esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Mfe  
Motivated by [23], we employed an MFE module at each layer of the decoder, as displayed in Figure 5. The MFE added a branch on the basis of the original RFB [22] module to enlarge the receptive field even more and added an asymmetric convolution layer [30] on top of the RFB-s to extract more discriminative features and enhance the robustness of the model without increasing the computational effort. On the side, a channel attention mechanism (CAM) [25] was added. In the MFE, firstly, a \(1\times 1\) convolution was chosen to shorten the channels to 32 to speed up the inference, and the output features are indicated by \(Fb_{i}\), where \(i\in\{1,2,3,4,5\}\). Next, for the second, third, and fourth branches, the original features were fed into three convolutional layers, successively, once again after the \(1\times 1\) convolution in the first layer, with convolutional kernel sizes of \(1\times(2i-1)\), \((2i-1)\times 1\), and \((2i-1)\times(2i-1)\), after which the features extracted from the corresponding branch were output. After obtaining the features of the five branches, the first four branches were concatenated, whereafter, there was a \(3\times 3\) convolution operation. The \(Fb\) was obtained by summing the features of the fifth branch, multiplying the \(Fb\) by the attention weights obtained by the CAM, and then summing them again, after an activation function to obtain the final MFE output. The overall process of the MFE is shown in the following equations:  
\[Fb_{1}=Conv(X), \tag{6}\]  
\[Fb_{i}=Conv_{i}^{3}\Big{(}Conv_{i}^{2}\Big{(}Conv_{i}^{1}(Conv(X))\Big{)}\Big{)}, \tag{7}\]  
\[Fb_{5}=Conv(X), \tag{8}\]  
\[Fb=Conv_{3\times 3}\Bigg{(}Concat\Bigg{(}\sum_{i=1}^{4}Fb_{i}\Bigg{)}\Bigg{)}+Fb_{5}, \tag{9}\]  
\[Y=\text{ReLU}(Fb*CAM(Fb)+Fb), \tag{10}\]  
where \(Fb_{i}\) is the output feature of each branch, and \(Y\) is the final output of the MFE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(i\) & \(Y_{A}^{i}/Y_{B}^{i}\) & \(D_{A}^{i}/D_{B}^{i}\) & \(D_{F}^{i}\) & \(D^{i}\) \\ \hline Decoder & 1
## 2 Results
### Correlation Analysis of Grassland Indexes to scPDSI  
The correlation coefficient of grassland indexes and scPDSI was analyzed because grassland dynamic is driven by global climate change (Figure 4). F\({}_{\rm v}\) and NPP were positively correlated with scPDSI, with a value of 0.12 and 0.85, respectively, whereas F\({}_{\rm b}\) accounted for \(-\)0.08. The areas with a positive correlation between F\({}_{\rm v}\), NPP, and scPDSI were approximately 84.08 and 93.88%. On the contrary, a negative correlation between F\({}_{\rm b}\) and scPDSI accounted for 57.43%. The grassland regions (2.02%) showed a significant positive correlation (\(p\) < 0.05) between F\({}_{\rm v}\) and PSDI, mainly distributed in Baotou, Hohhot, and south Ulaan Chab in IM. The grassland areas (8.28%) showed significant negative correlation (\(p\) < 0.05), mainly distributed in the Kent mountain area of MG and Tong Liao, Chi Feng, and Xilin Gol of IM. In the regions with a positive correlation between NPP and PSDI, 19.57% of them showed a significant positive correlation (\(p\) < 0.05), mainly distributed over the west, north, and central MG, and central IM.  
Figure 4: The spatial distribution and pixel frequency of correlation coefficient between scPDSI and three vegetation indicators ((**A–C**) are the correlation coefficient spatial distribution and (**a–c**) corresponding its pixel frequency).
