esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.2.1 Preprocessing Step  
A preprocessing step, which included different techniques, was applied to the original images in order to reduce the large variability of these images.  
Firstly, all of the images were converted to grayscale. Because the original images came from different hospitals and, consequently, from different X-ray machines, a histogram matching process was applied to every image, taking one of them as a reference [28]. Therefore, all images in the dataset were similar in terms of histogram distribution.  
Subsequently, rib shadows were suppressed from the X-ray images with a pretrained autoencoder model developed by [PERSON], which is publicly available in GitHub (www.github.com/hmchuong/ML-BoneSuppression). This makes it easier for the network to focus on relevant information within the lungs. Rib shadows suppression has been applied in other works related to lung cancer, pulmonary nodules, and pneumonia detection in chest radiography, proving to be a useful approach to help radiologists and machine learning systems when diagnosing lung related diseases [29; 30; 31; 32; 33].  
After this process, a contrast enhancement method, called Contrast Limited Adaptive Histogram Equalization (CLAHE) [34], was used to improve local contrast and enhance image definition.  
Figure 1 shows the whole preprocessing phase, where each algorithm's output is presented for three different examples.
## 4 Conclusions  
In this study, TPU was added to PP or MPP to form blends, and the blending morphology, crystallization behavior, melting behavior, and mechanical properties of blends were examined. The SEM images showed that MPP100 and MPP90/TPU10 absorbed higher imapct energy. The DSC and XRD results revealed that the addition of TPU did not affect the strucutre of PP/TPU blends but influenced its melting and crystallization enthalpy. The mechanical property test results showed that, among the blends, PP100 and PP90/TPU10 had greater tensile strength, a greater Young's modulus, and a greater flexural strength, while MPP100 and MPP90/TPU10 absorbed greater impact energy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sample Codes**} & **Tensile Strength** & **Young’s Modulus** & \multirow{2}{*}{**Energy (J)**} & **Flexural Strength** & **Impact Strength** \\  & **(MPa)** & **(MPa)** & **Energy (J)** & **(MPa)** & **(k/m\({}^{2}\))** \\ \hline MPP100 & 29.19 \(\pm\) 1.93 & 974 \(\pm\) 40.32 & 32.82 \(\pm\) 8.34 & 49.06 \(\pm\) 1.92 & 8.48 \(\pm\) 0.55 \\ MPP90/TPU10 & 29.79 \(\pm\) 1.55 & 977 \(\pm\) 26.67 & 43.33 \(\pm\) 10.24 & 49.88 \(\pm\) 0.78 & 4.93 \(\pm\) 0.25 \\ PP100 & 37.12 \(\pm\) 0.55 & 1046.79 \(\pm\) 37.47 & 21.01 \(\pm\) 2.68 & 58.60 \(\pm\) 1.39 & 2.76 \(\pm\) 0.21 \\ PP90/TPU10 & 32.36 \(\pm\) 0.86 & 1016.80 \(\pm\) 9.89 & 23.15 \(\pm\) 2.56 & 57.93 \(\pm\) 0.34 & 3.49 \(\pm\) 0.88 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of PP/TPU and MPP/TPU blends.  
Figure 4: Tensile and flexural behavior of PP/TPU blends and MPP/TPU blends: (**A**) tensile properties and (**B**) flexural properties.  
and had higher impact strength. It was surmised that MPP at the continuous phase may harm the hydrogen bond of TPU at the dispersive phase, which jeopardized its intrinsic high toughness. This study proposes a flexibly feasible design of polymer blends, which can be freely adjusted to suit the requirements of products and satisfy the user end.  
The authors would like to thank Ministry of Science and Technology of Taiwan, for supporting this research under Contract MOST 106-2622-E-035 -013 -CC3.  
In this study, the concepts and designs for the experiment, all required materials, as well as processing and assessment instrument were provided by [PERSON]. Data were analyzed, and the experimental results examined, by [PERSON]. The experiment was conducted, and the text composed, by [PERSON].  
The authors declare no conflict of interest.
