esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Crack Length Measurement with the DC-Potential Drop Method  
### Experimental Setup  
The crack length measurement with the DC potential drop method requires only a very simple experimental setup. As shown in Figure 1a, a constant, time-stable direct current is passed through the sample. The potential is measured between two grips arranged at the specimen in a distance \(y_{0}\) to the notch root. A good connection between the grips and the specimen is essential for a stable measurement. When screws or pressed-in pins are used, the resistivity of the connection may vary during cyclic loading leading to fluctuations in the measured potential values. This can be avoided by directly spot-welding the grips to the specimen surface [13; 22]. Due to the good conductivity of metallic materials, high currents as well as sensitive voltmeters are needed to measure a reasonable potential drop with adequate accuracy. The use of highly sensitive amplifiers in the control electronics of the testing machine allows a simple and simultaneous measurement of all data like force, stroke, and potential [23; 24].  
[PERSON] [8] developed a calibration function for center crack tension (CCT) and single edge notched (SEN) specimen to calculate the crack length from the measured potential drop. The same result was achieved by [PERSON] and [PERSON] [25] using a different mathematical approach. For SEN specimen, the crack length can be calculated by the so-called Johnson equation (Equation (1)):  
\[a=\frac{2\cdot W}{\pi}\cdot arccos\left[\frac{cosh\frac{\pi\cdot y_{0}}{2W}}{ cosh\left(\frac{U}{U_{0}}arcosh\frac{cosh\frac{\pi\cdot y_{0}}{2W}}{cos\frac{\pi \cdot W}{2W}}\right)}\right]. \tag{1}\]  
The crack length calculated from the potential drop \(U/U_{0}\) for a SEN-specimen with a width of 12 mm is shown in Figure 1b. The curves show that the resolution of the DCPDM is rising with crack length. Additionally, the sensitivity is influenced by the distance of the potential grips \(y_{0}\). To achieve a high resolution, the distance of the grips should be minimized. Unfortunately, this leads to a reduction of the measured potential, while the noise of the voltmeter or amplifier used for the potential measurement becomes more and more dominant and limits the accuracy of the measurement. Therefore, the optimal distance of the potential grips is always a compromise between these two opposing effects.  
Figure 1: **(a)** Schematic drawing of the setup of the direct current potential drop (DCPD) measurement; **(b)** the resolution of the direct current potential drop method (DCPDM) depends on the distance of the potential grips \(2y_{0}\) and the crack length a.
## 4 Findings and Discussion
### Environmental Impact Produced
#### 4.4.4 Effect on Wildlife  
Plastics can have considerable direct or indirect negative effects on animals [91], which can be produced by both higher and smaller size fractions. Smaller-sized fractions of plastics or macroplastics (\(>\)5 mm) have significant negative effects on wildlife via ingestion and entanglement, limiting feeding ability and mobility [92; 93]. The ingestion of plastic debris by birds, even of relatively low quantities, affects their morphometrics and levels of uric acid, cholesterol, and amylase [94], as well as their reproduction, due to chemical body burdens [95]. The immobilization of animals by plastic litter results in their death by suffocation, drowning, or strangulation. It also produces infections or causes amputations, possibly even causing the animal to stop eating to the point of starvation [92; 96].  
As with other sources of plastic pollution, both masks and gloves pose a risk of entanglement, entrapment, and ingestion. In fact, several cases of wildlife species entangled in disposable face masks [91], including seagulls, peregrine falcons, and hedgehogs, among others, as well as the death of an adult Magellanic penguin because of the ingestion of an FFP2 face mask [97], have been reported.  
In addition to ingestion and entanglement, the negative effects of face masks or other PPE are still unexplored.Finally, the use of PPE for the construction of common coot nests has been reported in The Netherlands [91], which could compromise both the nutritional requirements and development of the chicks [98], such as their reproductive success, because of its effect on the thermal and drainage properties of the nests [99].
# Marine Internet for Internetworking in Oceans: A Tutorial
## 6 Challenging Issues for Marine Internet  
Marine Internet is still in its infancy stage with several challenging issues to be addressed. The major challenges stem from handling heterogeneity and dynamics in communication and networking capabilities and air interfaces of network nodes composing marine Internet. These factors make it very hard to assure end-to-end performance in terms of reliability and QoS. Transient Internet users and mobile BSs (e.g., on-board BSs) make network security and mobility management very difficult. There are also many challenging issues for underwater networking, such as MAC protocols [68], routing protocols [69], end-to-end reliability control [70] and network security [74] for UANs. These issues have been discussed in the literature and are ignored here.