###### Abstract  
This paper presents a hybrid dielectric resonator antenna (HDRA) for circularly polarized (CP) radiation at 5 GHz, designed for WLAN applications. The antenna features a single probe feed that excites a combination of a circular ring patch and a cylindrical dielectric resonator (DR) element, achieving stable gain across a wide bandwidth. The parametric analysis and vector E-field distribution of the proposed antenna presents the optimization, and it is evidence of CP radiation, respectively. The hybrid DRA has a reflection loss (RL) bandwidth of 485 MHz, from 4740 to 5225 MHz, and an axial ratio (AR) bandwidth of 150 MHz, ranging from 4950 to 5100 MHz. It achieves a peak gain of 7.03 dBic at 5 GHz, making it suitable for missile tracking, data link communications, and IEEE 802.11n WLAN systems. Measurements of a prototype in an anechoic chamber show a close match with simulation results.  
CP radiation; dielectric antennas; hybrid antennas; IEEE 802.11n band; single-point probe feed
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 4 Simulation and Results  
To verify the effectiveness of the mega-constellation design method proposed in this paper, we carry out simulation experiments and contrastive analysis.  
In the simulation, a circular orbit is used for constellation design to maintain inter-satellite and satellite-ground communication's constant signal strength. Therefore, the orbit elements \(e\) and the \(\omega\) are set to 0.
## 2 Materials and Methods
### Process Design
#### 2.2.2 Pretreatment  
Before the cellulose and hemicellulose can be converted into hexose and pentose sugars, the lignocellulosic biomass must be pretreated to expose the polysaccharide. The biomass was mixed with sulfuric acid in a vessel, where thermal hydrolysis occurred. During thermal hydrolysis, the cellulose is converted to glucose, and the hemicellulose is converted to xylose according to Equations (1) and (2):  
\[162\text{ cellulose}+18\text{ water}\to 180\text{ glucose} \tag{1}\]  
\[132\text{ hemicellulose}+18\text{ water}\to 150\text{ xylose} \tag{2}\]  
In Figure 2, an equipment overview is given of the pretreatment section. Since SBP has a low lignin and a high pectin content, the substrate only requires a mild pretreatment [46]. Before SBP can be fermented, the cell-wall must be disrupted to expose the polysaccharide. This was done by first milling the SBP to obtain particles from 0.8-1.0 mm [46] for 60 min. After that, the particles were diluted with 2% _w/w_ sulfuric acid to obtain a dry matter content of 12% _w/w_. Then, according to [46] thermal pretreatment was performed by heating the solution for 30 min at 121 \({}^{\circ}\)C.  
Likewise, with SBP, GS was ground for 60 min to reduce its size. After that, dilute acid pretreatment was performed. This was done by treating the GS with a sulfuric acid solution (1% _w/w_ of solution) with a solid loading of 20% at 180 \({}^{\circ}\)C for 15 min [53]. Several studies investigated the effect of pretreatment on bioethanol production efficiency. However, it is not clear which technique favors maximal glucose and xylose release from the carbohydrate polymers, as well as is cost-efficient, as a financial analysis is required [54; 55; 56; 57; 58; 59; 60; 61].  
Figure 1: Process flow diagram of front-end operations in SuperPro Designer.
