esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual MAV Tracker with Adaptive Search Region
###### Abstract  
Tracking a micro aerial vehicle (MAV) is challenging because of its small size and swift motion. A new model was developed by combining compact and adaptive search region (SR). The model can accurately and robustly track MAVs with a fast computation speed. A compact SR, which is slightly larger than a target MAV, is less likely to include a distracting background than a large SR; thus, it can accurately track the MAV. Moreover, the compact SR reduces the computation time because tracking can be conducted with a relatively shallow network. An optimal SR to MAV size ratio was obtained in this study. However, this optimal compact SR causes frequent tracking failures in the presence of the dynamic MAV motion. An adaptive SR is proposed to address this problem; it adaptively changes the location and size of the SR based on the size, location, and velocity of the MAV in the SR. The compact SR without adaptive strategy tracks the MAV with an accuracy of 0.613 and a robustness of 0.086, whereas the compact and adaptive SR has an accuracy of 0.811 and a robustness of 1.0. Moreover, online tracking is accomplished within approximately 400 frames per second, which is significantly faster than the real-time speed.  
visual object tracker; fully convolutional neural network; adaptive search region; truncation prevention; path prediction +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Numerical Study of the Filtration Performance for Electrospun Nanofiber Membranes
## 4 Conclusions  
1. GeoDict software can be used to generate a more realistic electrospun nanofiber structure. Compared with the actual electrospun nanofiber membrane, it is also composed of nanofibers randomly arranged and also has a complex three-dimensional disordered cavity structure. The numerical model of electrospun nanofiber filters can provide channels for gas molecules while intercepting and filtering particles in the air. The spinning time determines the thickness of the electrospun nanofiber membrane and the degree of fiber coverage. As the spinning time increases, the corresponding coverage of the fiber membrane is higher, and the numerical model is more compact.
2. Based on the FlowDict module of GeoDict software, flow field simulations were performed using the parametrically constructed electrospun nanofiber filter model. The simulation results were compared with experimental data and predictions from empirical equations. Both the simulation results and empirical equation calculations for the filter media showed higher values than the experimental measurements. Among these, the numerical simulation results of the filter media showed the highest consistency with the experimental data, followed by the [PERSON] empirical equation, then the Happel model, with the Kuwabara model showing the lowest consistency. For example, at a spinning solution concentration of 7%, the relative error between the [PERSON] empirical equation calculations and the experimental values for filtration resistance is 41.1%, the relative error between the Happel model calculations and the experimental values is 47.7%, and the relative error between the Kuwabara model calculations and the experimental values is 59.5%.
3. Based on the FilterDict module of GeoDict software, the electrospinning nanofiber filter material model generated by the parametric model establishment method is used to simulate the filtration process. The simulated calculation value of the filtration efficiency of the fiber filter medium shows a high consistency with the experimental measurement value. This method can accurately evaluate the filtration efficiency of electrospun nanofiber membranes and predict the filtration performance of electrospun nanofibers.  
