esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
## 2 Freeze Program Design  
### Project Overview  
This design scheme for freezing is exemplified by the Sanya Estuary Channel Pipe Curtain Freezing Tunnel project. The project is situated in Tianya District, Sanya City, spanning the estuary of the Sanya River and connecting the north bank of the Hexi District with the south bank of the Luhuitou Peninsula. The construction tunnel has a total length of 3118 m. As an essential link connecting the Luhuitou Peninsula, the project is crucial for supporting the development of the southern sea area of the headquarters economy, enhancing the urban road network, improving regional transportation, and supporting the development of the South Border Sea Area of the Headquarters Economy, improving the urban road network, enhancing regional traffic, eliminating traffic bottlenecks, and easing the pressure of transit traffic in the old city. The project is surrounded by a sensitive environment, a unique geographic location, and a complex geological environment, passing through various unfavorable soil layers such as silty clay and highly permeable fine sand. The geology on the south bank is primarily composed of miscellaneous fill, clay, coral rubble (a strongly permeable layer), strongly weathered quartz sandstone, and moderately weathered quartz sandstone. On the north bank, the geology is dominated by miscellaneous fill, chalky clay, fine sand (a strongly permeable layer), and clay. Based on the conditions on both sides of the river, the tunnel in the estuary section is a single-cavity double-deck tunnel with upper and lower decks, and it is constructed using the pipe curtain freezing method.
## 6 Proposed Multiconstellation Positioning Algorithm
### \(X_{rb}\) Estimation (Stage 2)  
1. Apply steps 1\(\sim\)5 to all subsets and we get a set of subsample estimates and their corresponding scale factors, \[\begin{array}{c}\beta=\Big{\{}X_{p_{1}},\ldots,X_{p_{\overline{N}}}\Big{\}} \\ \widetilde{s}=\Big{\{}s_{p_{1}},\ldots,s_{p_{\overline{N}}}\Big{\}}\end{array}\] (28) Next sort up the scale vector \(\widetilde{s}\) in ascending order as \(\widetilde{s}_{asc}\). Take the 5 minimal scale factors from \(\widetilde{s}_{asc}\) and their corresponding estimates (the number 5 is determined empirically by the authors, it can be changed). Up to now we obtained the most robust 5 initial estimates \(\widetilde{\beta}=\big{\{}X_{1},\ldots,X_{5}\big{\}}_{asc}\), their relative scale factors \(\widetilde{s}=\big{\{}s_{1},\ldots,s_{5}\big{\}}_{asc}\) and corresponding subsamples \(\widetilde{P}=\big{\{}p_{X_{1}},\ldots p_{X_{5}}\big{\}}\).  
is the subsample used to compute \(X_{q}\) where \(X_{q}\in\widetilde{\beta}\). We apply the above steps 1\(\sim\)5 iteratively to the selected subsets \(\widetilde{P}\). The iteration stops only when the convergence point is achieved, \[\frac{\|X_{i}^{q}-X_{i-1}^{q}\|}{\|X_{i}^{q}\|}<k_{cc}:q=1,2,\ldots,5\] (29) \(k_{cc}\) is set as 0.001. The only difference in this step is the weighting function that we adopt Tukey bi-weight function type 2, \[w_{l,i}^{q}=\left\{\begin{array}{l}\left\{\left[1-\left(\frac{\widetilde{r} _{l,i}^{q}}{\alpha}\right)^{2}\right]^{2}\right.&\left|\widetilde{r}_{l,i}^{q }\right|\leq\alpha\\ 0&\left|\widetilde{r}_{l,i}^{q}\right|>\alpha\end{array}\right.\] (30) Once all the selected 5 subset estimates have got converged, the one with minimal scale factor will be taken as the most robust initial estimate \(X_{rb}^{0}\) for the final robust estimation.
2. Based on the robust initial estimate \(X_{rb}^{0}\) and its scale factor \(s_{rb}^{0}\), we use all \(n+m\) observations at this step to get the final robust estimate. The computation procedure is the same as step 1\(\sim\)5, or equivalently as the IRWLS algorithm in Appendix B. This iteration will not stop until the convergence point, Equation (29) is met, and the final estimate \(X_{rb}\) is obtained.
