esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
## 5 Conclusions  
We have proposed a framework for studying the effect of global time warping conditions in an audio-audio alignment task. We characterize the behavior of standard usages of DTW, along with several strategies for improving robustness to global time warping. Our main findings are (a) standard usages of DTW perform well when both sequences are comparable in length but completely fail to handle severe global time warping; (b) using \((0,1)\) and \((1,0)\) transitions in DTW leads to poor alignment quality and is not an effective solution; (c) several strategies like adding more extreme transitions and using non-uniform transition patterns can improve robustness to global time warping, but come with a computational cost and reduction in alignment quality, and (d) the most effective strategy we found is to perform sequence length normalization using downsampling before invoking DTW. This approach achieved the best alignment accuracy across a wide range of global time warping conditions, while simultaneously maintaining or reducing runtime compared to standard usages of DTW. Because it never degrades performance and may offer substantial improvement in both runtime and accuracy, we recommend using downsampling sequence normalization as a default practice when invoking DTW. For future work, we would like to (a) determine a set of best practices for handling severe time warping conditions in the subsequence DTW case, where the global time warp factor between both sequences is unknown a priori; (b) study to what extent our findings may generalize to other domains with different data characteristics; and (c) further explore the effect of sequence normalization when changing the lengths of _both_ sequences.  
Conceptualization, T.J.T.; Funding acquisition, T.J.T.; Methodology, T.J.T.; Software, J.K. and A.P.; Supervision, T.J.T.; Validation, J.K. and A.P.; Writing--original draft, T.J.T.; Writing--review & editing, J.K. and A.P. All authors have read and agreed to the published version of the manuscript. This material is based upon work supported by the National Science Foundation under Grant No. 2144050.
## 6 Discussion
### Validation and Case Studies  
The low relative error ranging from 2.5% to 5.8% between the DNN-predicted extended RUL and the FEA-calculated values indicates that the DNN model reflects the effects of different LSP parameters on bearing fatigue life. It shows that the proposed deep learning-based remanufacturing approach is validated. Additionally, there were similar increases in RUL observed in both DNN predictions and FEA simulations (29.33% and 30.31%). Therefore, the DNN model is reliable in predicting LSP remanufacturing performance.
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 2 Overview of the Designed System
### Transceiver Structure  
The following discussion is erected on the designed signal construction, and the transmitter diagram is depicted in Figure 2. For a quadrature branch, every single bit gets spread by PRN code at first, and then is modulated by local sub-carrier to generate a current FH-BOC signal. For the in-phase branch, a constellation is required for converting a series of original binary data bits into coded data symbols, which has highly promoted transmission efficiency, such as phase-shift keying (PSK) and quadrature amplitude modulation (QAM). These data symbols are arranged from series into parallel before IFFT and are CP appended after IFFT. Then, the complete OFDM symbols need to implement up-sampling, as mentioned earlier, through convolution with the impulse response of the matched filter. The numerically-controlled oscillator (NCO) generates sin wave or cosine wave in high frequency as the main carrier, where its real component is passed to the in-phase branch and the imagine component is passed to the quadrature branch. At last, the two signals are added up and sent to the channel.  
The receiver design is depicted in Figure 3. The main target is to exclude interferences from the unpredictable channel, meanwhile achieving valid separation of the combined signal based on the characteristics of the transmitted signal. At the beginning, there is a band-pass filter for allowing through transmitted signal within a specified frequency band  
Figure 2: Block diagram of designed transmitter.  
and blocking components with the outside-band frequency. Then, the received signal is duplicated and delivered into both the in-phase and quadrature branch.  
Delivered signals need a down-conversion from carrier frequency to a lower frequency at the baseband, which is achieved by multiplying with prompt local carrier from NCO. After being processed through a low-pass filter, the FH-BOC signal, as the quadrature component, needs an acquisition procedure, since it is obscure to find the exact Doppler shift and spreads code delay. Then, the tracking process is necessary for using the acquisition results to continuously synchronize a local replica of the spreading code and carrier with the received signal. Additioanlly, as for the in-phase component, the OFDM signal is required to go through a matched filter, in order to alleviate the negative influence from Doppler shift. After sampling at the symbol rate (with CP removing), the FFT procedure, and from the parallel to serial conversion, at last, the decipher on constellation mapping is demanded to recover transmitted data bits.
