esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art  
The analysis through the pedestrian models has developed considerably in recent years. In general, the microscopic approach offers a more detailed manners evaluation and can be classified into two categories: discrete and continuous models.  
The first type includes the discrete selection model, the reticular gas model, the cellular automaton model in which space is discretized to approximate the real people movement. Different models are described in literature, considering discrete selection model [10], reticular gas model [11] and cellular automaton model [12].  
Instead, continuous models are based on differential equations that describe dynamic movement in space. In the initial phase, a magnetic force model describes the pedestrian modes in an area and it was developed by borrowing an equation of motion used for magnetic fields [13]. In this way, the social force model applied to the evacuation analysis has been developed [14]. The force-based model makes it possible not only to accurately describe the dynamic movement in space but also to reproduce the phenomenon relate to the global results such as lane formations [15]. Although many studies focus on the general mechanism of human actions, few studies are focused on the application of the social force model to the pedestrian action in restricted spaces.  
The characteristics of the human operation must be evaluated considering the small size of the bridge on which each person must move and in cases of evacuation or in cases of maintenance must also consider the variability of operations and therefore the speed and precedence assumed compared to nearby people during the phases of movement. The main contribution of this study is the development of a microscopic model based on the theory of social force, which allows considering the characteristics of pedestrian ways of motion on an ancient footbridge. Moreover, an estimation approach is proposed to calibrate the model of social force based on real trajectory data. Model validation is conducted to confirm pedestrian performance.  
The model of social strength, like all models, is only a simplified representation of reality. In accordance with the calibration step, the parameters are tuned in such a way that the intended pedestrian manners match the actual action as close as possible to the scenario of interest. The first studies considered only the properties of the infrastructure (such as the width of the sidewalk) to characterize the level of service in the structures for walking. More recently, not only did the research consider the infrastructure characteristics, but they also took into account the properties of the vulnerable users movement (such as pedestrian density, travel time, queue length, etc.) to get a better idea of the quality of the offered service. Therefore pedestrian Level of Service LOS is defined as a general measure of the operating conditions on a given itinerary.
## 6 Conclusions  
Preliminary tests were performed for all reinforcement layer configurations. From the density test, it was found that the number of reinforcement layers did not directly affect the density of the finished composite or the absorbency of the used fabric. The material was evenly saturated with the resin/hardener mixture for all test runs. The use of reinforcement increases the hardness of the composite in the test case by about 17% relative to the pure matrix, regardless of the number of layers used. With the addition of another layer of reinforcement to the two-ply composite, the following increases appeared: the average impact strength (by about 20% between successive configurations), the average bending stress (when increasing the number of reinforcement layers by 50% and 33%, respectively), and the value of Young's modulus (by about 35% between successive configurations). The average tensile stress value increases by approximately 37% with the addition of a third layer of reinforcement. On the other hand, there is not much difference between the three-layer and four-layer configurations in the values of the determined stress obtained. In view of the above, specific tests were carried out for the three-layer sheets, which had the best strength-to-density ratio. Based on these tests, it was found that the weight of the samples increased by an average of about 8 percent after immersion in water. After drying, the samples quickly give up the absorbed water.  
Ageing tests, determining the material's resistance to six months of use in a temperate environment, showed that the strength properties increase under exposure to sunlight. For example, the average bending stress increased by about 23% and the average tensile stress by about 4%. UVB radiation, on the other hand, causes a strong degradation of the matrix and a significant reduction in the strength properties of the composite (average bending stress decreased by about 69% and average tensile stress decreased by about 29% relative to unaged samples).  
The multilayer composite material developed in this work can be successfully used for the skin of a racing car. It is an excellent alternative to the current commercially used materials of this type. Appropriate selection of components and determination of their share in the finished composite allow virtually unlimited possibilities for shaping its resultant properties. The research carried out in this study is a valuable basis for further considerations in the search for new material solutions characterised by the assumed set of functional properties.  
Methodology, M.S.; Validation, M.S.; Formal analysis, M.S.; Investigation, E.K. and M.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy. The authors declare no conflict of interest.
## 6 Discussion  
The proposed Chrono-ECAPA-TDNN (CET) model has demonstrated significant improvements in speaker identification performance, particularly in the challenging environment of air-ground communication. The experimental results show that CET effectively addresses the issues of complex noise and short speech, two common challenges in this domain. The incorporation of Bi-LSTM modules and multi-head attention mechanisms in the Chrono Block module has proven to be highly beneficial in capturing both global and local speaker features, as evidenced by the model's reduced EER and increased accuracy in comparison to baseline models such as ResNet34, ECAPA-TDNN, and MFA-Conformer.  