# Improved Particle Swarm Optimization for Sea Surface Temperature Prediction
###### Abstract  
The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading to significant improvements in SST trend predictions. Our experimental validation shows a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieve a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed Long-Short Term Memory (LSTM).  
sea surface temperature; sea surface temperature prediction; similarity measure; support vector machine; particle swarm optimization; local search +
Footnote †: journal: 15 March 2020
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 4 Performance Evaluation
### Results and Discussion
#### 4.2.3 The Accuracy of Denoising
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Denoising** \\ **Methods** \\ \end{tabular} & **Noisy** & \begin{tabular}{c} **Wavenet-Based** \\ **Denoising** [13] \\ \end{tabular} & \begin{tabular}{c} **Energy-Based** \\ **Filtering** \\ \end{tabular} & \begin{tabular}{c} **Entropy-Based** \\ **Filtering** \\ \end{tabular} & \begin{tabular}{c} **Fuzzy** \\ **Filtering** \\ \end{tabular} & \begin{tabular}{c} **Energy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} & \begin{tabular}{c} **Entropy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} \\ \hline \multirow{3}{*}{\begin{tabular}{c} Low SNR \\ Group \\ \end{tabular} } & SNR & 0.915 & 14.552 & 14.758 & 14.846 & 14.863 & 14.803 & **14.872** \\ \cline{2-9}  & STOI & 0.868 & 0.864 & 0.927 & **0.942** & 0.941 & **0.942** & 0.941 \\ \cline{2-9}  & PESQ & 1.413 & 1.551 & **1.826** & 1.659 & 1.642 & 1.643 & 1.635 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Medium SNR \\ Group \\ \end{tabular} } & SNR & 6.147 & 17.311 & 18.111 & **18.228** & 18.189 & 18.152 & 18.183 \\ \cline{2-9}  & STOI & 0.913 & 0.895 & **0.973** & 0.951 & 0.968 & 0.967 & 0.963 \\ \cline{2-9}  & PESQ & 1.757 & 1.824 & 2.172 & **2.448** & 2.368 & 2.364 & 2.371 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} High SNR \\ Group \\ \end{tabular} } & SNR & 13.555 & 19.639 & **21.047** & 21.028 & 20.997 & 20.966 & 20.991 \\ \cline{2-9}  & STOI & 0.951 & 0.914 & 0.982 & 0.980 & 0.982 & 0.979 & **0.985** \\ \cline{2-9}  & PESQ & 2.373 & 2.140 & **3.446** & 3.354 & 3.333 & 3.390 & 3.318 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Total \\ Group \\ \end{tabular} } & SNR & 6.872 & 17.167 & & & 18.049 & & \\ \cline{2-9}  & STOI & 0.910 & 0.891 & & & 0.967 & \\ \cline{1-1} \cline{2-9}  & PESQ & 1.847 & 1.838 & & 2.573 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of denoising performance for the final result of each method. Figure 21: The denoised results of a sample p226\({}_{0}\)03. Figure 22: Denoised results of a sample p226\({}_{0}\)22.
## 2 Granite Core Drilling Test under Low Confining Pressure  
To explore the mechanism of core discing under low confining pressure, based on the servo-controlled laboratory drilling test platform, intact granite core drilling tests on natural rock blocks under low confining pressure were carried out. The test platform is composed of a servo control system, drilling system, acquisition system, and confining pressure system, as shown in Figure 1. The internal structure of the confining pressure system is shown in Figure 2. The platform can control and collect drilling parameters (drilling pressure, drilling rate, bit rotation speed, bit torque, rock confining pressure) to achieve controllable drilling under low confining pressure conditions. The rock sample is intact granite with a hard texture, and it is light grey-white, with a massive, porphyritic structure. The compressive strength, tensile strength, cohesion, and internal friction angle of granite were obtained through a uniaxial compression test, Brazilian splitting test, and triaxial compression test, as shown in Table 1. The size of the rock sample is 100 mm \(\times\) 100 mm \(\times\) 100 mm. An impregnated diamond flat-tooth single-tube thin-wall drill bit with three cooling water ports was used in the test. Its outer diameter is 32 mm and inner diameter is 24 mm, as shown in Figure 3. Low confining pressure was applied to the rock samples, the initial rotation speed and different drilling rates were set, and the core drilling tests with uniform drilling rates were conducted. Figure 4 is the schematic diagram of the drilling test.  
Figure 1: Laboratory servo-controlled drilling test platform.  
Figure 3: Drill bit.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Density/kg\(\cdot\)m\({}^{-3}\)** & **Compressive Strength/MPa** & **Cohesion/MPa** & **Internal Friction Angle/\({}^{\circ}\)** & **Tensile Strength/MPa** \\ \hline
2598 & 246.21 & 39.49 & 56.94 & 21.42 \\ \hline \hline Water Way & Diamond Particle & & & \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the granite.  
Figure 2: Internal structure of confining pressure system.  
