esa-sceva/satcom-chunk-collection huggingface dataset:
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 3 Results  
### Animal and Transcriptome Sequencing  
After the Ashmead animals (three females and one male) were maintained with oriental fruit flies, which were fed guava (G), mango (M), and carambola (c), the antenna, head, breast, abdomen, and feet tissues were mixed together for RNA extraction and transcriptome sequencing. After data cleaning, we obtained a total of \(\sim\)622.32 million reads (\(\sim\)69.15 million reads on average) and assembled 24,201 to 34,302 genes using Trinity for all the samples (Table 1). After similar genes/transcripts were clustered and merged, we finally obtained 162,621 Unigenes for the Ashmead transcriptome, with an average length of 1425.14 bp. The N50, GC content, and size of the transcriptome were calculated as 3572, 41.88%, and \(\sim\)231 M, respectively (Table 1). Length distribution analysis showed that 53.05% of the total transcripts were longer than 500 bp and 11,101 transcripts (6.83% of the total transcripts) were longer than 5000 bp (Figure 1A). Last, we used BUSCO to evaluate the completeness of the assembled Unigenes and the results showed 99.6% of the assembled Unigenes were complete. In detail, out of the 255 evaluated BUSCOs in the dataset, 254 were complete, including 228 duplicated and 26 single-copy BUSCOs.
# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 5 Experimental Results and Analysis  
### Model Parameter Debugging  
The AC-YOLO model was used to train and test the insulator multi-defect image dataset; the batch size (Batchsize) was set to 8, the initial learning rate was set to 0.01, the momentum was set to 0.937, and the total number of training rounds (epoch) was set to 300. The Adam optimizer, which can obtain the optimal model weights, was used to optimize the process. The training process used the cosine annealing learning rate decay strategy to accelerate the convergence speed of the model, and the model evaluation index of the training process is shown in Figure 14.  
As shown in Figure 14, the total losses of AC-YOLO were kept low during the training process and had a faster convergence speed. The lowest loss value can be converged to less than 0.03, which is close to 0, and there was no overfitting phenomenon. As shown in Figure 14, the larger the mAP value, the higher the accuracy of detection and the better the network performance. From the figure, it can be seen that the AC-YOLO model gradually stabilized when the number of iterations reached 100, and the detection accuracy (mAP) reached up to 93.4% with the threshold set to 0.5, which is 5.1 percentage points higher than the detection accuracy of the original YOLOv5 model.  
Figure 14: Model evaluation index. (**a**) Total training loss comparison curve. (**b**) mAP0.5 comparison curve.
# FMFN: Fine-Grained Multimodal Fusion Networks for Fake News Detection
## 2 Related Work
### Multimodal Fake News Detection  
Multimodal fake news detection relies on multimodal information, rather than information from one modality of content. The process involves feature extraction and feature fusion. In feature extraction, textual feature extractors can be implemented using Bi-LSTM [20; 21], textCNN [22; 23], or BERT [24], and visual features are typically extracted by CNNs. In feature fusion, there are several typical methods as follows. [PERSON] et al. [4] exploit text content, image, and social context to produce a joint representation. An attention mechanism is leveraged to refine the visual representation. However, the refined visual representation cannot reflect the similarity between the visual representation and the social-textual representation, since the attention values are only calculated from the social-textual representation. [PERSON] et al. [5] are inspired by the idea of adversarial networks and thus propose an event adversarial neural network (EANN), which contains an event discriminator used to identify the event label of news, in addition to the feature extractors and the detector. To learn a more general joint representation, a minimax game is set up between the event discriminator and feature extractors. [PERSON] et al. [6] proposed a multimodal variational autoencoder (MVAE) for fake news detection, which is composed of an encoder, a decoder, and a fake news detector. The encoder first extracts textual features and visual features, which are converted to a sampled multimodal representation. Then, the decoder reconstructs the textual features and visual features from the sampled multimodal representation. Finally, the encoder, the decoder, and the detector are jointly trained to learn a shared representation of multimodal information. Nevertheless, the above three methods [4; 5; 6] obtain a joint representation by simply concatenating unimodal features without considering the dependencies between modalities. [PERSON] et al. [7] leverage an attention mechanism to fuse a number of word embeddings and one image embedding to obtain fused features, and further extract key features from the fuse features as a joint representation. Although the fusion considers inter-modality relations, it is not fine-grained enough.
## 5 Performance Metrics
### Measurements  
Four observations are used in GNSS positioning: pseudorange, carrier phase, Doppler shift, and SNR. The last two are provided straightforwardly in the GNSS event of the Android API; however, the pseudoranges and the carrier-phase data require some post-processing to be usable in a typical GNSS algorithm.  
