esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Anthropometric Differences  
#### 3.2.1 Anthropometric Differences According to Sex  
According to sex, we observed significantly higher values in the men in terms of weight, standing height, sitting height, arm span, arm perimeter, waist perimeter, calf perimeter, and styloid diameter of the wrist. The women had significantly higher values in most of the skinfold measurements such as the triceps, subscapular, abdominal, suprailiac, and thigh (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{2}{c}{**Variable**} & \multicolumn{6}{c}{**Study Groups**} \\ \multicolumn{2}{c}{**Analyzed**} & \multicolumn{2}{c}{**T**} & \multicolumn{2}{c}{**B**} & \multicolumn{2}{c}{**H**} & \multicolumn{2}{c}{**W**} & \multicolumn{2}{c}{**R**} & \multicolumn{2}{c}{**C**} & \multicolumn{2}{c}{**S**} & \multicolumn{2}{c}{**G**} & \multicolumn{2}{c}{**V**} \\ \hline \multirow{6}{*}{Weight} & T & 73.70 \(\pm\) 11.86 & 80.69 \(\pm\) 8.83 & 76.85 \(\pm\) 15.80 & 71.79 \(\pm\) 11.56 & 69.57 \(\pm\) 8.90 & 78.99 \(\pm\) 11.03 & 71.93 \(\pm\) 11.12 & 70.85 \(\pm\) 11.23 & 67.52 \(\pm\) 8.71 \\  & M & 78.92 \(\pm\) 10.44 & 86.16 \(\pm\) 6.35 & 86.29 \(\pm\) 5.07 & 79.23 \(\pm\) 7.65 & 71.52 \(\pm\) 8.07 & 83.09 \(\pm\) 8.57 & 75.46 \(\pm\) 9.84 & 77.45 \(\pm\) 9.11 & 75.39 \(\pm\) 4.51 \\  & F & 65.43 \(\pm\) 8.90 & 75.02 \(\pm\) 7.37 & 65.68 \(\pm\) 6.86 & 64.35 \(\pm\) 9.92 & 60.90 \(\pm\) 7.39 & 64.82 \(\pm\) 5.29 & 61.12 \(\pm\) 7.24 & 63.81 \(\pm\) 8.86 & 62.27 \(\pm\) 6.60 \\  & \(p\leq\) 0.001\({}^{*}\) & \(p\leq\) 0.001\({}^{*}\) & \(p\leq\) 0.001\({}^{*}\) & \(p\leq\) 0.001\({}^{*}\) & \(p\leq\) 0.001\({}^{*}\) & \(p\leq\) 0.001\({}^{*}\) & \(p\leq\) 0.001\({}^{*}\) & \(p\leq\) 0.001\({}^{*}\) \\ \hline \multirow{6}{*}{Standing height} & T & 173.98 \(\pm\) 9.38 & 180.75 \(\pm\) 6.61 & 177.10 \(\pm\) 9.31 & 168.78 \(\pm\) 7.36 & 174.16 \(\pm\) 8.82 & 174.41 \(\pm\) 6.87 & 174.68 \(\pm\) 9.26 & 170.52 \(\pm\) 9.06 & 170.40 \(\pm\) 7.12 \\  & M & 178.1
## 5 Performance Evaluation
### Implementation and Evaluation
#### 5.3.1 Part I-Algorithms  
This part evaluates the performance of the well-known MLNN algorithms introduced in Section 4 (i.e., KNN, DT, RF, SVM, MLP, CNB, LSTM and SLP) for predicting parking segment availability in smart cities based on accuracy, precision, recall, F1-Score and execution time.  
Table 3 presents the average cross-validation score of the algorithms given 30-min predictions.  
We can see that the computationally complex neural networks-based models such as LSTM, MLP and even SLP show generally the lowest performance. The results of neural networks-based models are as expected. Because generally neural networks are poor at time series forecasting. Among neural networks-based models, MLP has the best performance. It can be seen that SVM's accuracy is the closest to the accyracy of MLP. KNN and DT, as two simple methods, show promising performance. RF can outperform other algorithms. However, it is considered as an ensemble learning method.  
