esa-sceva/satcom-chunk-collection huggingface dataset:
# Availability of Primary Closure for Resection of Oral Cavity Cancer
## 3 Results
### Factors Associated with Primary Closure (Tables 2-4)

Table 2: Clinical characteristics of patients—primary closure vs. flap. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Clinical Characteristic** & **Primary Closure** & **Flap** & \(\mathbf{p}\)**Value** \\ \hline Age [years old, mean, (range)] & 65.25 (44–87) & 64.55 (15–86) & \(p=0.779\) \\ Gender (\%) & & & \(p=0.107\) \\ Male & 5 (5.88) & 37 (43.52) & \\ Female & 11 (12.94) & 32 (37.64) & \\ Neck dissection & & & \(p=0.307\) \\ Surgery with SND & 8 & 45 & \\ Surgery with mRND & 1 & 3 & \\ Surgery without neck dissection & 7 & 21 & \\ Stage & & & \(p=0.046\) * \\ I & 13 & 13 & \\ II & 3 & 15 & \\ III & 0 & 17 & \\ IV & 0 & 24 & \\ Operation time (h) & 4.02 & 8.81 & \(p=0.015\) * \\ Lesion site & & & \(p=0.120\) \\ Upper gingiva & 2 & 15 & \\ Lower gingiva & 3 & 30 & \\ Tongue & 5 & 8 & \\ Buccal mucosa & 1 & 7 & \\ Floor of the mouth & 2 & 5 & \\ Palate & 3 & 4 & \\ Follow-up [month, mean (range)] & 38.37 (12–80) & 32.92 (2–110) & \\ Period of hospitalization & 13.4 (7\(\sim\)21) & 26.7 (6–99) & \(p=0.0003\) * \\ \hline \hline \end{tabular}  
* Statistically significant difference (\(p<0.05\)).  
\end{table} 
Table 3: Three-way ANOVA of Table 2; dependent variable: operation time. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Df** & **SS** & **MS** & **F** & \(\mathbf{p}\) \\ \hline Neck Dissection (ND) & 1 & 87.21 & 87.21 & 1.2704 & 0.267153 \\ Reconstruction & 1 & 741.7 & 741.7 & 10.8039 & 0.002266 * \\ Method (RM) & & & & \\ Stage & 1 & 13.92 & 13.92 & 0.2027 & 0.655215 \\ ND\(\times\)RM & 1 & 10.34 & 10.34 & 0.1506 & 0.700227 \\ ND\(\times\)Stage & 1 & 4.89 & 4.89 & 0.0712 & 0.791087 \\ RM\(\times\)Stage & 1 & 0.09 & 0.09 & 0.0013 & 0.971476 \\ ND\(\times\)RM\(\times\)Stage & 1 & 0.03 & 0.03 & 0.0004 & 0.984249 \\ \hline \hline \end{tabular}  
* Df = degree of freedom; SS = Sum of squares; MS = mean squares; F = variance ratio.
* Statistically significant difference (\(p<0.05\)).  
\end{table} 
Table 4: Three-way ANOVA of Table 2; dependent variable: Period of hospitalization.
# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 2 Methodology
### Mid-Level Saliency Maps Generation by Bilateral Absorbing Markov Chain
#### 2.3.1 Absorbing Markov Chain for Saliency Detection  
To facilitate the understanding, we give a brief introduction to the principle of absorbing Markov chain [45; 46]. For a given set of states \(S=\{s_{1},s_{2},\ldots,s_{k}\}\), the probability of moving from state \(s_{i}\) to the next state \(s_{j}\) is expressed as the transition probability \(p_{ij}\), which does not depend on the chain before the current state. An absorbing Markov chain contains at least one absorbing state (\(p_{ii}=1\)), and starts from every transient state, a certain absorbing state can be reached. For an absorbing chain with \(r\) absorbing states and \(t\) transient states, the canonical form of the transition matrix \(\mathbf{P}\) is as follows,  
\[\mathbf{P}\rightarrow\left(\begin{array}{cc}\mathbf{Q}&\mathbf{R}\\ 0&\mathbf{I}\end{array}\right) \tag{9}\]  
where \(\mathbf{Q}\in[0,1]^{t\times t}\) represents the transition probability of any pair of \(t\) transient states, while \(\mathbf{R}\in[0,1]^{t\times r}\) represents the transition probability between any transient state and absorbing state. \(0\) is the \(r\times t\) zero matrix and \(\mathbf{I}\) is the \(r\times r\) identity matrix. Furthermore, the fundamental matrix \(\mathbf{N}\) is computed [45],  
\[\mathbf{N}=\left(\mathbf{I}-\mathbf{Q}\right)^{-1}=\mathbf{I}+\mathbf{Q}+ \mathbf{Q}^{2}+\cdots \tag{10}\]  
where \(n_{ij}\) of \(\mathbf{N}\) can describe the expected number of times from transient state \(s_{i}\) to transient state \(s_{j}\) in the absorbing chain.  
Then the absorption probability for each transient state to reach any absorbing state can be defined as [46],  
\[\mathbf{B}=\mathbf{N}\mathbf{R} \tag{11}\]  
where \(b_{ij}\) of \(\mathbf{B}\) indicates the absorption probability from transient state \(s_{i}\) to transient state \(s_{j}\)Traditional saliency detection models based on absorbing Markov chain generally mirror image boundary superpixels as absorbing nodes (or states), and all others as transient nodes. Then, the transition matrix **P** is constructed according to the similarity (the transition probability) between nodes. The saliency value is measured by the absorption probability, the higher the absorption probability of the node, the more similar to the absorbing nodes.
## 2 Statistical Analyses of Individual Hazards
### Assessing the Stability of Landslide Dams
#### 2.2.2 Some Methods of Calculating the Parameters  
Equation (6) involves the dam's height, width, length, and volume; the reservoir capacity of the barrier lake; the catchment area; and the material coefficient. Only the material coefficient is known. [PERSON] et al. [33] studied the landslide events in Taiwan and proposed a formula for the volume of a dam. However, the formulas were proposed on the basis of rainfall and earthquake conditions. On the basis of the form of this formula and the database, a new formula for calculating the volume of a dam in all cases is as follows:  
\[V_{D}=3.057V_{L}{}^{0.363} \tag{7}\]  
where \(V_{D}\) has a unit of \(10^{6}\) m and \(V_{L}\) has a unit of \(10^{6}\) m. The fitting results show that Equation (7) has a correlation coefficient of 0.675.  
According to the results when analyzing the database, the dam's width is mainly affected by the dam's volume and has little correlation with the dam's height. A formula for the dam's width can be fitted as follows:  
\[W_{d}=0.00592V_{D}{}^{0.746} \tag{8}\]  
The fitting results of the database show that Equation (8) has a correlation coefficient of 0.850.  
[PERSON] et al. [28] proposed an empirical formula based on 86 cases to calculate the length of a dam:  
\[L_{d}=224.87-1.915\times 10^{-3}V_{L}{}^{2}-0.056h^{2}+6.8V_{L}+8.683h \tag{9}\]  
where \(h\) is the depth of water in the river (m).  
After the volume, width, and length of the dam have been determined, the height of the dam can be easily calculated as follows:  
\[H_{d}=0.426\times\frac{V_{D}}{W_{d}^{0.84}L_{d}^{0.85}} \tag{10}\]  
Figure 4: Critical conditions for the stability of landslide dams.  
The fitting results of the database show that Equation (10) has a correlation coefficient of 0.799.  
