esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Phonetic Segments of Oesophageal Speech in People Following Total Laryngectomy
## 2 Material and Methods
### Frequency Domain Descriptors  
Spectral descriptors allow for the description of a speech signal with very high precision, which has a direct impact on the recognition of the speaker. These parameters are often used in speech recognition and improve the general recognition of the speaker. A very rich database of definitions of mathematical spectral parameter definitions can be found in [21; 22; 23; 24; 25]. Some of them have been implemented for the parameterization of phonetic segments of oesophageal speech following laryngectomy. The appropriate selection of spectral features enriches the definition of the oesophageal-speech pattern. The study of oesophageal speech in the frequency domain was carried out using signal windowing--which is the generally accepted standard when analyzing an audio signal. The length of the window for each phonetic segment was assumed to be 20 ms. Spectrum leakage was reduced by using a Hamming window and a 10 ms length overlap [2; 18; 19; 26]. This approach to the study allowed us to obtain a spectrum matrix for the analyzed phonetic segment. Then, for each signal spectrum, the descriptors values were found and their average value was calculated. An example spectrum matrix for the first phonetic segment of the word \"beczka\" (Seg. 1 \"be\") spoken by a laryngectomised woman is presented in Figure 6.  
This area of the cepstrum is the most different for oesophageal and physiological speech. This is due to the lack of the patient's larynx, which was removed by laryngectomy surgery. This area of the cepstrum in laryngectomees has flat characteristics--no larynx, no laryngeal tone.  
For the research, the spectral-domain descriptors of oesophageal speech below were used:  
1. Spectral centroid (\(SC\)) is a way of describing the shape of the power spectrum. It shows whether the spectrum is dominated by low or high frequencies. This descriptor also refers to the timbre of the sound and allows the separation of tonal sounds from noise. \[SC=\frac{\sum_{i=0}^{n}A(i)*i}{\sum_{i=0}^{n}A(i)}\] (6) where: \(A(i)\) is amplitude of the \(i\)-th component (harmonic), \(i\)--index of the \(i\)-th partial.
2. Irregularity of spectrum (\(lr\)) \[lr=log\left(20\sum_{i=2}^{N-1}\left|log\frac{A(i)}{\sqrt[3]{A(i-1)*A(i)*A(i+1) }}\right|\right)\] (7)
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 2 Preliminaries
### Software Environment of ADASs  
By analyzing the software environment of ADASs, we constructed a more targeted BOF exploitation. For example, the position of the system function in libc is involved in the ROP construction of the RCE process. The position of the system function varies in different versions of libc. The software components of ADASs mainly include operating systems, runtime environments and dependent libraries, and artificial intelligence algorithms. In our research on market devices, we found that the operating systems of most devices usually run on manufacturer-customized Linux systems or QNX systems, with Linux versions mostly between 2.6 and 4.13 and QNX between 6.5 and 7.1. Table 1 presents the operating systems and kernel versions of five commercial models of ADASs.  
Software operating environments and dependencies mainly include two types. One is the most fundamental operating environment that the operating system provides, such as libc. The libc information is our main focus in security research and plays a vital role in exploiting buffer overflow vulnerabilities. Table 1 lists the libc version information of five commercial ADAS devices. Another type is the third-party component Software Development Kit (SDKs) that the application depends on. This type often varies among different manufacturers and lacks a unified quantifiable indicator. For example, SAIC's immotors ADAS uses the NVIDIA Xavier development environment, while Tesla's Autopilot has entirely independently developed related suites and does not use such third-party components.  
Artificial intelligence algorithms are diverse, but when categorized, they mainly include supervised algorithms such as pattern recognition, SVM, regression algorithms, decision matrix algorithms, Ada-Boost, and unsupervised algorithms such as clustering [16].
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
## 2 Materials and Methods
### Reagents and Antibodies  
A crucial element for target recognition was a monoclonal antibody specifically designed to target the Lipopolysaccharides (LPS) of _Salmonella_ spp. It was used in conjunction with a polyvalent polyclonal antibody capable of detecting _Salmonella_ spp. with affinity for all O and H antigens. The antibodies used in this study were a polyclonal (rabbit) anti-_Salmonella_ antibody (Invitrogen S.A, Barcelona, Spain) and a _Salmonella_ LPS core monoclonal (mouse) antibody (Fisher Scientific SL, Madrid, Spain).  
To assess the primary antibodies, polyclonal and monoclonal antibodies were evaluated against target antigens sourced from _Salmonella enterica_. Specifically, the LPS from _Salmonella enterica_ L9516-5 MG were used. They were obtained from Sigma Aldrich (Darmstadt, Germany).  