One of the most notable contributions of this work is the Sub-center AAM-Softmax loss function, which significantly enhanced the model's classification ability by improving intra-class feature compactness and inter-class separation. This novel loss function, when combined with the data augmentation strategies, helped the model perform well even in noisy environments with short speech segments. In particular, the data augmentation techniques--such as speed perturbation, spectral masking, and noise suppression--playeda crucial role in boosting the model's robustness, making it more adaptable to real-world scenarios where speech quality can vary greatly.  
The results from the cross-domain testing further validate the generalization capability of CET. By achieving superior performance across multiple datasets, including VoxCeleb2, TIMIT, LibriSpeech, VoxCeleb1, and CN-Celeb, CET has shown that it is not only effective for air-ground communication but can also be applied to a broad range of speaker identification tasks in various domains.
# Drilling Condition Identification Method for Imbalanced Datasets
## 4 BiLSTM-GRU-Based Classification Model for Drilling Conditions
### BiLSTM-GRU Model  
The structure of the BiLSTM-GRU concatenated model is shown in Figure 7. The model's input is a three-dimensional tensor with the shape \((m,t,n)\), where \(m\) is the number of samples in the drilling sequence data, \(t\) is the time steps, and \(n\) is the number of features contained in the dataset. The model's output is the probability distribution for each class, which is used to predict the operational condition corresponding to the input data. After the data are input, they first pass through the BiLSTM layer, enabling the model to capture both forward and backward information from the input sequence. This allows the model to learn bidirectional dependencies and complex temporal features in the time-series data, providing rich representations for subsequent layers. Following the BiLSTM layer, the output sequence is fed into a dropout layer. During training, the dropout layer randomly sets a portion of the neuron outputs to zero, effectively \"dropping out\" a certain number of neurons. This prevents the model from relying excessively on specific neurons, thus enhancing its generalization ability on new data and mitigating overfitting. The output of the dropout layer is then input into the GRU model, which performs a higher-level time-series pattern extraction based on the BiLSTM model, emphasizing the key temporal information and improving the quality of temporal feature representation. The sequence output by the GRU is further passed through another dropout layer, further enhancing the model's generalization capability. Following the dropout layer, a fully connected layer is applied, where an activation function introduces nonlinearity, allowing the model to learn the complex nonlinear relationships between the drilling data features. The output of the fully connected layer is processed by the softmax activation function, converting the output into a probability distribution across the different classes. The class with the highest probability is selected as the predicted result, thus achieving the multi-class classification task for drilling conditions.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 2 Related Work
Each node owns a routing set that is a set of nodes with lower ranks and this set is propagated inside DIO messages. The routing set allows nodes to know whether a node is on a path to the destination or not. ORPL uses anycast instead of unicast to propagate a data packet. Nodes that receive the packet decide whether to forward it or not. If the destination of a packet is in the routing set, this packet continues to be relayed; otherwise, the packet is dropped. ORPL is proposed for static scenarios only. When considering mobility, rank and routing sets need to be updated on time according to the movement of nodes. Moreover, due to the fact that the updating of routing sets depends on the propagation of DIOs, timely updating costs too much overhead and is not practical. Dynamic Source Routing (DSR) and Ad hoc On-Demand Distance Vector (AODV) are unicast-based downward routing protocols. They both employ flooding methods to support route discovery and route maintenance. Compared with DSR, AODV further supports periodic advertisements and distance vector routing, which is more adaptive to dynamic scenarios. However, both DSR and AODV need to run route discovery and route maintenance very often in order to update routing tables in a timely manner in mobile scenarios. In this process, the flooding of requests causes congestion in the network, and route discovery and maintenance cannot run well. Improving AODV based on restricted broadcasting is a common method proposed in References [9; 10] for vehicular networks. However, these methods use geographic positions to assist broadcasting reduce the number of retransmissions, and thus require each node to be equipped with a Global Positioning System transceiver, which is difficult to achieve in WSNs and LLNs. Opportunistic source routing (OSR) [11] is a broadcast- and unicast-based downward routing protocol that introduces opportunistic routing into traditional source routing. OSR uses a bloom-filter mechanism to encode a downward source route, which reduces the length of a packet header while processing source routing. OSR uses multiple traffic-flow patterns, unicast, multicast, and broadcast. Unicast and multicast are the main ways to propagate information. In the case of failure of unicast and multicast, broadcast is used. OSR achieves a reduction in transmission count and a gain in reliability compared to standard RPL. However, OSR also needs route discovery and maintenance to deal with mobile scenarios, which meets congestion problems, similarly to DSR and AODV.
