esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
###### Abstract
At present, Convolution Neural Network (CNN) [24], Long Short-Term Memory (LSTM) [25], attention mechanism and other methods are mainly used for depth feature extraction in audio classification tasks and speech emotion recognition [20; 21; 22; 23; 26; 27; 28], which has achieved great improvement compared with the traditional model. However, on the one hand, existing models do not fully consider how to establish effective connections between speech; on the other, models are often based on a single-input feature classification, the accuracy of model is often unsatisfactory, and the ability of the model needs to be further improved [29]. Therefore, aiming at the SER classification task of discrete models, this paper proposes an emotional speech data augmentation method, using the machine-learning method to select from several global feature sets of speech emotion to reduce the dimensionality of the selected feature set, and conducts the feature fusion combined with the features extracted from the deep-learning model MHA-CRNN. In this paper, our main contributions are as follows:  
1. We propose an emotional speech data augmentation method, Mix-wav, migrated from the data augmentation method Mixup in the image recognition task. Experiments on two datasets, IEMOCAP and CHSE-DB, show that the proposed method improves the recognition accuracy of the model. 2. We propose a novel speech emotion feature-extraction model MHA-CRNN based on multi-head attention mechanism. Combining the advantages of CNN and LSTM effectively extracts the spectral features of speech and uses the multi-head attention mechanism to further pay attention to the emotion-related information in the time and frequency domain. 3. We use four global feature sets to conduct emotion classification experiment on IEMOCAP, chose the feature set with the best performance, and use two methods to conduct feature dimension reduction experiments to find out the feature subset with the best performance. 4. We use the selected global statistical features with the features extracted by the MHA-CRNN model to perform feature fusion. We tested our suggested SER model on benchmark IEMOCAP and self-built dataset CHSE-DB. It achieved 66.44% and 93.47%(Unweighted Average Accuracy, WAA), respectively. In the comparative analysis, our system showed our performed recognition results. The rest of this paper is structured as follows. The second section describes some related work in the field of SER. Section 3 describes our proposed data augmentation method. Section 4 describes the method and model architecture proposed in our paper. Section 5 describes the experimental setup, results and related analyses. Section 6 describes the conclusions of our paper and future work.
## 4 Discussion
Moreover, increased oxidative stress in melanoma cells has been shown to inhibit the PI3K/Akt/mTOR pathway through mTOR1 formation and phosphorylation of downstream targets [37]. Although intracellular molecular mechanisms are complex, these results indicate that this combination may represent a new therapeutic option via acute ROS formation; the involvement of mTOR1 and/or mTOR2 inhibition in this process requires further clarification. Platinum drugs exert their biological activity via reactive biotransformation products that bind to DNA; this results in the formation of DNA adducts that inhibit DNA replication, induce cell cycle arrest, and promote apoptosis. Carboplatin is a second-generation platinum agent that has fewer serious side effects than cisplatin. At present, none of the treatment regimens have demonstrated a significant overall survival benefit [38]. However, these platinum drugs are expected to be useful for a specific subtype of patients with HRPC, particularly in certain histological types such as neuroendocrine carcinoma. In this study, combination of Fe\({}_{3}\)O\({}_{4}\) NPs with carboplatin also decreased the viability of DU145 and PC-3 cells compared with carboplatin alone. Moreover, combined treatment with Fe\({}_{3}\)O\({}_{4}\) NPs and carboplatin in DU145 cells synergistically enhanced induction of apoptosis. ROS production may modulate this enhancement of carboplatin effects by Fe\({}_{3}\)O\({}_{4}\) NPs because platinum drugs have been reported to induce mitochondrion-dependent ROS production, which significantly contributes to cell killing by enhancing the cytotoxic effects exerted through the formation of DNA damage [39]. These drugs have been referred to as \"old drugs\". If they have substantial toxicity, the risk-benefit ratio may be positive by predicting sensitivity to the combination of old drugs and iron oxide NPs based on the genetic background (e.g., PTEN and p53). The use of MNPs in chemotherapy, gene therapy, hyperthermia, photochemical ablation, and photodynamic therapy has been proposed [19]. Encapsulating or attaching molecular drugs to iron oxide NPs helps selectively deliver chemotherapeutics to target cells, allows for decreased dosage, and minimizes off-target toxicity. In addition, the combination of hyperthermia and chemotherapy in the same MNPs-based nanotherapeutic system is relatively new. Enhancement of the effects of chemotherapy with application of concurrent hyperthermia is called thermo-chemosensitization, which is dependent on the synergistic effects of hyperthermia and chemotherapy [40]. The thermal enhancement of drug cytotoxicity is maximized at mild hyperthermia temperatures and does not require temperature as high as those used for hyperthermia therapy alone.
