esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Optimisation under Uncertainties with Neural Networks
## 2 Topology Optimisation under Uncertainties  
We are concerned with the task of topology optimisation with respect to a state equation of linear elasticity. This problem becomes more involved when stochastic data are assumed. In our case, this concerns material properties and the forces acting on the designed structure. These translate into the engineering world as material imperfections or fluctuations and natural forces such as wind or ocean waves. Such random phenomena are modelled in terms of random fields that often are assumed to be Gaussian with certain mean and covariance.  
It is instructive to first present the deterministic topology optimisation task, which we discuss in the following Section 2.1. Subsequently, in Section 2.2 we extend the model to exhibit random data, allowing an extension of the cost functional to also include the fluctuations of the data in terms of a risk measure. In our case, this is the so-called conditional value at risk (CVaR).  
For the sake of a self-contained presentation, we provide all equations that lead to the actual optimisation problem, which is given in terms of a gradient that evolves a phase field. Thus, the entire problem formulation can be understood and the required extensions to obtain the risk-averse formulation become clear. However, in case the reader is only interested in the proposed NN architectures, it might be sufficient to simply gloss over the most important parts of the problem definition, for which we provide a guideline as follows: The linear state equation is given in Equation (1), leading to the weak form in Equation (2) that is used for the computation of finite element solutions. These are required in the deterministic minimisation problem given in Equation (4), which is solved iteratively by computing the gradient step defined by Equation (6). A similar problem formulation, extended by an approximation of the CVaR risk measure, can be obtained in case of the risk-averse optimisation. This is given in Equation (9) and can be solved iteratively with gradient steps defined by Equation (11).  
The presentation of this section is based on [1; 2] where the optimisation problem computes the distribution of material in a given design domain described by a continuous phase field depending on the realisation of the random parameters. The optimum of this problem maximises stiffness while minimising the volume of material for the given data.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Structure
#### 3.5.1 Fully Connected  
The beamforming system makes use of digital and analog precoders that are structured in a certain way. We might say that completely and partly linked structures are just specific examples of connected beamformers. Results showed that compared to completely linked structures, partly connected ones are less efficient in the spectrum [55]. The spectral efficiency approaches that of a completely linked structure as the quantity of the RF chains grows. In comparison to the completely linked structure, the partly connected structure displayed superior performance in terms of energy efficiency [56].
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 3 Methodology
### Developed Algorithms
#### 3.4.3 BOCEDS Micro-Cluster Searching  
The goal of micro-cluster searching which is shown in Algorithm 3 is to find the closest target struct to add an existing point \(x_{i}\). There are three types of structs, namely, CMCs, which indicate a core micro-cluster set; PMCs, which indicate a potential micro-cluster set; and WMCs, which indicate a weak micro-cluster set. The output is the corresponding struct where the point is added after updating it. For the searching, Equation (6) is used which indicates having a distance of the point from the struct to its center smaller than the radius of the struct.  
\[d(X_{i},C)<R \tag{2}\]  
The searching follows priority by looking at the WMCs first, PMCs second and CMCs last. Hence, the result will be either one of these structs types or an empty set when the condition of \(d(X_{i},C)<R\) is not applied.
