esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem
## 4 Experiments and Results
### Exploratory Experiments
#### 4.2.2 Exploratory Experiment 2  
In the previous experiment, the EA forced the HHs into replicating the behavior of one heuristic, MaxPW. In this experiment, we try to overcome this situation by reducing the number of instances in the training set. Then, for this experiment, 30 new HHs were produced, but this time, only 60% of the instances in set \(\alpha_{1}\) were used for training purposes. These 60% instances were randomly selected once and used for producing the 30 HHs. As in the previous experiment, the maximum number of iterations for the EA was set to 200 and all the HHs were initialized by using a random sequence of 12 heuristics. We used the 30 HHs to solve set \(\alpha_{2}\) and summarized the results through the rankings and total profit (Table 5).  
Based on the results shown in Table 5, we observe a small improvement in Best-HH with respect to MaxPW. Although this supports our initial idea that we can improve the results obtained by the heuristics with a hyper-heuristic, the improvement is rather small and insignificant in practice. Furthermore, the success rate of Best-HH is (0.285, 0.0), which is exactly the same as MaxPW.  
For a more in-depth analysis about the performance of the best HH in this experiment, we plotted the performance of Best-HH, as well as of the best and worst heuristic for each particular instance in set \(\alpha_{2}\). For clarity, the results are sorted (from larger to smaller) on the profit obtained by Best-HH on each instance in the set. Figure 3 depicts the profit of each method across all instances. As we can observe, there are many cases where Best-HH obtains a smaller profit than the best heuristic for each particular instance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Heuristics**} & \multicolumn{4}{c}{**Hyper-Heuristics**} \\ \cline{2-7}
**Rank** & Def & MaxP & MaxPW & MinW & Best-HH & Median-HH & Worst-HH \\ \hline
1.0 & 85 & 100 & 0 & 87 & 0 & 7 & 0 \\
1.5 & 0 & 0 & 6 & 8 & 6 & 0 & 8 \\
2.0 & 1 & 1 & 5 & 4 & 5 & 16 & 1 \\
2.5 & 0 & 0 & 100 & 1 & 100 & 99 & 100 \\
3.0 & 2 & 0 & 3 & 0 & 3 & 10 & 5 \\
3.5 & 1 & 0 & 264 & 1 & 264 & 258 & 258 \\
4.0 & 0 & 0 & 16 & 1 & 16 & 4 & 25 \\
4.5 & 0 & 0 & 6 & 0 & 6 & 1 & 1 \\
5.0 & 11 & 21 & 0 & 78 & 0 & 5 & 2 \\
5.5 & 0 & 1 & 0 & 1 & 0 & 0 & 0 \\
6.0 & 18 & 195 & 0 & 183 & 0 & 0 & 0 \\
7.0 & 282 & 82 & 0 & 36 & 0 & 0 & 0 \\ \hline Profit & 241,081 & 343,872 & **467,145** & 376,148 & **467,145** & 467,103 & 466,711 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ranks and total profit obtained by the four heuristics and the best, median, and worst hyper-heuristics trained on set \(\alpha_{1}\) and tested on set \(\alpha_{2}\). The best result, measured in terms of total profit, is highlighted in bold.
# Automatic Construction of Fine-Grained Paraphrase Corpora System Using Language Inference Model
###### Abstract  
Paraphrase detection and generation are important natural language processing (NLP) tasks. Yet the term paraphrase is broad enough to include many fine-grained relations. This leads to different tolerance levels of semantic divergence in the positive paraphrase class among publicly available paraphrase datasets. Such variation can affect the generalisability of paraphrase classification models. It may also impact the predictability of paraphrase generation models. This paper presents a new model which can use few corpora of fine-grained paraphrase relations to construct automatically using language inference models. The fine-grained sentence level paraphrase relations are defined based on word and phrase level counterparts. We demonstrate that the fine-grained labels from our proposed system can make it possible to generate paraphrases at desirable semantic level. The new labels could also contribute to general sentence embedding techniques.  
paraphrase; text generation; language inference +
Footnote †: journal: 5  
where sentence 1 can be derived from sentence 2 but not the other way round because sentence 2 contains more information than sentence 1.  
A dataset with a more strict rule may label the second sentence pair as a negative case. Such variation can affect the generalisability of a paraphrase classification model. The presence of both symmetric and directional relations in a single class also affects the predictability of a paraphrase generation task. A generation model trained with randomly mixed relations would generated results of random relations.  
