esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitivity Analysis for Microscopic Crowd Simulation
## 2 Materials and Methods
### Bottleneck Scenario: Crucial for Improving Safety  
Bottleneck scenarios are widely investigated in experiments [40; 41; 42; 43; 44] and simulations [45; 46; 47] within the pedestrian dynamics community, since constrictions can lead to increased densities and delays in evacuations. In addition, the flow through the bottleneck can be used for capacity estimation [42]. We recreate the setup of the experiments by [PERSON] et al. [41] in our crowd simulation tool Vadere [13].  
Our investigation of the bottleneck scenario focuses on the density in front of the bottleneck, similar to [41]. There are several ways to measure the pedestrian density. A common measure is Voronoi density [48; 49], since it provides high accuracy and provides an output in number of agents per area, which is easy to understand. The Voronoi diagram identifies a Voronoi cell for each agent consisting of all points closer to this agent than to any other [49]. The density within the measurement area is calculated as the ratio between the number of agents in the measurement area to the sum of the areas of the Voronoi cells allocated to the agents within the area [11]. Figure 2 shows an exemplary Voronoi diagram. Agents who have more free space around them are assigned a larger Voronoi cell.  
Figure 3 depicts a snapshot of the simulation at 30 s. In this setup, 220 agents (blue) are moving from their origin (green) through the bottleneck, which is constructed with obstacles (grey) to their destination (orange). In addition, two measurement areas are shown in front of the bottleneck (red). The density is calculated for the smaller area, the larger area is used for calculating the Voronoi diagram in order to avoid artifacts on the edge.
## 3 Results and Discussion
### Samples Characterization
#### 3.1.1 TXRF Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Elements**} & \multicolumn{2}{c}{**FA**} & \multicolumn{2}{c}{**BA**} & \multicolumn{2}{c}{**CFA**} & \multicolumn{2}{c}{**FGD**} \\ \hline
**pH** & \multicolumn{2}{c}{**12.18**} & \multicolumn{2}{c}{**12.2**} & \multicolumn{2}{c}{**12.01**} & \multicolumn{2}{c}{**12.68**} \\ \hline P \({}^{*}\) & 24 & \(\pm\) & 17 & 9 & \(\pm\) & 3 & 6 & \(\pm\) & 2 & 9 & \(\pm\) & 2 \\ S \({}^{*}\) & 311 & \(\pm\) & 73 & 131 & \(\pm\) & 21 & 170 & \(\pm\) & 10 & 320 & \(\pm\) & 50 \\ Cl & 5029 & \(\pm\) & 3222 & 318 & \(\pm\) & 185 & 1.74 & \(\pm\) & 1.06 & 400 & \(\pm\) & 50 \\ K & 702 & \(\pm\) & 412 & 81 & \(\pm\) & 21 & 31 & \(\pm\) & 2 & 10 & \(\pm\) & 2 \\ Ca & 3488 & \(\pm\) & 1548 & 979 & \(\pm\) & 174 & 450 & \(\pm\) & 30 & 1700 & \(\pm\) & 200 \\ Cr & \(<\)LOD & \multicolumn{2}{c}{\(<\)LOD} & \multicolumn{2}{c}{0.21} & \multicolumn{2}{c}{\(\pm\) & 0.03} & \multicolumn{2}{c}{\(<\)LOD} \\ Mn & \multicolumn{2}{c}{\(<\)LOD} & \multicolumn{2}{c}{\(<\)LOD} & \multicolumn{2}{c}{\(<\)LOD} & \multicolumn{2}{c}{\(<\)LOD} \\ Fe & 0.62 & \(\pm\) & 0.40 & 0.07 & \(\pm\) & 0.03 & 0.27 & \(\pm\) & 0.01 & 0.12 & \(\pm\) & 0.04 \\ Cu & 0.38 & \(\pm\) & 0.08 & 5.6 & \(\pm\) & 0.7 & \multicolumn{2}{c}{\(<\)LOD} & 0.83 & \(\pm\) & 0.28 \\ Zn & 8.8 & \(\pm\) & 4.3 & 0.66 & \(\pm\) & 0.16 & 0.21 & \(\pm\) & 0.14 & 0.10 & \(\pm\) & 0.04 \\ Br & 193 & \(\pm\) & 13 & 1.88 & \(\pm\) & 0.20 & 0.20 & \(\pm\) & 0.03 & 1.67 & \(\pm\) & 0.18 \\ Rb & 7.6 & \(\pm\) & 0.9 & 0.19 & \(\pm\) & 0.02 & \(<\)LOD & \multicolumn{2}{c}{\(<\)LOD} \\ Sr & 16 & \(\pm\) & 1 & 5.5 & \(\pm\) & 0.6 & 3.43 & \(\pm\) & 0.18 & 2.84 & \(\pm\) & 0.34 \\ Ba & 1.28 & \(\pm\) & 0.96 & 0.78 & \(\pm\) & 0.49 & 0.76 & \(\pm\) & 0.40 & \(<\)LOD \\ Pb & 35 & \(\pm\) & 2 & 1.60 & \(\pm\) & 0.33 & \multicolumn{2}{c}{\(<\)LOD} & \multicolumn{2}{c}{\(<\)LOD} \\ \hline \hline \end{tabular} \({}^{*}\)Corrected values based on calibration curves; LOD—lower detection limit.  
