esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Gated Recurrent Unit  
A simple version of LSTM, GRU combines input and forget gates into a single update gate. It addresses the limitations of vanilla RNNs and demonstrates improved performance in sequence learning tasks, particularly by offering a more efficient training process and reduced complexity [21]. Additionally, GRU is more effective at recalling longer sequences and performs better with fewer training data [22]. Like the LSTM, it performs well with sequential data. GRUs are faster to train and more computationally efficient due to their reduced complexity, which makes them very helpful in real-time or resource-constrained settings. GRUs are commonly used in jobs involving historical prediction, natural language processing, and other applications where maintaining data over time is crucial [23]. Though usually not as resilient as LSTMs in very long sequences, the gating techniques handle vanishing and expanding gradient problems [24]. The architecture of the GRU model is illustrated in Figure 4.  
Figure 4: Structure of GRU model.  
Figure 3: Architecture of LSTM model.  
Here are the equations that describe each step in the GRU mechanism:  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]) \tag{7}\]  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]) \tag{8}\]  
\[\tilde{h}_{t}=\tanh(W\cdot[r_{t}\odot h_{t-1},x_{t}]) \tag{9}\]  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\tilde{h}_{t} \tag{10}\]  
The operations of the GRU consist of essential components that control the flow and time-based updates of information. The gates \((r_{t})\) (reset gate) and \((z_{t})\) (update gate) operate on the current input \((x_{t})\) and the previous hidden state \((h_{t-1})\) by employing sigmoid activation functions (\((\sigma)\)). These functions regulate the extent of information to retain or update. The candidate hidden state \((\tilde{h}_{t})\) is computed by utilizing the hyperbolic tangent function (tanh) on a combination of the current input and the reset-gated previous hidden state, thereby introducing non-linearity into the operation. Combining the candidate state \((\tilde{h}_{t})\) and the previous state \((h_{t-1})\) generates the final hidden state \((h_{t})\), with each state's contribution weighted by the update gate \((z_{t})\). This approach enables the model to effectively balance past information with new inputs, updating the state in a controlled way.
## 2 Materials and Methods
### Quantitative Real-Time PCR (qPCR)  
The total RNA was extracted from H9c2 cells with E.Z.N.A Total RNA kit I (Omega, Bio-Tek, Norcross, GA, USA, cat. no. R6834). Reverse transcription was performed using High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA, cat. no. 4368814). TaqMan Universal Master Mix used in this experiment was from Thermo Fisher Scientific (cat. no. 4364340). Both reverse-transcription and quantitative real-time PCR (qPCR) were performed using QuantStudio 3 (Thermo Fisher Scientific, Waltham, MA, USA). qPCR analysis was performed using TaqMan Gene Expression Assays (Thermo Fisher Scientific, Waltham, MA, USA): NKA alfa 1 (_Atp1a1_, cat. no. 4448892, Rn01533986_m1), NKA alfa 2 (_Atp1a2_, cat. no. 4448892, Rn00560879_m1), NKA alfa 3 (_Atp1a3_, cat. no. 4448892, Rn00560813_m1), NKA beta 1 (_Atp1b1_, cat. no. 4448892, Rn00565405_m1), NKA beta 2 (_Atp1b2_, cat. no. 4448892, Rn00560819_m1), NKA beta 3 (_Atp1b3_, cat. no. 4448892, Rn00755009_m1), Phospholemman (_Fayd1_, cat. no. 4448892, Rn00581299_m1), phospholamban (_Pln_, (cat. no. 4453320, Rn01434045_m1), SERCA1 (_Atp2a1_, cat. no. 4448892 Rn01508014_m1), SERCA2 (_Atp2a2_, cat. no. 4453320, Rn00568762_m1), SERCA3 (_Atp2a3_, cat. no. 4448892, Rn00563800_m1), 18S (cat. no. 4331182, Hs99999901_s1), and Cyclophilin A (_Ppia_, cat. no. 4331182, Rn0069093_m1). Results are reported as gene expression ratios (Equation (1)):  
\[\left(1+E_{reference}\right)^{Ct,\,reference}/\left(1+E_{target}\right)^{Ct,\, target} \tag{1}\]  
where \(E\) represents PCR efficiency, while \(Ct\) represents threshold cycle [47; 48]. Gene expression levels of evaluated proteins were normalized to geometric mean of endogenous controls 185 rRNA and Ppia [48]. Figure 2 shows the mRNA expression levels of distinct NKA and SERCA isoforms, as well as their respective regulators, FXYD1 and phospholamban.
