esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Circuit Design  
### Analog Front-End  
Amplification and filtering of the input signal are performed by a three-stage circuit shown in Figure 3. The first stage is an AC-coupled high-pass filter, using two MOS-bipolar pseudo-resistors as feedback elements [10], which enables the synthesis of high-value resistances without using large-area components. The mid-band amplifier gain is given by \(G_{1}\cong-C_{1}/C_{2}\), set to \(-67\) by taking \(C_{1}=10\) pF and \(C_{2}\) = \(150\) fF. The high-pass pole frequency is placed below \(10\) Hz to reject the offset and the slow voltage drift of the electrode. A tunable \(G_{M}\)-\(C\) high-pass filter with a cut-off frequency \(f_{HP}\) of about \(300\) Hz is introduced after the first stage. It is designed to reject the low-frequency signals, such as Local Field Potentials (LFPs) in the \(1-100\) Hz frequency range that can prevent a correct detection of the neural spikes or even saturate the amplifier, and cuts off the input-referred \(1/f^{2}\) noise due to the pseudo-resistors of the first amplification stage. After the selective high-pass filter, a second non-inverting gain stage is  
Figure 2: Block diagram of the receiver (**a**) and photo of the remote receiver with the detail of the graphical user interface (**b**).  
added to provide further signal amplification and to define the high frequency cut-off, \(f_{LP}\). The stage is a single-ended capacitive-coupled voltage amplifier with a gain \(G_{2}\approx 33\), achieved by using \(C_{3}=5\) pF and \(C_{4}=150\) fF. A capacitive-coupled structure was preferred to a purely resistive feedback amplifier to minimize the current drawn by the OTA output stage. The DC voltage at the amplifier input is determined by the pseudo-resistor elements in the feedback path, while the low-pass cut-off frequency is set by the gain-bandwidth product of the operational amplifier (\(GBWP_{2}\)) to about \(GBWP_{2}/G_{2}\cong 10\) kHz. Note that at very low frequencies this non-inverting stage has a unity gain in order not to amplify the offset of the operational amplifier.
## 5 Dietary Sources, Supplementation, and Formulation Strategies  
### Natural Dietary Sources  
Nicotinamide (vitamin B3) and pyridoxine (vitamin B6) are widely distributed in both plant-based and animal-based food sources, making them accessible components of the human diet. Nicotinamide and its related form, nicotinic acid, are found in significant amounts in meats (especially liver, poultry, and fish), whole grains, legumes, seeds, and fortified cereals [105, 168, 169, 170, 30]. Endogenously, nicotinamide is also synthesized through the tryptophan-kynurenine pathway, though this pathway becomes less efficient with age, increasing the importance of dietary sources. Pyridoxine and its bioactive forms--pyridoxal and pyridoxamine--are prevalent in foods such as bananas, avocados, whole grains, nuts, potatoes, and animal products including poultry, fish, and organ meats [171, 172, 173, 174, 175]. Among these, pyridoxal 5'-phosphate (PLP), the active coenzyme form of B6, is especially critical for amino acid metabolism and neurotransmitter synthesis. Food processing and preparation significantly affect vitamin content. For example, nicotinamide is relatively stable during cooking, whereas pyridoxine is sensitive to heat and can degrade under prolonged boiling or storage. Bioavailability also varies; nicotinamide from animal sources is typically more readily absorbed compared to that from plant-based sources bound within complex food matrices. Both vitamins have been incorporated into food fortification programs, particularly in countries with high prevalence of deficiencies or limited dietary diversity [176, 177, 30, 178]. In elderly populations, ensuring adequate intake from food alone can be challenging due to reduced appetite, gastrointestinal function, or dietary restrictions--making the case for supplemental forms even stronger. Monitoring dietary intake through food frequency questionnaires, 24 h recalls, or biomarker analysis (e.g., serum PLP or urinary metabolites) provides valuable insights into nutrient adequacy and informs public health strategies. Ensuring consistent and sufficient intake of nicotinamide and pyridoxine through a balanced diet--supported by functional food products when necessary--forms a foundational strategy to maintain muscle metabolic function, counter oxidative stress, and promote healthy aging [179, 180, 54, 106, 122, 54, 107].
