esa-sceva/satcom-chunk-collection huggingface dataset:
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
###### Abstract  
Following recent advancements in deep learning and artificial intelligence, spoken language identification applications are playing an increasingly significant role in our day-to-day lives, especially in the domain of multi-lingual speech recognition. In this article, we propose a spoken language identification system that depends on the sequence of feature vectors. The proposed system uses a hybrid Convolutional Recurrent Neural Network (CRNN), which combines a Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN) network, for spoken language identification on seven languages, including Arabic, chosen from subsets of the Mozilla Common Voice (MCV) corpus. The proposed system exploits the advantages of both CNN and RNN architectures to construct the CRNN architecture. At the feature extraction stage, it compares the Gammatone Cepstral Coefficient (GTCC) feature and Mel Frequency Cepstral Coefficient (MFCC) feature, as well as a combination of both. Finally, the speech signals were represented as frames and used as the input for the CRNN architecture. After conducting experiments, the results of the proposed system indicate higher performance with combined GTCC and MFCC features compared to GTCC or MFCC features used individually. The average accuracy of the proposed system was 92.81% in the best experiment for spoken language identification. Furthermore, the system can learn language-specific patterns in various filter size representations of speech files.  
A pacs: 2022  
Arabic; CNN; CRNN; classification; language identification; LSTM; Mozilla speech corpus; multi-lingual speech; RNN
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 3 Attitude and Orbit Determination Method with Angle-Only Measurements  
In this section, the autonomous attitude and orbit determination problem, with different conditions, are modeled based on the theory in Section 2.  
Case I: Both Orbits of \(S_{1}\) and \(S_{2}\) Are Known, and the Attitude of \(S_{1}\) Is Unchanged  
In this case, the navigation problem (19) is simplified as follows: the orbits of both \(S_{1}\) and \(S_{2}\) are known, and the attitude of \(S_{1}\) is unknown but unchanged (i.e., \(\mathbf{\omega}=[0\;0\;0]^{\mathrm{T}}\)). Thus, the state vector can be written as:  
\[\mathbf{X}=\mathbf{q}=[q_{0},q_{1},q_{2},q_{3}]^{\mathrm{T}} \tag{33}\]  
The state model is given by:  
\[\mathbf{\dot{X}}=\mathbf{F}(\mathbf{X})=\mathbf{q}=\mathbf{g}_{1}(\mathbf{q})=\frac{1}{2}\mathbf{\Omega}( \mathbf{\omega})\mathbf{q}=\frac{1}{2}\begin{bmatrix}0&-\mathbf{\omega}^{\mathrm{T}}\\ \mathbf{\omega}&-\mathbf{\omega}^{\times}\end{bmatrix}\mathbf{q}=[0,0,0,0]^{\mathrm{T}} \tag{34}\]  
The measurement is shown in Equation (17) and then the partial derivative of the intersatellite angle to the quaternion of spacecraft \(S_{1}\)\(\mathbf{H}_{k}\) is of the form:  
\[\begin{split}&\mathbf{H}_{k}=\left.\frac{\partial\mathbf{y}}{ \partial\mathbf{q}}\right|_{t=t_{k}}=\left.\frac{\partial\mathbf{R}_{E}^{\mathrm{ B}}}{\partial\mathbf{q}}\right|_{t=t_{k}}\frac{\mathbf{r}_{2}-\mathbf{r}_{1}}{\|\mathbf{r}_{2}- \mathbf{r}_{1}\|}\\ &=\left[\left.\frac{\partial\mathbf{R}_{E}^{\mathrm{B}}}{\partial q _{0}}\right|_{t=t_{k}}\left.\frac{\partial\mathbf{R}_{E}^{\mathrm{B}}}{ \partial q_{1}}\right|_{t=t_{k}}\left.\frac{\partial\mathbf{R}_{E}^{\mathrm{B} }}{\partial q_{2}}\right|_{t=t_{k}}\left.\frac{\partial\mathbf{R}_{E}^{\mathrm{ B}}}{\partial q_{3}}\right|_{t=t_{k}}\right]\frac{\mathbf{r}_{2}-\mathbf{r}_{1}}{\|\mathbf{r}_{2}- \mathbf{r}_{1}\|}\end{split} \tag{35}\]  
For the state model in Equation (34), the Jacobi matrix is given as:  
\[\frac{\partial\mathbf{F}(\mathbf{X})}{\partial\mathbf{X}}=\frac{\partial\mathbf{F}(\mathbf{q})}{ \partial\mathbf{q}}=\mathbf{0}_{4\times 4} \tag{36}\]  
Therefore, the STM has the following format:  
\[\Phi(t_{k},t_{0})=\mathbf{I}_{4\times 4} \tag{37}\]
## 3 Results and Discussion  
This section discusses the accuracy of the simulation and forecasting in a conceptual model (HEC-HMS), three ML models (ELM, SVR, and LSTM), and three coupled models (HEC-HMS-ELM, HEC-HMS-SVR, and HEC-HMS-LSTM). Data from 2002 to 2007 were used for the simulation, and data from 2005 to 2007 were applied to the forecasting models.
