esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Optimization and Evaluation on CBHF
### Counting Bloom Filter Structure  
Figure 10 shows the counting Bloom filter structure with several elements inserted in the filter. For each local counter in the filter, it is initially set to '0'. When an element \(x\) is inserted or deleted, the counters \(c[h_{1}(x)],c[h_{2}(x)],\ldots,c[h_{k}(x)]\) are incremented or decremented, respectively.  
The bit at the position \(h_{i}(x)\) is set to '1' whenever the counter \(c[h_{i}(x)]\) changes from 0 to 1. Conversely, the bit at position \(h_{i}(x)\) is set to '0' whenever the counter \(c[h_{i}(x)]\) changes from 1 to 0. Hence, the counting Bloom filter always correctly reflects the current set. Note that counters in the counting Bloom filter structure are only used for insertions and removals, but not for lookups. Therefore, the counting Bloom filter has the same false positive probability as the standard Bloom filter.  
In addition, the memory allocation of the counters is also important, namely, how largely each counter can become. In our previous paper [30], we have already obtained some conclusions about the probability that the counting value overflows:  
\[\mathbb{P}\{\max c\geq 2^{2}\}\leq 4.29\times 10^{-2}\times m, \tag{17}\]  
\[\mathbb{P}\{\max c\geq 2^{3}\}\leq 9.47\times 10^{-6}\times m, \tag{18}\]  
\[\mathbb{P}\{\max c\geq 2^{4}\}\leq 1.37\times 10^{-15}\times m, \tag{19}\]  
where \(c\) and \(m\) denote the counting value and the bit array size, respectively. These theoretical probabilities will help us construct the filters in the CBHF.
###### Abstract  
Rigid-reflector spaceborne antennas (RRSAs) are well-suited for high-frequency application scenarios due to their high surface accuracy. However, the low stowing efficiency of RRSAs limits the aperture diameters and further deteriorates the electromagnetic (EM) performances in terms of gain, resolution and sensitivity. After conducting systematic feature analysis with respect to several typical RRSAs, we propose a novel type of RRSA to solve the aforementioned problems. Inspired by the pose adjustment process for a higher stowing efficiency of traditional RRSAs, we also propose a new segmentation scheme of a reflective surface consisting of a deviation-angle panel that facilitates a higher stowing efficiency. Based on this scheme, its corresponding folded configuration is implemented by combining [PERSON]'s rotation theorem and the idea of parameter identification. In addition, we also compare the stowing efficiency of different schemes to verify the high stowing efficiency of the configuration. Finally, we perform mechanism/structure design and deployment dynamics to demonstrate that the antenna can be successfully deployed and exhibits excellent deployment quality. The results suggest that the proposed antenna possesses higher stowing efficiency than that of the same kind, with a stable deployment and interference-free process.  
rigid-reflector spaceborne antenna; deviation-angle panel; stowing efficiency; Euler's rotation theorem; parameter identification; deployment dynamics 202424385. [[https://doi.org/10.3390/s24020385](https://doi.org/10.3390/s24020385)]([https://doi.org/10.3390/s24020385](https://doi.org/10.3390/s24020385))
# Development of a Deep Neural Network Model for the
## 2 Data and Method  
Here, we created a scenario to replicate real mining sites in Dongtan, China, by deploying a network of 27 seismometer stations (Figure 1a). Each station can detect seismic waves originating from the earthquake source (Figure 1b). These seismometers are designed to monitor earthquakes occurring at depths of approximately 1000 to 2500 m within the network (Figure 1c).  
Figure 1: (**a**) Working face and seismometers in the No.6 mining area of DongTan coal mine, adapted from [PERSON] et al. [38]; (**b**) raw waveform data recorded by seismometers; (**c**) station\(-\)ray\(-\)source coordinate diagram. Green represents stations, red represents rays, and blue triangles represent the seismic sources, with background colors indicating varying velocities.  