Table 4 compares execution time of different models (in s). Training phase for LSTM algorithm is very time consuming in comparison to other algorithms. We can see that simple algorithms such as DT and KNN have better training time than more complex ones. Considering both training and testing execution times together, DT can be considered as one of the quickest algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Set** & **CNB** & **DT** & **KNN** & **MLP** & **RF** & **SLP** & **SVM** & **LSTM** \\ \hline Training & 0.0020 & 0.0015 & 0.0014 & 0.5869 & 0.2150 & 0.0046 & 0.0294 & 86.5 \\ Testing & 0.0009 & 0.0009 & 0.0023 & 0.0016 & 0.0196 & 0.0009 & 0.0155 & 0.0006 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Execution time of different models (in s).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metrics** & **CNB** & **DT** & **KNN** & **MLP** & **RF** & **SLP** & **SVM** & **LSTM** \\ \hline Accuracy & 77.97 & 78.96 & 78.20 & 75.74 & 79.15 & 64.84 & 75.37 & 53.56 \\ Precision & 80.13 & 81.09 & 80.61 & 77.54 & 80.94 & 69.53 & 78.65 & 85.41 \\ Recall & 77.97 & 78.96 & 78.20 & 75.74 & 79.15 & 64.84 & 75.37 & 53.56 \\ f1\_score & 76.18 & 78.64 & 77.87 & 73.72 & 78.68 & 61.45 & 72.17 & 43.52 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average cross validation score of models.
## Nomenclature  
\begin{tabular}{l l l} Symbol & Name & Units \\ C & linear concentration & mole/m \\ \(t\) & time & s \\ \(t^{\alpha}\) & fractional time (memory) & s\({}^{\alpha}\) \\ \(D\) & diffusion coefficient & m\({}^{2}\)/s \\ \(\alpha\), \(\beta\) & fractional orders & \\ \(m\) & delimits sub-/supra-diffusion & \\ \(\Gamma(x)\) & Gamma function & \\ \(x\) & space & m \\ \(\tau\) & projection plane variable & \\ \(s\) & Laplace Operator & \\ \(K\) & diffusion coefficient in space-time & m\({}^{2}\)/s\({}^{\alpha}\) \\ \(k\) & local space operator & m \\ \(n\) & sample number & \\ \(Cg\) & concentration in grain & mole/m\({}^{3}\) \\ \(L\) & grain size & m \\ \(A\) & transversal area of grain & m\({}^{2}\) \\ \(V\) & grain volume & m\({}^{3}\) \\ \(S\) & surface area of grain & m\({}^{2}\) \\ \(v\) & volumetric flow of diffused species & m\({}^{3}\)/s \\ \(\gamma\) & linear diffusion velocity & m/s \\ \(C_{0}\) & initial concentration & mole/m \\ \(T\) & temperature & K \\ \(K_{B}\) & Boltzman constant & (m\({}^{2}\)-kg)/(K-s\({}^{2}\)) \\ \(r\) & radius of sphere & m \\ \(\eta\) & viscosity & kg/(m-s) \\ \(\sigma\) & specific material coefficient & \\ \(\omega\) & angular frequency & rad/s \\ \(R_{e}\) & resistance & \(\Omega\) \\ \(L_{e}\) & inertance & Pa-m\({}^{-3}\)-s\({}^{2}\) \\ \(C_{e}\) & capacitance & F \\ \end{tabular}
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 3 Results and Discussion
### Firmness of Blackcurrant During Storage  
With storage, blackcurrant berry firmness decreased (Figure 2). In the beginning, the blackcurrant firmness was about 200 gm/mm. The control and chitosan-treated berries had similar effects before day 15. On day 15, the blackcurrant firmness with chitosan treatment had significant differences from the control treatment with about 10 gm/mm differences. Chitosan-treated berries were the firmest at almost 125 gm/mm. On the other hand, UVA and UVA-Chito treatment had large effects on blackcurrant firmness, which decreased to about 100 gm/mm under these two treatments. Chitosan combined with UVA had slightly better firmness maintenance, especially after one week. Chitosan's retention of fruit firmness was reported in many studies, which might due to its metabolic regulation abilities [35]. UVA has been considered as a non-important UV radiation compared with  
Figure 1: Effects of postharvest treatments on weight loss. Each treatment contains three replicates. Bars indicate values are the mean \(\pm\) standard deviations of replicates. Different letters correspond to mean values significantly different (\(p<0.05\)) by analysis of variance (ANOVA) and Tukey post hoc test across storage.  