## 4 Trustworthiness  
Specific to content analysis methodology, inter-coder reliability (akin to inter-rater reliability in quantitative research) is recognized as a way to improve trustworthiness [41]. Checking for coding consistency at two points in time supports quality assurance. A sample of data was initially coded. Then, coding was repeated 10 days later to evaluate and modify the coding frame [30]. Triangulation was achieved using different methods for data collection, in this case adherence data. Concept-driven validity was tested by comparing the results with what is already known to experts in the research topic [30]. As marked attrition and participant fatigue impacted the study, member checking was not done to avoid further burdening the participants. However, the resulting analysiswas reviewed by the facilitator, who agreed that the essence of the discussion had been captured appropriately. Although there was a small sample size, the concepts discussed were clearly delineated and similar between groups. Therefore, it is likely data saturation was achieved.
# Co-Efficient Vector Based Differential Distributed Quasi-Orthogonal Space Time Frequency Coding
## 6 Conclusions  
As opposed to traditional unitary matrix-based differential designs amenable to the AF cooperative protocol, we propose co-efficient vector based differential designs for DF cooperative networks where the relay nodes are required to differentially decode and retransmit information signals. We employ full rate full diversity quasi-orthogonal codes to meet the demands of high data rate transmission. We present the generalized STF mapping scheme and differential recipe for utilizing co-efficient vectors in cooperative networks with any number of relays. Through PEP analysis, we derive sufficient design criteria for our scheme to achieve full spatial, temporal, and frequency diversity. Using simulation results, we compare and contrast the unitary matrices and co-efficient vector designs in terms of computational complexity and BER performance.  
From the results, it is evident that, compared to the unitary matrices AF protocol, the performance of the differential DF scheme using co-efficient vectors is slightly better than that obtained using unitary matrices in the low SNR region while the performance improves significantly in the high SNR region. For example, at \(10^{-4}\) BER, the performance gain is about 1.2 dB. Additionally, it can be seen that for values of \(f_{D}\) between 78 Hz and 135 Hz the BER performance of our proposed differential DQSTFC scheme is better than that of the differential quasi-orthogonal DSTC-OFDM and DSFC schemes.  
We then study the performance of our differential DQSTFC scheme in severe multipath fading conditions. When \(\tau_{rms}\) is higher than 3.2 \(\upmu\)s, the coherence bandwidth is very low, such that coding in the frequency dimension introduces inter-carrier interference; thus, the  
Figure 5: Performance comparison between proposed Co-efficient Vector DQSTFC, DSTC-OFDM and DSFC in Time Selective Fading Channels.  
BER performance of our proposed scheme and the DSFC scheme degrade significantly. In this condition, however, DSTC-OFDM outperforms the other schemes.  
Future WorkThe reconfigurable intelligent surface (RIS) technology has generated considerable interest due to its advantages of low cost, easy deployment, and high controllability [39; 40; 41]. To further attempt to improve the performance of our proposed co-efficient vector differential DQSTFC scheme under different channel conditions, a RIS-assisted differential DQSTFC scheme will be investigated.  
Validation, G.O.; Formal analysis, N.N.; Investigation, O.S.; Supervision, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC was funded by the School of Physics, Engineering and Computer Science (SPECS), University of Hertfordshire, College Lane Campus, Hatfield AL10 9 AB, UK.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Design of State Space
#### 3.2.5 Packet Loss Rate  
Packet loss in a V2X network can be caused by congestion or random packet loss. Congestion occurs when the transmission rate V reaches the maximum capacity of the bottleneck link \(C\) or reaches the maximum window size \(cwnd_{max}\), resulting in packet loss, or congestion loss occurs when the buffer overflows. Random packet loss can be caused by signal collision, interference or attenuation in a V2X network. In addition, the random loss of packets is also affected by the data transmission rate \(H\), and the data transmission rate \(H\) conforms to a Poisson distribution.  