## 2 Background and Related Work  
### Background  
Currently, the convergence of several state-of-the-art technologies to create new system-of-system via digitalization within the electromagnetic domain redefines the capability of future airborne communication systems via increased levels of parallelization. Combined with high directivity antennas, novel systems now have the potential to satisfy modern operational requirements for robust networked communication with capabilities such as LPI/LPD, high data rate, security and low latency.  
Nonetheless, knowledge is still needed on how a targeted MANET performs in an operational context with different collaborative systems, in expected threat environments and with relevant data traffic requirements, as well as what prerequisites the solution imposes on employed multifunctional antenna systems.  
Overall, it is expected that such networks will find applicability in interoperable contexts between multiple platforms throughout different domains. In that respect, the defense industry needs to rapidly develop its familiarity with directed networks, especially for drones and tactical unmanned aerial vehicles (UAVs) for flexible, cost-adapted solutions.  
The background for this is that an increasing proportion of aerospace hardware is expected to consist of cooperative platforms, particularly between autonomous systems, thus enabling technologies for this transformation is vital from a future proofing perspective.
###### Abstract  
Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor's thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.  
reinforcement learning; transfer learning; railway propulsion system; induction motor; thermal model; optimization Article
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 3 Inductive Link Circuit Modeling
### Signal-To-Interference Ratio of the Uplink  
The power carrier is the major interference to the out-body uplink receiver. We evaluate the impact of the power carrier interference through calculating the signal-to-interference ratio (SIR) at \(V_{\text{d}}\) (the output of the RC divider). The SIR is defined in Equation (5), where \(V_{\text{d}}\) (\(j\omega_{\text{ul}}\)) is calculated based on the uplink transfer function of \(\frac{V_{\text{d}}(s)}{V_{\text{ul}}(s)}\) in Equation (4), and the divide ratio of the RC divider \(\frac{1}{1+sR_{\text{d}}\text{C}_{\text{d}}}\), \(V_{\text{d}}\) (\(j\omega_{\text{dl}}\)) is calculated based on the transfer function of \(\frac{V_{\text{load}}(s)}{V_{\text{o}}(s)}\) in Equation (3) and the divide ratio of the RC divider \(\frac{1}{1+sR_{\text{d}}\text{C}_{\text{d}}}\). When \(k\) is the minimum value (0.01) in the operating range, the SIR is about \(-100\) dB, which is hard to demodulate directly. Thus, the uplink receiving filter is introduced in the out-body receiver to improve the SIR. To improve the SIR to be above 0 dB, the order of the uplink filter is about four-orders according to Equation (6) provided in [22], supposing that the uplink filter is based on the Butterworth filter.  
\[\gamma_{\text{SIR}} = 20\log\left(\frac{V_{\text{d}}\left(j\omega_{\text{ul}}\right)}{ V_{\text{d}}\left(j\omega_{\text{dl}}\right)}\right), \tag{5}\] \[n=\frac{\log\left(\sqrt{10^{-0.17\gamma_{\text{SR}}}-1}\right)}{ \log\left(\omega_{\text{dl}}/\omega_{\text{ul}}\right)}. \tag{6}\]
# Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
## 3 Scalable Evolutionary Clustering Algorithms  
The stream clustering algorithms should handle the dynamic nature of streaming data, in which there is no control over the data arrival order, and the underlying data distribution can change over time. Such changes can be reflected in data clusters [23]. To illustrate such changes in Gaussian data distribution over time, assume that we look at the data stream in six moments represented in Figure 2a-f. Initially, Figure 2a illustrates five data distributions. After this, a new data distribution appears (which can be gradual or abrupt), shown in Figure 2b,c. Moreover, some data distributions can disappear, highlighted in red in Figure 2c,d, or merge with existing ones. Figure 2d,e illustrate two data distributions that are becoming closer over time, and then they merge, forming one dense data distribution, as in Figure 2f.  