## 3 Results
### Comparison of Climate- and Fuel-Optimised Configurations
#### 3.2.1 Location of Intermediate Stop Airport  
Climate and fuel-optimised refuelling airports per mission are identical for a small share of all investigated missions (6.9% of all flights, 14.3% of all flights with fuel efficiency gains through ISO). Besides the detour and offset factors associated, also the absolute position of the selected airport varies between both cases. A more detailed analysis of the resulting routes and their locations in Figure 7 confirms the hypothesised relation: the latitudes of the fuel-optimised ISO set-up are generally shifted towards the northern polar  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Reference Scenario** & **Fuel-Optimised ISO** & \multicolumn{2}{c}{**Climate-Optimised ISO**} \\ \hline Flight altitude & fuel-optimal, step & fuel-optimal, step & climate-optimised, \\  & climbs & climbs & constant \\ Share ISO missions & - & 48.23\% & 99.98\% \\ Avg. Distance & 7341 km & +0.04\% & +3.58\% \\ Avg. Flight Time & 08:57 h & +3.34\% & +9.57\% \\ Avg. Trip Fuel & 56.53 t & \(-\)1.92\% & +17.96\% \\ CO\({}_{2}\) emissions & 178.03 t & \(-\)1.92\% & +17.96\% \\ H\({}_{2}\)O emissions & 69.54 t & \(-\)1.92\% & +17.96\% \\ NO\({}_{\rm x}\) emissions & 868.91 kg & \(-\)2.42\% & +17.53\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of key parameters of an average flight of the underlying sample in reference case, fuel-optimised ISO and climate-optimised ISO.  
Figure 6: Zonal and vertical distribution of CO\({}_{2}\) emissions with fuel-optimised ISO (**left**) and climate-optimised ISO (**right**).  
region, while mid-latitudes are preferred in the climate-optimised case. On a longitudinal scale, in general, smaller and fluctuating changes can be observed.  
Figure 8 illustrates the location of preferred refuelling airports for the fuel-optimised and the climate-optimised ISO scenario. Refuelling airports in the climate-optimised context are more broadly distributed, and the additional number of take-offs and landings is below additional 20% per grid cell in most cases. For instance, we observe a large number of additional stops on the Arabian peninsula (126,211 additional take-offs and landings in this grid cell), which is associated with a relative increase of 5.8% in the grid cell. Furthermore, a high load is identified in the Newfoundland area caused by North Atlantic flights as well as in the Middle East region from South-East Asian flights. The two grid cells with the largest relative additional utilisation are located between 30 and 60\({}^{\circ}\) N latitudinally and between 30 and 60\({}^{\circ}\) E longitudinally. In these cases, take-offs and landings in these cells are more than doubled. In contrast to the climate-optimised case, we find higher peaks in individual grid cells relative to the current number of take-offs and landings for the fuel-optimised scenario. Consequently, adjustments in infrastructure would be required to make these regions capable of handling the additional amount of flights, independent of the selected ISO set-up.
## 4 Measurement Experiment
Figure 4: Experimental device for measuring angle positioning error, the tilt motion error around the Y axis, and the radial motion error along the Y direction of the target rotary axis. The measurement results after compensated and the repeatability values of the six DOF geometric motion errors are shown in Figure 5. The repeatability of each measurement point is half of the peak-to-peak value of the ten measurements, and the repeatability of the motion error of a certain degree of freedom is the maximum value of the repeatability of all measurement points. The experimental results show that after error model compensation, the repeatability deviation of the angular positioning error is 15.13; the repeatability deviation of the tilt motion error around the Y axis is reduced from 1.35 to 1.28''; the repeatability deviation of the tilt motion error around the X axis is reduced from 1.92 to 0.87''; the repeatability deviation of the radial motion error along the X axis is reduced from 1.12 to 0.74 um; the repeatability deviation of the radial motion error along the Y axis is reduced from 1.36 to 0.87 um; the repeatability deviation of the axial motion error along the Z axis is reduced from 0.53 to 0.34 um; the maximum reduction is 54.69%. According to Equation (9), the angular positioning error is only affected by the manufacturing error of the polyhedral prism working surface, and the manufacturing error of the polyhedral prism is determined as a fixed value. Therefore, the model's compensation does not affect the measurement repeatability of the angular positioning error. The measurement repeatability of the angular position error is determined by the repeatability of the target rotary axis itself, and the measurement results are essentially consistent with the nominal repeatability of the target rotary axis.