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
###### Abstract  
One of the major problems in network games has been that of latency (lagging) that game technology researchers are still tackling. Latency largely affects user satisfaction and it is often caused by insufficient hardware capacity or the internet speed that the user is employing. Even though online games are becoming more complex and the number of participants in these games is increasing continuously, users cannot properly deal with the requirements to play these games, as system upgrades and subscription changes to a higher speed internet service are costly. Instead of passing such a burden on to the users, the game companies should instead invest in providing an improved communication algorithm. Thus, the reliable user datagram protocol (RUDP)-based communication system design has been considered in this research instead of transmission control protocol (TCP) or user datagram protocol (UDP)-based systems. This could be a viable alternative system model. Many researchers agree that RUDP is a better protocol for the transport layer, but it seems that the large-scale testbeds that could support such an idea and carry out direct tests are very scarce, meaning that actual experimental implementation is difficult to achieve. This suggests that game designers or researchers need to rely on a small-scale testbed to collect performance data. Moreover, generating an analytic model from a small-scale testbed may not be viable due to the large number of elements involved in the implementation process, including latency. This study introduces an RUDP-based communication model created and tested by using the OPNET simulation tool. They are novel energy and cost-effective photonics technologies for access/metro networks.  
transport layer; RUDP; network games; OPNET simulation; leg; latency; transport layer architecture; computer architecture +
Footnote †: journal: Electronics Electronics
## 2 Machine Learning Prediction Methods
### Stacking Ensemble  
Though coming from the same branch, stacking is quite different from the random forest and boosting strategy in AdaBoost in several ways. In bagging, variance in the final ensemble model is reduced by the random selection of a subset of features as well as instances for each predictor to execute the parallel and independent learning. The outcomes are then aggregated by the averaging method to generate an ensemble prediction. Boosting, on the other hand, will pass the dataset through all the learners which are set sequentially. Each instance and learner are given the attribute, the so-called weight, that is going to be updated on each pass (instance) and each iteration (learner). The weighting procedure results in the uneven contribution of each learner to the final prediction, and uneven prioritization to each instance for the training process - which substitutes the output averaging process mechanism and randomization for training subset in the bagging concept.  
Figure 4: Demonstration of linear regression’s learning process.  
Figure 3: Illustration of artificial neural network.  
For stacking, each base predictor takes the whole dataset without any differentiation on the input and works in the canonical way to produce the result. The special property of this method lies in the aggregation mechanism. After the learning, the outputs from the predictors then become the inputs for the aggregator algorithm to produce the final prediction. The training set in the first learning process occupied by the base predictors is different with the one utilized by the aggregator algorithm because the dataset fed into the predictors has been transformed into the models which are later combined to form the new features. Fitting the aggregator algorithm onto the same instances causes a bias since the inputs are created from these instances. However, splitting two types of datasets raises another problem for a limited amount of data. To overcome this, the common \(k\)-folds cross-validation approach is usually adopted to provide more data for training both predictors and aggregators thereby facilitate a more accurate performance measure [17]. In practice, stacking usually considers multiple types of learners to build the prediction, while bagging and boosting are more common to have only homogenous learners. Besides the algorithms used, the design of stacking ensemble can also be altered by the stacking level. If the number of levels is more than 2, the layer in the middle will be filled with multiple aggregators. However, since increasing the number of levels will cost on the time computation, this parameter usually remains in default (i.e., level size = 2).
