esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving and Explainable AI in Industrial Applications
## 4 Industrial Impact and Remaining Challenges
### Remaining Challenges
#### 4.2.1 Bias and Fairness  
Data remain the base of every learning algorithm, and any issue in the underlying dataset will be reflected in the algorithm performance. One such example is data bias [70]. If datasets present biases, then these will be learned and reflected by the model predictions. Biases can also appear when data are not biased, e.g., due to design choices. Considering the training of a neural network for identifying animals, like dogs, for example, if 95% of dogs in the dataset have brown fur, then the model will not have a good performance on dogs with white fur, and the model might focus on color instead of key features to identify dogs in images.  
In the industrial environment, it is crucial to store data yielded by each hardware component (sensors, motors, etc.), so that an algorithm can extract relevant features and learn to predict accurately. Typically, equipment is running in a normal state, and breakdowns are very rare, e.g., once a week, month, or even year. Hence, omitting breakdown events will result in a biased dataset, because the model will react poorly when an abnormal event happens, thus decreasing the trust in its predictions.  
Another issue that can be encountered in datasets related to industrial applications is data noise [71]. Training only on noisy data may result in a biased model because, when presenting data without noise or with a decreased level of noise, the model may have a weaker performance. Trying to filter the noise from a dataset may not be a good choice either, since noise happens uncontrollably and is typically present in real-world applications. To address these issues, one needs to make sure that datasets include both normal and noisy data, representative for the actual use of the models.  
Another overarching topic is that of fairness [72]. There is no general definition since it is a term which spans across multiple domains such as computer science, psychology, and philosophy. A fair algorithm will be one that is not biased and does not discriminate against individuals, groups, or subgroups. If biases are identified in datasets, it is necessary to eliminate them before performing the training, to ensure that the model will not perpetuate them.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 4 System Model  
The system architecture includes APs, relay nodes, cellular users, and D2D devices as shown in Figure 1. The placement of network components was randomly dispersed within a geographical area using a Poisson point process. The Poisson point processes for the relay nodes, cellular users, and D2D users are represented by \(\varphi_{R}\), \(\varphi_{CU}\), and \(\varphi_{D}\), respectively. The given area is served by the APs with a total of \(U\) single-antenna users distributed all over the network. Users were assigned to APs based on their needs, with each AP serving a specific group of users. The densities of the relay nodes, cellular users, and device-to-device users are represented by \(\lambda_{R}\), \(\lambda_{CU}\), and \(\lambda_{D}\), respectively. In our network, we considered various channels, including the channel from an access point to a relay node \(|H_{AP_{i},R_{i}}|\), the channel from relay to relay \(|H_{R_{j},R_{i}}|\), the channel from the relay to the cellular user \(|H_{R_{j},CU_{i}}|\), the channel from the cellular to the relay user \(|H_{CU_{i},R_{i}}|\), the channel from the cellular to the cellular user \(|H_{CU_{i},CU_{i}}|\), and the channel from the D2D to the cellular user \(|H_{D_{i},CU_{i}}|\), all of which experience Rician fading with an exponent of \(\sigma\) and a path loss exponent of \(\gamma\). The notations used in paper and their descriptions are provided in Table 1.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 3 Distillation and deNoise Network(DiNo-Net)
### Fusion Method Based on Distillation  
An effective combination of these two defense strategies is achieved using knowledge distillation and KL divergence loss. The temperature in Equation (6) increases during the knowledge distillation process to smooth the logits related to the labels. This technique enables the student model to transfer its adversarial robustness readily from the teacher model. Additionally, as our analysis indicates, the expanded loss surface around the ground truth owing to the increased temperature aids in more stable adversarial denoising as shown in Figures 4 and 5. We utilized the soft distillation loss to integrate these into one comprehensive loss function. The combination of KL divergence loss and cross-entropy loss was applied in a ratio of 0.2 for the teacher model and 0.8 for the student model. This ratio has been confirmed to be optimal through these experiments.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
###### Abstract  
Escalating advancements in artificial intelligence (AI) has prompted significant security concerns, especially with its increasing commercialization. This necessitates research on safety measures to securely utilize AI models. Existing AI models are vulnerable to adversarial attacks, which are a specific form of assault methodology. Although various countermeasures have been explored, practical defense models are scarce. Current adversarial defense methods suffer from reduced accuracy, increased training time, and incomplete defense against adversarial attacks, indicating performance limitations and a lack of robustness. To address these limitations, we propose a composite defense model, the knowledge Distillation and deNoising Network (DiNo-Net), which integrates knowledge distillation and feature denoising techniques. Furthermore, we analyzed a correlation between the loss surface of adversarial perturbations and denoising techniques. Using DiNo-Net, we confirmed that increasing the temperature during the knowledge distillation process effectively amplifies the loss surface around the ground truth. Consequently, this enables more efficient denoising of the adversarial perturbations. It achieved a defense success rate of 72.7%, which is a remarkable improvement over the 41.0% success rate of models with only denoising defense mechanisms. Furthermore, DiNo-Net reduced the training time and maintained higher accuracy, confirming its efficient defense performance. We hope that this relationship will spur the development of fundamental defense strategies.  
