esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Augmentation  
In deep learning, lack of adequate data has been a remarkable dilemma from the very beginning. The reason behind this is that if a model learns from fewer samples for a specific class, that class will have less probability to be predicted correctly, as the model does not know the pattern well compared to other patterns with enough samples. As a result, misclassifications are bound to happen. With more data, deep models can generalize the learning procedure. Augmentation allows the architectures to explore additional learning which can further enhance the classification outcome. For example, suppose a cat picture is provided to the architecture but the picture is rotated 45 degrees to the left or 45 degrees to the right. Despite being rotated to left or right, the picture is still a cat picture. Moreover, if the picture is translated, zoomed or if noise is added with the picture, still the picture is a cat picture. Augmentation procedure computationally produces these sorts of images by processes such as rotating, translating, shearing, flipping, etc. These processes produce artificial samples that not only increase the training size but also increase the learning outcome of the classifier. In addition, data augmentation can be used to make equal samples for training classes to solve the imbalance issue. In this research, we have applied augmentation on the train data and then split the train data into training sets and validation sets. No augmentation was applied on the test set. Moreover, the test set was provided separately with the considered datasets.  
Augmentation was implemented with the compensation of the Augmentor Library [49]. When using the method of augmentation, the max left rotation, the max right rotation, and the probability of rotation of the rotation function was anchored to 3, 3, and 0.4 respectively. The values of grid width, grid height, probability, and magnitude of the random distortion function were adjusted to 4, 4, 0.4, and 4 respectively. Moreover, the percentage ranges and the probability of the zoom random function were adjusted to 0.9 and 0.2 respectively.
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 3 Results and Analysis
### Temporal and Spatial Dynamic Changes in NDVI
#### 3.2.2 Analysis of Spatial Variations in NDVI  
The spatiotemporal distribution characteristics depicted in Figure 5 indicate that the high-value areas of annual average NDVI (Figure 5a) are predominantly concentrated in basin oases and mountainous forest-grass belts, closely aligning with regions abundant in water and heat resources. Conversely, the low-value areas are predominantly situated in desert Gobi regions. Due to the extreme arid environment, the vegetation coverage is minimal, primarily consisting of sparse drought-resistant sand plants like suo suo (Haloxylon ammodendron). On a seasonal scale, summer NDVI (Figure 5c) shows the most significant spatial heterogeneity, with high-value areas widely covering irrigated agricultural zones within oases, underscoring the substantial promoting effect of water resource management and agricultural practices on vegetation growth. In contrast, the low-value areas are mainly distributed in the desert and Gobi regions of the southern basin. Compared to other seasons, the NDVI values in summer show only a slight increase but remain at low levels, resulting in overall minimal vegetation cover. Winter NDVI (Figure 5e), while having the smallest high-value area, demonstrates a trend of expansion toward the desert-oasis transition zone, potentially attributable to the establishment of artificial shelterbels and the implementation of ecological restoration initiatives. The spatial patterns of spring (Figure 5b) and autumn (Figure 5d) NDVI reflect the transitional dynamics of vegetation during greening and withering periods, respectively, with spring  
Figure 4: Interannual variation trends of annual and seasonal NDVI in the Turpan-Hami Basin from 2001 to 2020: (**a**) annual average NDVI; (**b**) spring average NDVI; (**c**) summer average NDVI; (**d**) autumn average NDVI; (**e**) winter average NDVI.  
NDVI showing a patchy distribution along oasis margins and autumn NDVI characterized by high-value areas retracting toward drought-resistant vegetation belts. Similarly, in desert and Gobi regions, NDVI values remain at low levels in both spring and autumn. Notably, the spatial distribution of NDVI exhibits a high degree of consistency with the boundaries of regional land use types (e.g., farmland, forest land, and deserts), underscoring the pronounced influence of human activities on local vegetation cover.'
