esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Nutritional Screening and Morphofunctional Assessment
#### 3.2.3 How to Establish the Degree of Malnutrition after Screening?  
**Recommendation 7**.: _The patient's degree of malnutrition should be previously established according to the latest ESPEN recommendations using the GLIM or SGA criteria (see Appendix A)._  
Grade of recommendation A, (98%) Strong consensus.  
Comment:  
There are many studies in this field that use the Subjective Global Assessment (SGA) for the diagnosis of malnutrition [58; 67; 68; 69; 70; 71; 72].  
GARIN members recommend nutritional diagnosis according to the latest ESPEN consensus guidelines [72], currently the GLIM or SGA criteria [11; 35].  
Based on the GLIM criteria, the diagnosis of malnutrition is reached when at least one phenotypic criterion (weight loss, age-related BMI, or Fat-Free Mass Index (FFMI)) coexists with another etiological criterion (reduction in intake/nutrient absorption or presence of inflammation/disease). The assessment of muscle mass becomes one of the phenotypic criteria in the diagnosis of malnutrition and the classification of its severity.  
The SGA scale is made up of several items that provide a score to predict the patient's nutritional status.
## 5 Conclusions and Future Work  
In this paper, we presented a new routing protocol for VANETs that includes four metrics (distance, trajectory, density and ABE) to make hop-by-hop forwarding decisions. Furthermore, the proposal is building-aware, avoiding those nodes in transmission range, but behind a building. This feature protects packets from being thrown out. Besides, a local buffer is used to temporarily store those packets when the routing protocol fails in finding a proper next forwarding node. We analyzed several metrics and developed an algorithm that includes four of them. The algorithm computes a global score value used to select the best next forwarding node among all the neighbors in transmission range.  
Firstly, we evaluated our proposal compared to other routing proposals present in the literature in two scenarios (a low and a medium density scenario). In terms of packets losses and throughput, MMMR improves all the protocols evaluated, due to the new way of selecting the next forwarding node. Compared to other existing routing protocols, we concluded that MMMR has a better performance in both scenarios, low and medium density.  
Secondly, we presented a performance evaluation, taking into account the forwarding decision based on each single metric compared to MMMR. The results show that MMMR, which makes the next forwarding decision using the equitable combination of the four metrics, is better than making the forwarding decision based only on one of the four metrics independently.  
Finally, we evaluated some combinations of the four metrics to analyze the behavior depending on the scenario. In this evaluation, we noticed that in the case of the 60 node scenario, the best results were obtained with MMMR. In terms of packet losses, MMMR was followed by the combination of weights \(distance+density\), while, in terms of average delay, by the combination of weights \(ABE+trajectory\). This makes sense, since in scenarios with a low density of vehicles, it is better to select nodes that are moving towards the destination. In the case of the 120 node scenario, the best results are obtained by the \(distance+density\) combination, followed in second place by MMMR.  
As future work, we plan to analyze other methods to obtain a global metric value. Furthermore, the computation of the weights of the metrics will be improved in a future work using machine learning techniques [25] to auto-configure the weights, varying throughout time, making the algorithm adaptable to the changing network conditions that are inherent in VANETs. Finally, we also will explore the available power level of each node as a metric to mark nodes as reliable next forwarding nodes.
# Radio-Frequency Undulators, Cyclotron Auto Resonance Maser and Free Electron Lasers
## 2 CARM as a Source of RF Undulator
\begin{table}
\begin{tabular}{l c} \hline \hline  & **CARM Wave Generation** \\ \hline beam energy & 420 kV (\(\gamma=1.82\)) \\ beam current & 60 A \\ pitch factor & \(\alpha=0.55\) \\ magnetic field & \(B_{0}=2.99\) T \\ cavity radius & 1 cm \\ operating mode & \(TE_{23}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main parameters settings to generate a CARM RF wave at \(\approx 150\) GHz with output power of \(\approx 5\) MW. Figure 9: Beam-wave cold cavity interaction in the Brillouin diagram (\(\omega,k\)) normalized to the cutoff frequency (\(\omega_{c}\)) and to the wave vector (\(k_{c}\)) using the parameters reported in Table 1. The transition from the condition of two operating frequency (two intersections \(\omega_{-}\), \(\omega_{+}\) continuous line) to that of grazing incidence (\(\omega_{-}=\omega_{+}=\omega_{g}\) dashed line) is obtained by changing, i.e., the magnetic field intensity from \(B_{0}=2.99\) T to 2.1 T.  
