esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 3 Preliminaries
### System Model  
The system model is shown in Figure 1, where all UAVs are managed in the cloud. The cloud formulates clusters and maintains the backbone routing table for communicating with the CHs. The cloud broadcasts central control commands to all UAVs through the CHs.  
In this system, all the UAVs are equipped with location-aware components and wireless communication interfaces so that they can obtain the velocity, direction and location information about themselves and also have the capability of routing. Through the cloud,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Clustering** & **Location** & \multirow{2}{*}{**Reliability**} & **Energy** & \multirow{2}{*}{**PDR**} & **Communication** \\  & **Overhead** & & & & **Efficiency** & **Safety** \\ \hline WCA [12] & High & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) \\ ACO [13] & Moderate & \(\times\) & ✓ & ✓ & ✓ & \(\times\) \\ GWO [14] & High & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) \\ HCC [15] & Low & ✓ & ✓ & \(\times\) & \(\times\) & ✓ \\ KMB [17] & High & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) \\ SRC [18] & Low & ✓ & ✓ & \(\times\) & \(\times\) & ✓ \\ SIC [19] & High & ✓ & \(\times\) & ✓ & ✓ & \(\times\) \\ DSWCA [20] & Low & \(\times\) & ✓ & ✓ & ✓ & \(\times\) \\ DTPCH-C & Low & ✓ & ✓ & ✓ & ✓ & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: DTPCH-C performance positioning with other clustering schemes in FANETs.  
the FANET will be clustered. Each cluster is composed of a CH and CMs. After clustering, CHs in each cluster divides the grid within their own communication range. According to the exchange of the HELLO message, the CH can obtain the position and speed of CMs. According to the communication grid position of CMs, the CH adjusts its signal transmission power, to save the energy and prolong the system lifetime. At the same time, the CH also predicts the movement of CMs, judges the position of CMs in the future, and adjusts the signal transmission power in time. Finally, the cloud will periodically repartition clusters and elect CHs by the weighted summation-based cluster head selection algorithm because of the real-time changing topology, which ensures normal communication in the network and avoids the influence of the excessive energy consumption of a node on the network lifetime.
## 3 Results
### Identification of Secondary Metabolite-Biosynthetic Gene Clusters  
Prediction of secondary metabolites using antiSMASH revealed that _Streptomyces_ sp. CH6 contained 22 BGCs encoding for non-ribosomal peptides (NRPS), polyketides (PKS), lanthipeptides, and terpen. Among them, 13 clusters exhibited different similarities to BGCs with known function (Table 3). The BGCs of three NRPSs and three PKS/NRPS hybrids were observed, while PKS alone was not found. Meanwhile, four gene clusters displayed high similarity (> 70%) to the reported BGCs: ectoine (80.7%), guangnanmycin (76.0%), birimositide (73.0%), and SGR PTMs (71.0%). Among the remaining nine BGCs, six exhibited some level of similarity (30-70%) with known BGCs, such as AmfS (61.0%), geosmin (57.3%), isorenieratene (56.7%), toptopetsamide (54.1%), hopene (53.9%), and detoxin S1 (30.6%) (Figure S3). The other three BGCs, such as detoxin S1, kinamycin, and julichrome Q3-3, showed low similarity values ranging from 13.2% to 19.8%.  
Notably, a large cluster 2 with a size of 133,857 bp exhibited 76% similarity with the known cluster, which was able to synthesize guangnanmycin, a congener of the well-known antitumor drug lead, leinamycin. It was about 1.8-fold larger than guangnanmycin produced by _Streptomyces_ sp. CB01883 (Figure 5). Further investigations revealed that the 30,020 bp scabichelin cluster was also present in cluster 2. The large BGC from the CH6 is unique and has not been found in any reference gene clusters from the MIBiG database.  