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Schedulers
#### 2.1.4 Earliest Completion First  
The earliest completion first (ECF) scheduler tries to make an intelligent scheduling decision by considering the RTT of the SFs, their available CWND, and the size of the data to be sent [30]. In an abstract form, for making the scheduling decision, it tries to estimate the arrival time of the packets at the receiver and chooses an SF so that the packets arrive in order at the receiver.
## 3 Results and Discussion
### Physical Characterisation: Granulometry and Densities  
The grain size distribution of HS1 and HS2 was analyzed and reported in the thesis of [PERSON] (2019) [13]. HS1 (referred to as C3) and HS2 (referred to as C2) exhibit notable differences in particle dimensions. HS2 has a wider size range compared to HS1, with particle widths ranging from 0.4 to 9 mm for HS2 and from 0.2 to 4 mm for HS1. Additionally, HS2 particles can reach lengths up to 45 mm, whereas HS1 particles vary between 1.5 and 20 mm. [PERSON] (2019) [13] categorizes HS1 as having a \"fine\" grain size and HS2 as having a \"coarse\" grain size.  
The apparent and skeletal densities of HS1 and HS2 are summarized in Table 5.  
The apparent densities of HS1 and HS2 are 135.1 and 103.0 kg\(\cdot\)m\({}^{-3}\), respectively, and their respective skeletal densities are 1338.4 and 1331.0 kg\(\cdot\)m\({}^{-3}\).  
These density values are generally consistent with those reported in the scientific literature, although they tend to be slightly lower. For instance, [PERSON] et al. (2021) [47] and [PERSON] et al. (2010) [52] reported apparent densities of 114 kg/m\({}^{3}\) and 113 kg/m\({}^{3}\), respectively, for hemp shiv, which are somewhat lower than the values observed in this study. Similarly, the skeletal densities reported by [PERSON] et al., (2018) [53], [PERSON] and [PERSON] (2018) [35], and [PERSON] et al. (2021) [47] are 1480 kg/m\({}^{3}\), 1450 kg/m\({}^{3}\), and 1247 kg/m\({}^{3}\), respectively. These values are notably higher than those found in HS1 and HS2.  
The discrepancies between the observed densities and those reported in the literature may arise from variations in the processing methods, sample preparation, and environmental conditions during growth and processing. The higher apparent density of HS1 compared to HS2 suggests that HS1 has a denser packing of particles or a smaller proportion of voids between particles.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
## 4 Experimental Research
### Dimension Reduction and Feature Visualization  
On account of discovering the spatial structure information hidden in the MI-EEG, the SE-isomap algorithm is used to reduce the dimension of OWP coefficients. The inter-class distance \(\sigma\) and target spatial dimension \(d\) are two important parameters in SE-isomap. Under some different parameters \(\sigma\), the proposed method is applied to dimension reduction for the training set and test set, respectively, and the most obvious three-dimensional features from the 16-dimension feature vector are visualized to discover the relationship and regularities between samples by a more intuitionistic way. Meanwhile, the accuracy of single experiment is provided to discuss the relationship between parameter \(\sigma\) and the classification accuracy. The experiment results of visualization are as below. In Figure 6, acc represents the classification accuracy on the test dataset.  
Figure 6: \(Cont\).  
As shown in Figure 6, the features of training sample sets whose dimension is reduced by the SE-isomap algorithm show the clustering characteristics in a three-dimensional space, and with the increase of \(\sigma\), the distance of the training datasets between different classes becomes larger at the same time. In Figure 6a, the aliasing still exists between the training samples. When the inter-class distance parameter \(\sigma\) increases, the separation of inter-class datasets is improved. It can be seen that the selection of parameter \(\sigma\) has a great influence on the feature visualization of EEG signals. It is clear from Figure 6b that the features of test samples show a linearly-separable distribution in the three-dimensional space. Although the visualization of the training samples is better when the inter-class distance parameter \(\sigma\) is larger, too large a value will bring a negative impact on the structure of the explicit mapping and also affect the true structural relationship between the data points, as shown in Figure 6c,d, which makes the distribution of the data points in the test set deviate significantly from their intrinsic geometry location, the overlapping phenomena occurring in a very small range due to the structural damage of some sample points, and ultimately leading to an unsatisfactory classification result. Therefore, it is very necessary to choose the value of \(\sigma\) reasonably for obtaining an ideal classification result.
