esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unsupervised Deep Learning System for Acoustic Scene Analysis
###### Abstract  
Acoustic scene analysis has attracted a lot of attention recently. Existing methods are mostly supervised, which requires well-predefined acoustic scene categories and accurate labels. In practice, there exists a large amount of unlabeled audio data, but labeling large-scale data is not only costly but also time-consuming. Unsupervised acoustic scene analysis on the other hand does not require manual labeling but is known to have significantly lower performance and therefore has not been well explored. In this paper, a new unsupervised method based on deep auto-encoder networks and spectral clustering is proposed. It first extracts a bottleneck feature from the original acoustic feature of audio clips by an auto-encoder network, and then employs spectral clustering to further reduce the noise and unrelated information in the bottleneck feature. Finally, it conducts hierarchical clustering on the low-dimensional output of the spectral clustering. To fully utilize the spatial information of stereo audio, we further apply the binaural representation and conduct joint clustering on that. To the best of our knowledge, this is the first time that a binaural representation is being used in unsupervised learning. Experimental results show that the proposed method outperforms the state-of-the-art competing methods.  
acoustic scene analysis; spectral clustering; auto-encoder network; deep learning Article
## 4 Classification Based on Taxonomy and Design Drivers
### Routing Algorithm  
The DCS scheme might incur high update traffic due to the lack of an optimally synchronized routing algorithm. Initial research in DCS focused on designing an efficient technique that could be used to map data to the rendezvous node/zone rather than concentrating on developing an optimal packet routing technique. Most of the DCS methods, for data storage and search routing, rely on a locating system (e.g., GPS) that places an energy burden on the WSN. However, in the last quarter of this decade a few _DCS_ techniques were proposed putting focus on enhancement of packet routing. These different routing algorithms can broadly be divided into two categories namely point-to-point routing and tree based hierarchical routing. In point-to-point routing the deployed network field is divided into zones or sectors and data usually propagates from one zone or rendezvous node to another in a multi-hop point-to-point fashion. In contrast, tree based hierarchical routing relies on a tree-construction technique dividing the whole network into a tree-structure and provides a mapping of data transfer paths with minimal assumption about the underlying infrastructure. DIM [13], KDDCS [42] are developed based on _K-D_ trees while HVGR [33] and PathDCS [32] were developed on top of hierarchical Voronoi Graph and path based tree structure respectively. Table 5 summarizes DCS schemes based on these two types of routing algorithm used.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Functionalities** & **Schemes** & **Method Used** \\ \hline Intra-Zone Load Balance & DLB [23] & Cover-up Scheme \\  & SDS [19] & Measuring Storage Usage Status (\(\sum\limits_{i=1}^{\times}S_{{}^{\cdot}}\)) \\  & DLB [23] & Extended Grid (Cover up grid) \\ Inter-Zone Load Balance & HVGR [33] & Proportional Assignment of Storage Task to Regions \\  & LB-DCS [24] & Sampling Density, Broadcast, Stripes, FatStripes \\  & KDDCS [42] & Weighted Split Median \\  & & Distributing Routing Load to All Possible Routes \\ Routing Load Balance & SDS [19] & Equals to \(\sum\limits_{j=1}^{\times}\begin{pmatrix}c-1\\ c-k\end{pmatrix}\times 2^{j}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of Load Balancing DCS Schemes.
# A Survey of Vehicular VLC Methodologies
## 4 Technical Challenges in Vehicular VLC and Solutions
### Weather Conditions  
The performance and reliability of VLC vehicle channel connections are significantly affected by challenging conditions and weather fluctuations, such as snow, fog, and rain. Figure 6 illustrates the impact of rain on V2V connections. When light encounters particles in the atmosphere, including diffraction, scattering, and absorption, it leads to the attenuation and dispersion of transmitted signal beams. These weather factors and fluctuations consist of larger particles that have a notable effect on the system's performance, hindering the connectivity of vehicles through VLC. The implications mentioned above become even more severe in unfavorable weather circumstances. Consequently, the range and reliability of the vehicular VLC system are negatively impacted. Initial studies have addressed various weather phenomena to understand their influence on the performance of vehicular VLC. These phenomena encompass fog, rain, snow, turbulence, and solar irradiance. Notably, fog has been extensively studied due to its direct impact on V2V connections, as demonstrated in experimental simulations when compared to snow and rain [23; 103; 104; 105].  
On the other hand, other analytical studies showed that under conditions of dry ice, which has proven to have a more significant effect than fog on vehicle connection (with dense fog representing the most challenging environment), it was possible to obtain reliable communication according to the experimental measurements, reaching about 20 m. This was accomplished using image sensors at the receiver side, while the connection using PDs was about 15 m, which was the maximum distance achieved using other modulation schemes [103; 104; 105]. The effects of weather and smoke particles on visible short-range communications were examined and evaluated in [106]. Other factors with a negatively impact are fires, pollution, and smoke particles, which are also considered significant issues.  
