esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model and Methodology of the Adaptive Protocol
### Figures, Tables and Schemes
#### 4.3.5 Future Rewards  
In this stage, rewards are given for the action performed in the previous step, which can be divided into three situations.  
(1) The node receiving data packets is not the sink node, and the energy level is normal. We assign each component of double-Q-values its own reward scheme, calculated as follows:  
\[DA_{nor}=\left\{\begin{array}{cc}\frac{Q_{J}^{\prime}(n)}{AD_{J}^{\prime}(n )}-1&\mathit{if}\frac{Q_{J}^{\prime}(n)}{AD_{J}^{\prime}(n)}-1<r_{DA}^{\max} \\ r_{DA}^{\max}&\mathit{else}\frac{Q_{J}^{\prime}(n)}{AD_{J}^{\prime}(n)}-1>r_{ DA}^{\max}\end{array}\right., \tag{10}\]  
\[E_{nor}=E_{r}^{s^{\prime}}/E_{i}^{s^{\prime}}, \tag{11}\]  
\[L_{s}(s_{i},s_{j})=\frac{\mathrm{Rec}_{s_{i}p}}{\mathrm{Rec}_{s_{i}p}},\] \[\mathrm{Rec}_{s_{i}p0}=\left[\frac{p_{i}(d)}{p_{i}(d)}\right]_{db} =-10\rho\log(\frac{d}{d_{0}})+X_{db}, \tag{12}\] \[L_{nor}=l_{s}(s,s^{\prime}),\]  
\[\begin{array}{c}radv=d(s,sink)-d(s^{\prime},sink),\\ radv_{avg}=\sum_{i=1}^{n}|radv_{i}|/n,\\ A_{nor}=radv/radv_{avg}.\end{array} \tag{13}\]  
\[T_{nor}=T(s,s^{\prime})/T_{avg} \tag{14}\]  
(2) The receiving node is \(sink\). The reward is a constant \(R_{s}\) when the chosen action sends the data packet to the sink node.  
\[R=R_{s}. \tag{15}\]  
(3) The receiving node is not sink, and the energy level is lower than the average energy of network nodes. In order to maintain the performance and life of the network, wesuggest not assigning the node the function of forwarding data when the energy level is too low to ensure that the basic monitoring service of the network is normal [36; 37]. Therefore, we offer a negative reward to avoid packets from the neighbor nodes.  
\[R=-R_{e}. \tag{16}\]  
Based on the above definition, corresponding to the double-Q-values, the future reward should also be divided into two parts to offer different rewards for its life cycle and real-time performance. At the same time, in order to avoid increasing the probability of forwarding to nodes far away from the sink, it is necessary to provide a discount value to the reward. Rewards \(R_{L}\) and \(R_{T}\) for state s are calculated as follows:  
\[R_{L}=\left\{\begin{array}{cl}\alpha_{l}\times R_{DA}+\beta_{l}\times R_{E}+ \gamma_{l}\times R_{L}&s^{\prime}\
eq sink\\ R_{s}&s^{\prime}=sink\\ -R_{e}&E_{s^{\prime}}\textit{ is low}\end{array}\right., \tag{17}\]  
\[R_{T}=\left\{\begin{array}{cl}\alpha_{t}\times R_{A}+\beta_{t}\times R_{T}&s ^{\prime}\
eq sink\\ R_{s}&s^{\prime}=sink\\ -R_{e}&E_{s^{\prime}}\textit{ is low}\end{array}\right.. \tag{18}\]
## 3 Results and Discussion
### Effects of Concentrated Mass Attached to Elastic Supports
The second and third natural frequencies showed clear differences based on the effect of the taper ratio. The natural frequencies of a single tapered beam with a concentrated mass at \(\overline{x}=1\) showed a large difference owing to the effect of the taper ratio, and the third natural frequency compared to other frequencies was most affected by the taper ratio. The effects of the concentrated mass and taper ratio for Cases A9-A12 with \(n=1\) are presented in Figure 9. When c = 0.5, the first natural frequency was more affected by the concentrated mass at \(\overline{x}=0\) than at \(\overline{x}=1\), and the fundamental frequencies for both \(c=0\) and \(c=0.5\) exhibited a similar effect at \(\overline{x}=0\). The effect of the taper ratio on these frequencies was greater when the concentrated mass was located at \(\overline{x}=1\) than at \(\overline{x}=0\). Figure 8: Effects of the concentrated mass and taper ratios on the first three natural frequencies of \(n=1\) with respect to Cases A5–A8. Figure 7: Effects of the concentrated mass and taper ratios on the first three natural frequencies of \(n=1\): Cases A1–A4. To compare the results for the single- and double-tapered beams with \(c=0.5\), the computed results are presented in Figure 13. As shown in Figure 13, the fundamental frequencies of the single- and double-tapered beams with \(c=0.5\) have almost the same value when the concentrated mass is located at \(\overline{x}=0\). The second and third natural frequencies showed a small difference compared with the effect of the concentrated mass attached at \(\overline{x}=1\). Except for the fundamental frequencies in cases A7 and A8, the natural frequencies for \(n=2\) than \(n=1\) were affected more by the taper ratio when the concentrated mass was attached at \(\overline{x}=1\) than when it was attached at \(\overline{x}=0\). The first natural frequency for Case A12 exhibited the most significant difference among all results. Figure 11: Effects of the concentrated mass and taper ratios on the first three natural frequencies of \(n=2\) with respect to Cases A5–A8. Figure 12: Effects of the concentrated mass and taper ratios on the first three natural frequencies of \(n=2\): Cases A9–A12.