Previous studies showed that a confining pressure higher than 35 MPa was necessary for core discing [27]. However, the granite core obtained by the drilling test under the condition of low confining pressure (0 MPa) on the test platform showed the characteristics of discing, as shown in Figure 5. Therefore, in order to explore the phenomenon and mechanism of granite core discing in laboratory drilling test, five groups of typical tests with confining pressure of 0 MPa were conducted, and the test conditions are shown in Table 2.  
The test steps are as follows: install the drill bit and rock sample; start the servo-controlled drilling test platform; supply cooling water inside the drill pipe drill under the set confining pressure, drilling rate, and rotation speed; collect and save the drilling parameters; exit the drill bit after drilling.
## 4 Discussion  
Based on the analysis in the previous section, the normalized FR represents the degree to which the FR of a microspeker is affected by the diffraction effects of the SMB. From another perspective, the normalized FR can be used to compensate the FRs measured on the SMB to obtain the results measured on an ideal infinite baffle. Therefore, \(p_{ideal}(\omega)=p_{\text{SMB}}(\omega)/H(\omega)\). Figure 6 depicts the compensation result of the FR of a microspeker driver measured on the SMB. Clearly, the compensated FR is consistent with that measured in a hemi-anechoic room, which can be considered as the results are measured on an ideal infinite baffle. The SMB diffraction effects are almost completely eliminated by the compensation process.  
Figure 5: Comparison of the normalized FRs between the measured and simulated results. (**a**) Results from the microspeker driver and (**b**) results from the closed-box microspeker module.  
Figure 6: Compensation of the SMB diffraction effects by the normalized FR.
## 2 System and Theoretical Model
### Hybrid PPM-BPSK-SIM Modulation Scheme  
A hybrid PPM-BPSK-SIM modulation is a multi-dimensional modulation method that combines subcarrier modulation and pulse position modulation. Here, we introduce by a 4-PPM, which is displayed in Figure 3. For a 4-PPM modulation symbol, the number of slots is \(L=4\), and the data bits that can be transmitted in each frame \(n=\log_{2}L=2\). If the n-bit data are written as \(M\), and the time slot position of the light pulse is recorded as \(l\), \(M\) can have four combinations: \(M=(0,0)\), \(M=(1,0)\), \(M=(0,1)\), \(M=(1,1)\), and the corresponding time slot positions are \(l=0\), \(l=1\), \(l=2\), \(l=3\), respectively. As shown in Figure 3, the \(L\)-PPM-BPSK is to change the original position of energy in \(L\)-PPM from pure intensity modulation to BPSK modulation. Therefore, after the combination of PPM and BPSK, one bit of BPSK information is added to each frame, and the number of transmitted bits is increased, which improves the transmission efficiency.  
Figure 3: 4-PPM-BPSK modulation schematic diagram.  
As present in Figure 4, the transmitter consists of an n-bit PPM encoder, a parallel serial converter and a transmitter filter with a rectangular pulse shape of duration. The PPM symbol is applied to the BPSK modulator on the radio frequency subcarrier. Before intensity modulation of the light source, a DC bias is added to the BPSK signal. Therefore, the modulated signal with \(L\)-PPM-BPSK-SIM is expressed as  
\[s(t)=\sum_{k=1}^{L}I_{k}[1+\varsigma\cos(\omega_{c}t+c_{k}\pi)g(t-(k-1)T_{s})], \tag{15}\]  
where \(I_{k}\) is the intensity of the \(k\)-th code, \(\varsigma\) and \(\omega_{c}\) represent the optical modulation index and the carrier frequency, respectively. \(c_{k}\in\{0,1\}\) is the code word of PPM, \(g(t)\) is the pulse shaping function, and \(T_{s}\) denotes the symbol interval [34; 35].  
The receiving front end is composed of photodetectors and amplifiers. So its resultant output signal for one symbol stream, which is proportional to the modulated signal, can be obtained as,  
\[r(t)=R\alpha_{c}\sum_{k=1}^{L}\!I_{k}[1+\varsigma\cos(\omega_{c}t+c_{k}\pi)g(t -(k-1)T_{s})]+n(t), \tag{16}\]  
where \(R\) is photodetector responsivity, \(\alpha_{c}\) is channel attenuation and \(n(t)\) is total receiver noise.