The probability of packet \(x\) loss under the size of congestion window \(S_{cw}\) is expressed by the probability density function \(P_{x}(S_{cw})\), which is defined as Formula (4).  
\[P_{x}(S_{cw})=\left\{\begin{array}{ll}\frac{(HS_{cw})^{x}e^{-HS_{cw}}}{x^{ 1}},&x=0,\ 1,\ 2,\ \cdots\\ 0,&\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \
## 2 Results and Discussion  
### Optimization of the Extraction Procedure  
The optimization of the extraction procedure was performed using a design of experiments (DoE). A two-level fractional factorial design was used as the screening design (SD) in order to understand which variables affect the response. After that, a second order model central composite design (CCD) was used to optimize the extraction methodology.
# Test-Retest Reliability of a 6 DoF Marker Set for Gait Analysis in Cerebral Palsy Children
## 4 Discussion
In this study, one subject presented mild spastic diplegia and a considerable gastrocnemius tone (as seen in Table 1), which often shows similar characteristics to idiopathic toe walking. In the first session, the subject was able to perform a normal heel strike at initial contact and during the stance phase of walking. However, during the dynamic trials in the second session, the gastrocnemius stiffness was significantly higher which caused some motion restriction at the ankle. As, in the static calibration trial, the subject was able to stand with both feet flat on the floor, the range of motion differences were wider from the start. The magnitude of this variation is visible in the scatter plots of the dorsi/plantar flexion (Figure 2). When we compare the kinematic data between sessions, there was an increase of 8\({}^{\circ}\) in hip flexion, a decrease of 13\({}^{\circ}\) in knee flexion and a total absence of ankle dorsiflexion in both lower limbs. These results are in accordance with the study of [PERSON] et al. [56] where CP children with toe walking often exhibited increased hip flexion and a decrease in knee flexion throughout the walking cycle. Furthermore, excessive plantar flexion may be responsible for changes in flexion, internal rotation and adduction of the hip as well as in the pelvic anterior tilt [33] which explains the reduced ICC on left and right anterior tilt (0.40 and 0.51, respectively) compared with the other kinematic variables of this segment, as seen in Table 4.  
Yet, due to co-spasticity of the muscles causing reciprocal movements across the joints and originating a wider variation in kinematic data, CP children are not able to change joint moments which results in a more reliable measure between the two assessment days [57]. This is evident in our results where the kinetic variables presented less variation (Table 5), in accordance with similar studies [16; 23]. Although there is no reliability analysis published with a 6 DoF model and kinetics variables, these results may be partially attributed to the small variations of the anthropometric measurements. Even though the two recorded sessions occurred several days apart, there was a small variation in marker placement between sessions (Table 2). Anthropometric measurements were considered excellent regarding ICC (ICC average \(\approx\) 0.98) and an absolute error of approximately 4 mm.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 2 System Description  
A multi-cell massive MIMO system is considered which consists of \(L\) cells numbered (1, 2,..., \(L\)). Each cell contains one base station with \(M\) antennas and \(K\) single-antenna users assuming (\(M>>K\)). Let the average power (during transmission) at the base station be \(P_{d}\) and the average power (during transmission) at each user be \(P_{u}\), the propagation factor between \(n\)th antenna of base station at \(j\)th cell and \(k\)th terminal at \(\ell\)th cell is defined by  
\[g_{jk\ell} = \sqrt{\beta_{jk\ell}}h_{jk\ell}, \tag{1}\] \[G_{j\ell} = H_{j\ell}\sqrt{D_{j\ell}}, \tag{2}\]where the matrix \(G\) is a \(M\times K\) channel matrix which represents independent fast fading, geometric attenuation and log-normal shadow fading. \(h_{jk\ell}\) is independently identically distributed (i.i.d) fast Rayleigh fading coefficient from the \(k\)th user to \(m\)th antenna of the base station with zero mean and unit variance. This block fading (\(h_{jk\ell}\)) will be constant during coherence interval \(T_{coh}\), while \(\sqrt{\beta_{jk\ell}}\) represents the geometric attenuation, shadow fading and path loss, which is independent over \(M\) antennas, and it is assumed to be constant over many coherence time intervals. Therefore, it is always predefined prior to the base station. This assumption is reasonable since the distance between the user and the base station is much larger than the distance among the antennas themselves. Note that the value of \(\beta_{jk\ell}\) changes very slowly with time. \(D_{j\ell}\) is a \(K\times K\) diagonal matrix where the elements of the diagonal comprise the vectors \(\beta_{jk\ell}\) and \(H_{j\ell}\) is an \(M\times K\) channel matrix. The communication strategy of the massive MIMO system is described through two main communication phases; uplink communication phase and downlink communication phase as shown in the following subsections.