adversarial attack; adversarial robustness; knowledge distillation; feature denoising +
Footnote †: journal: Applied sciences
## 3 Results
### The Dominance of Water Stress to Vegetation Growth in Global Drylands  
#### 3.3.1 The Dominance of CO\({}_{2}\), SW, and VPD to Vegetation Growth  
Figure 6 shows the contributions of CO\({}_{2}\), SW, and VPD to vegetation changes across global drylands from 1982 to 2018. The results exhibited great spatial heterogeneities in the pattern of contributions among CO\({}_{2}\), SW, and VPD. Rising CO\({}_{2}\) concentration produced positive contributions to vegetation growth in the middle- and low-altitude regions of global drylands (Figure 6a). For SW-stressed and VPD-stressed ecosystems, the area proportion of the latter (63.26%, Figure 6e) was larger than that of the former (62.78%, Figure 6c). The negative contributions for \(\triangle\)LAI (VPD I SW) were mainly distributed in western North America, southern Africa, central Asia, southeastern Australia, and western India (Figure 6e), while the strong positive contributions for \(\triangle\)LAI (SW I VPD) were detected in southern Africa, northeastern China, northern Australia, and western India (Figure 6c). Correspondingly, for the area proportion of non-water-stressed ecosystems, non-SW-stressed regions (37.22%, Figure 6c) were larger than those of VPD-stressed ecosystems (36.74%, Figure 6c), which indicated that VPD placed negative impacts on vegetation growth in a wider region of global drylands than SW. Furthermore, this study found obvious latitudinal heterogeneity in the latitude-averaged contribution of CO\({}_{2}\), SW, and VPD to vegetation change. Specifically, rising CO\({}_{2}\) produced positive contributions to LAI changes in most low-latitude areas in the northern and southern hemispheres (Figure 6b). Exacerbating SW deficits produced a greater constraint in the middle and low latitude areas of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Area Percentage (\%)**} & \multicolumn{4}{c}{**SW at Different Soil Depths**} \\ \cline{2-5}  & **0–7 cm** & **7–28 cm** & **28–100 cm** & **100–289 cm** \\ \hline Positive sensitivities & 74.88 & 80.29 & 80.39 & 68.43 \\ Negative sensitivities & 25.12 & 19.71 & 19.61 & 31.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Area percentage of the positive and negative sensitivities of LAI changes to SW at different soil depths in global drylands.  
Figure 5: Spatial patterns in the sensitivities of LAI to SW at the soil depths of (**a**) 0–7 cm, (**b**) 7–28 cm, (**c**) 28–100 cm, and (**d**) 100–289 cm in global drylands since the early 1980s.  
southern hemisphere (Figure 6d). Simultaneously, increasing VPD also produced notably negative impacts on vegetation changes in low-latitude areas of the northern hemisphere, exactly distributed in western North America (Figure 6f). Thus, rising atmospheric and SW deficits produced greater negative contributions to vegetation change; nevertheless, increasing CO\({}_{2}\) concentration positively contributed to the greening of global drylands from 1982 to 2018.
## 2 Methodology
### Unsupervised Anomaly Detection Using Spherical Detectors and Local Neighborhood Sampling
#### 2.4.4 Definitions  
* Euclidean norm: \(\|\cdot\|\).
* Random vector generation: \(\text{rand}\,(n)\) generates an \(n\)-dimensional vector with elements uniformly distributed in \([0,1]\).
* Bounds for each dimension: \(\text{bounds}=\text{mas}-\text{mins}\).  
Figure 3: Example result of negative selection algorithm on two-dimensional data. Different radii of the spherical detectors (red circles) were generated with decreasing radius strategy.  