This paper proposes a novel method to automatically generate fine-grained paraphrase labels using language inference models. In particular, we make the following contributions:  
* We defined a set of fine-grained sentence-level paraphrase relations based on similar relations at the word and phrase level.
* We developed a method utilising the language inference model to automatically assign fine-grained labels to sentence pairs in existing paraphrase and language inference corpora.
* We demonstrated that models trained with fine-grained data are able to generate paraphrases with specified directions.  
The labelling process leads to a detailed examination of several public corpora. We discover that corpora constructed for similar linguistic tasks have very different compositions of fine-grained relations. For instance, we find that:  
* Compared with Quora Question Pair (QQP), MRPC tolerates more semantic divergence in its positive class, which contains more directional paraphrases than equivalent ones.
* Compared with the Stanford Natural Language Inference (SNLI), Multi-Genre Natural Language Inference (MNLI) contains more diversified sentence pairs in all three classes.  
Such information may help researchers to design customised optimisation and to provide insights on observed performance variation.
## 4 Experiments
### Music Identification Experiment
#### 4.2.5 Signal-to-Distortion Ratio (SDR) Comparison of Distortion in the Spectrogram Frequency Band  
We experimented to see how the frequency band distortion of the spectrogram affects SDR. As shown in Figure 11, the spectrogram of music signals was divided into four bands (\(D1\)-\(D4\)) and multiplied by the weight for each band to distort it. The music signal without distortion is called \(D0\). We added the distorted music spectrogram to the speech spectrogram and restored it to the signal. Finally, the SDR was calculated using the distorted mixture signal and the reference signals with 16 kHz sampling rate. In addition, the identification experiment was performed using the distorted mixed signal. The fingerprinting database was extracted at 16 kHz sampling rate from 7295 songs. We used the distortion weight \(\alpha\) as 0.3. The 1000 samples mixed with 0 dB SNR were tested.  
Table 5 shows the SDR and identification results of the distorted mixed signal. The mean SDR of the undistorted signal \(D0\) matches the mixed SNR and shows the best identification accuracy. \(D1\)  
Figure 11: Experimental structure of SDR measurement according to distortion by frequency band.  
shows the lowest SDR value. On the other hand, \(D1\) shows better identification accuracy than \(D2\)\(-\)\(D4\). \(D2\)\(-\)\(D4\) show higher SDR performance than \(D1\), but lower identification accuracy than \(D1\).  
Since the SDR of separation performance is calculated based on the correlation of signal domain, it is sensitive to the low-frequency band with high energy values. A high SDR performance can be obtained by accurately estimating the low-frequency portion of the spectrogram. However, since speech signal has higher energy in the low-frequency band than music signal, maintaining the peak points of the high-frequency band at the music signal is important to music identification. For this reason, the identification performance is lower even with high SDR.
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 4 Curvature Rate Limits  
Two factors contribute to the establishment of curvature rate limits. Firstly, vehicle dynamics imposes constraints on the maximum steering rate achievable at a given velocity. For heavily loaded vehicles, such as semi-tractor trailers, this limitation corresponds to the speed at which significant lateral G-forces can be induced, potentially leading to rollovers [42]. The second source of limitations arises from the specific clothoid controller algorithm employed in this study (for detailed information, see [3]). In the first scenario, curvature saturation has been incorporated into the integration block (refer to Figure 2), where it integrates the product of \((x^{t})\) (curvature rate) and the vehicle's speed \(V\).  
To discover these saturation limits, a sequence of step-steer tests was conducted at varying velocities, each involving distinct step-steering angle inputs. The saturation limits were established based on the curvature values corresponding to the steering angle input at which the vehicle experienced a rollover. The outcomes of this series of simulation tests, presented in Figure 6, confirm the expected trend of decreasing curvature limits as the vehicle's speed increases.  
Figure 5: **(a)** Fictorial representation of deviation between the locally fitted clothoid and the reference path. **(b)** Minimum Fréchet distance vs. initial cross-track error.  
Figure 6: Curvature saturation limits based on roll-over speeds.
