esa-sceva/satcom-chunk-collection huggingface dataset:
# Physico-Chemical and Melissopalynological Characterization of Czech Honey
###### Abstract  
Geographical and botanical origin of honeys can be characterized on the basis of physico-chemical composition, sensory properties and on the basis of melissopalynological analysis. No comprehensive description of the characteristics of Czech honey has been published so far. This study provides insights that are important for correct classification. The study analysed 317 samples of authentic honey from randomly selected localities. Due to the diversity of the landscape, the typical honey of the region is blend honey with a predominance of blossom honey. According to the pollen profile and electric conductivity, the honeys were sorted into the following: Brassica honey (BH), Floral honey (FH), Fruit tree honey (PH), Honeydew (HD), Lime tree honey (LH), Robinia pseudoaccia honey (RH), and Trifolium honey (TH). Physico-chemical properties, including higher carbohydrates, were determined for the honeys and their pollen profiles were examined. The physico-chemical properties and pollen profile are partially in compliance with the description of European monofloral honeys, except for RH and TH. Although they had the highest proportion of acacia pollen, amounting to >10% of all the Czech honeys, these RH honeys differ from the European standard, so they cannot be considered acacia honey. Further, PH honeys and FH polyfloral honeys were described. Most honeys contained a significant proportion of rapeseed pollen, which is one of the common agricultural crops grown in the Czech Republic. All the analysed honeys met the parameters defined by the legislation. Due to direct on-site sampling, honeys were characterized by a low 5-(hydroxymethyl)furfurfurral (HMF) content (3.0 mg/kg) and high diastase activity (24.4 DN). Honeydew honeys had the highest proportion of higher carbohydrates, primarily of Melezitose (4.8 g/100 g) and Trehalose (1.3 g/100 g). The presence of higher carbohydrates was also confirmed in LH for Maltose (4.6 g/100 g) and Turanose (2.4 g/100 g).  
carbohydrates; colour of honey; pollen profile; trisaccharide; disaccharide +
Footnote †: journal: Applied Sciences
## 3 Experimental Setup
### ECG Databases and Computing Platform  
Two popular Physionet databases were used to evaluate the performance of our proposed algorithm, i.e., ECG-ID [10] and MIT-BIH [21]. These two databases have been used by several researchers to test the performance of the ECG authentication algorithm. In this paper, each database is used to test five different conditions, with variations in the application of beat detection, as well as DSC and residual configurations.  
The ECG-ID database contains 90 folders from 90 different people. Each folder contains several ECG records with a 500 Hz sampling frequency. Each record has been provided with 10 annotations related to the position of \(R_{peak}\) in the first 10 beats. The recording duration ranges from 20 s to 5 min. For testing purposes, only 179 files were used, i.e., two files from each folder, except the 74 th folder which has only one file. For training purposes, ten beats of the first file in each folder were obtained, so that around 900 beats were used. For validation purposes, ten beats of the second file were acquired. As the 74 th folder contains only one file, 10 beats from this file were used for both training and validation. The ECG beat extraction from 179 files from 90 folders produced 900 beats for training and 900 beats for validation. These validation beats were used to validate the CNN weights obtained in the training process.  
Besides using the ECG database of healthy people, our proposed algorithm was also tested with ECG records with several cardiac arrhythmia beats taken from the MIT-BIH database. The database contains 48 files, in which each file contains around 30 min of ECG recordings of cardiac arrhythmia patients. Each data file is complemented with the annotation file containing the \(R_{peak}\) position in the associated ECG recording. There are two channels provided for each of these ECG records, but in this paper only channel 0 (first channel) was used. For training and testing purposes, each ECG recording is divided into two, in which each half contains 325,000 sample points. The first half is used for training, while the second half is used for testing. For training, only 100 beats of the first half were used, producing a total of 4800 ECG beats. For validation, only 10 beats of the second half were used, producing a total of 480 ECG beats. These validation beats were used to validate the CNN weights obtained in the training process.  
