esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 7 Numerical Results
### Individual VoIP Channels  
In this experiment, each user has an individual bidirectional VoIP channel. We vary the number of ground users from 10 to 100 over two terrain sizes (100 m\({}^{2}\) and 10,000 m\({}^{2}\)) using the parameters listed in Table 3. We want to study the evolution of \(f_{\text{obj}}\) (i.e., the average rate of drones per hour of service), the power spent on communications \(P_{\text{Radio}}\) and the total required power (i.e., radio communication and hovering).  
Figure 7b illustrates the drone turnover rate (\(f_{\text{obj}}\)) in UAVs/hour. As introduced in Section 4, this value is the ratio between the number of drones \(D\) and the average flight duration \(\tilde{T}_{\text{Flight}}\). Results suggest that scenarios with large user sparsity (m\({}^{2}\)/U) tend to demand more of drones. For example, from 10 to 50 users, the 10,000 m\({}^{2}\) scenario requires more drones to satisfy the coverage constraint (\(C\geq C_{\text{min}}\cdot U\)) than that of 100 m\({}^{2}\). The average drone flight duration was around 25 min.  
Figure 6: Resolution time (\(T_{\text{solve}}\)).  
Figure 6(b) shows the average power spent in communications (i.e., \(P_{\text{Radio}}\)). In this graph, we can identify two behaviours:  
* For less than 50 users, drones spend more radio power in the smaller scenario than in the larger one. This can be explained as follows: while in the bigger scenario up to \((1-C_{\text{min}})\cdot U\) users can be excluded from coverage by locating drones far away from them, the smaller one has not enough terrain for the drones to exclude people (\(C=U\)), which in turn increases the average number of users associated to each aerial AP.
* For more than 50 users, the previous behaviour gets inverted when the user density is high enough. This is attributable to solutions where drones are placed at higher altitudes. In general, drones increase their altitude to cover a larger area (i.e., antenna directivity), which in turn reduces the received signal strength and the achievable data bit-rate (i.e., a slower MCS is selected), so the time to complete data transmission is longer which translates into higher energy consumption.  
Finally, Figure 6(c) represents the total power used by all UAVs broken down into mechanical power (i.e., \(P_{\text{Flight}}\)) and communications power (i.e., \(P_{\text{Radio}}\)). In our experiment, \(P_{\text{WiFi}}\) represents between the 14% and 16% of the total power expenditure, scaling with user density. This ratio is highly dependent on drone characteristics and the power consumption of the radio network interfaces. A careful selection of these aspects is a key factor for reducing the number of required UAVs per hour of service.
# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
## 4 Methods
### Proposed Method
The Inception V3 architecture contains 48 convolutional layers, the last of which is a fully connected layer with softmax activation followed by a layer with global average pooling. It has achieved state-of-the-art performance on several benchmark datasets, including the ImageNet large-scale visual recognition challenge (ILSVRC), where it achieved a top-5 error rate of 3.46%. BILSTM is a type of recurrent neural network (RNN) [34] that incorporates memory cells and can learn long-term dependencies in sequential data. There are several reasons for the selection of BILSTM for the proposed model is as follows: shoplifting detection typically involves analyzing temporal sequences of actions or events, such as movements, interactions, and object trajectories, to identify suspicious behavior. Bidirectional LSTM can be applied to challenges requiring sequence-to-sequence modeling. The ability to process the input sequence both forward and backward makes this variation of the conventional LSTM capable of capturing dependencies in both past and future contexts. By incorporating BILSTM into the hybrid system, it becomes capable of modeling the temporal dynamics of shoplifting activities, detecting patterns of behavior that may be indicative of shoplifting, and distinguishing them from normal activities. Figure 8: Architecture of Inception V3. The hybrid system can benefit from the strengths of both models. The Inception model can extract high-level visual features, while the BILSTM model can effectively analyze sequential data and capture temporal dependencies. The combination of these models enables the system to have a comprehensive understanding of both visual and temporal aspects, enhancing its ability to detect shoplifting incidents accurately. The process occurs as the input sequence in opposite directions: one that goes ahead and the other that goes backward. The final output sequence is created by concatenating the hidden state sequences that each LSTM generates. The work is shown in Figure 9.  