## 2 Improved EWT  
### EWT  
Perform fast Fourier transform (FFT) on the original signal \(f(t)\), then normalize the frequency range of the Fourier spectrum within \(2\pi\). According to the Shannon criterion, only discuss the characteristics within the interval \([0,\pi]\) during the signal analysis. Using the scale space method to adaptively divide the Fourier spectrum into continuous \(N\) intervals within \([0,\pi]\), next, obtain \(N+1\) interval boundaries, using \(\omega_{n}\) to represent boundaries, where \(\omega_{0}=0\), \(\omega_{n}=\pi\), and using \(\Lambda_{n}\) to represent divided intervals, that is:  
\[\Lambda_{n}=[\omega_{n-1},\omega_{n}],n=1,2,\ldots,N \tag{1}\]  
Add wavelet windows to \(\Lambda_{n}\). Establish the bandpass filter bank based on Littlewood-Paley and Meyer wavelet [17]. When \(n>0\), define the empirical wavelet function \(\psi_{n}(\omega)\) and empirical scale function \(\varphi_{n}(\omega)\) as Formulas (2) and (3), where  
\[\psi_{n}(\omega)=\left\{\begin{array}{ll}1,&\omega_{n}+\tau_{n}\leq\mid\! \omega\!\mid\leq\omega_{n+1}-\tau_{n+1}\\ \\ \cos\left[\frac{\pi}{2}\beta\left(\frac{1}{2\tau_{n+1}}(\mid\!\omega\!\mid- \!\omega_{n+1}+\tau_{n+1})\right)\right],&\omega_{n+1}-\tau_{n+1}\leq\mid\! \omega\!\mid\leq\omega_{n+1}+\tau_{n+1}\\ \\ \sin\left[\frac{\pi}{2}\beta\left(\frac{1}{2\tau_{n}}(\mid\!\omega\!\mid-\! \omega_{n}+\tau_{n})\right)\right],&\omega_{n}-\tau_{n}\leq\mid\!\omega\!\mid \leq\omega_{n}+\tau_{n}\\ \\ 0,&\text{others}\end{array}\right. \tag{2}\]  
\[\varphi_{n}(\omega)=\left\{\begin{array}{ll}1,&\mid\!\omega\!\mid\leq\omega_ {n}-\tau_{n}\\ \\ \cos\left[\frac{\pi}{2}\beta\left(\frac{1}{2\tau_{n}}(\mid\!\omega\!\mid-\! \omega_{n}+\tau_{n})\right)\right],&\omega_{n}-\tau_{n}\leq\mid\!\omega\!\mid \leq\omega_{n}+\tau_{n}\\ \\ 0,&\text{others}\end{array}\right. \tag{3}\]  
where \(\beta(x)\) is a polynomial that satisfies the transformation of Formulas (2) and (3) above. Its expression is \(\beta(x)=x^{4}\) (\(35-84x+70x^{2}-20x^{3}\)).  
Empirical wavelet coefficients include approximation and detail coefficients. The approximation coefficients \(W_{f}^{e}(0,t)\) are inner products of \(f(t)\) and \(\varphi_{1}(\omega)\); the detail coefficients \(W_{f}^{e}(n,t)\) are inner product of \(f(t)\) and \(\psi_{n}(\omega)\).  
\[W_{f}^{e}(0,t)=\langle f,\varphi_{1}\rangle=\int f(\tau)\overline{\varphi_{1 }(\tau-t)}d\tau=F^{-1}\Big{[}f(\omega)\overline{\varphi_{1}(\omega)}\Big{]} \tag{4}\]  
\[W_{f}^{e}(n,t)=\langle f,\psi_{n}\rangle=\int f(\tau)\overline{\psi_{n}(\tau- t)}d\tau=F^{-1}\Big{[}f(\omega)\overline{\psi_{n}(\omega)}\Big{]} \tag{5}\]  
After decomposing \(f(t)\), the AM-FM single components from low to high frequency can be obtained as follows:  
\[f_{0}(t)=W_{f}^{e}(0,t)*\varphi_{1}(t) \tag{6}\]  
\[f_{k}(t)=W_{f}^{e}(k,t)*\psi_{k}(t),\;k=1,2,\ldots,N \tag{7}\]  
Finally, the expression for the reconstructed signal is:  
\[f(t)=\sum_{n=0}^{N}f_{n}(t)=W_{f}^{e}(0,t)^{*}\varphi_{1}(t)+\sum_{n=1}^{N}W_ {f}^{e}(n,t)^{*}\psi_{n}(t) \tag{8}\]
# Map Matching Based on Seq2 Seq with Topology Information
## 4 Experiment
### Experimental Result
#### 4.4.3 Optimized Controlled Experiment  
In order to verify the validity of the data expansion method and the effect of the compressed dictionary on the training time and training accuracy, based on different data expansion conditions, the compression degree of dictionary compression is shown in Figure 15. Several groups of control experiments were set, and other experimental parameters remained unchanged as shown in Table 4. Accuracy is the absolute accuracy of the verification set.  