## 3 Experimental Results and Validation  
### Device Fabrication  
Keeping in mind the research aims of low cost, ease of fabrication, good quality factor, and improved sensitivity, the sensor was fabricated on an FR-4 substrate with a dielectric constant of 4.3. The fabricated sensor with the experimental measurement setup is shown in Figure 3. The sensor width was 30 mm and the length was 35 mm. The SMA connectors were manually soldered to feed the sensor. Oil samples were directly placed on the sensor's high sensitive electric field area for characterization and adulteration detection of different oil samples.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 3 Method  
First, a detailed analysis of the 5G New Radio protocols was necessary to assess whether they were potentially suitable for information hiding. Then, one of the protocols that offered the best characteristics for a covert channel was selected for further analysis of the hiding pattern collection [22] by checking, for each pattern, if its application on a field of the chosen protocol might result in a feasible covert channel. The taxonomy of [PERSON] et al. [4] defines patterns in order to categorize covert channel techniques. Originally, the taxonomy was limited to network covert channels. Since then, the categorization has been revised and expanded to a generic taxonomy [23] for all steganography domains. Nevertheless, the previous classification retains its validity and can still be applied in the domain of network steganography. After both the protocol to be exploited and the pattern to be used were selected, the theoretical concept of the covert channel was created. To ensure comparability and reproducibility of the results, a well-known method from the research field of steganography was used. The unified description method of [PERSON] et al. [24] is based on extensive analysis of publications in the steganography domain. It is designed to describe the covert channel technique or hiding method in a unified and structured way so that it is easier to compare and evaluate new methods. Finally, an experimental implementation of the proposed covert channel was evaluated in a test environment that simulated a real-world mobile network.
# Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
## 2 System Architecture
### An Example: Failure Recovery  
We use an example to illustrate how the new architecture works. In a harsh agriculture environment, suppose the vehicle sensor network policy requires \"data plane can maintain the basic communication when the controller is not available\". To implement this policy, the controller proactively inserts two rules in the first flow table: (match: connect_state = connection,  
Figure 3: Synchronizing the real-time connection state to the kernel datapath and user space for encapsulating a lookup key. The key containing the connection state can match stateful rules.  
actions = controller) and (match: connect_state = disconnection, actions: self-learning, go to table x). The self-learning rules are installed in flow table x.  
When a new packet (src_mac = sm1, ImPort = p1) arrives at the ingress port of a switch, it will be delivered from the datapath in the kernel to the first flow table in the user space for lookup. The packet will get a current connectivity state and then use it to match against the rules. If the controller communication with this switch is available, the packet will match the first rule and be sent up to controller for further processing. Otherwise, the packet will match the second rule and execute two actions in sequence.  
The first action is the self-learning action and should be executed on the packet. It will insert a new rule or update a corresponding rule in flow table x. If there is a rule whose dst_mac field is the same as sm1, the rule's action should be updated to p1. Otherwise a new rule will be installed: (dst_mac = sm1, action = out p1). After that, this packet is set to table x for lookup. This packet may match against self-learning rules and be forwarded. Of course, there can be a miss-flow to process the mismatched packet specifically.  
For a harsh agriculture environment, the above example can perform the failure recovery function. Communication in the vehicle sensor network will be uninterrupted even if the controller is not available.
# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
## 2 Methodology
### The Auto-Regressive Integrated Moving Average Models  
Wind speed prediction can be treated as a time series prediction problem. The auto-regressive integrated moving average model (ARIMA) [5] has been one of the most popular approaches to forecasting, as it is robust and easy to implement. The ARIMA model is comprised by three parts, i.e., the autoregressive (AR) model, moving average (MA) model, and an integrated part (I) achieved using the differential. In term of the non-stationary data, the ARIMA model preprocess the data using the differential to make the data stationary. Thus, the ARIMA model is suitable for the forecasting of the non-stationary time series. After that, the ARIMA model treats the future value of the prediction as a linear combination of the past observations and pure random errors. The ARIMA (\(p\), \(q\), \(d\)) forecasting model for time series \(y_{t}\) can be expressed as follows [26; 27]  
\[\Phi(B)\
abla^{d}(y_{t}-\mu)=\Theta(B)e_{t},\ \ t=1,2,\ldots,T \tag{5}\]  
where \(y_{t}\) is the time series at time period \(t\); \(\mu\) is a constant, representing the mean value of the time series; \(e_{t}\) is the random errors at time period \(t\); supposing that the random errors are independent and identically distributed, with a mean of zero and a constant variance, i.e., \(E(e_{t})=0\), \(Var(e_{t})=\sigma^{2}\); \(B\) is the backward shift operator, \(y_{t-p}=B^{p}y_{t},\forall p\geq 1\); \(d\) represents the order of difference; \(\
abla\) is difference operator, \(\
abla^{d}=(1-B)^{d}\); \(\Phi(B)\) is the polynomial of AR model, \(\Phi(B)=1-\sum_{i=1}^{p}\phi_{i}B^{i}\), \(p\), \(\phi_{i}\) are the order and coefficient of the AR model, respectively; \(\Theta(B)\) is the polynomial of the MA model, \(\Theta(B)=1-\sum_{j=1}^{q}\theta_{j}B^{j}\), \(q\), \(\theta_{j}\) are the order and coefficient of the MA model respectively.  