\end{table} 
Table 1: Results of the total reflection X-ray Fluorescence (TXRF) analysis and pH values of leaching test of raw powders. Values are expressed as the average \(\pm\) standard deviation of three TXRF measurements. Relative sensitivities for elements with \({}^{*}\) were calculated based on a calibration curve (see the experimental section). LOD—lower detection limit.
## 2 Geological Background and Sampling
### Garnet in Skarn Profiles  
Garnets from different parts of the profile exhibit distinct characteristics. In the sample TS-10-8 (Figure 6a) from the inner skarn, the predominant mineral assemblages consist of grossularite (70%), diopside (25%), actinolite (3%), and calcite (2%). The grossularite has And (andradite) end-member ranging from 42.78% to 74.50%, with an average value of 66.73%. The crystal form of almandine is commonly known as a pyritohedron, with larger particles measuring between 2 and 5 mm.  
The outer skarn samples include TS-10-7 to TS-10-4, and all their garnets are andradite. The mineral assemblage of TS-10-7 consists mainly of andradite (86%), diopside (10%), actinolite (2%) and chalcopyrite (2%). The end-member of And ranges from 96.66% to 99.68%, with an average value of 98.14%. The garnet is massive and homogeneous, partially penetrated by garnet veins and lacks a ring structure (Figure 6b). The mineral assemblage of TS-10-6 consists mainly of andradite (88%), calcite (10%) and quartz (2%). The end-member of And are 88.71% to 95.76%, with an average value of 91.55%. The andradite is mostly hexagonal and has a well-formed ring structure with a particle size ranging from 0.5 to 1.5 mm (Figure 6c). The mineral assemblage of TS-10-5 is made up of andradite (76%), pyroxene (20%) and calcite (4%). The terminal elements of andradite range from 54.75% to 99.55%, with an average value of 87.82%. The garnet is euhedral allomorphic. The mineral assemblage of TS-10-4 includes andradite (8%), pyroxene (74%), chlorite (3%), calcite (2%), chalcopyrite (5%) and magnetite (3%). The end-member of And ranges from 66.69% to 99.66%, with an average value of 91.50%. The decomposition characteristics of andradite are evident, and there is a chloritization phenomenon in the core. The core is rich in iron (Adr\({}_{91-99}\)Gro\({}_{0-7}\)), and the edge is rich in aluminum (Adr\({}_{62.3}\)Gro\({}_{35.5}\)) (Figure 6e).
## 3 Results  
### Electrical Characterization and Gas Temperature Measurement  
An example of recorded voltage and current waveforms, used for power calculation, is shown in Figure 3A.  
The SPD absorbed by the SDBD was \(6.81\pm 0.02\times 10^{-2}\) W/cm\({}^{2}\). The SPD is a key parameter of CAP treatment in air because it is known to directly affect the production of RONS (20; 21).  
Figure 3: Voltage (blue) and current (red) waveforms (**A**) and gas temperature measurements during 900 s of discharge (**B**).  
This value of SPD was measured using each available treatment volume; as expected, no difference could be detected.  
Figure 3B highlights the limited increase in temperature in the afterglow region during the discharge time. The maximum measured gas temperature was below 34 \({}^{\circ}\)C.
# Nutrient Sensing and Biofilm Modulation: The Example of L-arginine in _Pseudomonas_
## 2 Importance of Nutrients in _Pseudomonas_ Biofilms  
Bacteria can reshape their metabolism in response to the availability of carbon sources, nitrogen sources (both inorganic or organic, including amino acids, depending on the cellular background), ions and essential elements.  
