esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
###### Abstract  
In recent years, online ride-hailing has become an indispensable part of residents' travel mode. Therefore, the prediction of online ride-hailing travel demand has become extremely important. In the era of big data, the application of big data in the field of transportation is becoming more extensive. Based on the open data of ride-hailing trips in Haikou City, Hainan Province, provided by the Didi platform and combined with the rainfall data of Haikou City, this paper proposes a gate recurrent unit (GRU) model considering rainfall factors and rest days factors for short-term trip demand prediction. The K-fold cross-validation method is adopted to adjust the parameters of the model to the optimal ones through the training set. The improved GRU model is compared with the original GRU model and other classic models, and the model is evaluated by root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and \(R2\) score indexes. Finally, it is proved that the GRU model proposed in this paper greatly improves the prediction accuracy of short-term online ride-hailing travel demand.  
GRU; rainfall intensity; multiple factors; online ride-hailing travel demand prediction; big data 202214083390/su1407408333
## 5 Results and Discussion
### Grad-CAM Analysis Results  
Although YOLOv10 with PVT and shearing augmentation achieved the highest accuracy among all the tested configurations, contrast augmentation generally produced a higher average accuracy across the various architectures. In other words, shearing augmentation only improved performance for certain architectures, whereas contrast augmentation offered more consistent benefits. To further investigate these outcomes, a Gradient-weighted Class Activation Mapping (Grad-CAM) analysis was conducted.  
Figure 19: Validation results for each method.  
Figure 20: Results of testing validation and test sets in best model.  
Table 9 reveals that, among the Faster R-CNN models, contrast augmentation systematically outperformed shearing augmentation, with the Faster R-CNN with ResNet-101 showing a roughly five-point gap in both mean detection score (82.13 vs. 77.18) and coherence (68.97 vs. 64.12), while Faster R-CNN with ResNet-152 and MobileNetV3 exhibited smaller but consistent margins favoring contrast augmentation. In the YOLOv10 family, shearing augmentation emerged as the clear winner when paired with PVT, achieving 85.12 vs. 84.55 in mean detection score and 77.65 vs. 74.03 in coherence, whereas contrast augmentation remained better or about equal in YOLOv10 ViT and Swin Transformer. Overall, contrast augmentation provided higher average results across for detection accuracy and Grad-CAM alignment, indicating its greater consistency, while shearing augmentation's benefits appeared to be architecture-specific--most notably in YOLOv10 with PVT.  
Figure 21 illustrates example Grad-CAM heatmaps for different architectures using contrast and shearing augmentation. In Figure 21a, the Faster R-CNN using ResNet-101 and MobileNetV3 with contrast augmentation produced heatmaps that closely aligned with the building region, whereas ResNet-152 exhibited weaker alignment. With shearing augmentation, MobileNetV3 was the only one to demonstrate a clear increase in alignment, while the other architectures remain largely unchanged.  
In Figure 21b, YOLOv10 with PVT maintained a well-aligned heatmap around the building, but Swin Transformer and ViT showed reduced alignment using shearing augmentation. In contrast, contrast augmentation yielded similarly accurate building alignments for all three YOLOv10 backbones, suggesting that it provides a more universally effective approach than shearing augmentation.  
Figure 21: Examples of visual results from Grad-CAM analysis.