# Macro-Meso Mechanical Behavior of Loose Sand under Multi-Directional Cyclic Simple Shear Tests
## 3 Simulation Results  
Figure 11 shows the number of cycles at liquefaction (\(N_{l}\)) of performed cyclic simple shear tests. When CSR = 0, samples under three different shear paths, SL-0, OVAL-0, and FIG8-0, liquefied at the 31 st, 23 rd, and 11 th cycle, respectively. It showed that bi-directional cyclic shear stress accelerated the liquefaction, and the FIG8-0 shear path led to a faster liquefaction than the OVAL-0 shear path. It should be noted that in each cycle, the FIG8-0 tests experienced four loading-unloading changes in the y-direction, while OVAL tests changed only twice, as shown in Figure 10b.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Shear Path**} & \multirow{2}{*}{**The Consolidation Stress Ratio, CSR**} & \multicolumn{2}{c}{**The Direction of the Consolidation**} & \multirow{2}{*}{**Name**} \\  & & & **Shear Stress, \(\theta\)** & \\ \hline \multirow{4}{*}{SL} & 0 & / & SL-0 \\  & & 0\({}^{\circ}\) & SL-0.1-0 \\  & 0.1 & 45\({}^{\circ}\) & SL-0.1-45 \\  & 0 & 90\({}^{\circ}\) & SL-0.1-90 \\  & 0 & / & OVAL-0 \\ \cline{1-1}  & 0 & 0\({}^{\circ}\) & OVAL-0.1-0 \\ \cline{1-1}  & 0 & 45\({}^{\circ}\) & OVAL-0.1-45 \\ \cline{1-1}  & 0 & 90\({}^{\circ}\) & OVAL-0.1-90 \\ \cline{1-1}  & 0 & / & FIG8-0 \\ \cline{1-1} FIG8 & 0.1 & 45\({}^{\circ}\) & FIG8-0.1-45 \\ \cline{1-1}  & & 90\({}^{\circ}\) & FIG8-0.1-90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation conditions of undrained cyclic simple shear tests.  
Figure 10: Details of bi-directional cyclic simple test shear on the horizontal plane: (**a**) consolidation shear stress and (**b**) cyclic shear paths.  
When CSR = 0.1, SL-0.1-0, SL-0.1-45, and SL-0.1-90 liquefied at the 25 th, 11 th, and 8 th cycle, respectively; OVAL-0.1-0, OVAL-0.1-45, and OVAL-0.1-90 liquefied at the seventeenth, ninth, and seventh cycle, respectively; FIG8-0.1-0, FIG8-0.1-45, and FIG8-0.1-90 liquefied at the ninth, eighth, and third cycle, respectively. It can be seen that as \(\theta\) increases, samples were easier to liquefy.
# Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game
## 4 Results and Discussion
### Model Comparison  
At present, game theory has some mature application cases in finance, the Internet, medicine and other fields, but relatively few applications in the media field. The complexity of media data circulation, the diversity of media organizations, and the diversity of user behaviors increase the complexity of the application of game theory in the media industry. The model design in this article is based on evolutionary game theory, combines the industry attributes of nodes in the media ecology and introduces the role of regulators in order to adapt to the particularity of the media industry. In this article, the data element sharing model of convergence media ecology based on the evolutionary game is compared with the other three similar models, and the specific contents are shown in the following table. Similar model comparison is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Indicator** & **Indicator Advantage** & **[PERSON] [62]** & **[PERSON]** & **[PERSON]** & **[PERSON]** [65] & **This Article** \\ \hline Industry & Adapt to industry characteristics & Media & Intenet & Medical & Retail & Media \\ \hline Stability strategy & Strategy guidance & FALSE & TRUE & TRUE & TRUE & TRUE \\ \hline Based on media & \begin{tabular}{c} Adapt to the characteristics of the media industry \\ \end{tabular} & TRUE & FALSE & FALSE & FALSE & TRUE \\ \hline Regulator node & \begin{tabular}{c} Adapt to media industry standards \\ \end{tabular} & FALSE & FALSE & TRUE & TRUE & TRUE \\ \hline Data element requirement & \
\begin{tabular}{c} Adaptive data circulation \\ \end{tabular} & TRUE & FALSE & FALSE & FALSE & TRUE \\ \hline Degree of node participation & Promoting model activity & FALSE & TRUE & FALSE & FALSE & TRUE \\ \hline Node credit & Guarantee node credit & FALSE & TRUE & FALSE & FALSE & TRUE \\ \hline Incentive strategy & Encourage nodes to participate actively & FALSE & TRUE & FALSE & TRUE & TRUE \\ \hline Penalty strategy &
\begin{tabular}{c} Maintain the balance of ecological values \\ \end{tabular} & FALSE & TRUE & TRUE & TRUE & TRUE \\ \hline \end{tabular}
\end{table}
Table 5: Similar model comparison.  
Figure 19: Impact of \(F_{3}\).  