The input sequence is processed, start to finish, by the forward LSTM and in the opposite order by the backward LSTM. The current input and the prior hidden state are used to update the hidden state at each time step in the forward LSTM, whereas the current input and the following hidden state are used to update the hidden state at each time step in the backward LSTM. By joining the forward and backward LSTM outputs at each time step, the bidirectional LSTM's output is created. As a result, a series of concatenated hidden states is produced, encoding the input sequence's past and future dependencies.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 2 Data and Analysis Method
### Analysis Method  
#### 2.2.1 Single-Layer Assumption  
In ionospheric research, VTEC or the absolute TEC, which refers to the total electron content along the path that passes through the ionosphere perpendicularly, are generally used. In this paper, the single-layer (SL) assumption is adopted to convert to VTEC from STEC, where all free electrons are assumed to be contained in a shell of infinitesimal thickness at the height above the ground [23]. The altitude of this idealized layer is usually set to 350, 400, or 450 km above a spherical Earth, which approximately corresponds to the altitude of maximum electron density [7]. In this assumption, \(\alpha\) is the elevation of satellite S from receiver R, \(\chi\) is the zenith angle and P is the ionospheric piercing point (IPP), which is the intersection of the ionosphere and sight line. The zenith angle can be calculated as  
\[\chi=\arcsin\frac{R_{e}\cos\alpha}{R_{e}+h}, \tag{3}\]  
where \(h\) is the height of the SL assumption above the ground, and we set \(h=450\) km in this paper.  
According to Equations (1) and (2) in the \(TEC\) obtainment from GNSS, \(STEC_{sl}\) is a relative value due to the \(2\pi\) ambiguity in the phase measurement but \(STEC_{sp}\) contains more noise than \(STEC_{sl}\)[24]. High precise \(STEC\) can be obtained through carrier-phase smoothing over the pseudorange from GNSS observations after a baseline for the differential phase related \(STEC_{sl}\) is introduced as [9; 25].  
\[STEC_{G}=STEC_{sl}+\frac{\sum_{i=1}^{\pi}(TEC_{sp_{i}}-TEC_{gl_{s}})\sin^{2} \alpha_{i}}{\sum_{i=1}^{\pi}\sin^{2}\alpha_{i}}, \tag{4}\]  
where \(n\) is the number of measurements without a cycle slip. To reduce the multi-path effect at a low elevation angle, the square sine of the satellite's elevation is included as a weighting factor and the cutoff angle is set as \(25^{\circ}\) in this paper. Then the \(VTEC_{G}\) at the piercing point in the SL assumption can be calculated, where \(B_{s}\) and \(B_{r}\) represent the DCBs from satellites and receivers:  
\[VTEC_{G}=(STEC_{G}-B_{s}-B_{r})\cos\chi. \tag{5}\]  
Additionally, according to [PERSON] et al. [26], \(VTEC_{A}\), which represents the \(VTEC\) obtained from JASON-2 satellite altimetry, can be calculated as  
\[VTEC_{A}=-\frac{dR\cdot f_{kl}^{2}}{40.3}, \tag{6}\]  
where \(dR\) is the Ku-band ionospheric range correction, and \(f_{kl}\) is the Ku-band frequency of 13.575 GHz. Besides, \(VTEC_{A}\) needs a 21 s smoothing window to reduce the inherent noise effects of the altimeter [27].  
\(VTEC_{C}\), the \(VTEC\) obtained from the ionPrf product of COSMIC occultation, can be expressed as  
\[VTEC_{C}=VTEC_{0}+VTEC_{1}, \tag{7}\]  
where \(VTEC_{0}\) is the \(VTEC\) under the COSMIC satellite orbit, and \(VTEC_{1}\) is the \(VTEC\) above the COSMIC satellite orbit calculated by extrapolation [28].
