esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 5 Solving the ERRP Model Based on the Proposed Hybrid Algorithm
### Update Populations  
In the clustering operation, we create a new random individual to take the place of the cluster center. When operating updating populations, all the individuals need to be updated to take advantage of the newly generated individual. Firstly, a random value will be generated. A random cluster will be selected if the random value is less than the  
Figure 3: The flowchart of the ERRP solving model based on the HBSO.  
set probability. From this cluster, an individual will be chosen randomly, and a swap operation will be performed. If the random value is not less than the set probability, then two clusters will be randomly selected. Afterward, one individual from each cluster will be chosen randomly and crossed over. In the above two operations of the random selection of individuals, the center of the cluster will be selected when there is only one individual in the cluster. The resulting new individuals will be stored and await the next operations. The pseudocode for updating the population in the HBSO is shown in Algorithm 5.  
```
Input: individuals after clustering, \(p_{one}\) (the probability of selecting only one cluster to generate new individuals), \(p_{noncenter}\) (the probability of selecting the cluster center to generate new individuals), \(p_{tvaccenter}\) (the probability of selecting cluster centers to generate new individuals)
1 Update Population: randomly select one or two cluster(s) to generate new individuals;
2 if\(rand<p_{one}\)then
3 Choose one cluster at random;
4 if\(rand<p_{nocenter}\) or the cluster has only one individual then
5 The cluster center of this cluster is chosen;
6 else
7 Randomly choose an individual in the cluster other than the cluster center;
8 endif
9 Perform a swap operation on this individual;
10 else
11 Choose two clusters at random;
12 if\(rand<p_{tvaccenter}\) or only one individual in both clusters then
13 Select the cluster centers of these two clusters;
14 else
15 if only one cluster has just one individual in it then
16 Randomly select an individual in another cluster other than the cluster center;
17 else
18 Select an individual from each of the two clusters other than the cluster centers;
19 endif
20 endif
21 Perform crossover operations on these two individuals;
22 endif
23 Store the new individuals and update populations; Output: individuals after update
```  
**Algorithm 5**Update Population Strategy
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 5 Proposed WLAN Aware Cognitive MAC (WAC- MAC) Protocol
### Adaptive Backoff  
For channel access in IEEE 802.15.4, the CSMA/CA algorithm uses only a small range of backoff exponents values in the range of \(\{macMinBE,macMaxBE\}\), where the value of \(macMinBE\) by default is equal to 3, and the value of \(macMaxBE\) is 5. The selection of these backoff exponents by all nodes may result in collisions and cause energy loss in IoT nodes. To solve this issue, we propose an energy-based adaptive backoff scheme where nodes with lower battery time are allocated a lower backoff value, thus giving them a higher priority to transmit. A lower backoff value implies less wait required for channel access. The advantage of this scheme is that nodes with lower energy can completetheir transmissions before their energy gets depleted. Particularly, for energy harvesting-based IoT nodes, this scheme allows nodes to go quickly into sleep mode and harvest energy for future transmissions. Another advantage of this scheme is that nodes are allocated different \(macMinBE\) values, hence reducing the chance of selecting the same backoff value. Nodes are divided into three priority classes based on their energy level, as shown in the table 1.  
The energy level values are calculated based on the residual power of the sensors. Devices that belong to priority class (L1) get their macMinBE value decremented by a value of '2'. Devices that belong to priority class (L2) get their macMinBE value decremented by a value of '1'. Devices that belong to priority class (L3) get their macMinBE value decremented by a value of '0'. Figure 11 depicts the flow chart of proposed CSMA/CA with an adaptive backoff exponent scheme based on the energy level of sensor nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Energy Level** & **Priority Class** & **MacMinBE Value** \\ \hline
0\%–30\% & L1 & 1 \\
30\%–60\% & L2 & 2 \\ >60\% & L3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy Levels mapping to \(macMinBE\) Values. BE = backoff exponent.  
Figure 11: Proposed adaptive backoff exponent algorithm for IoT network node. NB = number of backoff.