# Empirical Evidences for Urban Influences on Public Health in Hamburg
## 5 Conclusions and Outlook
### Methodological Strengths and Limitations  
This study has provided the first empirical evidence to support the interdependencies between the variables in the model of UrbWelth [30]. In particular, it confirmed the relevance of all four quadrants studied, helping to identify key topics future urban planning can focus on in order to improve urban wellbeing and health. The analysis therefore functions to provide empirical evidence in urban planning, as well as options for governing UrbWelth and guidance in setting priorities.  
Interestingly, the results of [PERSON] could not be reproduced via the regression analysis, although the database is comparable [102]. None of the variables he identified as having a major impact on SRH have been significantly correlated with SRH in our data. This is probably due to the different approaches to the regression model: while [PERSON] deductively determined and specified the main influencing variables, our own analysis used a data-driven approach to determine significant independent variables that impact SRH. 'Backward regression' proved to be suitable in this regard.  
Unfortunately, due to the study design, no statements can be made regarding the confounding effect of BMI with regard to cold and heat stress. This possible correlation cannot be discussed on the basis of the available data, but should be taken into account when creating a future research layout. Moreover, our study does not provide access to objective health indicators. Thus, although it can be assumed that there is a connection between hyper-/hypo-tension and heat/cold stress, this cannot be investigated at this time. In this context, additional research may refer to available and comprehensive datasets like the Hamburg City Health Study (HCHS) [116]. Yet, data protection will limit the possibilities of georeferenced research with this data.  
Regarding the data on which the factor and regression analyses are based, it would be preferable to have a more even rate of return so that different regions, also representing different socioeconomic status, are evenly represented. On the other hand, SRH proved to be explained to a remarkable extent by the urban variables we identified. Overall, the fact that 27% of the variance could be explained on the basis of eight variables, 12% respectively for the five urban variables, has to be considered excellent.  
The study design makes it difficult to simply transfer the results to other parts of the city. In order to achieve this, more research is needed, based on a well-founded derivation of neighborhood types, for example.
## 1 Introduction
Figure 1: Schematic diagram of the operating principle for SOFCs. FS, a technique developed in 2010 by [PERSON] et al. at the University of Colorado, Boulder, involves use of an electric field while simultaneously heating the sample in a furnace [42]. UHS, developed in 2020 by [PERSON] et al., employs carbon materials to encase ceramic green body samples [43]. This method quickly generates a consistent high-temperature setting by applying an electric field to the carbon material, resulting in extremely fast sintering in around 10 s [44; 45]. Table 1 presents an overview of the key process characteristics, heating mechanism, advantages, and limitations associated with conventional sintering (CS) and alternative fast sintering techniques. The innovative current-assisted sintering techniques, such as FS and UHS, have several advantages in common. These include lower sintering temperatures, reduced processing times, and notable enhancements in material properties [46; 47; 48]. The purpose of this review is to provide a short summary of novel current-assisted sintering techniques (FS and UHS). Additionally, it seeks to summarize their developing history, delineate the most recent accomplishments, and identify the forthcoming problems in the field of SOFCs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sintering Technique** & **Major Mechanism** & **Typical Heating Rate (\({}^{\circ}\)C min\({}^{-1}\))** & **Typical Dwelling Time (min)** & **Typical Process Advantages** & **Major Limitations** & **References** \\ \hline \hline \multirow{4}{*}{CS} & The furnace & & & Time-consuming and energy-consuming. Samples exhibit coarse grains. Restricted low melting point \\  & transfers heat to the & & & Simple equipment, with no restrictions on sample size and shape. & \\ \cline{1-1}  & sample through & 1–10 & \(>\)120 & Simple equipment, with no restrictions on sample size and shape. & \\ \cline{1-1}  & conduction, and & & & shape. & materials that can be co-processed with the ceramic. \\ \cline{1-1}  & convection. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of conventional sintering with other fast sintering techniques. Figure 2: Sketches of different typical field-assisted and current-assisted sintering processes: (**a**) spark plasma sintering, (**b**) microwave sintering, (**c**) ultrafast high-temperature sintering, (**d**) flash sintering. ((**a**,**d**) reproduced from [39] with permission from Elsevier, (**b**) reproduced from [40], (**c**) reproduced from [41] with permission from Elsevier).