The geometric relationship for estimating the reservoir capacity of the barrier lake and the catchment area is shown in Figure 5. The reservoir capacity of the barrier lake and the catchment area can be calculated as follows:  
\[V_{l}=\frac{H_{d}^{2}L_{d}}{6i_{u}},\text{ for V - shaped valley} \tag{11}\]  
\[V_{l}=\frac{H_{d}^{2}L_{d}}{2i_{u}},\text{ for U - shaped valley} \tag{12}\]  
\[S_{C}=\frac{H_{d}L_{d}}{2i_{u}},\text{ for V - shaped valley} \tag{13}\]  
\[S_{C}=\frac{H_{d}L_{d}}{i_{u}},\text{ for U - shaped valley} \tag{14}\]  
where \(i_{u}\) is the upstream riverbed's inclination along the river (rad).
## 4 Problem Formulation
### Models of Fixed-Wing UAVs  
To simplify the dynamic model of UAV, we assume that each UAV flies at the predefined height, then the dynamics of the \(i\)-th UAV is described as  
\[\left\{\begin{array}{l}\dot{x}_{i}=v_{i}\cos\theta_{i}\\ \dot{y}_{i}=v_{i}\sin\theta_{i}\\ \dot{v}_{i}=a_{i}+f_{11}(v_{i},\theta_{i})+d_{i1}\\ \dot{\theta}_{i}=\omega_{i}+f_{22}(v_{i},\theta_{i})+d_{i2}\end{array}\right. \tag{14}\]where \((x_{i},y_{i})^{\top}\) represents the position of UAV \(i\) under initial frame, \(v_{i}\) and \(\dot{\theta}_{i}\) are linear velocity and angular speed of UAV \(i\), respectively, \(\mathbf{u}_{i}=(a_{i},\omega_{i})^{\top}\) is the control input, \(\mathbf{f}_{i}=(f_{i1},f_{i2})^{\top}\) is the unknown dynamics of UAV \(i\), and \(\mathbf{d}_{i}=(d_{i1},d_{i2})^{\top}\) is the unknown disturbance, which is bounded, i.e., \(\|\mathbf{d}_{i}\|\leq d\).  
**Remark 1**.: _The unknown dynamics illustrate the model deviation relative to the nominal system. The function \(\mathbf{f}_{i}\) is unknown but the function value \(f_{i}(v_{i}^{*},\theta_{i}^{*})\) can be measured. However, the external disturbance changes in different environment, which requires the robustness of the control law designed._  
Define \(\mathbf{\xi}_{i}=(x_{i},y_{i})^{\top}\), \(\mathbf{\zeta}_{i}=(\dot{x}_{i},\dot{y}_{i})^{\top}\), then Equation (14) can be transferred into state function  
\[\left\{\begin{array}{l}\mathbf{\dot{\xi}}_{i}=\mathbf{\zeta}_{i}\\ \mathbf{\dot{\xi}}_{i}=\mathbf{G}_{i}(\mathbf{u}_{i}+\mathbf{f}_{i}+\mathbf{d}_{i})\end{array}\right. \tag{15}\]  
where  
\[\mathbf{G}_{i}=\begin{bmatrix}\cos\theta_{i}&-v_{i}\sin\theta_{i}\\ \sin\theta_{i}&v_{i}\cos\theta_{i}\end{bmatrix}.\]  
It is obvious that the matrix \(\mathbf{G}_{i}\) is full rank, and \(\|\mathbf{G}_{i}\|=\max\bigl{\{}1,\sqrt{v_{i}}\bigr{\}}\).