## 3 Energy Dissipation Analysis  
### Energy Dissipation Capacity Under Harmonic Motion  
Compared with FVDs, the ECDs have quite different constitutive behavior for the force-velocity relationship. In view of these differences, we have to analyze their energy dissipation capacities as compared with FVDs. The dampers are assumed to be subjected to harmonic motion \(u=u_{0}\sin\left(\omega t\right)\). Here, we assumed the maximum damping force \(F_{max}\) in the ECD is the same as the maximum damping force in the FVD under the same harmonic motion. It should be noted that the maximum damping force of ECDs will occur at the critical velocity which is not necessary the same as the maximum velocity, while the maximum damping force of FVDs happens at the maximum velocity. Under the conditions of the same harmonic motion and the same maximum damping force, we will compare the energy dissipation capacities of ECDs and FVDs.  
For FVDs which is governed by Equation (1), the energy dissipated by the damper during a cycle of harmonic motion \(u=u_{0}\sin\left(\omega t\right)\) is [18]  
\[E_{\text{FVD}}=4\int_{0}^{u_{0}}F_{\text{FVD}}\text{d}u=\frac{2C_{d}\sqrt{\pi} u_{0}^{\alpha}\ ^{1}\omega^{\alpha}\Gamma\left(\frac{\alpha}{2}\ +\ 1\right)}{\Gamma\left(\frac{ \alpha+3}{2}\right)} \tag{3}\]  
where \(\Gamma(x)\) is the gamma function.  
For ECDs which is governed by Equation (2), the energy dissipated by the damper during a cycle of the same harmonic motion \(u=u_{0}\sin\left(\omega t\right)\) is  
\[E_{\text{ECD}}=4\int_{0}^{u_{0}}\frac{2F_{max}}{\frac{v}{v_{\sigma}}\ +\ \frac{v_{\sigma}}{v}}\text{d}u=\frac{4v_{\sigma}F_{max}\pi}{\omega}(1-\frac{v_ {\sigma}}{\sqrt{v_{\sigma}^{2}\ +\ u_{0}^{2}\omega^{2}}}) \tag{4}\]  
Figure 2: Comparison of fitting curve with measured points and FEM curve for ECDs.  
The detailed formula derivation is shown in Appendix A. It can be seen that \(E_{\text{ECD}}\) is a function of \(v_{\sigma}\) when \(F_{max}\), \(u_{0}\) and \(\omega\) are specified. In the following section, we will derive the optimal \(v_{\sigma}\) when the energy dissipation capacity \(E_{\text{ECD}}\) is maximized.
## 1 Introduction  
Melanogenesis is a physiological process that results in the synthesis of melanin pigment and has many functions in living systems [1]. Melanin is an important factor for protection of skin against deleterious UV irradiation. However, the excessive production of melanin causes hyperpigmentation [2] that is a skin pigmentation disorder in which skin becomes darker in color compared to the normal surrounding skin [3]. Tyrosine is a key enzyme in melanogenesis in melanocytes [4]. It acts as the catalyst for two rate-limiting steps of melanogenesis, the hydroxylation of tyrosine to 3,4-dihydroxyoxyphenylalanine (DOPA) and oxidation of DOPA to dopaquinone [5]. Therefore, the inhibition of tyrosinase activity is capitalized for inhibition of melanogenesis and the treatment of skin hyperpigmentation and whitening [2].  
Nonsteroidal anti-inflammatory drugs (NSAIDs) have many therapeutic benefits in the treatment of rheumatoid arthritis and other inflammatory conditions [6]. The major action of NSAIDs is based on inhibition of cyclooxygenase (COX), which is the rate limiting enzyme in the pathway facilitating the conversion of arachidonic acid to inflammatory prostaglandandins [7]. There are two different isoforms of COX, COX-1 and COX-2 [8]. COX-1 is expressed in essentially all mammalian tissues, and is responsible for prostaglandin production that maintains homeostasis. In contrast, COX-2 is manifested constitutively in brain, kidneys, and ovaries, and is activated in cells in response to inflammatory stimuli [9,10].  
