esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the literature [15; 16; 17; 18; 19], the viable solution for class imbalance or data insufficiency is data augmentation/enlargement. However, this approach becomes impractical when the training dataset lacks sufficient length to capture the fundamental discernible characteristics within the data. Additionally, any biases present in the original datasets will persist in the augmented dataset. Moreover, identifying an effective augmentation approach presents its own set of challenges. In this paper, we trained a biLSTM-based autoencoder with maximal overlap discrete wavelet transform (MODWT) layer to accurately predict the signal anomaly and detect the presence of structural damage and, based on this, to help determine the damage location. MODWT decomposes the input signals into multiple levels of details with different frequency resolution which enables RNN to capture both temporal and spectral features simultaneously. This enhances the RNN capability to detect signal anomalies that exhibit specific time and frequency scale. The proposed scheme exploits only pristine/healthy signals and is trained without any labeling. So, the problem of supervised-based learning methods such as class imbalance and data insufficiency can be avoided. Once the proposed DL algorithm identifies the anomalous exciter-sensor path, damage localization can be done with the help of an image reconstruction algorithm. Figure 1 represents the flow chart of the proposed methodology used in this paper. The rest of the paper is organized as follows: in Section 2, a brief description of the autoencoder, LSTM, and MODWT layer is presented, while in the next section, the proposed methodology along with the experimental setup is explained. In Section 4, anomaly detection and damage localization based on the proposed methodology are performed, while the conclusion is drawn in the last section.
# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
## 6 Conclusions  
In this paper, an emotion recognition method based on speech data and feature fusion is proposed. It has been verified on the public IEMOCAP and the self-built CHSE-DB dataset. First, in view of the small scale of each speech emotion dataset and the uneven classification of samples, the Mixup method in the field of image recognition is migrated and applied to one-dimensional audio sequence data. Then, on the one hand, to solve the problem that the optimal speech emotion feature set is still unclear, and in order to extract more representative fusion features, the LightGBM method is used to screen several global feature sets. On the other hand, based on the conventional use of the convolutional neural network, the multi-head attention mechanism is used to learn the emotional salient features, and then the long short-term memory network is used to analyze the time series. The information is analyzed and output. Finally, by fusing the extracted global statistical feature vector and the time-frequency domain vector, the emotion expression features from the two granularities are complemented, and the emotion discrimination ability of the model is effectively improved. The unweighted average test accuracy of 66.44% and 93.47% was obtained on the IEMOCAP and CHSE-DB dataset, respectively. According to the experimental results, our proposed method has higher accuracy compared with previous studies in the literature. Our proposed data enhancement method is widely applicable to multi-label audio databases and can alleviate the problems of insufficient and unbalanced data. At present, we are still lacking in the optimal set and feature fusion of emotional acoustic features, which will be the focus of our next work. In the future, we will also consider improving the network structure to improve the recognition rate and generalization ability. Considering the important position of emotion recognition in human-computer interaction, the improvement of the speed of system recognition is also a problem we need to solve in the future.  
[PERSON] conducted the research study, and wrote the paper; [PERSON] helped to edit the paper; [PERSON] made suggestions to this paper and guided the research study. [PERSON] and [PERSON] participated in the revision and inspection of the article. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Confusion matrix for IEMOCAP and CHSE-DB Dataset.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data and materials are available upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Case Study and Results  
### Experimental Setup  
The proposed modeling framework was applied to a real-world case study based on the public transportation system (PTS) of the city of Belfort, France. The goal was to evaluate the total energy consumption of a UAV-based delivery system operating along real bus lines, under the four mission scenarios defined in Section 3.2.
# Model Test on Grouting Properties of Alluvial Filler Soil
## 5 Theoretical Analysis of Grouting Volume and Its Quantitative Method
### Theoretical Grouting Volume
#### 5.2.2 Determination of Injection Rate of Soil Grouting  
The indoor test was conducted to determine the injection rate of grouting.  
(1) Before grouting, three sampling holes were set in the grouting area for drilling and sampling; the depth of drilling was the same as that of grouting.  
(2) The soil samples were sent to the laboratory, and the water absorption method was carried out to determine the soil porosity P\({}_{1}\) before grouting.  