* Neighborhood sampling: For a point \(x\) and parameter perc, a random point \(\tilde{x}\) in the neighborhood is generated as: \[\tilde{x}=x+(\text{rand}(n)\times 2-1)\odot\text{bounds}\times\text{perc},\] where \(\odot\) denotes element-wise multiplication.
# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 3 Methodology  
Figure 3 shows the workflow of this paper. To obtain images with high geometric accuracy, this paper investigates the performance of different fine line-time normalization methods in image-space. First, the imaging line-times of all inner CCDs were normalized by setting a nominal reference. After normalization, a homogenous CCD-line was produced through pre-processing of the installation error. In each homogenous CCD-line, all inner CCD images share the same imaging time in object-space. Finally, the phase congruency method was applied to calibrate overlapping pixels between adjacent inner CCD images.
## 2 Materials and Methods
### In Situ Measurements  
#### 2.2.1 Eddy Flux and Ancillary Measurement  
Eddy covariance (EC) system was fixed at the height of 23 m on the flux tower to measure the net ecosystem exchange of CO\({}_{2}\) (NEE). The EC system comprises an open-path infrared gas analyzer (LI-7500, LI-COR Inc., Lincoln, NE, USA) for the measurement of CO\({}_{2}\)/H\({}_{2}\)O concentrations and a three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) for the measurement of wind speed and virtual temperature. All raw EC data were recorded at 10 Hz frequency by a data logger (CR5000, Campbell Scientific, Logan, UT, USA) and then stored as the 30 min average of the EC data. Data quality control and processing were conducted using the EddyPro software (LI-COR Inc., Lincoln, NE, USA) for the production of NEE estimates. A flux partitioning algorithm [31] was employed to estimate EC-measured GPP (GPPEC) using NEE and daytime ecosystem respiration (R\({}_{\text{e}}\)).  
\[\text{GPP}_{\text{EC}}=-\text{NER}+\text{R}_{\text{e}} \tag{1}\]  
Ancillary environmental variables at canopy level were measured synchronously with EC flux measurements. The environmental variables included downward shortwave radiation (R\({}_{\text{in}}\)) and longwave radiation (R\({}_{\text{i}}\)) (CMP11, Kipp & Zonen, Delft, The Netherlands), PAR incident above the canopy (PAR\({}_{\text{canopy}}\)) (LI-190 SB, LI-COR Inc., Lincoln, NE, USA), air temperature (Ta) (HMP45C, Vaisala Group, Helsinki, Finland), air pressure (p) and atmospheric vapor pressure (ea) (CS105, Vaisala Group, Helsinki, Finland), wind speed (u) (A100R, Vector Instruments, North Wales, UK) and volumetric soil moisture content (SMC, 20 cm depth) (CS615-L, Campbell Scientific Inc., Logan, UT, USA).
# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
## 1 Introduction
However, it does not destroy the orthogonality among the subcarrier frequency components; therefore, ICI does not appear. The presence of FFO will destroy the orthogonality among the subcarriers [5]. LPWA technologies offer some solutions for low-cost, long-range connectivity, and low-power operations for the IoT system. They allow IoT devices to operate stably up to 10 years on a single battery charge. Therefore, these technologies are ideal for smart cities, tracking and tracing, metering, and smart agriculture applications. LPWA technologies and IEEE 802.15.4 were applied with limited mobility; however, in some cases, we have needed to use LPWA technologies with a high mobility channel. With high mobility, IEEE 802.15.4g SUN MR-OFDM will be affected by CFO, especially the Doppler shift. In [6; 7], the authors focused on the IFO estimation for IEEE 802.15.4g MR-OFDM, which ignored the effect and estimation of FFO. It uses only the preamble part to estimate and compensate the IFO in the entitled Physical Protocol Data Unit (PPDU) packet. Although FFO is very important, it causes non-orthogonality between subcarriers. In a mobility channel, the effects of CFO on the system vary in the time domain; therefore, we need the technical estimate and the compensated CFO in real time to combat the impact of the CFO on the system performance. In this paper, we analyze the impact of the Doppler shift on the IEEE 802.15.4g SUN MR-OFDM with high mobility between the transmitter and the receiver. In addition, we propose a novel method for the estimation and compensation of CFO in real time in the IEEE 802.15.4g SUN MR-OFDM. By using CP, which is the available part (cyclic-prefix part) in the MR-OFDM symbol frame, we can use it to estimate the CFO value without changing the architecture of symbol frames. In this way, the data rate of the system is not changed. The remainder of this paper consists of four sections. Section 2 describes the analysis of the Doppler effect in the MR-OFDM system. Section 3 provides an overview of the IEEE 802.15.4g specifications and includes the related studies on MR-OFDM compensation. In Section 4, we describe our contributions, that is, the effects analysis of the CFO on the MR-OFDM principle and of the proposed scheme on the OFDM compensation. Section 5 explains the performance evaluation with different configuration scenarios. Then, the final section concludes the paper.