From the comparison of the correlation models above, it can be seen that the model in this article has certain advantages in the application of convergence media ecology, which can reflect the media attributes related to nodes so as to encourage nodes to actively participate in data element sharing. At the same time, supervision nodes are introduced from the perspective of industry characteristics so as to ensure that the model is more suitable for industry standards. However, the model in this article needs further study in the aspects of artificial intelligence and network security.
## 2 Methods
### PSO Method  
Particle swarm optimization (PSO) is an evolutionary computation technique, which was proposed by Dr. [PERSON] and Dr. [PERSON] in 1995 [16]. It derives from the study of bird predation behavior. The algorithm was originally inspired by the regularity of bird swarm activities, then a simplified model was established using swarm intelligence. Based on the observation of the activity behavior of animal clusters, the particle swarm optimization algorithm uses the sharing of information by individuals in the group to make the movement of the whole group evolve process from disorder to order in the problem-solving space, so as to obtain the optimal solution.  
In PSO, we call the population formed by all particles a group. Each particle in the population can be regarded as the potential solution to a problem. The position and velocity of particle \(i\) in D-dimensional space are expressed as \(X_{i}\) = (\(x_{i1}\), \(x_{i2}\), \(\ldots\), \(x_{iD}\)) and \(V_{i}\) = (\(v_{i1}\), \(v_{i2}\), \(\ldots\), \(v_{iD}\)), respectively. The particles are constantly flying in the search space, and the speed is modified with their own flight experience and the experience of their peers. The particles continuously search according to the modified position; the change in position is realized by the change in velocity. The change in particle velocity and position is calculated by Equations (5) and (6) [19,22].  
\[v_{id}=wv_{id}+\mu_{1}r_{1}(p_{id}-x_{id})+\mu_{2}r_{2}(p_{gd}-x_{id}) \tag{5}\]  
\[x_{id}=x_{id}+v_{id} \tag{6}\]  
where \(d\) is the search direction (\(d\) = 1, 2, \(\ldots\), D), \(w\) is the inertia weight ranging from 0.4 to 0.9, \(\mu_{1}\) and \(\mu_{2}\) are positive constants for learning (\(\mu_{1}\) = \(\mu_{2}\) = 2), and \(r_{1}\) and \(r_{2}\) are two random numbers in the range [0, 1] [23]. The parameter \(w\) reflects the influence of the previous velocity of the particle on the current velocity. When the \(w\) value reaches the maximum, the PSO algorithm plays a leading role in the global optimization ability, and the smaller inertia weight will be beneficial to the local search ability.
## 4 Experiments and Results
### Data Preprocess  
Considering that the contrast between lesion areas and normal tissues in retinal fundus images is often imperceptible, it is difficult to distinguish between them with the naked eye and automated DR diagnosis based on deep learning [30]. Therefore, many methods [31; 32] employ various techniques to highlight the lesion areas in the images before performing DR diagnosis, assisting the network to better process the images. Following the above-mentioned methods, this paper applies the following preprocessing steps to the DDR dataset, DIARETDB1 dataset, and IDRiD dataset: First, the black borders are cropped to reduce computation time and suppress irrelevant noise information. Then, the images are uniformly resized to 512 \(\times\) 512 dimensions. Contrast Limited Adaptive Histogram Equalization (CLAHE) [33] is applied to enhance the images, with the processing parameters set as ClipLimit = 2 and GridSize = 8. Finally, the image pixel values are normalized from 0-255 to 0-1 for subsequent network processing.  
Figure 4 illustrates the comparison between the original retinal fundus images and the preprocessed images. It can be observed that after preprocessing, the irrelevant information at the edge of the images is cut, the retinal blood vessels become clearer, and the lesion areas become more prominent. This enhancement aids MLNet in the subsequent training, testing, and validation of the datasets.
# Multi-LEO Satellite Stereo Winds
## 4 Discussion  
In this section, we validate the retrieved stereo winds over the poles using three methods: ground point retrieval statistics, LiDAR layer-height comparisons, and comparisons with reanalysis winds. Finally, we discuss applications for the polar stereo-wind method.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 2 AI for Propagation Modeling
### Comparison between DL and ML Approaches
#### 2.3.3 Reliability and Accuracy  
The accuracy and reusability of the DL-based trained models is another concern. Since DL-based models use simulated data, their accuracy depends on the performance of ray tracing simulations, resolution, and availability of geographical images, which could vary from 25 to 400 square meters. Geographical images of the environment may not be available, such as in [32], where authors created their own custom images. Additionally, low-resolution images and the griding process averaging the data could contribute to more uncertainty and inaccuracies in the DL-based models.
