esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Definition of the Modelled Geophysical Gravity Signals-South America
#### 2.2.2 Lakes  
The other phenomenon of interest regards water level variations occurring in lakes. In South America, Patagonian lakes are sensitive to climate change since they intercept the waters melted from glacieved areas nearby [22]; these lakes are also quite extensive in terms of area and their seasonal water level oscillations involve several cubic kilometers of water (1 cubic kilometer of water corresponds to 1 Gt of mass). Other interesting lakes are located in the tropical area, and some of them are associated with hydroelectric power plants, where large and rapid variations of the storage water volumes are observed. Figure 2a reports the areas of major lakes (blue) and reservoirs (red).  
Figure 2: (a) Block-diagram showing the areal extents of the largest lakes (blue) and reservoirs (red) in South America. (b) Seasonal and long-period (LP) mass changes for selected lakes and reservoirs in South America. LP changes are given in logarithmic scale. The map shows the position of the various lakes: in red, artificial lakes, and in blue, natural lakes; the plots on the right and at the top show, as a function of longitude and latitude, the LP mass variations of the lakes (blue) and reservoirs  
(red). The plots are in log scale; the dashed lines indicate negative trends (mass loss), and the solid lines positive trends (mass increase). The plots on the bottom and left similarly report the yearly seasonal mass change (half peak2 peak). Color code as above. Large lakes include Titicaca (>8000 km\({}^{2}\)) and the two reservoirs Tucurui and Itaparica (also known as Sobradinho), the latter being the largest lake in South America.  
The mass variations occurring in these superficial water bodies are calculated in a similar way as that performed for the glaciers. The areal extents are constrained by the GLWD [23] database, while the time variations of water level are derived from the DAHITI database [24]. Both long-period variations and seasonal signal amplitudes (period 1 year) are considered.  
Figure 2b shows the long-term mass variations for about 16 lakes and 7 reservoirs, together with their locations in the map. The plots on the bottom and left side of the map report the yearly mass rates as functions of longitude and latitude. We can see that the largest mass variations are due to reservoirs (red lines), with peaks exceeding the 2 Gt/yr level. Natural lakes (blue) show lower variations, generally below 1 Gt/yr. We also considered the seasonal (annual and semi-annual variations) lakes variations, which are generally larger compared to long-period trends and for reservoirs can be quite large (>20 Gt, half peak-to-peak amplitude; Figure 2b).
# A Heterogeneous Parallel Algorithm for Euler-Lagrange Simulations of Liquid in Supersonic Flow
## 2 Algorithm and Code Structures
### CPU/GPU Heterogeneous Parallel Algorithm
#### 2.3.3 A Method for Droplet Location  
Of particular note is that, in our CPU/GPU hybrid computation mode, the three-dimensional relationships of the cells are weakened, making it hard to directly locate droplets and find their neighbors. Therefore, the information about which two cells share the \(i\)th face and which faces enclose the cell are stored to enhance the adjacent relationships of the cells. Then, combine the particle-locating algorithm of Chorda [41] to address the droplets. The process is illustrated in Figure 6. As mentioned above, the cell \(\Omega_{4}\) has the information of its enclosed faces, while the face has the information of which two cells share it. Figure 6a shows the criterion for determining whether the droplet is within the given cell. The determination criterion can be described as:  
\[Scalar_{i}=\overrightarrow{\Omega\mathbb{F}_{i}}\cdot\overrightarrow{F_{i} \mathrm{d}}, \tag{31}\]  
\[Inoroutface_{i}=\begin{cases}0,Scalar_{i}\leq 0\\ 1,Scalar_{i}>0,\end{cases} \tag{32}\]  
\[Inoroutcell=\sum_{i}^{N_{F}}Inoroutface_{i}. \tag{33}\]  
where the \(\overrightarrow{\Omega\mathbb{F}_{i}}\) is the vector of the cell-center pointing to the face-center, \(\overrightarrow{F_{i}\mathrm{d}}\) is the vector of the face-center pointing to the droplet. \(Inoroutface_{i}>0\) indicates the droplet is out of the \(i\)th face, while \(Inoroutface_{i}=0\) indicates the droplet is in the face. \(N_{F}\) is the total number of cell faces. Thus, when \(Inoroutcell=0\), the droplet is in the given cell. The initial cell containing the droplet is calculated by traversing the entire cells.  