The proposed algorithm was implemented in Python with Tensorflow with GPU [22] and Keras libraries [23], as well as other standard libraries, such as Numpy, Matplotlib, and Scipy. The experiments are performed on a computer with Intel Core i7-7700 CPU with a total of eight logical processors, memory of 8 GBytes, and an Nvidia GeForce GTX 1060 6 GB DDR5 graphics card, using the Microsoft Windows 10 64-bit operating system.
## 4 Kinematics, Workspace, Singularity, and Simulation
### Rotational and Joint Workspaces
The slices of the rotational workspace when the moving platform is rotated in clockwise and anticlockwise directions about the \(Z_{\text{m}}\)-axis for various angles of rotation (\(\phi\)) are shown in Figure 11.  
In Figure 11, the red colour slice is obtained for the angle \(\phi\) = 0 rad. The blue slices are obtained when \(\phi\) = \(\pm\)0.523 rad, the cyan colour slices are obtained for the angle \(\phi\) = \(\pm\)1.047 rad, and the yellow colour slice is obtained for \(\phi\) = 1.57 rad. Figure 8: Rotational and joint workspace considering the limits of legs: (a) rotational workspace, (b) rotational workspace in 2D for \(\phi\) = 0.52 rad, (c) joint workspace. The results of rotational workspaces shown in Figures 8-11 conclude the limitations in the orientation of 3 SPS/S spherical wrist robots. It was observed that the workspace reduced because of the limited ROM of the spherical joints. It was also observed that the workspace of the robot reduced when the moving platform rotated about the \(Z\)-axis. At 90\({}^{\circ}\) rotations about the \(Z_{\text{m}}\)-axis, the workspace is very limited. The workspace is symmetrical when the moving platform rotates about the \(Z_{\text{m}}\)-axis in both directions. Figure 10: Rotational workspace considering only the limits of legs (blue colour) and considering limits of legs and ROM of spherical joints (red colour) for \(\phi=0.52\) rad. \[\overset{\rightarrow}{n}=\overset{\rightarrow}{b}\times\overset{\rightarrow}{a} \tag{6}\]0 Rotational and joint workspace considering the ROM of spherical joint: (**a**) rotational workspace, (**b**) rotational workspace in 2D for \(\phi=0.52\) rad, (**c**) joint workspace. The total ROM of the custom-made spherical joint is found to be 40.02\({}^{\circ}\). The rotational workspace and joint workspace of the robot were plotted using MATLAB. The rotational and joint workspaces were obtained considering initially only the limits of the leg lengths and then later including the ROM of the spherical joints. It is found that the spherical joint's limited ROM causes a reduction in the workspace. Additionally, it is noted that, as the yaw angle (\(\phi\)) increases, the robot's workspace decreases. The workspace is quite limited at \(\phi\) = 1.57 rad. When the moving platform is rotated about the \(Z\)-axis in both directions, the workspace is symmetrical. Therefore, in order to achieve the full ROM of wrist flexion and extension motions, the spherical joints need to be redesigned. By observation, the spherical joints have no influence on the results presented in \[\overset{\rightarrow}{n}=\overset{\rightarrow}{b}\times\overset{\rightarrow}{a} \tag{6}\]1 Even though the spherical joint highly contributes to a reduced workspace, the other reasons will be the limitation in the stroke of the actuator, radii of the base and moving platforms, and the distance of the moving platform with respect to the base platform. If more ROM is required for wrist flexion and extension motions, then the spherical joints need to be replaced.
