esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Glycerol Concentration (g/L)**} \\ \cline{3-5}
**Equation** & \multicolumn{2}{c}{**Number of Data Points**} & \multicolumn{1}{c}{**100**} & **120** & **150** \\ \cline{3-5}  & & & **11** & **12** & **13** \\ \hline \(\frac{dX}{dt}=\mu X\) & 1 & \(\begin{array}{c}\mu_{\text{max}}\text{ (h${}^{-1}$)}\\ X_{\text{max}}\text{ (g${}_{N}$/L)}\end{array}\) & 0.0296 & 0.0252 & 0.0350 \\ \cline{3-5}  & & & \(\begin{array}{c}X\text{ (a${}t=0$ (g${}_{N}$/L)}\end{array}\) & 2.26 & 2.22 & 2.23 \\ \cline{3-5}  & & & \(\begin{array}{c}\text{SSE}\\ \text{R}^{2}\end{array}\) & 13.09 & 9.48 & 8.03 \\ \hline \(\frac{dPol}{dt}=q_{Pol}\)\(X\) & 3 & \(\begin{array}{c}q_{Pol\text{dimmax}}\text{ (g${}_{Pol}$/(g${}_{N}$/L)}\end{array}\)) & 0.0415 & 0.0420 & 0.0251 \\ \cline{3-5}  & & & \(\begin{array}{c}\text{Pol}_{\text{0}}\text{max}\text{ (g${}_{P}$/(L)}\end{array}\) & 40.14 & 48.86 & 162.87 \\ \hline \(q_{Pol}=q_{Pol_{\text{dimmax}}}\text{ (1}-\frac{Pol}{Pol_{\text{dimmax}}}\text{ }\end{array}\)) & 4 & \(\begin{array}{c}\text{SSE}\\ \text{R}^{2}\end{array}\) & 127.02 & 59.43 & 68.33 \\ \cline{3-5}  & & & \(\begin{array}{c}Y_{X[Gly}\text{ (g${}_{N}$/g${}_{Gly}$)}\end{array}\) & 0.2160 & 0.2749 & 0.1210 \\ \cline{3-5} -\frac{dCy}{dt}=\mu\frac{1}{Y_{X[Gly}\text{ (g${}_{N}$/g${}_{Gly}$)}}X+q_{Pul}\frac{1}{Y_{Pol [Gly}\text{ (g${}_{N}$/g${}_{Gly}$)}}X\end{array}\) & 5 & \(\begin{array}{c}Y_{X[Gly}\text{ (g${}_{N}$/g${}_{Gly}$)}\end{array}\) & 0.9997 & 0.6575 & 1.0586 \\ \cline{3-5}  & & & \(\begin{array}{c}\text{SSE}\\ \text{R}^{2}\end{array}\) & 445.2 & 187.1 & 1027.8 \\ \hline \multicolumn{5}{c}{SSE: Sum of squared errors.} \\ \end{tabular}
\end{table} 
Table 5: Governing equations and associated kinetic parameters related to biomass growth, glycerol consumption and polyols production during _Yarrowia lipolytica_ culture on different glycerol concentrations. The proposed model was not proved to be adequate to describe the data for the experiment with the 70 g/L initial glycerol concentrations. A close look at the experimental data (Figure 6) reveals a continuous increase in biomass, above 20 g/L values, even after glycerol depletion, at the expense of polyols. Such a behavior requires a different, rather unique modeling approach that was beyond the scope of the present work.
# Towards Facial Biometrics for ID Document Validation in Mobile Devices
## 3 Materials and Methods
### PCA Template Compression  
The designed template provides a holistic description of the face, which may include some redundancy. To eliminate it and make the system more compact, we employ compression techniques and evaluate the effect of template compression on the performance of validation. As an automatic approach for compressing the designed descriptor, we use the well-known principal components analysis (PCA) algorithm.  
PCA is used for reducing the dimensionality of a template by projecting its elements onto a lower number of principal components, while at the same time maximizing the variance of the data.
