esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The silicon dioxide NP (637246 Aldrich) used in this study were acquired from Sigma Aldrich, Dramensveien, Oslo, Norway. As reported by manufacturer, the Silica NP had a primary particle size of 10-15 nm (spherical). The NP were dispersed in DIW and DIW containing 3-MercaptopropyI Trimethoxysilane (MPTMS) as stabilizing fluid. MPTMS (175617 Aldrich) was also acquired from Sigma Aldrich. Berea sandstone (outcrops) cores used in this work were acquired from Koucurek Industries Inc., Caldwell, TX, USA. The properties and mineral composition of the used cores are listed in Table 1. Quartz (00653 Sigma-Aldrich) and kaolinite (03584 Sigma-Aldrich) mineral powders were acquired from Sigma Aldrich with chemical compositions: SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) 2 SiO\({}_{2}\) 2H\({}_{2}\)O, respectively. The specific surface area of the mineral powders are 0.62 m\({}^{2}\)/g and 8.56 m\({}^{2}\)/g, respectively [22] previously determined by nitrogen adsorption.  
The nanofluid preparation was done using ultrasonic processor: UP400S (400 watts, 24 kHz) by Hielscher Ultrasonics. A TurbiScan Lab instrument by Formulation Inc. (Worthington, OH, USA) was used to determine the stability and concentration of silica nanofluids. A dual beam UV/Vis spectrophotometer: UV 1700 PharmaSpec by Shimadzu Corporation (Nakagyo-ku, Kyoto, Japan) was used to determine the concentration of the stabilizing fluids in the effluents. The particle size and zeta potentials of the NP in the prepared nanofluids were measured using a Zetasizer Nano ZSP from Malvern Instruments (Malvern, Worcestershire, UK). The zeta potential of the mineral powders was measured using an Acousustisizer II S/M Flow-through System by Colloidal Dynamics. Scanning Electron Microscope (SEM) imaging of the barea cores saturated with the nanofluids was performed on a SUPRA 35 VP instrument by Zeiss with an integrated energy-dispersive X-ray spectroscopy (EDX) analyzer by EDAX. Core flood studies were conducted to study the adsorption and transport of NP in the porous medium. A schematic of the core flood apparatus is shown in Figure 1. The setup consists of a core holder connected to a piston cylinder filled with injection fluid. The prepared core was wrapped in a plastic sleeve. The wrapped core with the plastic sleeve was inserted in a cylindrical rubber sleeve and loaded into the core holder. As shown in Figure 1, another pump and cylinder filled with confining oil (Tellus S2 V 32) was used for applying confining pressure. The outlet valve of the core was connected to automated liquid handler (GX-271 by Gilson Inc. Middleton, WI, USA) to collect the effluent at regular intervals. Pressure gauges are connected at the inlet and outlet valve to record the differential pressure drop across the core. The experimental methods used in in this study are explained in detail in the following sections.  
Figure 1: Schematic of the core flood setup. DIW: Deionized water; P: Pressure.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Data Preprocessing  
#### 4.5.1 Eye-Tracking Data Preprocessing  
Raw gaze data were filtered to remove blinks and other artifacts. QE onset was defined as the initiation of the final fixation occurring before the racket's forward swing, with a minimum duration of 100 ms. QE offset was determined at the last frame of the fixation.
## 2 Methods  
### Dataset  
Data for our study were obtained from the Agence Nationale des Frequences [23], a public administrative body in France tasked with planning, managing, and monitoring the use of public radio frequency spectrum, including private usage, in accordance with Article L. 41 of the Postal and Electronic Communications Code. Our dataset consists of 3.578 electric field strength measurements collected throughout Paris between 2022 and the first half of 2024 (Figure 1) using portable IoT sensing devices. These measurements were carried out within the framework of Article L.34-9-1 of the French Postal and Electronic Communications Code. The dataset includes frequency ranges from 100 kHz to 6 GHz, covering a variety of sources such as mobile telecommunication systems at different frequencies, FM and radio broadcasting, television (TV), professional mobile radio networks (PMR), HF services (short-wave, medium-wave, and long-wave), radar, Wi-Fi, and cordless phones (DECT). The electric field strength (V/m) was recorded as a spatial average over a six-minute interval. Measurements were taken at three different heights that corresponded to the average human body: \(h_{1}=1.70\) m, \(h_{2}=1.50\) m, and \(h_{3}=1.10\) m, ensuring a complete representation of exposure levels.  