As described in [44], the pseudorange needs to be constructed based on the received and transmitted times given in the event. Several checks of the tracking state should be performed for each constellation to ensure correct measurements. In our analysis, we used the instructions provided by [44], but we only checked for adequate code lock and Time of Week (TOW) decoded. The carrier phases in Android devices are provided as \"Accumulated Delta Ranges\", which correspond to the regular carrier-phase measurements multiplied by the wavelength of the GNSS signal, thus in meter units and not in cycle units.  
For both the pseudoranges and the carrier phases, we performed a double time difference on the measurements, similarly to [10]. The double time difference allows for discarding the residual drift induced by the velocity changes, leaving only the acceleration changes which are assumed too small for both satellites and users in our study cases. It also reduces or cancels out some errors present in the GNSS measurements, such as atmospheric error or satellite orbit/clock error, as two consecutive measurements at a regular small rate (here 1 Hz) should be tainted by similar errors. Consequently, the error left can be assumed to be related mostly to the receiver tracking noise. While other papers have used different error estimations for measurements, such as the Code-Minus-Carrier combination [11; 12], the double time difference method showed adequate results for a preliminary analysis. For Doppler measurements, only one time difference is needed as the measurement is already a velocity/rate estimation. For C/N0, it is more interesting to study its value range; thus, there is no difference in performance.  
The results are provided as logged, with minimum filtering of the data for a more realistic analysis. However, to minimize the number of outliers and end up with biased statistics, we filtered some of the measurements based on a threshold, as a basic navigation filter would do. The threshold values were chosen empirically, based on the standard deviation and expected precision of each type of data. Consequently, the thresholds were set as follows: pseudorange \(\Rightarrow\) 300 m, Doppler \(\Rightarrow\) 30 m and phase \(\Rightarrow\) 30 m.  
For each scenario, a statistic table for each GNSS measurement is provided, along with the percentage of measurements included/removed from the filter. For pseudoranges, Doppler and phase errors, we used box plot graphs to represent the different statistics.  
For Carrier-to-Noise ratio (C/N0), we choose to represent the data in \"violin plots\", which are mostly similar to box plots but provide additional information on the data distribution. It is specifically interesting to study the L1/L5 differences and the range of CN0 values.
## 4 Conclusions  
1. Under the same numerical simulation conditions, compared with the conventional straight cone nozzle, the velocity increase rate of the BSCN could reach 13.45%. This can provide valuable scientific references for the hydraulic mining of high-pressure water jets in oil shale drilling.
2. CFD numerical simulation results showed that the circular groove in the inlet and outlet sections could effectively reduce the fluid resistance. The drag reduction effect of the circular groove arranged in the outlet section of BSCN was better than that arranged in the inlet section.
3. Based on the results of genetic algorithm optimization analysis and CFD numerical simulation, the optimal structural parameters of the BSCN were as follows: Fixed values were inlet diameter D = 15 mm, inlet length L1 = 20 mm, outlet diameter d = 4 mm, L3/d = 3, and contraction angle \(\theta\) = 30\({}^{\circ}\). Variable values for the inlet section of L1, namely, the groove width, slot pitch, and groove depth, were 3.9 mm, 5.2 mm, and 5.5 mm, respectively, and the number of circular grooves was 2. In the outlet section of L3, the groove width, slot pitch, and groove depth were 2.25 mm, 3 mm, and 5.5 mm, respectively, and the number of circular grooves was 2.  
Future work is planned to manufacture the real object of the BSCN and conduct a series of experiments. At the same time, in addition to the bionic unit used in this study, we can also try to find different types of bionic units to explore nozzles with better hydraulic performance.  
Conceptualization, J.Z. and Y.L.; Methodology, J.Z.; Software, J.Z. and Z.D.; Formal analysis, J.Z., Y.L. and X.Q.; Writing--original draft preparation, J.Z.; Writing--review and editing, X.X.; Funding acquisition, Y.L., X.Q. and J.L.; Visualization, J.Z., Y.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the Natural Science Foundation of China (No. 42002307), Fundamental Research Funds for the Central Universities, China (No. 2652019070).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the editor and the reviewers for their helpful comments.  
The authors declare no conflict of interest.