## 5 Conclusions  
To solve the problem of signal waveform distortion of existing seismic-signal-denoising methods in removing the co-frequency band noise, further improve the SNR of seismic signals, and enhance the quality of seismic signals, this paper took the U-Net as the basic framework, combined atrous convolution and residual dense blocks, and designed a denoising ARDU model for seismic co-band noise. The ARDU model connects atrous convolution and residual dense blocks to form a feature extraction unit of the model's encoder. Among them, the residual dense blocks can deepen the network's depth and enhance the feature extraction ability of the network on the premise of mitigating gradient vanishing and gradient exploding. Atrous convolution can expand the receptive field without increasing the network parameters so as to obtain more abundant signal features, reduce waveform distortion, protect effective signals, and improve the quality of seismic signals. To test the feasibility and effectiveness of the ARDU model, the Stanford Global Seismic dataset was used to construct a training set and a test set and the model was trained and tested on it. The denoising results were compared with the signals containing labels, and the SNR, \(r\), and RMSE were calculated to quantify the denoising performance of the model. Finally, the following conclusions are drawn: (1) The ARDU model can effectively remove the co-band noise in seismic signals, improve the SNR of seismic signals, and enhance the quality of seismic signals, and (2) compared with wavelet threshold denoising, the DnRDB model, and the U-Net model, the ARDU model has the least distortion and the best denoising effect.  
**Author Contributions:** Formal analysis, J.C. and J.Z.; investigation, N.C.; methodology, J.C.; software, L.L.; writing--original draft, J.C., L.W. and J.Z.; writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
Figure 15: Denoising results of actual seismic signals by different denoising methods.  
**Funding:** This work was supported by Langfang Science and Technology Research and Development Plan Self-funded Project (grant No. 2022011037), the Fundamental Research Funds for the Central Universities (grant no. ZY20180112), the research and practice project of higher education reform in Hebei Province from 2019 to 2020 (grant no. 2019 GJJG471), Scientific Research Plan Projects for the National Key Research and Development Programme of China (grant no. 2018 YFC1503801), the Scientific Research Project Item of Hebei Province Education Department (grant no. QN2018317), the second batch of new engineering research and practice projects of the Ministry of Education (grant no. E-SKWLFIXL20202607), and the 2020 education research and teaching reform project of the College of Disaster Prevention Technology (grant no. JY2020A12).  
**Data Availability Statement:** Data source: ref. [30].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 2 Materials and Methods
### Proposed Segmentation System
#### 2.2.1 GMM-HMM Based VAD  
In the VAD of the proposed system, three possible classes are defined as output in each acoustic frame: voice, applause or silence. We decided to split applause and silence in two separate classes because applause is common in our Bertsolaritza database and the acoustic nature of both classes is very different. The classification of recordings is made using a frame-level GMM with an HMM post-smoothing [25]. We used 13 MFCC values with \(\Delta\) and \(\Delta^{2}\) values calculated applying a 25 ms window and 10 ms frame period. For the initial frame classification, independent GMMs are trained per each class using Expectation Maximization [26]. Frames are classified using these GMMs, but this frame level classification can create fast label changes that do not fit well to the data. This is why we used an HMM to smooth the resulting sequences. The HMM has fixed transition probabilities and forces the frame labels to remain in the same class for minimum durations depending on the likelihood values of the GMMs. We used a probability of 0.0001 outside the transition matrix diagonal for this purpose. To classify the segments, the likelihood of observation provided by each model is calculated using expression (1):  
\[P(o|s_{i})=\sum_{j=1}^{M}w_{ij}N(o|n_{ij},\Sigma_{ij}), \tag{1}\]  
where \(o\) is the MFCC vector, \(w_{ij}\), \(\mu_{ij}\), \(\Sigma_{ij}\) are the weight, mean and diagonal covariance of the component \(j\) of the state \(s_{i}\) and \(M\) is the number of Gaussian components.