Modeling and predicted wind speed using the ARIMA model mainly includes the following three important steps:  
1. Model order identification. The stationarity detection of the time series is carried out. The time series should be converted to a stationary time series using differential operation if a non-stationary series is detected. Then, the differential order \(d\) can be determined. After that, the model orders \(p\) and \(q\) can be determined according to the AIC criteria by calculation of the Auto-Correlation function (ACF) and the Partial ACF (PACF).
2. Estimation of the model parameters. The maximum likelihood method is usually adopted to estimate the model parameters.
3. Diagnostic checking and prediction. Whether the model is suitable for the series is determined, and the future wind speeds are predicted by the constructed ARIMA model.
# Investigation on Cutting Blasting Efficiency of Hard Rock Tunnels under Different Charge Diameters
## 4 Cutting blasting Experiments
### Experimental Results and Analysis  
Since it was very difficult to keep the rock conditions completely consistent in practical mine engineering, ten cutting blasting experiments were performed for each charge diameter to avoid the adverse effect of rock conditions on the experimental reliability. After the cutting blasting experiments, the final cutting depth of the cutting cavity was measured and the hole utilization was calculated. The measurement of the residual hole length used for the indirect calculation of cutting depth is shown in Figure 10. The statistics of the cutting blasting effects under the three different charge diameters are shown in Table 7.  
Figure 10: Measurement of residual hole length.  
Figure 9: Rock vertical slope used for a cutting blasting experiment.  
As illustrated in Table 7, when the cutting holes adopted \(\Phi\) 32 mm blasting holes and \(\Phi\) 27 mm cartridges, the average final cutting depth and average hole utilization were 1.76 m and 70.4%, respectively. When the \(\Phi\) 42 mm blasting holes and \(\Phi\) 35 mm cartridges were employed in those cutting holes, the average final cutting depth and average hole utilization increased to 2.05 m and 82.0%, respectively. When the cutting holes used \(\Phi\) 50 mm blasting holes and \(\Phi\) 42 mm cartridges, the average final cutting depth and average hole utilization reached 2.35 m and 94.0%, respectively. The improvement of the final cutting depth and hole utilization powerfully confirmed that increasing the charge diameter could significantly improve the cutting blasting efficiency of hard rock tunnels.  
The main reason for the above blasting results was that the increase in charge diameter could enhance the blasting stress field intensity and crack evolution capacity in the cutting cavity, and then the hard rock in the cutting cavity could be broken into small rock fragments that were easy to be discarded. The typical rock fragmentations under different the working conditions are shown in Figure 11. It is obvious that the rock fragmentation decreases significantly with the increase in the charge diameter. The sharp contrast of rock fragmentation under different charge diameters also strongly proves the validity of the results of the theoretical analysis and numerical simulation.
## 1 Introduction
The findings reveal the performance of the latest TMPA products in an extreme event and will guide the application of TMPA products for a hydrological model. The uncertainties quantified for TMPA products can help algorithm developers to further improve the algorithm. It is worth noting that there is a big difference between the sampling volumes of rain gauge and TRMM sensors. The inhomogeneity of the precipitation may cause a non-uniform beam filling (NUBF) problem for remote sensing instruments, which is a common problem to the remote sensing community and has been reported in many previous studies (e.g., [PERSON] and [PERSON] [21], [PERSON] and [PERSON] [22], [PERSON], _et al_. [23] and [PERSON], _et al_. [24]). Other studies have also shown that the NUBF may lead to uncertainty in the evaluation of remote sensing measurements through _in-situ_ (e.g., rain gauge) measurements [25, 26]. In this study, we averaged rain gauge measurements within an individual pixel of TRMM data in order to mitigate the evaluation uncertainty related to the NUBF effect. The area-averaged rain gauge measurements can reduce the intrinsic difference of sampling volume between rain gauge and satellite. We assumed that rain gauge measurements represent the precipitation truth within the corresponding pixel of satellite data. This kind of assumption is a common way to evaluate remote sensing products and has been widely applied by the remote sensing community [1, 27, 28, 29, 30, 31]. Considering that the TRMM products to be evaluated have the same spatio-temporal resolution, to better address the differences for these TRMM products, the uncertainty analysis associated with the NUBF effect has not been included in the current study. This paper is organized as follows. Section 2 introduces the study domain and the data used in the analysis. Section 3 analyzes the spatial characteristics and error quantification for the rainfall in terms of total rainfall accumulation, temporal variation, and rainfall contingency as a function of rainfall intensity. A summary of analysis results and the conclusion of the study are given in Section 4.