# Omnidirectional Haptic Guidance for the Hearing Impaired to Track Sound Sources
## 5 Discussion
In other words, the same results should be obtained when the same experiment is conducted with vehicle sounds instead of the sound of a motorcycle engine. This property of being independent of sound quality is also a drawback in another sense. This is especially apparent when the system is used outdoors. The system reacts to wind noise, which it is not necessary to alert the user about. In order to solve this problem, it will be necessary to add a function to the system that can recognize environmental sounds through artificial intelligence. In this study, the number of sound sources to be tracked was limited to one. Ideally, a hearing assistance system should be able to track the locations of multiple sound sources, to analyze the sound quality of each, and to then prioritize them in order to provide notification. However, tracking multiple sound sources and analyzing the sound quality using artificial intelligence is too computationally demanding to be implemented on a mobile platform, which may violate constraints 2 and 3 in Section 1. The proposed system notifies the user of the direction of the loudest sound source in the vicinity. The acoustic signal detected by the microphone is a composite of sounds arriving from various directions. Physically, sound is a scalar quantity because it is pressure fluctuation in the atmosphere, but it can be treated as a vector quantity with a propagation direction by using two microphones. We assume that the direction of a composite vector, which is a combination of several vectors of different sizes, can be approximated by the direction of the vector with the largest amplitude. Strictly speaking, we track one sound source per time frame, and the detected sound source is not limited to one but may switch to others as time passes. In other words, even if the hearing-impaired person happens to be looking to the right when crossing the street, if the sound of a truck approaching from the left (corresponding to the rear) is loud, the system can alert the person to the presence of the approaching truck. Finally, we compared the function of this system with that of the previous hearing assist system [39]. This system is similar in that it can present the direction of a single sound source in all horizontal directions (including lateral and backward directions) to the subject, but it has an additional function of estimating the distance to the sound source. By adding analog vibration patterns for presenting the direction of the sound source, we were able to show the direction of a moving sound source with an accuracy of less than \(5^{\circ}\). The high accuracy of the direction overcame the uncertainty in the estimation of the distance and made it possible to show the approach and departure of the sound source to the subject.
## 2 Results and Discussion
### LF-NMR Analysis  
The water distribution and dynamics were investigated in bigeye tuna during cold storage. From Figure 3a, the CPMG signals decayed slower with increased time, suggesting increased water mobility, and the trend was closely correlated with the different water content in the samples during cold storage. From the multi-exponential fitting of the transverse relaxation data, the T\({}_{2}\) relaxation spectra of different storage times was obtained (Figure 3b). Three water populations were defined as T\({}_{2\text{b}}\), T\({}_{21}\) and T\({}_{22}\), which were present for bound water, trapped water and free water, respectively [33]. Relaxation time, T\({}_{2\text{b}}\), of the bigeye tuna was approximately 0.2-1.74 ms, and the trend was not obvious, which might have been due to water molecules tightly associating with protein [34]. T\({}_{21}\) was considered to be located in the protein-dense myofibrillar network with a relaxation time of approximately 24-132 ms. Both T\({}_{21}\) value and the amplitude of T\({}_{21}\) declined with increasing storage time. Moreover, T\({}_{21}\) significantly decreased during process, and the variations in 4 \({}^{\circ}\)C group was more apparent than that of the 0 \({}^{\circ}\)C group. The water molecular mobility increased with the temperature rising. This result was consistent with the previous study that could be explained by destruction of the pores of tuna muscle and water loss, which was due to bacterial growth [7]. The component peak with the longest relaxation time, T\({}_{22}\), with a relaxation time of 403-1032 ms, was located outside of the myofibrils. With increasing storage time, the T\({}_{22}\) increased at 0 \({}^{\circ}\)C and 4 \({}^{\circ}\)C. This phenomenon was observed by the water migration and trapped water moving to free water, suggesting the spoilage of the fish [35]. The quantitative intensity of the MRI images is shown in Figure 3c. The H proton density decreased as cold storage time increased. The higher the temperature was, the more significantly the H proton density decreased, which suggests that high temperature accelerated water loss. This result was consistent with the transverse relaxation curve analysis and drip loss analysis.  
Figure 3: Changes in Carr–Purcell–Meiboom–Gill (CPMG) relaxation decay curves (**a**), transverse relaxation time (**b**) and corresponding relative intensity of the T\({}_{1}\) weighted images (**c**) for bigeye tuna at 0 \({}^{\circ}\)C and 4 \({}^{\circ}\)C.
## 4 Experimental Results  
To validate the proposed approach for fast batches processing of sparse representation and recovery in the delay-Doppler domain, experiments are conducted using simulated and field signals with \(\alpha=0.1\). The experimental setup is as follows: first, the effectiveness of the proposed approach is verified using simulated signals. Second, the proposed approach is further validated by detecting two ship targets. Finally, a comparison of the real multiplication times is performed between the traditional and the proposed approach to highlight its computational efficiency advantage.  