### Review of the State of the Art
Such comparisons can provide useful insights for a more comprehensive structural health assessment by enabling the identification of the structure's responses to different loads and the study of possible interactions between vibration modes. Recent international studies have begun to address this gap [22; 23], demonstrating that a comparative analysis between multiple excitation sources enables the detection of deterioration or damage patterns that might otherwise go unnoticed. Data fusion techniques in SHM, including approaches that combine ambient and controlled source vibrations, emphasize the importance of integrating data from multiple excitation types to capture a complete dynamic profile of structures. These studies highlight that the lack of combined data sources, such as ambient vibration, traffic, and seismic activity, limits the precision of diagnostics in concrete and steel buildings [24]. A recent study compared records of ambient vibrations and seismic events in a 13-story concrete building, showing how vibration modes and natural frequencies vary depending on the excitation source. The authors emphasize that these differences in response to ambient and seismic loads help identify specific structural vulnerabilities that may not be captured when only a single type of excitation is used [25]. This work emphasizes the importance of adopting a comprehensive approach to SHM, highlighting how the combination of records from multiple excitation sources over time can offer a more detailed and in-depth perspective on structural behavior in complex environments. The application of this model in seismic areas underscores the need for extended structural monitoring that captures not only the impact of major events but also dynamic variations induced by environmental loads and nearby activities.
## 4 Experiments and Analysis
### Model Performance Evaluation
#### 4.2.3 Detection Efficiency  
This experiment evaluated the time costs of function homology detection for GMN+ and baseline methods on different function sizes, i.e., the number of basic blocks. The primary time cost components involve generating function embeddings and calculating similarity scores. The results depicted in Figure 8 show the distinct time costs associated with different methods, where the horizontal axis represents the CFG size and the vertical axis represents the time cost of detection. The time costs of FIT are significantly higher than those of the other methods, escalating linearly with CFG size. The higher time costs for FIT stem from its use of LSTM to generate basic block embeddings, which are computationally intensive. The SAFE method, which uses a bidirectional RNN to aggregate instruction embeddings into function embeddings, incurs slightly higher time costs than the graph embedding method used by GMN+. GMN+ demonstrates marginally higher time costs than GMN due to its integration of an additional attention mechanism. Moreover, the application of NLP methods to automatically generate basic block embeddings, characterized by heightened dimensions, also increases the computation time of GMN+.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 2 System Modeling  
The system model demonstrated in Figure 1 presents a three-hop mixed RF/FSO/UWOC system employing DF relaying protocol. In the proposed model, the source node shown by \(S\) sends the information signal towards the destination node denoted as \(D\). We assume that no direct communication is feasible between the source and the destination nodes due to different environmental obstacles and long distance; hence, communication between these two takes place with the help of two DF relays, termed \(R_{1}\) and \(R_{2}\). The source \(S\) is situated at the distant location and sends the signaling information towards the destination node \(D\), such as ocean divers. The \(S\) in the first hop sends the information signal towards the first relay \(R_{1}\), which is mounted on the lighthouse. This link (\(SR_{1}\)) is assumed to be an RF link. The received information signal at \(R_{1}\) is decoded and converted into the FSO signal and forwarded towards the second relay, \(R_{2}\), which is mounted on a floating vehicle (FV) over the sea surface, such as a boat used by the ocean divers. The second relay, \(R_{2}\), decodes the received FSO signal, converts it to a visible light signal, and forwards the optical signal to the divers under the sea surface via UWOC link. Here, the Doppler effect is ignored and the UWOC link is assumed to be static.  