Conceptualization, W.H.; Methodology, W.H.; Software, W.H. and H.Z.; Validation, W.H.; Formal analysis, W.H.; Investigation, W.H. and S.C.; Data curation, X.M.; Visualization, L.C.; Supervision, F.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Feasibility of Developing Sustainable Concrete Using Environmentally Friendly Coarse Aggregate
## 4 Results and Discussion
### Mechanical Properties
This impacts the axial shortening and short term vertical deflection of structural members under compression load. The modulus of elasticity in Figure 4 tended to increase with time while following a similar trend observed in compressive and tensile strengths. The elastic modulus ranged from 20,748 to 29,672 MPa for combined RCA-25 and 32,526 to 37,879 MPa for combined RCA-40 concrete between 7 and 90 days. Elastic modulus values have a high variability, thus experimentally observed values are allowed to vary between \(\pm\)20% of the calculated value Equations (3) and (4), based on AS 3600 [62]. The predicted elastic modulus for combined RCA-25 and combined RCA-40 are 19,581, 22,409, 29,259 MPa and 29,101, 30,599, 34,615 MPa, respectively, at 7, 28, 90 days. The elastic modulus of the combined RCA concretes is observed to be higher than the prediction provided in AS3600; however, most results fall within the allowed 20% tolerance limit. Thus, data indicates that the elastic modulus of combined RCA concrete is comparable to that of quarried aggregate concrete of similar compressive strengths. \[E_{c}=0.043\;p^{1.5}\sqrt{f_{cmi}}\;\text{MPa};\;\text{when}\;f_{cmi}\leq 40 \;\text{MPa} \tag{3}\]  
\[E_{c}=\left(0.024\sqrt{f_{cmi}}+0.12\right)p^{1.5}\;\text{MPa};\;\text{when} \;f_{cmi}>40\;\text{MPa} \tag{4}\]  
where \(E_{c}\), \(p\), and \(f_{cmi}\) are elastic modulus, density, and mean compressive strength, respectively. The Poisson's ratio of conventional aggregate concrete is usually in the range from 0.11-0.21, but typically stay in the range from 0.15-0.20 [64]. The measured Poisson's ratio values of combined RCA-25 and combined RCA-40 concretes at 7, 28 and 90 days are 0.11, 0.14, 0.15 and 0.14, 0.16, 0.17,  
Figure 3: (a) Flexural strength and (b) Splitting tensile strength development of Combined RCA concrete. respectively. All these measured values are within the 0.11-0.21 range and are comparable to quarried aggregate concrete.
# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
## 2 Materials and Methods
### Related Work  
With the growth of artificial intelligence, some studies are beginning to emerge involving deep learning techniques in the context of IRDs, involving classifiers to detect these diseases based on ophthalmic examinations [22]. However, in the context of IRDs, the use of deep learning techniques as a tool to aid diagnosis is still an under-explored area. One of the reasons for this problem is the small size of the dataset used to train deep learning classifiers, because there are insufficient amounts of data to train these classifiers efficiently. This is the main limitation presented by some authors in their studies [8; 9; 23].  
In other areas of retinal research, studies involving generative models for creating synthetic eye exams to enhance small datasets have been considered a viable solution to overcome this limitation identified by some authors [8; 9; 23; 24]. A preliminary search was conducted to determine what had already been studied in this area within the context of IRDs. However, no systematic reviews were found, and only two studies specifically addressing the use of these techniques in IRDs were identified. Thus, the application of synthetic data to augment small datasets and train classifiers has not yet been widely explored for these diseases.  
With the results obtained, it was necessary to broaden the scope of the search for this systematic review of retinal diseases and not just IRDs, as we had already seen in the great scarcity of studies.  
These findings demonstrate that there is great potential for research in this context in IRDs that is not yet being explored, and that there is a lot of work involving generative models to increase data in these diseases.  
The lack of systematic reviews on this subject reinforces the need for a review that collects all of the existing knowledge on this topic. By consolidating existing research on generative models in retinal diseases and evaluating their potential application in IRDs, this study will constitute an important reference for future research and clinical applications. This could allow future researchers to know what has already been done on this subject and to apply these techniques in their studies. This will allow for an increase in studies involving generative models in IRDs to be able to generate synthetic data to augment reduced-size training datasets. Consequently, it is intended to eliminate the limitations mentioned in the literature, the scarcity of data for training classifiers, and improve their efficiency [8; 9].
## 3 Literature Review and Research Questions
### Profitability of IS Adoption  
The business environment is characterized by major complexity in all of its functions and elements. Therefore, the information that is obtained from internal or external business environments needs to be processed and managed in a very systematic way to minimize uncertainty and to maximize profit at the same time. Profitability is the characteristic that can define the existence and longevity of a company in a very competitive environment. The advances in technology have made it possible to manage complexity and information more easily than ever before. Thus, many companies started to be interested in IS to manage the vast amount of information they have available during their operations [1; 16; 23].  