Initially introduced in [13], this section presents three evolutionary algorithms developed and compared in the present study, designed to cluster scalable batch streams and dynamically estimate the number of clusters. In [12], the algorithms use sequential and parallel approaches to process data objects one-by-one, resp  
Figure 2: Illustration of changes in data distribution over time. The blue and red spheres around data highlight such changes, where a new data distribution is emerging in (**a–c**) (highlighted in blue), a data distribution disappears in (**c,d**) (highlighted in red), and two data distributions are merging over time in (**d–f**) (highlighted in blue).  
sequence, such as traditional data stream clustering algorithms. However, the analysis of single objects sequentially shows a severe computational bottleneck towards the distributed processing. In advance of the present work, all the compared algorithms used the Discretized-Stream (DS) [11] approach to properly handle the data streams in the MapReduce model, taking full advantage of the distributed computation and scalability of the framework. In the DS model, the t-th micro-batch \(B_{t}\) consists of an unsorted set of data points \(B_{t}=\{x_{p},x_{p+1},\ldots,x_{p+q}\}\), where \(p\) is the offset of the micro-batch and \(q\) is the size of the micro-batch. A micro-batch \(B_{t}\) can be interpreted as one of six scenarios illustrated in Figure 2 from a to f. When the micro-batch arrives, it is distributed through the nodes of the system so that \(b_{1}\cup b_{2}\cup\ldots\cup b_{m}=B_{t}\), where \(m\) is the size of the distributed system, and \(b_{j}\) represents a subset of \(B_{t}\) stored in a node. Note that MapReduce implementation replicates all the data in a few nodes to ensure reliability and fault tolerance. Moreover, the time window established by the DS model is the union set of the last micro-batches \(W=B_{t}\cup B_{t-1}\cup\ldots\cup B_{t-|W|}\), where \(|W|\) is the window size. Note that the time window is not a computational resource limitation but a forgetting mechanism related to the data evolution [1].
_applied sciences_  
_Article_  
Organic Beet Leaves and Stalk Juice Attenuates the Glutathione Peroxidase Increase Induced by High-Fat Meal in Dyslipidemic Patients: A Pilot Double-Blind, Randomized, Controlled Trial  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1,  
[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1,  
[PERSON] 3, [PERSON] 4, [PERSON] 2 and [PERSON] 5,*(c)  
1 College of Nutrition, Federal University of Goias, Goiania 74605-080, Brazil;  
[EMAIL_ADDRESS] (A.C.d.O.); [EMAIL_ADDRESS] (A.P.G.); [EMAIL_ADDRESS] (L.C.R.); [PERSON]ESS] (R.A.S.C.); [EMAIL_ADDRESS] (T.M.S.); [EMAIL_ADDRESS] (R.M.S.); [EMAIL_ADDRESS] (M.A.H.)  
2 College of Pharmacy, Federal University of Goias, Goiania 74605-170, Brazil;  
[EMAIL_ADDRESS] (M.A.C.S.); [EMAIL_ADDRESS] (L.C.C.)  
3 College of Applied Sciences, State University of Campinas, Limeria 13484-350, Brazil;  
[EMAIL_ADDRESS]  
4 Laboratory of Immunology and Inflammation, University of Brasilia, Brasilia 70910-900, Brazil;  
[EMAIL_ADDRESS]  
5 Department of Nutrition, College of Health Sciences, University of Brasilia, Brasilia 70910-900, Brazil  
* Correspondence: [EMAIL_ADDRESS]  
**Featured Application: Organic beet leaves and stalks had better nutritional compositions than conventional ones. The organic beet leaves and stalk juice attenuated the increase in GPx activity in the postprandial period. The consumption of beet leaves and stalks may contribute to treatment of the conditions associated with dyslipidemia.**  
The beet, including the leaves and stalk (BLS), is a vegetable with great antioxidant potential. The aim of this study was to evaluate the differences in the nutritional composition and the concentration of total phenolics between the organic and conventional BLS and to verify whether the ingestion of an organic BLS juice containing different concentrations of polyphenols modulated some biomarkers of oxidative stress after the ingestion of a high-fat meal in individuals with dyslipidemia. A controlled, randomized, double-blind, crossover clinical trial with a washout period of 1 week was carried out. A 12-h fasting blood sample was collected. Afterward, the participants consumed a high-fat meal, followed by the ingestion of a placebo or one of the organic BLS juices containing either 32.0 or 77.5 mg/100 mL of polyphenols. Blood samples were obtained 30, 60, 120 and 180 min after the first blood collection. The composition analysis showed that organic BLS had a better nutritional composition than conventional ones. The high-fat meal induced postprandial hypertriglyceridemia after 120 min (\(p<0.001\)) and increased the concentration of malondialdehydehyde after 30 min (\(p<0.001\)). In addition, there was a significant increase in GPx in 30 min (\(p=0.026\)). This increase was attenuated in the group that received the highest dose of polyphenols when compared with the placebo after 30 min (\(p=0.045\)). Therefore, the organic BLS juice containing 77.5 mg of polyphenols was efficient at modulating one of the postprandial mechanisms of enzymatic antioxidant protection in individuals with dyslipidemia.  