Sensors_**2012**, _12_, 4352-4380; doi:10.3390/s120404352  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Reverse Localization Scheme for Underwater Acoustic Sensor Networks**  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Computer Systems and Communication, Universiti Teknologi Malaysia, UTM Skudai 81310, Johor, Malaysia; E-Mails: [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] (A.S.I.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 16 February 2012; in revised form: 18 March 2012 / Accepted: 19 March 2012 / Published: 29 March 2012_  
**Abstract:** Underwater Wireless Sensor Networks (UWSNs) provide new opportunities to observe and predict the behavior of aquatic environments. In some applications like target tracking or disaster prevention, sensed data is meaningless without location information. In this paper, we propose a novel 3D centralized, localization scheme for mobile underwater wireless sensor network, named Reverse Localization Scheme or RLS in short. RLS is an event-driven localization method triggered by detector sensors for launching localization process. RLS is suitable for surveillance applications that require very fast reactions to events and could report the location of the occurrence. In this method, mobile sensor nodes report the event toward the surface anchors as soon as they detect it. They do not require waiting to receive location information from anchors. Simulation results confirm that the proposed scheme improves the energy efficiency and reduces significantly localization response time with a proper level of accuracy in terms of mobility model of water currents. Major contributions of this method lie on reducing the numbers of message exchange for localization, saving the energy and decreasing the average localization response time.  
**Keywords:** localization; underwater wireless sensor networks; reverse localization scheme
# Construction of a Dynamic Diagnostic Approach for a Fuzzy-Interval Petri Network
## 3 Illustration of the Modeling Approach
### Fuzzy Interval Petri Grating Modeling  
In order to solve the issue of concentration variation and thus the range constraints on this concentration [17], we must therefore take into account the other three parameters. The following equation is considered to give the relationship between the C concentration and the ingredient masses (for each lot produced):  
\[C=\frac{m_{1}+m_{2}}{Ve} \tag{7}\]  
where C is the concentration of the final product in kg/mm\({}^{3}\);  
m\({}_{1}\) is the mass of ingredient 1 in kg;  
m\({}_{2}\) is the mass of ingredient 2 in kg;  
Ve volume of water in mm\({}^{3}\).  
m\({}_{1}\) = C1 \(*\) V1 and M2 = C2 \(*\) V2.  
where Ci and Vi are the product Qi (i = 1, 2) concentrations (in kg/mm\({}^{3}\)) and volumes (in mm\({}^{3}\)), respectively.  
This connection can be expressed as follows:  
\[C=\frac{C_{1}h_{1}S+C_{2}h_{2}S}{Ve} \tag{8}\]  
The following are the parameters' interval restrictions:  
C [Cmin, Cmax: The product will be rejected if the concentration C value falls outside this range.  
C1 [C1 min, C1 max].  
C2 [C2 min, C2 max].  
Ve represents the amount of water in mm\({}^{3}\): \(V\in\) [Ve min, Ve max].  
S is the tank section measured in mm\({}^{2}\).  
\[C_{\min}\leq\frac{C_{1}h_{1}S+C_{2}h_{2}S}{Ve}\leq C_{\max} \tag{9}\]The tolerances for each parameter must be determined before building the model. To do this, we suggest a statistical approach based on precise values obtained from statistical production data. The procedure for using this method of computation and the presumptions taken into account are described in the next subsection.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 3 PM2M Overlay Network over PRACH  
### Earlier Work on Overlays in LTE  
In [18], an IEEE 802.15.4-based VANET was proposed for implementing the VANET Control Channel (CCH) in urban areas. IEEE 802.15.4 has a low power consumption feature, which is an advantage over IEEE 802.11p's high power consumption. In [19; 20; 21], a comparative study between IEEE 802.11p and LTE aimed to evaluate their suitability for different vehicular applications and finding, using analytical and simulation modelling, that the latter has a distinct advantage over IEEE 802.11p-based VANET for transmission of safety messages was shown [22]. Other studies have shown that LTE supports mobility and provides higher network capacity compared with IEEE 802.11p [20]. However, LTE was found not to provide sufficient reliability in terms of safety messages transmitted over PRACH, as the network can easily become overloaded [21; 23]. The main culprit is the four-way handshake, which for small safety messages limits the capacity and increases the latency [10]. Additionally, the data rate for VANET safety messages remains constant regardless of the distance to eNodeB and the closeness of congestion, while in IEEE 802.11p, it can be adaptively adjusted according to the channel quality [24].  
Before the introduction of the IEEE 802.15.6 standard, IEEE 802.15.4 and Bluetooth were considered as feasible physical and MAC layer protocols for healthcare applications. In [25], the authors did a comparative study between the IEEE 802.15.4 and IEEE 802.15.6-based MAC protocols for healthcare systems. The study showed that IEEE 802.15.4 cannot reliably transmit real-time medical data because it does not support high data rate applications and priority among traffic classes. The simulation study showed that IEEE 802.15.4 is suitable only for applications that require a data rate below 40 kbps, while applications requiring higher data rates should use the IEEE 802.15.6-based network. In [26], it was shown that there is no interference when WiMAX or LTE are integrated with IEEE 802.15.6. To enlarge the radio coverage, the authors in [27] integrated IEEE 802.15.6 with LTE because they showed that existing architectures were not suitable for the scenarios of high mobility due to the channel quality fluctuation.  