Limited by the weakened three-dimensional relationships, a search method for the droplet is developed by traveling through the cells by droplet trajectory, which joins the initial and final droplet position. Figure 6b gives an example of one path. The key point is to choose which face of the current cell the droplet exits from and then use the face to locate the neighboring cell. The search process is repeated until the cell is addressed.  
Figure 7 summarizes the general two-phase computational procedure, the solid boxes summarize the overall processes on the CPU and GPU, and the dashed box represents the specific solution processes of the two-phase governing equations, which are implemented  
Figure 6: Droplets locating process (**(a)** Judgement criterion; **(b)** Example of a typical path; **(c)** Flow diagram of droplet locating).  
on the GPU. Of particular note is that, in the event of droplet breakup, a temporary array on GPU and CPU is used to store and transfer the newly produced droplets to the global array on GPU to lessen data transfer. This is also the reason why the droplet breakup takes the most proportion of the time cost.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
###### Abstract  
A novel sample preparation method based on the use of the Arrow solid-phase microextraction device was used to extract and preconcentrate volatile organic compounds (VOCs) from milk samples prior to their determination by gas chromatography-mass spectrometry (GC-MS). The experimental parameters of the solid-phase microextraction (SPME) Arrow method were evaluated in terms of fiber type, sample volume, extraction temperature, extraction time, stirring rate and salt addition. Under the optimum extraction conditions, the SPME Arrow was compared with conventional SPME fibers to evaluate the effectiveness of the SPME Arrow method. Evaluation of the conventional SPME procedure was also performed under optimized conditions, for appropriate method comparison. Due to the larger sorption phase volume of SPME Arrow, a higher sensitivity and reproducibility were observed for the determined chromatographic peaks in comparison with conventional SPME fibers. The use of Carbon wide range (WR) SPME Arrow/polydimethylsiloxane (CAR/PDMS) SPME Arrow fibers leads to a compound-dependent improvement of a factor of 4-5x over the classical SPME setup. Moreover, the relative standard deviation (RSD) of the total volatiles for a conventional SPME procedure was 12.5%, while for SPME Arrow it was 6.2%. Finally, the novel method was successfully employed for the analysis of commercially available milk samples. The findings of this study indicate that SPME Arrow can be effectively used for the determination of volatile organic compounds in complex food matrixes.  
SPME arrow; GC-MS; gas chromatography; milk; volatile compounds +
Footnote †: journal: materials
###### Abstract  
COVID-19 has deeply impacted clinical strategies in dentistry and the use of surgical masks and respirators has become critical. They should adapt to the person's facial anatomy, but this is not always easy to achieve. Bellus3D Company proposed to apply their face scan software, used with selected smartphones and tablets, to design and 3D-print a bespoke \"Mask Fitter\" to improve the sealing of surgical masks and respirators. Twenty dental staff participants were face scanned and a Mask Fitter for FFP2 respirators was designed and 3D-printed. Participants were asked to wear their Mask Fitter over one week and then completed a survey. Questions were asked about wearing comfort, sealing confidence, glasses or loupes fogging, both with and without the Mask Fitter. Dental staff gave positive feedback, with levels of comfort during daily use reported as similar with and without the Mask Fitter; and a higher confidence in achieving a proper seal, ranging from a 10% confidence rating of a proper seal without the Mask Fitter to 75% with the Mask Fitter. Moreover, fogging problems decreased considerably. The tested Mask Fitter device could represent an easy and low-cost procedure to improve the facial adaptation of the FFP2 respirator.  
