esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.3.2 Calculation of the Standardized Precipitation Evapotranspiration Index (SPEI)  
The standardized precipitation evapotranspiration index (SPEI) was used to characterize drought degree [5]. The different time scales of SPEI (running from 1 to 48 months) represent the precipitation deficit or precipitation surplus over the preceding period. In this study, we calculated the three-month SPEI (denoted as SPEI\({}_{3}\)) to determine the droughts of the Loess Plateau. The SPEI time scale has been shown to reflect seasonal variations in drought conditions [37]. The SPEI index is calculated as follows:  
Step 1: Calculation of potential evapotranspiration (PET):  
\[\text{PET}=16\bigg{(}\frac{\text{N}}{12}\bigg{)}\bigg{(}\frac{\text{M}}{30} \bigg{)}\bigg{(}\frac{10\text{T}}{\text{H}}\bigg{)}^{\text{A}} \tag{2}\]  
where T represents the monthly average temperature (\({}^{\circ}\)C), N represents the maximum sunshine hours, M represents the number of days in the current month, and H represents the heat index. A is a constant determined by the heat index, H: A = (\(0.492+0.179\text{H}-7.71\)) \(\times\) (\(10^{-5}\text{H}^{2}\) + \(6.75\times 10^{-7}\text{H}^{3}\)).  
Step 2: Calculation of the differences between monthly precipitation (\(P\)) and potential evapotranspiration (PET) at different time scales:  
\[\text{D}_{\text{i}}=\text{P}_{\text{i}}-\text{PET}_{\text{i}} \tag{3}\]  
where \(\text{D}_{\text{i}}\) is the difference between precipitation and evapotranspiration, \(\text{P}_{\text{i}}\) is the monthly precipitation, and \(\text{PET}_{\text{i}}\) is the monthly potential evapotranspiration.  
Step 3: Calculation of SPEI using the log-logistic probability distribution of three parameters to normalize the \(\text{D}_{\text{i}}\) (the results of step 2) and then obtain the SPEI series [38].  
To analyze the drought characteristics of the Loess Plateau, we divided five drought grades using China's meteorological drought grade standard, geographical characteristics of the Loess Plateau, and a related research [39; 40] (Table 1). The smaller the SPEI value, the more severe the drought.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Standardized Precipitation Evapotranspirationipation Index (SPEI)** & **Drought Magnitude** \\ \hline
0 \(<\) SPEI & No drought \\ \(-1\)\(<\) SPEI \(\leq\) 0 & Mild drought \\ \(-1.5\)\(<\) SPEI \(\leq-1\) & Moderate drought \\ \(-2\)\(<\) SPEI \(\leq-1.5\) & Severe drought \\ SPEI \(\leq-2\) & Extreme drought \\ \hline \hline \end{tabular}
\end{table}
Table 1: Drought classification is based on the standardized precipitation evapotranspiration index (SPEI).
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
## 3 Preliminaries
### Sensor Activity Control Algorithm  
This study proposes the SACA, which is a sensor control algorithm with high robustness, fast convergence, and power-saving capabilities. The sensor and base stations use random access technology to send and receive packets. The base station calculates the difference d between the total number of packets received at the current time (round) and the target value, and adds the difference \(d\) in the feedback packet, which is sent to the sensor. The sensor uses a weight value to determine whether it should switch its state. SACA mainly has two sub processors to control the number of active sensors, namely, the sensor adjustment procedure for the weight value of sensors and the transformation procedure for the state of sensors. In the state transition process, the active sensor determines whether it should continue in the active state or switch to the sleep state in the next round based on the difference recorded in the feedback packet. Sleep sensors do not need to receive feedback packets from the base station to achieve true dormancy. The sleep sensors gradually increase their weight value in each round to increase the probability of changing to the active state. In the adjustment procedure for the weight value of the sensors, the sensor decides whether to change the state or maintain the original state in the next round according to its weight value, \(W_{i}\).  
Assume that \(m\) sensors and a base station are randomly deployed in the monitoring area. Before the system starts operating, the sensor randomly selects a state (e.g., an active or sleep state). Each sensor contained a weight value, \(W_{i}\), with an initial value of 0. The weight value, \(W_{i}\), affects the probability that the sensor changes its state in the next round. Each packet is transmitted from the sensor to the base station in one hop, and vice versa.