The linear regime is characterized by the small signal gain, which exhibits, along the longitudinal coordinate the transition from low- to high-gain regime, displayed in Figure 11. The gain curve vs. the normalized detuning \(\Delta\),  
\[\Delta=2(1-\beta_{z}/\beta_{p})/(\beta_{\perp}^{2}(1-\beta_{p})^{-2})\delta_{0}, \tag{22}\]  
where \(\beta_{p}=v_{p}/c\) and \(\delta_{0}=1-\beta_{z}/\beta_{p}-\Omega_{CH}/\omega_{+}\), exhibiting the characteristic bell-shaped form. Before concluding this section, we would like to clarify two points we have just touched on. We mention that the gyrotron mode may grow too. In Figure 12 we report the temporal growth rate vs. the wave vector \(k_{z}\), using the parameters reported in Table 1, exploring the two resonances at \(\omega_{+}\) and \(\omega_{-}\). In Figure 13, we report the low-frequency mode \(\omega_{-}\), at different z-values inside the waveguide. The gain is significantly larger and could be dom  
Figure 11: Gain vs. the normalized detuning \(\Delta\) at different z-position for the \(\omega_{+}\) resonance calculated using the analytical solution in the SSA regime with the parameters reported in Table 1.  
Figure 12: Frequency value (left axis) corresponding to the interaction of the beam (dashed line) with the dispersion curve of the operating mode \(TE_{23}\) (continuous line) and growth rate (dot-dashed line) using the the parameters reported in Table 1.  
CARM mode. The associated power growth can be suppressed by suitably seeding the up-shifted mode. The second point concerns the evaluation of the CARM efficiency, which, unlike the magnetic case is significantly larger and reaches values around tens of percent. The reasons underlying these large values are the characteristic feature of the device itself. The interaction occurs in such a way that the gain curve is larger and the interaction is resonant for a longer time [33] thus following a behavior not dissimilar by tapered FEL device [43].
## 3 Results and Discussion
### HPLC-PDA Method Validation  
#### 3.2.1 Specificity  
The specificity was evaluated by confirming the retention times (\(t_{\rm R}\)) and absorption spectra from HPLC-PDA chromatograms. For \(L\). _chinensis_ extract and the standard solutions, the retention times (\(t_{\rm R}\)) were 7.445 min for L7 GC, 9.445 min for CDR, 10.121 min for C7 dGlu, and 15.034 min for DSM. In addition, a comparative analysis of the absorption spectra was performed in the range 190-400 nm. The PDA absorption maxima (\(\lambda_{\rm max}\)) were the same at 254.4 nm and 347.4 for L7 GC, 266.2 nm and 336.6 nm for CDR, 252.0 nm and 347.4 nm for C7 dGlu, 252.0 nm and 346.2 nm for DSM. Thus, we chose an absorption wavelength of 340 nm for the simultaneous analysis of L7 GC, CDR, C7 dGlu, and DSM (Figure 3).
# Towards Environmentally Friendly Accelerometers Based on Bacterial Cellulose
## 2 Materials and Methods
### Characterization Methods  
The thermogravimetric measurements, obtained through a Shimadzu DTG-60 instrument, were performed by heating the samples, weighing between 8 and 11 mg, from 35 to 700 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\). Scanning electron microscopy (SEM) images have been recorded on a Cambridge 90 instrument, equipped with an energy dispersive X-ray microanalysis (EDX) facility. ATR Fourier transform IR (FTIR) spectra were recorded directly on samples, without any preliminary treatment, at a resolution of 2.0 cm\({}^{-1}\) in the range 4000 to 650 cm\({}^{-1}\) by a PerkinElmer Spectrum 100 spectrometer. The temperature dependence of storage modulus (E\({}^{\prime}\)), loss modulus (E\({}^{\prime\prime}\)), and tan delta (tan \(\delta\) = E\({}^{\prime\prime}\)/E\({}^{\prime}\)) has been evaluated, in tension mode, by using a 2000 TA DMA (Triton Technology Ltd.) and applying a sinusoidal force to a rectangular sample, in the range 25-200 \({}^{\circ}\)C, at a working frequency of 1 Hz and a 2 \({}^{\circ}\)C/min rate.
## 4 Methodology  
Figure 13 presents the comprehensive methodological framework of our study. This architecture addresses the challenge of understanding how different delay mechanisms affect information diffusion across diverse social network structures.  
Figure 11: Average path length by graph type. Box plots show standard statistical summaries with outliers marked as circles.  
Figure 12: Modularity by graph type. Box plots show standard statistical summaries with outliers marked as circles.  