3D-face-scanning; 3D-printing; surgical mask; FFP2 respirator; mask filter +
Footnote †: journal: Applied Sciences  
production of aerosols and droplets during routine dental procedures cannot be completely avoided, hence the generation of highly contaminated microbial aerosol [17; 18]. PPE amongst the dental profession has become increasingly important, and use of facial masks, particularly, has also been recommended for most of the population during any kind of contact with other individuals. One of the fundamental pre-requisites of a surgical mask is its ability to properly adapt to the person's facial anatomy, creating a good seal to limit as much as possible the spread of bacteria and viruses. A good face mask seal is not always easy to achieve, especially since not all human faces allow an easy adaptation of the mask. To help address these surgical mask adaptation uncertainties, Bellus3D, a USA based company, (Bellus3D, Campbell, CA, USA) has developed a system for which a custom adapted low-cost Mask Fitter can be easily and rapidly produced. The Mask Fitter has already been reported to be potentially effective in an ophthalmology related study in which a quantitative fit test (QNFT) was performed [19]. More recently, another study still performed with a QNFT on dentists reported that the 3D printed frame fitted over a surgical mask offers advantages comparable to those offered by FFP2 respirators in terms of marginal fit. However, surgical masks do not offer the same protection in terms of filtering capacity, thus the use of surgical masks with mask fitter could be considered only as a replacement in case of a FFP2 shortage [20].  
The aim of the present study was to evaluate several aspects of the use of the Mask Fitter produced and delivered to a cohort of dental staff, in general practice settings, particularly concerning low-, medium- and long-term (full day) wearability.
# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 3 Detecting Echo Chambers in Online Social Platforms
### Step 3: Debate Network Construction  
Once we have identified a set of users labeled with respect to their ideology on the controversy, the next step consists of defining their interaction network. The concepts of exposure, affiliation, and interaction with like-minded individuals is a crucial aspect of echo chambers. Accordingly, it becomes necessary to define when an interaction between two users takes place. Most of the previous approaches [19; 20; 48] rely on the follow or friend relationship to define the connections among users. However, not all online social platforms allow discovering who follows whom and, thus, retrieving such information, e.g., Reddit. Further, we believe that it is quite unrealistic that a user has a direct relationship with all the users they follow or that consume all their contents.  
For such reason, we build the interaction graph among the previously labeled users through the who-comment-whom relation, since such a feature is available across all the main online platforms, as in the case of textual contents. Formally, we define the interaction network as a graph \(\mathcal{G}\) where each node represents a user, and two nodes \(u\) and \(v\) are connected if and only if \(u\) directly replies to a post or a comment of \(v\) or vice versa. Each node \(u\) is also associated with a discrete label \(a_{u}\in\{l_{1},l_{2},\ldots,l_{n}\}\) where \(n\) is the number of sides/ideologies in the considered controversy. Further, each edge \((u,v)\) is described by a weight \(w_{u,v}\in\mathbb{N}\) that represents the number of interactions between two users. In our case study, we model the interaction network as an undirected graph. Depending on the specific task it would make sense to use a directed network instead; however, such a choice mainly depends on how strict is the given definition of echo chamber. In our rationale, we believe that the echo chamber components should somehow be insulated from opposite views. Thus, neither incoming information from the rest of the network nor outgoing ones are allowed to a certain extent.
## 3 Results
### Factors Influencing Changes in Vegetation NDVI
#### 3.2.1 Relationship Between Temperature and NDVI  
From 2000 to 2020, the annual average temperature in Aksu fluctuated significantly (Figure 6), with a minimum of 9.92 \({}^{\circ}\)C in 2012 and a maximum of 11.98 \({}^{\circ}\)C in 2020. NDVI exhibited a positive correlation with temperature, with a correlation coefficient of 0.34 (R\({}^{2}\) = 0.1157). Although this correlation coefficient is relatively low, it reflects the unique characteristics of the arid environment in Aksu, where vegetation growth is less sensitive to temperature changes due to the dominance of drought-resistant species such as Haloxylon and Tamarix. These plants are well-adapted to high-temperature conditions, relying more on water availability than on temperature for growth.  