# Emerging Optical Techniques for the Diagnosis of Onychomycosis
## 3 Imaging Techniques
### Optical Coherence Tomography (OCT)
The hyper-reflective area was shown to correspond to the characteristics of the dermatophytoma \"fungal ball\", at a depth of 0.55 to 0.98 mm below the nail plate. Moreover, cuticula and proximal fold were depicted as highly scattering structures in contrast to the low scattering matrix. Finally, compared to established diagnostic procedures for onychomycosis, OCT seems to be more reliable than KOH preparation and nail clippings culture which are both susceptible to type II (false negative) errors [35]. From a methodological point of view, the OCT approach to onychomycotic nail plate diagnosis can be compared to the histopathological screening of nail plate serial sections for the presence of hyphae. Cross-correlation with histology showed that in onychomycotic nails high-scattering structures are conglomerates of hyphae [35]. However, with OCT there is no need for nail plate sampling and an almost indefinite number of vertical sections are available for subsequent evaluation. Yet a distinct limitation of OCT is the fact that alterations in the hyponymium and the surface of the nail plate cannot be evaluated with the same efficacy as the nail plate. Nevertheless, in a  
Figure 3: Optical and OCT images of nail infected by _T. rubrum_ (culture-confirmed by direct KOH microscopy). The white line (9 mm) in the clinical picture (**a,b**) indicates the vertical plane that the OCT image was taken at (**c,d**). The multiple hyper-reflective, poorly defined, wisp-like streaks seem to originate from the hyponymium towards the nail plate at oblique angles relative to the nail’s surface, as if the fungal hyphae move towards undigested keratin (chemotaxis). Images were acquired with NITID OCT (DermaLumics, Madrid, Spain). prospective trial on 50 patients with suspected onychomycosis [39], OCT had the lowest specificity of 42.9%. Moreover, OCT had negative predictive value of 75%, higher than CLSM (68%), histopathology (63.6%) and culture (40.4%), but lower than PCR (91.3%). Both positive (i.e., the probability that nails with positive screenings are indeed affected by onychomycosis) and negative (i.e., the probability that nails with negative results are indeed not affected by onychomycosis) predictive values of OCT were 75%. The high number of false positive outcomes was attributed to the OCT's inherent inability to differentiate between hyphae or fungal spores within the nail plate and structural artefacts (e.g., trapped air) that add image noise to the optical signal.
## 2 Methods
### Simulated Annealing  
Since [PERSON] et al. [17] proposed the simulated annealing algorithm based on statistical physics, it has become an important tool for resolving nonlinear inversion issues. In this paper, we utilize the Metropolis algorithm to find the global optimum solution of Equation (2). Metropolis algorithm is a fixed temperature for the classical general scheme of the simulated annealing. In the inversion procedure, we use a temperature of 100 \({}^{\circ}\)C.  
To numerically find the globally optimal solution of the objective function (2), we assume the model parameter \(\mathbf{m}\) is the only variable to be inverted, \(\mathbf{m}=d\). The model space \(\mathbf{m}\) has a variation of plus or minus 30 km of the ISC-EHB depth for every iteration in the inversion. The model space must be greater than 0. Here are the instructions for how to do the simulated annealing procedure using the classical scheme.  
Figure 1: (**a**) Schematic diagram showing seismic ray paths of PKIKP (blue line) and pPKIKP (magenta line), traveling from the source (red star) to a station at the Earth’s surface (black triangle), CMB and ICB represent core-mantle boundary and inner-core boundary, respectively. (**b**) Relationship between theoretical pPKIKP-PKIKP time interval and focal depth at the epicenter distance 150\({}^{\circ}\).  
Basic variables are declared and set to their default values. In the inversion technique, the following parameters were set: (1) An initial model parameter \(m_{0}\) was generated randomly, (2) the initial temperature \(T=T_{0}\) is set to 100 \({}^{\circ}\)C, (3) The maximum number of iterations in the inversion procedure, (4) fitting error threshold \(\epsilon\).