# An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning
## 3 Implementation Based on PER-D2 MQN Algorithm
### Per-D2 mqn
Finally, M-DQN is introduced by adding a \"log-policy\" term of the probability of the strategy to the instant reward, i.e., by replacing \(r_{t}+\alpha\tau ln\pi_{\overline{g}}(a_{t}|s_{t})\) instead of \(r_{t}\) to strengthen the signal and optimize the learning process. Part of the pseudo-code for the PER-D2 MQN algorithm is provided in Algorithm 1. ```
Input: Prioritized Experience Replay Buffer (\(\mathbf{P}\)) Output: Trained network with parameters \(\mathbf{\theta}\)
1 Initialize \(\mathbf{P}\)
2 Initialize online network and target network with random weights and biases
3 for episode = 1 to Edo
4 Initialize state \(s\) (Reset Environment)
5 for\(t=1\) to episode_stepdo
6 Observe state \(s\) and input state \(s\) into online network
7 Select action \(a\) using \(\varepsilon-greedy\)
8 Execute action \(a\) in the environment, get \(r\), \(s^{\prime}\), and flag
9 Store experience (\(s\), \(a\), \(r\), \(s^{\prime}\), \(done\)) into \(\mathbf{P}\) with maximum priority \(\mathbf{\psi}\) of \(\mathbf{P}\)
10 ifsize of \(\mathbf{P}>\) batch - size then
11 Sample batch from \(\mathbf{P}\) based on priority weights \(\mathbf{\omega}\)
12 forach (\(s_{i}\), \(a_{i}\), \(r_{i}\), \(s^{\prime}_{i}\), \(done\)) in batchdo
13 Compute \(Q(s_{i}\), \(a_{i})\);
14 Compute Munchausen log policy term: \(\log\pi(a_{i}|\,s_{i})=Q(s^{\prime}_{i}\,a_{i})-\log\sum_{j}\exp(Q(s_{i},b))\)
15 Compute Munchausen reward: \(r^{\prime}_{i}=r_{i}+\pi\log\pi(a_{i}|\,s_{i})\)
16 Compute target: \(y_{i}=r_{i}^{\prime}+\gamma(1-done_{i})\sum_{a}{}^{\prime}\pi(a^{\prime}|\,s^ {\prime}_{i})(Q^{\prime}(s^{\prime}_{i},\ a^{\prime})-\log\pi(a^{\prime}|\,s^ {\prime}_{i})\)
17 Compute target: \(\psi_{i}=|y_{i}-Q(s_{i},\ a_{i};\theta)|+e\)
18 Compute loss: \(L=\frac{1}{N}\sum_{i}\omega_{i}\psi_{i}^{2}\)
19 Update priorities in \(\mathbf{P}\)
20 Gradient descent on loss
21 ifglobal_step % target update == 0 then
22 Update target network \(\mathbf{\theta}^{-}=\mathbf{\theta}\)
23 ifdonethen
24 Break
25 Increment global_step
26 if\(\varepsilon>\varepsilon_{min}\)then
27\(\varepsilon^{*}=\varepsilon_{decay}\)
```  
**Algorithm 1**PER-D2 MQN Algorithm
## 3 Methodology
### The ReMAHA Algorithm
#### 3.2.2 Inter-Cluster Weighted Mahalanobis Distance Sorting  
Directly generating new data using a genetic algorithm can lead to excessive divergence in the generated data, potentially causing a loss of original features [36]. Given the large volume of traffic accident data, this study first employs mini batch K-means to cluster minority class sample data. This process groups data with similar features into clusters, which are then used for inter-cluster sorting of the sample data.  
Additionally, traditional Mahalanobis distance, although addressing the issue of dimension measurement not resolved by Euclidean distance, neglects the varying influence of features on labels. Applying feature weighting can lead to more precise distance calculations between sample points. Here, it is assumed that the sample for calculating the distance originates from the same cluster, with a cluster center represented as \(C\) = (\(c_{1}\), \(c_{2}\),..., \(c_{n}\)), and the point for Mahalanobis distance calculation as \(X\) = (\(x_{1}\), \(x_{2}\),..., \(x_{n}\)). Following that, the feature importance diagonal matrix \(B\) is generated using the relief-F algorithm. The weighted Mahalanobis distance is then represented as follows:  
\[D_{M}(X,C)=\sqrt{\left(X-C\right)^{T}B\Sigma^{-1}B^{T}(X-C)} \tag{9}\]  
\(\Sigma\) represents the covariance matrix, and the formula for calculating the covariance is as follows:  
\[\text{cov}(X,Y)=\frac{\sum\limits_{i=1}^{n}\left(x_{i}-\overline{x}\right)(y _{i}-\overline{y})}{n-1} \tag{10}\]
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 2 Data  
In the next sections, the data routinely processed by AUTOWADE are introduced, together with those used for validation purposes.