Figure 4: The relationship between \(g_{\alpha}(x)\), \(x\), and \(\alpha\). As \(\alpha\) approaches \(0\), the properties of \(g_{\alpha}(x)\) tend to \(0\) norm.
## 3 Materials and Methods
### Data Preprocessing
#### 3.1.5 Adaptive Regulation of Fluctuations  
The sales of fast-moving consumer goods (FMCG) exhibit significant fluctuations over time. Exploring the fluctuation patterns of FMCG sales can contribute to more accurate sales forecasting [26]. The actual fluctuation rate of FMCG sales can be calculated using Equations (6) and (7).  
\[r=\log\left(\frac{x_{1}}{x_{2}}\right) \tag{6}\]\[RV=\sqrt{\sum_{t=1}^{T}r_{t}^{2}} \tag{7}\]  
where \(x_{1}\) represents the sales volume of the current month, \(x_{2}\) represents the sales volume of the next month, \(r\) denotes the logarithmic return, \(T\) is the time period, and \(RV\) is the realized volatility.  
The fluctuation module applies weighted processing to months with significant sales fluctuations, allowing for a deeper understanding of complex sales data and making it more suitable for medium- and long-term sales forecasting [27; 28]. The specific process is as follows:  
1. Input Data: \(X_{\text{data}}\): Time data, where each time point is denoted as \(X_{\text{date}}\). \(X_{\text{fluctuations}}\): Key fluctuation months. weight: The weight factor for fluctuation adjustments.
2. Normalize Monthly Data: Normalize the monthly data to map it to the range \([-0.5,0.5]\), making it suitable for model training. The normalization formula is as follows: \[\text{values}_{\text{date}}=\frac{\text{x}_{\text{date}}}{12}-0.5\] (8)
3. Define Time Window: Specify a _time_window_ to define the range of months around each key fluctuation month.
4. Compute Fluctuation Month Range: Calculate the fluctuation month intervals by expanding around each key fluctuation month (\(X_{\text{fluctuations}}\)) using the defined time window: \[\text{month\_range}=X_{\text{fluctuations}}\pm\textit{time\_window}\] (9)
5. Adjust Values within the Fluctuation Range: Add the specified weight to the normalized time values corresponding to the fluctuation month range: \[\text{values}_{\text{month\_range}}=\text{values}_{\text{month\_range}}+ \text{weight}\] (10)
6. Return Final Values: Output the adjusted values, which now reflect the weighted impact of fluctuations over the specified months.  
This study first employs the Random Forest-based Recursive Feature Elimination (RF-RFE) method to quantitatively identify key features, enhancing feature selection precision and improving predictive performance and model reliability. Once the relevant features have been identified, they form the foundation for the subsequent model construction. Building on this, the study proposes an innovative hybrid model, STL-DCSInformer-ETS, which integrates the complementary strengths of STL decomposition, an enhanced DCSInformer model, and the ETS model.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Monitoring  
Monitoring of nanorobots is an essential task requiring the integration of different imaging technologies. Some of these medical imaging techniques are well-establishedand can be/have been used for this purpose. Despite many advancements in imaging techniques, the lack of high resolution and precise in vivo imaging approaches is a huge challenge in the translation of nanorobots into clinical applications [116]. The precise control and visual servoing of nanorobots in a very high dynamic and complex environment depends on the imaging technique used in such environment, and there is no one-for-all solution in this regard.