The objective of this study was to train a model using synthetic data that could predict wave velocity and earthquake locations based on the P-wave arrival times at each station. Our methodology consisted of three phases, namely data generation, model training, and model testing. Uniquely, our approach did not follow a strict sequential order for these phases. We began by generating initial data with an assumed velocity model, used it for training, and subsequently generated more data with different velocity models for further training.  
The core of our research lies in training a model that can be iteratively improved with the influx of newly acquired field recording data, regardless of its current state of training. Such recorded data can be continuously acquired through seismometers deployed near the mining site.  
Each iteration may employ different methodologies; however, if the methods are scientifically sound, the subsequent model would invariably surpass its predecessor in performance. Thus, we proposed a novel approach to solving the problem of seismic source inversion--continually training models through neural networks, interspersed with various methods.  
Each iteration of our model represented a step forward, with the process of moving to the next step being independent of the previous one. Essentially, our training improved the model incrementally, and this improvement was achieved using different, relatively independent methods at each step. Consequently, the model could be refined to a very high degree of accuracy through unlimited iterations. Each iterative model remained independent of the others, and the methods employed in each step was different and independent. Therefore, it was feasible to develop a modular iterative inversion model training approach.  
This iterative process allowed us to incorporate the acquired velocity knowledge to enhance the model while also reducing the computational cost associated with training. It ultimately yielded a high-precision model suited to a specific scenario--in this case, mining-induced seismicity--although the methodology is also applicable to hydropower, bridges, and tunnels.
# Polynomial Expressions for the Thermal Efficiency of the Parabolic Trough Solar Collector
## 4 Conclusions  
The objective of this work was to investigate different mathematical expressions for the determination of thermal efficiency in a parabolic trough solar collector (PTC). A detailed literature review was conducted in order to highlight what are the usual expressions in the existing literature. This work examined 31 different models which are specific cases of a generalized formula. A numerical model about the thermal efficiency of the PTC was developed and it has been validated by literature experimental data. This model regards the LS-2 PTC module which is a usual module that is used in studies about PTC and the respective applications. The results of this work can be extended and applied for all the PTC modules and not only for the examined one because the usual PTC characteristics have been selected. Below, the most important conclusions are given:  
* The use of the third-order term \"\(\Delta T^{3}\)/G\({}_{\rm b}\)\" is the most important term which can lead to relatively accurate results, especially in cases with a low number of terms.  
Figure 7: Comparison of the suggested model (\(a_{0}\), \(a_{3}\), \(b\)) with other usual models for \(G_{b}=1000\) W/m\({}^{2}\).  
* The use of the temperature difference term \"\(\Delta T\)\" is important and it can lead to high improvement compared to the respective formulas without it.
* 5}\cdot\Delta T\), (R\({}^{2}\) = 99.92%
- MAPE = 0.13%) or in a general form: \(\eta_{col}=\text{a}_{0}+\text{a}_{3}\cdot\frac{\Delta T^{3}}{G_{b}}+b\cdot \Delta T\)
* It is found and discussed that there is a need for using the proper terms and not using many terms in order to achieve high accuracy.
* In the future, there is a need for extending the present work by taking into consideration the wind speed, the solar angle and the flow rate. Moreover, there is a need for studying extra PTC modules with different characteristics than the present one, for example with a non-selective absorber, with a non-evacuated receiver and with different working fluid.  
E.B. conceptualization, methodology, investigation, writing--review and editing, writing--original draft preparation; C.T. supervision, writing--review and editing, writing--original draft preparation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Radiator Enablers for Wireless Communication Evolution
## 2 Challenges, Enabling Technologies, and Antenna Developments
### Antenna Evolution to Provide New Degrees of Freedom
#### 2.2.3 The Holographic Metasurface Technology  
In the case of RIS, the element radiators are usually at resonance; thus, their dimensions are comparable to the operating wavelength. In many cases, this fact may be an unnecessary constraint. On the other hand, gMIMO uses an extremely large number of elements that should be extremely broadband, which require the support of an equally broadbandbeamforming feeding network. By relaxing the size of the element and the need for a demanding feeding network, one can consider the holographic metasurface technology for use at the access point (be it a BS or an orbiting air- or space-born platform). Indeed, it is not the resonant element placement but the placement of its feeding port that defines the achieved array factor. Indeed, the shape of the produced beams can be assigned not to the feeding network alone but mainly to the scattering holographic metasurface. This is viable since the working principle of the metasurfaces is the exploitation of the leaky wave scattering on the holographic host radiating aperture. In this way, a holographic metasurface can control both the phase and amplitude of the incident wave with high precision, enabling the reconstruction of complex wavefronts or the manipulation of waves in intended ways.  