## 3 Technical Overview
### BL Conversion Matrix  
The \(BL\) conversion matrix in Figure 4 represents the conversion of the sound energy from dB SPL to \(BL\) (Phons) for specific frequencies. The rules that govern the proposed model are concentrated in this matrix. The frequency as the reference point of \(BL\) and dB SPL calculations reflects the central frequencies of an octave band analysis. The frequencies of interest are (a) 250 Hz, (b) 500 Hz, (c) 1000 Hz, (d) 2000 Hz, (e) 4000 Hz, (f) 8000 Hz, (g) 16,000 Hz. For each frequency, a sequence of calibrated pure tones of five seconds with 1 dB difference from 30 dB SPL to 94 dB SPL is constructed. The Glasberg and [PERSON] model [19] with the implementation of the Genesis toolbox [38] analyzes each audio sample.
# A Workflow for Creating Gastric Computational Models from SPARC Scaffolds
## 2 Materials and Methods
### Computational Models
#### 2.1.2 Muscle Fiber Generation  
A Laplace-Dirichlet rule-based algorithm [9] was used to compute the muscle fiber orientations within the three FEM models in accordance with the reported orientations in [14]. These fiber orientations defined the principal conduction axes in the propagation of gastric slow waves. [PERSON]'s equation was solved on the FEM models for a scalar quantity (\(u\)) using Chaste, subject to Dirichlet boundary conditions as illustrated in Figure 2b. The Dirichlet boundary conditions of \(u_{C}=1\) and \(u_{C}=0\) were defined at the lesser curvature (LC) and greater curvature (GC), respectively for the CM layer. A separate Laplace solution with an arc-shaped boundary condition of \(u_{L}=1\) at the fundus and the boundary condition of \(u_{L}=0\) at the pylorus was solved for the LM layer.  
For the volume meshes (i.e., fixed and varying thickness 3D FEM models), fiber orientations were computed for each tetrahedral element. First, a 3 \(\times\) 1 gradient vector of \(u\) (denoted as \(g_{u}\)) was computed:  
\[g_{u}=\begin{bmatrix}v_{2}(u)-v_{1}(u)\\ v_{3}(u)-v_{1}(u)\\ v_{4}(u)-v_{1}(u)\end{bmatrix} \tag{3}\]  
where \(v_{i}(u)\) corresponds to the Laplacian solution at the \(i\)-\(th\) tetrahedral node (i.e., each of the four vertices of the element). Then, the fiber orientation, \(f\), was defined as  
\[f=\frac{\mathbf{J}^{-1}g_{u}}{\|\mathbf{J}^{-1}g_{u}\|} \tag{4}\]  
where \(\mathbf{J}^{-1}\) is the 3 \(\times\) 3 inverse Jacobian matrix.  
The gradient of \(u_{L}\) along the longitudinal direction was calculated and used for the fibers in the LM layer, while the fibers in the CM layer were computed using the gradient of \(u_{C}\) along the circumferential direction. The same approach was used in the surface mesh to compute the fibers for each triangular element but only the gradient in the circumferential direction was used to compute the fibers.  
Figure 2: (**a**) A schematic displaying how varying thickness was applied to a cubic Hermite element in the surface mesh. Local coordinates \(\xi_{1}\) and \(\xi_{2}\) of an element were used compute the surface normal at each node. The longitudinal muscle (LM) and circular muscle (CM) layers were created by projecting the nodes normal to the surface by the thickness values at each node, where the surface j (blue) corresponds to the serosal surface and the surface k (green) corresponds to the interface between the CM and submuc layers. (**b**) The boundary conditions (\(u_{L}\), \(u_{C}\)) used in Laplace solutions to compute longitudinal and circumferential fiber orientations. The green region corresponds to the location of pacemaker cells initiating slow waves in the simulations.
## 5 Discussion
### Research Implications  
The main practical implications of this study are the identification of enablers analogous to the three links that could promote the adoption of this type of technology, such as government support and regulations. These are considered the main drivers because they create the enabling conditions for the promotion of technologies and facilitate their adoption by the actors in each link. Likewise, ease of use is concentrated in the production and marketing links because the complexity of the technologies limits their adoption (see Figure 9).  