## 3 SIF-Controlled In-Situ Fatigue Experiment
### Processing of Experimental Data  
With all the AE equipment applied to the specimen, fatigue loading began again at loading bounds of 1.38 kN and 13.85 kN and a loading frequency of 2 Hz. AE events were simultaneously recorded by the AE system shown in Figure 7. Figure 9 shows the overall process flow that the AE data goes through to be analyzed and used in the ensuing novel artificial intelligence system. The process begins with the microcrack growth event which emits strain wave energy that propagates from the tips of the crack (\"step 1\"). This event is then sensed by the PWAS and picked up within the AEWinTM system (\"step 2\"). The raw time-domain waveform is stored in the system and then recovered for further processing and analysis (\"step 3\"). In this processing and analysis of the raw signal, a noise cancellation algorithm is applied to eliminate internal system noise captured with the signal. This cleans up (and removes any noise-related frequencies) the time domain signal as seen in the upper plot of \"step 4\" in Figure 9. The fast Fourier transform of the signal is then performed to acquire the frequency domain information of the AE wave, as shown in the lower plot of \"step 4\" in Figure 9. Finally, the Choi-Williams transform (CWT) is performed on the wave (\"step 5\"). The Choi-Williams distribution (CWD) is a time-frequency transform that is a Cohen class member, which is related to parameters such as the instantaneous median frequency and the instantaneous power (integral over all frequencies at each time) [38; 39]. This CWT is a type of wavelet transform that yields an intensity plot that gives information of both the time and frequency domain of the AE wave simultaneously. This CWT result will be the crucial liaison point between the raw signal captured and the application of a convolutional neural network (CNN) for artificial intelligence processing of the AE signals.  
Continuous fatigue loading may cause disbonding of the PWAS from the specimen. This would result in the capturing of false AE signals. Throughout fatigue loading, the bonding of the PWAS sensor to the specimen was tested using electromechanical impedance spectroscopy (EMIS). The results of this periodic EMIS monitoring are shown in Figure 10, where a good bonding is observed throughout the entirety of the experiment, ensuring no erroneous AE signals.  
Figure 9: Flowchart of acoustic emission signal processing from microcrack growth event to the time-frequency intensity plot of Choi–Williams transform.
## 4 Results and Analysis
### Pressure Difference  
As an indirect indicator of the thermal performance, \(\Delta\)P reflects the overall energy consumption of the air-cooling BTMS. Since two evaluation criteria (T\({}_{\rm max}\) and \(\Delta\)T) should be regarded as priorities, \(\Delta\)P is regarded as a secondary indicator to evaluate and compare the designs when their cooling performances (T\({}_{\rm max}\) and \(\Delta\)T) are close. In the EV accumulator system, the energy used to cool the battery pack is the parasitic energy from the battery pack itself, so the lower energy consumption (lower \(\Delta\)P) of the BTMS could save more energy to extend driving mileage or for other EV functions, such as electronic control systems, lights, heating, ventilation, and air conditioning, etc. The \(\Delta\)P values of seven designs at five flow rates are shown in Figure 10.  
As mentioned in Section 2.3, \(\Delta\)P is only dependent on the inlet velocity and the intrinsic internal structure of the system. Its values have a constantly rising trend with the increase of the flow rates for each design and the rankings of the \(\Delta\)P values of each design remained unchanged at all flow rates (from high to low): Design 4, Design 5, Design 6, Design 1, Design 7, Design 2, and Design 3. Design 4 can be considered as the worst design in this study due to having the worst cooling performance and highest energy consumption. Design 3 has the lowest \(\Delta\)P values at five flow rates due to its constant vertical spacing arrangement and largest cell spacing. Although Design 7 has a gradient vertical spacing  
Figure 8: Temperature differences of 7 designs at 5 flow rates.  
Figure 9: Air flow velocity streamlines and temperature contour of Design 6 at 10.56 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s.  
arrangement, it has the second lowest \(\Delta\)P due to having the largest cell spacing among the GVSG (the inlet areas of Design 7 and Design 3 are the same). A similar trend can be found in Design 2 and Design 6. The constant vertical spacing designs have lower \(\Delta\)P values than the gradient vertical spacing ones when the total cell spacings (inlet/outlet sizes) are the same.
## 3 Results and Discussion
### Biological Investigation  
#### 3.4.1 Wst-8 Assay  
WST-8 results (Figure 7) show that 1 day after seeding PCL_45S5 has lower OD values compared to neat PCL electrospun mats used as control; on the other hand, PCL_BGZn and PCL_BG10 showed comparable OD values.  
Figure 6: FTIR spectra before immersion (0d) in SBF and 1, 7, 14, 21 days after immersion in SBF for: PCL (**a**); PCL_45S5 (**b**); PCL_BG10 (**c**); and PCL_BGZn (**d**). Relevant indicated peaks are discussed in the text.  