# A Novel FFT_YOLOX Model for Underwater Precious Marine Product Detection
## 2 Methods
### FFT_Filter  
In this section, a brief review of an FFT_Filter module is introduced in Figure 2 for global feature extraction. To illustrate, the input feature map is set to \(x\in\mathcal{R}^{H\times W\times C}\), where \(H\times W\), \(C\) represent the spatial resolution and number of channels, respectively. In FFT_Filter, a 2D FFT is performed to convert the feature map into spectral domain [34]:  
\[\mathbf{X}=F[x]\in\mathbf{C}^{H\times W\times C} \tag{1}\]  
\[X[u,\;v]=F(x[m,\;n])=\Sigma_{m=0}^{M-1}\Sigma_{n=0}^{N-1}x[m,n]e^{-j2\pi(\frac{ mn}{M}+\frac{uw}{N})} \tag{2}\]  
where, \(F[x]\) denotes a Discrete Fourier Transform (DFT) operator, as illustrated in formula (2). \(X[u,\;v]\) represents to the spectrum of the sequence \(x[m,\;n]\) at frequencies \(w_{v}=2\pi\frac{vm}{M}\) and \(w_{u}=2\pi\frac{uw}{M}\) in 2D domain. Inspired by the spectral convolution theorem [IF2-46], updating a single spectral domain value can globally affect all original data, which sheds light on design and the efficiency of the operator to capture global information in a feature map with a non-local receptive field. In the proposed FFT_Filter, a learnable matrix \(\mathbf{FFT_Kernel}\in\mathcal{C}^{H\times W\times C}\) is proposed to element-wise multiplicate with matrix \(\mathbf{X}\) in the complex domain \(\mathbf{C}^{H\times W\times C}\):  
\[\mathbf{\hat{X}}=\mathbf{FFT\_Kernel}\odot\mathbf{X} \tag{3}\]  
where \(\odot\) denotes the element multiplication. Additionally, Formula (3) is an efficient way to carry out a convolution with a global filter, of which size is \(H\times W\). Then, an inverse DFT operator is used to revert the feature map into a real number field from its complex domain. Finally, the input feature map is merged with the inversed DFT result by an element add operator. In addition, to replace each 3 \(\times\) 3 filter in the original CSPDarknet block in YOLOX-s, an average pooling is performed to satisfy the situation that stride is greater than 1.  
Figure 1: The framework of FFT_YOLOX, (**a**) the proposed modified YOLOX framework, (**b**) the proposed CSP_FFT block.  
Figure 2: The architecture of FFT_filter.
## 3 Experiments
### Experiments of Action-Gesture Joint Uav Control  
The experiments of the overall proposed system were composed of two parts: a (1) simulation experiment of the joint cross-validation over the action-gesture recognition; and (2) a real flight experiment, performed by applying the proposed system to control a UAV under a real urban scene. Figure 23 presents an example of the simulation experiment of our system, with a test video of 2.5 h in length (including a total of approximately 270,000 images with 6 types of actions and gestures). The UAV pilot sent the flight command through gestures and actions, and only the correctly recognized gestures and actions could lead to the correct flight command.  
It can be observed from Table 9 that that, when combining the actions and gestures directly, the accuracy of the combination result was lower than the correct rates of the actions and gestures, respectively. This is because such a combination could be considered as the inner product of the action and gesture recognition results, and the false alarms from either result would lead to incorrect judgment. However, after applying the proposed joint cross-validation over the action-gesture recognition results, the final recognition accuracy achieved was 99.4% (tested in our offline dataset).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CNN Model** & **Accuracy Top-1** & **Accuracy Top-5** \\ \hline AlexNet [48] & 53.17\% & 82.89\% \\ \hline Inception-v3 [49] & 53.63\% & 83.88\% \\ \hline VGG-19 [50] & **55.24**\% & 84.91\% \\ \hline ResNet-101 [29] & 54.74\% & **85.08**\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative experimental results of scene understanding algorithms on datasetPlace365.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**CNN Model** & **Accuracy Top-1** \\ \hline AlexNet [48] & 93.76\% \\ \hline Inception-v3 [49] & 95.28\% \\ \hline VGG-19 [50] & 96.41\% \\ \hline ResNet-101 [29] & **97.02**\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative experimental results of scene understanding algorithms when dataset Place365 is categorized into 6 classes according to Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Action Accuracy** & **Gesture Accuracy** & **Action-Gesture Combination** & **Action-Gesture Cross-Validation** \\ \hline
95.8\% & 96.7\% & 92.5\% & **99.4**\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: The recognition accuracy of each module in our system when Recall Rate is 0.98.(trained and tested by our datasets, _accuracy_ just refer to the recallf rate of Equation (7)).  