Moreover, the weak correlation can be attributed to the significant influence of non-climatic factors, such as irrigation and land management practices, which play a dominant role in sustaining vegetation in this region. For instance, the widespread use of irrigation systems decouples vegetation growth from natural temperature fluctuations. Additionally, the spatial heterogeneity of temperature changes and the limited seasonal growth periods in this region further reduce the direct impact of temperature on NDVI.  
This result underscores the importance of considering additional variables, such as water resource management and soil conditions, in future analyses to better capture the driving factors of vegetation dynamics in arid and semi-arid regions like Aksu.
## 2 Methods and Materials  
### Online Correction Method: Pulsed Radiation  
#### 2.1.1 Recombination in Pulsed Beams  
The collection efficiency of a radiation detector irradiated with a pulsed beam is well described by the equation of Boag in the near-saturation regime (\(f>0.9\)) [21]:  
\[f=\frac{1}{u}\log(1+u) \tag{1}\]  
The variable \(u\) depends on the polarization voltage, \(V\), dose per pulse, \(D_{0}\), and the geometry and properties of the sensitive material. We can just summarize this dependence as,  
\[u\sim\frac{c}{V} \tag{2}\]  
where \(c\) is proportional to \(D_{0}\).  
Equations (1) and (2) are behind the development of the two-voltage-method for air-filled detectors irradiated with pulsed beams [20; 22]. In the case of liquid-filled detectors, the ionized charge per pulse depends on the applied voltage due to initial recombination, and more complex methodologies are necessary to obtain the collection efficiency [14; 15; 23].  
In the case of a significant contribution of free electrons to the collected charge, the following could be used alternatively [24]  
\[f=\lambda+\frac{1}{u}\log\left[1+\frac{e^{\lambda(1-\lambda)u}-1}{\lambda}\right] \tag{3}\]  
with \(\lambda=1-\sqrt{1-p}\), and \(p\) the free-electron fraction in the chamber.
## 2 Literature Review and Hypotheses
### Media Richness
On the other hand, FtF does not allow much time for analysis and discussion of different viewpoints, especially when participants are shy or are not ready to share which will lower the participation interaction and affect creativity performance [33]. Furthermore, a relaxing learning situation will help elicit a greater level of creative thinking from the learners. Research [34] shows that creativity rarely emerges under stressful circumstances and thus it is important to keep learners in a relaxed mental state where the subconscious can do the nurturing and forming thoughts that give rise to creative thinking. Therefore, considering the interactivity and advances of the CMC communication format, its media richness has grown significantly in recent years [35; 36].
## 6 Open Challenges, Future Prospects and Research Directions
### Hybrid Adaptive Optics Techniques  
Investigating hybrid AO techniques can offer more comprehensive solutions for FSO communication systems.  
Combining Traditional and Novel Approaches: Exploring hybrid AO techniques that combine traditional methods with novel approaches, such as digital holography, can further enhance the robustness and efficiency of FSO communication. Digital holography involves capturing and reconstructing the wavefront information using digital sensors and processing algorithms. Integrating this technique with conventional AO methods can enhance the system's ability to correct for complex distortions and improve overall performance.  
Enhanced Turbulence Mitigation: Hybrid AO techniques can provide more comprehensive solutions for mitigating turbulence effects by leveraging the strengths of different approaches. For instance, digital holography can offer high-resolution wavefront reconstruction, while traditional AO methods can provide real-time correction. Combining these techniques can result in more effective turbulence mitigation and better communication link stability.