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 2 Materials and Methods
### Ground-Based Tower Albedo
The sill represents the maximum spatial variation in the subset surrounding the tower and is used as an indicator of heterogeneity, spatial extent, and strength of the spatial correlation within the satellite subsets. This assessment is often used for identification of suitable MODIS and VIIRS validations sites [54]. Evaluation of Landsat albedo products generally do not have to conform to this representativeness requirement, as in most cases, several Landsat pixels lie within the tower footprint. However, we note that the MODIS derived BRDFs are not specifically spatially linked to the tower pixels as they are paired with Landsat surface reflectances based on the classification algorithm. However, the assessment can be used as a metric for the homogeneity of the surrounding landscape and has served as a benchmark for suitable MODIS and VIIRS validation efforts in the past. As such the assessment can provide additional information on the added capabilities of higher resolution albedo products to capture heterogeneous landscapes.
# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 2 Characterization of 'Unit Element' for Reservoir Modeling
### Delta Front Environment
As a result, these sandstones generally have a lower content of plastic debris and matrix, and possess favorable physical properties. In contrast, the sediments at the bottom of the mouth bar primarily consist of fine- to very-fine-grained sandstone, indicating weaker hydrodynamic conditions and a higher content of matrix and plastic particles (heterolithics). Furthermore, the fine-grained sediments contain relatively high levels of carbonate and other cements, resulting in poorer reservoir physical properties. These plastic-rich grain lithic sandstones or calcareous cemented sandstones found at the base of the mouth bar serve as internal architectural elements within the unit mouth bar proposed in this study, acting as flow barriers. The top surface of an individual mouth bar corresponds to a fourth-order interface, while the calcareous cementation at the base represents a third-order interface [58; 59; 60]. Leveraging extensive morphology data derived from numerous outcrops and existing literature [57; 61; 62], a preliminary dataset of mouth bar unit elements has been developed.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
### Optical Compensation
#### 1.1.2 Dispersion Compensating Gratings  
The well known fiber Bragg gratings (FBGs) are used in this technique [9], to provide compensation against the dispersion effects using frequency selective fading (FSF). FSF occurs because of ultra violet (UV) light at the optical fiber's core. Group delay ripple, produced due to the high deviation of frequency from the mean dispersion slope, is one of the major performance liming factors of this technique. On the contrary, dispersion compensation modules (DCMs) [10] using FBG is not suitable for the broadband transmission due to narrow operating wavelength range [11].
## 4 Model Optimization Based on Stepwise Regression
### Model Determination  
To rectify the pointing errors, we substituted the above data into Equation (15) and solved the parameters by OLSE. The statistical analysis was then performed using SPSS Statistics, IBM, New York, USA where the VIFs of all variables exceeded 100 and some even surpassed 1000. The results indicated that the full parameter model (FPM) had severe multicollinearity that needed to be removed. Next, the stepwise regression technique was adopted to obtain the optimized parameter model (OPM) as follows, where the significance levels of entry and exclusion were set at 5% and 10%, respectively, using the F-test.  
\[\begin{cases}\delta A=\Delta\beta_{0}p_{1}+\Delta\gamma_{0}p_{2}+\Delta\alpha_ {4}p_{4}+(\Delta A+\Delta\alpha_{2}+\Delta\alpha_{3}+k_{A2})+\varepsilon_{A} \\ \delta E=\Delta r_{9}+\Delta xq_{6}+(\beta_{1}-\Delta E-\Delta\beta_{4}-2\tan( E_{b})+k_{E2})+\varepsilon_{E}\end{cases} \tag{22}\]  
\[\begin{cases}\delta A=-\Delta\beta_{0}\sin(A_{b})\tan(E_{b})-\Delta\gamma_{0} \cos(A_{b})\tan(E_{b})+\Delta\alpha_{4}\sec(E_{b})\\ \quad+(\Delta A+\Delta\alpha_{2}+\Delta\alpha_{3}+k_{A2})+\varepsilon_{A} \\ \delta E=\Delta r_{h}\sec(E_{b})+\Delta xh_{x}\sec(E_{b})+(\Delta\beta_{1}- \Delta E-\Delta\beta_{4}-2\tan(E_{b})+k_{E2})+\varepsilon_{E}\end{cases}\]  
Based on the hypothesis of normality, the model has passed the \(F\)-test and \(t\)-test, indicating that the regression equation and coefficients are significant. Meanwhile, there is no multicollinearity between the independent variables as \(VIF\leq 2\), which results in better robustness of the parameters estimated by OLSE. In addition, the model works well with all three criteria mentioned above, where the \(C_{p}\)-statistic criterion is prioritized due to the prediction purpose.  