## 4 Results and Discussion  
### Training Set Loss  
In this experiment, the network model is constructed based on the deep learning framework Tensorflow 2.5.0 and the Python programming language, the GPU used is RTX3060, the CPU is Core I5-12400F, and the computationally efficient Adam optimization algorithm is used to train the model. The training hyperparameters are shown in Table 3.  
Figure 8: Signal decomposition process by discrete wavelet transform.  
As can be seen from Figure 9, the loss of the four bearingss decreases gradually with the increase in iterations. The loss of bearing 1-3 decreases from 0.26 to 0.046, the loss of bearing 2-3 decreases from 0.41 to 0.048, and the loss of bearing 3-2 decreases from 0.42 to 0.053. The loss of the three bearings is smaller, and all of them are in the vicinity of 0.05, which indicates that the constructed CNN-GRU-MHA model has a small error and can fit the training set data well. To verify that the model trained on the training set data can be well applied to the test set and validation set, the structure and parameters of the feature extraction layer of the CNN-GRU-MHA model under the three experiments are frozen separately, the feature extraction capability of the model is retained, and the top layer of the model is retrained. The loss of the model after 100 rounds of reiteration on the test set is specifically shown in Table 4, and to avoid chance, the experiment is repeated five times to take the average value of the loss.  
As can be seen from Table 4, the migration experiments of the four bearings have good results, and the loss under variable load conditions is in the range of 0.04-0.05.
## 2 Materials and Methods
### GC-MS Experiments and Transcriptome Experiments
#### 2.5.2 GC-MS Analysis of VACs  
Agilent 7890B chromatography and a 5977B mass spectrometer equipped with a DB-5 MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 m, Agilent J&W Scientific, Folsom, CA, USA) were used to carry out the qualitative and quantitative analysis of volatile aromatic compounds. The extracted sample was directly injected into the injection port of the gas chromatograph-mass spectrometer, desorbed at 250 \({}^{\circ}\)C for 5 min, and the injection port temperature was set at 230 \({}^{\circ}\)C. High-purity helium (Purity \(\geq\) 99.999%) was used as the carrier gas. The initial oven temperature was set at 40 \({}^{\circ}\)C for 1 min, increased to 230 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and held for 1.5 min, then increased to 250 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/minand kept for 2 min. Mass spectrometry was recorded in the ionization mode of electron impact ion source (EI) energy of 70 eV. The ion source temperature was set to 230 \({}^{\circ}\)C, and the quadrupole temperature was 150 \({}^{\circ}\)C. Mass spectrometry data were extracted by full scan mode (SCAN) with a mass scanning range of 40-500 \(m/z\). In the process of mass spectrometry analysis, all samples were mixed as quality control (QC) samples to test the stability of the system mass spectrometry platform during the whole experiment. MS-DIAL 2.74 software (accessed on 3 August 2024) analyzed the GC-MS raw data for peak detection, peak recognition, MS2 Dec deconvolution, and peak alignment. They then compared with MS in the NIST database ([[https://webbook.nist.gov/chemistry/](https://webbook.nist.gov/chemistry/)]([https://webbook.nist.gov/chemistry/](https://webbook.nist.gov/chemistry/)), accessed on 5 August 2024) to determine the species of VACs. VACs with MS matching scores greater than 80% were retained, and the final substance type was determined after manual identification and comparison. The relative content of volatiles was calculated by the standard internal method. Detailed information is provided in the Supplementary Information Table S1. The formula is as follows:  
f[relative content of volatiles (\(\mathrm{\SIUnitSymbolMicro g}/\mathrm{kg}\))] = peak area of target \(\times\) 0.1 \(\mathrm{\SIUnitSymbolMicro g}/\)(peak area of internal standard \(\times\) 1000 g).  
\[\mathrm{Retention\ index\ formula:\ RI=n\times 100+100\times(\Pi-Tn)/(Tn+1-Tn)}\]  
Screening of Differential VACs and Marker Aroma Compounds  
Multivariate statistical analysis was used to screen the differential VACs in the samples. The relative content of VACs was used as the analysis data, and the PLS-DA model of VACs in different pepper fruit samples was constructed by (PLS-DA). The VACs in the model were screened by variable weight value (VIP) > 1, and a _t_-test was performed to verify its significance.