From the data expansion control test in Figure 15, it can be seen that when the expansion multiple is between 10 and 50 times, the absolute accuracy of the model will be greatly improved, and when the expansion multiple is above 50 times, the accuracy tends to be stable and oscillates between 89.57% and 89.93%.  
The experimental results show that the data expansion method is effective to improve the absolute accuracy of the model.  
It can be seen from the controlled experiments in Figure 16 that when the compressed data range is between 1/5 and 1/10, the accuracy rate of the model is maintained between 88.11 points and 89.04 points, and the training speed is increased from 276 ms/Step to 27 ms/Step. When the compression degree was increased to 1/15, the absolute accuracy of the model began to decline significantly to 62.37 points, and when the compression was continued to 1/20, the absolute accuracy of the model dropped to 43.37 points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LSTM** & **Bi-LSTM** & **Bi-LSTM-Attn** & **Textual Model** \\ \hline Training set & & & & \\ accuracy & 83.51\% & 85.32\% & 86.71\% & **89.93\%** \\ Test set accuracy & 80.23\% & 82.34\% & 84.74\% & **89.88\%** \\ Training set loss & 0.4131 & 0.3691 & 0.3271 & 0.2311 \\ Test set loss & 0.5221 & 0.4561 & 0.3711 & 0.2298 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model Comparison Experiment.  
Figure 15: Data expansion controlled experiment.  
The experimental results show that the data compression method is effective for improving the training speed, and has limited influence on the accuracy of the model within a certain compression range. Beyond a certain compression range, the absolute accuracy of the model will be seriously affected.
## 4 Experiments
### Speech Emotion Recognizer Setup  
As we commented in Section 3.2, for the feature extraction, we extracted 512-dimensional embeddings from the last layer of the feature encoder of the xlsr-Wav2 Vec2.0 network. After that, we calculated the average of these embeddings and compared the performance of three algorithms: SVC with 'RBF' kernel, k-NN, and MLP. For the SVM, we varied the regularization parameter between 1, 10, and 100. For the k-NN, we modified the number of neighbors to 10, 20, 30, and 40. Finally, for the MLP we tested architectures with one or two layers, always with 80 neurons each, except for the last output layer that always had eight neurons. The rest of the parameters took the default values set in the sklearn library.  
Concerning the training configuration and hyper-parameters for the Fine-Tuning experiments, we selected a batch size of 100 samples, adequate to the capacity of our GPU; and a maximum number of training epochs of 10, since higher values showed an increment in the overfitting of the model and did not improve the validation metrics.  
As we were solving a classification task, we utilized the cross-entropy loss implemented in the Hugging Face library [44]. To optimize this objective function, we employed the default optimizer of the library, AdamW, with a learning rate of 0.001, betas with values0.9 and 0.999, and epsilon of \(1\times 10^{-6}\), fine-tuning the previous weights learned from the English set of CommonVoice dataset.  
Notice that the xlsr-Wav2 Vec2.0 model was adapted to include an MLP on top of the transformer module and an average pooling layer. The average pooling compacted the timesteps at the output for having a single vector of 1024 dimensions per recording. This vector passed to an MLP with a hidden layer of 1024 neurons, activated with a tanh function and an output layer of eight neurons that returned the final probabilities of each class thanks to its softmax activation.
# Improving Lossless Image Compression with Contextual Memory
## 2 Related Work
The blending prediction weights are selected based on the texture of the surrounding pixels and a Pearson correlation coefficient is computed for adjusting these weights. The final prediction also takes into account a template matching prediction. The CBPB algorithm was also ported to parallel execution via a CUDA implementation [10]. Vanilc is a lossless image compression framework described in [11] for 8 bpp, multichannel color images, and 16 bpp medical 3D volumes. The main contribution of the paper is a pixel probabilitydistribution predictor based on a weighted least squares approach that uses a weighting function that generalizes some of the proposed contextual schemes in the literature and provides good results when it comes to the non-stationarities in the image while having only a few tuning parameters. A lossless image compression algorithm is described in [12]. It is based on multi-resolution compression for progressive transmission. It improves on prior work from [13], where the image is decomposed into a pyramidal data structure and an edge adaptive hierarchical interpolation is applied for coding and progressive transmission. The prediction accuracy is improved here by using context-conditioned adaptive error modeling and by passing the estimates through an error remapping function. In this way, it improves both the final bitrate and the visual quality of the reconstructed images at intermediate stages. Another lossless medical imaging compression algorithm using geometry-adaptive partitioning and least square-based prediction is described in [14]. Because of the similarities of the images obtained from the same imaging method, a prior segmentation via geometry adaptive partitioning and quadtree partitioning of the image allows a good selection of a least squares optimized predictor for sections of the image. For lossless compression of 3D medical images, an extension of the minimum rate predictors from 2D to 3D has been developed in [15]. Here, 3D-shaped predictors were introduced to benefit from the volumetric redundancy, and, then, volume-based optimizations are applied, and hybrid 3D block splitting and classification is done. The algorithm was also extended from 8 bpp images to 16 bpp images because they provide better diagnostic quality. Lossless compression of multi-temporal hyperspectral images can also exploit the temporal correlations besides the spatial and spectral ones. In [16], the fast lossless predictor, a variation of the least means square applied to the causal context [17], has been extended to 4D to incorporate the temporal aspect in the prediction. The residuals are computed as the difference between the prediction and the current pixel and are then encoded using the fast Golomb-Rice coding.
# Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors
## 7 Conclusions  
In this paper, we investigated the problem of sensor trajectory optimisation for tracking a moving target using multiple cooperative passive sensors. The problem is formulated as POMDP, and a \(N\)-step ahead sensor trajectory scheduling is considered to steer the measurement decision process that maximises a reward function. Two statistical reward functions, the Expected RID and the determinant of FIM, both of which are derived in closed-forms, are considered. We show that the Expected RID function approximated through linearisation effectively yields a much weaker correlation between the two sensor states and thus the trajectory scheduling performance using it is as poor as using the Trace of FIM. On the other hand, the Determinant of FIM is an appropriate statistical reward function to be used. This conclusion is confirmed by the simulation results presented. Although this work is based on tracking a moving target by two bearing-only sensors scenario, the results can also be applied to other types of sensors which are required to be used jointly to observe the underlying target state, such as Doppler radars. The underlying application areas may consist of those whose decision-making performances depend on the cooperation of multiple dependent sensors such as smart cites, energy and robotics [30; 31; 32] and practice of game theory [33].  
Conceptualization, X.W. and B.H.; methodology, X.W. and B.R.; validation, X.W., B.R. and B.M.; formal analysis, X.W.; investigation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W., B.R., B.H. and B.M.; supervision, B.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Asian Office of Aerospace Research & Development (AOARD)/AFRL under Grant No. FA2386-15-1-4066 and Australia Research Council under Grant No. ARC DP130103582.  
We thank [PERSON] from the Defence, Science, Technology Group, Australia for his helpful comments on the simulation study of this work.  
The authors declare no conflict of interest.
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Heuristic Algorithm
#### 4.1.5 Pigeon-Inspired Optimization  
The Pigeon-Inspired Optimization algorithm (PIO) is a swarm intelligent optimization algorithm designed to simulate pigeon homing behavior. First of all, three kinds of pigeon swarm optimization models were proposed: the map model based on the geomagnetic field, the pointer operator model based on the sun and the landmark operator model based on the landmark operator model. Secondly, a general direction was identified through the map and pointer operator, and then, the landmark operator was used to correct the current direction until the best track was found. Figure 15 shows a PIO map and compass operator model and a pigeon flock optimization (PIO) track chart. [PERSON] et al. [83] proposed a co-evolutionary Pigeon-Inspired Optimization (CPIO) algorithm based on a cooperation-competition mechanism. The search and track (ST) method is introduced to obtain the lowest-cost trajectory, and the dynamic two-stage closed search (DTCSCS) problem of UAV formation under range constraints (RC) and orientation constraints (OC) is solved. [PERSON] and [PERSON] [84] proposed a multi-objective social learning pigeon-inspired optimization algorithm (MSLPIO), which uses iterative learning to update waypoint positions, adding social learning factors and dimension-related parameter setting methods, which solves the problem of weak convergence of a traditional Genetic Algorithm. [PERSON] et al. [85] proposed a dynamic discrete Pigeon-Inspired Optimization algorithm based on hybrid architecture (\(D^{2}PIO\)), constructed and updated the probability mapping by using Bayesian formula, adopted the response threshold S-type function model (RTSM) for target allocation during attack execution, and finally used B-spline curve to generate feasible trajectory. The problem of search-attack task planning for multiple UAVs is solved. [PERSON] et al. [86] proposed a multi-UAV collaborative trajectory planning method based on the Cauchy mutant pigeon intelligent optimization algorithm (ECM-PIO); the algorithm uses the Cauchy mutation operator for optimization, expanding the search range and reducing the risk of falling into local optimization, which solves the shortcomings of the