## 4 Field Tests of the GFZ-WISE
### Data Recording, Analysis and Results
Considering the general information about the subsoil structure that can be obtained by a visual inspection of both the H/V and RWDC, and the fact that the H/V is flat, only a linearized inversion of the RWDC for a frequency range between about 2.5 Hz and about 10 Hz, was performed (Figure 9). In general, for simple S-wave velocity structures, a reasonable input model for the inversion procedure can be obtained by considering the simple rules of assigning 110% of the velocity at a certain frequency to a depth equal to half the wavelength (evaluated as velocity/frequency) of the corresponding frequency [4]. However, in order to verify the robustness of the inversion procedure, we performed the iterative inversion starting from two extreme velocity profiles (Figure 9). That is, we started the inversion from input models that definitely under- and over-estimate the real S-wave velocity profile. It is worth to note that, independent of the starting model, after only few inversion steps, there is a convergence towards almost the same velocity model, which is able to fully justify the observed data. Therefore, even if the good fit of data cannot be considered always as an absolute measure for the high quality of the inverted model, we think that the estimated S-wave velocity model is a reliable representation of the subsoil structure down to about 90 meters (Figure 9). Figure 8: Results of the surface-wave analysis. _Main panel_: Rayleigh-wave dispersion curves. Experimental phase velocities (_gray dots_), theoretical limit for the spatial aliasing (_dotted line_), and theoretical limit for the spatial resolution (_black line_). _Inset:_ horizontal-to-vertical spectral ratio curve. Average H/V values (_black line_), and the 95% confidence interval (_gray area_). Unfortunately, _a priori_ information on the S-wave velocity at the site was not available. Thus, a direct comparison of the estimated S-wave velocity profile with an alternative one was not possible. However, the tests we are carrying out with the GFZ-WISE aim primarily to verify the methodological aspects, as the communication efficiency between the sensors and a user's laptop for the data retrieval, and the station performance while recording real seismic noise. Nevertheless, the obtained S-wave velocities are compatible with those observed for the same kind of sediments by [PERSON] _et al._[32].
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 5 Simulations and Parameter Effects
### Simulation Results
#### 5.2.1 End-to-End Delay  
The end-to-end delay, as previously defined, is the average time difference between the time the message was sent and the time the message was received by the sink node. In Figure 2, this time is plotted against the number of nodes in a fixed-area network for each of the five parameters considered in the MFWF.  
The average end-to-end delay linearly increases as the number of nodes in the network increases for each of the MFWF parameters. However, the sink distance parameter (\(f3\)) results in the lowest delay as the number of nodes increases. The variations in this parameter between different numbers of nodes are also much more subtle than those for the other parameters. These findings, as anticipated, highlight the importance of the sink location to the end-to-end delay; that is, the closer the sink node is to a source/neighbouring node, the faster the delivery. The effects of the residual energy (\(f1\)) and message  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Factor** & **Parameter** & **Equation** \\ \hline \(f1\) & Residual energy & \(Energy=\frac{RE}{E}\) \\ \hline \(f2\) & Next-node distance & \(NeighbourDist=\frac{1}{\max[(X-nX_{||}|Y-nY|)}\) \\ \hline \(f3\) & Sink distance & \(sinkDist=1-\frac{nToDistDistance}{sToDistDistance}\) \\ \hline \(f4\) & Node density & \(nDensity=\frac{localDensity}{N}\) \\ \hline \(f5\) & Message priority & \(msg=\begin{cases}0&low\\ 1&high\end{cases}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The manipulated factors mapped to the parameters and their associated equations.  
Figure 2: The impact of parameter activation on the average end-to-end delay as the number of nodes increases from 50 to 250 nodes.  
priority (_f_5) on the end-to-end delay are similar and shorter than those of the next-node distance (_f_2) and node density (_f_4). Of the five controlled factors, the one associated with the next-node distance parameter (_f_2) results in the the longest delay and the largest variation as the number of nodes in the network increases.