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
## 3 Results  
### Effect of CAP on Wound-Related Monospecies Biofilms  
Monospecies biofilms formed by MRSA, _P. aeruginosa_ or _E. faecalis_ were exposed to CAP. Figure 1 shows the results for antibiofilm effect. The median value (CFU/mL) of the non-treated control for _E. faecalis_ was 1.0 \(\times\) 10\({}^{9}\); for treatment groups the median values (CFU/mL) were 5.8 \(\times\) 10\({}^{7}\) (reduction of 1.2 log, \(p\) < 0.05), 5.2 \(\times\) 10\({}^{7}\) (reduction of 1.3 log, \(p\) < 0.01), 1.4 \(\times\) 10\({}^{7}\) (reduction of 1.9 log, \(p\) < 0.001) and 3.3 \(\times\) 10\({}^{7}\) (reduction of 1.5 log, \(p\) < 0.01) after 1, 3, 5 and 7 min of CAP exposure, respectively. The median value (CFU/mL) of the non-treated control for MRSA was 6.4 \(\times\) 10\({}^{8}\); for treatment groups the median values (CFU/mL) were 8.0 \(\times\) 10\({}^{6}\) (reduction of 1.9 log, \(p\) > 0.05), 3.0 \(\times\) 10\({}^{6}\) (reduction of 2.3 log, \(p\) < 0.05), 8.0 \(\times\) 10\({}^{5}\) (reduction of 2.9 log, \(p\) < 0.001) and 1.4 \(\times\) 10\({}^{6}\) (reduction of 2.7 log, \(p\) < 0.001) after 1, 3, 5 and 7 min of CAP exposure, respectively. The median value (CFU/mL) of the non-treated control for _P. aeruginosa_ was 5.0 \(\times\) 10\({}^{7}\); for treatment group the median values (CFU/mL) were 4.0 \(\times\) 10\({}^{6}\) (reduction of 1.1 log, \(p\) > 0.05), 4.0 \(\times\) 10\({}^{6}\) (reduction of 1.1 log, \(p\) > 0.05), 4.0 \(\times\) 10\({}^{6}\) (reduction of 1.1 log, \(p\) > 0.05) and 2.2 \(\times\) 10\({}^{5}\) (reduction of 2.4 log, \(p\) < 0.05) after 1, 3, 5 and 7 min of CAP exposure, respectively.  
Figure 1: Median and interquartile range of MRSA, _P. aeruginosa_ and _E. faecalis_ viable cells (CFU/mL) recovered after exposure of monospecies biofilms to (CAP) for 1, 3, 5 and 7 min. Kruskal-Wallis test and Dunn’s multiple comparison * \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 3 Methodology
### Initialization  
In this phase, we initialized the Spark session and made necessary modifications to its default configuration options, based on a comprehensive analysis of system performance and technical constraints. We aimed to modify these instructions to optimize resource usage and to guarantee the optimal execution of \(O\) in the experiment \(P\).  
Let the total memory allocated to each executor Mem\({}_{\text{exec}}\) be represented as follows:  
\[\text{spark.executor.memory}=2000\,\text{MB}\]  
For example, Mem\({}_{\text{exec}}\) refers to the maximum amount of memory that each Spark executor can access. The setting we run with makes sure that the executors are all working within the resource boundaries of existing virtual machines (VMs), thus getting the best out of them before burdening the system. Additionally, we assigned each executor a number of cores Cores\({}_{\text{exec}}\):  
\[\text{Cores}_{\text{exec}}=\text{spark.executor.cores}=2\]  
The driver memory Mem\({}_{\text{driver}}\) was set to the following:  
\[\text{Mem}_{\text{driver}}=\text{spark.driver.memory}=3\,\text{GB}\]  
This value was determined through empirical testing, as smaller values significantly degraded system performance. Furthermore, we reduced the periodic garbage collection interval GC\({}_{\text{interval}}\) to  
\[\text{GC}_{\text{interval}}=\text{spark.cleaner.periodicGC.interval}=1\, \text{minute}\]  
This adjustment helped to mitigate the risk of memory overload by forcing frequent garbage collection, ensuring unused memory was quickly reclaimed.
## 3 Temperature-Based ET Models: Advantages, Drawbacks, and Evolution of Research
### Single-Source Surface Energy Balance Models
#### 3.1.2 The Simplified Surface Energy Balance (SSEB)  
The SSEB model and its variations have undergone significant development and evaluation for estimating evapotranspiration in different regions. Initially applied in Afghanistan, the SSEB model successfully estimated ET in irrigated areas over multiple years, demonstrating good agreement with field reports and other models [31]. Further evaluations in the Southern High Plains and Texas showcased the accuracy of the SSEB approach in estimating ET, capturing a substantial portion of the measured ET variability while requiring minimal data [32; 33].  
The model was refined and assessed by leveraging the METRIC (the Mapping Evapor-transpiration at High Resolution with Internalized Calibration) model as a reference. A comparative analysis of the evaporative fraction estimated by both models was conducted [34]. In regions characterized by relatively uncomplicated topography, the improved SSEB model demonstrated a robust agreement with the METRIC model, as indicated by a superior R2.  
