esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Phylogenetic Trees Construction and Multiple Sequence Alignment  
For constructing the phylogenetic tree of PsHMGR1, firstly, HMGR proteins with high homology to PsHMGR1 were found by NCBI Blastp from ten species of plants, namely _D. turbinata, T. wilfordii, Ricinus communis, P. trichocarpa, Nelumbo nucifera, P. euplratica, Mangifera indica, P. alba, Pistacia vera,_ and _Jatropha curcas_. Subsequently, 27 HMGR protein sequences from 25 representative species were downloaded based on reported HMGR studies from 3 animals (_Homo sapiens, Rattus norvegicus_ and _Drosophila alonomicas_), 3 fungi (_Saccharomyces cerevisiae, Ganoderma lucidum_ and _Lachnellula hyalina_), 3 bacteria (_Zobellia galactanivorans, Streptomyces malasisensis_ and _Breibacterium linens_), 2 lower plants (_Chlorokybus atnolnyticus_ and _Mesostigma viride_ from the Chlorophyta), 3 Pteridophyta (_Alsophila spinulosa, Ceratopteris richardii_ and _Adiantum capillus_), 2 Bryophytes (_Physcomittella patens_ and _Marchantia polymorphica_), 2 Gymnosperms (_Pinus tabuliformis_ and _Ginkgo biloba_), and 7 Angiosperms (1 basal angiosperm _Amborella trichropoda_, 2 monocots _Oryza sativa_ and _Zea mays_, and 4 eudicots _Olea europaea_, _Vitis vinifera_, _P. trichocarpa_ and _Arabidopsis thaliana_) [24; 26]. Information on these HMGR proteins is shown in Table S1. Finally, the phylogenetic tree was constructed by 'One Step Build a ML Tree' in TBtools V2.136 software with a bootstrap value of 5000 [63].  
Homologous proteins of PsTPS1 were found and downloaded in NCBI Blastp, including _P. delavayi, P. lactiflora_, and _L. formosana_. Also refer to the papers [18; 35; 61] to download TPS proteins from TPS-a, TPS-b, TPS-c, TPS-d, TPS-e/f, and TPS-g subfamilies, including _A. thaliana, Solanum lycopeneum, Medicago truncatula, Abies grandis, Melia azadarach, P. alba, Vitis riparia, V. vinifera_, and _Rosa hybrida_. Information on the TPS proteins is shown in Table S2. Then, the phylogenetic tree was constructed employing the MEGA-X V10.2.6 software with the neighbor-joining (NJ) method, and a bootstrap value of 1000 was used to verify the phylogenetic tree. Finally, the generated tree was visualized by online software iTOL.  
Open reading frame (ORF) analysis and multiple sequence alignment of PsHMGR1, PsTPS1, and proteins from other species were carried out with DNAMAN V9.0 software. Motifs were analyzed with reference to the reported paper [8; 24; 64; 26].
# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 6 Challenges and Future Research Directions
### Limited AD Detection Methods with Respect to Non-English Languages  
Almost all existing studies focus on developing detection methods to detect fake voices speaking English, although six official languages are included in the United Nations' list of official languages [48]. For example, the authors are aware of no existing studies focusing on Arabic. Indeed, Arabic is the world's fourth most widely spoken language behind Chinese, Spanish, and English, with over 230 million native speakers [49]. It consists of three core types: Classical Arabic (CA), Modern Standard Arabic (MSA), and Dialect Arabic (DA) [50]. CA is the official language of the Quran; MSA is the official language of the present era; and DA is the spoken language of everyday life that differs between regions [50]. The reason for highlighting the Arabic language in this section is because the Arabic language has a unique challenge in alphabet pronunciation, which the traditional techniques of audio processing and ML learning models cannot deal with [51]. It contains three crucial long vowels named Fatha, Damma, and Khasra [52], which if pronounced incorrectly will change the meaning of the sentence [51]. The authors [53] therefore pointed out that the performance of any model in a specific language will not be the same in other languages, especially in languages that have limited available data, such as Arabic and Chinese. There was only one attempt by [24], where the authors collected CA data based on imitation fakes. For this reason, we can directly understand the lack of detection methods for non-English AD. We therefore encourage the research community to meet this research gap by proposing a new detection method to detect other languages, such as Arabic.