The framework consists of four primary components (Figure 13):
* Data Layer: This foundation includes both real Reddit thread networks (capturing authentic social interaction patterns) and synthetic networks (enabling controlled experimental conditions). The diversity of network types (Random, Scale-Free, Small-World, Tree, Community) allows for systematic comparison across topological variations representative of different social media interaction patterns.
* Diffusion Simulation Layer: At this level, we implement three distinct delay mechanisms (constant, uniform, exponential) that model different patterns of user re  
Figure 13: Information diffusion analysis framework.  
sponse timing in social networks. The simulation engine applies population dynamics principles to model how information spreads through network connections with parameterized transmission probabilities (0.1-0.4), representing different content engagement likelihoods.
* Analysis Layer: This component transforms simulation outputs into actionable insights through feature engineering, model training, and evaluation. The machine learning models (particularly Gradient Boosting and Random Forest) identify key predictive factors that determine information coverage across network types.
* Application Layer: Finally, our framework translates analytical findings into practical recommendations for social media stakeholders. These applications include optimizing information dissemination strategies, predicting viral content potential, and designing more effective network structures for specific communication goals.
* Each component addresses specific challenges in information diffusion modeling: the data layer provides ecological validity, the simulation layer captures temporal dynamics, the analysis layer identifies key predictors, and the application layer ensures practical relevance.
## 4 Performance Evaluation
### Aggregate Throughput  
Figures 7 and 8 present the results in terms of aggregate throughput. For HMC-MAC protocol, it is clear that when the number of generated packets increases the aggregate throughput increases. All other protocols reach saturation (Figure 7) or rise slowly (Figure 8) when the number of generated packets becomes greater than \(8\) pkts/sec/node due to the high number of collisions and frame repetitions. For the  
Figure 6: Traffic generation in periodic and burst modes.  
cluster method, the number of packets collected by the \(NC\) does not exceed \(100\) packets/sec and reaches saturation when the number of generated packets exceeds \(4\) pkts/sec/node.  
The performances of our proposition are close to those obtained with HMC without segmentation, for low traffic rates (under \(6\) pkts/sec/node for periodic traffic scenario and under \(3\) pkts/sec/node for burst traffic scenario). On the other hand, for higher traffic rates HMC-MAC copes much better than the other protocols. This is due to the fact that HMC-MAC divides the network into two groups. It always takes into consideration the channel usage in the groups before choosing a channel. This means, with HMC-MAC we have \(16\) channels for each group, whereas in HMC without segmentation and the other methods we have \(16\) channels for all the nodes. In addition, segmenting the network and defining  
Figure 8: Aggregate throughput for burst traffic.  
Figure 7: Aggregate throughput for periodic traffic.  
the receivers during each interval helps HMC-MAC to avoid sending frames for nodes that are not in reception mode on the right channel. This leads to higher packet delivery rate with HMC-MAC.  
By comparing the periodic and the burst traffic generations, we note that HMC-MAC outperforms other protocols even more in burst traffic generation. For example, with \(12\) pkts/sec/node, when we use a periodic traffic generation, HMC-MAC increases the received packets at the sink by around \(76.8\%\), \(34.7\%\), \(20\%\) and \(10.4\%\) compared to cluster method, random method, 2-hop method and HMC without segmentation method, respectively. When we use a burst traffic generation, the number of packets received by the sink is increased by almost \(77\%\), \(45.7\%\), \(39.4\%\) and \(32.5\%\) respectively. This is due to the same reasons stated earlier and shows that HMC-MAC is able to better manage the access to the medium under high burst traffic than other protocols that suffer more from packet loss due to lack of rendez-vous between senders and receivers.  
We can also notice that HMC without segmentation outperforms the 2-hop method and that 2-hop method is more efficient than the random method. This is due to the fact that 2-hop does not take into consideration the channel usage in its 3-hop neighborhood and suffer form the type of collisions explained in 3.2. It is also important to note that we are using a slotted CSMA/CA algorithm in all the evaluated methods. Slotted CSMA/CA is a variant of non-persistent CSMA/CA, and this is why we reach a saturation point and not a decrease point.
## 3 Results and Discussion
### Optimization Process
#### 3.4.3 Verification of Predictive Models  
Optimization of the prepared functional biscuits was performed to measure the optimum levels of independent variables (amounts of RPC and SAFA) to achieve the desired response goals. The antioxidant potential and sensory characteristics were specified as desired to be maximized. To evaluate the sufficiency of the proposed mathematical models, verification experiments were carried out at the predicted conditions derived from the RSM analysis. The predicted and experimental results are presented in Table 6.  