# MTRSRP: Joint Design of Multi-Triangular Ring and Self-Routing Protocol for BLE Networks
## 4 The Proposed MTRSRP
### Multi-Triangular Ring Formation  
In this scenario, all bridges are of the S/S type and are restricted to connecting only two piconets. They are classified into two roles based on their positions: the intra-ring bridge (B_intra), which sequentially connects piconets within the same ring, and the inter-ring bridge (B_inter), which links piconets from different rings to form a network comprising multiple interconnected rings. These rings are numbered from the innermost layer (layer 0) to the outermost layer (layer _Nr_).  
Figure 1 provides an interconnected example of four triangular ring networks, demonstrating the connection layout of the multi-ring networks and the functions of each component. In this illustration, the blue nodes denote the master nodes, green nodes signify the intra-ring bridge nodes (B_intra), red nodes represent the inter-ring bridge nodes (B_inter), and black nodes indicate the slave nodes. According to R7, each ring typically links three piconets, resulting in core components of each ring comprising three master nodes and three B_intra nodes.  
In addition, we designed a binary encoding mechanism for each piconet (based on the master unit). Encoding was divided into two parts. The first part encodes the piconets on different rings, as described previously. Starting from layer 0 and proceeding with binary coding from the innermost to the outermost ring, a k-bit binary code can encode 2\({}^{k}\) rings. In principle, _Nr_ should be less than or equal to 2\({}^{k}\). The code between the rings is called the inter-ring ID and is placed at the beginning of the first part, forming a complete encoding mechanism. The second part encodes piconets at different positions within each ring. This part requires only two fixed bits encoded in a counterclockwise direction, referred to as the intra-ring ID, specifically 01, 11, and 10. Masters with the same intra-ring ID on different rings are connected through an inter-ring bridge node (B_inter). Figure 2 shows the address of the encoded packet format for the piconet and scatternet.  
Once the design framework and coding rules are established, and with knowledge of the total number of network nodes N, the calculation of a certain number of _Nr_ is performed based on the constraints outlined in R1-R4. This calculation enables the formation of a multi-ring network, which is then used to implement a self-routing protocol for data packet transmission. Subsequently, the bridge node design for the multi-ring network was categorized into three cases:  
Case 1: Only one B_inter and one B_intra bridge exist between two piconets.  
Case 2: One B_intra bridge connects two piconets within a ring, whereas two B_inter bridges link piconets across the ring.  
Case 3: Two B_intra bridges interconnect two piconets within a ring, and one B_inter bridge connects the piconets across the ring. However, designing two B_intra and two B_inter bridges is not feasible, as it would result in the number of nodes within some piconets exceeding seven, which violates constraint R1.
# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 1 Introduction
Furthermore, the climate in this region has exhibited a trend toward warming and wetting, which in turn has affected WUE dynamics [29]. Hence, studies examining the effects of climate change and human activities on WUE in drylands in China have garnered considerable attention. For instance, a study utilizing eddy covariance (EC) tower data and remote sensing-based Normalized Difference Vegetation Index (NDVI) data to investigate the spatial variation in WUE across aridity gradients suggested that Prec and elevation were the primary factors of this spatial variation [30]. Another study on the WUE dynamics and driving factors in the Tibetan Plateau in China's drylands between 1981 and 2010 indicated that climate change was the primary factor behind changes in WUE, with Tem being the most significant factor [31]. Additionally, a report indicated that increases in NPP were largely affected by changes in Prec, and factors including Prec and Tem also dominated the changes in WUE [32]. Nevertheless, current studies have only focused on the effects of climate change and human activities on WUE in China's drylands at the single scale, and few studies have comprehensively investigated the driving forces behind changes in WUE at time series and spatial differentiation scales. Moreover, the precise extent to which climate change and human activities have contributed to variationsin WUE in China's drylands remains unclear. Therefore, there is a pressing need to conduct extensive research on the complex factors that underlie changes in WUE. This study thoroughly examined the effects of climate change and human interventions on Water Use Efficiency (WUE) across China's arid regions from both spatial and temporal perspectives, quantifying the degree of their contributions. The primary objectives of this study were as follows: (1) to analyze the spatial and temporal patterns of WUE in China's drylands using remote sensing and meteorological data; (2) to quantitatively evaluate the contributions of various climatic factors and human activities to the changes in WUE and to investigate the underlying mechanisms of multi-year spatial differentiation in WUE; and (3) to explore the effects of changes in WUE on China's dryland ecosystems and to predict future ecosystem changes. The results of this research offer a solid scientific foundation for the conservation of ecosystems and strategies for adapting to climate change. They deepen our comprehension of how China's dryland ecosystems are evolving in response to global warming and help to guide the execution of ecological initiatives within the country.