Figure 22: Scenes understanding results of ResNet-101 [29] over real aerial images.  
Finally, we applied our system to control the flight action of a UAV (as illustrated in Figure 19) under a real urban scene. In total, the flight experiments were approximately 3 h long, and approximately 324,000 images were tested under five types of flying conditions (buildings, factory, woods, indoors, and square). The total accuracy of final flight command sent to the UAV was over 99.5%. Figure 24 presents an example of our real flight experiment.
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 2 Preliminaries
### Specification  
Following [PERSON] and [PERSON] [5], we let the specification of the program consist of a safety specification and a liveness specification. The safety specification identifies bad things that the program should not do. We define a safety specification as a set of (bad) transitions. Specifically,  
**Definition 7** (Safety).: _A safety property is specified in terms of a set of bad transitions, \(\delta_{b}\), that the program is not allowed to execute. Thus, a sequence \(\sigma=\langle s_{0},s_{1},\ldots\rangle\) satisfies the safety property \(\delta_{b}\) iff \(\forall j:j>0:(s_{j},s_{j+1})\
otin\delta_{b}\)._  
Liveness, on the other hand, identifies good things that the program should do. We define a liveness property in terms of a set of leads-to properties. A leads-to property is of the form \(L\leadsto T\) where \(L\) and \(T\) are two state predicates and the leads-to property requires that if the program reaches a state in \(L\) it eventually reaches a state in \(T\). Specifically,  
**Definition 8** (Leads-to).: _A leads-to property is specified in terms of \(L\leadsto T\), where both \(L\) and \(T\) are state predicates. A sequence \(\sigma=\langle s_{0},s_{1},\ldots\rangle\) satisfies the leads-to property iff \(\forall j:s_{j}\in L:(\exists k:k\geq j:s_{k}\in T)\)._  
Finally, a specification consists of a safety property and a liveness specification (in terms of a set of leads-to properties).  
**Definition 9** (Specification).: _A specification spec, is a tuple \(\langle Sf,Lv\rangle\), where \(Sf\) is a safety property and \(Lv\) is a set of leads-to properties. A sequence \(\sigma\) satisfies spec iff it satisfies \(Sf\) and \(Lv\)._  
**Definition 10** (Satisfaction).: _Program \(p\)\(k\)-satisfies specification spec from \(S\) in environment \(\delta_{e}\) iff the following conditions hold:_  
* \(S\) _is closed in_ \(\delta_{p}\cup\delta_{e}\)_, and_
* _Every_ \(p[]_{k}\delta_{e}\) _computation that starts from a state in_ \(S\) _satisfies spec._  
We note that from the above definition, it follows that starting from a state in \(S\), execution of either a program transition or an environment transition results in a state in \(S\). Transitions that start from a state in \(S\) and reach a state outside \(S\) will be modeled as faults (cf. Definition 12). We define an invariant for a program as a subset of states such that if the program starts from that subset, it satisfies its required specification.  
**Definition 11** (Invariant).: _State predicate \(S\) is an invariant of \(p\) for specification spec iff_  
* \(S\
eq\emptyset\)_, and_
* \(p\)_\(k\)-satisfies spec from_ \(S\)_._
## 4 Experiments and Analysis
### Evaluation Metrics  
Hits@k is an evaluation metric commonly used to assess the performance of ranking-based models. It measures whether the correct answer appears within the top k positions in the predicted results. This metric calculates the average proportion of quadruples whose ranks are less than or equal to k across all quadruple predictions. A higher value of this metric indicates better predictive accuracy of the model.  
\[\text{Hits@k}=\frac{1}{|\text{S}|}\sum_{i=1}^{|\text{S}|}1(\text{rank}_{i}\leqslant \text{k}) \tag{16}\]  
S represents the set of quadruples, with \(|\text{S}|\) being the total number of quadruples in the set and \(\text{rank}_{i}\) denoting the predicted rank of the i-th quadruple. \(1(\text{rank}_{i}\leqslant\text{k})\) is the indicator function, which returns 1 when the rank of the i-th quadruple is less than or equal to k, and returns 0 otherwise. For each quadruple, if its rank is less than or equal to k, it is considered a hit, with a contribution value of 1. The sum of hits across all quadruples is then divided by the total number of quadruples to obtain Hits@k. In the experiments, k values of 1 and 10 are chosen as evaluation metrics, which not only reflect the precision of the answers but also capture the range of correct answers.