Insignificant differences (\(p>0.05\)) between the predicted and experimental response values confirmed that the proposed models were accurate and adequate for the optimization of amounts of two ingredients to baking functional biscuits with acceptable levels of RPC and sensory characteristics.
## 2 Materials and Methods
### Numerical Homogenization  
The second part of the calculation was performed based on the principles of numerical homogenization. Based on the energy equivalence between the simplified shell model and the full three-dimensional, the finite element model can be represented by an appropriate definition of displacements at the outer RVE nodes for both membrane and bending behavior. The generalized displacements at each node on the RVE surface are related to the generalized strains. Therefore, the relationship between the generalized constant strains and the position of the external nodes on the RVE boundary is expressed by the following transformation:  
\[\mathbf{u}_{i}=\mathbf{A}_{i}\ \epsilon_{i}, \tag{18}\]  
where \(\mathbf{u}\) is the node displacement vector, and \(\epsilon\) is the strain vector. Here, for a single node (\(x_{i}=x\), \(y_{i}=y\), and \(z_{i}=z\)) one can derive the \(\mathbf{A}_{i}\) matrix adopted for the RVE model.  
\[\begin{bmatrix}u_{x}\\ u_{y}\\ u_{z}\end{bmatrix}_{i}=\begin{bmatrix}x&0&y/2&z/2&0&xz&0&yz/2\\ 0&y&x/2&0&z/2&0&yz&xz/2\\ 0&0&0&x/2&y/2&-x^{2}/2&-y^{2}/2&-xy/2\end{bmatrix}_{i}\begin{bmatrix} \epsilon_{x}\\ \epsilon_{y}\\ \gamma_{xy}\\ \gamma_{xz}\\ \gamma_{yz}\\ \kappa_{x}\\ \kappa_{y}\end{bmatrix}_{i} \tag{19}\]Matrix \(\mathbf{A}_{i}\) determines the relationship between the displacements and effective deformations that are applied to nodes in boundary conditions to which the stiffness of the entire model is condensed. The total energy of elastic deformation is:  
\[E=\frac{1}{2}\mathbf{u}_{e}^{T}\ \mathbf{K}\ \mathbf{u}_{e}=\frac{1}{2}\mathbf{e}_{e }^{T}\ \mathbf{A}_{e}^{T}\ \mathbf{K}\ \mathbf{A}_{e}\ \boldsymbol{e}_{e}, \tag{20}\]  
where \(\mathbf{K}\) is the global stiffness matrix. Taking into account that the finite element model is subjected to bending, tension, and transverse shear for the shell (or plate). The internal energy is:  
\[E=\frac{1}{2}\boldsymbol{e}_{e}^{T}\ \mathbf{A}_{\mathbf{k}}\ \boldsymbol{e}_{e} \{area\} \tag{21}\]  
The stiffness matrix of a homogenized composite is easy to extract from a discrete matrix because:  
\[\mathbf{A}_{k}=\frac{\mathbf{A}_{e}^{T}\ \mathbf{K}\ \mathbf{A}_{e}}{area}. \tag{22}\]  
The matrix \(\mathbf{A}_{k}\) is the ABDR matrix which can be saved as:  
\[\mathbf{A}_{k}=\begin{bmatrix}\mathbf{A}_{3\times 3}&\mathbf{B}_{3\times 3}&0 \\ \mathbf{B}_{3\times 3}&\mathbf{D}_{3\times 3}&0\\ 0&0&\mathbf{R}_{2\times 2}\end{bmatrix}. \tag{23}\]  
where \(\mathbf{A}\) contains the tensile and shear stiffnesses, \(\mathbf{B}\) contains the combination of tension and bending stiffness, \(\mathbf{D}\) contains the bending and torsional stiffness, and \(\mathbf{R}\) contains the transverse shear stiffness.