SSEB stands out as one of the most straightforward methods found in the literature for regional-scale mapping of ET. In an overall assessment, the performance of its approach exhibited levels of accuracy that are on par with other data-intensive techniques, such as SEBAL, as reported by [PERSON] et al. (2008) [PERSON] et al. (2008). Nevertheless, the reliance of SSEB on ancillary data is minimal, and its proficiency in estimating ET in dryland regions is notable. However, in topographically intricate mountainous areas, where the variability in evaporative fractions is more pronounced, emerge the inherent challenges of adequately representing the intricate interplay of radiation and heat transfer processes on steep, irregular slopes using a simplified index model. This underscores the imperative for further extensive research endeavors in such environments.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Exploiting Path Diversity
#### 4.2.6 M-Rpl  
[PERSON] et al. proposed M-RPL, a multipath extension of RPL which supports temporary multipath routing only while congestion occurs over a path [22]. Each node detects congestion by monitoring packet delivery ratio (PDR) on receiving each packet. To calculate PDR, each node must have the information of the expected data rate of each child node. Therefore, each node uses DAO messages to send its parent node the information of the current forwarding rate. If the average PDR during a congestion interval (CI) decreases below a predefined threshold, the node is deemed congested.  
Once a node detects congestion, it sends a congestion notification (CN) to its child nodes using a periodic DIO message. A node receiving a DIO message with CN starts forwarding packets using both the original parent and any other node chosen from the parent table. To avoid forwarding packets to congested nodes, each node saves the CNs of its neighbors in the parent table. If a child node does not hear CN from its parent any more, it stops splitting traffic.  
For performance evaluation, the authors compared M-RPL to the basic RPL (with MRHOF) over the IEEE 802.15.4 MAC via Cooja simulation. M-RPL significantly outperformed RPL in terms of average throughput particularly when the data rates were high. M-RPL also showed slightly lower per-bit energy consumption than RPL. However, M-RPL did not reduce the end-to-end delay significantly because the delay of M-RPL increased while constructing multiple paths on detecting congestion. As congestion became persistent, the end-to-end delay stabilized for both protocols and M-RPL showed a shorter delay than RPL.
# Biorefinery: The Production of Isobutanol from Biomass Feedstocks
## 1 Introduction  
Energy and the environment are two major issues facing the world. Due to climate change and the demand for renewable transportation fuels, the production of environmentally friendly biofuels has aroused great interest. Compared with fossil fuels, biofuels are more sustainable and highly renewable, which has attracted much attention [1; 2; 3; 4; 5; 6; 7].  
In the past few years, researchers have focused on the production of biofuels from edible crops [8]. This is not a long-term solution. From the perspective of economy and sustainability, the ultimate goal is to convert low-cost non-edible biomass resources into high-value biofuels and other chemical products [9; 10; 11; 12; 13]. Therefore, people have proposed the biotransformation of lignocellulosic biomass, most of which is agricultural waste. With the development of biorefinery, the potential industrialization of lignocellulosic biomass processing to release monosaccharides that can be fermented and converted into high-value chemicals has become a reality. Cellulosic isobutanol already occupies a place in the fuel sector [14].  
Ethanol has obvious disadvantages compared to fossil fuels, but most current research in biorefinery is still based on ethanol production. We need to expand the types of chemicals that can be produced to facilitate further development of biorefinery. Compared with ethanol, most higher alcohols have lower hygroscopicity, higher energy density, higher octane number, and properties that are closer to gasoline, so they have a higher compatibility with existing equipment and higher operating safety factors [15; 16; 17]. Isobutanol is an important industrial compound. It is used in lubricants, coatings, adhesives, automobile spray paint, and as an intermediate for the synthesis of many drugs. In addition, isobutanol derivatives are widely used in the chemical industry [15]. As a new generation of biofuel, isobutanol has many advantages (Table 1) and uses [18; 19; 20]. Therefore, developing of biorefinery to produce biomass isobutanol is in line with market needs and sustainable development.  