The performance of a healthcare system through interconnecting IEEE 802.15.6 with IEEE 802.11e-based WLAN for a medical information system was studied in [28]. The proposed architecture used the RTS/CTS mechanism for accessing the medium in the WLAN, which caused overload in the network and coexistence issues. That work also did not consider the presence of backoff error due to the interference in the physical layer while accessing the medium to transmit the packets. In [26], it was shown that there is no interference when WiMax or LTE are integrated with the IEEE 802.15.6 network. It was proposed in [27] that LTE supports reliable data transmission with high data rates for real-time health messages over large coverage areas.
## 4 Energy Balance
The lower LE rates observed in the dry season can be attributed to reduced water availability for plants, resulting from lower rainfall rates [10; 57; 58]. Table 2 shows that, in most cases, the residual values are higher than the fluxes. Regarding the behavior of H, substantially higher values were observed during the dry season (88.8 Wm\({}^{-2}\) in 2009 and 117.8 Wm\({}^{-2}\) in 2012) compared to the wet season (41.8 Wm\({}^{-2}\) in 2009 and 53.6 Wm\({}^{-2}\) in 2012). It is noteworthy that H values during the year of intense drought surpassed those observed during the year of intense rainfall. This distinction is due to the influence of biomass in regulating the energy exchanges between the atmosphere and the surface in the Caatinga. The ecosystem experiences alternating periods of high and low biomass, which are entirely controlled by rainfall [20; 59]. Another noteworthy result shown in Table 2 refers to the residual of the energy balance, R, which accounted for 40% of Rn in 2009 and approximately 30% of Rn in 2012. In other observational studies, this residual is approximately 25% [10; 19]. Energy balance calculations in tropical forests have exhibited higher residues, with values fluctuating between 16% and 25% [8]; in FLUXNET towers, the range is wider, ranging from 4% to 36% [60]. The SITE model has consistently demonstrated a tendency to underestimate H values, particularly during the dry season, as reported by [32] in the Caatinga and [34] in the Amazon. Caatinga plants close their stomata in response to low water availability, which effectively reduces water transfer to the atmosphere, resulting in decreased evapotranspiration. Consequently, the energy consumption required to heat the air (H) increases [61]. According to the literature, the annual proportion of Rn converted into H in the Caatinga varies from approximately 40% to 60% [10; 19; 22]. Similar ranges of values have also been observed by [62] in the semiarid region of the Loess Plateau in China and by [61] in the Mongolian Plateau. In the present study, this percentage was less than 40% during the dry season in both years, indicating values lower than those reported in the literature. This finding strongly suggests that the SITE model tends to underestimate H.  
According to [56], the characteristics of Caatinga vegetation directly influence the daily cycle of thermohygrometric variables and the variables that compose the energy balance. In addition, atmospheric conditions also play a decisive role in the variations of H and LE values at different times of the year in the Caatinga.
## 3 Materials and Methodology
### Development of a Modeling Framework to Estimate Traffic Volume
It is important to acknowledge that the model currently demonstrates moderate performance, with an adjusted \(R^{2}\) of 0.747-0.768 and \(MAP\) of \(\sim\)37%, primarily due to the focus on selected topological parameters while excluding spatial variables that influence traffic flow dynamics. Similar findings were observed in the study by [PERSON] and [PERSON] [35] on the Hong Kong street network and AADT datasets, which achieved an \(R^{2}\) of 0.8 using street-based topological representations. Their results also highlight the challenges of capturing all influencing factors, reinforcing that moderate accuracy metrics, as in our study, can still provide meaningful insights into traffic flow patterns when focusing on topological properties. Similarly, [PERSON] et al. [45], who studied London streets and tweet location data, found that natural streets were the best representation for predicting traffic flow. Their findings suggest that traffic predictability arises from the scaling hierarchy of streets--ranging from many least-connected to a few most-connected--rather than individual travel behavior or spatial factors. This alignment with established research underscores the validity of our approach and model performance, demonstrating that despite moderate performance metrics, topological assessments remain a critical factor in understanding traffic flow dynamics and provide valuable first-order approximations, particularly in data-scar contexts. However, as highlighted by [PERSON] and [PERSON] [56] and [PERSON] and [PERSON] [57], the omission of essential travel demand modeling parameters can lead to biases in the estimation of critical variables. For example, excluding spatial factors has been shown to inflate the impact of AADT, overstating minor road AADT by 40% and major road AADT by 14%. These findings emphasize that relying solely on topological parameters may provide an incomplete representation of traffic volume dynamics, potentially skewing predictions and limiting the framework's applicability. Therefore, we strongly suggest incorporating spatial variables in the future development of the model to further enhance accuracy and robustness.