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 2 TORC2 Structure and Function  
Only Tor2 can serve as the catalytic subunit in yeast TORC2, whereas TORC1 is functional when its catalytic subunit is either Tor1 or Tor2 [10; 11]. The small \(\beta\)-propeller (\"WD40 repeat\") protein Lst8 is present in both TORC1 and TORC2, presumably because, as shown for its mammalian ortholog (mLST8), it binds tightly to and greatly stabilizes the TOR kinase fold [18; 19; 20]. Aside from Lst8, however, the other known subunits in yeast TORC2, namely Avo1 (mammalian ortholog is mSIN1), Avo2 (appears to be fungal-specific), Avo3/Tsc11 (mammalian ortholog is Rictor), Bit2 and Bit61 (mammalian counterparts are ProtoT1/PRR5 and ProtoT2/PRR5L), and Slm1 and Slm2 (also fungal-specific), are all separate and distinct from those in TORC1, as reviewed in detail elsewhere [5; 17; 21]. Structural, genetic, and biochemical analyses have revealed that TORC2 is only insensitive to rapamycin because the C-terminus of Avo3 blocks the ability of rapamycin-bound FKBP12 (Fpr1 in _S. cerevisiae_) to bind to the FKBP12-rapamycin binding (FRB) domain of Tor2; deleting a portion of the Avo3 C-terminus renders TORC2 sensitive to rapamycin inhibition [15]. In a yeast cell where such an _avo3_ truncation (_avo3_\(\Delta\)C) is combined with a dominant point mutation (_TOR1-1_) in the FRB domain of Tor1 that blocks its association with rapamycin-Fpr1 [4], TORC2 can be uniquely inhibited by addition of rapamycin [15].  
Using budding yeast (_S. cerevisiae_) as the experimental organism, it has been shown by us [22; 23; 24; 25; 26] and others [27; 28; 29; 30; 31] that TORC2 plays an essential role in sensing the status of the plasma membrane (PM) and controlling the reactions that ensure PM homeostasis. In addition to influencing reactions that affect PM lipid and protein composition, TORC2 action also modulates assembly and function of the actin cytoskeleton and actin-driven endocytosis [26; 32; 33; 34].
## 3 Datasets and Methodology  
### Data Sources and Pre-Processing  
The meteorological information originated from two sources. One of these sources was the China Meteorological Data-Sharing Service System ([[http://cdc.cma.gov.cn/](http://cdc.cma.gov.cn/)]([http://cdc.cma.gov.cn/](http://cdc.cma.gov.cn/)), accessed on 13 February 2023), which contains the common maximal and minimal temperatures and precipitation taken from 42 meteorological stations between 2000 and 2022. These data were used for calculating extreme climate indices using the Rclimdex(1.0) software, and the sunshine percentage was used to calculate the monthly solar radiation. The other source was the National Earth System Science Data Center (National Science and Technology Infrastructure of China) ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), (accessed on 9 July 2023), accessed on 20 July 2023), which provided monthly mean temperatures and precipitation from 2000 to 2022 at a space resolution of 0.008333\({}^{\circ}\) (more or less 1 km). We sampled the data to a space resolution of 500 m as raster data, and interpolated the monthly solar radiation data to an identical resolution by means of ANUSPLINE. The monthly mean temperatures were used to calculate the NPP parameters in the CASA model, as well as monthly average precipitation, and monthly solar radiation.  
NDVI data were derived from the 2000-2022 MODIS13A1 data product, which was supplied by the National Aeronautics and Space Administration (NASA) Data Center. The spatial resolution was 500 m; in addition, the time interval was 16 days. From the MODIS13A1 data, we selected images of the vegetation between April and October. Synchronously, the maximal value synthesis (MVC) approaches were utilized, with the aim of attaining month-by-month NDVI data. The NDVI data were then used to calculate the NPP and FVC.  
The data for the vegetation types comprised 1:1,000,000 vegetation type maps of China from the Western China Environmental and Ecological Science Data Center ([[http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)]([http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)), accessed on 6 August 2023); these data were applied to the CASA model for the purpose of figuring out the NPP.
## 3 Results and Discussion
### Node Elevation Estimation  
To design a WDN, it is mandatory to determine the elevations at each node. By assuming the pipe diameter of 0.5 m, the elevations at each node are estimated and can be observed in Figure 9. Each WDN is denoted with a number (i.e., first WDN: WDN 1, second WDN: WDN 2, third WDN: WDN 3). From nodes 1 to 9, the elevations for all structures are equal. In the case of the first WDN, the node elevations vary from a minimum of 1.1718 m observed for node 4 to a maximum of 14.5025 m observed for node 10. This is also true in the case of the second and third WDNs; however, the maximum node elevations for these two WDNs (2 nd and 3 rd) occurred at node 10 with values of 24.5538 and 24.506 m, respectively.  