## 3 Proposed Methodology
### Feature Extraction
#### 3.1.2 Prewitt and Gaussian  
The Prewitt filter is an edge recognition filter that improves the image's edges. Prewitt filter was used for the pristine and damaged images in this study, followed by a Gaussian filter to minimize noise. The Prewitt filter can be defined by following equation,  
\[G_{p}=\begin{bmatrix}-1&0&1-1&0&1-1&0&1\end{bmatrix}*I\quad\text{and}\quad G _{q}=\begin{bmatrix}-1&-1&-1&0&0&0&1&1\end{bmatrix}*I \tag{2}\]  
where \(G_{p}\) and \(G_{q}\) are the Prewitt filters for identifying vertical and horizontal edges, respectively, and \(I\) is the input image. The Gaussian filter is defined as follows,  
\[G(p,q)=\frac{1}{2\pi\sigma^{2}}\exp\left(-\frac{p^{2}+q^{2}}{2\sigma^{2}}\right) \tag{3}\]  
Figure 2: Overview of the proposed framework for Blind Image Quality Assessment. The framework includes three main stages: Feature Extraction, Feature Selection, and Quality Prediction.  
where \(G(p,q)\) denotes the Gaussian filter at location \((p,q)\), and \(\sigma\) denotes the standard deviation of the filter.
## 3 Results and Discussion
### Display of Inter-District Traffic Relations  
Figure 9 shows the inter-district relationships for Variant A, Variant B and Variant C. There can be found a decrease in the direction of traffic flows between the Narodna trieda-Sturova and Narodna trieda-Palackeho districts by comparing the results between Variant A and Variant B. On the other hand, there is an evident increase in traffic flows in the Jantarova-Palackeho district. With the expected decrease in the number of vehicles of 10% in Variant C, this negative phenomenon is eliminated to the values corresponding to Variant A, i.e., the current state. Traffic flow in Figure 9 represents vehicles per hour, specifically between 7:00-8:00.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Centroids** & **1.** & **2.** & **3.** & **4.** & **5.** & **6.** & **7.** & **8.** & **9.** & **10.** & **Total** \\ \hline
1. [PERSON] & 0 & 77 & 31 & 123 & 154 & 46 & 108 & 93 & 62 & 15 & 709 \\
2. [PERSON] & 61 & 0 & 14 & 54 & 68 & 20 & 48 & 41 & 27 & 7 & 340 \\
3. Juzna trieda & 22 & 12 & 0 & 20 & 25 & 7 & 17 & 15 & 10 & 2 & 130 \\
4. [PERSON] & 74 & 41 & 16 & 0 & 82 & 25 & 57 & 49 & 33 & 8 & 385 \\
5. [PERSON] & 188 & 105 & 42 & 167 & 0 & 63 & 146 & 125 & 84 & 21 & 941 \\
6. [PERSON] & 33 & 18 & 7 & 30 & 37 & 0 & 26 & 22 & 15 & 4 & 192 \\
7. [PERSON] & 25 & 14 & 6 & 22 & 28 & 8 & 0 & 17 & 11 & 3 & 134 \\
8. [PERSON] & 15 & 8 & 3 & 14 & 17 & 5 & 12 & 0 & 7 & 2 & 83 \\
9. [PERSON] & 51 & 28 & 11 & 45 & 57 & 170 & 40 & 34 & 0 & 6 & 442 \\
10. [PERSON] & 3 & 2 & 1 & 3 & 4 & 1 & 3 & 2 & 1 & 0 & 20 \\ Total & 472 & 305 & 131 & 478 & 472 & 345 & 457 & 398 & 250 & 68 & 3376 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Historical matrix for Variant A, (source elaborated by authors).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Centroids** & **1.** & **2.** & **3.** & **4.** & **5.** & **6.** & **7.** & **8.** & **9.** & **10.** & **Total** \\ \hline
1. [PERSON] & 0 & 74 & 30 & 12 & 148 & 44 & 103 & 89 & 59 & 15 & 574 \\
2. [PERSON] & 59 & 0 & 13 & 53 & 66 & 20 & 46 & 39 & 26 & 7 & 329 \\
3. Juzna trieda & 22 & 12 & 0 & 20 & 25 & 7 & 17 & 15 & 10 & 2 & 130 \\
4. [PERSON] & 71 & 40 & 16 & 0 & 79 & 24 & 56 & 48 & 32 & 8 & 374 \\
5. [PERSON] & 183 & 102 & 41 & 163 & 0 & 61 & 142 & 122 & 81 & 20 & 915 \\
6. [PERSON] & 32 & 18 & 7 & 29 & 36 & 0 & 25 & 22 & 14 & 4 & 187 \\
7. [PERSON] & 20 & 11 & 5 & 18 & 23 & 7 & 0 & 16 & 14 & 2 & 116 \\
8. [PERSON] & 15 & 8 & 3 & 13 & 17 & 5 & 12 & 0 & 7 & 2 & 82 \\
9. [PERSON] & 51 & 28 & 11 & 45 & 57 & 17 & 40 & 34 & 0 & 6 & 289 \\
10. [PERSON] & 3 & 2 & 1 & 3 & 4 & 1 & 3 & 2 & 1 & 0 & 20 \\ Total & 456 & 295 & 127 & 356 & 455 & 186 & 444 & 387 & 244 & 66 & 3016 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Historical matrix for Variant B, (source elaborated by authors).