## CFD Methodology
### Results and Validation
Lastly, these design parameters are used on a 3D CFD model calculation of the final system to validate the design obtained, considering a wide range of operation points around the design point. In Figure 13, the results of the 3D calculations are shown for a given operation point. On the one hand, Figure 13a shows point of lower mass flow of the design point (0.2 kg/s), in which the final water column height is kept at the optimal height of 90 cm. This means that the simplification of a 2D column represents with enough accuracy the 3D behavior, validating the pre-design with the simplified geometry. On the other hand, Figure 13b shows how when increasing the mass flow way over the design (0.42 kg/s), the final water column height increases above the optimal height, but does not reach overflowing conditions, thus validating the reactor height selected previously. Figure 13: Three-dimensional (3D) validation of the design obtained. Stabilized bubbling of the operation point: (**a**) 0.2 kg/s; (**b**) 0.42 kg/s.
## 5 Experimental Evaluation
### Performance Metrics  
Performance measurements for analytical evaluations of the schemes involve cache hit rate, latency (delay), and network backhaul traffic. Key parameters like the cache size of the MAN servers, number of active users, number of video requests, and transmission power are varied to observe the correlation of the edge cache network performance and varying parameters.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Mathematical Foundation  
HGE-ACN recognizes sign language words using graph-based temporal modeling and Adaptive Convolutional Networks. The approach relies on the dataset, a matrix of frames, frame height, width, and channel count (e.g., RGB). N keypoints with geographic coordinates and confidence scores indicate the landmarks of each frame's hand and stance. Matrix representations of each frame's number of frames, height, breadth, and channels form the HGE-ACN's mathematical supports.  
To improve the mathematical foundation, the values of \(x,\ y,\ \text{and}\ c\) should be constrained within bounded intervals instead of unrestricted in \(\mathbb{R}\). The Equation (1) is as follows:  
\[\begin{array}{l}V=\{\mathrm{f}_{1},\mathrm{f}_{2},\ldots\ldots\mathrm{f}_{n }\},\ \mathrm{f}_{\mathrm{t}}eD\subseteq\mathbb{R}^{\mathrm{H}\times W\times C}\\ D=[a_{H},a_{H}]\times[a_{W},a_{W}]\times[a_{C},a_{C}]\end{array} \tag{1}\]  
Figure 1: Block diagram of HGE-ACN framework.  
In Equation (1), D represents a bounded domain, and f\({}_{t}\) represents the frame at time t. H and W represent the frame's height and width, and C represents the number of channels. This guarantees that the model operates within a well-defined mathematical space.  
Each frame f\({}_{t}\) contains N keypoints corresponding to hand and pose landmarks:  
\[\begin{split} k_{t}=\{k_{1}^{t},k_{2}^{t}\ldots k_{n}^{t}\},k_{i}^{t }=\left(x_{i}^{t},y_{i}^{t},c_{i}^{t}\right)\end{split} \tag{2}\]  
In Equation (2), \(x_{i}^{t}\) and \(y_{i}^{t}\) represent the spatial coordinates of the ith keypoint, and \(c_{i}^{t}\) represents the confidence score of the keypoint.  
The HGE-ACN model optimizes learning using hyperparameters for robust feature extraction, efficient spatial-temporal modeling, and computational efficiency. Key hyperparameters are detailed in Table 1, providing notations and values.