## 4 Conclusions  
The contamination by PTEs is a severe concern for soils worldwide, and proper attention should be paid to overcoming this problem. Proper mitigation approaches are needed, and this study concludes that the concentration of Cu, Co and Pb in roadside soils was found less in contrast with Indian soil limits. We tried to predict the level of these elements using modelling techniques. We found that amid all the applied techniques, we can conclude that MLP-based models perform better than BT-based models for predicting the Cu level with RMSE (0.0519 to 0.0943) and CC (0.5619 to 0.9488) and Pb level with RMSE (0.0066 to 0.1084) and CC (0.3490 to 0.8562), while BT models perform better than MLP-established models in predicting the Co levels with RMSE (0.0343 to 0.0455) and CC (0.7092 to 0.9159), respectively. Further BT-based regression models indicate that pH and phosphorus are the imperative variables in the retention of Cu, Co and Pb in the soil. These findings were supported by [PERSON]'s correlation analysis. Out of applied input soil variables in this study for model building, only phosphorus and pH exhibit a positive correlation with Cu, Co and Pb, and this may be why both these variables are imperative for the retention of Cu, Co and Co Pb in the soil of the studied region. The findings of the modelling techniques in the prophecy of Co, Cu and Pb helps ecological researchers to estimate the sites of effluence, causes and guidelines where the PTEs are disseminating. Planning appropriate environmental supremacy methods requires the mitigation of PTEs pollution in the environment. The present work provides significant  
Figure 10: Taylor diagram for Co (a), Cu (b) and Pb (c) based models. The colored dots epitomize the models in the corresponding legend.  
information about the predicting power of machine learning techniques for Co, Cu and Pb concentration prediction and the models in which all datasets are grouped into a single learning framework. With the greater recitals and valuable characteristics of the generalized models, the projected scheme was effectively established for a set of Cu, Co and Pb; it should be executed further to a bigger dataset to form a widespread model.  
Conceptualization, V.K., A.K.; methodology, A.K.; software, A.K., P.S.; validation, S.P., A.R.-S., V.K.; formal analysis, A.K., P.S.; investigation, V.K.; resources, A.K.; data curation, A.K., P.S.; writing--original draft preparation, V.K.; writing--review and editing, V.K., P.S., A.R.-S.; visualization, A.K., P.S.; supervision, V.K.; project administration and funding acquisition, V.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
ARS would like to acknowledge the Foundation for Science and Technology (FCT) and CIIMAR (Grant Numbers UIDB/04423/2020, UIDP/04423/2020 and research contract CEECIND/03794/2017).  
All authors declared that there is no conflict of interest.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 4 Application and Usage of Brainwave-Based Fine-Tuned Model in Architectural Spaces
### Application of Fine-Tuned Model to Architectural Space: 360\({}^{\circ}\) VR Experience
#### 4.2.5 Conclusions  
This study applied the fine-tuned Model Version 8 in a 360\({}^{\circ}\) VR environment to conduct an exploratory analysis of EEG-based emotional responses in architectural spaces with differing spatial characteristics. The comparison between a highly artificial environment (DDP) and a nature-rich environment (Long Island City) provided insights into how brain-region activation patterns correlate with model-classified emotional states and how urban and natural environments elicit distinct emotional responses across individuals.  