## 2 Numerical Model Settings
### Gas Explosion Model
#### 2.2.2 Mathematical Model  
Gas explosion is a combustion reaction process, and the reaction is rapid. The conservation equation satisfied by the selected process is as follows.  
Equation of continuity:  
\[\frac{\partial p}{\partial t}+\frac{\partial(pu)}{\partial x}+\frac{\partial(pv )}{\partial y}+\frac{\partial(pw)}{\partial z}=0 \tag{4}\]  
Energy of continuity:  
\[\frac{\partial e}{\partial t}+u\frac{\partial e}{\partial x}+v\frac{\partial e }{\partial y}+w\frac{\partial e}{\partial z}=0 \tag{5}\]  
Momentum of continuity:  
\[\left\{\begin{array}{l}\frac{\partial u}{\partial t}+u\frac{\partial u}{ \partial x}+v\frac{\partial u}{\partial y}+w\frac{\partial u}{\partial z}=- \frac{1}{p}\frac{\partial p}{\partial x};\\ \frac{\partial v}{\partial t}+u\frac{\partial v}{\partial x}+v\frac{\partial v }{\partial y}+w\frac{\partial v}{\partial z}=-\frac{1}{p}\frac{\partial p}{ \partial y};\\ \frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+v\frac{\partial v }{\partial y}+w\frac{\partial w}{\partial z}=-\frac{1}{p}\frac{\partial p}{ \partial z}.\end{array}\right. \tag{6}\]  
Equation of state:  
\[p=p(\rho,T)=\rho RT. \tag{7}\]  
where P is pressure; x, y, and z are the parameters of the rectangular coordinate system; \(u\), \(v\), and \(w\) are the velocities in each of the three coordinate directions; \(\rho\) is the fluid density; \(T\) is the temperature; \(R\) is the gas constant; and \(e\) is the specific energy, \(e=p/(\gamma-1)+\rho(u^{2}+v^{2}+w^{2})/2\) (\(\gamma\) is Gas Index).  
Figure 3: Three-dimensional diagram of the explosion model.
## 4 RoCoMAR Algorithm
### Link Quality Gain by using Robotic Relays
Also, after \(l\) reinforcement processes, the quality of the \(j^{th}\) link becomes  
\[R_{j,j+1}^{rei}=\left(1-\frac{1}{2}e^{-\frac{\Gamma(l+1)^{2}}{d^{2}_{(j,j+1)} }}\right)^{m(l+1)} \tag{9}\]  
From Equations (8) and (9), the end-to-end link quality gain, \(G_{q}\), for the route due to the adding number of relay nodes of \(l\), can be obtained. That is  
\[G_{q}=\frac{\left(\prod\limits_{ \begin{subarray}{c}i=1\\ i\
eq j\end{subarray} }^{n}R_{i,i+1}\right)R_{j,i+1}^{rei}}{\prod\limits_{i=1}^{ n}R_{i,i+1}}=\frac{\left(\prod\limits_{ \begin{subarray}{c}i=1\\ i\
eq j\end{subarray} }^{n}R_{i,i+1}\right)R_{j,j+1}^{rei}}{\left(\prod\limits_{ \begin{subarray}{c}i=1\\ i\
eq j\end{subarray} }^{n}R_{i,i+1}\right)R_{j,j+1}^{ori}}=\frac{R_{j,j+1}^{ rei}}{R_{j,j+1}^{ori}}\approx\frac{\left(1-\frac{1}{2}e^{-\frac{\Gamma(l+1)^{2}}{d^{2}_{(j,j+1)} }}\right)^{m(l+1)}}{\left(1-\frac{1}{2}e^{-\frac{\Gamma}{d^{2}_{(j,j+1)}}} \right)^{m}} \tag{10}\]The Figure 4 shows the relationship between the quality gain obtained and the distance from the transmitter to the receiver. It is assumed that one relay node is added to the route, _i.e_., \(l=1\). In addition, we use the transmitted power of \(P_{t}=1\,mW\), carrier frequency of \(f=2.4\,GHz\), signal bandwidth of \(W=54\,MHz\), and packet length of \(m=12,000 bits\), (_i.e_., \(1500 Bytes\)) for our evaluations. As shown in Figure 4, the quality gain tends to increase as the distance increases. However, quality gain is 1 when the distance is less than 200 m, which implies that no extra relay nodes are necessary.