Transparency, traceability, and reduction of transaction costs appear as enablers or facilitators in the marketing and processing links. Regarding the first two, the literature shows them as the main motivators for adopting technologies because they increase consumer confidence in the products that reach the market. Furthermore, they have higher quality and safety derived from using automation technologies, in addition to the verification that can be conducted through Blockchain. For transaction costs, these links also consider that Food Informatics technologies can reduce the logistics costs associated with the trade of agri-food products, bringing the end customer closer to the links mentioned above.  
While training and expertise are barriers in the processing and marketing links, training and expertise are enablers or facilitators in the production link. This is particularly relevant mainly due to the complexity of the technologies deployed and the greater infrastructure required in these links. In addition to examining adoption determinants, this study offers a bibliometric analysis of the evolving scientific interest in Food Informatics technologies. The analysis explores the global research landscape, focusing on institutions and researchers that have incorporated these technologies into agri-food systems. The study highlights the growing recognition of Food Informatics as a valuable tool for enhancing food chain performance.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 1 Introduction  
Fifth-generation (5G) new radio (NR)--the first stage of the 5G systems--has led to various user-experience services, shorter latency, and higher user data traffic compared to the previous generation mobile communication systems such as LTE/LTE-A. These remarkable successes have been enabled by various techniques such as massive multiple-input-multiple-output (MIMO) systems and network slicing in various communication layer technologies [1, 2]. For the application of massive MIMO, employing an extremely high-frequency band is a highly effective technique in 5G NR to facilitate a significantly reduced antenna size and mitigated interferences due to significant propagation-pass-loss [3, 4]. To shape several beams using a massive number of antenna elements such as a uniform-linear-array (ULA) in the mmWave environment, a useful technique is mapping the one-logical port based on several antennas to a one-synchronization-symbol-burst (SSB) beam [5]. To exploit multi-beam-based architecture with narrow beam coverage in massive MIMO networks, we need to investigate beam switching (BS) schemes that have not been seriously investigated previously.
## 5 Conclusions  
We presented a joint detection and classification (JDC) network that performs singing voice detection and pitch estimation simultaneously. The main network uses a CRNN architecture that consists of convolutional layers with residual connections and Bi-LSTM layers. The main network is trained to classify the input spectrogram into pitch labels quantized with a high resolution or a special non-voice label. The auxiliary network is trained to detect the singing voice using only multi-level features shared with the main network. We also examined the joint melody loss function that optimizes the JDC network to combine more tightly the tasks and the three different voice detection outputs from the two networks. Through the experiment, we provided a better understanding of how the main network and auxiliary network work for voice detection. We also showed that the knowledge sharing between two networks helps perform the melody extraction task more effectively. We showed that the proposed JDC network has consistently high performance for all test datasets and outperforms previous state-of-the-art methods based on deep neural networks. Finally, we illustrated failure cases, which may provide ideas for future work to improve the melody extraction performance further.  
Figure 6: Case examples of singing melody extraction from the MedleyDB dataset: (**a**) good example (“MusicDelta-Gospel”) and (**b,c**) bad examples (“CelestialShore-DieForUs”, and “MatthewEntwistle-Lontano”).  
**Author Contributions:** Formal analysis, S.K.; investigation, S.K.; methodology, S.K.; writing--original draft preparation, S.K.; writing--review and editing, J.N.; supervision, J.N.  
**Funding:** This research was supported by BK21 Plus Postgraduate Organization for Content Science (or BK21 Plus Program), and Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02036962).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multiscale Analysis for Improving Texture Classification
## 2 Multiscale and Multiresolution Analysis  
Multiscale and multiresolution analysis are related techniques that involve analyzing a problem or system at multiple scales or levels of resolution. However, they differ in the specific approach used to perform the analysis. The multiscale analysis involves studying a problem or system simultaneously at multiple scales or levels of detail and can be done using various techniques, such as scale space representation, multiscale features, or multiscale modeling. The multiscale analysis aims to understand how the behavior at one scale is influenced by the behavior at other scales and to identify patterns or features present at multiple scales. On the other hand, multiresolution analysis involves decomposing a problem or system into multiple scales or levels of resolution using techniques such as wavelet decomposition or pyramid decomposition. This allows one to analyze the problem or system at different scales separately and capture patterns or features present at different scales. The goal of multiresolution analysis is often to represent a problem or system compactly or to extract robust features to changes in scale.  