## 4 Performance Evaluation
### Performance Tuning of WR-Store
In addition, we conduct the performance evaluation on four real data sets to find their best parameter settings. Table 7 shows the measured throughput on real data sets as \(WR_{L}\) and \(WR_{D}\) are varied. Here, we simply calculate the average throughput by assigning the same weight into read, write, and delete operations. As a result, we can find the best parameter setting, which is represented in bold. For the remaining experiments on real data sets, we use the best parameter setting for each data set as shown in this table. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data Sets**} & \multirow{2}{*}{**Throughput (OPS)**} & \multicolumn{6}{c}{**Parameters**} \\ \cline{3-8}  & & **2\_D\_1L** & **2\_D\_2L** & **2\_D\_3L** & **1D\_4L** & **4\_D\_1L** \\ \hline \multirow{4}{*}{_ID-Geo_} & Read & 23,085 & 54,749 & 4928 & 20,095 & 8791 \\  & Write & 368 & 8516 & 2045 & 6140 & 3903 \\  & Delete & 13,057 & 18,289 & 2558 & 11,116 & 4265 \\  & Average & 12,170 & **27,185** & 3177 & 12,450 & 5653 \\ \hline \multirow{4}{*}{_ID-HashTag_} & Read & 25,635 & 58,849 & 11,411 & 40,369 & 52,908 \\  & Write & 9718 & 20,107 & 18,081 & 25,413 & 24,374 \\ \cline{1-1}  & Delete & 38,835 & 30,053 & 6077 & 18,783 & 23,022 \\ \cline{1-1}  & Average & 24,729 & **36,337** & 11,856 & 28,188 & 33,435 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The best settings of WR-Store for real data sets. Figure 8: Performance of WR-Store as \(WR_{D}\) and \(WR_{L}\) are varied. (**a**) Read throughput; (**b**) Write throughput; (**c**) Delete throughput. BerkeleyDB 4365.17 times, 230.23 times, and 1.48 times, respectively; LevelDB 239.80 times, 725.28 times, and 766.18 times, respectively. Here, we note that WR-Store is not enough scalable to the write operations compared to the read and delete operations. This stems from the fact that the Windows registry already stores a data set required by the operating systems while the other key-value stores do not. We also note that BerkeleyDB is scalable to the write operations, but its performance degradation is much severe for the read and delete operations.
# Different Configurations of the Non-Minimal Prismatic Tensegrities
## 4 Examples  
### Quartex Module  
The procedure of prestressing the non-minimal quartex prism presented in Section 3.1 is extended here in order to show that lengthening the additional members, in comparison to shortening them, can also result in a stable configuration with additional members being bars. The initial data is given in Section 3.1 (the stiffness of additional struts remains as for additional cables). The prestressing coefficients in different prestressing scenarios are as follows: (1) \(ca\) = 1.05, (2) \(ca\) = 1.1, (3) \(ca\) = 1.15 and (4) \(ca\) = 1.2. The results are presented in Table 3, where other dimensions after prestressing are also given. The graphical behavior of the prism is presented in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prestress Level** & \(c_{a}\) = 1.05 & \(c_{a}\) = 1.1 & \(c_{a}\) = 1.15 & \(c_{a}\) = 1.2 \\ \hline \hline \multicolumn{5}{c}{Additional struts} \\ natural lengths (m) & 11.243 & 11.779 & 12.314 & 12.849 \\ \hline \hline \multicolumn{5}{c}{Prestressed configuration (no index)} \\ \hline Angle \(\theta\) (deg) & \(-8.93\) & \(-17.06\) & \(-24.38\) & \(-30.97\) \\ \hline \hline \multicolumn{5}{c}{Struts (m)} & 13.613 & 13.609 & 13.605 & 13.600 \\ \hline \hline \multicolumn{5}{c}{Base cables (m)} & 7.079 & 7.099 & 7.125 & 7.154 \\ \hline \hline \multicolumn{5}{c}{Cross cables (m)} & 10.738 & 10.821 & 10.941 & 11.086 \\ \hline \hline \multicolumn{5}{c}{Added cables (m)} & 11.239 & 11.747 & 12.221 & 12.655 \\ \hline \hline \multicolumn{5}{c}{Radius (m)} & 5.006 & 5.020 & 5.038 & 5.059 \\ \hline \hline \multicolumn{5}{c}{Height (m)} & 10.281 & 10.546 & 10.792 & 11.017 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results on the quartex prism.  
Figure 10: Two quartex physical models: (**a**) minimal and (**b**) non-minimal for \(\theta=-10^{\circ}\), as well as (**c**) a simple device for creating repetitive lengths of cables.  
Please note that the prism presented in Figure 11 possesses eight bars (thick lines denotes compression). Changes in relative lengths and the values of the rotation angle \(\theta\) are presented in Figure 12a, while changes in forces are presented in Figure 12b. These values are supplemented by the results presented in Section 3.1. The non-minimal quartexes presented in Figure 11 can be classified as a \"structures with tensegrity features\" (\(T,C,S\) and \(I\)).  
Figure 11: Prestressing the non-minimal quartex by lengthening additional struts for (**a**) \(c_{\text{a}}=1.05\), (**b**) \(c_{\text{a}}=1.1\), (**c**) \(c_{\text{a}}=1.15\) and (**d**) \(c_{\text{a}}=1.2\).  
Figure 12: Charts: (**a**) the relative lengths and angle of rotation changes and (**b**) forces—both in a function of the prestressing coefficient of additional members.