Two types of secondary array antibodies were employed: GAM (goat anti-mouse) anti-mouse IgG-Alexa 647 and GAR (goat anti-rabbit) anti-rabbit IgG-Alexa 647, both sourced from Abcam (Cambridge, UK). These secondary antibodies were integral to the experimental setup. To complete the array of sensor probes, a rabbit anti-Salmonella polyclonal antibody (Abcam, Cambridge, UK) was introduced. As a negative control, a rabbit anti-fish polyclonal antibody from Eurofins Immunolab (Reinbek, Germany) was used. These sensor probes were crucial in the experimental setup, enabling the detection and differentiation of target elements.
# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
## 5 Knowledge-Based Abstraction Operations
### Automatic Abstraction Operators
#### 5.2.1 Automatic Abstraction in Our Running Example  
The process model in Figure 3, although less complex than the initial one (see Figure 1), is still not easy to read at first glance, mainly due to the long chains of activities and interactions. A higher-level view of the main procedures performed in this process can be useful for facilitating the interpretation. Such a view can be obtained thanks to the KB presented in Figure 4. The user can exploit the KB in a naive way, trying, as a first option, the execution of the abstraction algorithm in a fully automatic mode, indicating, e.g., level 2 of the KB as the desired abstraction level. Notably, at level 2, all the medical procedures (patient evaluation, therapeutic procedures involving both preparatory and intervention phases, patient monitoring, and rehabilitation) are defined in their more general form. After running the automatic algorithm, the system provides the model in Figure 7 as output.  
The model in Figure 7 is much more readable and clearly shows most of the main procedures performed for the treatment of patients. However, the user recognizes, in this model, a path in which all three treatments (_r-TPA proc_, _TPA ia proc_, and _PH proc_) are potentially performed on a single patient, with a transition from the _r-TPA proc_ procedure (upper branch after _Evaluation_) to the _TPA ia proc_ procedure, and finally, to the pharmacological procedure (_PH Proc_, lower branch). This is not correct from the medical viewpoint. Thus, the user needs to backtrack to the model in Figure 3 to apply alternative abstraction strategies, different from the automatic one, as discussed in the following section.  
Figure 7: Process model abstracted at level 2 through the fully automatic algorithm.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 5 Conclusions  
In aquaculture, the normal growth of fish is closely related to the density of marculture. Therefore, in order to solve the density statistics of marculture cages, this paper adopts the Swin Transformer neural network algorithm, an accurate segmentation scheme for remote sensing images of cages based on the Swin Transformer and ensemble learning strategy. In order to verify the effectiveness of the scheme proposed in this paper, comprehensive comparison experiments are conducted on U-Net, Mask-RCNN, SOLOv2, UCTransNet, Attention U-Net and Swin Transformer in the experiments. The experimental results show that the segmentation scheme proposed in this paper can produce optimal segmentation results with mIoU reaching 82.34%.  
Although the scheme proposed in this paper has produced a good segmentation effect on the remote sensing images of cages, there are still shortcomings. On the one hand, for the problem that some of the cages cannot be segmented, future attempts can be made to incorporate temporal features and spatial features. On the other hand, the scheme proposed in this paper is not effective in segmenting small targets, and future attempts can be made to combine image pyramids and multi-scale sliding window methods to improve the segmentation accuracy of marculture cages. At the same time, the seasonality of aquaculture and how to extract high-definition remote sensing images in harsh environments can also be studied in the future.  
Conceptualization, Z.H., W.W. and L.X.; methodology, L.X. and C.Z.; software, L.X. and C.Z.; validation, Z.H., L.X. and C.Z.; formal analysis, Z.H., L.X. and C.Z.; investigation, Z.H. and W.W.; resources, Z.H.; data curation, L.X. and C.Z.; writing--original draft preparation, L.X. and C.Z.; writing--review and editing, Z.H.; visualization, L.X.; supervision, Z.H.; project administration, Z.H. and W.W.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hainan Province Science and Technology Special Fund (Grant No. ZDYF2022 SHFZ039), the Natural Science Foundation of Hainan Province (Grant No. 620 RC564) and the National Natural Science Foundation of China (Grant No. 61963012 and Grant No. 62161010).  
Not applicable.  
The dataset associated with this paper can be found in the online version, at [[https://gitee.com/hainu_hzh/remote-sensing-image-datasetof-marculture-cage.git](https://gitee.com/hainu_hzh/remote-sensing-image-datasetof-marculture-cage.git)]([https://gitee.com/hainu_hzh/remote-sensing-image-datasetof-marculture-cage.git](https://gitee.com/hainu_hzh/remote-sensing-image-datasetof-marculture-cage.git)) (accessed on 26 June 2022).  