Expanding the sample size and incorporating diverse environmental conditions, advanced data-preprocessing methods and deep learning models such as CNN-LSTM would  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**User**} & \multicolumn{2}{c}{**DDP (A)**} & \multicolumn{3}{c}{**Long Island (B)**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Emotion**} & \multicolumn{2}{c}{**Emotion**} & \multicolumn{2}{c}{**Fine-Tuning**} \\  & \multicolumn{2}{c}{**Fine-Tuning (\%)**} & \multicolumn{2}{c}{**Connectivity Diagram**} & \multicolumn{2}{c}{**Fine-Tuning (\%)**} \\  & & & **(\%)** & & **(\%)** \\ \hline \multirow{2}{*}{\#1} & Frontal and parietal lobe & Happy & 57.42 & Frontal and temporal lobe & Happy & 40.23 \\  & activity & Neutral & 32.18 & Neutral & 39.34 \\  & Sad & 10.40 & activity & Sad & 20.43 \\ \hline \multirow{2}{*}{\#2} & Occipital and temporal lobe & Happy & 35.13 & & Happy & 30.48 \\  & activity & Neutral & 50.02 & Occipital lobe activity & Neutral & 55.23 \\  & Sad & 14.85 & & Sad & 14.29 \\ \hline \multirow{2}{*}{\#3} & Happy & 48.96 & \multirow{2}{*}{Frontal and parietal lobe} & Happy & 44.60 \\  & & Neutral & 33.14 & activity & Neutral & 35.10 \\  & & Sad & 17.90 & Sad & 20.30 \\ \hline \multirow{2}{*}{\#4} & Temporal and occipital lobe & Happy & 42.33 & \multirow{2}{*}{Frontal, temporal, and occipital lobe activity} & Happy & 34.78 \\  & & Neutral & 41.55 & & Neutral & 36.45 \\ \cline{1-1}  & & Sad & 16.12 & & Sad & 28.77 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of participants’ connectivity diagram trends and fine-tuning model emotion classification results.  
enhance the applicability of EEG-based emotion analysis in architectural design and user experience research.  
It is important to note that this study was conducted with an exploratory objective and did not include rigorous statistical validation due to its limited sample size and controlled conditions. However, the findings highlight the potential of EEG-based emotion recognition in VR environments and demonstrate how fine-tuned AI models can be used to analyse individual emotional states within built environments, contributing to future research in emotion-aware architecture and adaptive spatial design.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 4 Performance Evaluation
### Results from the Grid Topology
#### 4.1.2 The Impact on Coding Conditions and Coding Benefit Function  
By comparing arbitrary combinations of different coding conditions (CCC and PCC) and coding benefit functions (EXQL and ETX), we can comprehensively evaluate the performance of each coding condition and coding benefit function. To judge the performance in the multi-rate scenario, a flow was selected with different flow rates.  
Figures 11 and 12 show the total throughput and total delay under different rates. When the rate was less than 150 kbps, the total throughput of PCC was better than for CCC, and the delay time of PCC was weaker than for CCC, because PCC had more coding opportunities. Moreover, given the lower rate and fewer number of packets transmitted, the network was not too congested. ETX and EXQL had similar performances in the case of a low flow rate. However, when using a flow rate greater than 200 kbps, CCC could achieve better throughput and less delay time than PCC, while the network state deteriorated. According to the judgment of the state of the coding node, EXQL also performed better than ETX in a more congested network. This result also verifies that the coding node state had a large impact on the performance.  
Figure 10: Effective coding benefit ratio.  
Figure 9: Encoded and decoded packet ratios.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 3 Hardware and Software of WT in Sport
### Network Layer  
Due to the power and size limitations in WTs, the processing capacity is limited, and therefore, if more complex outputs are desired, the data obtained should be included in the process with wireless communication methods such as Bluetooth, Wi-Fi, Zigbee,etc. In the third layer, WT communicates wirelessly, transmitting data to devices with higher processing capacity such as computers, smartphones, gateways, and similar devices, enabling the production of more sophisticated output. The computational operations performed at this level are called fog computing. Cloud computing is used if these devices have difficulty in performing the desired operations or if more advanced services are needed. Fog computing involves processing data from WTs at nearby gateways or cloud services with higher processing capacity. At this stage, data is transferred to cloud-based services for deeper analysis, data mining and other complex operations. Wearables for cloud computing communicate directly with servers with very high processing capacity, either stand-alone or via a smartphone, computer, or gateway device. At this stage, fog or cloud computing can be used as needed.