Figure 5: Comparison of guangnanmycin and scabichelin BGCs between _Streptomyces_ sp. CH6 and previously reported _Streptomyces_ sp. CB01883.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Cluster** & **Type** & **Length (bp)** & **Most Similar Known Cluster** & **Similarity (\%)** & **Potential Activity** \\ \hline
1 & NRPS-like, NRPS, nucleoside & 114,856 & Detoxin S1 & 19.8 & - \\
2 & TransAT-PKS, NRPS, NRPS-metallophore & 133,857 & Guangnammycin & 76.0 & Anticancer [13] \\
3 & Terpene & 21,104 & Detoxin S1 & 30.6 & - \\
4 & Lanthipeptide-class-iii & 22,655 & AmfS & 61.0 & - \\
5 & HgI-KS, kprepre & 55,247 & Isoorientate & 56.7 & Antioxidant, anti-UVB \\
6 & Terpene & 26,704 & Hopene & 53.9 & - \\
7 & NRPS, TIPKS & 49,405 & SGR PTMs & 71.0 & Antiformal, antibiotic, anti-acid \\
8 & NI-siderophore & 33,324 & Kinamycin & 13.2 & Antibacterial, anticancer [36] \\
9 & Ectoine & 10,399 & Ectoine & 80.7 & Antioxidant [37] \\
10 & NRPS, TIPKS, TSPKS, NRPS-like & 57,731 & Totopopetsamide & 54.1 & - \\
11 & Terpene & 21,173 & Julichrome Q3-3 & 16.5 & Antibacterial, anticancer [38] \\
12 & Terpene & 22,436 & Geosmin & 57.3 & Antibacterial [39] \\
13 & NRPS, lanthipeptide-class-ii & 32,791 & Birmingham & 73.0 & Antibacterial [39] \\ \hline \end{tabular}
\end{table}
Table 3: List of proposed secondary metabolite biosynthetic gene clusters present in _Streptomyces_ sp. CH6 genome.
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
## 2 SCL Decoding and Its Operational Complexity
### Operational Complexity of the SCL Decoding for the SCRC Scheme
Although a parallel sorting is preferred for fast decoder realization, a serial sorting is also taken into account for minimal complexity reference. The quick sorter that requires \(2l_{i}{\rm log}_{2}(2l_{i})\) comparisons for \(2l_{i}\) paths is considered for a serial sorter. For a parallel sorter, we adopt the bitonic sorter [17] requiring \((l_{i}/2){\rm log}_{2}(2l_{i})({\rm log}_{2}(2l_{i})+1)\) comparisons because it gives a good complexity-speed tradeoff. If we assume the bitonic sorter, the number of comparisons at the \(i\)-th decoding step, \(s_{i}\), is computed as  
\[s_{i}= \begin{cases}(l_{i}/2){\rm log}_{2}(2l_{i})({\rm log}_{2}(2l_{i})+1),& \text{if }i\in\mathcal{A}\text{ and }2l_{i}>L,\\ 0,&\text{otherwise}.\end{cases}  \tag{3}\]  
We evaluate the sorting complexity in the number of additions since a comparison is equivalent to an addition. Figure 2: The two node operations for the log-likelihood ratio (LLR) calculation. Finally, the calculation of PM should be counted. PM calculation can also be approximated by a single addition as follows [25]:  
\[\text{PM}_{i}^{l}= \begin{cases}\text{PM}_{i-1}^{l},&\text{if }u_{i}^{l}=(1/2)(1- \text{sgn}(\text{LLR}_{i}^{l})),\\ \text{PM}_{i-1}^{l}+|\text{LLR}_{i}^{l}|,&\text{otherwise},\end{cases}  \tag{4}\]  
where \(l(1\leq l\leq 2l_{i})\) is the path index and \(\text{LLR}_{i}^{l}\) is the LLR for \(u_{i}^{l}\)[25]. The total decoding complexity \(\text{C}_{\text{S}}\) for the SCRC scheme is the sum of the complexities of the node and sorting operations, and the PM calculation as  
\[\text{C}_{\text{S}}=\sum(l_{i}+l_{i}n_{i}+s_{i})=\sum\text{C}_{\text{S}}^{i}, \tag{5}\]  
where \(\text{C}_{\text{S}}^{i}\triangleq l_{i}+l_{i}n_{i}+s_{i}\) is the temporal complexity of the \(i\)-th decoding step of the SCRC scheme. We omit CRC computation in complexity evaluation because it is composed of bit operations which are much lighter than real additions.