Figure 8: Head losses based on pipe diameter for the studied WDNs.
# Prospects for the Development of High Energy Density Dielectric Capacitors
## 3 Analysis of Dielectric Cells Using Nanoparticles Embedded in a Host Polymer
### Multilayer Device Analysis  
The dielectric devices analyzed consist of three layers. The first device treated has a high \(\varepsilon\) layer between two lower \(\varepsilon\) outside layers which have very high resistivity and high breakdown strength. This arrangement is termed the sandwich configuration. The electric fields in the layers are related to their dielectric constants.  
\[\varepsilon_{1}\text{E}_{1}=\varepsilon_{2}\text{ E}_{2},\text{ V}_{0}=2\delta_{2}\text{ E}_{2}+\delta_{1}\text{ E}_{1},\]  
where subscript 1 applies to the inner layer and subscript 2 to the outside layers and  
\[\text{E}_{1}=(\text{V}_{0}/2\delta_{1})\text{ p}/(1+1/2\text{p}),\text{ E}_{2}=\text{V}_{0}/2\delta_{2}/(1+1/2\text{p})\text{ where p}=(\delta_{1}/\delta_{2})(\varepsilon_{2}/\varepsilon_{1})\]  
\[\Delta\text{V}_{1}=\text{V}_{0}/2\text{p}/(1+1/2\text{p}),\Delta\text{V}_{2}= \text{V}_{0}/2/(1+1/2\text{p})\]  
The energy stored in the device is  
\[\text{EN}_{\text{t}}=\frac{1}{2}\text{ C}_{1}\ (\Delta\text{V}_{1})^{2}+2\ (\frac{1}{2}\text{ C}_{2}\ (\Delta\text{V}_{2})^{2})\]  
\[\text{EN}_{\text{t}}/(\text{A}\ \varepsilon_{1}/\delta_{1})\text{V}_{0}^{2}=(1+2 \text{p})/(8(1+1/2\text{p})^{2})\]  
\[\varepsilon_{\text{dev}}/\varepsilon_{1}=(\delta_{\text{dev}}/\delta_{1}) \ (1+2\text{p})/(4(1+1/2\text{p})^{2})\]  
For the example, consider \(\delta_{1}=3\ \mu\), \(\delta_{2}=1\ \mu\), \(\varepsilon_{1}=60\), \(\varepsilon_{2}=4\), \(\text{p}=0.20\), \(\varepsilon_{\text{dev}}/\varepsilon_{1}=0.485\), \(\varepsilon_{\text{dev}}=29\).  
Next, the reverse sandwich arrangement will be considered. In this case, the insulating \(\varepsilon_{2}\) layer is between two high \(\varepsilon_{1}\) layers. In this case,  
\[\text{E}_{1}=(\text{V}_{0}/\delta_{1})\text{ p}/(1+2\text{p}),\text{ E}_{2}=\text{V}_{0}/\delta_{2}/(1+2\text{p})\text{ where p}=(\delta_{1}/\delta_{2})(\varepsilon_{2}/\varepsilon_{1})\]  
\[\Delta\text{V}_{1}=\text{V}_{0}\ (\text{p}/(1+2\text{p})),\Delta\text{V}_{2}=\text{V}_{0}/(1+2 \text{p})\]  
\[\text{EN}_{\text{t}}/(\text{A}\ \varepsilon_{1}/\delta_{1})\text{V}_{0}^{2}= \text{p}/2/(1+2\text{p})\]  
\[\varepsilon_{\text{dev}}/\varepsilon_{1}=(\delta_{\text{dev}}/\delta_{1}) \ (\text{p}/(1+2\text{p})^{2})\]  
For the example, consider \(\delta_{1}=5\ \mu\), \(\delta_{2}=0.25\ \mu\), \(\varepsilon_{1}=60\), \(\varepsilon_{2}=4\), \(\text{p}=1.33\), \(\varepsilon_{\text{dev}}/\varepsilon_{1}=0.42\), \(\varepsilon_{\text{dev}}=25\). For both examples of the multilayer device, the problem is the high electric field in the blocking layer.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 2 Modelling the Network
### Bit Error Rate and Packet Error Rate  
A bit error is defined as the total number of bits of a data stream over a communication channel that is affected by the noise. Bit error rate (\(BER\)) is defined as the number of bit errors divided by the total number of transferred bits [46].  