The first studies conducted about the business performance of companies that had adopted IS showed no promising results regarding the profitability of ICT adaptation. Promising results firstly appeared in the mid-1990s, although there were also contradicting findings, which suggest that there are probably other variables influencing the effect of ICT on business performance (e.g., the sector they operate in) [24; 25; 26].  
[PERSON] et al. [10], in their study of Southeastern Europe's companies--including Greece--with regard to profitability, found that companies in that area are more focused on dealing with suppliers and less with customers. In other words, they try to gain profit from the customers, but the amount of profit they gain from their businesses is not determined. There are also studies that are focused on specific IS systems [27], examining the post implementation practices of ERP systems and their relationship to financial performance.  
The focus of the second research question is set on the profitability of IS adaptation and it is expressed as follows:  
RQ2: Is IS adoption considered as profitable in direct economic terms by Greek manufacturing companies?
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 3 TPC-CQF Improved from CQF
### CQF-Based Queue Scheduling Mechanism  
CQF is a specific configuration of time-aware shaping (TAS), as shown in Figure 2.  
The CQF mechanism distributes traffic across distinct queues, each handling specific traffic types. Queues Q\({}_{5}\) and Q\({}_{4}\) guarantee service for audio/video bridging (AVB) traffic, while Q\({}_{3}\) reserves bandwidth for non-time-sensitive traffic. Queues Q\({}_{0}\) and Q\({}_{2}\) employ best-effort forwarding for regular Ethernet traffic, and Q\({}_{1}\) handles the lowest-priority background traffic. Given the high latency sensitivity of time-sensitive flows (TSFs), they are assigned to high-priority queues (typically Q\({}_{6}\) and Q\({}_{7}\)). These TSF queues operate under an XOR-gated mechanism: during fixed T-second time slots, one queue exclusively receives packets while the other transmits. This design ensures TSF forwarding within two slots. Assuming switch processing keeps pace, latency over \"n\" hops ranges between (n - 1)T and nT.  
Despite guaranteeing TSF forwarding within bounded slots, the traditional CQF approach presents significant limitations. A key drawback is its inability to provide differentiated forwarding services for TSFs with varying priority levels. All TSFs are scheduled within the same queue pair (Q\({}_{6}\)/Q\({}_{7}\)), lacking the flexibility to meet diverse QoS demands effectively. Furthermore, under traditional scheduling, if multiple TSFs arrive simultaneously, subsequent packets may be discarded based solely on first-come first-served order. Consequently, leveraging TSF delay redundancy to reduce packet loss represents a key factor for enhancing overall scheduling capability. Addressing this trade-off--potentially sacrificing minimal end-to-end latency to improve TSF service reliability--constitutes a primary objective of this work.
## 1 Introduction
This is one of the main gaps in knowledge and suitable areas for future research reported also by the ICNIRP in [43]. Studies in this field often report selectivity of biological reactions related mainly to the frequency, amplitude, length of exposure and to the sensitivity of different cell lines [44]. However, results typically remain inconsistent, with questionable repeatability and relevance. One of the common aspects of the studies in this research field is lack or absence of the MF background measurement or monitoring within the exposure area. This is another possible reason why the effect of these fields remains controversial, but still a challenging research area. This work tries to help address the problem with ambiguous results, focusing on the precise implementation of the chosen methodology and experimental protocol setup incorporating exposure system optimization with a focus on static MF measurement within the incubator and exposure system. The research aim is verification of IPR theory via continuous exposure of the yeast strain _Saccharomyces cerevisiae_ to a time-varying ELF MF. As is shown further, setting the exposure conditions to meet the IPR demands within the laboratory environment is not an easy task, which can not be achieved without accurate measurements of magnetic background as well as monitoring of relevant quantities within the microbiological experiments. After sufficient optimization of the exposure setup and in accordance with the chosen methodology, a significant set of experiments is performed to obtain data for subsequent statistical analysis, from which the conclusions regarding the biological effects of the chosen ELF MF are drawn, together with a discussion about the need for MF monitoring as a crucial part of IPR mechanism (as well as any other) validation.
