esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 2 Ground RFI Impacts on GEO SAR Signal
### Average Transmitted Power Requirements in the Presence of RFI  
RFI will degrade the SINR performance of a fixed GEO SAR system (e.g., specified average transmitted power, antenna size). To meet the requirements for achieving the good performances of remote sensing applications, we should determine the proper average transmitted power.  
Considering the incoherent RFI and combining (2), the Noise-Equivalent Sigma Zero (NESZ) \(NESZ_{T}\) can be expressed as  
\[NESZ_{T}(\theta_{d},\psi)=\frac{4\pi R^{4}\lambda^{2}k_{B}(T_{RFI}+T_{th})}{P_ {a}A_{t}{}^{2}F^{2}(\theta_{d},\psi)L_{loss}^{2}T_{a}\rho_{a}\rho_{g}}, \tag{7}\]  
where, especially, in the multi point-like RFI source case,  
\[T_{RFI}=\frac{h_{I}A_{t}}{4\pi k_{B}R^{2}}, \tag{8}\]  
is the RFI equivalent brightness temperature, \(h_{I}=\sum\limits_{i=1}^{N}\frac{P_{a}\rho_{a}F(\theta_{a},\psi_{a})}{B_{IJ}}\) is the power spectrum density, \(B_{I}\) is the bandwidth of the point-like RFI source and \(T_{a}\) is the integration time of GEO SAR. In the distributed RFI source case, \(T_{RFI}\) is the brightness temperature caused by the noise-like RFI.  
Based on (7), in both the point-like RFI source case and the distributed RFI source case, the SINR will have the following relationship in dB  
\[SINR=\sigma_{0}-NESZ_{T}, \tag{9}\]  
According to (9), with respect to the fixed target (specified \(\sigma_{0}\)), to satisfy the SINR requirement for SAR applications with a good performance (e.g., to keep the SINR above 10 dB in SAR interferometry applications for making the coherence of the interferogram above 0.9 [34]), we should design the average transmitted power of the GEO SAR system as  
\[P_{ar}=\frac{4\pi\lambda^{2}R^{4}k_{B}(T_{RFI}+T_{th})SINR_{r}}{A_{t}{}^{2}L_{ loss}^{2}T_{a}\rho_{a}\rho_{g}\sigma_{0}}, \tag{10}\]  
where \(P_{ar}\) is the required average transmitted power of GEO SAR and \(SINR_{r}\) is the required SINR.
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 4 Applications of Chitosan-Based Nanocomposites
### Antifouling Paints and Coatings
Chitosan functionalized with polyelectrolyte brushes were tested against bacteria in laboratory experiments and against biofouling in static field experiments in the Mediterranean sea, showing promising antifouling activities [134]. Chitosan has been applied to create membranes with antifouling properties (Table 3). Chitosan membranes had strong antibacterial properties [135] and growth of gram-positive bacteria was reduced more significantly than gram-negative bacteria. Cellulose membranes with smaller pore sizes were modified with higher molecular weight chitosan in order to improve their activity against gram-positive _Staphylococcus aureus_ and gram-negative _E. coli_ bacteria [136]. A forward osmosis (FO) membrane was fabricated using layer-by-layer assembly of chitosan and graphene oxide nanosheets on a porous support layer, which was found to be effective in fouling rejection of alginate in laboratory experiments [137]. Similarly, ultrathin antibacterial protective nanocoatings were prepared from ionic derivatives of chitosan using a layer-by-layer deposition methodology [138]. Silicone and glass surfaces coated with this nanocoating were able to prevent the adsorption of proteins and biofilm formation by _S. aureus_. The antifouling effect of chitosan nanocoating was found to depend on its thickness. Chitosan-modified polyacrylonitrile (PAN) hollow fibre membranes were reported to have better antibacterial properties against _E. coli_ and _Bacillus subtilis_ compared to unmodified PAN membranes [139]. Composite polyethersulfone (PES) nanofiltration membranes synthesized by blending _O_-carboxymethyl chitosan (_O_-CMC) and iron oxide (Fe\({}_{3}\)O\({}_{4}\)) nanoparticles were found to have considerably higher water flux, permeation, and fouling resistance compared to the unfilled PES membrane [140]. In a similar study, PES membranes prepared using _O_-CMC and silver nanoparticles were found to prevent protein fouling as well as having a good antibacterial property towards _E. coli_ and _S. aureus_[141].