###### Abstract  
The increasing demand for more sensors inside vehicles pursues the intention of making vehicles more \"intelligent\". In this context, the vision of fully connected and autonomous cars is becoming more tangible and will turn into a reality in the coming years. The use of these intelligent transport systems will allow the integration of efficient performance in terms of route control, fuel consumption, and traffic administration, among others. Future vehicle-to-everything (V2X) communication will require a wider bandwidth as well as lower latencies than current technologies can offer, to support high-constraint safety applications and data exhaustive information exchanges. To this end, recent investigations have proposed the adoption of the millimeter wave (mmWave) bands to achieve high throughput and low latencies. However, mmWave communications come with high constraints for implementation due to higher free-space losses, poor diffraction, poor signal penetration, among other channel impairments for these high-frequency bands. In this work, a V2X communication channel in the mmWave (28 GHz) band is analyzed by a combination of an empirical study and a deterministic simulation with an in-house 3D ray-launching algorithm. Multiple mmWave V2X links has been modeled for a complex heterogeneous urban scenario in order to capture and analyze different propagation phenomena, providing full volumetric estimation of frequency/power as well as time domain parameters. Large- and small-scale propagation parameters are obtained for a combination of different situations, taking into account the obstruction between the transceivers of vehicles of distinct sizes. These results can aid in the development of modeling techniques for the implementation of mmWave frequency bands in the vehicular context, with the capability of adapting to different scenario requirements in terms of network topology, user density, or transceiver location. The proposed methodology provides accurate wireless channel estimation within the complete volume of the scenario under analysis, considering detailed topological characteristics.  
m 2020200111 September 202020
# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
## 2 Deep-Learning-Based Traffic Parameter Estimation
### MobileNetV2 Architecture  
MobileNet is a deep learning architecture known for its computational efficiency, making it suitable for resource-constrained environments such as mobile devices and embedded systems [33]. Its main features are depth-wise separable convolutional layers and inverted residuals. It is composed of an input layer, convolutional layers, and fully connected layers. This study demonstrates the application of MobileNetV2 to a multi-class image classification task, specifically focusing on \(\phi\)-OTDR waterfall image classification. The MobileNetV2 architecture employed in this study is depicted in Figure 4, showing the respective dimensions in each layer of the CNN. Images are presented to the MobileNetV2 with dimensions of \(224\times 224\) in RGB color mode, as required by the model. The total data are divided into training and validation sets, with a validation split of 0.2 for vehicle counting and 0.15 for average speed estimation. MobileNetV2 is pre-trained using ImageNet weights. After the MobileNetV2 layers, a global average pooling layer is set, followed by a fully connected (FC) layer with one-hundred-forty neurons, a dropout rate of 0.1, and finally an output layer with six neurons for vehicle counting estimation and fourteen neurons for speed estimation (as for ViT). L1 and L2 regularization are also applied to the dense layer in the classification head, with L1 = 0.001 and L2 = 0.0005. The Softmax activation function is used. A learning rate scheduler is set with an initial value of \(lr=0.001\), decaying by 50% every 10 epochs. The categorical cross-entropy loss function is used. The model is trained for 100 epochs, as in the previous case. The MobileNetV2 model has approximately 2 \(\times 10^{6}\) parameters.
## 1 Introduction
Second, conventional data-driven models ignore the intrinsic temporal dependency, which involves the effect of preceding actions on present actions [21; 22]. Actually, measured landslide displacement data contain temporal dependencies [23; 24]. The abovementioned limitations can be addressed from the following perspectives. The first is to utilize mutual information index describing nonlinear relationships by the amount of related information that is jointly owned by two or more variables [25] for input variable selection. The second solution is to recognize intrinsic temporal dependencies by deploying advanced modeling techniques. A promising solution is the recurrent neural network (RNN) [26]. The temporal dependency in monitoring data can be captured by adopting a sequential approach, thereby improving the ability to model dynamic systems. In addition, the \"decomposition-ensemble\" learning paradigm can also be considered a promising tool for analyzing series with complex nonlinearity characteristics and enhancing prediction accuracy [27; 28; 29; 30; 31]. The effectiveness of the \"decomposition-ensemble\" has already been confirmed in a variety of fields. Based on the \"decomposition-ensemble\" principle, a novel \"decomposition-ensemble\" learning model integrating EEMD and RNN was proposed in this study to enhance the performance of landslide displacement prediction. The Muyubao landslide located in the Three Gorges Reservoir area was selected as a case study to verify the performance of the proposed model.