## 4 Discussion
### Recommendations for Future Research  
The findings of this study provide some insights into the effectiveness of MR technology and PowerPoint presentations for teaching the human muscular system, and also into the moderating role of age in instruction method effects. However, there are some limitations and gaps that should be addressed by future research.  
First, the sample size was relatively small and consisted only of undergraduate students from two educational institutions, which limits the generalizability and external validity of the findings. Future research should use larger and more diverse samples that incorporate different age groups, education levels, and cultural backgrounds, as this will increase the representativeness and robustness of the findings.  
Second, the study did not account for individual differences in learning styles, preferences, and motivations, which may have influenced the learners' engagement with different instruction methods, along with subsequent performance. Future research should measure and control for these factors, and examine how they interact with the instruction methods and the learning outcomes.  
Third, the study used a single topic (the human muscular system) and a single type of assessment (multiple choice tests) to measure the learning outcomes, which may not capture the full range of knowledge and skills that learners acquired from the instruction methods. Future research should use multiple topics and types of assessments (such as open-ended questions, practical tasks, and/or case studies) to provide a more comprehensive and holistic evaluation of the learning outcomes.
# Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey
## 2 Routing Protocols
### Main Challenges Facing UWSN Routing Protocol Designers  
In this context, to better design an OR routing protocol for UWSN, a number of challenges that UWSN routing protocol designers encounter are listed and briefly discussed below, as in [36]:  
1. Limited bandwidth and data rate: UWSNs suffer from limited available bandwidth (i.e., acoustic waves use a frequency between a few Hz and tens of kHz) and low data rate (i.e., the transmission rate hardly exceeds 100 kbps). The limited accessible acoustic bandwidth depends on the communication range and acoustic frequency.
2. High propagation delay: The UWSNs use an acoustic channel for communication between the underwater sensor nodes themselves and with the sink(s). In the acoustic channel, the propagation speed is five orders of magnitude lower than in the radio channel. This high propagation delay (0.67 s/km) can significantly decrease the throughput of the network.
3. High noise and interference: Basically, there are two kinds of noises, man-made and natural. These noises are caused by water currents, machines, marine mammals, and shipping. The noise under water is much more serious than in the terrestrial environment. The interference is essentially caused by the surface, the bottom, or animals and contamination reflections.
4. High bit error: Due to the shadow zones caused by animals, water currents, and human-made noise, the acoustic channel suffers from a high bit error rate and temporary losses of connectivity.
5. Limited resources: In UWSNs, sensor nodes are constricted resource devices (i.e., they have limited energy and memory). Therefore, after deploying the sensors in an underwater environment, it becomes difficult and costly to replace or recharge the node batteries due to the harsh underwater environment. Moreover, underwater sensor nodes are vulnerable to deterioration and damage due to corrosion and pollution.
6. Topology changes: Due to the flow of water, the underwater sensor nodes cannot stay in one location; instead, they move randomly, which gives UWSNs a mobile or changeable topology.
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 2 Passive Constant Flow Regulators--General Considerations
### Materials  
Silicon, glass, and poly(dimethylsiloxane) (PDMS) are the most common materials in contact with the working fluid in the passive constant flow regulators reported here. Titanium [7; 16] was also considered for implantable devices. Early microfluidic and Micro Total Analysis Systems (\(\upmu\)TAS) were made in glass and silicon due to the availability of process equipment and etching recipes. Since the Whitesides' group manufactured, in 1998, microfluidic devices in PDMS by soft lithography, PDMS became the preferred material for rapid prototyping of microchannel systems for use with biological samples [17; 18; 19]. Indeed, PDMS is an inexpensive, optically transparent, gas and vapor permeable, hydrophobic, relatively inert, and biocompatible polymer that, moreover, cures at low temperatures. PDMS is soft and facilitates removal from Si molds for micron-scale feature replication.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Temperature** (\({}^{\circ}\)**O** & **5** & **10** & **15** & **20** & **25** & **30** & **35** & **40** \\ \hline Water dynamic & & & & & & & & \\ viscosity (mPa-s) & 1.520 & 1.308 & 1.139 & 1.002 & 0.890 & 0.797 & 0.719 & 0.653 \\ Relative water flow & & \(-34.1\%\) & \(-23.4\%\) & \(-12.0\%\) & 0.0\% & 12.6\% & 25.7\% & 39.3\% & 53.5\% \\ rate error (\%) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect of dynamic viscosity change with temperature on relative flow rate accuracy of passive constant flow regulators. The reference temperature is \(20\ ^{\circ}\)C.  