## 3 System Principle
### LED Recognition
Given an input vector \(X^{k}=\left(x_{1}^{k},x_{2}^{k},\ldots,x_{m}^{k}\right)^{T}\) and expected output vector \(P^{k}=(p_{1},p_{2},\ldots,p_{n})^{T}\), for network learning, the number of neurons in the input layer and output layer is m and n respectively. The number of hidden neurons has great influence on the prediction accuracy of a BP neural network. At present, \(l=2n+1\) is usually used to calculate the number of hidden layer nodes. Calculate the output of the hidden layer and output layer using:  
\[y_{j}=f\left(\sum_{i}w_{ji}x_{i}-\theta_{j}\right)=f\left(net_{j}\right) \tag{12}\]  
\[z_{l}=f\left(\sum_{i}v_{ji}y_{i}-\theta_{l}\right)=f\left(net_{l}\right) \tag{13}\]  
In the formula, \(y_{j}\) is the output of hidden layer; \(w_{ji}\) is the weights between input and hidden layer; \(x_{i}\) is the input layer; \(\theta_{j}\) is the threshold of hidden layer; \(v_{ji}\) is the weights between the hidden and output layer; \(\theta_{l}\) is the threshold of output layer; and \(f\) is excitation function of S type, \(f(x)=\frac{1}{1-e^{-x}}\). The error of the output node is:  
\[E=\frac{1}{2}\sum_{i}(t_{l}-f(\sum_{j}v_{ij}f\left(\sum_{i}w_{ji}x_{i}-\theta_{ l}\right))^{2} \tag{14}\]  
In the formula, \(t_{l}\) is the expected output value of the output node. By modifying the weights and thresholds from the output layer, the error back propagation to modify the weights of each layer to minimize network error, as follows:  
\[w_{ji}(k+1)=w_{ji}(k)+\eta^{\prime}\delta_{j}^{\prime}x_{i} \tag{15}\]  
\[\theta_{j}(k+1)=\theta_{j}(k)+\eta^{\prime}\delta_{j}^{\prime} \tag{16}\]  
In the formula, \(\eta\) is the learning coefficient, and \(\delta_{j}^{\prime}\) is the node error. In general, three layers of the BP network can be done in any mode of classification; thus, we chose the three layers of the BP network in our case. There are six types of feature data, so \(n=6\). According to \(l=2n+1\), the number of neurons in the hidden layer is 13. Therefore, a 6-13-6 BP network is structured, which is shown in Figure 14.  
Figure 14: BP network structure of LED-ID classification and recognition.
## 3 The Principle of the Leak Location Method Based on the AE Signal Energy Attenuation  
### AE Signal Energy Attenuation  
The energy of the AE signal reduces as the propagation distance increases due to geometric spreading, structural scattering, and energy absorption [14]. The weakening effect of the pipe on AE signals is similar with the low-pass filter [10], in that the cut-off frequency decreases as the propagation distance increases. When the AE signals propagate in one direction along the pipe wall, the energy attenuation of the AE signal can be described by an exponential decay [31]:  
\[E=E_{0}e^{-kt_{i}}, \tag{12}\]  
where \(E\) is the energy of the sensor signal, \(E_{0}\) is the AE signal energy at the leak point and \(k\) is the attenuation coefficient. The AE signal energy is proportional to the integral of the square of the signal amplitude [32], and can be written as:  
\[E=\int_{0}^{\Delta T}v^{2}dt, \tag{13}\]  
where \(\Delta T\) represents the length of sample time in seconds, and \(v\) represents the instantaneous signal amplitude in voltages. If the distance between the vibration source and the sensor is constant, the weakening effect of the pipe on the AE signal will be also constant, irrespective of any leakage-induced signals or impulse response signals. The closer the sensor is to the leak source, the greater the energy of the measured signals. Then the energy ratios of two measured signals will gradually increase or decrease as the distance between the vibration source and one of the two sensors increases. Therefore, a pipeline leakage can be located by the signal energy ratios without knowing the wave speed or the time delay estimation.