Biorefinery involves many steps of biomass treatment, biomass conversion, fermentation, product purification, fermentation processes, etc. In this article, we discuss the industrial microorganisms used in biorefinery, review the metabolic engineering strategies of isobutanol production by microorganisms, and introduce the current situation and developmental prospects of isobutanol production from various biomass feedstocks.
# Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System
## 5 Experiments  
### Experimental Setup  
The real-time experimental setup provides a user-friendly developing environment for fast prototyping and implementing derived control algorithms. The control system is developed in the MATLAB/Simulink (R2023a) environment and executed in real time using the dSPACE DS1104 control card (dSPACE GmbH, Paderborn, Germany). The Simulink model is converted into C code and loaded onto the dSPACE card. Tests are conducted through the ControlDesk interface of dSPACE by accessing Simulink variables (Figure 5).  
The position control of the winch platform was achieved by utilizing feedback from a laser distance sensor to monitor the system's height. The parameters of the experimental winch system are given in Table 1.
## 5 Discussion
### Digestate Post-Treatment and Handling After AD  
Digestate post-treatment significantly impacts LCA results by influencing system emissions and the efficiency of mineral fertilizer substitution. Various post-treatment technologies alter material stability, nutrient composition, water content, and agricultural suitability, ultimately affecting system sustainability. The first stage of post-treatment involves separating the solid fraction (SF) and liquid fraction (LF), primarily through mechanical methods, which reduce volume and humidity, facilitating transport and land application while maintaining a lower GWP compared to more energy-intensive filtering or drying processes [59; 89].  
Further processing of digestate depends on its intended final use. Composting stabilizes organic matter, reduces pathogens, and improves digestate quality as an organic fertilizer, requiring minimal energy input while enhancing nutrient retention. This leads to a higher-quality product capable of substituting a greater quantity of synthetic fertilizers [89]. Additionally, increasing total solids (TS) in compost reduces ammonia volatilization and nitrate leaching, significantly lowering eutrophication and acidification potential.  
The final stage of digestate handling, land application, is often the primary contributor to emissions in acidification and eutrophication impact categories [99; 101; 106]. To mitigate these environmental impacts, it is recommended to prevent excessive digestate application on land and to implement specific application techniques such as soil injection, which reduces nutrient losses and associated emissions [94].
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 3 Uncertainty Estimation Approach
### Confidence Intervals for the \(R\) Estimation  
When clinical studies are performed on patients, new data are obtained, which must be analyzed to determine if they are correct. For carrying out this, confidence intervals are constructed on-line to ensure that the test is correct with a confidence level \(\alpha\).  
The last sample's variations are analyzed. These variations are measured by standard deviation (s), which is calculated by means of the \(\mathrm{s_{n}}\) estimator, considering the process defined by Y and the set of samples.  
In a process defined by \(Y\) with \(N\) clinical studies set \(Y_{n}\), \((n=1,\ 2,\cdots,N)\), each clinical study has \(M\) samples \(\left[Y_{n}\right]_{m}\), \((m=1,2,\cdots,M)\). The variations are measured by the standard deviation \(\mathrm{s_{Y}}\), which is estimated through the corrected \(\mathrm{s_{n}}\).  
From a statistical point of view, a measurement is considered incorrect if it is an atypical value. The criteria to decide is built by considering the probability that the current measurement will fall outside a range defined by a standard deviation estimator, considering the necessary compensations for the used estimators.  
Now, we will determine a criterion to eliminate measurements that, with a high probability, may be wrong, since values will be far from most of the measurements, known as atypical data.  