## 5 Data Analysis and Results
### Analysis of PLS-SEM Results
#### 5.1.1 Measurement Model Assessment  
SEM is composed of two-step process measurement and structural model assessments. The measurement model assessment is the first step of the model assessment, to ensure that every construct is measured correctly. Reliability and validity are the primary requirement for measurement model assessment to measure the strength of the suggested model. According to [PERSON], [PERSON] [116], \"for internal consistency of the measurement model composite reliability and Cronbach's \(\alpha\) were applied.\" The validity of the constructs was evaluated by applying \"average variance extracted (AVE)\" and \"cross-factor loadings.\" The reliability and validity results of the specified constructs were summarized in Table 5. As recommended by [PERSON], [PERSON] [116] for the reliability of the constructs, the value above 0.7 is a satisfactory score for the internal consistency of the survey. For all defined constructs, the results showed that composite reliability and Cronbach's \(\alpha\) are above the satisfactory value of 0.7, which surpasses the suggested score, except CCA5 and VS5. Furthermore, the minimum score of 0.50 is considered to be an acceptable value of AVE for each construct [116]. As depicted in Table 5, the validity of scale items was above 0.5, which exceeded the threshold value. The next step after convergent validity verification is discriminant validity. The \"discriminant validity\" was evaluated by analyzing correlations between the constructs [190]. As revealed in Table 6, the Square root of AVE for defined constructs had a higher value in comparison to correlation co-efficient with other latent constructs. Therefore, \"convergent and discriminant validity\" was approved in the assessment of the measurement model [190]. Consequently, based on the above assessments, the validity and reliability of the constructs for the measurement model have been accepted and meet the recommended values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Constructs** & **Items** & **OL (0-0.7)** & **CA (>0.6)** & **CR (0-0.7)** & **AVE (>0.5)** \\ \hline \multirow{4}{*}{CC Adoption} & CCA1 & 0.789 & & & \\  & CCA2 & 0.831 & & & \\ \cline{1-1}  & CCA3 & 0.843 & 0.842 & 0.888 & 0.615 \\ \cline{1-1}  & CCA4 & 0.77 & & & \\ \cline{1-1}  & CCA5 & 0.677 & & & \\ \hline \multirow{4}{*}{Compatibility} & CT1 & 0.804 & & & \\ \cline{1-1}  & CT2 & 0.831 & & & \\ \cline{1-1}  & CT3 & 0.818 & & 0.842 & 0.887 & 0.612 \\ \cline{1-1}  & CT4 & 0.749 & & & \\ \cline{1-1}  & CT5 & 0.704 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Constructs’ reliability and validity.
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### End Host-Based SHIM Layer Approach  
The Level 3 Multihoming Shim Protocol for IPv6 (Shim6) [8] is a host-based shim layer multihoming solution. Shim6 uses an Upper-Layer Identifier (ULID) and a Locator in its addressing scheme. So, as for LISP, there is overloading of the IP address semantics. While Shim6 was designed for multihoming, by dynamically adding and removing ULID-Locator bindings, host mobility can be realised [3]. Also proposed is a different approach called SEMO6: SEamless MObility using Shim6 [9]. However, the implementation and deployment capability of those proposals are not clear, and they were not progressed within the IETF. Further work on Shim6 did demonstrate how site mobility could be supported, but it did not combine mobility multihoming for individual hosts [10].  
Dynamic Internet Mobility for End Systems (DIME) [11] is based on Shim6. While the current version only supports mobility, there is a proposal that a key component of the approach, the Stack-Trans, can be further developed to support multihoming. However, the Stack-Trans is an additional network entity, and it uses a network address translation (NAT) approach to map between a Host Identifier (HI) for a host and an IP address, which acts as a Locator. An implementation in Linux of the mobility part was documented, but there are no full proposals or implementations available for supporting both multihoming and mobility at the time of writing.  
Our approach does not require network address translation, is implemented in Linux, and has been used and tested with commercial equipment to show deployment capability.
## 3 Methodology
### Validation and Comparison  
We used the stratified random sampling approach to acquire validation samples. First, the MODIS land cover products [38] were used to partition the study area into several strata (forest, grassland, wetland, cropland, built-up and barren land, and water body). Second, random points were generated in each stratum, and we made areas of interest (AOIs) as square buffers for the points (100-m \(\times\) 100-m), as shown in Figure 8b. Third, we checked each AOI with the VHR images and selected and labeled the relatively pure land cover AOIs by referencing both the VHR images from Google Earth and auxiliary information (if any). The AOIs without clear land cover information were excluded. For AOIs with available auxiliary information, such as field photos, street views, or paddy field POIs, the auxiliary information was used to better clarify land cover types. For AOIs without any auxiliary information, the field shape, size, temporal evolution (using Google Earth's time machine), and vegetation index time series were considered to label the land cover information. Finally, a total of 482 AOIs were generated to validate the resultant paddy rice and non-paddy rice map, including forests (62 AOIs), grasslands (150), wetlands (100), croplands (100), built-up and barren lands (70), and water bodies (50). A confusion matrix for the paddy rice map was calculated to evaluate the accuracy of the results.  