Moreover, one more advantage of holographic metasurface technology should be noted. There are cases when a limited field of view system is needed (a BS antenna covering a specific sector or a satellite serving a clearly defined coverage area on the planet), or when we need to achieve more directivity than the array antenna can provide. In this case, the antenna radiators overlapping is the appropriate choice (ref. [24]). Metasurface antennas lend themselves naturally to this concept since the leaky wave from a feeding port can travel much of the available metasurface host aperture before it is radiated away. Thus, in this way, one more constraint (the radiating elements' non-overlapping one) is relaxed, and more antenna ports can be accommodated in a limited area.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 2 The NOMA Link-Level Simulator
### Receiver Design
#### 2.4.1 Perfect SIC  
Perfect or ideal SIC works on the assumption that the NEU has perfect knowledge of the FEU data, and the interference is perfectly canceled at the NEU. Perfect SIC gives the maximum upper bound of the performance. The detection of NEU in terms of perfect SIC can be expressed as [30],  
\[\widehat{x_{1}}=\frac{y_{1}-\sqrt{P_{2}}x_{2}}{\sqrt{P_{1}}} \tag{2}\]  
where \(y_{1}\) and \(\widehat{x_{1}}\) denote the received and estimated decoded signals at the NEU, respectively. \(\sqrt{P_{1}}\) and \(\sqrt{P_{2}}\) are powers allocated to the NEU and FEU, respectively. \(x_{2}\) is a perfectly decoded signal of the FEU whose knowledge is available at the NEU. Signal detection involves compulsory demodulation and an optional channel decoding process, provided that channel coding is applied at the transmitter side.
## 2 Material and Methods
### TEC Driver
#### 2.1.3 Lighting  
The radiant flux required for the subjects of interest varies between species and organisms. Certain fungal species require white light while plant species mostly require red and blue components [24]. The literature also indicates the changes in chemical composition in plants in response to red and far-red light [25; 26]. Events such as flowering and germination could be triggered by varying the spectral components and circadian rhythms [27; 28]. This, therefore, requires light source with adjustable spectral composition and power. Narrow-band LEDs covering a wide range of wavelengths and wide-band white LEDs, as shown in Figure 3, are widely available from different manufacturers [29].  
One of the goals of this work was to design and include a light source with adjustable spectrum. Spectral power distribution of typical white LEDs with different color temperature (3000-6500 K) could be used as the primary source and the additional wavelengths that influences various biological processes or trigger events could be included using narrow-band LEDs. A complete range of spectral requirements for various species is still unknown and needs to be studied. A preliminary LED lighting unit with a combination of neutral white, narrow-band red, blue, green, and support for inclusion of UV/far-blue and IR/far-red LEDs was designed.  
Figure 3: Normalized spectral power distribution of LEDs available for lighting system design: (**left**) white LED series compared [29]; and (**right**) narrow-band LED series compared [29].  
LED Driver for Spectrum Regulation  
Spectrum adjustment and light-based event triggering require that the individual channels (i.e., white, red, blue, green, UV/far-blue, and IR/far-red) be independently adjusted. LEDs used in this system are daisy-chained modules of three or four LEDs connected in series and operating at 12 V. As shown in Figure 4, every color channel has a MOSFET based low-side driver to adjust the radiant spectral power using PWM signals. These vertical columns of daisy-chained modules are combined into groups that are operated using relays. This grouping is done such that the first group corresponds to the central vertical column and the preceding groups correspond to the adjacent columns. The overall advantage of this setup is that the radiant spectral power and the total area covered by the LEDs can be varied as required.
# Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser
## 7 Conclusions  
In this paper, an image motion measurement method based on an inertial reference laser was proposed, and an experimental verification system was developed. Based on this system, image motion measurement experiments under various experimental conditions were carried out, and the degraded images were restored using the obtained image motion data. The results show that the image motion measurement method based on an inertial reference laser achieved a higher measurement accuracy with a relative error of less than 0.12 pixels (RMS) compared with the measurement data of the Hartmann wavefront sensor. When the pixel jitter was less than 3.5 pixels, the image motion measurement method based on inertial reference laser significantly improved the image quality and increased the MTF of the image to 1.61-1.88 times of the original image. The obtained image motion data could also be fed back to the image stabilization mirror to compensate for the influence of on-orbit disturbance on the image quality in real time and to effectively improve the image quality of the optical remote sensing satellite. The proposed method has a high measurement accuracy and speed, is not affected by ground lighting conditions, and can be used in other occasions that require a high-precision image motion measurement.  
Conceptualization, S.W., H.W. and R.Y.; data analysis, R.Y. and Y.G.; investigation, R.Y.; methodology, S.W., H.W. and R.Y.; project administration, H.W. and K.Y.; resources, H.W. and J.Z.; software, T.J.; validation, R.Y., X.S. and T.Y.; writing--original draft, R.Y.; writing--review and editing, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National key research and development plan, grant number 2016 YFB0500502.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 1 Introduction  
### Background  
Recently, deep learning with convolutional neural networks (CNNs) has proven to be highly effective for computer-aided detection (CAD) in medical image analysis. The trend in CNN architectures recently has been towards ever deeper and wider networks with dense connectivity. For example, ViT-G/14 [1] and ViT-MoE-15B [2] were the top two CNN architectures in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition in 2021 [3]. The ViT-G/14 and ViT-MoE-15B architectures contain 1.843 G and 14.70 G parameters, respectively. Furthermore, ViT-G/14 requires 965.3 G floating point operations (FLOPs) per single image, which is a very computationally costly and power-hungry solution. Perhaps even more significantly, larger networks require more training data to be able to generalize to new data [4]. In many medical image analysis applications, access to properly-labeled truth imagery is limited, especially for rare diseases [4]. Data collection and truthing in medical imaging can be cost-intensive, time-consuming, and requires expert analysis. Transfer learning can help to reduce the amount of application-specific data required for training. However, large amounts of data may still be needed to obtain the desired reproducibility and generalizability, even with transfer learning [5; 6]. Data augmentation is another approach to dealing with limited training data. However, data augmentation can be very challenging in some medical imaging modalities such as chest radiographs [7].  
Modular CNN architectures are a promising approach for complex problem-solving that may be able to help address the challenges described above. Some modular methods are inspired by the structure and function of the human brain. Recent findings in neuroscience reveal a high level of modularity and hierarchy of neural structure in the human brain [8]. In the early 1980s, neuroscientific research categorized the central nervous system (CNS) in the human brain as a massively parallel and self-organizing modular system [9; 10; 11]. The CNS consists of distinctive regions. Each region develops as a functional module. The modules are densely connected and interact with one another to accomplish complex perception and cognitive tasks in an efficient manner [9]. Traditional CNN architectures often use repeating structures such as layers or groups of layers. However, the networks are generally trained as one monolithic entity with all learnable parameters being updated simultaneously.
## 3 System Design
### Client
#### 3.3.1 Map  
There are two forms of data visual representations in this system: map and statistical plot. Flat maps are frequently used for climate studies. Meanwhile, 3D globe displays has the advantage in providing more intuitive view about the global location and other information which may be useful for climate study (e.g., terrain) [24, 38]. However, this paper will not evaluate the trade-off between flat maps and 3D globes. Therefore, both are integrated into the system. Due to the advanced capabilities in dynamically visualizing multidimensional geographical data online, Google Map and Google Earth (GE) [39] are selected for displaying maps of climate data at the client side. Instead of using one map as other climate analysis tools, e.g., VISUS [24], maps can be attached to as many as six windows. Users can compare variables in parallel to acquire knowledge of spatiotemporal patterns. Moreover, all map operations on the six windows are linked together so that all map events occur on six windows synchronously. The map views presented in front of users are always focused on the same area. With comparison during view changes, users can find, for example, that one variable has high value close to the Equator and low value near polar areas and that another variable has an opposite behavior.  