The influence of nutrients and media composition was already recognised in early mechanistic studies of _Pseudomonas_ biofilm development. In _P. fluorescens_, for example, it was observed that different carbon sources (glucose, citrate or glutamate) stimulated biofilm formation through partly overlapping pathways [10]. In _P. putida_, nutrient exhaustion leads to rapid biofilm dispersal, a process that indirectly involves the turnover of the large adhesin LapA [19]. The stringent response, mediated by (p)ppGpp, has been shown to participate in starvation-induced dispersal of biofilms by positively regulating phosphodiesterase BifA and negatively regulating LapA and its transport system [20]. In _P. aeruginosa_, nutrient availability determines not only biofilm formation and dispersal, but also the biofilm structure, as predicted by in silico models and further corroborated in vivo [21]. Although there is still limited knowledge regarding the effects of specific nutrients, their importance reflects the fact that biofilm formation is an active, energy-demanding process, as well as a means for bacteria to occupy favourable, nutrient-rich environments.  
The molecular mechanisms by which different nutrients influence biofilm formation or stability are being explored from various perspectives, from signal transduction through chemoreceptors [22] to population dynamics and evolutionary aspects [23]. In a recent study using _P. fluorescens_ Pf01 as a model organism, the impact of a wide array of nutrients on biofilm formation was analysed, using glycerol as a carbon source to define the effect of compounds that may not be strictly \"nutrients\" for this microorganism but still act as environmental signals [24]. This work allowed identifying organic molecules with an effect on expression or functionality of enzymes involved in c-di-GMP turnover, thus modulating biofilm formation. Relevant molecules include specific sugars, organic acids and amino acids. Among these, L-arginine stands out as a key element based on growing evidence not only in _Pseudomonas_, but also in many other bacterial species.
## 2 Materials and Methods
### Sensory Analysis  
The sensory analysis was conducted by eight official panelists, led by a panel leader, following the guidelines of UNI EN ISO13299 [PERSON] et al. (2024) in a sensory analysis-equipped laboratory according to ISO 8589 [PERSON] et al. (2024). Two samples, CTRc and HSc5%, were evaluated by each panelist during each session. To minimize color differences between the samples caused by varying doughs, a red light was used during the evaluation. Panelists were instructed to evaluate both samples, with distilled water provided to cleanse their palates. A minimum 5 min break was taken between each sample. To ensure a common lexicon, three training sessions were conducted with the judges. After the training, a focus group determined the appropriate descriptors to use. Table 2 outlines the descriptors, definitions, and associated reference standards employed in the sensory analysis. Each descriptor was rated on an unstructured continuous scale, from 0 (absence of the characteristic) to 10 (maximum intensity), based on bibliographical sources [PERSON] et al. (2024); [PERSON] et al. (2024). The same scale was used to assess the descriptor of each panelist's personal judgment, reflecting their subjective level of agreement. To better understand the differences in the sensory attributes and the overall rating between the samples, a principal component analysis (PCA) was performed.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Descriptors** & **Sensory Attribute Definitions** & **Standards and Reference Materials** \\ \hline Hardness & Force required to compress the sample with the back teeth & Ripe banana and raw carrot \\ \hline Crunchiness & Amount of noise generated when the sample is chewed at a fast rate with the back teeth & Value 0 corresponds to a dried apple piece, while 10 corresponds to a fresh celery piece \\ \hline Pastiness & Amount of soft, smooth mass that does not release moisture during chewing & Watermelon and peanut butter \\ \hline Astringent & Sensation of drying, drawing-up or puckering of any of the mouth surfaces & Diluted tannic acid solution (0.06–2 mg/mL) \\ \hline Sweetness & Basic taste associated with sugar (sucrose) & Diluted sucrose solution (0.5–6 g/L) \\ \hline Bitterness & Basic taste associated with caffeine & Diluted caffeine solution (0.03–0.2 g/L) \\ \hline \end{tabular}
\end{table}
Table 2: Descriptors used for sensory assessment and related reference standards.
# Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment
## 1 Introduction
* We propose the BD-FL by combining blockchain with federated learning in the edge computing environment. BD-FL uses the distributed characteristics of blockchain and edge computing to solve the problem of a centralized server in that the local device trains the local model and the edge server aggregates the global model. BD-FT also introduces an incentive mechanism to encourage local devices to actively participate in model training, increasing the number of samples and improving the model accuracy. * We propose a preference-based stable matching algorithm in BD-FT, which binds local devices to appropriate edge servers, improving the utilization of edge server resources and reducing the delay of data transmission. We propose the R-PBFT algorithm, which optimizes the network topology and the consistency protocol and designs a dynamic reputation mechanism, reducing the communication overhead of the blockchain consensus process and improving the model training efficiency. * We performed extensive simulation experiments to evaluate the proposed BD-FL. Experimental results show that BD-FL effectively reduces the model training time by up to 19.7% and 34.9%, respectively, compared with several federated learning methods with different matching algorithms and a state-of-the-art blockchain-based federated learning method. The R-PBFT algorithm can reduce the communication overhead of the consensus process and improve the training efficiency of BD-FL by 12.2%. The rest of this paper is organized as follows. Section 2 introduces related work. Section 3 presents the methodology, including the system architecture, BD-FL, and R-PBFT. Section 4 gives the experimental evaluation. Section 5 concludes this paper.
# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
## 3 AMR Framework  
A framework of CNN-BiLSTM-DNN is proposed, as shown in Figure 1. The CNN-BiLSTM-DNN model is composed of a CNN, BiLSTM, an attention mechanism, and a fully connected (FC) deep neural network, which can be used to extract and classify spatiotemporal features by using their complementarity and synergy. Incorporating an attention mechanism into the model can greatly enhance its ability to represent data, effectively filtering out irrelevant information and improving the recognition accuracy of the target of interest. This ultimately leads to a significant boost in overall model performance. To eliminate noise from the input signal, it is first filtered by the minimum pooling layer and the average pooling layer. Then, the I signal and the Q signal pass through two one-dimensional convolution layers, respectively, and then, the spliced signal passes through a two-dimensional convolution layer. Then, it fuses with the I/Q signal and finally extracts the spatial features of the signal through another two-dimensional convolution layer. Then, the signal passes through two bidirectional LSTM layers to extract the signal's time characteristics. Finally, the classification results are output by the fully connected layer. Three-channel input, spatial feature mapping, temporal feature extraction, and fully integrated classification make up the functional parts of the model.
## 3 Methodologies
### Gaussian Process Regression (GPR)  
GPR is one of the best methods for providing uncertainty measurement. It follows the Bayesian approach to regression. It is an efficient technique for small data sets. GPR follows a nonparametric approach. A nonparametric approach is an approach that is not limited by its functional form. A probability distribution can be defined for a specific function or for all the functions that fit the data. Since GPR has a nonparametric approach, it computes the probability distribution of functions that fit the data. Two main aspects of GPR are Gaussian Process Prior (GPP ) and Kernel function. Since GPR depends a lot on the prior values, GPP plays a vital role in computing algorithms for GPR. The equation for GPP can be computed from (13). RBF (Radial Basis Function) is considered as a kernel in this paper. This function is calculated using (14).  
\[f(x)\sim GP(m(x),k(x,x^{{}^{\prime}})) \tag{13}\]  
\[k(x,x^{{}^{\prime}})=\partial_{f}^{2}\exp(-\frac{1}{2l_{m}^{2}}\left\|x-x^{{} ^{\prime}}\right\|^{2}), \tag{14}\]  
where \(m(x)\) is mean function, \(k(x,x^{{}^{\prime}})\) is a covariance function, \(\partial_{f}^{2}\) is signal variance and \(l_{m}\) is the length scale of the system.
## 5 Benchmark Testing and Performance Analysis of MRSO
### Evaluating the MRSO Algorithm alongside Metaheuristic Methods with Classical Benchmark Functions
& \(6.82\times 10^{-1}\) & \(1.05\times 10^{-1}\) & \(4.2\times 10^{-1}\) & \(3.84\times 10^{-1}\) & \(9.63\times 10^{-1}\) & \(6.99\times 10^{-1}\) & \(1.59\) \\  & \(p\),value & \(4.24\times 10^{-3}\) & \(1.23\times 10^{-15}\) & \(1.01\times 10^{-11}\) & \(3.73\times 10^{-1}\) & \(1.11\times 10^{-5}\) & \(1.95\times 10^{-2}\) & \(2.85\times 10^{-4}\) \\  & Ranking & 8 & 8 & 8 & 8 & 7 & 7 & 7 \\ \hline \multirow{4}{*}{CSA} & Average & \(8.45\times 10^{-1}\) & \(3.27\times 10^{-1}\) & –1.17 & –\(1.02\times 10^{-1}\) & \(-1.04\times 10^{-1}\) & \(-1.05\times 10^{-1}\) \\  & Std. & \(8.82\times 10^{-18}\) & \(1.45\times 10^{-14}\) & \(6.78\times 10^{-16}\) & \(2.26\times 10^{-1 Regarding statistical significance, the MRSO shows significant results in two out of the seven functions. For example, in F18, the MRSO has a \(p\)-value of 3.36 \(\times\) 10\({}^{-4}\), indicating that its performance is statistically significant compared to most of the other algorithms, including the MRA and the WSO. In F19, the MRSO's \(p\)-value of 8.21 \(\times\) 10\({}^{-3}\) confirms that it performs significantly better than several algorithms, including the SCA and the DOA. However, in functions like F20 and F21, the MRSO does not achieve statistical significance, with \(p\)-values of 1.05 \(\times\) 10\({}^{-1}\) and 6.44 \(\times\) 10\({}^{-1}\), respectively, suggesting that its performance is less conclusive in these cases. Overall, the MRSO demonstrates statistically significant improvements in two functions, while maintaining competitive performance across the rest. Table 5 highlights the MRSO's strengths in terms of average performance, stability, and statistical significance in the fixed-dimension multimodal functions. While it excels in several key areas, there are opportunities for further refinement in functions where its performance falls behind other metaheuristic algorithms. To rank the algorithms, we used the [PERSON] mean ranking score [39], which measures how well each algorithm performed across all 23 classical benchmark functions. A lower score means better performance. As shown in Table 6, the MRSO received a ranking score of 10.2, placing it in the middle of the group. The MRA (7.5) and the CSA (8.1) performed better than the MRSO, taking the top spots. The TSA and the DOA followed closely with scores of 11.3 and 10.6, respectively. The SCA, the EHO, and the WSO ranked lower with scores of 12.8, 12.4, and 13.2, while the LCA had the lowest performance with a score of 15.1. These results show that while the MRSO did well in some benchmarks, especially in multimodal and fixed-dimension functions, the MRA and the CSA were stronger overall across all the tested functions.