## 4 SWellEx-96 Experimental Data Analysis  
### Experiment Description  
Data from the SWellEx-96 experiment were used. The SWellEx-96 experiment was conducted by the Marine Physical Lab in the littoral waters just outside the port of San Diego in May 1996. The environmental information and data are available online [20]. Therefore, they are widely used to verify the performance of underwater target detection [21], localization [22], tracking [23], and underwater acoustic inversion [24] methods.  
The receiving arrays used in the experiment include a vertical line array (VLA), a tilt line array (TLA), and two horizontal line arrays (HLA). The experiment is divided into two events: S5 and S59. Sound sources in both events travel along the contour line and are suitable for signal processing in a range-independent sound field environment, and event S59 has one more interference source than event S5. Here, the data of the vertical array from event S5 are used to verify the performance of the proposed range localization method.  
Figure 4: Comparison of radial velocity estimates with theoretical values (the black solid line in the figure) versus time.  
Figure 5: Estimated source range.  
Figure 6 shows the ship trajectory for event S5, with two sources being towed at depths of 54 m and 9 m, respectively. The deeper source emits comb-like single-frequency signals at various signal-to-noise ratios (SNR) and frequencies from 49 to 400 Hz, and the shallower source emits 9 single-frequency signals with a frequency range of 109 to 385 Hz. The sampling rate is 1500 Hz. The SSP was measured using a CTD several times during the 75 min duration of the experiment.  
The environmental parameters of the waveguide and the configuration of the VLA are shown in Figure 7. The depth of the highest array element is 94.125 m and the depth of the lowest one is 212.25 m.  
Figure 6: Map of the ship trajectory for event S5.  
Figure 7: Environmental parameters of the waveguide and configuration of the VLA.
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 2 Background
### Multi-Target Models for Sets of Trajectories  
1. Multi-target dynamic models: At time step \(k\), existing targets \(x_{k-1}\) survive to the next time step with probability \(p^{5}(x_{k-1})\), or perish with probability \(1-p^{5}(x_{k-1})\). If a target survives, its Markov state transition function is \(g_{k}(\cdot|x_{k-1})\). At each time step, newly detected targets \(x_{k}\) are independent of other surviving targets and follow a PPP with a birth intensity of \(\lambda_{k}^{B}(x_{k})\).
2. Multi-target measurement models: The measurement set \(z^{k}\) for targets is divided into two parts: one consists of actual measurement sequences generated by extended single-target detection by sensors, modeled as PPPs with measurement likelihoods of \(\mathcal{\ell}_{k}(z_{k}|x_{k})\) produced by each \(x_{k}\in\mathcal{X}^{ds}\) and with a Poisson intensity of \(\gamma_{k}(x_{k})\). The other consists of clutter measurements induced by measurement noise, modeled as a PPP with a Poisson intensity of \(\lambda_{k}^{C}(z_{k})=\gamma_{k}^{C}\mu_{k}^{C}(z_{k})\), a Poisson rate of \(\gamma_{k}^{C}\) and clutter intensity of \(\mu_{k}^{C}(z_{k})\).