## 8 Experiments and Result Analysis
### Experimental Environment and Data  
Here we used the JavaWeb technology and used the Eclipse tool as the development environment to develop the support tool for the proposed method. This tool is an updated and upgraded version of the design pattern detection tool we developed in [22; 23; 24], named PatternDetectorByDL 3.0 (Version number: 3.0; Creator: [PERSON], et al.; Location: Beijing, China). The tool takes the source code or UML model (class diagram and sequence diagram) of the system to be identified as input and automatically divides the system to be identified into subsystems and second-level subsystems according to the pattern to be identified, selected by the user, and converts them into colored UML models, then input into the corresponding design pattern classification model that has been trained and saved for judgment and merge the judgment results to obtain the final pattern instances, and finally display the detection results on the interface.  
The running environment of the experiments was: Windows 10 (Version number: 10; Creator: Microsoft; Location: Seattle, USA) operating system, Genuine Intel (R) CPU (number of cores: 4, number of threads: 8), 16.00 GB memory, 2.40 GHz main frequency and 500 GB hard disk.  
The open-source projects JHotDraw, JRefactory and JUnit contain a large number of design pattern instances, and the documents of these projects record the use information of design patterns in detail. Therefore, they are used to verify the design pattern detection methods in many references. In this study, JHotDraw 5.1, JRefactory 2.6.24 and JUnit 3.7 were selected as experimental data.  
Figure 9: Schematic diagram of the judgment and merging of the four second-level subsystems for the State pattern.
## 2 Materials and Methods
### Questionnaire  
The same questionnaire was supplied in both non-informed and informed groups. At the beginning, demographic data, education level, monthly spending on food items and eating habits were collected. After food neophobia was measured using ten items (five positively and five negatively) rated on a 7-point scale from disagree to agree, developed by [PERSON] and [PERSON] [48] and validated in the Italian language by [PERSON] et al. [49].  
The sum of the rate given to ten statements (reversing the neophilic items) provide the individual food neophobia scale (FNS) scores. FN levels both for non-informed and informed conditions, and the segmentation of respondents as low, medium and high, were determinate according to [PERSON] et al. [49]. Participants responded also to statements on the meaning of entomophagy and previous consumption of insects.  
As, in Italy, it is not allowed to sell edible insects, four images (F1-4) representing different kinds of edible insect and insect-based products were presented to participants and they were asked for each product to indicate the hedonic liking on a 9-point scale (1 = extremely dislike and 9 = extremely like) and the evoked sensations between curiosity, disgust, appetite and \"other\", with the possibility to indicate other sensations.  
The insect products were selected at four levels of visibility: whole and visible (F1, mix of edible insect), whole and coated (F2, chocolate-coated grasshopper and scorptions), ground and invisible (F3, crickets and silkworm flour), processed and invisible (F4, protein bars). All the images were displayed to participants with a short description of the product [26].  
In addition, 9 food values similar to those used in previous studies were identified [34; 39; 40]. For each of these food values, we assessed how they could influence the propensity to consume insect-based foods. This assessment was carried out by giving the participants four questions, in Likert format, for each of the 9 food values identified. The 9 food values and their definitions are given in Table 1, while the Likert format questions used for each of them are given in Appendix A (Table 1). For each question, respondents were asked to express their evaluation on a scale from 1 (always true) to 5 (never true).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Food Values** & **Definition** \\ \hline Healthiness & When a product is good for health \\ Naturalness & Extent to which food is produced without modern technologies \\ Taste & Extent to which consumption of the food is appealing to the senses \\ Price & The price that is paid for the food \\ Safety & Extent to which consumption of food will not cause illness \\ Convenience & Ease with which food is cooked and/or consumed \\ Appearance & Extent to which food looks appealing \\ Origin & Where the agricultural commodities were grown (local, national or abroad) \\ Environmental impact & Effect of food production on the environment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Food values.
## 5 Proposed Approach
### Prediction Module
#### 5.4.2 Arima  
The ARIMA model (or integrated autoregressive models with moving average) by [PERSON] and [PERSON] [70], on the other hand, starts from the assumption that between two  
Figure 3: Example of forecast.  
observations of a series, the one that alters the level of the series is called the disturbance. A general Box-Jenkins model is indicated as ARIMA (\(p\), \(d\), \(q\)) where AR = autoregression (autoregression), \(p\) is the order of AR, \(I\) = integration (integration), \(d\) is the order of I, \(MA\) = moving average and \(q\) is the order of MA. If the series is not stationary, that is, if the mean and the variance are not constant over time, it is integrated after having performed a possible transformation of the data, usually of the logarithmic type. In this way, a stationary series is obtained. The proposed procedure of [PERSON] and [PERSON] is iterative and is used for the identification, estimation, and verification of an ARIMA model. It has the purpose of building a model that adapts to the observed time series and represents the generating process of the series itself:  
1. Verification of the stationarity of the series;
2. Identification of the ARIMA model;
3. Estimation of the parameters \(p\), \(d\) and \(q\);
4. Verification of the model.  
If the estimated model passes the verification stage, then it can be used for predictions. Otherwise, the identification, estimation, and verification phases are repeated iteratively [70].  