**Keywords: antioxidants; polyphenols; postprandial period**
# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 6 Discussion
### Q1: What Are the Most Researched Areas of AI Implementation for MEMS Sensors?  
Figure 9 illustrates a time-based heat-map analysis of the MEMS application problem area where the authors implemented AI-based solutions. As seen from the heat map, there are 12 problem areas where the application is more popular. The problem types are  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Year** & **Algorithm Type** & **Category** & **Data Type** & **Data Size** & **Problem Type** & **MEMS Sensor** & **Key Findings** & **Ref.** \\ \hline
2022 & DL & Regression & Synthetic & 29,984 & MEMS Design & FEA & DL-based model to effectively, quickly, and accurately predict MEMS structural design pattern. The data were captured from MEMS resonators. & [85] \\ \hline
2020 & Transfer learning, Faster R-CNN & Classification & Real & 25,464 & Surface Defect & Fabrication process & The authors proposed classification-based approach for automatic inspection using CNN models to identify the wafer surface defects with an accuracy ranging from 98\% to 99\%. & [86] \\ \hline
2018 & RGRN and DSCN & Classification & Real and Synthetic & - & Surface Defect & Fabrication process & The authors proposed deep ML-based model to find mixed and single pattern defects separately with an overall accuracy of 86.17\%. & [88] \\ \hline
subcategories under MEMS sensors and MEMS manufacturing and design, e.g., \"Thermal Calibration\", and \"Predictive Maintenance\" is used in MEMS manufacturing.  
Almost 20% of the articles deal with the problems related to \"Fault Detection and Diagnosis\". For MEMS design, almost 14% of articles are covered. \"BioMEMS\", \"Human Activity Recognition\", and \"Surface Defect\" cover 10% of the study each. The rest of the problem types cover 36% of the study.  
The MEMS sensor types used for data analysis differ in these articles. Among all other sensors, the most used gyroscope and accelerometer data are 50%. At the same time, MEMS manufacturing-related articles were only 25%. Figure 10 shows the various purposes of using AI algorithms. As can be seen, the classification and regression tasks are most intensively used, which explains the high percentage of fault detection, thermal calibration, and production issue-related articles.  
Figure 10: Purpose of used AI algorithm used in the articles.  
Figure 9: Time-based articles categorization for AI application in MEMS filed according to the types of problems.
## 2 Assessment of Available RT Orbit and Clock Products and RT ZTDs
### Long-Term Quality of Operational RT ZTD Production  
The RT ZTD from the demonstration campaign is evaluated for 18 European stations during the initial year of the GNSS4 SWEC Real-time Demonstration campaign. Two GOP solutions using the IGS03 product are compared with respect to the EUREF 2 nd reprocessing combined tropospheric product [39]: (1) GOPR--standalone GPS solution, and (2) GOPQ--GPS + GLONASS solution. In Table 3, we can observe a systematic error in ZTD of about 2 mm in the long-term evaluation, similar as observed in the simulated real-time processing in the benchmark campaign, see Section 2.2. Although GLONASS observations are down-weighted by a factor of 2 in our solution in order to reflect the lower quality of GLONASS precise products, a small positive impact on the ZTD is observed in terms of mean bias (10%) and mean SDEV (7%), both calculated over 18 stations.  