###### Abstract  
Reducing the effect of leg length discrepancy (LLD) on gait abnormalities while other abnormal conditions such as spasticity, joint contractures or weak muscle strength are exhibited is challenging. This study aimed to evaluate the impact of mild LLD on lower limb biomechanics, on participants with anatomic LLD with and without other clinical abnormalities. A motion capture system was utilized on 32 participants to measure lower limb kinematics and dynamic leg length (DLL) throughout the gait cycle, calculated as the absolute distance from the hip joint center, either to the heel, ankle joint center, or forefoot. The Pearson correlation coefficient found that LLD was associated with 5 kinematic variables only when LLD appeared with no other clinical abnormalities present (r = 0.574 - 0.846, \(p<0.05\)). When clinical abnormalities were present, the random forest classification accuracy was lower (64% versus 80%), implying that the used kinematics are low predictors for anatomic LLD, revealing a higher asymmetrical clearance index (the difference between the maximal stance phase and the minimal contra-lateral swing phase DLL) and a different kinematic variable importance index. Clinical abnormalities in pathological gait will in all probability significantly affect gait deviations, affirming mild anatomic LLD as inconsequential. A functional measurement can offer a better estimate as to the side and extent of the functional discrepancy.  
Keywords: gait analysis; leg length discrepancy; functional; dynamic +
Footnote †: journal: Applied Sciences
## 5 Proposed Method
### Temporal Penalization in Sub-Matrices  
Observational data indicate that overload probability increases as the event horizon approaches. This trend, validated through an explanation technique detailed in Section 6, emphasizes the importance of temporally proximal instances in assessing overload risk (Figure 4). To leverage this insight, we implement a temporal penalization filter, which reduces the influence of earlier events within each channel of the encoder's input matrix (Figure 5). This approach enhances the predictive accuracy of models in real-world scenarios [10].  
The temporal attenuation matrix adjusts the importance of events over time while preserving the angular differences that form the Gram matrix.  
* Purpose of the Attenuation Matrix: The attenuation matrix modulates the influence of events based on temporal proximity, scaling each time step uniformly within the input matrix. This scaling affects absolute magnitudes without altering relative differences between elements.
* Gram Matrix Construction: The Gram matrix calculates angular differences between variable pairs, which remain invariant under uniform scaling. Thus, the attenuation matrix does not affect these differences, preserving the matrix's integrity.  
The attenuation filter maintains intrinsic relationships while emphasizing temporal importance, ensuring the structural integrity of the Gram matrix is retained during modulation. The resulting matrix \(\mathcal{F}\) has elements in the interval \([1.5,1]\). It is applied systematically to each of the \(R\) channels in the generated multivariate time-series representations, denoted as \(G\) (Figure 6).  
\[G^{\prime}_{i,j}=\mathcal{F}_{i,j}\odot G_{i,j}, \tag{6}\]  
where \(\odot\) denotes element-wise multiplication. In the top left corner, the angles of the matrix are amplified, whereas in the bottom right corner, the angles are maintained. This effect identifies the most significant value at the current time (the first time in each window), allowing the network to emphasize this previous time.  
Figure 4: Gradation of importance across selected pairs in overload prediction. Blue to red gradient signifies a transition from lesser to greater significance.  
Figure 5: Depiction of the temporal penalization filter applied per channel.
## 4 Results and Discussion
### Ablation Study
Finally, our proposed network consists of a multichannel learning framework that plays a significant role in performance improvement. Additionally, group convolution can be utilized to reduce the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural Network** & **Parameters** & **Model Weight** & **Accuracy** \\ \hline RS-1 & 265,929 & 1.1 MB & 92.61\% \\ \hline RS-2 & 256,354 & 980 kb & 92.12\% \\ \hline RS-3 & 283,142 & 1.668 MB & 93.40\% \\ \hline RS-4 & 259,258 & 1.52 MB & 91.55\% \\ \hline RS-Proposed & 253,274 & 920 KB & 94.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters, model weight, and accuracy of several models in ablation study. Figure 9: Performance comparison of different network configurations used in ablation study. model complexity while maintaining its effectiveness. However, we can conclude that our proposed network performs better and has a significant classification accuracy compared to other network configurations.