## 4 Case Study: Clipped Area  
### Echigo Plain  
The Echigo (Niigata) Plain is located in the northwest of the frame ID 046D_05292_131313 (Figures 5a and 10). It is an alluvial plain formed by the Shinano and Agano Rivers, surrounded by sea to the north and higher topography elsewhere (Figure 10). Snow cover is present in winter months in both the mountainous areas and the lowlands.  
In addition to processing the whole frame (Section 3), we performed LiCSBAS processing after Step 0-3 (GACOS correction) for a rectangular subset covering the Echigo Plain region by clipping this area from the entire frame (Step 0-5). In addition, we masked (Step 0-4) the mountainous areas to the southeast, where most of the interferograms are decorrelated by vegetation and snow. Only one interferogram (20171203-20171227) was then excluded, due to unwrapping errors at Step 1-2, leaving 305 unwrapped interferograms for time series and velocity estimation. We selected GNSS station 960566 as a reference point, because the observed GNSS displacements are stable compared to other stations in this region and the associated InSAR-derived time series are reliable. As a result, clear displacement signals are detected at Niigata, Ojiya, and Sanjo cities (Figure 10b). In the following sections, we describe the details for each area as examples to test the extraction of both long-term subsidence signals and seasonally varying recoverable signals from the InSAR time series corroborated by independent datasets.  
Figure 9: Temporal evolution of velocity uncertainties from the first acquisition (i.e., 25 November, 2014) to each subsequent acquisition with a log scale on the y-axis. Solid and dashed lines denote the theoretical values calculated from Equation (4) with \(\sigma_{e}\) of 7.5 mm and 9.0 mm, respectively, and sampling intervals of 70, 24, 12, and 6 days. Blue dots denote the STD of the difference between InSAR and GNSS LOS velocities. Orange dots and error bars denote the average and STD of the InSAR LOS velocity STD computed by the bootstrap method.
# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 4 The Proposed Risk-Sensitive Intelligent Control Method
### The Structure of the Control Method
#### 4.1.1 Structure and Loss Function of Value Network
Then, the operating state of the servo motor \(s\), the reference trajectory during the control task \(g\), the duty cycle output of the strategy network \(a\), and the previously generated quantile positions \(\tau_{i}\) are fed to the neural network. Through the above calculations of the value network, a set of monotonically increasing quantile outputs \(Z_{i}\) are obtained, which together with the input quantile positions \(\tau_{i}\) describe the probability distribution of the cumulative discount returns. In this way, both the probability distribution of the current time step \(Z(s_{t},g_{t},a_{t})\) and the probability distribution of the next time step \(Z(s_{t+1},g_{t},a_{t+1})\) can be obtained. The target probability distribution can be  
Figure 6: The structure of the coding part of the value network. obtained by adding the probability distribution of the next time step \(Z(s_{t+1},g_{t},a_{t+1})\) and the reward signal of the current time step \(r(s_{t},g_{t},a_{t})\). By minimizing the Wasserstein distance between the probability distribution of the current time step and the target probability distribution, the approximate distribution output by the network can gradually approach the true probability distribution. Therefore, the objective function of value network \(J(\theta)\) can be expressed as Equation (8). \[ \begin{split}& J(\theta)=E_{(s_{t},g_{t},a_{t},s_{t+1})\sim D_{d_{t +1}}\sim\pi}\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{N}\frac{sign(u_{ij})+1}{2}smooth _{L_{1}}(u_{ij})\hat{\tau}_{j}\\ & u_{ij}=Z_{i}(s_{t},g_{t},a_{t})-\big{[}r(s_{t},g_{t},a_{t})+ \gamma\big{(}Z_{j}(s_{t+1},g_{t},a_{t+1})-\alpha log\pi(a_{t+1}|s_{t+1},g_{t}) \big{)}\big{]}\\ smooth_{L_{1}}(u_{ij})=\left\{\begin{array}{l}0.5(u_{ij})^{2}, \,if|u_{ij}|<1\\ |u_{ij}|-0.5,\,others\end{array}\right.\\ &\hat{\tau}_{j}=\frac{\tau_{j-1}+\tau_{j}}{2},\tau_{0}=0\end{split}  \tag{8}\]  
where \(\mathcal{D}\) represents the experience buffer that stores the interactive data within the environment, \(\gamma\) is the discount factor, \(\alpha\) is the temperature coefficient, and \(\pi(a_{t+1}|s_{t+1},g_{t})\) is the policy function that will be described next. Equation (8) represents the updated objective function of the value network. By sampling in the replay buffer D, the calculation method used is the negative of the absolute value of the difference between the target of the control trajectory and the actual control result.
# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 4 Experiments
### Experimental Settings
#### 4.1.2 Evaluation Metrics  
We adopt the widely used metrics--including recall, normalized discounted cumulative gain (NDCG), precision, and F1--to evaluate the performance of personalized top-K item lists. For better understanding, Table 2 shows the evaluation metric for four users with three recommendation items. Specifically, the metric recall is the fraction of correctly predicted positive samples to the total positive samples. Thus, higher recall indicates a better performance. The formula is shown below as Equation (13).  
\[Recall@K=\frac{1}{|U|}\sum_{u}\frac{TP}{TP+FN} \tag{13}\]  
where, \(|U|\) is the number of users, \(TP\) and \(FN\) are the number of true positive and false negative samples, respectively. True positive means the number of items in the top-N recommendation that hit the target. False negative means the number of the positive items test set that was falsely identified as the negative items.  
Precision is the number of correctly predicted positive instances over the total number of predicted positive instances. Higher precision also indicates a better performance. See Equation (14) for details. Additionally, we use the F1 score to balance equally between Recall and Precision. The formula is shown in Equation (15)  
\[Precision@K=\frac{1}{|U|}\sum_{u}\frac{TP}{TP+FP}=\frac{1}{|U|}\sum_{u}\frac{x}{K} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**User ID** & **Gender** & **Age** & **Item ID** & **Time** \\ \hline
001 & 1 & 54 & 67540 & 21 October 2015 \\
002 & 0 & 32 & 1453670 & 10 November 2015 \\
003 & 1 & 30 & 12356 & 12 May 2017 \\
004 & 0 & 27 & 67540 & 22 April 2018 \\
005 & 0 & 45 & 3457091 & 10 July 2019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of e-government service interaction entries.  
\[F1@K=\frac{2*Precision@K*Recall@K}{(Precision@K+Recall@K)} \tag{15}\]  
where \(FP\) is the number of false-positive instances. 'False positive' means the number of items in the top-N recommendation list that were falsely identified as the target items. The NDCG evaluates the gap between the predicted ranked item list and users' real interaction list. The closer the NDCG value is to 1, the better the model performance is.  
\[NDCG@K=\frac{1}{|U|}\sum_{u}\frac{DCG@K}{IDCG@K} \tag{16}\]  
\[DCG@K=\sum_{i=1}^{K}\frac{2^{r_{i}}-1}{\log_{2}(i+1)} \tag{17}\]  
\[IDCG@K=\sum_{i=1}^{|REL|}\frac{2^{r_{i}}-1}{\log_{2}(i+1)} \tag{18}\]  
where, \(r_{i}=1\) if the target test item in the position \(i\); otherwise, \(r_{i}=0\); \(|REL|\) indicates that the recommendation list is sorted in the best way. In the implicit feedback scenarios, \(IDCG@K=1\).
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 2 MXenes as Supercapacitor Electrodes
### Advantages of MXenes in Supercapacitors
#### 2.2.1 MXenes with Various Materials: A Journey to Enhanced Supercapacitors  
Research into supercapacitor technology has been characterized by a relentless pursuit of higher energy density, faster charging and discharging, and longer cycle life. The versatility of MXenes and their compatibility with various materials have opened new avenues for achieving these goals.  
[PERSON] et al. [27] conducted pioneering research on MXene-Carbon Nanotube (CNT) composites, producing a sandwich-like structure by alternately filtering MXene and CNT dispersions. This innovative approach led to significantly improved volumetric capacitance, reaching 350 F cm\({}^{-3}\) at 5 Ag\({}^{-1}\). By comparison, pure MXene or randomly mixed MXene-CNT papers fell short of this performance. These findings point to the potential of MXene-CNT composites in supercapacitor applications. Additionally, an MXene/rGO (reduced graphene oxide) composite demonstrated an exceptional volumetric capacitance of 435 F/cm\({}^{3}\) with outstanding cycling stability [27]. These results suggest the versatility of MXenes, not only when combined with CNTs but also with other carbon-based materials.  
[PERSON] et al. [28] contributed to the MXene supercapacitor field by constructing an asymmetric yarn supercapacitor. This innovative device utilized a 100 um thick MXene-CNT anode and MnO\({}_{2}\)/CNT cathode electrodes combined with a PVA/LiCl gel electrolyte, achieving an energy density of 100 mWh/cm\({}^{2}\). Such creative applications underscore the adaptability of MXenes in forming diverse supercapacitor designs.  