From Equation (22) we can see that the azimuth error relates closely to the angular positions of the axes. By contrast, the elevation error involves more, since \(h_{r}\) and \(h_{x}\) are calculated from the target position and platform attitude.
## 3 Results
On the basis of the results yielded by the ddPCR of artificially contaminated sponges, the time of incubation adopted for this analysis was 9 h at 37 \({}^{\circ}\)C. All positive sponges are reported in Table 5. The results showed the ability of ddPCR to detect STEC, while Real-Time PCR was not so efficient. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**150 CFU**} & \multicolumn{4}{c}{**15 CFU**} & \multicolumn{4}{c}{**1.5 CFU**} \\ \hline
**Inchubation Time** & **strx1** & **strx2** & **eae** & **strx1** & **strx2** & **eae** & **strx1** & **strx2** & **eae** \\ \hline
24–(-1) & no call 1 & no call & no call & no call & no call & no call & no call & no call \\
24–(-2) & 2480 & 2405 & 2580 & 2250 & 1998 & 1789 & 3600 & 3610 & 4005 \\
24–(-3) & 250 & 239 & 250 & 220 & 198 & 185 & 358 & 364 & 392 \\
24–(-4) & 26.4 & 24.2 & 27 & 21.8 & 20.3 & 19.7 & 36.3 & 37 & 38.3 \\
24–(-5) & 2.2 & 2.5 & 2.3 & 7.3 & 5.1 & 6.5 & 10.4 & 9.8 & 9.2 \\
24–(-6) & 0.7 & 0.9 & 1.2 & 0 & 0 & 0 & 1.2 & 0.7 & 0.9 \\
24–(-7) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
24–(-8) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
24–(9) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular} Notes: 1 no call: saturation condition.  
\end{table} 
Table 4: Number of copies/uL detected by droplet digital PCR in different dilutions prepared from sponges artificially contaminated with different concentrations of _E. coli_ O157:H7 (150, 15, and 1.5 CFU/mL) and incubated at 37 \({}^{\circ}\)C for 24 h. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **strx1** & **strx2** & **eae** \\ \hline Sponge 4 & 78 & 0 & 3.4 \\ Sponge 5 & 3 & 13.2 & 6.3 \\ Sponge 6 & 0 & 38 & 0.37 \\ Sponge 8 & 0 & 8.8 & 139 \\ Sponge 12 & 0.26 & 0 & 9 \\ Sponge 13 & 0.46 & 0 & 0.12 \\ Sponge 24 & 0 & 91.2 & 58.9 \\ Sponge 27 & 15 & 0 & 9.7 \\ Sponge 29 & 79 & 0 & 8.3 \\ Sponge 30 & 3 & 0 & 5.9 \\ Sponge 31 & 0.23 & 0.98 & 12.9 \\ Sponge 32 & 0 & 14.2 & 4.7 \\ Sponge 35 & 0 & 19.8 & 33.2 \\ Sponge 36 & 71.4 & 0 & 35 \\ Sponge 40 & 25.5 & 0 & 85.4 \\ Sponge 41 & 45.9 & 0 & 43.1 \\ Sponge 42 & 0 & 48.8 & 67.2 \\ Sponge 43 & 0 & 65.9 & 98.1 \\ Sponge 44 & 0 & 75.1 & 58.1 \\ Sponge 45 & 0 & 54.1 & 65.3 \\ Sponge 49 & 0.5 & 0 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of copies/uL detected by droplet digital PCR in sponges used for the microbiological control of carcass of cattle slaughter in various areas of the Campania region. All sponges were incubated at 37 \({}^{\circ}\)C for 9 h.
## 7 Future Research Directions
### The Development of Satellite Systems  
Ships must mostly rely on satellite connectivity in deep-sea conditions. An autonomous ship, or more specifically a remote-controlled ship, may send a large amount of sensor data in uplink direction, i.e., from the ship to the satellite and to the remote operator [5]. On the contrary, only a small amount of control data are received in the downlink direction. This is opposite compared to how the satellite systems have been designed, since they support high downlink data rates and limited uplink data rates. Even though more satellites are being developed and new high-throughput systems are arriving, it is be important to study how they can support scalable maritime scenarios where a large amount of ships need to be served in the same satellite service area. In addition to analysis and simulations, it would be very useful to perform wide-scale measurement campaigns with the latest satellite equipment to determine capabilities and restrictions.
# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 3 Methodology  
### Network Architecture  
To better segment brain tumor MRI images, the network adopts a U-shaped symmetric structure, as shown in Figure 1. Corresponding to the decoder on the right is the encoder on the left. On the right side of the decoder is the deep supervision module, and at the bottom of the network architecture is the bottleneck layer. The left encoder consists of three layers, each of which is referred to as an encoding block. Each encoding block is composed of three parts: two convolutions with a kernel size of \(3\times 3\times 3\) and a stride of 1. To reduce the impact on the batch and improve segmentation performance, an evolving normalization-activation layer (EVO Norm) [28] is placed after each convolution. Following the normalization is the PCSAB attention module for extracting fine features, in turn followed by the downsampling. The downsampling uses maximum average pooling with a kernel size of 2 to reduce the image size to half of the original. On the right side of the network is the decoder, which is also divided into three layers. Each layer contains a decoding block, which is divided into three parts. The first part is an upsampling module, which includes trilinear interpolation and convolution with a kernel size of 1 \(\times\) 1 \(\times\) 1 and a stride of 1 to restore the image size, doubling the size of the image. The second part is the SLRFB attention module, which follows its upsampling counterpart to enhance the receptive field. The third part consists of two convolutions with a kernel size of 3 \(\times\) 3 \(\times\) 3 and a stride of 1, with EVO Norm normalization following each convolution. Between the corresponding layers of the encoder and decoder, skip connections are used to transmit information. The difference from the traditional 3D U-Net in this part is that the information passed from the encoder is concatenated with the information processed by the SLRFB attention module before being passed to the DoubleConv module for processing. At the bottom of the network is the bottleneck layer, which includes two parts. First, there are two convolutions with a kernel size of 3 \(\times\) 3 \(\times\) 3 and a stride of 1, with EVO Norm normalization following each convolution. Second, there is the CRRASPP module to enhance the interaction between different pieces of information. At the end of the network, a convolution with a kernel size of 1 \(\times\) 1 \(\times\) 1 and a stride of 1 is used as the classifier to restore the image size to 128 \(\times\) 128 \(\times\) 128, and the number of channels is changed to three, each channel storing the obtained WT, TC, and ET results, respectively.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 3 Challenges and Motivations
### Technological Advancements in IoT and Blockchain for Agriculture  
#### 3.2.1 Evolution of IoT in Agriculture  
With IoT technology, both agriculture and livestock management have achieved greater efficiency, smarter operations, and sustainable practices. The following are key developments in IoT within these sectors:  
Agriculture:  
* Advancements in smart farming: IoT enables the integration of sensors across agricultural landscapes, facilitating real-time tracking of variables such as crop development, soil conditions, and weather changes, thus propelling the adoption of precision farming techniques.
* Efficient resource management: using IoT devices leads to significant reductions in wastage and a minimized environmental footprint by optimizing essential resources like water and fertilizers.
* Operational automation: automation of farm equipment, exemplified by automated irrigation systems and drones, enhances operational efficiency and diminishes the need for manual labor, thanks to IoT in agriculture.  
* Supply chain management and product traceability: in agricultural supply chains, IoT technology has improved transparency and traceability substantially, thereby boosting food safety standards and reinforcing consumer confidence. Livestock:
* Livestock health monitoring: livestock behavior and health are monitored using IoT technology, which helps in the timely detection and treatment of diseases and health issues.
* Feed management and animal welfare: IoT technology allows for more precise feed distribution and also enables monitoring of animal welfare, ensuring their growth in optimal environments.
* Ranch automation: IoT technology has made ranch management more automated, such as automatic feed distribution systems and health monitoring devices, reducing the need for human labor.
* Product quality tracking: in livestock management, IoT technology is also utilized to track the quality of meat and dairy products, from the ranch to the consumer, ensuring the high quality and safety of the products.  
In summary, IoT technology's expansion in agriculture and livestock management profoundly enhances production efficiency, resource utilization, and product quality. These innovations not only refine conventional practices but also open avenues for more sustainable and eco-friendly methods of production. As technological progress continues, these sectors are expected to evolve further towards heightened automation and intelligence.