The authors declare no conflict of interest.
## 6 Simulation Results
### Economic Analysis through One Year  
From the previous subsection, one parameter configuration was selected for each PRRC method to be tested using a whole year's data. These configurations were selected considering the minimum amount of degradation without having PRR violations. Additionally, the mean of the SOC and the variance of the output PRR were considered. The SOC at the end of the day is an indicator of SOC variations and is also used as a selection criterion. With these criteria in mind, the following PRRC methods were considered for this section:  
* RS with a PRR\({}_{\text{max}}\) of 10%.
* SMA with a window size W = 10.
* EMA with a window size W = 30 and a smoothing factor \(\alpha\) = 0.123.
* FOLPF with a sampling period T\({}_{\text{s}}\) = 60 s and a T\({}_{\text{f}}\) = 500 s.
* SOLPF with a sampling period of T\({}_{\text{s}}\) = 60 s, a damping ratio \(\zeta\) = 0.707, and a natural frequency \(\omega_{\text{n}}\) = 1/25 rad/s.
* ELES with a smoothing factor \(\alpha\) = 0.06.
* Proposed PDS with a window size W = 5 and a disturbance coefficient \(\mu\) = 10%. This coefficient was selected since it represented errors in estimations of about 1 MW, which is reasonable according to the literature presented in Section 3.  
First, a battery degradation analysis was made to each of the PRRC methods. Then, the estimated cost per kWh per year was compared. Finally, the violations throughout the year were analyzed. The solar radiation data for this test was selected from 20 May 2019 to 19 May 2020. Results were summarized at the end of this section. It is important to mention that, as the SOC at the end of each day is not the same as the one at the beginning of the day, the mean SOC tended to drift during the year until it reached the maximum or minimum limits. To account for this issue, a proportional SOC controller was added at the output of each PRRC controller. The mathematical expression for this controller is dictated by:  
\[\overline{\text{P}}_{\text{ess}}\text{=P}_{\text{ess}}-\text{K}_{\text{soc}} (\text{SOC}_{\text{ref}}-\text{SOC}). \tag{26}\]  
Figure 19: Comparison of the effect of selecting the window size using the PDS method.  
where \(\overline{\text{P}}_{\text{ess}}\) is the adjusted power reference for the ESS, \(\text{P}_{\text{ess}}\) is the output of the PRRC method, \(\text{SOC}_{\text{ref}}\) is the desired SOC at the end of the day, and \(\text{K}_{\text{soc}}\) is the proportional control constant, which is selected small to not affect the dynamics of the PRRC.
## 4 Numerical Experiments
### Results
\(N\)—amount of points in the FFT analysis, \(K=\lceil M/H\rceil\)—amount of time instants for which the FFT has to be applied for \(M\)—signal length in samples, and \(H\)—window shift in samples. Figure 10: Absolute value of estimated errors of the Doppler rate estimation—initial error and after correction. time was reduced to \(t_{S}=0.17\) s and \(t_{R}=90.73\) s. For the purposes of calculations, a computer with an Intel i7-7700 HQ 2.8 GHz processor, 16 GB DDR4 RAM, an SSD hard drive, and a 64-bit Windows 10 system was used. The calculations were performed in the Matlab environment. Consequently, by reducing the distribution quality and for the window shift \(H=2\), the processing time was additionally decreased to \(t_{S}=0.06\) s and \(t_{R}=29.62\) s with nearly the same readability of the distribution. This analysis shows how the processing time may be easily manipulated and reduced with almost conserved resolution, allowing enhanced estimation of the motion parameters to be performed.
## 3 Methods and Materials
### Markov Decision Process
* \(N_{comm}\) is the total number of communications sessions. * \(N_{recharge}\) is the total number of recharging sessions. * \(N_{total}\) is the total number of waypoints to be touched in the simulation. The weights \(w_{1},w_{2},w_{3}\) and \(w_{4},w_{5}\) sum up the unity. In the simulation results presented at the end of this section, the weights are: \(w_{1},w_{2}=0.25\), \(w_{3},w_{4},w_{5}=0.5\). The algorithm works well when the reward is between 0 and 1. Therefore, the final value of \(R_{scenario_{results}}\) is set to be at most equal to unity. For the simulations in this paper, the system had enough resources to communicate and recharge only two times over the ten touched waypoints. The \(R_{distance}\) is evaluated as in Equations (2) and (3), where:  
* \(\beta\) is a factor defining the equation's sensitivity. * \(D_{opt}\) is an optimal distance estimate to touch all the \(N_{waypoint_{max}}\). * \(D_{evaluated}\) is the travelled distance between the touched waypoints during the simulation. Equation (2) tends toward one when the x evaluated in Equation (3) tends toward zero. The factor \(\beta\) quantifies the boundaries from which there is a sensitive increment of the reward related to distance, as showed in Figure 4. For the simulations of this paper \(\beta\) is assumed to be equal to 1000. \[R_{distance}=e^{(-\frac{x^{2}}{\beta})} \tag{2}\]  
\[x=D_{opt}-D_{evaluated} \tag{3}\]  
The optimal distance between a set of waypoints can be evaluated with routing algorithms. The results of the routing algorithms can be used in the MDP framework to provide a heuristic suggesting a preferential movement sequence. Different scenarios can have quite different reward formulations. Therefore, the algorithm requires a first tuning to define it.