\[BER=\frac{number\ of\ bit\ errors}{number\ of\ transferred\ bits} \tag{1}\]  
The packet error rate (\(PER\)) is defined as the number of packets with errors divided by the number of packets transmitted.  
\[PER=\frac{number\ of\ packet\ with\ errors}{number\ of\ packets\ transmitted} \tag{2}\]  
The relation between \(BER\) and \(PER\) depends on the average packet length \(L\). Assuming BER is evenly distributed, \(PER\) is the probability that there is a bit error in the packet with length \(L\).  
\[PER=BER\times L \tag{3}\]  
Table 1 shows the relationship between \(BER\) and \(PER\) for two different packet lengths. Column 1 shows \(BER\). The corresponding \(PER\) for packet lengths of 1250 bytes and 1500 bytes are shown in Columns 2 and 3, respectively.  
The first packet length (i.e., frame length) was chosen due to the simplicity of the relation it creates between BER and PER. The packet length of 1250 bytes leads to a \(10^{-4}\) factor between BER and PER, and the average Ethernet packet length is likely a bit smaller than the maximum packet length. The second packet length (1500 bytes) was selected because it is the average size of an Ethernet frame. The average frame length heavily depends on the network environment (corporate or internet service provider) and the traffic direction (up- or down-stream) [6]. The Riverbed simulation in this study consists of one large file transfer from a server node to a client node. Because our simulation transfers the one file over the link from the server to the client with a maximum transmission unit (MTU) of length 1500 bytes, an average frame size of 1500 bytes is a realistic assumption.  
We observe that a high BER (\(10^{-4}\)) leads to a PER of 1 or more. This means that every packet is dropped out and it is impossible to transfer data over the link. With an evenly distributed BER, only frames smaller than 1250 bytes have a chance to get sporadically through.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(BER\) & \(PER\) (\(L\) = 1250 Bytes) & \(PER\) (\(L\) = 1500 Bytes) \\ \hline \(10^{-1}\) & 1000 & 1200 \\ \hline \(10^{-2}\) & 100 & 120 \\ \hline \(10^{-3}\) & 10 & 12 \\ \hline \(10^{-4}\) & 1 & 1.2 \\ \hline \(10^{-5}\) & \(10^{-1}\) & \(1.2\times 10^{-1}\) \\ \hline \(10^{-6}\) & \(10^{-2}\) & \(1.2\times 10^{-2}\) \\ \hline \(10^{-7}\) & \(10^{-3}\) & \(1.2\times 10^{-3}\) \\ \hline \(10^{-8}\) & \(10^{-4}\) & \(1.2\times 10^{-4}\) \\ \hline \(10^{-9}\) & \(10^{-5}\) & \(1.2\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between \(BER\) and \(PER\) for two different packet lengths.
## 4 Presentation and Discussion of Findings
### Reliability of Models Using Root Square Mean Error (RMSE)  
According to AgriMetSoft [76] the root square means error (RMSE) is the difference between the predicted model and observed values. The spread of the individual differences provides an aggregated predicted power of the model. The R squared values can also be used to calculate the RMSE by dividing the number of samples and then the square root of the values. The R squared values are usually between 0 and 1. The larger the R square values, the more reliable the model is. In the RMSE, the smaller values near 0 reflect absolute reliability of the model. The formula for RMSE is also given as:  
\[RMSE=\frac{\sqrt{\sum_{i=1}^{n}(\text{XObs,i- Xmodel,i})^{2}}}{\text{n}} \tag{4}\]  
where X(Obs,i) is the observation value and X(model,i) is the forecast value.  
The _RMSE_ for the models in level 1, 2 and validation are indicated below.  
Table 11 displays the various models, levels 1, 2 and validation, the number of samples, R squared and RMSE values for each model.  