2. Estimate the model parameter \(\text{m}_{k}\) in the objective function (2), and the temperature \(T_{k}\), for the \(k\)th iteration. The acceptance of the accepting model parameter \(\text{m}_{k+1}\) from the current \(\text{m}_{k}\) is given by the following transition probability: \[P(\Delta F)=\begin{cases}1&\text{if }\Delta F<0\\ \exp(\frac{\Delta F}{T})&\text{if }\Delta F\geq 0\end{cases}\] (3) where \(\Delta F\) is the difference of the objective function (2), that is, \(\Delta F=F(\text{m}_{k+1})-F(\text{m}_{k})\). The Boltzmann constant is not included in Equation (3). If \(\Delta F<0\) then the transition probability is equal to 1, indicating that the system energy tries to go to the lower energy states. Thus, the new model parameter is accepted. However, if \(\Delta F\geq 0\) then we should calculate the probability function \(P(\Delta F)\) (The value of \(P(\Delta F)\) takes a value from 0 to 1), and then a random number R between 0 and 1 is generated. If \(P(\Delta F)>\text{R}\) then the model parameter will be updated to \(\text{m}_{k+1}\), otherwise the \(\text{m}_{k+1}\) will be rejected.
3. Evaluate the objective function \(F(\text{m}_{k})\). If \(F(\text{m}_{k})<\epsilon\) then the inversion procedure will be terminated, otherwise, \(k=k+1\), going to step 2.
## 3 Problem Statements  
Figure 2 presents the stability issue of the slope with the MHB failure criterion. The geometry of the studied problem was denoted by the slope height \(H\) and the slope angle \(\beta\). In order to make the applicability of the calculation results more transparent, the following assumptions were adopted:  
Figure 1: Tangential line to the modified Hoek–Brown (MHB) yield criterion.  
Figure 2: Simplified model for a rock mass slope. Point \(A\) and \(C\) are the end points of the sliding surface; \(H\) is the slope height; \(\beta\) is the slope angle; \(k_{h}\) is the horizontal seismic acceleration coefficient; and \(W_{\gamma_{t}}\) is the work rate of the rock weight.  
(1) The ground surfaces were assumed to be horizontal, and the impact of pore water pressure was not considered in this work.  
(2) The failure model was presumed as a rotational body around the same axis through \(O\) with the same angular velocity \(\omega\).  
(3) The influence of seismic loads is assumed as a horizontal force acting on the failure model.  
The third assumption is the classical pseudo-static approach, which has been widely used to conduct the seismic stability assessment of slopes with linear Mohr-Coulomb failure criterion [20,21].
## 4 Experimentation
### Raw AE Signals Characteristics  
Power spectral density (PSD) is the averaged power of a signal in the frequency domain. PSD is a worthy illustrator of energy levels lying on the band of frequency showing which component of the frequency is outplaying in a particular signal. Figures 16-21 are the normalized PSDs generated using the MATLAB(r) Signal Analyzer application showing power levels on all frequency components for all cases. It is perceived that every case has a different PSD signature greatly dependent on the workpiece and tool type.  
Figure 16: Power spectral density (PSD) for Case-1 of new (blue), used (yellow), and worn-out (red) tool with maximum leakage settings.  
Figure 15: Amplitude comparison of all three categories for Case-1 with new (blue), used (yellow) and worn-out (red) tools are shown.  
Figure 19: Power spectral density (PSD) for Case-4 of new (blue), used (yellow), and worn-out (red) tool with maximum leakage settings.  
Figure 17: Power spectral density (PSD) for Case-2 of new (blue), used (yellow), and worn-out (red) tool with maximum leakage settings.  
Raw acoustic signals in the time domain also contain useful statistical features that are distinct from each other, resulting in good classification practice [5; 26; 27]. Figures 22-27 represent the six statistical features, RMS, mean, variance, skewness, kurtosis, and standard deviation, that have been calculated for the raw AE signal and are represented by bar graphs. Each figure represents the comparison of each statistical feature extracted for each case. Intuitively, frequency and time domain features failed to express promising trends or orders that are essential for attaining good training and classification for this experiment; there are no significant levels of unique or matching feature trend behaviors intra-categorically or inter-cases, respectively.  
Figure 21: Power spectral density (PSD) for Case-6 of new (blue), used (yellow), and worn-out (red) tool with maximum leakage settings.  