A unique fabrication technique was employed by researchers who utilized a Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) MXene-CNT composite electrode formed by photolithography followed by vacuum filtration. This approach yielded an impressive areal capacitance of 61.38 mF cm\({}^{-2}\) at 0.5 mA cm\({}^{-2}\). This enhanced performance can be attributed to CNT intercalation between the MXene layers, which facilitates ion diffusion [29].  
Furthermore, Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) MXene-multiwalled CNT (Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) MXene-MCNT) composite cathodes, in combination with polypyrrole-coated MCNT anodes, assembled with Na\({}_{2}\)SO\({}_{4}\) electrolyte, created asymmetric supercapacitors exhibiting an areal capacitance of 0.94 F cm\({}^{-2}\) within a voltage window of 0-1.6 V [30]. These remarkable results underscore the potential for MXene-based supercapacitors to become a pivotal energy storage solution.
## 5 Discussion
### Implications for Using HeadUp Vest  
The main implications, the mechanism, and the theoretical explanation of using a vest to maintain heads up are:  
1. Physiological Monitoring and Performance Optimization
2. Injury Prevention and Management
3. Enhanced Tactical and Strategic Decision-Making
4. Psychological and Emotional Wellbeing
5. Personalized Training and Rehabilitation Programs  
The HeadUp vest, paired with AI technology, could significantly impact football by monitoring physiological parameters and biomechanical data. AI algorithms analyze these data to provide insights into a player's physical condition and performance metrics. This could help maintain homeostasis and adapt to changes in training loads, enhancing performance and reducing injury risk. The technology also aligns with the Supercompensation Theory, ensuring optimal training stress and recovery.  
AI can predict injury patterns and risk factors through historical and real-time data analysis. Real-time alerts allow for adjustments in activity. The HeadUp vest uses cumulative load theory and kinetic chain theory to prevent injuries from repetitive stress and imbalances in body segments during movement.  
The HeadUp vest uses AI to provide in-game data on team and individual performance, enabling coaches to make strategic adjustments. It also provides detailed information on ecological dynamics and situational awareness, enhancing team performance and enhancing responsiveness during matches.  
The HeadUp vest monitors psychological indicators like stress and emotional states, providing personalized recommendations. It uses AI to analyze data, supporting the biopsychosocial model and cognitive load theory. This holistic approach helps maintain players' mental resilience and focus, promoting optimal performance.  
The HeadUp vest uses AI to create personalized training and rehabilitation programs based on individual needs and progress. It uses the Individual Differences Principle and Periodization Theory to optimize training outcomes and adjust programs dynamically based on real-time data.
###### Abstract  
Cloud-based technology, which is now well established, helps in reducing costs and providing accessibility, reliability and flexibility. However, the Yemen higher educational institutions (HEIs) have not yet embraced the technology due to security and privacy concerns, lack of trust, negative cultural attitudes (i.e., tribalism), and most importantly, lack of digital devices experience in educational settings as well as lack of knowledge and technical know-how. Thus, this study proposes a conceptual model of cloud computing adoption in Yemen HEIs by investigating the influence of technology, organization and environment (TOE) factors. In addition, this study investigates the moderating effect of tribalism culture in the relationships between the identified factors and cloud computing adoption. The study employed the quantitative approach to determine the factors that influence cloud computing adoption in Yemen HEIs through a questionnaire survey. Data were collected from 328 respondents in 38 HEIs and analyzed using partial least square (PLS) structural equation modelling (SEM). The results indicate that relative advantage, reliability, compatibility, security, technology readiness, top management support, regulatory policy and competitive pressure have positive significant impacts on the cloud computing adoption, except tribalism culture with negative significant impact. The study also found that tribalism culture moderates the relationship between compatibility, reliability, security, relative advantage, regulatory policy and cloud computing adoption. This study contributes to the TOE adoption model by including the cultural factor as a moderator towards cloud computing adoption in Yemen HEIs. The study also provides a model and insights for HEIs, technology consultants, vendors and policy makers in better understanding of the factors that influence cloud computing adoption in least developed countries (LDCs), specifically, Yemen.  