# Machine Learning Subjective Opinions: An Application in Forensic Chemistry
## 4 Results  
### Beta Distribution Assumptions  
The theory of binary subjective logic is well established [5]. The two mutually exclusive and comprehensive states represent a Bernoulli distribution and the conjugate prior is a beta distribution. However, the ML methods applied as ensembles are not guaranteed to produce a set of predicted probabilities of class membership that fit a beta distribution. Tests of the beta distribution were made by the Kolmogorov-Smirnov distance (D) statistical test (i.e., the K-S test) [43]. The test statistic compares the empirical cumulative probability distribution to the cumulative probabilities for the reference beta distribution. The reference beta distribution is based on the fitted \(\alpha\) and \(\beta\) shape parameters for each validation sample. The Null hypothesis is that the empirical distribution was drawn from the reference distribution. If the measured statistic is greater than the critical value, \(D_{n,\alpha}\), the Null hypothesis is rejected. The critical value is \(D_{100,0.05}=0.1358\)[43].  
Table 2 gives the results for the K-S test on the beta distribution fitting data for LDA, RF and SVM base models. The \"Samples\" column gives the number of in silico samples used to train each of the 100 ensembles. The \"Mean \(D\)\" column gives the average value of D calculated for the 1117 validation samples. The \"Non-Beta Fits\" and \"% Non-Beta Fits\" columns give the number and percentage, respectively, of the 1117 samples where the predicted probabilities of class \(x\) membership were statistically different from a beta distribution at the 5% significance level. Increasing the training set size for the LDA models improved the fraction of validation samples that fit a beta distribution. The RF ML models showed the opposite trend, with an increasing number of validation samples' predicted class membership probabilities not conforming to a beta distribution. This trend is not understood, nonetheless, the percentage of non-beta distributions remained below 5% up to a training set size of 2000. However, as the size of the RF training set increased, the median uncertainty showed statistically significant improvement and the ROC AUC also exhibited statistically significant improvement up to a training set size of 60,000. These trends are discussed further in Section 4.2, Tables 3 and 4. The decreasing median uncertainty for indicates a narrowing of the fitted beta distribution which results from increased similarity among the predicted probabilities from all members of the ensemble. The SVM ML models gave the poorest performance regarding the beta distribution fit. The further analysis of the observed trend for the RF base models will be studied in the future as it is beyond the scope of this work.
## 3 Results and Discussion
### Principal Components Analysis (PCA) of the Raman and SORS Spectra  
As simple visible inspection did not allow for the assessment of the quantitative levels of the three adulterants, PCA was used to analyse the three binary oil mixtures on the four analytical methods (vials for all three platforms and through the bottle for SORS) in order to look for any trends in the PCA ordination scores space that may be related to changing levels of the oils.  
For brevity we shall here highlight Figure 3, which is a PCA scores plot of EVOO and pomace OO where Raman data were collected in vial mode using a CBEx 1064 nm instrument. It is clear from this plot that there was a concentration-dependant gradient along the PC1 axis, which accounted for \(\sim\)29% of the total explained variance (TEV).  
Figure 2: Raman spectra of olive oil collected using SORS directly through bottles. The different authentic extra virgin olive oil-adulterant mixtures are shown in different colours. Spectra are offset in the \(y\)-axis so that the features can be more easily observed. Also provided are the origins of the main bands in these oils with assignments taken from [41]. Also highlighted are bands not used within the data processing; these are seen in Supplementary Information.  
