esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Complexity Channel Estimation Based on a Least-Squares Algorithm in OFDM Systems
## 4 Comparison of Computational Complexity with the LS Method  
The equation for the estimation of a CIR by the LS method in [11] is shown as follows:  
\[\hat{\mathbf{h}}_{LS}=((\mathbf{F}_{P}^{out})^{H}\mathbf{F}_{P}^{out}+\alpha \mathbf{I})^{-1}(\mathbf{F}_{P}^{out})^{H}\hat{\mathbf{H}}_{P}^{out} \tag{22}\]  
where \(\mathbf{F}_{P}^{out}\) is an \(N_{P}^{out}\times N_{P}^{out}\) partial Fourier transform matrix with \((f_{P}^{out})_{k,n}=e^{-\beta\pi nk/N}\), \(k\)\(\in\) pilot, \(n\) = 0, 1,..., \(N_{P}^{out}-1\). Additionally, \(\mathbf{I}\) is an \(N_{P}^{out}\times N_{P}^{out}\) identity matrix. When the relevant parameters are determined, \(((\mathbf{F}_{P}^{out})^{H}\mathbf{F}_{P}^{out}+\alpha\mathbf{I})^{-1}( \mathbf{F}_{P}^{out})^{H}\) of (22) can be calculated in advance and stored in the memory. The acceptable channel maximum delay of the LS method is \(N_{P}^{out}\). The channel maximum delay of our method is less than or equal to \(N_{p}/2\). For a fair comparison of our method with the LS method [11], let the channel maximum delay be equal to \(N_{p}/2\). Therefore, the content of \(((\mathbf{F}_{P}^{out})^{H}\mathbf{F}_{P}^{out}+\alpha\mathbf{I})^{-1}(\mathbf{F }_{P}^{out})^{H}\) in (22) will change, and the matrix dimension changes to \(0.5N_{p}\times N_{P}^{out}\) and has \(0.5N_{p}N_{P}^{out}\) complex numbers to be stored in the memory. The computational complexity of Equation (22) is \(0.5N_{p}N_{P}^{out}\) complex multiplications.  
Consider the number of multiplications after CFRs estimation at the pilot subcarriers until the CIR estimation. The computation of our method was divided into two parts. Part 1 uses (17) and (21) to estimate the CFRs at the virtual pilot subcarriers. The number of multiplications of this part is \(N_{p}^{out}N_{p}^{l}\). Part 2 performs the \(N_{p}\)-points IFFT to obtain the CIR. The number of multiplications of this part is \((N_{p}/2)\text{log}_{2}(N_{p})\)[26]. Therefore, the computation of the proposed method requires \(N_{p}^{out}N_{p}^{l}\) + \((N_{p}/2)\text{log}_{2}(N_{p})\) complex multiplications, which is much fewer than \(0.5N_{p}N_{p}^{out}\) in general cases. Comparisons with relevant parameters are described in subsequent simulations.
## 3 Results and Discussions
### Damping Effect  
To study effects of the structural damping on the payload's response to sinusoidal top excitation, five different cable damping ratios were considered, while the other parameters remain the same. As the decay test of wire rope is scarce, there is a lack of tested data on the damping ratio. In these cases, the damping ratio varies from 0.01 to 0.40, which is close to the tested value reported in [27]. In addition, the payload is lowered with a velocity of 0.2 m/s up to 3000 m depth, while the top of the cable is imposed with a sinusoidal excitation (\(H_{s}=0.3\) m, \(T_{s}=9\) s).  
The envelope of the traction in the cable is plotted in Figure 10. It shows that the damping ratio of the wire rope has little effect on the traction on the top of the cable. This is mainly because the structural damping of the cable is relatively small compared to the hydrodynamic damping of payload. If the hydrodynamic force acting on the payload significantly decreases, the structural damping of the cable will have more significant effects on the system's response.  
Figure 10: Influence of the damping ratio on the top traction.  
Figure 9: System response under a rapid downwards crane tip motion for different lowered depths: (a) payload motion; (b) force on the payload.  