# A Review of Experimental Techniques for Measuring Micro- to Nano-Particle-Laden Gas Flows
## 3 Specific Measurement Methods
### Optical Particle Characterization
#### 3.1.3 Scattering Intensity Measurements
In this regime, the particle scattering intensity only depends on the incident light wavelength and particle size; thus, increases in particle size decrease the angular range of the diffracted light. In this case, the diffracted light intensity is independent of the optical constants of the particle material and may be used to measure particles of different or undetermined refractive indices. In contrast to a 0\({}^{\circ}\) scattering angle, a scattering angle of 90\({}^{\circ}\) usually produces the weakest scattered light and receiving light signal. However, in practice, limited operating space and the requirement of non-intrusive detection usually results in setups having \(\varphi~{}=~{}90^{\circ}\). Given that there should be only one particle in the measurement volume to avoid significantly overestimating particle size, this determines the upper concentration limit of the scattering intensity techniques. There are two ways to solve this problem: (1) use a microparticle-laden gas flow with particles smaller than the measurement volume, which can be generated using a micro-mechanical device, e.g., a particle-laden micro-jet flow in a particle counter, although this involves a sampling stage; and (2) use an optical arrangement that achieves a small enough measurement volume, which can be carried out online. Other parameters, such as tracer particles and blockages of incident light or scattered light, also have a marked effect on particle characterization using scattering intensity measurements. It is important to note that scattering intensity measurement techniques inevitably fail to characterize particles in the Mie scattering regime, because of their non-monotonic relationship with dimensionless diameter \(X_{M}\) and scattering intensity \(I\). Mie/laser-induced fluorescence (LIF) ratio technology was specifically developed to overcome this problem [40; 41; 42; 43]. In this technique, the ratio of fluorescence intensity to scattered light is used as an analog signal to replace the conventional scattering intensity. Using fluorescent tracers, it is presumed that fluorescence intensity is proportional to droplet volume (\(d_{p}\)\({}^{3}\)), while Mie scattering intensity is proportional to \(d_{p}\)\({}^{2}\); thus, the intensity of fluorescence and Mie scattering can be distinguished according to their varied wavelengths, and particle size can be estimated using their intensity ratio. This method has reduced the influence of other parameters on the detection, but leads to a dependence on particle size and fluorescent concentration, as well as any  
Figure 9: Scattering intensity as a function of the Mie parameter for two scattering angles. related calibration. Currently, only droplets can be characterized using Mie/LIF ratio measurements, since there are still several unresolved issues for measuring solid particles and non-spherical particles.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 2 Background
### Low Power Wide Area Network (LPWAN)
#### 2.1.2 SigFox  
This was the first LPWAN technology that was proposed for an IoT market in 2009 [12]. It offers an end-to-end LPWAN connectivity solution by itself or by cooperating with other network operators. The physical layer of SigFox employs ultra-narrowband (UNB) modulation, and the end devices connect to the base station, which are equipped with cognitive software-defined radios (SDRs), utilizing binary phase shift keying (BPSK) modulation in an ultra-narrow sub-GHz ISM band [13]. Benefiting from UNB, SigFox is in possession of high spectrum efficiency and it experiences low noise levels and, thus, results in high receiver (Rx) sensitivity, ultra-low power consumption, and inexpensive antenna designs.
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 3 Results
### Changes in LULC after the Flood and Assessment of Accuracy
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Classes**} & \multicolumn{4}{c}{**Pre-Flood**} & \multicolumn{4}{c}{**During the Flood**} & \multicolumn{4}{c}{**Post-Flood**} \\ \cline{2-13}  & **UA** & **PA** & **OA** & **K** & **UA** & **PA** & **OA** & **K** & **UA** & **PA** & **OA** & **K** \\ \hline Crop/agricultural land & 82.35 & 98.98 & & & 91.94 & 80.28 & & & 91.84 & 81.82 & & \\ Built-up area & 98.98 & 97.23 & & & 96.02 & 93.53 & & & 88.73 & 88.73 & & \\ Barren land & 84.81 & 94.37 & & & 93.85 & 93.85 & & & 93.10 & 73.97 & & \\ Sand & 95.85 & 94.29 & & 94.64 & 96.15 & & & 80.79 & 83.59 & 0.90 & 0.85 \\ Water/wetlands & 98.90 & 93.75 & & & 80.11 & 84.56 & & & 90.30 & 83.71 & & \\ Deposited material & 81.56 & 84.53 & & 90.59 & 93.33 & & & 90.91 & 98.90 & & \\ \hline \end{tabular} UA = user’s accuracy, PA = producer’s accuracy, OA = overall accuracy, K = Kappa coefficient.  