Besides providing static views of maps, the web client also supports temporal animated maps which can help detect the continuous dominant patterns through time [40, 41]. If users conduct animations on multiple map windows, they will obtain the general spatial patterns showing how different variables change over the same time period. For example, from 1960s to 1990s, land surface temperature increases and the largest change appear around polar areas. At the same time, vegetation coverage decreases correspondingly, but the biggest change is found at places close to the Equator.  
In addition, some widgets are provided for better making maps such as setting transparent colors for image layers in maps so that many layers presenting different information can be overlaid together.
## 2 Methodology
### RandomForest (RF) and the RandomForest Spatial Interpolator (RFSI)  
Among the many machine learning (ML) methods, RF has shown the most promising results as an interpolator [6; 7; 8]. When used in this manner, it functions in the same way as the traditional RF. The dataset is randomly sampled with replacement to create a new dataset, which is then used to create a decision tree model. This process of sampling the dataset and constructing a decision tree model repeats until the maximum number of trees has been reached, at which point, training concludes. The main difference is that position data (x and y coordinates) must be included to allow the model to capture spatial covariance.  
However, using coordinate information as the sole form of spatial data may not be enough to capture the target variable's spatial structure. Hence, the RandomForest spatial interpolator (RFSI) model was conceptualized to compensate for this deficiency. The RFSI attempts to mimic how spatial structure is modeled in traditional statistical interpolators, where it is assumed that variables decrease in similarity as the distance between them increases. By adding information regarding observation values from nearby observations and the distance to these neighbors, the RF algorithm can better capture the spatial relationship, leading to better performance [8]. Equation (7) shows the basic formulation of the RFSI model.  
\[\hat{Z}(x)=f(Z(x_{1}),\ldots,Z(x_{m}),d(x,x_{1}),\ldots,d(x,x_{m}),a_{1},\ldots,a_{n}) \tag{7}\]  
where \(\hat{Z}(x)\) is the RFSI interpolator, \(Z(x_{m})\) is the observed value at the \(m\)th-nearest-neighbor, \(d(x,x_{m})\) is the distance between estimation location x and the observed value at the \(m\)th-nearest neighbor, and \(a_{n}\) is the \(n\)th auxiliary variable.  
This study considers only the five closest neighbors because, when the observation distance is set to 1 km, there are not enough measurements to use a larger neighborhood size.
## 2 Methods
### Monte Carlo Estimation Model
#### Exposure Categories and Scenarios  
We evaluated the model for a set of exposure scenarios defined by (1) geographical location, (2) base station cell type, (3) level of indoor coverage, and (4) usage behavior. Three different geographical locations based on the number of citizens within 500 m from the base station were defined by the operator that provided the data:  
* rural: 0-800 citizens, bystander distance \(r=25\) m;
* suburban: 801-4000 citizens, bystander distance; \(r=10\) m;
* urban: \(>\)4000 citizens, bystander distance \(r=5\) m (\(r=1\) m for trains/urban centers).  
Measured LTE cells were categorized as macrocells (rural, suburban, and urban) and additionally as mini and microcells (urban as well as indoor). Additionally, we simulated a shrinking cell size corresponding to 6, and 12 dB \(RSRP\) increases at the cell edge. The increase in indoor coverage was simulated in the range from 0% to 100% for each cell type. We also defined four user types based on their daily data usage:  
* non-user: does not own a mobile phone;
* light user: uses 100 MByte/day;
* medium user: 1 GByte/day;
* heavy user: 10 GByte/day.