# UAV Image Small Object Detection Based on RSAD Algorithm
## 3 Proposed Method  
### Establishment of the Backbone Network  
The object detection algorithm's foundation, the backbone network, directly influences how well it performs in terms of object detection [36]. As we all know, medium and large object recognition is more advantageous with deeper backbone network layers since more information can be gleaned from the image. Because there are a lot of small objects in UAV images and the background is complicated, the deeper layer backbone network's feature  
Figure 2: Display of the VisDrone UAV dataset and object’s width and height distribution.  
extraction effect on small objects is less effective than it is on medium and large objects. As the layers are deepened, the information pertaining to small objects is also lost or even completely lost. At the same time, considering the practical application of the algorithm, the number of parameters of the backbone network should not be too large, otherwise it is difficult to be integrated into the UAV system.  
In this study, in order to compare the quality of different backbone networks oriented to real applications, we collected and analysed the accuracy values (based on LSVRC dataset) reported in the literature are shown in Figure 3[37; 38; 39; 40; 41; 42]. From the figure, it can be seen that ResNet-152, ResNet-101, and ResNet-50 achieved the top three accuracies. The reason why the ResNet backbone network has such a good performance is because of the use of the residual structure. The proposed residual structure cleverly solves the problem of accuracy degradation by skip-connection, which will allow early feature s(x) to propagate further in the architecture (H(x)), as opposed to something like VGG-16 as in SSD. Figure 4 shows the difference between the normal structure and the residual structure. However, considering the limited resources of the UAV. The number of parameters of the backbone network and the size of the computation affect whether it can be applied on UAVs or not. In this study, the number of parameters and the amount of computation of these backbone networks are calculated by Pytorch in Python and are shown in the following table.  
From Figure 3 and Table 1, it can be seen that AlexNet and BN-AlexNet are poor both in terms of accuracy and the number of parameters and flops. The BN-NIN, Enet, GoogLeNet, ResNet-1 and ResNet-34 has suitable Flops, few number of parameters, but less accuracy. The VGG-16 and VGG-19 though have a good accuracy, the parameters and flops  
Figure 4: Comparison of normal and residual structures.  
Figure 3: Different backbone network accuracy.  
are too large. The ResNet-50, ResNet-101 and ResNet-152 have a better accuracy, however, The ResNet-101 and ResNet-152 are complicated compared to ResNet-50. Comprehensive consideration, the ResNet-50 is more appropriate for UAV images. As a result, ResNet-50 is used in this study as the RSAD backbone network, retaining the multi-scale feature map identification capabilities of the SSD object detection algorithm.
## 7 Conclusions  
In this work, the proposed PDM-UWOC system utilizing the on-off keying (OOK) modulation scheme demonstrates promising results for achieving high-speed network connectivity underwater. The modeling of the UWOC link was carried out in MATLAB(tm), and the PDM-based transmitter and receiver were designed in OptiSystem(tm) software. The numerical simulation results indicate that the NRZ modulation scheme outperforms the RZ scheme in terms of BER at varying distances. The NRZ scheme exhibits lower BER values, providing better communication performance over longer distances compared to the RZ scheme. Additionally, the eye diagrams of both encoding schemes show clear openings, confirming the successful transmission of high-speed data at 9.5 m underwater. Furthermore, the investigations conducted at different salinity levels highlight the impact of water salinity on communication performance. As salinity increases, the BER values also increase, indicating a degradation in the system's performance. Higher salinity levels introduce additional signal impairments, leading to a higher likelihood of errors in transmitted data. These findings emphasize the importance of considering salinity levels in designing andoptimizing UWOC systems. Overall, the proposed PDM-enabled UWOC system with NRZ encoding demonstrates its potential for providing seamless and reliable communication in underwater environments, considering both freshwater and varying salinity conditions.  
Conceptualization, S.C.; methodology, S.C. and A.S.; software, S.C. and A.S.; validation, S.K. and S.S.; formal analysis, S.C. and L.W.; investigation, A.S. and R.U.; resources, A.P.; data curation, R.U. and A.P.; writing--original draft preparation, S.C.; writing--review and editing, L.W.; visualization, S.K.; supervision, L.W.; project administration, S.C. and L.W.; funding acquisition, S.C. and L.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
This research project is supported by the Second Century Fund (C2F), Chulalongkorn University, Thailand.  
The authors declare no conflict of interest.