UVC and UVB effects, but some studies have pointed out its known unknown functions, such as on photosynthesis and biomass of plants [36]. The study of UVA's effect on fruit firmness during postharvest is limited. The loss of firmness is largely correlated with solubilization and depolymerization of the cell wall, which can be modified by UV radiation [32]. UVA reduced cucumber (_Cucumis sativus_ L.) yellowing and softening during storage at 10 \({}^{\circ}\)C but not at 5 \({}^{\circ}\)C [37]. This indicated that UVA effects are based on other storage conditions as well. Further research and storage condition modification can be conducted in the future for blackcurrant postharvest storage.
## 2 Numerical Investigation
### Governing Equations  
The continuity equation and momentum equation applied in this study can be expressed in a conservation form as follows:Continuity equation:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{1}\]  
where \(u_{i}\) is the instantaneous velocity in the i direction.  
Momentum equation:  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}\Big{(} \rho u_{i}u_{j}\Big{)}=-\frac{\partial P}{\partial x_{j}}+\frac{\partial\tau_ {ij}}{\partial x_{j}}\rho f_{i} \tag{2}\]  
where \(P\) is the static pressure, \(\tau_{ij}\) is the viscous stress tensor, and \(f_{i}\) is the body force.  
In Newtonian fluids, \(\tau_{ij}\) can be expressed in terms of the velocity gradients, as shown in Equation (3).  
\[\tau_{ij}=\mu\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{ j}}{\partial x_{i}}\bigg{)}-\frac{2}{3}\mu\bigg{(}\frac{\partial u_{m}}{ \partial x_{m}}\bigg{)}\delta_{ij} \tag{3}\]  
where \(\mu\) is the fluid dynamic viscosity and \(\delta_{ij}\) is the Kronecker delta.  
Equations (2) and (3) can be used to obtain the Navier-Stokes equation (Equation (4)):  
\[\begin{array}{l}\frac{\partial}{\partial t}\big{(}\rho u_{i}\big{)}+\frac{ \partial}{\partial x_{j}}\Big{(}\rho u_{i}u_{j}\Big{)}\\ =-\frac{\partial P}{\partial x_{j}}+\frac{\partial}{\partial x_{j}}\Big{(} \mu\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{i}}\bigg{)}-\frac{2}{3}\mu\bigg{(}\frac{\partial u_{m}}{\partial x _{m}}\bigg{)}\delta_{ij}\bigg{)}+\rho f_{i}\end{array} \tag{4}\]  
To simplify the problem, we used the method of averaging the Navier-Stokes equation. Equation (5) shows the RANS equation:  
\[\begin{array}{l}\frac{\partial}{\partial t}\Big{(}\overline{\rho u_{i}} \Big{)}+\frac{\partial}{\partial x_{j}}\Big{(}\overline{\rho u_{i}}\overline {u_{j}}\Big{)}\\ =-\frac{\partial\overline{P}}{\partial x_{j}}+\frac{\partial}{\partial x_{j}} \Big{(}\overline{\mu}\bigg{(}\frac{\partial\overline{u_{i}}}{\partial x_{j}} +\frac{\partial\overline{u_{i}}}{\partial x_{i}}\bigg{)}-\frac{2}{3}\overline {\mu}\bigg{(}\frac{\partial\overline{u_{m}}}{\partial x_{m}}\bigg{)}\delta_{ij }\Big{)}+\frac{\partial}{\partial x_{j}}\bigg{(}-\overline{\rho u_{i}^{\prime }u_{j}^{\prime}}\bigg{)}\\ +\overline{\rho f_{i}}\end{array} \tag{5}\]
# Towards an Efficient Chipless RFID System for Modern Applications in IoT Networks
## 2 The RFID Tag Design  
In this paper, we propose a tri-state coupled line microstrip resonator as a chipless RFID tag already presented in [17]. The proposed tag has 14 resonators as shown in Figure 1.  