3. Dynamic model for sets of all trajectories: For the set of surviving trajectories \(\mathbf{X}_{k-1}\) at time step \(k-1\), each trajectory \(X=(t,x^{1:v})\in\mathbf{X}_{k-1}\) has a survival probability \(p^{S}(X)=p^{S}(x^{v})\). If the trajectory continues to survive in the next time step, its transition probability density is \[g_{k}(\tilde{t},y^{1:\overline{v}}\Big{|}X)=\delta_{t}[\tilde{t}]\delta_{v+1 }[\overline{v}]\delta_{x^{1:\overline{v}}}(y^{1:\overline{v}-1})g_{k}(y^{ \overline{v}}\Big{|}x^{v})\] (1) Newly initiated trajectory sets \(\mathbf{X}_{k}\) independently follow a PPP with an intensity of \[\lambda_{k}^{B}\Big{(}t,x^{1:v}\Big{)}=\delta_{k}[t]\delta_{1}[v]\lambda_{k}^{ B}(x^{v})\] (2) Given the complete set \(\mathbf{X}_{k}\) of trajectories, both surviving and newly initiated, at time step \(k\) for each \(X=\big{(}t,x^{1:v}\big{)}\in\mathbf{X}_{k}\), \(t+v-1\leq k\), the survival probability is \(100\%\), i.e., \(p^{S}(X)=1\), and the trajectory's transition density is \[g_{k}\Big{(}\tilde{t},y^{1:\overline{v}}\Big{|}X\Big{)}=\delta_{t}[\tilde{t}] \times\begin{cases}\delta_{v}[\overline{v}]\delta_{x^{1:\overline{v}}}\big{(}y ^{1:\overline{v}}\big{)}&\overline{\omega}<k-1\\ \delta_{v}[\overline{v}]\delta_{x^{1:\overline{v}}}\big{(}y^{1:\overline{v}} \big{)}\big{(}1-p^{S}(x^{v})\big{)}&\overline{\omega}=k-1\\ \delta_{v+1}[\overline{v}]\delta_{x^{1:\overline{v}}}\big{(}y^{1:\overline{v} -1}\big{)}p^{S}(x^{v})g_{k}\big{(}y^{\overline{v}}\big{|}x^{v}\big{)}& \overline{\omega}=k\end{cases}\] (3) Thus, the current moment's survival probability of a trajectory depends solely on the final state of the trajectory in the previous moment and is independent of other moments. The probability density of the measurement sequence \(w_{k}\) produced by a single trajectory \((t,x^{1:v})\in\mathbf{X}_{k}\) is \(\ell_{k}(\mathbf{w}_{k}|t,x^{1:v})=\ell_{k}(\mathbf{w}_{k}|x^{v})\).
## 3 System and Threat Model
### TCC Communication Architecture  
Only limited works have focused on the thermal covert channel due to the complexity of the attacks (e.g., require expert knowledge and skills) and noisy traces [32]. A thermal covert channel relies on a pair consisting of a transmitter and a receiver, which is shown in Figure 3. To start the transmission process, first, the receiver and the transmitter require achieving agreement on the transmission frequency for the thermal covert channel. Then, the transmitter reads the sensitive information and data and packetizes them with a so-called Error Correcting Code (ECC) [2]. When the packet is created, it requires to be changed to the temperature signal for transmission. Basically, the transmitter sends thethermal signal by controlling the power consumption of the transmitter core [33; 34]. It means that the transmitter runs computation-intensive code with a particular purpose to generate a high temperature, inserting idle CPU cycles to cool down the chip and generate a low temperature [3; 5]. For instance, a program is used to change the temperature based on the bit, e.g., if '0' then no change (inserting idle cycles to cool down the chip) and if '1' then the temperature is made high followed by a low to keep a low average temperature. On the receiver side, it gets the thermal signal from its local temperature sensor and transforms it into binary bitstreams. Then the receiver checks the ECC code for the integrity of the packet. If the received packet is recognized and contains the particular preamble field (e.g., 1010101) throughout the transmission process, the receiver extracts the data fields from the packet; otherwise, the receiver rejects the binary bitstreams [35].
## 4 Recurrent Neural Networks (RNN)-Based Model  
Recurrent Neural Networks (RNN) were widely used in numerous NLP tasks, such as the analysis of Twitter messages, for example. In this paper, we will adopt them as our first solution for the intent classification task. When applied to text inputs, RNN models have the ability to process these inputs sequentially, performing the same operation, \(h_{t}=f_{W}(x_{t},h_{t-1})\), on each of the different words that constitute the text input sequence (i.e., their corresponding word embeddings, to be more exact), where \(h_{t}\) is the hidden state, \(t\) the time step, and \(W\) the weights of the network.  
Since this operation is formulated in such a way that the hidden state at each time step depends on the previous hidden states, the order of the words in our text sequences becomes particularly important. Consequently, RNNs allow us to handle input sentences of variable length, which turned out to be an essential aspect given the nature of our problem.  
There are many different architectures for RNNs. Among them, we opted for the so-called long short-term memory (LSTM) networks [31]. LSTM networks are a kind of RNN that introduce a gating mechanism that ensures proper gradient flow through the network, thus helping to prevent the vanishing (and exploding) gradient problem that typically exists with standard RNNs. One of the most interesting features of LSTMs is that they are capable of learning long-term dependencies. Specifically, a common LSTM unit is basically composed of a cell, an input gate, an output gate, and a forget gate. These gates allow the network to control and update for each cell its corresponding state by deciding where information in a sequence of data is placed and that it is preserved from that provided by the previous cells, as well as what information is provided to the next ones.