# A Survey of Deep Learning for Data Caching in Edge Network
## 3 Deep Learning Outline
### Recurrent Neural Network (RNN)
#### 3.3.2 Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)  
Recently, an efficient way to cope with long-term dependencies in practical is employing gated RNN, including LSTM [43]. Then, we compare with the recurrent neuron in simple RNN: Internally LSTM introduces three gates to control signal propagation, where input gate \(I\) decides the partition of input signal to be stored, forget gate \(F\) controls ratio of last moment memory to be kept until next period (the name \"forget gate\" may be a little misleading because it actually represents the ratio to be remembered), and output gate \(O\) influences the proportion of current state to be delivered; Externally LSTM has four inputs embracing one input signal and three control signals for three gates. All these four signals are derived via the calculation of current network input and last moment delivered state (i.e., the output of last time state, called hidden state \(h_{t-1}\)). The data flow before the output gate is named inner state \(c_{t}\). Intuitively, \(c_{t}\) contains something new learned via the input and the previous knowledge through \(h_{t-1}\). In other words, some old memories still have an influence on the current decision so the gradient vanishing is eased. The side effect of LSTM is that the implementation of gate mechanism is very complicated, which makes both training and testing slower. Because of the temporal causal, LSTM or common RNN does not support parallel training like CNN. One explanation of the name \"Long Short-Term Memory\" is given by [PERSON] in the book \"Neural Networks and Deep Learning\" ([[https://nndl.github.io/](https://nndl.github.io/)]([https://nndl.github.io/](https://nndl.github.io/))): the hidden state \(h\) stores the historical information, which is viewed as a kind of memory. In the simple RNN, this state is updated at each time slot, which is called short-term memory. Besides, the parameter of neural network is updated at each iteration, which is regarded as long-term memory. The life of \(h\) is longer than the short-term memory and that is why it is named LSTM.  
A simpler version is employing gated recurrent unit (GRU) [47]. On one hand, the role of input gate and forget gate in LSTM are complementary. So, GRU combines these two gates and utilizes a single gate \(Z\) to control the balance between the previous memory and newly learned knowledge. On the other hand, GRU removes the output gate since both hidden state \(h\) and inner state \(c\) of LSTM contain the information of last state. Moreover, the candidate state \(h^{\prime}\) in GRU depends on current input and the result of reset gate \(R\). Compared with LSTM, GRU has fewer parameters, which indicates its training is faster than LSTM.
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 6 Conclusions  
In this paper, we conducted a comparative analysis between three promising producer MM schemes, each representing a different approach: AFIRM (routing-based), Map-Me (data plane-based), and Kite (anchor-based).  
We ascertained and compared the performance of the schemes to accommodate latency-sensitive and real-time applications. These applications generate traffic classified according to its sensitivity to latency and do not benefit from network caches due to their real-time nature. We evaluated, using NdnSIM, all three schemes with the same network, traffic, and mobility settings.  
The results showed that the data plane-based approach represented by the Map-Me scheme held the right balance to handle producer mobility in delay-sensitive applications. The routing-based approach yielded a negligible, slightly lower data retrieval delay at the cost of a much higher overhead.  
Further investigations can be conducted using different network topologies and for various specific environments. One such environment is that of vehicular ad hoc networks (Vanet) where road side units (RSU) act as central entities, and hence the anchor-based category may prevail. The question arises, however, of the conformity of the NDN specifications and the implementation in ndnSIM.  
Methodology, L.A., A.B., A.G. and S.A.-A.; Software, A.G.; Formal analysis, L.A., A.B., A.G. and S.A.-A.; Investigation, L.A.; Writing--original draft, L.A., A.B., A.G. and S.A.-A.; Writing--review & editing, L.A. and A.B.; Supervision, A.B.; Project administration, A.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deanship of Scientific Research at King Saud University.  
Not applicable.  