The R square value for the first level excluded the monthly profit and loss statements and was able to predict 30.8% of the independent variables. The RMSE indicates a value of 0.048, thus, providing a very good fit for the model. An enhanced study with the multilayer perceptron dependent variable in level 2 and validation provided R square values of 72.5% and 72.5% of the independent variables. They provide an excellent RMSE fit of 0.073 for both levels. The RMSE value of 0.073 proves that the predictions are very reliable.
* Next-Generation Seismometers: Advancements in seismometer technology continue to improve the sensitivity and accuracy of ground motion detection, enabling better earthquake characterization [22; 23; 24]. In addition, data analysis for these purposes is improving rapidly. Some of the most representative approaches for evaluating advanced technologies in seismic and volcanic monitoring are covering some of these topics:
* Machine Learning and AI: Advanced machine learning and artificial intelligence techniques are employed to process and analyze vast amounts of seismic data. These technologies can identify patterns, predict seismic events, and enhance early warning systems [25; 26; 27].
* Advanced Data Visualization: Data visualization tools, including virtual reality (VR) and augmented reality (AR), create immersive experiences for researchers and emergency responders, allowing them to explore seismic data in three dimensions [28; 29; 30].
* Crowdsourced Data: Mobile apps and citizen science initiatives enable seismic data collection from smartphones. These crowdsourced data streams can supplement traditional monitoring networks and provide real-time information [31; 32; 33].
* Real-time Data Streaming: High-speed, real-time data streaming and cloud computing are essential for quickly processing and analyzing seismic data, facilitating rapid earthquake alerts and early warning systems [34; 35; 36].
* Blockchain for Data Security: Blockchain technology is being explored to ensure seismic data security and integrity, especially in critical applications such as early warning systems [37; 38; 39]. When integrated and combined, these technologies offer a comprehensive approach to seismic monitoring, enabling better earthquake forecasting, early warning, and post-event assessment. Some of these technologies may vary depending on the region's seismic or volcanic activity and the available resources.  
Figure 1: Time–frequency record of apparent resistivity calculated at the USME station of the Geophysical Network of the National University of Colombia. On the left is the location map of the Mw5.9 earthquake that occurred on 28 August 2023, in the Panama Fracture Zone (as suggested by the focal mechanism), which has been linked to apparent resistivity anomalies generated by fluid mobility under pore pressure gradients from the seismic source. On the right, the temporal variation in apparent resistivity with the location of the earthquakes at depth is observed (upper panel). The middle panel represents the resistivity anomaly for the average at each depth. The lower panel shows the variations in the Kp solar index, ruling out possible anomalies induced by solar activity. The hypocentral solution has been provided by the USGS [40].  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author thanks the support of the Universidad Nacional de Colombia and MINCIENCIAS through the projects registered on the HERMES platform: 58331, 54189, 53392, 52625, 51929, 51307 and 46899.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 3 Results
### Analysis of Triaxial Test Results
#### 3.3.2 Analysis of Shear Strength Parameters  
The shear strength parameters (cohesion c and internal friction angle \(\phi\)) of the mixed soil containing recycled aggregate and stabilizer vary with changes in their respective dosages. Figure 8 illustrate the comparison of shear strength parameters under different dosages of recycled aggregate and stabilizer.  
Figure 7: Peak deviatoric stress of specimens with different mix ratios under confining pressures of 200–400 kPa.  
In Figure 8a, it can be observed that \(\phi\) increased overall with the increase in stabilizer content. This was likely due to the hydration reaction of the stabilizer, which generated cementitious material that formed stronger inter-particle contacts, thereby enhancing the specimen's shear resistance. Conversely, as the recycled aggregate content increased, \(\phi\) slightly decreased at lower dosages and declined further at higher dosages. This was likely because excessive recycled aggregate increased internal voids within the specimen, leading to insufficient particle contact and reduced mechanical performance.  
Figure 8b shows that c significantly increased with higher stabilizer content, reaching its maximum when the stabilizer content was 20%. This was likely due to the large amount of cementitious material produced by the stabilizer's hydration reaction, which effectively fills inter-particle voids and strengthens particle bonding. The effect of recycled aggregate content on ccc indicates that it was maximized at 20% dosage. However, as the dosage increased to 40% and 60%, c gradually decreased. This decline was likely caused by the inability of cementitious material to fully fill the voids between recycled aggregate particles at high dosages, resulting in a loose overall structure.