Unlike the forced vibration of the payload under a sinusoidal top excitation, the cable-payload system's response to a rapid top motion is a kind of decaying vibration. The effects of both the hydrodynamic damping and the structural damping on the potential snap loads are studied. First, three cases with the payload's drag coefficients of 0.7, 3.5, and 7.0 are compared. As shown in Figure 11, under the top excitation expressed in Equation (18), the payload's drag coefficient has a significant effect on the traction in the cable.  
Then several cases in which different structural damping ratios ranging from 0 to 0.8 are simulated. As shown in Figure 12a, for the cases with a payload drag coefficient of 0.7, the system's response in the case of \(\zeta=0.1\) is very close to that of \(\zeta=0\). Also, if the structural damping ratio increases from 0 to 0.4, the maximum snap load decreases by about 2%. If the payload's drag coefficient is larger--say, \(C_{d,p}=7.0\)--the effect of structural damping is smaller, as shown in Figure 12b. Generally, effects of the structural damping on the system response is insignificant. However, consideration of the structural damping of the rope can give a more accurate prediction of the system response.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 3 Alternative Categorization
### Category Theory and RFPA  
Incorporating the principles of category theory [47] into the analysis of RFPA designs brings a structured approach to examining the interconnections between time, frequency, and space. As pointed out by [PERSON], \"Mathematics is the art of giving same name to different objects\" [48]. The application of category theory provides a unique lens through which the complexity of RFPA can be unraveled, focusing on three primary analytical dimensions. Further mathematical details about the implications of category theory in RFPA design are outlined in the Appendix A, where an important distinction between the nature of the relations among these three factors (time, frequency, and space) is also emphasized.  
For instance, time is represented by the abstract symbol \(A\). Similarly, frequency can be represented by \(B\) and design space as \(C\). This abstraction helps to simplify by transforming the complicated RFPA design into a conceptual space where decisions about power, efficiency, linearity, and other critical factors are made. In this context, the morphism functions or mappings between these domains receive specific interpretations relevant to RFPA design and analysis. For example:  
1. A morphism, \(f:A\to B\), illustrates the transformations of the signal from the time domain to the frequency domain, similar to calculating the Fourier coefficients to find the ideal impedance conditions required to achieve optimum efficiency levels.  
Figure 7: Typical back-off efficiency levels of RFPA (representational).  
An identity morphism, \(f:A\to A\), highlights the invariant properties within the time domain, such as in case of a Class J/B continuum, where the variation in a certain parameters results in a wide varies of waveforms. The composite morphism, \(g\circ f:A\to C\), represents the transition across time to space, which describes the process of optimizing the signal parameters to achieve the maximum possible efficiency across varied power and frequency levels.  
Translating category theory concepts into practical methodologies within RFPA design not only enriches the existing literature but also paves the way for innovative strategies. At the physical level, the morphisms defined within the context of category theory translate into the techniques that have been developed and documented in the RFFA literature, as well as potential methodologies that could emerge in the future. Such a structured approach provides clarity and coherence in understanding the intricate dynamics of RFPA systems.  
Furthermore, this categorization reveals another remarkable advantage: the ability to predict the existence of inverse classes [49] or explore the concept of load modulation in Doherty power amplifiers, as reported by [PERSON] et al. [50]. This means that potential design solutions or optimizations can be anticipated without the need to delve into extensive and time-consuming design procedures.
# An Analysis of the Wind Parameters in the Western Side of the Black Sea
## 3 Results
### Seasonal Average Values  
The seasonal average values of the wind speed were computed by following partitions: winter--DJF (December-January-February), spring--MAM (March-April-May), summer--JJA (June-July-August), and autumn--SON (September-October-November). As can be seen from Figure 6, winter is the most dynamic season. In this season, wind speed values were registered in the range of 8.6 m/s (December)-9.3 m/s (January). Further, the next season that presents dynamic variations is autumn. During these months average values of 6.1 m/s in September, 7.0 m/s in October and 8.0 m/s in November were recorded. In spring lower values of the wind speed were noticed: March--7.4 m/s, April--6.3 m/s, May--5.5 m/s. Nevertheless, the lowest energetic season is summer. The lowest wind speed average value was recorded in July (4.6 m/s), which is closely followed by June (4.9 m/s) and August (5.5 m/s).