\end{table} 
Table 3: Accuracy assessment (%) of the LULC before, during, and after the 2014 flood. Figure 4: Change in LULC in Muzzaffargarh District according to Landsat 8 images before, during, and after the 2014 flood.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 2 Models Developed for Network Coverage and Capacity Problems
### Capacitated Network Area Coverage with Existing Services  
The problems of existing services were discussed widely in the [71]. Here, the discussion was issued in the context of covering of fire brigade locations through an extension of the MLCP. Since the existing facilities may prove to be spatially inefficient, the systematic analysis is essential for evaluating both short and long-term costs. In the context of this paper, the existing services are an important part of the deployment since it is not a trivial or inexpensive task of changing the position of the existing gNodeB nodes. In most scenarios, we need to extend, enhance or replace the existing service centres by new service centre configurations. In addition to the previous model, we had to add another constraint that will add the existing services to the solution explicitly as follows:  
\[\forall i\in E_{f}:\ x_{i}=1, \tag{31}\]  
where \(E_{f}\) represents the set of existing service centres.  
The whole extended model of Section 2 would change as follows for the use case of deployment in the area with existing services.  
Minimize  
\[(1+\varepsilon)\sum_{i\
otin E_{f}}w_{i}x_{i}+\sum_{i\in E_{f}}x_{i} \tag{32}\]  
subject to:  
\[\forall j\in J:\;\sum_{i\in I}a_{ij}x_{i}\geq 1 \tag{33}\]  
\[\forall j\in J:\;\sum_{i\in I}a_{ij}y_{ij}=1 \tag{34}\]  
\[\forall i\in I:\;c_{i}x_{i}\geq\sum_{j\in J}a_{ij}y_{ij}b_{j} \tag{35}\]  
\[(\forall i\in I)(\forall j\in J):\;y_{ij}\leq x_{i} \tag{36}\]  
\[\forall i\in E_{f}:\;x_{i}=1 \tag{37}\]  
\[(\forall i\in I-E_{f})(\forall j\in I-E_{f})(i\
eq j):\;d_{ij}\geq(x_{i}+x_{j} -1)d_{\min} \tag{38}\]  
\[(\forall i\in I-E_{f})(\forall j\in E_{f}):\;d_{ij}>d_{\min}x_{i} \tag{39}\]  
\[\forall i\in I:\;x_{i}\in\{0,1\} \tag{40}\]  
\[(\forall i\in I)(\forall j\in J):\;y_{ij}\in\{0,1\}, \tag{41}\]  
where \(\varepsilon\) is the additional cost of building a new service centre as compared to keeping an existing one. The objective function may be modified in the same way as in Equation (22), considering the existing nodes. Interference must be eliminated for all pairs of newly added centres and also for new and existing centre, which is satisfied by Equations (38) and (39). The other parameters are the same as in the model provided in Section 2.1, and for its solvability, the same assumptions must be satisfied.
# Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
###### Abstract  
With the advancement of Artificial Intelligence (AI) and the Internet of Things (IoT), research in the field of emotion detection and recognition has been actively conducted worldwide in modern society. Among this research, speech emotion recognition has gained increasing importance in various areas of application such as personalized services, enhanced security, and the medical field. However, subjective emotional expressions in voice data can be perceived differently by individuals, and issues such as data imbalance and limited datasets fail to provide the diverse situations necessary for model training, thus limiting performance. To overcome these challenges, this paper proposes a novel data augmentation technique using Conditional-DCGAN, which combines CGAN and DCGAN. This study analyzes the temporal signal changes using Mel-spectrograms extracted from the Emo-DB dataset and applies a loss function calculation method borrowed from reinforcement learning to generate data that accurately reflects emotional characteristics. To validate the proposed method, experiments were conducted using a model combining CNN and Bi-LSTM. The results, including augmented data, achieved significant performance improvements, reaching WA 91.46% and UAR 91.61%, compared to using only the original data (WA 79.31%, UAR 78.16%). These results outperform similar previous studies, such as those reporting WA 84.49% and UAR 83.33%, demonstrating the positive effects of the proposed data augmentation technique. This study presents a new data augmentation method that enables effective learning even in situations with limited data, offering a progressive direction for research in speech emotion recognition.  