To prepare the data for analysis, we used a variety of scikit-learn library [24]. This library offered flexibility and powerful tools to present the results effectively. Our final dataset contains eight key variables:  
ID: This unique identifier helps track each antenna location.  
EMF (V/m): This variable represents the total electromagnetic field strength (summed across all bands) measured at each antenna.  
Urbanization degree: This variable, coded with 8 values based on [25], indicates the level of urbanization in a specific area.  
Population (expressed as the number of people per **100 m \(\times\) 100 m**): This variable, extracted from a population density dataset [26], represents the number of people living within a 100 m \(\times\) 100 m square around each antenna.  
Built-up volume (m\({}^{3}\)): Derived from a spatial dataset [27], this variable indicates the total volume of buildings in the area surrounding each antenna.  
Built-up surface (m\({}^{2}\)): This variable, extracted from a dataset [28], represents the total surface area of buildings in the area, including residential and non-residential uses.  
Building height (m): This variable, derived from a building height dataset [29], indicates the average building height in the vicinity of each antenna.  
Settlement characteristics: This variable, based on the GHS-BUILT-C dataset [30], describes the inner structure and functionality of the built environment around each antenna.  
Figure 1: The distributions of measurement points.
# Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition
## 2 SAR Image Recognition with Sparse Weighting Spatial Pyramid Pooling
### Local Feature Extraction and Descriptor Quantization  
Considering that the SIFT feature has the characteristic of invariance to scale, orientation, and affine distortion, in this study, dense-SIFT descriptors were extracted to express an SAR image [14]. The image \(I\) can be denoted by a set of local features' descriptors as: \(I=[d_{1},d_{2},\ldots,d_{N}]\in\mathbb{R}^{D\times N}\), where \(d_{i}\) represents the \(D-\)dimensional SIFT description vector. Given a codebook \(B=[b_{1},b_{2},\ldots,b_{M}]\in\mathbb{R}^{D\times N}\), the K-Means clustering algorithm [13] was adopted with the Euclidean distance to cluster local features into groups, and the generated centers of each group were taken as the codebook.  
Sparse coding [10] was applied to encode the local feature vectors, as follows:  
\[\arg\underset{V}{\text{min}}\sum_{i=1}^{N}||d_{i}-Bv_{i}||_{2}+\lambda||v_{i}|| _{1}. \tag{1}\]  
The feature vector for a descriptor \(d_{i}\) becomes an M-dimensional vector \(v_{i}\). \(v_{i}\) is the corresponding vector to the descriptor \(d_{i}\). The sparse coding vectors are obtained by the feature-sign search algorithm [20] by solving Equation (1) here.  
The next step is the spatial pooling step aiming at obtaining more discriminative image representation from each sub-region. This study applied the construction of a three-level spatial pyramid. At every resolution \(e\), \(e\) = 0, 1, 2, a grid was constructed such that there were \(2^{e}\) resolution cells along every dimension, i.e., the 1 \(\times\) 1, 2 \(\times\) 2, 4 \(\times\) 4 grid structures; thus, the sub-regions were \(K\) = 21 in total. We let \(V\) be a collection containing \(T\) local feature codes acquired from a sub-region. In addition, the max-pooling strategy was used to concatenate all the descriptors for the sub-region, which can acquire many discriminative features to SAR image spatial variations. The expression of the max-pooling is as follows:  
\[t_{k}=\max(v_{1},v_{2},\cdots,v_{T}) \tag{2}\]  
where max represents the element-wisely maximization for the involved vectors. The local features were pooled in all the sub-regions and concatenated to form the image representation \(f=[t_{1};t_{2};\cdots;t_{K}]\).  