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 7 Micromegas for Neutron Detection
### Micromegas for Beam Loss Monitoring  
The principle of neutron detection with a Micromegas has been used for accelerator instrumentation, and more precisely for beam loss detection, which is very different from physics measurements. The future European Spallation Source (ESS) is integrating a system of 84 Micromegas based neutron Beam Loss Monitor (nBLM) for the lower energy part of the linac (most of them will be installed in the region of 3-100 MeV beam energy). The ESS linear accelerator will provide a proton beam of up to 2 GeV with an intensity of 62.5 mA to a Tungsten target. The high-intensity beam may create irreparable radiation damage in case of a beam loss in a short time, and therefore even smaller fractions of beam loss need to be avoided and monitored to prevent activation of the surrounding materials.  
The goal of the nBLM system [103; 104] is to monitor small losses as well as to give a fast response in the case of a malfunctioning of the machine. Two complementary modules have been designed for these purposes, and the configurations differ only by the the neutron/charged particle converter used. In both cases, the nBLM detector was designed to be sensitive to fast neutrons produced by the beam loss while presenting a low sensitivity to thermal neutrons and photons from the background. The first design uses a hydrogen-rich converter (e.g., polypropylene) as the cathode of the Micromegas. Fast neutrons can create proton recoils by interacting in the medium, while the recoils of neutrons with energies below 0.5 MeV do not have enough energy to escape the target. This module is intrinsically blind to neutrons below 0.5 MeV. However its efficiency is smaller than the second one [105].  
The second module encapsulates the Micromegas detector within a 4-cm thick polyethylene moderator. An additional coating around the moderator absorbs thermal neutrons, while the fast neutrons are slowed down in the hydrogen-rich material and lead to the Micromegas chamber. An enriched boron carbide (\({}^{10}\)B\({}_{4}\)C) layer of 1.5 \(\upmu\)m is used as a converter in front of the Micromegas (as in the previous case, deposited in the cathode).  
This detection model has a much higher detection efficiency for neutrons but also a time delay due to the moderation process of up to 200 us. Both nBLM detector models use a bulk Micromegas, and the detected neutron energy ranges between 0.1-10 MeV [106]. The design of the detector is shown in Figure 23.  
The innovations of these beam loss monitors compared with the standard ones in use currently is the capability to detect event by event and, therefore, to enlarge the sensitivity to small losses as well as allowing the discrimination neutron/gamma based on their deposited energy. A fast current amplifier is used for this capability. The signal is digitized by 250 MHz ADCs and processed in real-time by a dedicated Field Programmable Gate Array (FPGA) [107]. The system can switch automatically in charge mode in case of a high neutron flux resulting in a pile-up.
## 6 Conclusions  
To apply facial expression recognition to an application-level system that can be used in environments such as driving or patient monitoring, both continuous and discrete facial expression measurement is required. In addition, because the limitations of computational resources in the system must be considered, research is needed to optimize both facial expression recognition accuracy and computational efficiency. The proposed method learns based on a teacher-bounded loss function that uses both discrete and continuous emotion labels and demonstrates a knowledge distillation method. Our experimental results confirm the synergy between these two approaches. In the experiments, the MAC of the proposed method was about 3.9 G, which is much smaller than the computational load of the teacher model (16.99 GMAC), and the accuracy degradation was approximately 1.3%, with the error in continuous measurements of valence and arousal levels being approximately 0.02 and 0.01, respectively. The performance of the proposed method has a facial expression recognition classification accuracy of 73.62%, a valence RMSE of 0.31, and an arousal accuracy of 0.28. Although this is lower than that of the teacher model, it exhibits SOTA-level accuracy and a computation load of 3.94 GMAC, which is much smaller than the 16.99 GMAC required for the teacher model. In addition, when MobileNetV2 is used as a student model, the computational efficiency is 50 times higher at 0.3 GMAC, albeit with reduced accuracy. Thus, the proposed model can be used for a wide range of continuous emotion estimation applications, from edge devices to servers.  