## 2 Materials and Methods
### Algorithm for Downscaling
#### 2.3.2. Regression between GPP and Topographic Factors  
The regression takes advantage of valuable spatial heterogeneity information from the ancillary data at 500 m spatial resolution. The relationship between topographic factors and MODIS GPP at 1 km is modeled by multiple linear regression:  
\[GPP_{R\_obs}(\mathbf{x})\ =\ b_{0}+b_{1}H_{R}+b_{2}COS(S_{R})+b_{3}COS(A_{R})+b _{4}LAI_{R} \tag{3}\]  
where \(GPP_{R}\_obs}(\mathbf{x})\), \(H_{R}\), \(S_{R}\), \(A_{R}\) and \(LAI_{R}\) represent the MODIS GPP(gC d\({}^{-1}\)), altitude (m), slope (deg), aspect (deg) and LAI (m\({}^{2}\) m\({}^{-2}\)) at a 1 km resolution, respectively. Since \(GPP_{R}\_obs}(\mathbf{x})\), \(H_{R}\), \(S_{R}\), \(A_{R}\) and LAI\({}_{R}\) in Equation (3) are known, \(b_{0}\), \(b_{1}\), \(b_{2}\), \(b_{3}\) and \(b_{4}\) can be estimated. Currently, a wide range of fitting methods has been applied in the theoretical framework of regression, such as generalized least squares (GLS) and ordinary least squares (OLS). In this paper, the OLS algorithm was adopted to estimate the regression coefficients.  
The relationship in Equation (3) is assumed to be universal at different resolutions [32; 33], and thus the relationship modeled at a 1 km resolution can be implemented at a 500 m resolution. Based on this assumption, the regression prediction \(GPP_{r1}(\mathbf{x})\) is calculated as  
\[GPP_{r1}(\mathbf{x})\ =\ b_{0}+b_{1}H_{r}+b_{2}COS(S_{r})+b_{3}COS(A_{r})+b_{4}LAI _{r} \tag{4}\]  
where \(H_{r}\), \(S_{r}\), \(A_{r}\) and \(LAI_{r}\) represent the altitude (m), slope (deg), aspect (deg) and LAI (m\({}^{2}\) m\({}^{-2}\)) at a 500 m resolution, respectively. Variables \(b_{0}\), \(b_{1}\), \(b_{2}\), \(b_{3}\) and \(b_{4}\) are regression coefficients.
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 5 Conclusions  
To enhance the anti-offset ability of the underwater wireless power transfer system and improve the transmission efficiency of the system, this paper proposes a coupling mechanism design method and a transmitting array switching control strategy for the array underwater wireless power transfer system. By analyzing the influence of the size ratio of the transmitting coil and the receiving coil on the mutual inductance change when the system is offset, the optimal size ratio of the transmitting coil and the receiving coil is determined.  
By comparing the reflection impedance of the receiving end of the power supply coil and its surrounding adjacent coils, the position of the receiving coil is estimated, so as to realize the optimal power supply coil switching. The theoretical method studied in this paper was verified by experiments. The experimental results show that the UWPT system designed in this paper can expand the proposed receiving coil position estimation method and transmitting array switching control method. When the receiving coil is offset, the optimal transmitting coil switching control can be realized according to the position of the receiving coil, and the transmission efficiency of the system is maintained above 80%.  
Notably, receiver-side adaptation (e.g., DC-DC control or variable load regulation) could further enhance system performance by optimizing impedance matching and stabilizing output power under misalignment. While this work focuses on transmitter-side array design to expand the anti-offset range, integrating receiver-side control is a promising direction for future research to achieve even higher efficiency and power stability.  
H.X.: Methodology, Software, Investigation, Validation, Formal Analysis, Visualization, Writing--Original Draft; W.L.: Conceptualization, Funding Acquisition, Resources, Supervision, Writing--Review & Editing; Z.Z.: Conceptualization, Methodology, Supervision, Writing--Review & Editing; Y.W.: Software, Validation, Visualization, Writing--Original Draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
No conflicts of interest exist in the submission of this manuscript. I would like to declare that the work described was original research that has not been published previously, and not under consideration for publication elsewhere.