(3) After grouting, three sampling holes were also set in the grouting zone; the depth of drilling was the same as that of grouting.  
(4) The water absorption experiment was performed to obtain the soil porosity P\({}_{2}\) after the grouting.  
(5) The ratio of the porosity after grouting to the porosity before grouting is the injection rate of soil, \(\eta=\rm{P}_{2}/\rm{P}_{1}\).  
The test results showed that the porosity of the soil masses before grouting was P\({}_{1}=0.304\), and the porosity of the soil masses after grouting was P\({}_{2}=\)0.131. The volume of pore in the injected soil can be obtained by dividing the volume \(V\) of the soil in the grouting zone by the porosity P\({}_{1}\), that is, \(V_{g}=6.422\) m\({}^{3}\). The injection rate of soil is \(\eta=0.431\).
## 4 Hardware-in-the-Loop Real-Time Simulation
### Circular Curve Performance  
Figure 9 shows the heading error, yaw rate of each module, tire sideslip angle of tires, and folding angle curve of the SRT under the proposed method and the same condition in Section 4.1. It can be seen that the heading error, yaw rate, and folding angle of the vehicle reach their maximum values when entering the curve, and the overshoot is minimal. The above errors and vehicle status remain stable when the curve radius remains unchanged and converge to 0 when returning to the straight line. The maximum error in the heading error does not exceed 0.06 rad, and the yaw rate of each module and the folding angle of every hinge are consistent. Only the folding angle of the second hinge is larger than the other two because of the dimension difference between the middle modules and others, which reflects good curve passing stability and anti-folding stability. The sideslip angle of the tires on each axle is within a small range of \(-0.006\) to \(0.014\) rad, which can result in lower tire wear. At the same time, the tires are in a linear working area, ensuring vehicle controllability. The sideslip of the first, second, and sixth axle wheels are closing, while the third and fifth axle wheels are the same. Due to the \"virtual axle\" method, the sideslip of the fourth axle wheels is closing to 0.  
Figure 8: Simulation results under different control strategies: (**a**) lateral deviation; (**b**) hinge force; (**c**) width of turning passageway; (**d**) test line.
## 3 Results  
### Isometric Force Outcomes  
Table 2 presents the descriptive and inferential statistics for the peak force (PF) and impulse (IP) across five time points (PRE, POST, 5 min, 10 min, and 15 min) and three limb occlusion pressure (LOP) conditions (0%, 40%, and 80%). The values are expressed as mean \(\pm\) SD in kilograms (kg) and Newtons (Nw) for PF and Newton-seconds (Nw-s) for IP.  
A significant main effect of time was found for the PF (F (4, 116) = 3.07, \(p\) = 0.02, \(\eta^{2}\) = 0.11) and a marginal effect for the IP (F (4, 116) = 2.41, \(p\) = 0.05, \(\eta^{2}\) = 0.09) under the 0% LOP condition. Post hoc analysis showed a non-significant decrease in the PF from PRE to POST (mean difference = 58.53 Nw, \(p\) = 0.07), followed by a return to baseline at 10 min (mean difference PRE-10 min = \(-\)4.22 Nw, \(p\) = 1.00). The largest decline was observed from POST to 10 min (-62.75 Nw), which reached statistical significance after Holm correction (\(p\) = 0.05). Effect sizes for PF comparisons were small to moderate, with the PRE-POST contrast showing a Cohen's d = 0.35 and a 95% CI of [\(-\)0.21, 0.91].  
For the IP, the PRE-POST comparison showed a statistically significant reduction (mean difference = 304.29 Nw-s, \(p\) = 0.04), with a small effect size (Cohen's d = 0.28). All subsequent comparisons (e.g., POST-10 min, POST-15 min) were not significant (\(p\) > 0.05), with effect sizes remaining small ([PERSON]'s d \(\leq\) 0.28). No significant time effects were observed for the PF or IP under the 40% or 80% LOP conditions (\(p\) > 0.05). Due to minimal fluctuations, no post hoc tests were conducted in these conditions.