Smoke is one of the main challenges that must be considered, as the attenuation using the smoke model generated in the field of fire engineering is independent of wavelengths, as the highest field of view along the road is about five meters. \"Visibility\" refers to the maximum distance at which an object can be recognized apart from its background. In the context of two different external VLC settings, an investigation of the effects of fog and smoke was carried out. Moreover, the idea of visibility as a starting point for smoke and fog models uses the same kinds of equations to explain the optical attenuation, they induce to adjust the smoke and fog effect ratio. The channel attenuation coefficients were calculated using an energy simulator on the receiving side, considering the actual radiation pattern used in the LED, which reached about one kilometer [106; 107].
# A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control
## 4 Simulation Results
The aggregate performance of the PID controller, LQR controller, fuzzy controller, and MRAPC with MIT rule controller appears to be satisfactory, as demonstrated in Figure 13 and Table 6, where the root mean square error (RMSE) and normalized root mean square error (NRMSE) for simulations are less than 1 m along the \(x\)-axis. Although, the LQR controller is superior to others because it reduces overshoot, rising time, and settling time along the \(x\)-axis. Nevertheless, the performance of PID, LQR, fuzzy, and MRAPC with MIT rule appears to be good, as demonstrated in Figure 14 and Table 7, where the root mean square error (RMSE) and normalized root mean square error (RMSE) for simulations are below 1 m along the \(y\)-axis. In terms of overshoot, rising time, and settling time along the \(y\)-axis, the LQR controller performs better. According to Figure 15 and Table 8, the LQR controller achieves superior results along the \(z\)-axis than other control approaches because there is no overshoot, the rising time is shorter, and the settling time is shorter. In Figure 16, the LQR controller stabilizes the roll angle in 4 s, which is a much quicker period than the findings of other controllers. Figure 16: Roll (\(\phi\)) Control result depending on step input for all control systems. Figure 15: Step response result of all control systems along the \(z\)-axis  
The LQR controller, as shown in Figure 17, requires a shorter time to stabilize the pitch angle than other controllers. Figure 18 demonstrates that the system has a negligible yaw angle in the case of the LQR controller structure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Controllers**} & \multicolumn{5}{c}{**Performance Index (\(x\)-axis)**} \\ \cline{2-7}  & **Setting Time \(T_{S}\)** & **Rise Time \(T_{r}\)** & **Overshoot (\%)** & **Peak Time \(T_{p}\)** & **RMS Error** & **NRMS Error** \\ \hline PID & 24.3 & 1.2 & 4.2 & 5.1 & 0.16 & 0.11 \\ \hline LQR & 4.18 & 2.55 & 0.0 & 30 & 0.21 & 0.21 \\ \hline Fuzzy Logic & 36.48 & 3.15 & 38.64 & 8.8 & 0.23 & 0.17 \\ \hline MRAPC with MIT & 17.24 & 7.1 & 119.58 & 22.5 & 0.22 & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of all controllers for the longitudinal position (\(x\)-axis). Figure 17: Pitch (8) Control result depending on step input for all control systems. Figure 18: Yaw (\(\Downarrow\)) Control result depending on step input for all control systems.
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 4 Case Study
### Simulation Experiment I
Table 5 and Figures 7a and 8a show that the estimated rotational speed value of the EKF based on the elementary PSO algorithm under either the PI controller or improved SMC controller fluctuates within a range after sudden load addition, oscillates and does not stabilize at a fixed value, while the estimated rotational speed value of the EKF based on the immune PSO algorithm can stabilize at 923.1 rpm and 1001 rpm. Therefore, the data show that the parameters of the EKF covariance matrix obtained using the optimization of the immune PSO algorithm are better than those obtained using the optimization of the elementary PSO algorithm. The rise time, peak time, and regulation time under both the PI and SMC controllers are all within 0.2 without much difference, but the overshoot magnitude using the improved SMC control is significantly less than that of the PI controller. The improved SMC controller is also shown to be more resistant to disturbances and more stable by comparing the steady state error and load recovery time after sudden load application. A comparison of the speed difference between Figures 7b and 8b shows that the value of the SMC speed error is very small and almost negligible, but the control under the PI controller is clearly affected. The parameters of the particle swarm search with the same controller improved using the immune algorithm are significantly better than those optimized by the basic particle swarm algorithm in terms of steady-state performance, and the degree of curve smoothing is shown to be superior. The electromagnetic torque comparison diagram of the motor shows that the control strategy after immune PSO under the control of the improved SMC controller is the best. There is no significant difference between the rotor position angle PI and SMC control effect, both of which are optimized using the immune particle swarm optimization algorithm.
# Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network
## 2 Materials and Methods
### Mathematical Models
#### 2.1.6 Class Imbalance Problem  
Although class imbalance results from lack of available data, imbalanced data also appears in the cases of classification tasks for many classes or sparse class data, where majority labels, having a nature of one-hot encoding, is 0. High class imbalance is natural in many real-world datasets for classification tasks, such as image recognition, medical diagnosis, and fraud detection (summarised by, e.g., [13]).  
The general measure of class imbalance in research is defined:  
\[\rho=\frac{max_{c}\{|S_{c}|\}}{min_{c}\{|S_{c}|\}} \tag{12}\]  
where \(\rho\) is maximum imbalance between class, \(S_{c}\) is a set of examples in class \(c\), and \(max_{c}\{|S_{c}|\}\) and \(min_{c}\{|S_{c}|\}\) is maximum and minimum class size over all classes. For example, if maximum class size is 1000 and minimum class size is 10, then \(\rho\) is 100.  
Even though class imbalance is widespread, the general majority of classifiers operate on an assumption that classes in the training dataset are balanced. Therefore, in cases of class imbalance, many classifiers exhibit bias towards the classes that represent majority cases due to its increased prior probability. In this case, the classifier might ignore minority classes that tend to be misclassified [12].  
[PERSON] and colleagues showed that class imbalance interferes with backpropagation, resulting in much smaller length of gradient component in minority classes [14]. Thus, majority class dominates net gradient, which is responsible for weights adjustment. This results in reduced loss only for large class groups [14].  
In this study, we tackle the data imbalance issue using specific methods, namely: focal loss, structuring output as hierarchy and transforming input data as text embeddings.
## 3 Results
### Cost of Transportation
We also calculated the average cost of transportation for \(\uptau\)/W and USD/kg PV waste and listed these values in Table 4.  
As discussed, the cost of transportation depends on the type of product being transported (and the consequent weight and size). [PERSON] [27] compared the overall costs of landfill disposal with recycling (USD/W) of PV modules, including transportation. This study showed that small quantities and high costs make the recycling option relatively expensive, and that the transportation step represents an approximate additional USD 2.64/module for CdTe modules (varying depending on location and quantity). However, the overall conclusion of his study conclusion is in accordance with our results, which show that low truck loads result in high transportation costs. It is important to highlight that different approaches might be used to calculate the cost of transportation related to the distance between the collection point and the recycling plant, which can be in terms of distance travelled and consequent amount of energy used and time, economic costs etc. [47]. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**c-Si**} & \multicolumn{2}{c}{**CdTe**} & \multicolumn{2}{c}{**CIGS**} \\ \cline{2-6}  & **\(\upphi\)/W** & **\(\upepsilon\)/kg** & **\(\upepsilon\)/W** & **\(\upepsilon\)/kg** & **\(\upepsilon\)/W** & **\(\upepsilon\)/kg** \\ \hline
1000 km & \(0.87\pm 0.13\) & \(10.34\pm 1.54\) & \(2.23\pm 0.05\) & \(16.64\pm 0.44\) & \(2.5\pm 0.04\) & \(13.46\pm 0.23\) \\
2000 km & \(1.00\pm 0.15\) & \(11.93\pm 1.77\) & \(2.58\pm 0.06\) & \(19.19\pm 0.51\) & \(2.89\pm 0.05\) & \(15.52\pm 0.26\) \\ \hline \end{tabular}
\end{table} 
Table 4: Average cost of transportation of different PV technologies. Figure 1: The cost of transportation of different type PV modules with diesel-fueled trucks (note that after 1500 km the unit cost (USD/km) of truck transportation decreases about 40%).
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 5 Conclusions  
The IoT is emerging in several application-specific smart devices that are used in different domains, such as healthcare, homes, cities, urban and industrial infrastructures, transport, etc. The number of IoT implementations is exponentially escalating and is expected to reach billions in the next few years. Both IoT networks and RPL are resource-constrained; therefore, the security risks are higher in RPL-based IoT networks. Several solutions have been proposed to address the security attacks using different approaches. Machine learning is one such solution domain that has gained recognition for attack detection model development due to the big data generated by IoT devices. In the existing literature, the concurrent detection of PS-R and SN-W attacks is insufficiently addressed. Therefore, to address this gap, ProSenAD model is developed based on multiclass classification approaches. This research critically analyzes the parameters important for developing such a system. Evaluation metrics are also explored for performance analysis because standard classification-related metrics such as accuracy, precision, and recall are inadequate for ProSenAD evaluation. The proposed ML-based model has outperformed the ML-LGBM and GRU-DL benchmarks in terms of the number of attacks detected, and GB, XGBoost, and LGBM, in terms of accuracy, PPV, sensitivity, CE, CK, and MCC. ProSenAD shows improved performance in both use cases, i.e., PS-R and SN-W, respectively.  