The information of each resonator can be encoded with two frequencies (\(f_{1}\) and \(f_{2}\)); therefore, 28 possible frequencies can be configured, as depicted by its S-parameters in Figure 2.  
Figure 1: Porotype of the high coding capacity tag [17].  
Figure 2: The simulated S\({}_{21}\) response of the tag [17].  
It is worth mentioning that the frequency separation between \(f_{1}\) and \(f_{2}\) is 50 MHz, which can be varied by changing the widths of the arms, thus, making our design bandwidth flexible by enhancing the tag's capacity without the need of broadening the required frequency bandwidth.  
Furthermore, the proposed tag can be configured with other arm connection combinations, thereby yielding various bit combinations for storing information. Additionally, the bit encoding capability of the proposed tag in the high frequency spectrum suggests that its size can be further reduced and, consequently, the cost, if the tag is redesigned for high frequency applications such as object localization and robotics where accurate position resolution is required. This would also enable the tag to encode greater number of bits as the bandwidth increases, which is one the primary requirement of heterogeneous networks such as the IoT [18].
## 4 Results
### Exploration of Recommendation Rate Influencing Factors  
Correlation analysis revealed significant correlations between doctor recommendation rates and multiple variables: post-consultation evaluation (r = 0.602, \(p<\) 0.001), final patient completion count (r = 0.562, \(p<\) 0.001), and number of thank-you letters (r = 0.554, \(p<\) 0.001) demonstrated moderate positive correlations. Article count showed no significant correlation with recommendation rate (r = 0.122), and thus was excluded from subsequent multilinear regression analysis to enhance analytical precision. Pearson correlation coefficients between variables are presented in Table 3. Standard errors between correlations are presented in Table 4.  
Figure 3: Subnetworks of Doctors by Different Professional Titles. (**a**) Chief Doctor Subnetwork; (**b**) Deputy Chief Doctor Subnetwork; (**c**) Attending Doctor Subnetwork; (**d**) Resident Doctor Subnetwork. We used Python 3.8.8 to generate a correlation heatmap (Figure 4). The coordinate axes encompassed indicators including doctor recommendation rate, online work quantity, personal webpage visits, article count, total post-consultation patient count, post-consultation evaluation, thank-you letter count, and gratitude gift count. Numerical values within grid cells represent Pearson correlation coefficients (r) between the corresponding variables. Additionally, grid cell color intensity reflects correlation coefficient magnitude, with darker colors indicating more significant inter-variable correlations. Multiple regression model goodness-of-fit test results demonstrated that the model could explain 45% of the dependent variable variance (R\({}^{2}\) = 0.450). F-test results (F = 864.142, \(p\) < 0.001) indicated statistically significant overall regression model performance. Standardized regression coefficient analysis revealed that post-consultation evaluation (diagnosis_evaluation) possessed the highest standardized regression coefficient (\(\beta\) = 1.419), signifying its most substantial influence on the dependent variable. Thank-you letter count (thanks_letter) also exhibited significant influence (\(\beta\) = \(-\)0.833), with a negative directional effect. Doctor online work quantity (doctor_online_work_num) (\(\beta\) = \(-\)0.012) and final patient count (patient_final_finish) (\(\beta\) = 0.109) demonstrated relatively minor impacts on recommendation rate. \begin{table}
\begin{tabular}{l l} \hline \hline  & **Standard Error** \\ \hline Doctor Recommendation Rate & 0.4424 \\ Doctor Online Work Quantity & 1924.798 \\ Personal Webpage Visits & 232.508 \\ Article Count & 84.311 \\ Total Post-Consultation Patient Count & 741.467 \\ Post-Consultation Evaluation & 115.195 \\ Thank-You Letter Count & 52.065 \\ Gratitude Gift Count & 169.859 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Standard Errors Between Correlations.