## 4 Experimental Results And Analysis
### EER (Equal Error Rate) and the Threshold (th)  
The last step in adjusting our model for perceptual purposes is the selection of the threshold. The aim is to differentiate recordings with different content and to detect files with perceptual manipulation. This is achieved by balancing FAR (false acceptance rate) and FRR (false rejection rate). FAR is the ratio of the number of false acceptances (i.e., when two recordings of different content are classified as perceptual modification) to the number of attempts, while FRR is the ratio of the number of false rejections (i.e., when two recordings with the same perceptual content are classified as different content) to the number of attempts. For forensic purposes, FAR implies that a new recording can be used as (false) evidence and FRR implies that evidence that has been perceptually manipulated can be discarded as valid evidence. Neither case is desirable, so it is necessary to find the best point where both parameters have the same value, i.e., the EER.  
Figures 6-8 show the FRR and FAR values for different threshold values for each type of perceptual manipulation.  
For MPEG-4 AAC manipulation, according to Figure 6, the EER value is obtained when the threshold is 0.37, where the values of FAR and FRR are equal to 0.2%. It should be noted that for threshold values lower than EER, the value of FAR is extremely low, and for threshold values higher than EER, the value of FRR decreases. EER is the trade-off between both performances. The above results mean that discrimination between perceptual manipulation or different content is correct in the  
Figure 6: EER for MPEG-4 AAC manipulation. The x-axis represents the threshold (0–1), and the y-axis represents the error rate (0–100).  
Figure 7: EER for volume adjustment manipulation. The x-axis represents the threshold (0–1), and the y-axis represents the error rate (0–100).  
99.8% of cases (i.e., 100 \(-\) EER). It is also worth noting that with 124,750 BER values, the resolution is \(1/124,750=8\times 10^{-6}\); for 500 BER values, the resolution is \(2\times 10^{-3}\). This means that with this dataset, the lowest possible values of FAR and FRR are \(8\times 10^{-4}\)% and 0.2%, respectively.  
In terms of volume adjustment, for th = 0.22, the FRR equals 0% and the FAR equals \(208\times 10^{-6}\)%. Since the resolutions of FRR and FAR are not equal, the threshold of 0.22 is assigned as point EER, with a value of \(208\times 10^{-6}\)%. The use of this threshold results in discrimination between perceptual manipulation and different content being correct in 99.9979% of cases.  
For re-sampling manipulation, the EER value is 0.2% for a th = 0.37. The discrimination accuracy is equal to the MPEG-4 AAC compression case.  
It is important to note that both FLAC manipulation and re-quantization work without false classifications. This means that, despite the threshold value, in all cases the data are correctly classified.
# A Weighted PageRank-Based Bug Report Summarization Method Using Bug Report Relationships
## 4 Experimental Set-Up  
### Research Questions  
There are mainly three kinds of relationships between bug reports. Past research only used the duplicates relationship to improve the quality of a bug report summary. In this paper, we intend to use two other relationships, blocks and depends-on, to improve the quality of a bug report summary. Therefore, we select the following main research questions:  
1. Can our proposed bug report summarization method improve the quality of a summary by using the dependency relationships (blocks, depends-on)?
2. How does the number of bug reports' dependency relationships affect summary quality?
## 4 Numerical Simulation
### Governing Equations  
The flow and the heat transfer are assumed to be steady state. Air properties are assumed to be constant and evaluated at the inlet temperature of air flow. The radiation heat transfer is neglected.  
Considering all of these assumptions, the thermo-hydrodynamic evolution of the airflow is governed by the following fundamental equations:  
Conservation of mass:  
\[\
abla\cdot v=0 \tag{1}\]  
Conservation of momentum:  
\[(v\cdot\
abla)v=-\frac{\
abla p}{\rho}+v\
abla^{2}v \tag{2}\]  
Conservation of energy:  
\[(v\cdot\
abla)T=\lambda\
abla^{2}T \tag{3}\]  
A part of the electrical energy crossing the SC is transformed into Joule heating. The heat dissipated from a SC, \(\dot{Q}\left[W\right]\), is calculated as a function of the current rate:  
\[\dot{Q}=ESR\times l^{2} \tag{4}\]  
The heat flux calculated above is implemented as a heat flux source with a volumetric density, \(s\) [W\(\cdot\)m\({}^{-3}\)]:  
\[s=\frac{\dot{Q}}{L\times\frac{\pi}{4}D^{2}} \tag{5}\]  
Also, a spatial thermal conductivity variation for each SC is considered.  