## 3 Results and Discussion
### Observed Climate and NDVI Trend for 1982-2011
Solid black lines in the map show the subcontinental division (only for the Eurasian continent) defined in Figure 1b. In the sub-continental scale analysis, we detected a statistically significant NDVI increase in seven of the nine sub-continental regions (Table 1). The two exceptions were the Boreal Eurasia North West region and the Tropical Asia South region. However, these regions had positive but minor increases in the NDVI. In the Boreal Eurasia North West region, interannual variations in the NDVI gradually increased from 1982 to 2000 and decreased after 2001, except for 2011 (Figure 4a). In the Tropical Asia South region, the NDVI had large interannual variations without significant increasing or decreasing trends from 1982 to 2000 (Figure 4i). Figure 4: Interannual variations in the NDVI and annual GPP in each subcontinental region (Figure 1b and Table 1). Normalized NDVI anomalies and modeled GPPs (based on the average for the 1982–2011 period) are shown.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 4 Deep Learning-Based Detectors  
As stated in the latter, the objective of this research is to understand the collective behavior of primary users by analyzing the temporal patterns of sensing data gathered by multiple cooperative SUs. Cooperative spectrum sensing has been shown to be a successful method of enhancing the detection performance. In contrast to single-node sensing, CSS employs multiple distributed nodes to improve the reliability in a collaborative way. The fusion center gathers sensed information from each SU to make the ultimate decision. Nonetheless, since the fusion center relies on binary results, it cannot incorporate confidence information, resulting in limited decision-making capabilities. To address this limitation and achieve our objective, we propose three deep neural network-based detection algorithms for CSS.
# Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
## 3 Stackelberg Game Model and Problem Statement  
### Common Algorithm  
Most algorithms based on existing Stackelberg game theory adopt utility function using earnings to define macro base station, but in reality, spectrum pricing will bring large signaling overhead. Based on this, references [29; 30] made improvements by defining the utility function as average throughput capacity of macro base station and standard distortion rate of femtocells, respectively. However, each base station in the heterogeneous network was assumed as an independent individual in-game model, which attempted to seek maximal energy efficiency without considering feelings of other individuals in the network or the overall performance of system. To consider the overall  
Figure 2: The spectrum shared scheme.  
performance of the system, and to improve the performance of the system to reduce the signaling overheads has become our primary concern. Meanwhile, when applying game models to solving the problem of cross-layer interference, adopting backward induction [13] to obtain Nash equilibrium can only be applied to the special case of a finite perfect information discrete game. The Nash equilibrium range of most games should be continued. In terms of our research, there has not been a solution for Nash equilibrium with a continuous value range.  
In the following parts, we will discuss one kind of optimal algorithm for solving the above problems. The game flow in the following part can be summarily described. The femtocells describe the amount of parameter \(\beta\), which is the rate between the final user number included in the femtocell service scope and the initial user number of the femtocell. The macro base station continues to choose \(\alpha\) as the strategy, where \(\alpha\) expresses the spectrum retained by the macro base station.
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 4 The Rogue Node Detection Scheme
### Analysis of Fog Node Communication  
Since the fog node and the central computing node communicate with each other in the above-mentioned algorithm, in this subsection, we analyze the communication delay among fog nodes. If the fog node can directly communicate with the central computing node, the delivery time between fog node \(a\) and the central computing node is denoted as:  
\[T_{a,cent}=\frac{S_{data}}{C_{a,cent}}=\frac{S_{data}}{Wlog_{2}\left(1+\frac{p_{ c}g_{0}(a,cent)d^{-\beta}}{1+\sigma^{2}}\right)} \tag{3}\]  
where \(S_{data}\) is the size of the transmission data, \(C_{a,cent}\) is the throughput from fog node \(a\) to the central computing node, \(P_{v}\) is the transmission power of the vehicle, while \(\zeta(a,cent)\) is the small-scale fading channel power gains from the fog node \(a\) to the parked vehicle central computing node and \(W\) is the bandwidth. Once the fog node cannot directly communicate with the central computing node, we have:  
\[T_{a,cent}=\frac{L(a,cent)}{2R}\frac{S_{data}}{2C_{v}} \tag{4}\]  
in which \(L(a,cent)\) is the distance from node \(a\) to the central computing node, \(C_{v}\) is the throughput of vehicle-to-vehicle delivery and \(R\) is the coverage range of the vehicle. Note that \(C_{v}\) can be acquired using a similar calculation method as \(C_{a,cent}\).
