internal forces of supports, during excavation could be fully considered. The CVISC rheological model was capable of simulating the time-dependent characteristics of mucky soils and seepage effects to help understand and predict the behavior of deep excavations in soft soils. This case study can be used as a reference for similar engineering projects.
## 1 Introduction
[28] observed that the PAW irrigation of tomato plants improved their growth, endogenous RONS, defense hormones, and expression of key pathogenesis related genes. [PERSON] et al. [29] showed that PAW improves tolerance against low temperature and hypoxia stresses during germination through the changes in carotenoids, cysteine, and \(\gamma\)-glutamylcysteine content. They attributed the effect to H\({}_{2}\)O\({}_{2}\) and NO. H\({}_{2}\)O\({}_{2}\) can activate various defense mechanisms through the redox signaling pathway, while NO formed from NO\({}_{2}\)\({}^{-}\) and NO\({}_{3}\)\({}^{-}\), can activate the protective processes under the stress conditions. The PAW possessed outstanding abilities in improving seed germination, seedling growth, and microbial decontamination. [PERSON] et al. treated mung bean seedlings with the PAW and found changes in enzymatic and hormone activities indicating that PAW can reduce oxidative damage, increase antioxidant enzyme activities and promote seed germination and seedling growth [17]. Our group investigated the effect of PAW composition (activity), i.e., concentrations of H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\), on wheat seeds and plants [18]. We found that seeds cultivated in the PAW interact mainly with H\({}_{2}\)O\({}_{2}\) in the early growth stages during imbibition and germination, while NO\({}_{3}\)\({}^{-}\) and NO\({}_{2}\)\({}^{-}\) are metabolized once the germination starts and during the early plant growth. The experiments showed that, for prospective application of the PAW, further optimization of water composition is necessary. Encouraged by the previous results, we decided to further investigate the effect of PAW. The objective of this work was to compare the effects of PAW and solutions of H\({}_{2}\)O\({}_{2}\) and NO\({}_{3}\)\({}^{-}\), in order to better understand the role and contribution of individual RONS to plant growth and physiology. The experiments were performed with pre-grown lettuce plants and the evaluated parameters were visual appearance and growth parameters of plants, photosynthetic pigment content, photosynthetic rate, and activity of antioxidant enzymes. So far, the effect of cold plasma relevant to lettuce plants has been mostly mentioned in context of improved food quality and inactivation of pathogen microorganisms from fresh-cut leaves. The plasma was applied to lettuce leaves directly [30], indirectly as plasma activated water [31; 32; 33; 34], or as plasma activated air in the process for food sanitation [35]. Besides, only few papers have dealt with the effect of plasma or PAW on lettuce seeds, their germination and seedling growth parameters [36; 37; 38]. We are not aware of any other research papers discussing the effect of PAW on lettuce plants, neither comparing it with the effect of H\({}_{2}\)O\({}_{2}\) and NO\({}_{3}\)\({}^{-}\) solutions. In this respect, we assume that the presented work investigating the effects of PAW and solutions of H\({}_{2}\)O\({}_{2}\) and NO\({}_{3}\)\({}^{-}\) on lettuce growth and its selected physiological parameters is unique and original.