Conceptualization, K.L., E.C.L. and S.K.; methodology, K.L. and S.K.; software, K.L. and S.K.; validation, K.L. and S.K.; formal analysis, K.L. and S.K.; investigation, K.L. and S.K.; resources, K.L. and S.K.; data curation, K.L. and S.K.; writing--original draft preparation, K.L. and S.K.; writing--review and editing, E.C.L.; visualization, K.L. and S.K.; supervision, E.C.L.; project administration, E.C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Since the data used in this study is a public open dataset, it can be used by contacting the data holder.  
The authors declare no conflict of interest.
# Numerical Analysis for Shear Behavior of Binary Interfaces under Different Bonded Conditions
## 4 Discussion  
Under the influence of a variety of factors, a large number of partially bonded rock-mortar interfaces usually exist in rock engineering structures, which is rarely explored in the current research. Therefore, by assigning different bonded conditions to the numerical models of rock-mortar interfaces, the shear behavior of partially bonded rock-mortar interfaces was investigated in this study. Meanwhile, the fully bonded binary interface in the course study was simulated by assigning a rock-mortar interface model with the 100% bonded condition. Based on the comparison between the numerical results of the rock-mortar interface model with 100% bonded conditions and those of partially bonded rock-mortar interfaces, the comparison between the simulation results of this study and those of the current work was realized so as to analyze the influence of bonded conditions on the deformation pattern, strength characteristics, fracture mode and stress distribution from both macro and micro viewpoints. This is also the most important innovation of this study, which makes up for the deficiency of current relevant research to a certain extent. It contributes to the in-depth understanding of the shear mechanical interaction between rock and mortar, especially the variation of shear mechanical properties under different bonded  
Figure 12: Stress distribution in fully bonded condition.  
degrees. Meanwhile, studying the change of interface shear resistance under different bonded degrees can provide a scientific basis for optimizing the construction process and improving the quality and life of the project.  
Of course, there are some areas for improvement in this study. One of the biggest shortcomings is the failure to validate the simulation results against experiments due to the difficulty of sample preparation. Nevertheless, it is still believed that the findings are of great value for the following research since some measures to ensure the validity of the simulation results have been taken. In addition, these are qualitative analyses of the rock-mortar binary interface and further research will be conducted from a quantitative perspective to form a systematic and complete theoretical system for better application in guiding engineering construction. If it can be combined with intelligent algorithms, it will greatly simplify the application and achieve more satisfactory results.
## 5 Application Domain  
After conducting a qualitative content analysis of the shortlisted papers, many different application domains and research areas were identified from the literature. It was found that, within the context of building and infrastructure inspection and monitoring, robots have been used for five different applications, namely, (a) maintenance inspection, (b) construction quality inspection, (c) as-built/as-is modeling, (d) progress monitoring, and (e) safety inspection. These application domains are explained in detail in the following subsections. The frequency of work in each of these application domains is presented in Figure 4.
## 2 Methodology for Selection of Studies  
### Methodology: PRISMA Framework  
The PRISMA methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), updated in 2021 [22], is used for conducting the literature review documented in this manuscript. PRISMA ensures transparency, reproducibility, and a high standard of academic rigor by guiding researchers through four distinct phases:  
* Identification: An exhaustive search is conducted across multiple databases to compile a comprehensive pool of studies potentially relevant to the research objectives. This step involves designing detailed search strategies tailored to the scope of the review and employing bibliographic tools to manage and organize retrieved records.
* Screening: Duplicate records are identified and removed, and studies are evaluated based on predefined inclusion criteria through title and abstract reviews. This phase narrows the dataset to studies most likely to meet the review's objectives while maintaining transparency and consistency in the selection process.