With these assumptions, for the solid, conservation energy is expressed as:  
* In the SCs:  
\[\left(\lambda\
abla^{2}\right)T+s=0 \tag{6}\]  
Figure 5: (**a**) Computed domain and (**b**) detail in the SCs area.  
In the sheath wall:  
\[\lambda\
abla^{2}T=0 \tag{7}\]
## 5 AI-Empowered Communication with Perceptual Constraint
### AI-Empowered Semantic Communication
#### 5.2.2 Generative Modeling in User Equipment  
Due to the increasing hardware computing capability, UE such as smartphones is increasingly capable of performing intelligent tasks involving generative models. In wireless communications, UE encompasses a wide range of devices, including smartphones, tablets, specialized Internet of Things (IoT) devices, and industrial sensors, all of which are capable of transmitting and receiving reliable and efficient communication services. UE features software-defined encoders and decoders for audio, video, and other data formats, as well as support for wireless communication protocols such as LTE and 5G NR. More recently, LLMs have been deployed on edge devices, such as smartphones. Applications like prompt compression [92] enable extended model inference (EMI) and native model inference (NMI), which apply prompt compression and inference on the same devices and facilitate interactions between edge devices and cloud servers, respectively.  
Recent research indicates that the joint source-channel coding (JSCC) scheme, combined with a generative model for reconstruction, can achieve substantial reductions in bandwidth while significantly enhancing the perceptual quality of the reconstruction. Utilizing VAEs, ref. [12] reimagines communication systems as a joint optimization of the  
Figure 4: Generative models deployed on user equipment (UE), base stations (BSs), and core network (CN) in AI-empowered communication [105; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133].  
transmitter (Tx) and receiver (Rx) in an end-to-end manner for reconstruction tasks. The source node (Tx) encodes the data using neural networks that create compact representations while ensuring reliability. For example, a pre-trained generative model based on GANs is employed in [126], and the destination node (Rx) utilizes a generative model to reconstruct the data. In [131], a diffusion model is deployed at the receiver to achieve high-perceptual-quality wireless image transmission. In [134], common modulation schemes are incorporated into the encoded representation as well. In [100], a JSCC video transmission scheme is proposed using deep neural networks. Additionally, Rx may provide feedback to the source node, enabling dynamic adjustments to enhance communication quality [135].
## 2 Path Planning Algorithm Based on the Markov Decision Process (MDP)  
### Markov Decision Process  
The target region is first divided into multiple orthogonal grids. For path planning, an action that the AUV takes only depends upon the present state, not on the previous ones that it has experienced. Here, the state specifically refers to the appearance of the AUV inside a grid, rather than the movement process of the AUV itself. Since each grid usually ranges from several hundreds of meters to several kilometers, which is much larger than the actual size of the AUV (usually 1 to 10 m), the AUV has enough time and space to adjust its state inside the grid. Therefore, the AUV's movement can be treated as a Markov process. A tuple including five parameters (\(S\), \(A\), { \(P^{a}_{s,s^{\prime}}\)}, \(\gamma\), \(R\)) is used to describe this process. Here, \(S\) is a state set, providing information of the position and velocity of the AUV. \(A\) denotes an action set, which the AUV takes to move from its present grid to neighbors. { \(P^{a}_{s,s^{\prime}}\)} denotes a transition probability matrix, defined as:  
\[P^{a}_{s,s^{\prime}}=P\big{[}S_{t+1}=s^{\prime}\big{|}A_{t}=a,S_{t}=s\big{]}, \tag{1}\]  