The authors would like to thank the Deanship of Scientific Research at King Saud University for funding and supporting this research through the initiative of DSR Graduate Students Research Support (GSR).  
The authors declare no conflict of interest.
In order to investigate the triggering mechanism of rainfall-induced landslides, instrumental monitoring studies that lasted for two years, laboratory tests, and numerical analyses to establish accurate parameters and, thus, to establish the triggering mechanism more realistically were performed. The variations in matric suction, volumetric water content, groundwater level, and rainfall amount were measured with sensors in the landslide area. The geotechnical properties of the ground were determined by laboratory tests. Using data obtained from in-situ monitoring and laboratory tests, stability analyses were performed. The landslide was investigated by means of additional parametric analyses performed based on transient flow analyses using the rainfall data and subsequent stability analyses. The rainfall intensity duration thresholds were obtained for both dry and wet periods separately as a result of the numerical analyses performed by considering the site-specific measured seasonal matrix suctions and groundwater levels.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Reinforcement Learning Algorithm
```
1:procedureD2D Route selection
2: /* Source node selects a route to offload traffic from MC BS */
3:\(MCBS\) receives a route map (i.e., \(\{k_{bb},k_{2},k_{3},k_{4}\}\) from the CC and sends it to \(fc_{s}\)
4: /* Stage 1 */
5:for time \(t_{1}\); \(fc_{s}\) reprioritize D2D routes in the route map do
6:\(k=\{k_{2}>k_{3}>k_{4}>k_{bb}\}\)
7:if\(k_{2}\) or \(k_{3}\) or \(k_{4}\) is not available then
8:\(fc_{s}\) use backbone route \(k_{bb}\)
9:endif
10:endfor
11: /* Stage 2 */
12:for time \(t_{n+1}\), \(n\in|N|\); \(fc_{s}\) reprioritize D2D routes based on traffic intensity \(\Psi_{t_{n}}^{k_{n}}\) and Equation (4) do
13: Estimate \(\Phi_{t_{n},OFF}^{c_{n},h_{n},k_{n}}\) using Equations (2) and (8)
14:if\(\Psi_{t_{n}}^{k_{n}}<\Phi_{t_{n},OFF}^{c_{n},k_{n}}\) /* traffic intensity is not updated */ then
15: update \(\Psi_{t_{n}}^{k_{n}}\leftarrow\sum(\Phi_{t_{n},\beta_{OFF}}^{c_{n},k_{n}})\)
16:endif
17: Calculate dynamic learning rate \(\alpha(s_{t_{n}}^{k_{n}},a_{t_{n}}^{k_{n}})\) using Equation (10)
18: Update the Q-value using Equation (4)
19:for\(fc_{s}\)do select a D2D route with the maximum Q-value from routes \(\{k_{2}>k_{3}>k_{4}\}\)
20:endfor
21:endfor
22:endprocedure
```  
**Algorithm 2** RL mechanism at distributed nodes and BSs. Figure 5: Experimental setup for the hybrid route selection with three D2D routes and a backbone route via MC BS.
## 3 Materials and Methods  
This section describes the materials and methods used in this study. When adopting new technologies, decision-makers want to evaluate the system before its acquisition. Using simulators helps estimate coverage and the main parameters of a 5G deployment scenario.  
In general, physical objects affect the coverage of the 5G network in an industrial environment where heavy machinery, production lines, or compound materials are ubiquitously in working fields. Antenna position, frequency band, or transmitting power also affect radio coverage. Antenna position or transmitting power changes can improve or deteriorate the radio signal coverage. Radio simulators can provide visual representation features to understand the effect of these changes.  
Additionally, network simulators allow data transmission and analysis with virtual equipment representing either a valid user or a non-authorized user. Virtual equipment can provide security measures to guarantee safety across the network, where the simulation of firewalls or encryption techniques can help to increase network security.  
Using 5G digital twins allows radio and network behavior simulation to represent common issues about 5G technologies. This study aims to provide a practical application scenario of enabling 5G technologies, providing an initial testing approach to non-academic adopters.