## 1 Introduction
### Multi-Spectral Sensors
Going up in scale, [PERSON] and [PERSON] [35] showed that discrimination and mapping of Amazonian land cover types were improved in off-nadir viewing, using data from MISR satellite sensor. We mounted downward-looking sensors at an oblique angle of 55\({}^{\circ}\) off nadir (Figure 2). The measurements were directed towards the dominant area in the eddy covariance flux footprint (according to the most frequent wind direction). We aimed for roughly westward or eastward azimuths to avoid either back scattering or forward scattering effects in the solar principal plane roughly along the N-S direction at noon, and to approximate the sensor view azimuth angles of most polar-orbiting satellites. Small adjustments had to be made in order to mount the mast at an easily accessible spot, moreover, with solid foundation. The upward-looking multispectral sensors measured with a cosine correction diffuser, thus sampling irradiation from the whole upper hemisphere with equal response across the sensor aperture from any direction. The downward-looking multispectral sensors measure conically, thus enabling computation of hemispherical-conical reflectance factors (HCRF, following the nomenclature by [PERSON] _et al._[36] and [PERSON] _et al._[37]. We further note that the term \"reflectance\" in this paper refers to \"HCRF\"). Since there was a tendency for water films covering sensor aperture holes, we covered the downward-looking sensors with a small roof to protect them from rain. The geometrical properties of the downward-looking multispectral sensors (not PAR, since sensors for PAR reflection measurement were always facing vertically downward with hemispherical FOV) are given in Table 3.  
Optical sensor footprint sizes vary between _ca._ 1,400 and 6,900 m\({}^{2}\) for the sites with conical measurements of reflected radiation, depending on sensor FOV and height of measurements. Using a fixed sensor to completely match a flux tower footprint is impossible given that fluxes are dynamic and the footprint area is very difficult to predict [2]. We strived at placing our optical towers carefully within the target ecosystems of the flux towers in order to facility the comparison of the two measurements. The sites are homogeneous, and phenological transitions viewed by our towers occur more or less simultaneously across each flux tower footprint area.
# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 9 Discussion
### Past Trends in Two Decades and Current Area of Interest  
Question 1: \"What has been the trend of HMLV research for the past two decades?\".  
By charting the literature found according to the industrial sectors, 71% of the research was not industry specific. The industries with prominent HMLV research problems were the semiconductor, electronics, and engineering support industries. Regarding the research area, 63% were about production and operation management. This showed the heavy emphasis on production scheduling and production planning. Therefore, there are opportunities for research in the other aspects of the value chain.  
Question 2: \"What are the current areas of interest in HMLV research?\".  
From 2017 to 2022, Industry 4.0 technologies have seen growing interest in topics, such as robotics, artificial intelligence, the internet of things, and additive manufacturing. Beyond algorithms and mathematical models for production planning, the concept of big data advocates production data collection to allow comprehensive data analysis for the optimisation of decision-making systems.
# Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm
## 2 5G NR Time Service Signal Model
### Integration of 5G NR Time Service Signal with 5G Signal  
In summary, the signal power of our 5G NR time service signal is much lower in practice than that of the 5G signal. It appears to be \"submerged\" within the 5G signal, does not occupy the 5G signal frequency band and does not interfere with the original 5G signal, as shown in the Figures 3 and 4 below:  
The system principle diagram for the integration of the 5G NR time service signal with the 5G signal is shown in the Figure 5 below:  
Figure 4: Power Spectral Density Diagram of 5G NR Time Service Signal within the 5G Signal.  
Figure 3: Time Domain Diagram of 5G NR Time Service Signal within the 5G Signal.  