cloud computing adoption; technology; organization and environment (TOE) model; structural equation modelling (SEM); higher education institutions (HEIs); least developed countries (LDCs)
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 6 Results and Discussion
### Storm Challenges
At the same time, the Spanning Tree and MPResiSDN(\(k=1\)) schemes only use path 1, which is currently under the storm challenge. The sensor nodes on path 1 are affected by random noise caused by the storm challenge. The noise flips some of the bits in the packets, causing them to be dropped by the link layer. This causes a severe problem for the Spanning Tree and MPResiSDN(\(k=1\)) schemes because they do not have alternative paths to use. As a result, the overhead sometimes goes to zero because no throughput or goodput are generated. At other times, overhead increases because duplicates are generated due to re-transmissions. The results of the delay under the storm challenge are similar to the previous results for delays under the fire challenge and normal operation. Figure 13(d) shows the CDFs of the delays for each of the four schemes under the storm challenge. The delay varies between 5 and 6 ms. This indicates that there is no relationship between using alternative paths and the delay in delivering the data. In other words, using alternative paths does not affect the delay.
# Measuring Ocean Surface Current in the Kuroshio Region Using Gaofen-3 SAR Data
## 4 Discussion
The method used in this paper does not need prior wind field information to estimate wave motion, but reasonably introduces variables representing Bragg wave phase velocity and residual orbital velocity into the equations, and finally directly solves the current information. The experimental results show that this procedure is feasible. Note that the actual sea surface environment is complex and changeable, and it is difficult to consider all the characteristics of the marine environment in one method. In this article, the experiment is located in the Kuroshio region, which has the characteristics of high current velocity and uniform ocean surface backscattering, and is less affected by strong sea surface motion phenomena such as hurricanes and vortices. These are the basic environmental features for the success of the experiment. In summary, the method proposed in this paper is suitable for SAR systems with a medium incidence angle and high resolution, and the appropriate number of sub-apertures is selected according to the azimuth beam width and processing process. The sea surface current field with high velocity and uniform backscattering, such as Kuroshio, Agulhas current and Gulf Stream, and so forth, is an ideal measurement object. The marine environment is required to be stable without being affected by complex ocean motion phenomena.
## 3 Results
### Characterization of the Se-Enriched DB-Yeast  
To better understand the differences of DB-yeast and Se-enriched DB-yeast, SEM and EDS were performed to investigate the surface morphology and Se element content (Figure 3). As shown in Figure 3A, DB-yeast was yellow (Figure 3A, inset) and spherical. As shown in Figure 3B, Se-enriched DB-yeast was sandy beige (Figure 3B, inset) and spherical but the surface was surrounded with some debris. As shown in Figure 3C, the Se element content of DB-yeast was only 0.02%. As shown in Figure 3D, the Se element content of Se-enriched DB-yeast was 0.54%, indicating that Se was successfully accumulated by DB-yeast fermentation. The Se-Met content of the DB-yeast and Se-enriched DB-yeast was further analyzed by LC-MS (Figure 4). The results showed that Se-Met was not detected in DB-yeast (\(<\)0.05 mg/kg) (Figure 4B), whereas it was found in Se-enriched DB-yeast (up to 1.23 \(\pm\) 0.02 mg/kg) (Figure 4C), which proved that Se-enriched yeast was obtained after DB-yeast fermentation.  
Figure 2: Effects of (**A**) Se concentration and (**B**) Se addition time on Se content and biomass of Se-enriched DB-yeast fermentation.  
Figure 4: Determination of l-selenomethionine (Se-Met) of DB-yeast and Se-enriched DB-yeast by LC–MS. (**A**) Se-Met standard substance (10 mg/L). (**B**) DB-yeast. (**C**) Se-enriched DB-yeast.  
Figure 3: SEM and energy-dispersive spectrometry (EDS) for DB-yeast and Se-enriched DB-yeast. (**A**) SEM of DB-yeast. The inset image is powdery DB-yeast (yellow). (**B**) SEM of Se-enriched DB-yeast. The inset image is powdery Se-enriched DB-yeast (sandy beige). (**C**) EDS of DB-yeast. (**D**) EDS of Se-enriched DB-yeast.  
The effects of Se on the amino-acid composition of DB-yeast and Se-enriched DB-yeast were also analyzed (Table 1). The total amino-acid content of Se-enriched DB-yeast (43.3 g/100g) was 9.9% higher than DB-yeast (39.4 g/100 g). For the relative ratio of each amino acid, 11 kinds of amino acids, including the alkaline amino acids Arg, Lys, and His, were increased by 1.59%, 0.67%, and 0.11%, respectively, whereas, six kinds of the amino acids, including acidic amino acids Glu and Asp and the sulfur-containing amino acids Cys and Met, were decreased by 3.85%, 0.07%, 0.35%, and 0.07%, respectively, from Se-enriched DB-yeast compared with DB-yeast.