# Unconventional Extraction Methods of Oleaginous Yeast Cell Pretreatment and Disruption
## 5 Conclusions  
The aim of this research was to evaluate the impact of several methods of permeabilization and disintegration of cell wall structures and cell membranes on the efficiency of microbial oil extraction from oleaginous yeast cells of the _Y. lipolytica_ species. The research conducted in this study indicated that none of the unconventional methods used, such as US, PEF, or HPP, led to effective disintegration and release of lipid bodies from cells. Extraction of microbial lipids was possible only after using solvent extraction. However, it can be clearly stated that as a result of using appropriate techniques for processing biological material (such as PEF or US), a better efficiency of microbial oil extraction from yeast cells can be achieved. Still, optimization experiments should be further conducted in order to enhance the permeabilization effect. Based on the results published by other authors, there is an unreached potential for unconventional methods. In the future, it would be advisable to conduct research to answer the question of whether the use of PEF in the flow would improve the permeabilization effect. Moreover, some of the methods tested in this work, such as the use of sonication to improve the extraction of microbial oil from yeast cells, require further experiments. It seems that the use of ultrasonics may bring better results in terms of extraction of cell contents after applying more stringent process conditions.  
Conceptualization, A.F.; methodology, A.F., K.W. and D.N.; investigation, A.P., A.F., K.J., K.W., B.Z., D.N. and J.M.; resources, A.F. and D.N.; data curation, A.F., A.P. and K.W. writing--original draft preparation, A.F., A.P., K.W. and J.M.; writing--review and editing, A.F., A.P. and B.Z.; visualization, A.P. and K.J.; supervision, A.F. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by sources of the Ministry of Education and Science within funds of the Institute of Food Sciences of Warsaw University of Life Sciences (WULS) for scientific research. Research equipment was purchased as part of the \"Food and Nutrition Centre--modernisation of the WULS campus to create a Food and Nutrition Research and Development Centre (CZiZ)\" co-financed by the European Union from the European Regional Development Fund under the Regional Operational Programme of the Mazowieckie Voivodeship for 2014-2020 (Project No. RPMA.01.01.00-14-8276/17).  
Not applicable.  
The data presented in this study are available on request from the corresponding author (A.F.). The data are not publicly available due to privacy.  
The authors would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for assistance in operating the microscope, PEF, and high-pressure devices.  
The authors declare no conflict of interest.
## 2 State of the Art  
Before constructing new roads or modifying existing ones, cities need to perform counting and conduct surveys about the amount of actual traffic. For motorized traffic, the vehicles are often classified by their weight, the number of wheels, or the number of axes. This categorization varies from country to country but is mostly well defined. In the weight class of micromobility vehicles, no such clear definition exists yet, and most surveys have only differed between pedestrians and bicycles thus far. A study by [PERSON] et al. [3] states that currently, only 8% of the questioned state agencies in the US store data on micromobility, compared with 100% for bicycles and 92% for pedestrians. Additionally, out of 10 questioned companies, only 4 reported the capability of counting micromobility vehicles, and only 3 offered the capability of counting bicycles, pedestrians, and micromobility at the same time. One of these systems is a mobile app that requires the voluntary participation of people to install the app, and the other systems are camera-based or use a combination of different sensors for counting and classification. For the future, it will be important to be capable of distinguishing subclasses such as cargo bikes, bicycles with trailers, or e-scooters.  
Pedestrians and micromobility traffic undergo significant variation due to commuting, weather, and seasonal changes, making regular surveys necessary. These can be performed either manually or be automated by sensor systems. Manual counting is mostly used to establish a baseline or as a reference for automated systems because of its high accuracy. However, it is not applicable on a large scale or in continuous data acquisition due to the high amount of labor required. Installing an automated counting device in each street of a city is also not feasible. Therefore, mobile counting devices are often used to perform short countings, and the average annual daily bicycle traffic is estimated. A study by [PERSON] et al. [4] investigated the best duration for counts. The complete results are shown in Table 1 and suggest an optimal duration of one week.