###### Abstract  
Phase change material (PCM) is an attractive solution for improvement of thermal performance in buildings, and have excited a vast amount of research in recent years. There are however practical challenges with ensuring adequate phase transitions of the PCM to exploit the passive heat storage benefits. Night ventilation (NV) with free cooling have surfaced as one of the most promising methods to properly utilize PCMs and maximize energy savings. This work deals with a novel spackling compound enhanced with microencapsulated PCM. The product is intended for use at inner walls and ceiling surfaces of buildings and is suited for new and retrofitting building applications. Ensuing former experimental studies, a validated simulation model is developed and used to study the PCM with natural and hybrid NV strategies in an office building during summer conditions in Oslo, Norway. Cooling load reduction and energy savings are analyzed with varying air flow rates of 0.5-5 air changes per hour (ACH) and 2-4 mm PCM layer thickness. It is shown how increasing air flow rates and PCM thickness greatly enhances energy performance, but at a diminishing rate. Although the NV alone can reduce the cooling load by 11.5% at 1 ACH, 40.2% at 3 ACH and 59.8% at 5 ACH, one can achieve further reduction up to 19.5%, 78.2% and 95.5% for the respective ACHs with 4 mm PCM. The natural NV provides more energy savings compared to the hybrid strategy. As energy requirement by fans increases with the increase of air flow rates in the hybrid strategy, the energy savings eventually start to reduce. The hybrid strategy can save 38% energy at most with 3 ACH, and the savings is increased to 50% with the inclusion of 4 mm PCM. On the other hand, the natural strategy saves 56% of energy at the same air flow rate, and 69% with 4 mm of PCM.  
micro-encapsulated phase change material (MPCM); phase change material (PCM); spackling: gypsum boards; night ventilation (NV); passive cooling; office cubicle; cooling load; energy savings; retrofit +
Footnote †: journal: Applied Sciences
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 7 Conclusions  
To improve the performance of sparse UWSNs, the 2 hop-AHH-VBF protocol tackles the void hole problem. Routing decisions are made on the basis of neighbor node distribution in the ATD. Two-hop neighbor information before forwarding the data packet reduces the probability of void holes. The relative distance of the forwarding node and the neighbor information leads to optimal forwarder selection, which improves PDR. The simulation results validate that the performance of 2 hop-AHH-VBF scheme beats the AHH-VBF routing scheme in terms of energy consumption and PDR. Energy per dropped packet is saved as the packet delivery is improved at the cost of end-to-end delay. While compared with the WDFAD-DBR and AHH-VBF counter routing techniques, 2 hop-AHH-VBF performs efficiently, even in sparse UWSNs.
# Tensor-Based Algorithms for Image Classification
## 2 Prerequisites
### MNIST and Fashion MNIST  
The MNIST data set [18], see Figure 1, contains grayscale (The methods described below can be easily extended to color images by defining basis functions for each primary color.) images of handwritten digits and the associated labels. The data set is split into 60,000 images for training and 10,000 images for testing. Each image is of size \(28\times 28\). Let \(d=784\) be the number of pixels of one image, and let the images, reshaped as vectors, be denoted by \(x^{(j)}\in\mathbb{R}^{d}\) and the corresponding labels by \(y^{(j)}\in\mathbb{R}^{d^{\prime}}\), where \(d^{\prime}=10\) is the number of different classes. Each label encodes a number in \(\{0,\ldots,9\}\), and the entries \(y^{(j)}_{i}\) of the vector \(y^{(j)}\) are given by  
\[y^{(j)}_{i}=\begin{cases}1,&\text{if $x^{(j)}$ contains the number $i-1$},\\ 0,&\text{otherwise},\end{cases} \tag{1}\]  
i.e., \(y^{(j)}=[1,0,0,\ldots,0]^{\top}\) represents \(0\), \(y^{(j)}=[0,1,0,\ldots,0]^{\top}\) represents \(1\), etc. This is also called one-hot encoding in machine learning.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(X=[x^{(1)},\ldots,x^{(m)}]\) & data matrix in \(\mathbb{R}^{d\times m}\) \\ \(Y=[y^{(1)},\ldots,y^{(m)}]\) & label matrix in \(\mathbb{R}^{d^{\prime}\times m}\) \\ \(n_{1},\ldots,n_{p}\) & mode dimensions of tensors \\ \(r_{0},\ldots,r_{p}\) & ranks of tensor trains \\ \(\psi_{1},\ldots,\psi_{p}\) & basis functions \(\psi_{\mu}\colon\mathbb{R}^{d}\to\mathbb{R}^{n_{p}}\) \\ \(\Psi_{X}\) / \(\Psi_{X}\) & transformed data matrices/tensors \\ \(\Xi\) / \(\Xi\) & coefficient matrices/tensors \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation used in this work.  