## 2 Current CORNERSTONE Platforms
### Thermal Phase Shifters  
The high thermo-optic coefficient of Si [57] can be both advantageous and disadvantageous for a PIC designer. For resonant devices in particular, the high thermo-optic coefficient typically requires temperature control to maintain the stability of the device, which introduces additional power consumption [58]. Alternatively, several thermal resonant device designs have been explored by other groups, but these typically deviate from standard CMOS processes [59].  
On the other hand, the high thermo-optic coefficient of Si means that efficient thermal phase shifters can be fabricated on SOI substrates, typically using TiN based heaters [60]. The efficiency of the phase shifter is largely dependent on thermally isolating the Si waveguide structure so that the heat generated in the TiN heater is confined to the waveguide and does not dissipate through the substrate. This is demonstrated in Figure 4, which shows the electrical power required to achieve a pi phase shift reducing from 19.4 mW/\(\pi\) for a rib waveguide configuration (Figure 4b) to 8.4 mW/\(\pi\) for a strip waveguide configuration (Figure 4a). This is based on a 200 um long, 600 nm wide and 50 nm thick TiN heater filament layer with Al + Au heater contact pads. The flip-side to the higher thermal isolation of the strip waveguide configuration is that the device has a lower switching speed  
Figure 3: Bidirectional angled multimode interferometer (MMI) showing the (**a**) top-view device schematic and (**b**) measured device performance (reprinted from [52], Royal Society, 2015).  
of 54 kHz compared to 69 kHz for the rib waveguide configuration (Figure 4c). Other groups have investigated methods of further improving the heater efficiency, for example by undercutting the Si substrate, which can result in a 50\(\times\) improvement [61].
# End-to-End Performance Evaluation of MEC Deployments in 5G Scenarios
## 5 Conclusions and Future Work  
This paper presented a methodology to evaluate MEC deployments with 4G/5G access and the results of its application. The methodology consists in using detailed system-level simulators (namely, Simu5G and CoFluent) to simulate the communication and computation parts of an E2E service with MEC, and joining them into a flexible co-simulation framework. We have used the above tool to compare MEC deployment options, namely 4G, 5G non-standalone, and 5G Standalone, as for their suitability to support an infotainment service with MEC. Our results show that 4G radio access is going to be the bottleneck from a QoE perspective, allowing only a small number of users, and virtually leaving most of the MEC computation power untapped. With 4G, large jitters will impair the service even in a relatively uncongested RAN. Deploying 5G, already in NSA but especially in the SA option, is going to make a large difference. With 5G, the same service will be able to accommodate considerably more users before MEC hosts become a bottleneck.  
Due to the inherent flexibility of the above framework, this work can be extended in several directions. For instance, in-car task offloading from car to MEC, e.g., critical workloads to be offloaded to MEC in the case of sudden blade failure in the car.  
Conceptualization, A.V., G.N., and G.S.; software, A.V. and G.N.; validation, A.V. and G.N.; writing--original draft preparation, A.V., G.S., and D.S.; writing--review and editing, A.V., G.N., G.S., and D.S. All authors have read and agreed to the published version of the manuscript.  
Work partially supported by the Italian Ministry of Education and Research (MIUR) in the framework of the Cross-Lab project (Departments of Excellence). The subject matter of this paper includes description of results of a joint research project carried out by Intel Corporation and the University of Pisa. Intel Corporation reserves all proprietary rights in any process, procedure, algorithm, article of manufacture, or other results of said project herein described.  