* Eligibility and Inclusion: A detailed full-text evaluation of the remaining studies is performed. Each study is assessed using specific criteria for relevance, methodological rigor, innovation, data quality, and impact. Studies meeting the defined threshold for quality and alignment are included in the synthesis. This phase ensures that only high-quality, pertinent studies are retained for the review.
* Synthesis: The selected studies undergo comprehensive analysis to integrate their findings, methodologies, and contributions. This phase involves summarizing, comparing, and synthesizing evidence to address the research questions and provide a cohesive understanding of the topic.  
Figure 1 illustrates the roadmap for implementing the PRISMA methodology in the systematic literature review process. A detailed flowchart outlining the entire procedure for narrowing down articles from the initially identified items in prestigious digital databases is presented in Appendix A, Figure A1.  
Figure 1: Roadmap for implementing the PRISMA methodology in the literature review.
## 3 Proposal of an IoMT Architecture and Platform to Enable Pervasive Healthcare
### Performance Evaluation
There are several studies ([42; 43; 44]) that validate the performance of the CoAP, MQTT and REST protocols considering the delivery of predefined payloads in an independent environment without interference from other actors and the results differ from those obtained in this manuscript. However, we prefer the validation of the proposed platform considering a real use scenario where factors such as those described above influence the performance. This allows the definition of better criteria regarding the performance of the platform. A performance comparison with similar platforms would be interesting to better evaluate our proposal in terms of state-of-the-art. However, a fair comparison seems to be very difficult (or impracticable) given the differences in platform objectives, network architectures, and topologies, just to name a few features. Moreover, open source healthcare platforms were not identified. As such, a construction effort of other platforms would be needed, in order to make an effective comparison. An additional issue was identified: many healthcare platforms focus their efforts on specific diseases, and several times use proprietary data formats and protocols, different from our proposal. Our platform aims to cover the domain of healthcare as a whole, following widely adopted standards, enabling semantic interoperability as well as considering a big data approach. Applications with ultra-low latency requirements may be affected considering the results. The better latencies were obtained using the CoAP BP which does not meet the requirements for ultra-low latencies applications, such as telesurgery or wireless service robots. In this sense the platforms enable the implementation of novel communications protocols as BPs, such as the proposed by [45]. Either way, the proposed platform is not focused on such types of applications and the experiments show encouraging results in terms of healthcare data standardization, processing, and sharing. For remote pervasive monitoring and wearable IoMT devices data aggregation, the platform meets the latency requirements (please see Table 1 in [46]).
## 2 Related Work
### Statistical Anomaly Detection  
The most traditional method to detect an anomaly is to utilize statistical techniques such as calculating the mean and standard deviation of a dataset. By identifying data points that fall outside of a certain threshold, these methods can flag potential outliers.  
[PERSON] et al. [13] introduces a generalized probabilistic monitoring model (GPMM) designed to handle both random and sequential data for process monitoring. It unifies various probabilistic linear models and establishes connections between different monitoring methods. Using the expectation-maximization (EM) algorithm, the model estimates parameters, derives monitoring statistics, and investigates the equivalence between these statistics and those from classical multivariate methods. The model's effectiveness is demonstrated through numerical examples and application to the Tennessee Eastman process.  
[PERSON] et al. [14] presents an unsupervised fault detection and diagnosis method called Sparse Distribution Dissimilarity Analytics (SDDA), which combines distribution dissimilarity with a lasso penalty. This method addresses shortcomings in existing techniques by maximizing the dissimilarity between normal and abnormal data distributions, enabling accurate detection and diagnosis of process faults, including those with small magnitudes. The method is validated through both simulations and real industrial processes, showing superior performance compared to traditional methods.  
[PERSON] et al. [15] presents a novel fault detection method designed for complex industrial systems. The proposed MoniNet framework integrates both temporal and spatial information using a cascaded monitoring network, which enhances the detection accuracy of process anomalies. The method is validated using real industrial data, demonstrating its effectiveness in identifying faults more accurately compared to traditional methods.