Besides the validation, we also compared three pixel-based classification results obtained from three different data sources (Landsat-8, Sentinel-2, and MODIS) along with our object-based results in some randomly selected case regions. The comparison checks the intensity of salt-and-pepper effects that result from data of different spatial resolutions, as well as the advantages of object-based classification over pixel-based approaches to alleviate such effects when moderately high-resolution data are used. For the pixel-based classification, the random forest algorithm [39] available in GEE platform is used, whose number of tree is set as 500 and other parameters as the default. It is often used as a benchmark algorithm because good accuracy could be achieved with a limited number of training samples and default parameter settings [40].  
Figure 7: Top and bottom panels show the time profiles for the optical and microwave signals over an exemplar object, respectively. In the top panel, blue dots denote valid observation (not flagged as cloud or cloud shadow in QA bands) while orange crosses denote invalid observation (flagged as cloud or cloud shadow). In the bottom panel, all observations are valid because SAR data are independent from weather and illumination conditions, thus they marked as blue dots. The corresponding observation images in the time profile are given in the middle two rows, displayed in true colors for Sentinel-2 and a grey image for Sentinel-1 VH.
# Failure Mechanism of Multilayer Ceramic Capacitors under Transient High Impact
###### Abstract  
In recent years, penetrating weapons have been used more and more to attack increasingly hard targets; therefore, the impact of such a penetrating process has increased to an extremely high level. As an important component of a fuze, the reliability of the ceramic capacitor in high-impact environments is key for the normal working of the fuze. In this paper, we found that a high-impact causes parameter drift of the multilayer ceramic capacitor (MLCC), which further causes the fuze to misfire. This paper mainly studies the internal mechanism of the MLCC's parameter drift during high impact. Firstly, transient physical phenomena, such as capacitance fluctuation and the leakage current increase of the ceramic capacitor under a high acceleration impact, were studied experimentally by a Machete hammer, revealing the relationship between the capacitance change, leakage current change, and acceleration under different working conditions. Secondly, a mechanical model of the ceramic capacitor is established to simulate the change in capacitance value, which shows that the main factor of the capacitance change is the deformation-derived change in the facing area between the electrodes. Lastly, an equivalent circuit model is established to simulate the change in the leakage current, which shows that the main factor of the leakage current change is the piezoelectric resistance of the ceramic dielectric.  
multilayer ceramic capacitor; high impact; failure mechanism; leakage current; mechanical model +
Footnote †: journal: Applied Sciences
## 5 Simulation Results and Discussions
At a high SNR regime, an irreducible MSE floor introduced by frequency-offset induced ICI is observed in the frequency estimation schemes. Because of the non-symmetrical distribution of CPs, the MSE performance of the conventional scheme using full CPs exhibits a severe error floor, which decreases with increasing \(N_{R}\), compared to that of the proposed scheme. Therefore, the pilot subset selection serves to remove a bias resulting from the use of randomly distributed CPs, and the MSE can be minimized by the proper selection of pilot subset. As expected in Equations (36) and (37), the detection ability can be enhanced with \(N_{R}\). Such physical behaviors is attributed to the fact that the use of multiple receive antennas increases the number of statistically independent observation samples used for frequency-offset estimation. More importantly, the performance gap between the conventional and proposed scheme becomes remarkable as \(N_{R}\) increases. Figure 2: MSE of the frequency-offset estimators in the ATSC 8 k mode when \(\varepsilon=0.05\) and \(\xi=50\) ppm: (**a**) CFO estimation; (**b**) SFO estimation.