For the presented three-hop mixed system, the RF link is assumed to be modeled by Nakagami-\(m\) distribution, the FSO channel is modeled by the Gamma-Gamma fading statistics considering the impact of pointing errors, atmospheric turbulence, and angle-of-arrival (AOA) fluctuations, denoted as \(\theta_{FOV}\), and the UWOC link is assumed to be modeled by EGG distribution, respectively. It has been assumed that the optical signal transmission underwater is remarkably influenced by absorption and scattering, rather than the turbulence caused by the temperature gradient and air bubbles under the water.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 2 Related Work
[19] proposed the EARN mechanism, an enhanced ADR algorithm that adapts coding rate (CR) based on the capture effect, optimizing data transmission success rate and energy consumption. Although EARN improves energy efficiency, its focus is primarily on uplink communication, lacking system-level optimization for the entire LoRa network. [PERSON] et al. [20] introduced the ADRx adaptive data rate algorithm, which dynamically adjusts link margin values in real time to overcome the limitations oftraditional ADR in estimating channel quality, thus improving data extraction rate (DER) and energy efficiency. By dynamically adjusting ED data rates, ADRx optimizes ED power consumption while also enhancing overall network performance. [PERSON] et al. [21] applied the ordinary kriging (OK) function to analyze the spatiotemporal correlation of signal-to-noise ratio (SNR) across different time points, proposing the K-ADR algorithm. This algorithm accurately detects changes in link conditions and predicts future link status in real time, improving ADR performance under challenging channel conditions. However, as overall channel conditions deteriorate, the packet delivery rate advantage of K-ADR diminishes, though improvements in ED energy consumption are still observed. [PERSON] et al. [22] proposed the ND-ADR algorithm, which dynamically adjusts link parameters by combining RSSI and SNR values. This algorithm provides a more accurate assessment of communication quality and dynamically adjusts network parameters, improving communication reliability in harsh environments and reducing power consumption. In terms of physical layer optimization, [PERSON] et al. proposed the XCopy algorithm [23], which improves SNR by repeatedly retransmitting weak signals and coherently accumulating them, providing more than 10 dB of gain in low-SNR environments. This makes communication more reliable, particularly for EDs in harsh conditions. [PERSON] et al. [24] introduced the FLoRa algorithm, which improves the energy efficiency and reliability of firmware updates over-the-air (FUOTA) in LoRa networks through incremental updates, channel coding, and beamforming techniques. This effectively addresses the low energy efficiency and unreliable transmission issues in traditional FUOTA. [PERSON] et al. [25] proposed the one-shot method, which aggregates data from multiple nodes, significantly improving transmission accuracy and efficiency, especially in large-scale LoRa networks. Although these physical layer optimization algorithms perform well in improving communication reliability and energy efficiency, they also increase the hardware cost for EDs. In contrast, ADR algorithms flexibly adjust ED parameters, leveraging LoRa's features to optimize link connectivity and network performance. Table 1 compares the different algorithms, listing their optimization approaches and objectives.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 3 Proposed Algorithm
### Spectral Efficiency Analysis when \(\mathbf{N_{s}\
eq N_{t}^{RF}=N_{r}^{RF}}\)  
Figure 2 shows the system performance as an SNR function when the number of RF chains \(\mathbf{N_{t}^{RF}=N_{r}^{RF}=8}\) and number of data streams \(\mathbf{N_{s}=\{2,4,8\}}\) for the Alt-MMSE algorithm, pure digital beamforming algorithm, OMP algorithm [20], Phase Pursuit [18], MO-AltMin [24], and PC-SIC [19]. It can be seen from Figure 2 that, when the number of data streams is small, the performance difference of the algorithms is not large, but as the number of data streams \(\mathbf{N_{s}}\) increases, the performance gap between the algorithms becomes increasingly obvious, and the performance of the Alt-MMSE algorithm is more obviously and significantly better than the performance of the other algorithms, and closer to the performance of pure digital beamforming. The simulation showed that the performance advantage of the Alt-MMSE algorithm is significantly greater than that of other beamforming algorithms [16; 17; 18; 19; 20; 24] when number of data streams \(\mathbf{N_{s}}\) is large.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of transmitting antennas \(\mathbf{N_{t}}\) & 256 \\ \hline Number of receiving antennas \(\mathbf{N_{r}}\) & 64 \\ \hline Antenna arrangement & UPA \\ \hline Antenna spacing \(\mathbf{d}\) (mm) & \(\uplambda\)/2 \\ \hline Number of clusters \(\mathbf{N_{cl}}\) & 5 \\ \hline Number of scatterers ray \(\mathbf{N_{ray}}\) & 10 \\ \hline Angle of Arrival (AoA), Angle of Departure (AoD) distribution & Laplacian \\ \hline Number of data streams \(\mathbf{N_{s}}\) & 8 \\ \hline Signal-to-noise ratio (SNR) & 30 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 2: \(Cont.\)  