For future work, the LIoTN-RPL dataset will be further diversified by simulating other prevalent attacks from the protocol-specific and SN-inherited categories. Currently, the dataset is limited to benign network traffic data and attack data from two categories of the attacks identified in the paper. We plan to evaluate the results considering additional parameters and benchmarks for the two attack categories mentioned earlier.  
**Author Contributions:** Conceptualization, F.Z., N.Z.J., S.N.B., and N.A.K.; data curation, F.Z., N.Z.J., S.N.B., M.M., S.A., and N.A.K.; formal analysis, F.Z., N.Z.J., S.N.B., M.M., S.A., and N.A.K.; methodology, F.Z., N.Z.J., S.N.B., and N.A.K.; resources, F.Z., N.Z.J., S.N.B., M.M., S.A., and N.A.K.; supervision, N.Z.J., S.N.B., and N.A.K.; writing--original draft, F.Z., N.Z.J., S.N.B., and N.A.K.; writing--review and editing, F.Z., N.Z.J., S.N.B., M.M., S.A., and N.A.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Taif University Researchers Supporting Project Number (TURSP-2020/73), Taif University, Taif, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be available on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Methodology
### Data Preprocessing  
The multivariate LSTM model utilised features such as Close (the closing price), Sent_Positives, and Sent_Negatives for the primary analysis. Additional experiments incorporated the Total Volume of Tweets feature to assess its effect on prediction accuracy. The Close feature served as the target variable across all experiments, with sentiment data acting as auxiliary predictors.  
MinMax scaling was applied to standardise values to a range between 0 and 1 to address scale disparities among features. The dataset was divided into training (60%), validation (20%), and test (20%) subsets, preserving temporal relationships to maintain the integrity of time-series data.  
Later in Section 5, we explore an alternative data partitioning strategy of 70%-15%-15% to evaluate its impact on some of our models' performance. This adjustment aimed to assess the robustness of our best models, the Multi-LSTM-Sentiment and the Tuned Multi-LSTM-Sentiment models (see Sections 3.3.1 and 3.3.2), under different training conditions and data distributions.  
Input sequences were structured with a length of 48 (equivalent to a two-day window of hourly data), enabling the model to learn temporal dependencies. The data were reshaped into the LSTM-required format of \"samples, time steps, features\", where the training set encompassed 9838 samples, and the test set included 2424 samples.  
For the last mode, the Hybrid LSTM-Volume model, the preprocessing steps were adjusted slightly to focus on two features: Close (closing price) and Total Volume of Tweets. The Close feature remained the target variable, while tweet volume data acted as an auxiliary predictor. This setup allowed the model to combine price information with social media activity for more robust predictions.  
The reshaped dataset comprised 12,310 samples, each with 48 time steps and 2 features. The training set included 9838 samples, while the test set contained 2424 samples, maintaining the same data partitioning strategy and sequence structure as before.
# Thermal Conductivity Calculations for Nanoparticles Embedded in a Base Fluid
## 4 Discussion
### Liquid Nanolayer Thickness, \(h\)  
The interface between the liquid and nanoparticles plays an important role that influences the thermal conductivity in NFs, which is known as the interface effect [97]. The liquid molecules form a layered structure close to the nanoparticle's surface [98]. This layer of atoms (nanolayer) around the particles is more ordered and its thermal conductivity is higher than that of the bulk liquid [56; 99]. However, the nanolayer plays a key role in heat transfer but is not solely responsible for the thermal conductivity enhancement in NFs [32; 100]. Recently, [PERSON] et al. [101] reported that the phonon frequency plays a vital role in the liquid and nanoparticles in NFs. An increase in the nanolayer molecule density and the ordered molecules causes an increase in the phonon group velocity and the phonon scattering at the particle-nanolayer interface will decrease. Thus, as the nanolayer thickness increases, the effective thermal conductivity will increase [83; 97; 102]. Many authors reported that thenanolayer thickness, \(h\), is in the range of 1-3 nm [56; 63; 64; 72; 99; 100; 103], while others have stated that a thickness of the nanolayer within the range of 1-3 nm does not affect the enhancement of the thermal conductivity in NFs [104]. Several authors [63; 102] considered thenanolayers' thermal conductivity, \(K_{layer}\), as being equal to \(2K_{m}\) or even 3-5\(K_{m}\) with this range of thickness (1-3 nm) without getting a precise fitting to the experimental data at a wide range of particle volume concentrations.  
In the present work, the effect of the nanolayer without the assumption of nanoparticle agglomeration was taken into account. To the best of our knowledge, the value of the nanolayer thickness, \(h\), has unfortunately not been measured experimentally; therefore, it was used as an adjustable parameter (their values can be seen in Table 1), and according to Equation (6), the thermal conductivity ratio that provided the best fit to that of the experimental data was calculated.