## 3 Preliminaries, Models, and Definitions  
### Preliminaries  
\(N\) nodes are thrown in the target area (rectangular or cubic) uniformly and randomly. Each node then moves to the determined position with the aid of an autonomous underwater vehicle (AUVs), and each node is fixed at the position by an anchor after its deployment position is calculated by the deployment algorithm. In addition, sensor nodes communicate with one another through acoustic channels and maintain connectivity with the sink node via one-hop or multi-hop paths. The following assumptions are made:  
1. A node adopts the Boolean sensing model [26], and the sensing radius of the node is fixed.
2. All nodes have the same states, including initial energy, sensing radius, and communication radius, before the node deployment. Moreover, the communication radius of the node can be adjustedaccording to the demand of the algorithm but should not exceed the maximum communication radius, \(R_{c}^{\max}\) which is determined by the physical device during the node deployment.
3. With the similar assumptions in [24] as inspiration, the sink node is fixed at the center of the water face. By contrast, the other nodes can move freely in all directions, and their real-time locations can be determined using a localization algorithm or global positioning satellite devices during the node deployment.
# Brewing with 10% and 20% Malted Lentils--Trials on Laboratory and Pilot Scales
## Results and Discussion
### Pilot-Scale Brewing Trial
#### 3.2.3. Lautering Time  
Filtration of the produced word samples was completed in less than 95 min for all the mashes with filtration times of 93.6 \(\pm\) 1.2 (BM), 81.7 \(\pm\) 1.5 (BM + 10% LM) and 77.3 \(\pm\) 2.1 min (BM + 20% LM). As previously observed at the laboratory scale, filtration with the addition of lentil malt decreased the time of this crucial and normally time-consuming process step. The addition of 10% lentil malt reduced the filtration time by approximately 12%; with the addition of 20% lentil malt it was reduced by 17%. This could be explained by the higher glucan content in barley malt (see Section 3.1.3). With the decrease in the amount of glucan, the viscosity of the mash decreases and filtration is faster. [PERSON] et al. [43] also showed a lower viscosity for Congress word made with 30% malted lentils compared to all-barley malt wort. The results suggest that lautering time may be reduced by approximately 15 min when adding 20% malted lentils. The industry standard for lautering time is around 1.5 h and it is often the bottleneck in the brewhouse. Saving 15 min per brew could potentially add the option of up to two additional brews per day (assuming 10 brews/day on average).
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Comparison Against Related Research
#### 6.8.3 Used Classifiers  
Most of the existing approaches on finding a mobile continuous user authentication system implement traditional classification models like k-NN, SVM, or similar. Examples would be [26] or [27]. Our system in comparison relies on the HMM, which in general is considered a statistical model, where through the forward algorithm, the likelihood of one input sequence belonging to the created model is calculated. The HMM classifier is also employed and researched in [28] (where the dataset from [26] was used), as well as [29], whose authors also consider accelerometer and gyroscope data.  
HMM Advantages  
The main advantage of relying on them instead of the previously mentioned classifiers is that when training a HMM, only data of the authenticated user is necessary. This means that a user who wants to train such a system can do it offline without the need for remote data. This ensures that the user's data is not unnecessarily transmitted over the network as well. Furthermore, the HMM is capable of online training. This means that, as soon as new touch gestures from the authenticated user are detected, the HMM can retrain itself with those, to better model the user's input behavior. Therefore, the model trains the user's ever-adapting input habits.  
HMM Disadvantages  
However, it has to be noted that using HMM as classifiers for these tasks does also come with several drawbacks. For example, the internal parameters, namely the hidden states and the mixtures used for configuring the classifier are not known to us. This is because for such complex sequences of data finding meaningful hidden states is a hard task, as opposed to a simple model, which for example does use the weather as hidden states, and models it as hot, mild, and cold. For finding the best number of states we are therefore relying on the Baum-Welch algorithm, where, from the available data, a model is fitted. This, however, does also introduce the problem that most of the time only local optimums are found. Furthermore, the model does generally tend to overfit when increasing the number of hidden states and mixtures. In our implementation, we use the configuration with the least amount of parameters, which does perform well enough, to select the number of hidden states and mixtures.