Spectral Efficiency Analysis when \(\mathbf{N_{s}}=\mathbf{N_{t}^{\text{RF}}}=N_{r}^{\text{RF}}\)  
Figure 3 shows the system performance of the algorithms as a function of SNR at \(\mathbf{N_{t}^{\text{RF}}}=\mathbf{N_{r}^{\text{RF}}}=\mathbf{8}\). It can be seen from Figure 3 that, when number of data streams \(\mathbf{N_{s}}\) is equal to the number of RF links (\(\mathbf{N_{s}}=\mathbf{N_{t}^{\text{RF}}}=\mathbf{N_{r}^{\text{RF}}}\)), the performance of the OMP-based hybrid beamforming algorithm is significantly lower than that of pure digital beamforming, and the gap becomes increasingly obvious as number of data streams \(\mathbf{N_{s}}\) increases. The performance of the proposed algorithm is always better than the performance of the other existing algorithms, and is very close to the system performance of the pure digital beamforming algorithm. It can be seen that the relationship between number of RF chains and number of data streams \(\mathbf{N_{s}}\) also affects the performance of the algorithm.  
Figure 3: System performance when number of data streams equals RF links (\(N_{s}=N_{t}^{RF}=N_{r}^{RF}\)).  
Figure 4: System performance when number of data streams is \(\mathbf{N_{s}}=\{\mathbf{2,4,8}\}\) and SNR = 0 dB.
## 3 Results
### Importance Analysis of Driving Factors  
Land use change is the result of the combination of natural, social, economic and other factors, which reflects the intensity and rationality of land use, development intensity, economic input, policy orientation, and other factors in the study area. Comprehensive analysis of the driving factors of land use change process is helpful to better understand the mechanism and trend of land use change. In order to provide scientific reference for formulating more reasonable land use policies and promote the sustainable development of regional economy and ecology. The RF model can use Out-Of-Bag (OOB) data to calculate the importance of feature variables from a large dataset, thereby revealing the complex relationship between feature variables. In this paper, the RF model is used to analyze and quantify the influence of driving factors such as neighborhood interaction in multi-land change. Figure 9 shows the top 10 most important drivers of LUCC.  
According to the importance of driving factors in the RF model, it can be concluded that: (1) neighborhood probability is the most important driving factor in the process of cropland expansion. It indicates that the development of cropland is largely affected by historical land use/cover patterns and follows a certain time development law and neighborhood effect. (2) The point density of the light rail station plays an important role in the expansion of the impermeable layer. The location of the light rail station is highly concentrated in the downtown area, indicating that the urban development of Chongqing is still concentrated in the central city. (3) In shrub and forest, the slope is the most important factor. (4) In the process of water area development, the importance of the initial probability of transformation into the water area is as high as 0.1097, which is far greater than other driving factors. (5) In the process of Grass expansion, the neighborhood probability of cropland plays the most important role, and the neighborhood probability of grass also gives great reference value. Both urban grass and cropland belong to artificial-natural mixed ecosystems, and their development has similar characteristics.  
Figure 9: Importance of driving factors.
## 5 Measurements and Evaluation
### Color Gamut and Color Reproduction Fidelity  
In a full color holographic display, color reproduction fidelity is not actually guaranteed even though color gamut of the display system is wider than conventional ones. Figure 11a shows the measured color gamut of our display, which is ~160% wider than CIE 1976 NTSC standard (1953). On the contrary, Figure 12d shows the measured color fidelity for the 24 colors in a standard Macbeth color chart. Blue dots represent desired values and red dots are measured ones. As shown in Figure 12c, optically reconstructed hologram image is corrupted with speckle and other noise, so that colors corresponding to two modulation levels (125 and 255) are not clearly distinguished. As a result, color separation is very bad. Quantification of this result is under study.