The proposed 5G NR time service signal can be initially expressed by the following formula:  
\[S^{D}(t)=S_{2}(t)+S_{c}(t)+\phi^{D} \tag{7}\]  
\[S_{2}(t)=A^{D}D^{D}PN^{D}(t-\tau)cos\left[\omega^{D}t+\varphi_{0}^{D}\right] \tag{8}\]  
\[\omega^{D}=2\pi\left(f_{RF}+f_{d}^{D}\right) \tag{9}\]  
Combining the above equations, the final 5G NR time service signal that can be received by the terminal is expressed as:  
\[\begin{array}{ll}S^{D}(t)&=A^{D}D^{D}PN^{D}(t-\tau)cos\left[\omega^{D}t+ \varphi_{0}^{D}\right]+Re\left\{\sum\limits_{k=1}^{k=N}X_{k}e^{i\omega_{k}t} \right\}cos(\omega_{c}t)\\ &+Im\left\{\sum\limits_{k=1}^{k=N}X_{k}e^{i\omega_{k}t}\right\}sin(\omega_{c} t)+\phi^{D}\end{array} \tag{10}\]  
In the equation, \(A^{D}\) represents the amplitude of the 5G spread spectrum downlink signal received by the terminal, \(D^{D}\) represents the modulation data of the 5G spread spectrum downlink signal received by the terminal, \(PN^{D}\) represents the spread spectrum code of the 5G spread spectrum downlink signal received by the terminal, \(f_{RF}\) represents the center frequency of the 5G RF signal, \(f_{d}^{D}\) represents the Doppler frequency between the terminal and the base station, \(\varphi_{0}^{d}\) represents the initial phase of the terminal carrier, \(\theta^{D}\) represents the noise introduced after the 5G spread spectrum downlink signal is received by the terminal and passes through the channel. The resulting 5G NR time service signal can be transmitted by the base station to the terminal receiving equipment. Once the terminal equipment receives the signal, it can complete terminal time calibration.
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
###### Abstract  
The thermal performance of the propeller driver system is very important for underwater vehicles. A new kind of cylindrical heat sink is designed for a certain propeller driver system. The performances of the heat sink are analyzed, mainly using numerical methods. The thermal influences of structure parameters, such as base thickness, fins length, and fin number, are studied for the heat sink with an orthogonal experimental method. The results show that all three parameters have positive impacts on the heat dissipation of the driver. Compared with the fin numbers and the fin length, the base thickness has a relatively small impact on the working temperature of the driver. Compared to the initial design, the maximum temperature of the propeller driver drops by 22.3% with the designed novel cylindrical heat sink in the studied cases.  
propeller driver; thermal analysis; numerical simulation; structural optimization +
Footnote †: journal: Applied sciences
# The Impact of Power Definitions on the Disaggregation of Home Loads for Smart Meter Measurements
## 4 Results
### Appliance Data  
The proposed study considered a variety of common household appliances. For each appliance, a dataset of power consumption measurements was collected. Table 1 lists the specific appliances tested and their corresponding power consumption profiles.  
The algorithm was applied to each observation aggregation load (OAL) of every aggregated power signal associated with each appliance. This process identified the disaggregated load for each OAL. It is worth noting that appliances with varying power consumption levels during their operating cycle can be effectively modeled as state machines with multiple operational levels. This modeling approach does not impact the overall procedure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Appliance Name** & **Power (VA)** & **Number** \\ \hline LED Lamp (LED) & 10 & 10 \\ Fluorescent Lamp (Flu) & 72 & 3 \\ Vacuum Cleaner (VC) & 1180 & 1 \\ Washing Machine (WM) & 2140 & 1 \\ Dish Washer (DH) & 2120 & 1 \\ Refrigerator (Ref) & 105 & 1 \\ Toaster (Toa) & 703 & 1 \\ Microwave Oven (MO) & 1310 & 1 \\ Coffee Machine (CM) & 1405 & 1 \\ Oven (Ove) & 2550 & 1 \\ Air Conditioner (AC) & 1165 & 2 \\ TV LCD (LCD) & 46 & 2 \\ Laptop (Lap) & 105 & 3 \\ Mobile Phone (MP) & 10 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Appliance power consumption levels.