in which \(P^{a}_{s,s^{\prime}}\) is the probability when the AUV changes from its current state \(s\) to its successor state \(s^{\prime}\) by taking an action \(a\). Thus:  
\[\sum_{s^{\prime}}P^{a}_{s,s^{\prime}}=1\text{ and }P^{a}_{s,s^{\prime}}, \tag{2}\]  
\(\gamma\in[0,1]\) is a discount factor, which is used to adjust the proportion between the present and future values. In our simulation, we choose the discount factor \(\gamma=0.95\). \(R\) is a reward function which is a function of \(S\) and \(A\), i.e., \(R:S\times A\to R\). \(R^{a}_{S}\) denotes the reward for the AUV to take an action \(a\) in the present state \(s\). Its value is predicted by \(A\) and \(S\) at step \(t\), i.e.,  
\[R^{a}_{S}=E[R_{t+1}|A_{t}=a,S_{t}=s], \tag{3}\]  
in which \(R_{t+1}\) is the reward that the AUV gets at the next step \(t+1\). Thus, the [PERSON] decision process is described as:  
\[s_{0}\xrightarrow{a_{0}}s_{1}\xrightarrow{a_{1}}s_{2}\xrightarrow{a_{2}}s_{3 }\xrightarrow{a_{3}}s_{4}\xrightarrow{a4}\ldots \tag{4}\]  
The goal is to find an optimal strategy from all possible actions to maximize the expectation of the system's total reward, i.e., \(\max[R(s_{0})+\gamma R(s_{1})+\gamma^{2}R(s_{2})+\ldots]\). The Bellman equation [25] is used to iteratively solve the [PERSON] decision process. When the AUV takes an action in the present state \(s\) following a strategy \(\pi\), the expectation of total reward functions become:\[\begin{array}{ll}V(s)&=E[R(s_{0})+\gamma R(s_{1})+\gamma^{2}R(s_{2})+\ldots|s_{0} =s,\pi]\\ &=E[R(s_{0})+\gamma(R(s_{1})+\gamma R(s_{2})+\ldots)|s_{0}=s,\pi]\\ &=E[R(s_{0})+\gamma V^{\pi}(s_{1})|s_{0}=s,\pi],\end{array} \tag{5}\]  
in which \(R(s)\) indicates the reward in the current state \(s\).
## 2 Methodology
### Scope of Study and Research Questions  
The scope of the study is to provide an in-depth analysis of the LoRaWAN technology and its integration with ML. The study also provides a comprehensive understanding of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Sigfox** & **LoRaWAN** & **NB-IoT** \\ \hline Deployment Standardization & Proprietary & LoRa Alliance Standard since 2015 & 3 GPP Standard \\ Frequency Bands & Unlicensed sub-GHz ISM bands & Unlicensed sub-GHz ISM band & Licensed frequency bands & (GSM or LTE) \\ Modulation & BPSK in Ultra Narrow Band (100 Hz) & Chirped Spread Spectrum (LoRa) & QPSK in OFDMA (Downlink) and FDMA (Uplink) \\ Data Rate & Up to 100 bps & 300 bps to 50 kbps & Up to 200 kbps (Downlink), 20 kbps (Uplink) \\ Payload Length & Up to 12 bytes & Up to 243 bytes & Up to 1600 bytes \\ Communication Classes & Uplink initially, later bidirectional & A (bidirectional), B, C & Standalone, Guard Band, In-Band \\ Maximum Range & Exceeding 40 km & Urban = 5 km, Rural = 20 km & \textless{100 km (Urban), up to 10 km} \\ Maximum Devices per Cell & 50k [8] & 50k [8] & Over 100,000 [8] \\ Energy Consumption & Very low & Low to moderate & Low to moderate \\ Latency & Variable & Low to high, based on classes & Low due to synchronous \\  & & & communication \\ Scalability & Constrained & Highly scalable & Very scalable (Over 100,000 devices) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance overview and technical comparison [5; 8; 13].  
the state-of-the-art ML algorithms, network simulators, and datasets that are acquired by the authors to optimize resource consumption in LoRaWAN technology. To identify the scope of the study, we have formulated the following research questions:  
1. What state-of-the-art methods exist for optimizing LoRaWAN energy efficiency and enhancing network performance?
2. How can ML and AI algorithms be used to optimize resource constraints to enhance energy efficiency and LoRaWAN's performance?
3. What are the parameters that are optimized to enhance the performance of LoRaWAN networks?
4. What gaps and opportunities exist for future research in the fields of LoRaWAN performance and energy efficiency optimization?