Inspection of this plot revealed that pure EVOO is located on the most negative part of principal component 1 (PC1) (located on the left-hand side of this plot) and that increasing the pomace OO content caused the spectra to follow a trend to the most positive side of the PC1 axis (located on right hand side). The corresponding PCA loadings plots (data not shown) highlighted that the main peaks involved in these trends were at 1265 and 1657 cm\({}^{-1}\), as also seen for sunflower-EVOO mixtures (Figure S4). These vibrations are from unsaturated fatty acids and specifically for 1265 cm\({}^{-1}\) from =C-H deformation of _cis_ R-HC=CH-R and for 1657 cm\({}^{-1}\) from C=C stretch, _cis_ -RHC=CHR.  
Similar trends were seen for all these multivariate analyses and the PCA scores plots of the Raman spectra collected from mixtures of EVOO with sunflower oil, pomace OO and refined OO using all three handheld instruments, which are provided in the Supplementary Materials (Figures S5- S7). The exception was the PCA scores plots of the spectral data collected using the SORS instrument in vial mode that did not show any clear separation for mixtures of EVOO with pomace and refined oil in the first PC, and the concentration gradient clustering was achieved on the PC2 axis, with a TEV of 15.5% and 10.9%, respectively, for EVOO-pomace OO (Figure S6C) and EVOO-refined OO (Figure S6C).
## Appendix E Correctness of Algorithm 5 (Slitherlink Solver)  
Let \(Y_{G,h}\) be the output by Algorithm 5 for \((G,h)\).  
**Lemma 30**.: _If a \(0,1\)-sequence \(\pi\) ends in \(\mathbf{1}\) in \(Y_{G,h}\), then \(E(\pi)\) is a solution for \((G,h)\)._Proof.: By Lemma 27, If \(\pi\) ends in a node \(n\
otin\{\mathbf{0},\mathbf{1}\}\), then \(E(\pi)\) is a path matching such that every vertex in \(V\setminus V^{\geq e_{i+1}}\) has degree \(0\) or \(2\) in \(E(\pi)\) and \(\mathrm{mate}_{\pi}=\mathrm{mate}_{n}\). Moreover, it is easy to show by induction on \(i\) that \(|E(\pi)\cap D|=\mathrm{count}_{n}(D)\) for all \(D\in\mathrm{dom}(h)\).  
If \(\pi\) ends in \(\mathbf{1}\), by definition of \(\mathrm{GN}\), \(E(\pi)\) is a cycle such that \(|E(\pi)\cap D|=h(D)\) for all \(D\in\mathrm{dom}(h)\). That is, \(E(\pi)\) is a solution for \((G,h)\).  
**Lemma 31**.: _If a \(0,1\)-sequence \(\pi\) of length \(i\) ends in \(\mathbf{0}\) in \(Y_{G,h}\), then \(E(\pi)\cup E^{\prime}\) is not a solution for \((G,h)\) for any \(E^{\prime}\subseteq E^{\geq e_{i+1}}\)._  
Proof.: Suppose that \(\pi=\pi^{\prime}\cdot b\) ends in \(\mathbf{0}\) where \(b\in\{0,1\}\). Let \(n=n(\pi^{\prime})\), \(i=|\pi|\) and  
\[c(D)=\begin{cases}\mathrm{count}_{n}(D)&\text{ if }b=0\\ \mathrm{CU}(\mathrm{count}_{n},e_{i})(D)&\text{ if }b=1\end{cases}\]  
for all \(D\in\mathrm{dom}(h)\). The proof of Lemma 25 shows that if \((\mathrm{mate}_{n},i)\) has a fixed end or \(\mathrm{mate}_{n}\) declines \(e_{i}\), then the lemma holds. It is enough to discuss the case where \((c,i)\) is incompatible with \(h\). In this case, either \(c(D)>h(D)\) or \(c(D)+|D\cap E^{\geq e_{i+1}}|<h(D)\) for some \(D\in\mathrm{dom}(h)\). In the former case, \(|E(\pi)\cap D|=c(D)>h(D)\) implies that \(|(E(\pi)\cup E^{\prime})\cap D|>h(D)\) for any \(E^{\prime}\subseteq E^{\geq e_{i+1}}\). That is, \(E(\pi)\cup E^{\prime}\) cannot be a solution. In the latter case,  
\[|E(\pi)\cap D|+|D\cap E^{\geq e_{i+1}}|=c(D)+|D\cap E^{\geq e_{i+1}}|<h(D)\]  
implies that \(|(E(\pi)\cup E^{\prime})\cap D|<h(D)\) for any \(E^{\prime}\subseteq E^{\geq e_{i+1}}\). That is, \(E(\pi)\cup E^{\prime}\) cannot be a solution.  
**Corollary 32**.: \(Y_{G,h}\) _represents the set of all solutions for \((G,h)\)._  
Proof.: By [PERSON] 30 and 31.