speech emotion recognition; data augmentation; CGAN; DCGAN; artificial intelligence; deep learning +
Footnote †: journal: _Sci._  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Experimental Test Scheme
### Dust Suppression Solution Preparation  
To better understand the relationship between changes in surface tension and viscosity and the atomization characteristics, as well as to achieve uniformity in the fog field and improve dust removal efficiency, we chose dodecyl alcohol as the preferred experimental test solution based on its wide application, dust suppression effect, and physical and chemical properties. Water was used as the test solution for the control experiment. The mass concentration of the dodecyl solution was set at 2.0% and 5.0%, respectively. The surface tension and viscosity of the dust suppression liquid were measured by the experimental equipment, as shown in Figure 1, with the test environment temperature set at 25 \({}^{\circ}\)C and the humidity at 53% RH.  
Based on the pre-test results, when the mass concentration of the dodecyl alcohol solution exceeds 5%, its surface tension is similar to that of water, making it unsuitable for comparative experiments with water. Additionally, its viscosity becomes too high, leading to suboptimal dust reduction through the spray and rendering the optimization of atomization effects meaningless. Therefore, the test solutions were selected within a viscosity range of 0.89 \(\times\) 10\({}^{-3}\) Pa-s to 1.46 \(\times\) 10\({}^{-3}\) Pa-s and a surface tension range of 33.6 mN\(\cdot\)m\({}^{-1}\) to 71.97 mN\(\cdot\)m\({}^{-1}\). The viscosity gradient is approximately 0.25 \(\times\) 10\({}^{-3}\) Pa-s, and the surface tension gradient is about 20 mN\(\cdot\)m\({}^{-1}\). Within this range, the surface tension and viscosity of the dust suppression solution can ensure stable spraying without causing nozzle clogging or damage. Moreover, it can be compared with water to systematically explore the relationship between the changes in surface tension and viscosity and the atomization effect.
## 2 System Description
### Data Embedding
\tag{5}\]  
This equation indicates that the portion of the frequency-domain image where the low-frequency information is concentrated is set to one, while the remaining portion is utilized for embedding data. Finally, as depicted in Figure 1, the 2D-IDFT operation was employed to convert the frequency-domain image back into the spatial domain to be displayed on the electronic screen. Mathematically, this can be expressed as  
\[\mathbf{D}_{t}[i]=\mathbf{F}_{P}^{H}\mathbf{D}_{F}[i]\mathbf{F}_{Q}^{H}, \tag{6}\]  
where \(\mathbf{F}_{P}^{H}\) and \(\mathbf{F}_{Q}^{H}\) are Hermitian transposes of the 2D-DFT matrices. Thus, the spatial domain image is rendered through the electronic display, and the data are transmitted simultaneously through the image. Figure 2 depicts the data embedded in the frequency-domain image and their effect on the spatial-domain image. Figure 2a illustrates the location of the sub-band, where the data and pilots are loaded. In particular, the white region in the frequency-domain image represents the sub-band region, where the data and pilots are embedded. As mentioned previously, to introduce fewer detectable artifacts, the data and pilots were loaded in the high-frequency range. The low-frequency components of an image generally exhibit smooth color variations, whereas the high-frequency components exhibit sharp variations. Because the low-frequency regions of an image contain the primary perception of the human eye, it is preferable to embed the data in the high-frequency region. In this way, during the simultaneous transmission of images on the screen, any image distortions in sequential images are almost imperceptible to the human eye. Figure 2b shows the corresponding spatial-domain image. For comparison, a data-embedded image (without pilots) is displayed in Figure 2c [19]. Both images appear similar in the spatial domain, and even minor differences are imperceptible to the human eye. Figure 2: Illustration of data embedding in the frequency domain (**a**) location of high-frequency sub-bands, (**b**) the corresponding spatial-domain image to be displayed on the screen, and (**c**) data-embedded image for comparison [19].