[PERSON] is affiliated with Intel, which funded some of the activities that are described in this paper. [PERSON] took part in the writing, contributing in particular to the background description and to the scenario definition. The funder did not influence the claims or the results that are presented in this paper.
# A Knowledge Graph Embedding Model Based on Cyclic Consistency--Cyclic CKGE
## 2 Related Work
### Method Based on Vector Translation  
TransE [23] is the first paper to use the translation model for knowledge graph embedding research. The model is simple, easy to train and has strong explanatory properties. The basic idea is to represent head entities, relations and tail entities as vectors, respectively. However, TransE [23] can only handle 1-1 relational patterns, and it is not effective for complex relationships such as 1-N, N-1 and N-N relational patterns. Based on the TransE [23] model, a number of subsequent research results were extended, including the use of projection vectors or matrices to transform the head and tail entities into relational vector Spaces. During this period, many models began with Trans, such as TransH [24], TransR [31] and TransD [32]. TransH treats relationships as hyperplanes and projects head and tail entities into the hyperplane of a particular relationship. Although the TransH [24] model allows each entity to have a different representation under different relations, it still constructs entities and relations in the same semantic space, which limits the representation ability of TransH to some extent. TransR projects entities and relationships into different Spaces and transforms them on the corresponding relational Spaces. Under the same relation, the head and tail entities share the same projection matrix. However, the types or attributes of the head and tail entities of the relationship may be very different. The projection from the entity space to the relation space is an interaction process between the entity and the relation, so it is unreasonable for TransR [31] to only associate the projection matrix with the relation. Compared with TransE [23] and TransH [24], due to the introduction of spatial projection, the TransR [31] model has significantly more parameters and higher computational complexity. TransD [32] further simplifies TransR [31] by decomposing the projection matrix into the product of two vectors.
# Advances in the Synthesis and Physiological Metabolic Regulation of Nicotinamide Mononucleotide
## 3 Biosynthesis of NMN
### Metabolic Engineering for NMN Synthesis
#### 3.2.2 Metabolic Engineering of the NRK Pathway  
Human NRKs were reported to have optimal pH values of 6.5-9.0, and they are very unstable at pH values outside of this range, as well as being sensitive to heat [58]. Immobilization is a commonly used strategy for improving the performance and robustness of enzymes. Moreover, immobilized enzymes can be recycled repeatedly in the production process, realizing an efficient and economical way to synthesize NMN. [PERSON] et al. [59] expressed the NRK from _S. cerevisiae_ at high levels in soluble form in _E. coli_ and immobilized it using a metal affinity tag to optimize the enzyme performance. [PERSON] et al. [60] expressed yeast-derived a-agglutin anchoring protein Aga2 and human NRK-2 enzymes in tandem, which enabled NRK-2 to be efficiently displayed on the cell wall of _Saccharomyces cerevisiae_. Under optimized conditions, the substrate conversion rate reached 98.2%. NRK-2 showed good pH and heat stability, and it could be reused in industrial production. At present, NR substrates are generally synthesized by chemical methods and are expensive, which limits their large-scale industrial applications. After the study of NR as the core metabolic intermediate pathway, a variety of different substrate synthesis pathways were discovered. [PERSON] et al. [61] found that NRK was able to phosphorylate ribose-1-phosphate and ribose nicotinamide. Using ribose, NAM, and ATP as substrates, NMN was obtained via a one-pot reaction in a multienzyme catalytic system, with a substrate conversion rate of 92% after 4 h. [PERSON] et al. [62] used adenosine and phosphate to generate NMN under the catalysis of purine-nucleoside phosphorylase (PNP) and NRK, to which they added polyphosphate kinase (PPK) derived from _Pseudomonas aeruginosa_ to realize ATP recycling. [PERSON] et al. [11] designed the NR phosphorylation pathway to generate NMN, which could achieve an NMN yield of 3 g/L after screening and optimizing the multienzyme cascade catalytic system of nucleoside phosphorylase (PyNP), PNP, PPK2, and NRK with uridine as a substrate.