Considering a set of \(G\) training images from \(C\) classes, \(F=[f_{1},f_{2},\cdots,f_{G}]\), where \(f_{g}\) is the feature vector, which is the pooling result of the \(g\)th image. Correspondingly, \(F\) is partitioned as \(F=\left[F_{1}^{T},F_{2}^{T},\cdots,F_{K}^{T}\right]^{T}\) and \(F_{k}\in R^{d\times G}\) with \(d<G\). Distinctly, the \(g\)th column of \(F_{k}\) is the feature vector for the \(k\)th sub-region of the \(g\)th image, and \(G\) represents the image number of the training dataset. Analogously, a test image \(y\) is divided into \(y=[y_{1};y_{2};\cdots;y_{K}]\).
# Photonic-Assisted Scanning Receivers for Microwave Frequency Measurement
## 3 Experimental Results
A total insertion loss of 34 dB for the optical filter was experimentally measured. There was mainly two factors contributing to the loss. One was the fiber-to-chip loss of around 29 dB, which arose from the pair of vertical grating couplers. This can be improved via optimizing the coupling design between SOI waveguides and optical fibers, where a coupling loss of less than 1 dB has been demonstrated in integrated filters [30]. This can be adopted to vastly reduce the insertion loss of the filter and thus achieve a better signal-to-noise ratio of the system. The other factor was the waveguide propagation loss as the light propagates along the micro-ring resonator, which is around 2.5 dB/cm. Significant reduction of the propagation loss can be realized via adopting a monolithic waveguide of wedge geometry, high-aspect-ratio Si\({}_{3}\)N\({}_{4}\) waveguides, and waveguide based on high-quality silicon oxynitride films [31; 32; 33; 34]. In [31], an optical delay line fabricated on a silicon chip was presented, and it exhibited an average measured waveguide loss of 0.08 \(\pm\) 0.01 dB/m in long spirals. It is expected that with the optimization of the optical waveguide and integrated fiber-to-chip couplers, it will be possible to boost the detected signal intensity, thus achieving an improved signal-to-noise ratio. Apart from the effect of the signal-to-noise ratio, it is noted that the measurement resolution is also dependent on the spectral line shape of the optical filter adopted in the scanning system. Given the same bandwidth, optical filters with a Lorentzian profile--such as fiber Bragg gratings--and ring resonators are preferred, as they exhibit superior recovery accuracy than the ones with a Gaussian profile [35]. Additionally, advanced super-resolution methods such as atomic norm minimization [28] can be adopted to further improve the performance of the scanning system.
## 2 Methodology
### Neural Network Model  
#### 2.6.1 Selection of Neural Network Type  
For the model, two NN architectures were considered, the recurrent neural network (RNN) and the feedforward neural network (FFNN), the architecture of which is shown in Figure 5. FFNNs are simple and effective for static data [32]; however, they lack the ability to model temporal dependencies and treat each input independently. RNNs, on the other hand, are explicitly designed to handle sequential data by maintaining an internal state that allows them to retain information from previous time steps [33]. This property makes RNNs particularly well-suited for time-series tasks, such as predicting the pressure and flow rate measurements in a filter press system. In this work, the RNN is implemented using long short-term memory (LSTM) units, which are effective at learning long-term dependencies and mitigating issues such as vanishing gradients, which can occur in standard RNNs. Its downside is that with enough accumulated data, it needs significantly more computational power to train. In this work, both the FFNN and RNN were implemented in order to compare them against each other.  
The selection of these two architectures was made to evaluate the trade-off between FFNNs' simplicity and efficiency for static data, and the temporal modelling capabilities of LSTM-based RNNs. Other architectures, such as gated recurrent unit (GRU) or convolutional neural networks (CNNs), were considered but not selected for this work. GRUs, while simpler than LSTMs, do not capture long-term dependencies as effectively, which can limit their accuracy in applications requiring extended temporal memory. However, their simpler structure, with fewer gates, results in faster training times and reduced com  
Figure 4: Schematic overview of the data acquisition and communication architecture for the chamber filter press system. The setup includes a Delphin data logger connected to sensors for pressure and flow rate measurement, which communicates with a control unit via OPC UA. The control system, represented by a PiXtend V2 board, interfaces with peripheral devices such as a touchscreen and user input terminals through HDMI/USB. Data transfer and remote monitoring are facilitated through a router, enabling Wi-Fi connectivity and LTE-based transmission to a file server for storage and further analysis. Developers and operators can access the system remotely or locally for control and data export.  
putational overhead, making them attractive for scenarios where model efficiency and real-time responsiveness are critical [34]. Despite these advantages, in this work, LSTMs were preferred due to their superior ability to retain information over longer sequences, which is essential for accurately modelling the temporal dynamics of filter press operations. CNNs, which excel in tasks like spatial pattern recognition, are less suited for time-series forecasting tasks where sequential relationships are paramount.