Figure 7: WST–8 analysis: graph of OD at 450 nm for all electrospun mats 1 day and 7 days after seeding. **\(p<0.05\)** (one-way ANOVA) and a table reporting the ratio ODd7/ODd1.  
7 days after seeding, PCL_45S5 and PCL_BG10 showed higher OD values compared to PCL, while PCL_BGZn had a comparable value (Figure 7). To highlight the effect of the presence of BGs on cell viability the ratio between the OD at 7 days (ODd7) and the OD measured after 1 day (ODd1) from the seeding was calculated for all samples and is shown in Figure 7. It is relevant to notice that this value is higher for all composites mats with respect to neat PCL while PCL_BG10 shows the highest value. This behavior could be due to the ions released by bioactive glass particles. In fact, it is known that bioactive glasses influence cell proliferation with an increase in the level of differentiation markers such as ALP, osteoclast, and osteepontin and they improve osteogenesis by controlling genes involved in the cell cycle toward mature osteoblasts [84]. Additionally, BGs release ions such as Si, Ca, Na, P that can activate the osteogenesis related signaling pathway [85]. Therefore, the incorporation of bioactive glass particles enhances cell viability 7 days after seeding compared to PCL electrospun fiber mats. Additionally, 45S5 and BGMS10 seem to have higher effect than BGMS_2 Zn. Although PCL_BGZn showed OD values higher than those of PCL, it showed lower cell viability compared to PCL_45S5 and PCL_BG10 samples. This behavior is probably due to Zn ions released that could become toxic in an in vitro environment characterized by the absence of fluid exchange as is the case inside the human body [86; 87].  
The morphology of cells was investigated by means of fluorescence microscopy 7 days after seeding. The fluorescence staining corroborates the results obtained by WST-8 assay. All samples (Figure 8) showed cells in their characteristic shape spread on electrospun mats.  
It is possible to observe that the cells seeded on PCL_BG10 and PCL_BGZn showed a more elongated morphology with respect to neat PCL and PCL_45S5, suggesting that the presence of Mg and Sr could affect cell adhesion on the fibers [88; 89; 90].  
Figure 8: Fluorescence images of actin filaments (red) and cell nuclei (blue) for PCL (**a**), PCL_45S5 (**b**), PCL_BG10 (**c**), and PCL_BGZn (**d**) 7 days after the seeding.
# An Ordered Universal Accumulator Based on a Hash Chain
## 6 Conclusions  
We introduce an ordered universal accumulator scheme based on a novel hash chain structure, addressing key limitations of existing cryptographic accumulators. The proposed scheme enables the efficient and secure management of dynamic sets by providing both membership and non-membership witnesses while eliminating the need for a trusted administrator. By leveraging the simplicity and immutability of hash chains, the scheme achieves self-certification and batch verification, offering robust security properties and consistent representation of accumulated sets.  
Figure 5: Hash-chain-based secure communication scheme.  
Compared to hash-tree-based accumulators, the proposed model significantly reduces storage requirements, making it an ideal choice for resource-constrained environments such as IoT and mobile systems. While the HC accumulator has slightly higher witness generation costs, its overall efficiency and scalability outweigh these drawbacks, especially in scenarios requiring lightweight and decentralized solutions.  
The scheme's ability to integrate privacy protection, data integrity, and traceability highlights its potential for diverse applications, including blockchain systems, secure communication, and authentication protocols. Future work will focus on optimizing the model for dynamic datasets, improving witness generation efficiency, and exploring its integration with emerging technologies such as artificial intelligence and the Industrial Internet of Things. These efforts aim to further expand the utility of hash chain accumulators across academic and industrial domains.  
Conceptualization, W.J. and Y.G.; methodology, J.L.; software, J.L. and H.Z.; validation, W.J., J.L. and Y.G.; formal analysis, J.L. and Y.G.; investigation, Y.G.; resources, J.L.; data curation, J.L.; writing--original draft preparation, Y.G. and J.L.; writing--review and editing, [PERSON] and H.Z.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2022 YFB2703000.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.