Figure 6 shows the comparison of the GOPR solution with respect to the EUREF 2 nd reprocessing combined product during the first year of the RT demonstration campaign. Monthly mean ZTD biases, standard deviations and their 1-sigma scatter calculated over all 18 stations indicate a long-term stability of the operational real-time production with a small seasonal effect in SDEV due to a less accurate troposphere modeling during the summer period [19].
## 3 Overview of Physical Unclonable Functions
### PUF Implementation
#### 3.2.4 SRAM-PUF (Static Random-Access Memory PUF)  
SRAM-PUF utilizes the random variations in the state of memory cells in static RAM. The start-up values of the SRAM cells are sensitive to manufacturing differences and environmental conditions, creating a unique and unpredictable pattern.
# A Survey of Advances in Landscape Analysis for Optimisation
## 5 Opportunities for Further Research  
This survey highlights that fitness landscape analysis techniques are being applied in contexts beyond evolutionary computation. The wider scope of landscape analysis introduces interesting challenges and offers many opportunities for further research. Three ideas are discussed here.  
Technique 26 in Table 1 proposes an approach to analysing the search space of coevolution by tracking differences between the objective and subjective landscapes. This notion of coupled landscapes, where landscapes are dynamic and a change in one effects the other, may be applicable in contexts wider than coevolution. For example, in generative adversarial networks (GANs), the error landscapes of the generator and discriminator networks are coupled in a similar way to the landscapes of coevolution. It would be interesting to investigate whether landscape analysis could be used in the context of GANs to better understand the dynamics of adversarial training.  
Most of the research in landscape analysis is restricted to single objective search spaces. However, many real-world problems have multiple conflicting objectives. Technique 32 in the survey [50] is an important contribution as it provides a first set of numerical features for characterising local features of multiobjective problems. More work is needed in applying this approach to a wider range of problems and algorithms and also adapting the approach for use in continuous search spaces. There is still a gap in techniques for practically characterising global features of multiobjective search spaces. PLOS-nets [30; 31] have been proposed for capturing the global structure of multiobjective landscapes, but it is still not clear how this approach can be scaled to large-size problems.  
Surrogate modelling has become an important technique for managing optimisation problems that have computationally expensive objective functions. Initial investigations into landscape analysis of surrogate functions [68] were not very successful and further work is needed to identify landscape analysis techniques that are suitable for characterising surrogate functions so that the analysis is indicative of the characteristics of the actual landscape.
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
## 3 Small-Strain Tests for Unsaturated Sand
### Test Method  
During the preparation of the unsaturated sand specimen (\(\phi\)50 mm \(\times\) 100 mm, \(\rho_{d}\) = 1.337 g/cm\({}^{3}\)), the \(\rho_{d}\) was consistent with that used in the previous SWCC test. Firstly, a sand sample with 5% water content was prepared by mixing oven-dried sand and air-free water; then, the sample was put in a sealed container for 12 h to make the moisture uniform. The sand specimen is formed with five layers of the sample in a saturator using the vibrating and tamping method and the surface was carefully scarged before the construction of the next layer, to strengthen the connections between the layers. The specimens were placed into a vacuum cylinder and saturated by the vacuum saturation method. As the saturated sand specimen could to be disturbed very easily, it was frozen first and then installed on the improved BE-RC pedestal. The back pressure was maintained at 50 kPa and the net stress of the specimen was maintained at 100 kPa. After the specimen was thawed, based on the axis translation technique, the confining pressure was set to 150 kPa, the pore air pressure was 50 kPa and the pressurization time was uniformly set to 120 min. The BE test was carried out first on the saturated sand specimen, followed by the RC test, to determine parameters, such as the shear wave velocity and shear modulus of the sand under different strains. After this, different levels of matrix suction were applied step by step, according to Table 2. The net stress and back pressure of the specimen remained unchanged and the pore air pressure and confining pressure changed based on the axial translation technique. When the suction equilibrium criterion was reached, the BE test was carried out first, followed by the RC test. Following the same method, small-strain shear modulus tests were conducted on unsaturated sand at net stresses of 200 kPa, 300 kPa and 400 kPa, respectively.