# High Reliability Evaluation and Lifetime Prediction of 50 GHz Athermal AWG Module
## 5 Lifetime Estimation
### Accelerated Life Test
#### 5.1.3 Lognormal Distribution and Life-Stress Relationship  
The life distribution model was based on a lognormal distribution, and the life-stress relationship uses the Arrhenius relationship, which is widely used in accelerated life tests by temperature, as shown in Equation (12) below.  
\[\zeta(\text{T})=\text{A}\cdot\exp\left[\text{E}/(\text{kT})\right] \tag{12}\]  
where \(\zeta(\text{T})\) is the lifetime distribution parameter; E is the activation energy (eV); k is the Boltzmann constant (8.617 \(\times\) 10\({}^{5}\) eV/\({}^{\circ}\)C); T is the absolute temperature (K, \({}^{\circ}\)C + 273.15); and A is a constant according to the material properties and test conditions.  
\begin{table}
\begin{tabular}{c c} \hline
**Temperature**\({}^{\circ}\)**C** & **n** \\ \hline
71 & 16 \\
83 & 8 \\
95 & 4 \\ \hline \end{tabular}
\end{table}
Table 4: Test conditions.  
Figure 11: Lognormal probability plot.  
The acceleration factor (AF) from the life-stress relationship is shown in Equation (13) below.  
\[\text{AF}=\zeta(\text{T}_{\text{d}})/\zeta(\text{T}_{\text{a}})=\text{exp}\ [(\text{E}/\text{K})\text{-}(1/\text{T}_{\text{d}}-1/\text{T}_{\text{a}})] \tag{13}\]  
where T\({}_{\text{d}}\) is the lifetime under operating conditions, and T\({}_{\text{a}}\) is the lifetime under accelerated conditions.  
The activation energy (Ea) estimated from the software was 0.482. Therefore, as a result of calculating the acceleration factors for each acceleration stress, as shown in Figure 12, the life-stress relationship for temperatures 71 \({}^{\circ}\)C, 83 \({}^{\circ}\)C, and 95 \({}^{\circ}\)C shows that the lifetime reduced as the temperature increased.
###### Abstract  
With their compact design and versatility, CubeSats have emerged as critical platforms for advancing space exploration and communication technologies. However, achieving reliable and efficient communication in the dynamic and constrained environment of low Earth orbit (LEO) remains a significant challenge. Beam-steering antenna systems offer a promising solution to address these limitations, enabling adaptive communication links with improved gain and coverage. This review article provides a comprehensive analysis of the state-of-the-art in CubeSat communication, concentrating on the latest developments in beam-steering antennas. By synthesizing the findings from recent studies, the key challenges are highlighted, including power constraints, miniaturization, and integration with CubeSat platforms. Furthermore, this paper investigates cutting-edge techniques, such as phased array systems, metasurface-based designs, and reconfigurable antennas, which pave the way for enhanced performance. This study can serve as a resource for researchers and engineers, offering insights into current trends and future opportunities for advancing CubeSat communications through innovative antenna systems.  
CubeSat communications; beamforming antennas; leaky wave antenna; metasurface 2025  
142025  
2
# Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma
###### Abstract  
Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal adenocarcinoma (EAC). Unfortunately, response to neoadjuvant chemotherapy (NAC) is poor (20-37%), as is the overall survival benefit at five years (9%). The EAC genome is complex and heterogeneous between patients, and it is not yet understood whether specific mutational patterns may result in chemotherapy sensitivity or resistance. To identify associations between genomic events and response to NAC in EAC, a comparative genomic analysis was performed in 65 patients with extensive clinical and pathological annotation using whole-genome sequencing (WGS). We defined response using Mandard Tumor Regression Grade (TRG), with responders classified as TRG1-2 (\(n=27\)) and non-responders classified as TRG4-5 (\(n=38\)). We report a higher non-synonymous mutation burden in responders (median 2.08/Mb vs. 1.70/Mb, \(p=0.036\)) and elevated copy number variation in non-responders (282 vs. 136/patient, \(p<0.001\)). We identified copy number variants unique to each group in our cohort, with cell cycle (_CDKN2A_, _CCND1_), c-Myc (_MY_), RTK/PIK3 (_KRAS_, _EGFR_) and gastrointestinal differentiation (_GAT46_) pathway genes being specifically altered in non-responders. Of note, _NAV3_ mutations were exclusively present in the non-responder group with a frequency of 22%. Thus, lower mutation burden, higher chromosomal instability and specific copy number alterations are associated with resistance to NAC.  
(TRG) [12] with responders defined as TRG \(\leq\) 2 and non-responders as TRG \(\geq\) 4. Mandard grade was scored by a specialist gastrointestinal pathologist blinded to the clinical data at the treating cancer center.