Figure 1: (**a**) Samples of the MNIST data set. (**b**) Samples of the fashion MNIST data set. Each row represents a different item type. (**c**) Corresponding labels for the fashion MNIST data set.  
The fashion MNIST data set [19] can be regarded as a shoe-in replacement for the original data set. There are again 60,000 training and 10,000 test images of size \(28\times 28\). Some samples are shown in Figure 0(b) and the corresponding labels in Figure 0(c). Given a picture of a clothing item, the goal now is to identify the correct category, which is encoded as described above.
## 7 Performance Evaluation and Simulation Results
#### 7.2.2 Low Social Media Interaction. Node's Density = 200 Nodes/km\({}^{2}\)  
As it happened in the previous case with a very low social media interaction, here as we increase the nodes' density the network connectivity increases and there is a higher chance to establish a forwarding path with nodes whose confidence level is higher. Therefore, as \(w_{TS}\) increases (we give more importance to the confidence level than to the QoS) the measured \(\overline{TS}\) will increase (see blue line in Figure 10).  
As we can see, we get the lowest packet losses (17.5%) when \(w_{TS}=0\), i.e., when we select the path with best QoS performance. The highest packet losses (23%) is obtained when \(w_{TS}=1\), i.e., when we do not consider QoS performance of the paths but just their TS values. We can consider a good trade-off when \(w_{TS}=0.6\) as we give a good importance to the tie strength parameter with not a big increase in the packet losses (18.4%). Regarding the average end-to-end packet delay, for \(w_{TS}=0.6\) it is near 0.9 s and very similar for \(w_{TS}=0\), see Figure 11.  
Figure 8: Percentage of packet losses and average tie strength of the forwarding path. Scenario with low social media interaction (TS normal distribution with \(\mu=2\) and \(\sigma=1\)). Nodes’ density = 100 nodes/km\({}^{2}\).  
Figure 9: Average end-to-end packet delay. Scenario with low social media interaction (TS normal distribution with \(\mu=2\) and \(\sigma=1\)). Nodes’ density = 100 nodes/km\({}^{2}\).  
Results for a High Social Media Interaction. TS Normal Distribution with Mean \(\mu=3\), Standard Deviation \(\sigma=1\)  
For a scenario with a high social media interaction between users, the tie strength (TS) between consecutive nodes in the forwarding nodes from source to destination is going to be more significant in the forwarding decision and will have a higher impact in the \(\overline{TS}\) measured in the simulation.
# Wheeled Permanent Magnet Climbing Robot for Weld Defect Detection on Hydraulic Steel Gates
## 4 Experimental Validation
### Weld Defects Detection
#### 4.1.1 Dataset Collection  
As shown in Figure 11, common types of weld defects on metal surfaces include undercuts, pores, weld bumps, and weld pits. These defects can lead to stress concentration at the weld region, significantly increasing the risk of failure under external loads. Prior to training, a set of images containing representative weld defects was collected.  
Figure 11: Common categories of weld defects. (**a**) Undercuts; (**b**) Pores; (**c**) Weld bumps; (**d**) Weld pits.  
Given the challenges associated with obtaining sufficient defect images at an early stage, the sample size was initially limited, making it difficult to train a high-precision classification model. To address this, data augmentation techniques were applied. The dataset was enhanced by adjusting the image structure and introducing artificial noise, thereby improving the robustness and generalization capability of the classification model while mitigating the risk of overfitting. The distribution of various defect types after enhancement is shown in Table 2. After augmentation, the dataset was expanded to 2400 images.