## 3 Characteristics of the Studies
### FA Methodologies and Tools  
The first method reported to measure the fractal dimension was the \"yardstick method\" used in 1993 by [PERSON] et al. [39] and consisting of a ruler to manually measure the perimeters of nuclei from normal and cancerous keratinocytes from histological specimens that were magnified up to \(\times\)1400, photographed and further enlarged 2.6 times, with a black-and-white scanned photograph at a final resolution of 1 pixel equal to 35 nm.  
Then, [PERSON] et al. [32; 33] adopted the box-counting method to measure the nuclear periphery, its inner structure complexity, and the tumor-associated vasculature FD. In their works, the authors reported a significant positive correlation between FD and the size of the neoplastic nuclei, with FD values generally higher in carcinomas than in the control group and a statistically significant difference in FD between well-differentiated tumors and moderately or poorly differentiated ones.  
Since then, the box-counting method and its variants have become the most used method. Box-counting was refined year after year, owing to the rising technological and informatics improvements of specific tools to acquire high-definition images, such as CDD cameras, which replaced scanned analog conventional photographs. Image processing software is consistently more accurate and precise in performing textural analysis in the RGB channel; this is then transformed into grayscale and later into binary images for better definition through semi-automatized and automatized image analyses with neural network supports.
## 2 Mathematical Formulations
### Proposed Scheme for the Identification of Suitable Locations for DG Placement  
The performance of a system dominated by DG is highly influenced by the locations where the DGs are placed in the system. Consequently, a suitable location is where the influence of the DG will positively impact the system; thus, suitable locations for DG placement are of paramount importance. This is because using poor locations for DG placement in a power network could be highly detrimental and uneconomical as this will result in a significant increase in losses and a significant reduction in the system voltage profile. To avoid the aforementioned challenges, this paper suggests the use of a novel approach, the coupling factor (CF) method. This approach is mainly network topology-based and is characterized by simple mathematical formulations. This approach of CF is non-iterative and therefore swiftly identifies weak points suitable for DG placement.  
Consider a simple two-bus distribution system, the CF of the line having an impedance of \(z_{jk}\), connecting bus \(i\) and bus \(j\), whose voltages \(v_{j}\) and \(v_{k}\) can be easily derived from the system matrix of the coupling strength using the atomic theory concept [41]. According to [PERSON] et al. in references [42; 43; 44], the pull-push attractive force matrix, as stated by Coulomb's law, which represents the coupling strength matrix (CSM) of the transmission line between any two buses \(j\) and \(k\) can be modeled by  
In terms of the branch admittance between transmission lines \(j\) and \(k\) (\(y_{jk}\)), we can write Equation (47) as  
\[\left[CSM\right]_{jk}=c\left(v_{j}v_{k}\right)(y_{jk})^{2} \tag{47}\]  
where \(c\) is a constant and \(v_{j}\) and \(v_{k}\) are the voltages associated with buses \(j\) and \(k\). It is worth noting that the charges associated with buses \(j\) and \(k\) are directly proportional to voltages \(v_{j}\) and \(v_{k}\), respectively. The introduction of min-max scaling to the off-diagonal elements of the CSM results in a normalized value of the off-diagonal elements of the CSM and is defined as the CF of the node. This is expressed as  
\[CF=\frac{\left[CSM\right]_{jk}-min[CSM]_{jk}}{max[CSM]_{jk}-min[CSM]_{jk}} \tag{48}\]  
Since the voltage is expected to remain constant and close to unity, it can be seen from Equation (48) that the CSM is directly proportional to the square of the transmission line branch admittance. Hence, the weakest transmission line in any given network can be easily identified. This corresponds to the line associated with the lowest coupling factor (CF).