The surface energy of PDMS can be tuned to increase its wettability. Also, PDMS can be sealed reversibly to itself or other materials. Last but not least, the elasticity of PDMS has been widely exploited to create membrane pumps and check valves in active microfluidic devices for cell sorting and biochemical assays [20]. PDMS was also considered to make passive flow regulators because this material enables the rapid prototyping of complex three-dimensional (3D) structures. On the other hand, PDMS presents, compared to Si and glass, some disadvantages that can preterite its use as raw material for specific microfluidic applications that require long-term stability, reproducibility, well-controlled surface chemistry, high accuracy, stiffness, high temperature, compatibility with organic solvents, or low gas/vapor permeability. Si/glass biomedical electromechanical systems (BioMEMS) are more expensive to produce at low volume and require interconnections to the other parts of the fluidic systems. Silicon is a rigid material with a well-defined Young's modulus by contrast to the hyperelastic PDMS, whose mechanical properties are highly dependent on its degree of cross-linking and stretching. Si membranes exhibit a highly reproducible displacement and no fatigue, and the electronic properties of silicon enable the smart integration of reliable sensors [21]. Also, the negative charge surface of glass and silicon support electroosmotic flow (EOF).
# Novel Drug Combinations in Lung Cancer: New Potential Synergies Between 5-FU and Repurposed Drugs
## 3 Results
### Impact of Resveratrol on the Cytotoxicity of Levosimendan and 5-FU in Lung Cancer Cells  
In our previous experiments, Levosimendan did not exhibit significant anticancer effects in the A549 lung cancer cell line as it did in bladder and prostate cancer cell lines [41]. To enhance its antitumor activity, we explored the combination of Levosimendan with Resveratrol, a compound known for its potent anticancer properties. Resveratrol was specifically studied in the A549 cell line because it shares common receptors with this line, including B\({}_{2}\) adrenergic [53,54] and EGFR receptors [55,56], which are implicated in cancer progression and treatment response. Additionally, given the established efficacy of 5-FU, we investigated whether Resveratrol could also augment the effects of 5-FU at its IC\({}_{50}\) concentration in A549 cells.  
As shown in the accompanying data (Figure 12), the combination of Resveratrol with Levosimendan demonstrated a significant reduction in cell viability. Specifically,  
Figure 11: Clonogenic assay of the human lung cancer cell line treated with IC\({}_{50}\) concentration of 5-FU and Levosimendan, both alone and in combination. (**A**) Image displaying the colonies formed by the human cancer cell line after plating 100 cells and incubating for 14 days. (**B**) The number of colonies was quantified, with error bars representing the mean \(\pm\) SD from three independent experiments. ns: non-significant;* statistically significant vs. control at \(p<0.05\);** statistically significant vs. control at \(p<0.01\).  
the combination of 50 \(\upmu\)M Resveratrol with 1 \(\upmu\)M Levosimendan led to a 31% decrease in cell viability compared to Levosimendan alone, while 100 \(\upmu\)M Resveratrol combined with 0.1 \(\upmu\)M Levosimendan resulting in a 33% reduction in cell viability. Moreover, when combining Resveratrol with the IC\({}_{50}\) concentration of 5-FU, similar trends were observed. These findings suggest that Resveratrol enhances the cytotoxic effects of both Levosimendan and 5-FU, reinforcing its potential role as a synergistic agent in cancer therapy.  
In relation to the morphological analysis (Figure 13), treatments with Resveratrol alone showed a visible reduction in the number of cells as the drug concentration increased, with noticeable alterations in cell phenotype at the highest concentration of Resveratrol. Similar observations were made with the combination of Resveratrol and the IC50 concentration of 5-FU. Furthermore, in the combination of Levosimendan with Resveratrol, a reduction in the number of cells was also evident as the concentration of Resveratrol increased and that of Levosimendan decreased.