In short, multiscale analysis involves studying a problem or system at multiple scales simultaneously. In contrast, multiresolution analysis decomposes a problem or system into various scales and analyzes them separately. Both techniques can help understand phenomena or processes that occur over a range of scales and for extracting features that are robust to changes in scale.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description
### Particle Swarm Algorithm Combined with Pareto Efficiency Process
Define fitness_function(x):  
Input: particle position (x)  
Output: objective 1 (\(f_{1}\)), objective 2 (\(f_{2}\)), objective 3 (\(f_{3}\)),..., objective \(n\) (\(f_{n}\))  
Define objective_constraints(\(f_{1}\),\(f_{2}\),\(f_{3}\),...,\(f_{n}\)):  
Input: objective 1 (\(f_{1}\)), objective 2 (\(f_{2}\)), objective 3 (\(f_{3}\)),..., objective \(n\) (\(f_{n}\))  
Output: whether constraints are satisfied (True/False)  
Initialize_particles(num_particles, dim):  
Input: number of particles (num_particles), dimension (dim)  
Output: particle positions matrix (particles), particle velocities matrix (velocities)  
Update_particles(particles, velocities, p_best, g_best, w, c1, c2):  
Input: particle positions (particles), particle velocities (velocities), personal best positions (p_best), global best position (g_best), inertia weight (w), cognitive component (c1), social component (c2)  
Output: updated particle positions (particles), updated particle velocities (velocities)  
Pareto_sort(particles, fitness_values):  
Input: particle positions (particles), fitness values (fitness_values)  
Output: indices of Pareto front (pareto_front)  
MOPSO_algorithm(num_particles, dim, num_iterations, w, c1, c2):  
Input: number of particles (num_particles), dimension (dim), number of iterations (num_iterations), inertia weight (w), cognitive component (c1), social component (c2)  
Output: best particle position (g_best), best particle fitness values (g_best_fitness)  
Pseudocode steps:  
Define fitness_function(x). Define objective_constraints(\(f_{1}\),\(f_{2}\),\(f_{3}\),...,\(f_{n}\)). Initialize particles using Initialize_particles(num_particles, dim). For each iteration from 1 to num_iterations:  
Calculate fitness values for each particle using fitness_function. Update personal best positions (p_best) and fitness values (p_best_fitness), If current fitness values are better than personal best values. Use Pareto_sort to get Pareto front indices. Update global best position (g_best) to the best solution in Pareto front. Output the best particle position (g_best) and the best particle fitness values (g_best_fitness).
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 1 Introduction
The results suggested that protein content could be successfully estimated (\(R\) = 0.990 for multiple linear regression (MLR), \(R\) = 0.987 for PLS, while carbohydrate (\(R\) = 0.801 for MLR, \(R\) = 0.755 for PLS), oil (\(R\) = 0.823 for MLR, \(R\) = 0.723 for PLS) and ash (\(R\) = 0.926 for MLR, \(R\) = 0.810 for PLS) required more studies to obtain better prediction results. Similarly, [PERSON] et al. [20] evaluated the potential of NIR spectroscopy for predicting the nutritive value of grain corn with high moisture content. The coefficients of determination in the calibration of dry matter, acid detergent fiber, in vitro organic matter digestibility, pH and ammonia nitrogen were all higher than 0.9. [PERSON] et al. [21] developed a single-kernel NIR instrument for predicting maize grain attributes, where crude protein content and kernel mass were more predictable than tryptophan, lysine and oil. Although many scholars have applied NIR spectroscopy for seed and grain moisture detection, the studies have mainly been based on the NIR spectrometers for laboratory use, consisting of complex optical path structure and not suitable for application in a farmland environment. Therefore, late long-grain nonglutinous rice with a wide planting area was selected as the research object. The main objective of this paper is to present a moisture detection method for rice grain based on NIR spectroscopy characteristic band, as well as to design and develop a rapid-detection sensor based on a single-band 1450 nm. As a result, a portable, low-cost and high-precision sensor for measuring rice grain moisture was developed, which is of great significance for enhancing detection efficiency and monitoring of rice grain quality in real time.