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Consensus Functions
#### 2.4.3 Non-Negative Matrix Factorization  
NMF is the clustering ensemble method based on a non-negative matrix factorization process. It was first introduced by [PERSON] and [PERSON] [56] and popularized in an article by [PERSON] and [PERSON] [57]. NMF corresponds to the problem of factorizing a given non-negative data matrix \(V\) into two matrix factors, i.e., \(V\approx WH\), while requiring \(W\) and \(H\) to be non-negative. At this point, while W refers to the dimensionally reduced data matrix, \(H\) represents the associated coefficient matrix (weights associated with \(W\)). In theory, although various extensions have emerged to propose the clustering feature, the original model formulation does not include a clustering objective and was first presented as a dimensionality reduction algorithm.  
In this method, the distance between partitions is defined using Equation (14):  
\[\mu(P,P^{{}^{\prime}})=\sum_{i,j=1}^{n}\mu_{ij}(P,P^{{}^{\prime}}) \tag{14}\]  
where \(\mu(P,P^{{}^{\prime}})=1\) if \(c_{i}\) and \(c_{j}\) belong to different clusters in the other and belong to the same cluster in one partition, otherwise \(\mu(P,P^{{}^{\prime}})=0\). The consensus partition (final) is determined by the median partition model using \(\mu\)[8].
###### Abstract  
This study aimed to explore the potential of predicting diabetes by analyzing trends in plantar thermal and plantar pressure data, either individually or in combination, using various machine learning techniques. A total of twenty-six participants, comprising thirteen individuals diagnosed with diabetes and thirteen healthy individuals, walked along a 20 m path. In-shoe plantar pressure data were collected and the plantar temperature was measured both immediately before and after the walk. Each participant completed the trial three times, and the average data between the trials were calculated. The research was divided into three experiments: the first evaluated the correlations between the plantar pressure and temperature data; the second focused on predicting diabetes using each data type independently; and the third combined both data types and assessed the effect of such to enhance the predictive accuracy. For the experiments, 20 regression models and 16 classification algorithms were employed, and the performance was evaluated using a five-fold cross-validation strategy. The outcomes of the initial set of experiments indicated that the machine learning models were significant correlations between the thermal data and pressure estimates. This was consistent with the findings from the prior correlation analysis, which showed weak relationships between these two data modalities. However, a shift in focus towards predicting diabetes by aggregating the temperature and pressure data led to encouraging results, demonstrating the effectiveness of this approach in accurately predicting the presence of diabetes. The analysis revealed that, while several classifiers demonstrated reasonable metrics when using standalone variables, the integration of thermal and pressure data significantly improved the predictive accuracy. Specifically, when only plantar pressure data were used, the Logistic Regression model achieved the highest accuracy at 68.75%. Those predictions based solely on temperature data showed the Naive Bayes model as the lead with an accuracy of 87.5%. Notably, the highest accuracy of 93.75% was observed when both the temperature and pressure data were combined, with the Extra Trees Classifier performing the best. These results suggest that combining temperature and pressure data enhances the model's predictive accuracy. This can indicate the importance of multimodal data integration and their potentials in diabetes prediction.  
diabetes prediction; thermal analysis; plantar pressure; machine learning +
Footnote †: journal: _Algorithms_  
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