## 1 Introduction
### Contributions  
Our work has a different approach to the problem of detecting jamming, facilitating other jamming related works such as jammed area mapping, jammer localization and tracking and consequent decision making for different anti-jamming actions for various tactical operations. The major contributions are listed as follows:  
* _Contour mapping_, based on different lower cut-off values of jamming indices of nodes of the WSN (akin to altitude contours on a geographical relief map) is made possible. This is a better alternative to the present trend of plotting the jammed area, as recommended by different authors such as [PERSON] _et al._[7], [PERSON] _et al._[8], [PERSON] _et al._[9], and others, because instead of dividing the geographical extent of the WSN into jammed and non-jammed areas, it provides a jamming gradient to the whole area.
* _Flexibility in extent of jammed area mapping_ is possible which would give more working space to the battle field commander, e.g., in a defensive battle, during the _pre-contact-with-enemy_ stage, when the density and health of the WSN is the best, the jammed area may be bounded by a contour of jamming index of 75% (say) and the same may expand to 50% and 25% during the _contact-with-enemy_ stage and _counter-attack_ stage, as the battle progresses and the density and health of the WSN goes on depleting.
* _Holistic decision_ regarding the jammed condition of a node, based on the node parameters and its neighborhood conditions is taken at the base station, and not at the node level, as done in the existing methods. This not only improves the quality of the decision and survivality of the decision making process, but also takes off the extra burden of taking such decisions from the already resource-starved nodes under siege of a jammer.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 6 Simulations and Results
### Simulation Scenarios  
The simulations of the MNM concept were divided into three simulation scenarios, which are intended to illustrate the advantage of using multiple layers of the MNM model compared to the deployment of only WSN networks in the area affected by the disruption of fixed 5G and IoT infrastructure.  
The simulation presupposes that there is an area where the fixed infrastructure has been disrupted and there is only one functional AP. Another prerequisite is the location of the WSN wireless sensors in the area intended for data collection, while the occurrence of mobile MANET devices is also considered in the same area. In the network disruption scenario, UAVs of DRONET networks are also present, arranged in the area to cover the MANET network.
# The Spatial Structure of Passively Simulated Atmospheric Boundary Layer Turbulence
## 4 Results and Discussion  
### Statistical Parameters of Turbulence  
The turbulent spectrum of three fluctuating velocity components of BL1 at \(z=0.9\) m is shown in Figure 3, where \(S_{u}(f)\), \(S_{v}(f)\), and \(S_{w}(f)\) represent the spectral density of along-wind, lateral, and vertical wind fluctuation components. The measured spectrum agrees well with the theoretical models in the low-frequency range, which is consistent with other heights and turbulence flows. However, the fitting accuracy of the two theoretical models differs in the high-frequency range. The [PERSON] spectrum model retains a good fitting accuracy, but the Dryden model has a higher attenuation rate at high frequencies, resulting in deviations from the test results. Figure 3 also shows the fitting result of the \(-5/3\) exponential attenuation curve, which shows that the simpler \(-5/3\) exponential attenuation theory can be used for description in the high-frequency attenuation area. This result demonstrates that theoretical simulation can better describe the spectral properties of passively stimulated turbulence, but there are some differences in the high-frequency inertial subregion and the dissipation region. The integral length scale can be obtained by fitting the von Karman spectrum model using the nonlinear least square method. Table 3 displays the statistical parameters of the three turbulence fields.  
Figure 3: The turbulent spectrum of BL1: (**a**) along\(-\)wind fluctuation spectrum; (**b**) lateral fluctuation spectrum; (**c**) vertical fluctuation spectrum.  
According to the statistical turbulence parameters of several wind fields, the simulated wind field is basically consistent with the natural atmospheric turbulence, which can meet the research requirements. The length scales and the turbulence intensities for the three velocity components in Table 3 deviate slightly from the conditions typically seen in the atmospheric boundary layer. There are some limitations to the experimental observations due to the insufficiency of the commonly used wind tunnel [8; 37]. For example, the turbulence intensity is different from the actual situation (i.e., \(I_{u}>I_{p}>I_{w}\)), and the difference between the three components is smaller than the field measurement result of the natural wind. This indicates that the wind velocity fluctuations of the \(w\) component of the simulated BL flows are stronger than those of the natural atmospheric boundary layer turbulence.
# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 3 The Proposed Model
### Data Cleaning  
Data cleaning is the first step in the proposed model. The purpose of this task is to remove unnecessary words and symbols from the text messages in order to improve the performance of the machine learning model. The elements to be removed at this step are:  
* Punctuation: remove unnecessary punctuation marks and symbols from the text.
* Capitalization: remove the capital-letters by transforming all words to lower-case.
* Arabic and English stop-words: Stop-words refer to the most common words in a language that are not important for understanding the text. These include words such as \"the\", \"is\", \"a\", \"which\", and \"on\" for English language and for Arabic language. In NLP tasks, it is often useful to remove these types of words before training the models in order to reduce the amount of 'noise'.
## 3 Results
### Correlation between XCO\({}_{2}\) and Biosphere Parameters
The overlapped lines are 8.0 ppm in solid black and 4.0 ppm in dotted black as shown in Figure 3.  
It can be seen from Figure 6 that XCO\({}_{2}\) decreases sharply when NDVI increases quickly during the growing season in summer. A stronger negative correlation in the forest region (\(-0.68\) in FeR) compared to the cropland region (\(-0.50\) in CwR) at the same latitude zone can be found. PCCs between seasonal variations of XCO\({}_{2}\) and NDVI is up to \(0.8\) over GcA, CnI, and CnC. The rainy season over GcA boosts the grass growth and promote the CO\({}_{2}\) uptake. Strong correlation presented in CnI and CnC, imply that the seasonal crop farming from planting, growing to harvesting may have a large impact on the seasonal variation of CO\({}_{2}\) uptake and release. Moreover, from Figure 6, we can see that there is an offset between the time of XCO\({}_{2}\) troughs and NDVI peaks. The time delay, as lags of month, over the six regions of interest are also indicated in Figure 6. Comparing with phase of NDVI, the phase of XCO\({}_{2}\) is generally delayed by a lag of about 0-2 months, which generally agrees with results from Table 3 in [62] by [PERSON] et al., which shows the delayed time between seasonal amplitudes of XCO\({}_{2}\) and boundary layer CO\({}_{2}\) to be within 2 months [62], with generally a greater difference in high latitude regions compared to low ones in NH. This offset is probably due to the time delay in XCO\({}_{2}\) response to terrestrial biosphere activities associated with the mass of CO\({}_{2}\) in the column and vertical mixing and convection of CO\({}_{2}\) from the surface to the column profile [58,62,63]. In this study, we define the time delay to be the lags of months between XCO\({}_{2}\) and NDVI that make their negative correlation the strongest. We can see the delayed time over the three regions (FrC, FeR, and CwR) in high latitude regions are over one month, and larger than the other three regions at low latitudes (CnC, GcA, and CnI), probably indicating a relatively slow vertical mixing due to very large changes of the CO\({}_{2}\) mass at high latitudes with strong biosphere absorption. The global pattern of this phase delay effect is elaborated in Section 3.3. Figure 6: Seasonal variations of monthly-averaged seasonal GM-XCO\({}_{2}\) (dXCO\({}_{2}\), black lines) and NDVI (green lines) from June 2009 to May 2014 in six selected regions of interests as shown in Figures 1 and 5a. Vertical error bars represent one standard deviation from its mean value. The PCC values given in the figure titles, between seasonal variations of GM-XCO\({}_{2}\) and NDVI, are averaged values for all grids within the region of interest. The time delay, in terms of lags of months, of the NDVI time series to maximize the negative correlation with XCO\({}_{2}\) is \(1.6\) months for FnC, with a minimum averaged PCC of \(-0.82\). They are \(1.0\) month and \(-0.85\) for FeR, \(1.8\) months and \(-0.83\) for CwR, \(0.6\) months and \(-0.85\) for CnC, \(0.8\) months and \(-0.86\) for GcA, and \(0.3\) months and \(-0.81\) for CnI.