In our application case, this procedure was automated using auto-ARIMA, which provides an automatic ARIMA algorithm. The auto-ARIMA process tries to identify the most optimal parameters for an ARIMA model. The problem with this automatic model is that due to stationarity problems, it may not find a suitable ARIMA model that will converge. The reason why we chose an automatic model lies in the desire to find a simple procedure that can be used without technical expertise on ARIMA. In fact, with this process, there is the risk of making the series excessively stationary, affecting the final results. Therefore, it was decided to initialize auto-ARIMA with the standard parameters proposed by the library itself [71].
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 1 Introduction
It is worth noting that high availability and reliability, as well as low latency are needed to ensure URC, but the relative significance of these may vary across use cases. In this work, we address link adaptation from an URC perspective. In adaptive modulation and coding (AMC) protocols, the transmission rate is selected based on a channel quality indicator (CQI), which is a part of the CSI measured by the intended receiver, and fed back to the transmitter. However, if the channel undergoes fading, the measured CSI becomes outdated due to the unavoidable feedback and processing delays. The channels at the time instant of transmission, can thus differ substantially from the measured channels. With outdated CSI, the outage performance is severely degraded, and the channel capacity is affected [6; 7]. In addition to mobility induced variations in the channels, the use of radio resources in interfering cells may also change between the measurement and the transmission instants. For example, changes in precoding may change the interference [8]. In order to ensure that a transmission is received within the tight latency window required by URC, robust link adaptation is needed. Consequently, transmissions can be received with very high reliability.
## 3 Noise in PLC Networks
### Summary of Noise Signals in NBPLC-PRIME Networks  
To sum up, the main features of the noises presented in this section are summarized in Table 2. Although these noises are not \"pure\", Table 2 also tries to map them onto a simplified version of the traditional noise classification presented in Section 3.1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Type** & **Noise** & **Colored** & **Impulsive** & **Features** \\ \hline \multirow{4}{*}{**Normative**} & Normative 1 & \multirow{2}{*}{X} & Peaks every 8 kHz in the whole PRIME frequency band \\ \cline{3-5}  & (synthesized) & & & Peaks every 1.953 kHz in the whole PRIME frequency band \\ \cline{2-5}  & Normative 2 & \multirow{2}{*}{X} & Peaks every 1.953 kHz in the whole PRIME frequency band \\ \cline{3-5}  & (synthesized) & & & 2 peaks at 41.5 kHz (95 dBuv) and 83 kHz \\ \hline \multirow{4}{*}{**Measured in the field**} & Community & \multirow{2}{*}{X} & \multirow{2}{*}{(100 dBuv)} \\  & antenna & & & (100 dBuv) \\ \cline{2-5}  & TV receiver & X & & Maximum values up to 90 dBuv \\ \cline{2-5}  & Water pump & X & & Maximum values up to 65 dBuv \\ \cline{2-5}  & Electronic & \multirow{2}{*}{X} & \multirow{2}{*}{(-28.9 dBuv)} & Affect only the upper part of the PRIME frequency band with maximum values up to 85 dBuV \\ \hline \multirow{4}{*}{**Measured in the lab**} & Renault Twicy & \multirow{2}{*}{X} & Main peak at 48 kHz (\(-30.28\) dBV) \\ \cline{2-5}  & Renault Zoe & X & & Almost flat around \(-65\) dBV \\ \cline{2-5}  & Nissan Leaf & \multirow{2}{*}{X} & \multirow{2}{*}{2 peaks at 54 kHz and 82 kHz (\(-60\) dBV)} \\ \cline{2-5}  & PV inverters & & & Three peaks at 48 kHz (\(-22.9\) dBV), 64 kHz (\(-28.9\) dBV), and 80 kHz (\(-27.3\) dBV) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of noise signals in NBPLC-PRIME networks.  
Figure 17: Noise associated to the inverters.