## 2 Related Work  
### Object Detectors  
Common object detectors are divided into two categories, one-stage and two-stage. Two-stage object detectors are represented by the R-CNN [18] family, including Fast R-CNN, Faster R-CNN [19], and R-FCN [20]. One-stage object detectors are represented by YOLO [21; 22; 23; 24], SSD [25], and RetinaNet [26]. In object detection, the object detector usually consists of three parts: the backbone, neck, and head. The backbone usually consists of a convolutional cascade used for feature extraction. The backbone is generally divided into two categories, heavyweight and lightweight. Heavyweight backbone networks can extract more features, but the extraction speed is slow. The lightweight-level backbone network extracts features fast but extracts few features. Common heavyweight backbones are the VGG [27], ResNet [28], VIT [29], etc. There are also lightweight backbones such as ShuffleNet [30; 31], MobileNet [32; 33; 34], and GhostNet [35]. ShuffleNetv1 [30] is a neural network structure specially designed for devices with limited computing resources. It mainly adopts pointwise group convolution and channel shuffle technologies, which greatly reduces the computational overhead while preserving the model accuracy. ShuffleNetv2 [31] further improves Shufflenetv1 and puts forward four principles for designing a lightweight network: 1. When the input and output channels are the same, the MAC (Memory Access Cost) is the minimum. 2. Too many packets will increase the MAC. 3. Fragmentation is not friendly to parallel acceleration. 4. An element-by-element operation will increase the memory consumption. MobileNetv1 [32] was proposed by Google in 2017, mainly focusing on the use and deployment of a convolution neural network's mobile terminal. In short, it is to replace the conventional convolution with the VGG network with depthwise convolution. The subsequent MobileNetv2 [33] is improved by replacing the ReLU6 activation function with a linear activation function and designing an inverted residual block. MobileNetv3 [34] uses the NAS (network architecture search) to search and determine the network structure, introduces the Squeeze and Excitation structure based on the block of MobileNetv2, uses the h-swish activation function, and improves the tail structure of MobileNetv2 network. Common necks are the FPN [36], PANet [37], and BiFPN [38]. The head is used to predict the category information and the bounding box of the object and is usually divided into two categories: the decoupled detection head and coupled detection head. The number of coupled detection head parameters is small, but the effect is average. The decoupled detection head has a large number of parameters but is effective.
## 3 Results and Discussion
### In Situ Biological Field Test Results  
#### 3.2.1 TOC Concentration Variation  
On days 0, 36, and 130 of the in situ biological field test, 40 L SOE + 5 kg FL, 230 L SOE + 20 kg FL, and 130 L SOE + 10 kg FL were injected, respectively. The FL was adjusted to 2% concentration with in situ groundwater before being blended with SOE. After each chemical injection, a groundwater volume of three times the chemical volume was injected to propel the chemicals. Layered injection was adopted. Because the TCE contamination depth of the site was 4.5 to 12 m, a double packer system was used to inject the chemicals to 5, 6, 7, 10, 11, and 12 m beneath the surface. The TOC concentration variations in each monitoring well after the chemical injections are presented in Figure 6. The results revealed that, because of the special structure of the Machette tube in the double packer injection system, the IW retained no groundwater or chemicals. Therefore, no water quality data were obtained in the IW. After the first injection, the TOC concentration of each monitoring well exhibited no apparent increase, probably because the first injection calculation underestimated the hydraulic conductivity, causing an insufficient amount of chemicals to be injected. After adjustment, the second injection was conducted on day 36  
Figure 5: Effects of different emulsions on chlorinated pollutants in the microcosm tests: (**a**) TCE and (**b**) _cis_-1,2-DCE.  
and TOC concentrations increased in each monitoring well. The CW TOC concentration remained at 1-3 mg/L, suggesting that the study site lacked electron donors, which prevented reductive dechlorination through natural attenuation of TCE. Because OW1 was only 1 m away from the IW, a TOC increase was observed soon after each injection. The TOC concentration in OW1 after the third injection (393 mg/L) was higher than the highest TOC concentration (243 mg/L) observed after the second injection. These results indicated that SOE+FL was effectively adsorbed into soil pores and continued to release electron donors to reach effective reductive dechlorination. In addition, the TOC concentration of 94 mg/L observed at OW1 on day 90 after the SOE+FL injection demonstrated that SOE+FL had at least 90 days of slow-release effect. Because OW2 and OW3 were farther from the IW, their TOC concentrations began to increase on day 90, with the increasing slope greater at OW2 than at OW3. The maximum TOC concentration reached at OW2 and OW3 was 282 mg/L at OW2. On day 160, the TOC concentration at OW2 and OW3 was 144 mg/L. Because the TOC concentration of the IW upstream area was maintained at 200 mg/L or higher, a decrease in OW3's TOC concentration was not observed. These overall results revealed that the SOE+FL transmission reached at least 5 m when a double packer system under 2 bar pressure of injection was used.