# Deep Reinforcement Learning for UAV-Based SDWSN Data Collection
###### Abstract
* Optimized UAV Path: We propose an optimized UAV flight path using Deep Reinforcement Learning (DRL) techniques, specifically based on Deep Q-Learning. This approach addresses the NP-hard nature of the formulated approximation problem. Moreover, integrating this learned UAV path with SDWSN enhances the overall system's efficiency and flexibility. * Dual Objective: Our primary objectives are twofold: minimizing the total energy consumption of the UAV system during each data collection round and improving the packet delivery rate to the UAV receiver. Achieving this balance ensures an effective and sustainable system. The remainder of this paper is structured as follows: Section 2 discusses the related work. Sections 3 and 4 present the system model and RL proposed algorithm. Section 5 evaluates the model. Finally, in Section 6, the conclusion of the work is discussed.
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 7 Conclusions
Therefore, this approach facilitated the process of corpus gathering for this specific task as fewer data were needed for obtaining a high-enough-quality voice-cloning model. Regarding future work, these automatic evaluations can be complemented by means of a subjective evaluation based on the real MOS using human evaluators. Moreover, the acquisition of novel metrics that leverage prosodic information could complement the characterisation of heterogeneous data in order to automatically identify the subsets exhibitinghigher variability within a specific voice-cloning dataset, accompanied by corresponding ablations for assessing the impact of their heterogeneity in the final quality. Finally, these evaluations could be performed on different voice-cloning frameworks, featuring other TTS paradigms and extending it to voice-conversion approaches, in order to check whether these results can be extrapolated to the broad spectrum of architectures. Conceptualisation, A.G.-D. and A.A.; methodology, A.G.-D. and A.A.; software, A.G.-D.; validation, A.G.-D. and A.A.; formal analysis, A.G.-D. and A.A.; investigation, A.G.-D. and A.A.; resources, A.G.-D. and A.A.; data curation, A.G.-D.; writing--original draft preparation, A.G.-D.; writing--review and editing, A.G.-D. and A.A.; visualisation, A.G.-D. and A.A.; supervision, A.A.; project administration, A.A. All authors have read and agreed to the published version of the manuscript. This work has been partially funded by the Spanish Public Business Entity Red.es, attached to the Ministry of Economic Affairs and Digital Transformation, under the C005/21-ED programme and in the context of the IANA project with reference 2021/C005/00152165. Not applicable. The two public corpora presented in this study are openly available at [[https://github.com/carffm01/my-speech-datasets](https://github.com/carffm01/my-speech-datasets)]([https://github.com/carffm01/my-speech-datasets](https://github.com/carffm01/my-speech-datasets)) (accessed on 4 July 2023) and [[https://www.openslr.org/109/](https://www.openslr.org/109/)]([https://www.openslr.org/109/](https://www.openslr.org/109/)) [36] (accessed on 4 July 2023). Due to the particularities involving the personality of the corpus _XRay_, the data will not be shared. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} CNN & Convolutional Neural Network \\ GAN & Generative Adversarial Network \\ GMM & Gaussian Mixture Model \\ HMM & Hidden Markov Model \\ HQ & High Quality; refers to speakers _Tux_ and _Hi-Fi TTS_ \\ MFCC & Mel Frequency Cepstral Coefficient \\ MOS & Mean Opinion Score \\ MFA & Montreal Forced Aligner \\ NISQA & Non-Intrusive Speech Quality Assessment \\ SNR & Signal-to-Noise Ratio \\ TTS & Text-To-Speech \\ VAD & Voice Activity Detection \\ \end{tabular}