Figure 20: Power spectral density (PSD) for Case-5 of new (blue), used (yellow), and worn-out (red) tool with maximum leakage settings.  
Figure 23: Spectrograms (training samples) of new, used, and worn-out tool conditions for Case 2.  
Figure 24: Spectrograms (training samples) of new, used, and worn-out tool conditions for Case 3.  
Figure 22: Spectrograms (training samples) of new, used, and worn-out tool conditions for Case 1.  
Figure 27: Spectrograms (training samples) of new, used, and worn-out tool conditions for Case 6.  
Figure 26: Spectrograms (training samples) of new, used, and worn-out tool conditions for Case 5.  
Figure 25: Spectrograms (training samples) of new, used, and worn-out tool conditions for Case 4.
## 4 Synthesis and Summary
### Biases and Limitations  
Despite the fact that this study has provided some valuable insights, it is essential to acknowledge its limitations, which stem from the publication retrieval process and the chosen analytical methodologies. Firstly, the exclusive utilization of the WoS database was necessitated by its adoption, attributed to variations in data standards. This constrained choice might have led to the inadvertent exclusion of pertinent papers concerning USV globally, potentially introducing biases in the outcomes and impeding a comprehensive elucidation of discernible patterns and developments. Moreover, this paper, based on existing research, primarily utilizes the WoS as the database for data retrieval. Future research could consider incorporating a wider range of databases to enrich the scope of the study and facilitate comparative analysis.  
Secondly, to facilitate a broader understanding of the structural dynamics and evolutionary trends in USVs, this study focused exclusively on articles published in the English language, which is the predominant medium of academic discourse. Regrettably, a significant number of USV articles written by researchers from Non-English-speaking maritime powers, including countries like China, South Korea, Japan, Russia, etc., are published in their domestic language journals. Consequently, they are not included in the WoS database. The exclusion of these contributions could impact the comprehensiveness of the results.  
Additionally, there are several common limitations that this paper may have encountered. These include potential publication bias, as certain types of articles or journals may be overrepresented or underrepresented in the chosen database, thus impacting the generalizability of the findings. The selected time frame for the analysis may influence the assessment of trends and patterns, as research topics and publication rates can evolve. The choice of bibliometric indicators, such as citation counts, can introduce sources of bias, as they may not capture the full impact or relevance of a paper. Moreover, the design of the search query, such as the confine of keywords and the combination of conjunctions, may also do a disservice to the final result.  
Lastly, it is imperative to recognize that the scope of the database itself may not encompass all relevant articles, potentially resulting in the omission of some significant contributions to the field.
###### Abstract  
The perception of automatic generation control (AGC) has a massive part in delivering eminence power in an interrelated structure. To acquire eminence power by monitoring the fluctuations of frequency and tie-link power, an appropriate controller strategy is essential. This work explores AGC learning under the traditional situation. In this study, we employ a cascade controller with proportional amalgamation with a tilt-integral-derivative with a filter (TIDN) and fractional order integral-derivative (FOID), named TIDN-FOID. In order to acquire the controller's attributes, a meta-heuristic optimization algorithm spotted hyena optimizer (SHO) is employed. Several investigations express the excellency of the TIDN-FOID controller over other controllers from outlook regarding the lessened level of peak_overshoot, peak_undershoot, and settling_time for the considered structure. The structure comprises thermal, biodiesel units in area 1, thermal, and geothermal units in area-2, and hydrothermal units in area-3. Both biodiesel and GPP have a better effect on system dynamics even in the presence of time delay. Action in the redox flow battery is also examined, providing a noteworthy outcome. Eigenvalue assessment is carried out to comment on the stability of the system. TIDN-FOID parameter values at nominal conditions are appropriate for a higher disturbance value without the need for optimization.  
automatic generation control; geothermal power plant; redox flow battery; biodiesel plant; spotted hyena optimizer; particle swarm optimization; controller; time delay