To do so, the Chebyshev approximation [25] is used  
\[P\left(\frac{\left|Y-\mu_{Y}\right|}{\sigma_{Y}}\geq k\right)\leq\frac{1}{k^{ 2}}, \tag{26}\]  
\[P\left(\frac{\left|Y-\mu_{Y}\right|}{\sigma_{Y}}<k\right)\geq 1-\frac{1}{k^{2}}, \tag{27}\]  
where \(\mu_{Y}\) and \(\
# Bulk and Rayleigh Waves Propagation in Three-Phase Soil with Flow-Independent Viscosity
## 3 Wavefield Solution for Bulk and Rayleigh Waves
### Rayleigh Wave
 \tag{48}\]  
with  
\[\Re_{11}=2(\mu_{e}+\mu_{v}i\omega)\gamma_{1}^{2}+\bigg{[}C_{11}+ C_{14}i\omega+C_{12}p_{(1)}^{\text{us}}+C_{13}p_{(1)}^{\text{ss}}\big{]}k_{p1}^{2},\] \[\Re_{12}=2(\mu_{e}+\mu_{v}i\omega)\gamma_{2}^{2}+\bigg{[}C_{11}+ C_{14}i\omega+C_{12}p_{(2)}^{\text{us}}+C_{13}p_{(2)}^{\text{ss}}\big{]}k_{p2}^{2},\] \[\Re_{13}=2(\mu_{e}+\mu_{v}i\omega)\gamma_{3}^{2}+\bigg{[}C_{11}+ C_{14}i\omega+C_{12}p_{(3)}^{\text{us}}+C_{13}p_{(3)}^{\text{ss}}\big{]}k_{p3}^{2},\ \Re_{14}=2(\mu_{e}+\mu_{v}i\omega)k_{R} \gamma_{4},\] \[\Re_{21}=2(\mu_{e}+\mu_{v}i\omega)\gamma_{1}k_{R},\ \Re_{22}=2(\mu_{e}+\mu_{v}i\omega) \gamma_{2}k_{R},\ \Re_{23}=2(\mu_{e}+\mu_{v}i\omega)\gamma_{3}k_{R},\] \[\Re_{24}=(\mu_{e}+\mu_{v}i\omega)\big{(}k_{R}^{2}-\gamma_{4}^{2} \big{)},\ \Re_{31}=\Big{[}C_{21}+C_{24}i\omega+C_{22}p_{(1)}^{\text{us}}+C_{23}p_{(1)}^{ \text{ss}}\big{]}k_{p1}^{2},\] \[\Re_{32}=\Big{[}C_{21}+C_{24}i\omega+C_{22}p_{(2)}^{\text{us}}+C_{ 23}p_{(2)}^{\text{ss}}\big{]}k_{p2}^{2},\ \Re_{33}=\Big{[}C_{21}+C_{24}i\omega+C_{22}p_{(3)}^{\text{ss}}+C_{23}p_{(3)}^{ \text{ss}}\big{]}k_{p3}^{2},\] \[\Re_{41}=\Big{[}C_{31}+C_{34}i\omega+C_{32}p_{(1)}^{\text{ss}}+C_{ 33}p_{(1)}^{\text{ss}}\big{]}k_{p1}^{2},\ \Re_{42}=\Big{[}C_{31}+C_{34}i\omega+C_{32}p_{(2)}^{\text{us}}+C_{33}p_{(2)}^{ \text{ss}}\big{]}k_{p2}^{2},\] \[\Re_{43}=\Big{[}C_{31}+C_{34}i\omega+C_{32}p_{(3)}^{\text{us}}+C_{ 33}p_{(3)}^{\text{ss}}\big{]}k_{p3}^{2},\ \Re_{34}=\Re_{44}=0\]  
Figure 2: The semi-infinite system of Rayleigh wave propagation. Finally, the characteristic equation of the Rayleigh wave is derived from Equation (48) as  
\[ \begin{array}{cccc}\left|\Re_{11}&\Re_{12}&\Re_{13}&\Re_{14}\right|\\ \Re_{21}&\Re_{22}&\Re_{23}&\Re_{24}\\ \Re_{31}&\Re_{32}&\Re_{33}&\Re_{34}\\ \Re_{41}&\Re_{42}&\Re_{43}&\Re_{44}\end{array} =0 \tag{49}\]  
By solving Equation (49), the wave speed (\(v_{R}\)) and attenuation coefficient (\(\delta_{R}\)) of Rayleigh are solved as  
\[v_{R}=\omega/\text{Re}(k_{R}),\;\delta_{R}=\text{Im}(k_{R}) \tag{50}\]