# Big Data Perspective and Challenges in Next Generation Networks
###### Abstract  
With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.  
Keywords:5G; data analysis; mobile communication; wireless communication +
Footnote †: journal: Future Internet
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Data Extraction and Categorization
#### 2.4.1 Population and Age  
Eleven studies presented the ages of the participants in time intervals, varying between 18 and 67 years of age [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. Four studies covered the age by reporting only the global mean, varying between \(\overline{x}\) = 23 and \(\overline{x}\) = 29.16 [59; 60; 61; 62]. One study reported the mean age by severity of condition (\(\overline{x}\) = 30.0 to \(\overline{x}\) = 50.3 [63]), and another study discriminated the mean age by sex (\(\overline{x}\)?= 37 and \(\overline{x}\)?= 33 [64]). In 5 studies, the age of participants was not reported [65; 66; 67; 68; 69]. The population in studies was most often males. Three studies included only male participants [53; 58; 62]. Fourteen studies had both males and female participants [48; 49; 50; 51; 52; 54; 55; 56; 57; 60; 61; 63; 64; 66], one study included a non-binary participant [60], and in five studies, the gender was not reported [59; 65; 67; 68; 69].  
The population was mostly healthy without complaints of any relevant respiratory or cardiac disorder, as reported by fourteen studies [48; 49; 50; 52; 54; 55; 56; 57; 61; 64; 65; 66]. Among the remaining eight studies, one focused solely on participants without neurological changes [53], another on those without medication usage [62], and one on participants with suspected sleep apnea disorders [63], and in five studies, the specific condition was not reported [51; 60; 67; 68; 69].
## 2 Materials and Methods
### Extraction and Determination of Total Phenolic Content  
The phenolic compounds in the CSO were extracted [27]. Briefly, 2.5 g of CSO was dissolved in 5 mL hexane, and 3 mL of methanol/water (60: 40, vol/vol) was added and vortexed for 2 min. After that, the samples were centrifuged at 3500 rpm for 10 min, and the hexane phase was collected and re-extracted with 3 mL of methanol/water solution. After 2 extraction procedures, the methanolic phase was collected for further study.  
The total phenolic content (TPC) of the methanolic phase of CSO was determined using the Folin-Ciocalteu colorimetric method. Briefly, 80 uL of standard (gallic acid) or CSO, 20 uL Folin-Ciocalteu reagent, and 100 uL 7.5% w/v Na\({}_{2}\)CO\({}_{3}\) were mixed and incubated at room temperature (RT) for 30 min and measured at 765 nm using a microplate reader (SpectraMax(r) M3 Multi-mode microplate reader). The TPC of the CSO was denoted as ug gallic acid equivalent (GAE) per gram of the oil [28]. The working standard of the gallic acid was prepared by diluting stock solution to a concentration of 1.25-25 ug/mL.
## 4 Results and Discussion
### Experimental Results
#### 4.2.1 Performance Comparisons
_(2) Model Efficiency Analysis_. Relying on TT and GPUM, we observed that FedDeep achieved an acceptable performance with respect to model efficiency. To improve prediction accuracy of the model as much as possible, the baselines spent more time on training and utilized more GPU memory in the multi-urban \(PM_{2.5}\) forecasting, especially for the deep learning-based baselines. For instance, for total training time, the TT of FedDeep was significantly shorter than LSTM, CNN-LSTM, and GAT-LSTM. This revealed that time overheads can be reduced through a federated training approach. In addition, due to the small-scale structural design, the GPU memory usage of FedDeep was less than the other deep-learning-based baselines. This was due to the small size of the processed individual urban regions on the ECSs, so FedDeep had a relatively low GPU memory overhead, even with a large number of urban regions. Moreover, although FedDeep and FedDeep(w/o) were comparable in their prediction accuracy, FedDeep's training time and GPU memory usage were significantly lower than FedDeep(w/o)'s. Thus, this proved the effectiveness of our proposed model in terms of efficiency.