Mimosine [\(\beta\)-[\(N\)-(3-hydroxy-4-oxypyridyl)]-\(\alpha\)-aminopropionic acid] is a non-protein amino acid containing an alanine side chain bound to the nitrogen atom of a pyridine ring. It is found in several tropical and subtropical plants, which possesses a wide range of biological activities [11,12]. It is hypothesized that mimosine could be used as a source of bioactive compounds and could be useful in the design of novel drugs. Recently, we reported on a series of mimosine tetrapeptides, which are more effective than mimosine to inhibit neuraminidase and tyrosinase as well as the oncogenic/ageing kinase PAK1 [12,13]. Interestingly, melanogenesis and COX activation are among a variety of PAK1-dependent phenomena [13]. Thus, in continuation of our earlier work, we synthesized a small library of enantiomeric imimosine dipeptides and found that they inhibit at least COX and melanogenesis in B16F10 melanoma cells more effectively than mimosine.
## 2 Small-Signal Model
In the same way, the output voltage is processed, ignoring the high-order infinitesimals, and the small-signal AC model after linearization of the CCM flyback converter is obtained by the Laplace transform:  
\[\left\{ \begin{array}{l}sL_{\text{m}}\hat{i_{1}}(s)=D\hat{u}_{\text{in}}(s)- D^{\prime}\hat{u}_{\text{o}}(s)n+[V_{\text{in}}+V_{\text{o}}n]\hat{d}(s)\\ sC\hat{u}_{\text{o}}(s)=D^{\prime}\hat{i_{1}}(s)n-\frac{\hat{u}_{\text{o}}(s)}{ R_{\text{L}}}-I_{1}\hat{d}(s)n\\ sC\hat{u}_{\text{o}}(s)=D^{\prime}\hat{i_{1}}(s)n-\frac{\hat{u}_{\text{o}}(s)}{ R_{\text{L}}}-I_{1}\hat{d}(s)n\end{array} \right. \tag{7}\]  
The transfer function from system output voltage \(u_{\text{o}}\) to duty cycle \(d\) is:  
\[G_{\text{vd}}(s)=\frac{\hat{V}_{\text{o}}(s)}{\hat{d}(s)}=\frac{D^{\prime}n( V_{\text{in}}+V_{\text{o}}n)-sL_{\text{m}}\frac{V_{\text{o}}}{R_{\text{L}}D^{ \prime}}}{L_{\text{m}}Cs^{2}+\frac{L_{\text{m}}}{R_{\text{L}}}s+(D^{\prime}n) ^{2}}=\frac{\frac{L_{\text{m}}V_{\text{o}}}{R_{\text{L}}D^{\prime}}\left(D^{ \prime}n(V_{\text{in}}+V_{\text{o}}n)\frac{R_{\text{L}}D^{\prime}}{L_{\text{m }}V_{\text{o}}}-s\right)}{L_{\text{m}}Cs^{2}+\frac{L_{\text{m}}}{R_{\text{L}} }s+(D^{\prime}n)^{2}} \tag{8}\]  
From Equation (8), it can be seen that the transfer function from the output voltage \(u_{\text{o}}\) to the duty cycle \(d\) of the CCM mode flyback converter has a right-half-plane zero.
## 5 MTMD Applications on Taizhou Bridge
### Parametric Analysis on the MTMD Application
#### 5.4.1 Number of TMDs  
The control efficiency of the lateral and vertical MTMD systems is investigated with different numbers of TMDs, as shown in Figure 15. During the process, the other three parameters are constant. These three parameters for lateral MTMD are \(\mu_{0}=0.02\), \(\zeta_{0}=2\%\), \(\delta_{0}=0.5\), while those for vertical MTMD are \(\mu_{0}=0.02\), \(\zeta_{0}=2\%\), \(\delta_{0}=0.2\). The lateral buffetting response is dominated by two modes, so the selection of frequency bandwidth ratio needs to cover both of the two modes. In such a consideration, the frequency bandwidth for lateral MTMD is wider than the vertical one.  
According to Figure 15, the control efficiency firstly increases with the augment of the number of TMDs, but the rising trend gradually levels off and finally becomes stable. This phenomenon is consistent in both lateral and vertical MTMDs. When the number of TMDs is small (e.g., it is taken as two), the MTMD system will mainly dedicate itself to mitigating the vibration components characterized by the two frequencies, even though the MTMD system is equipped with a wide frequency bandwidth. However, as the number of TMDs increase, the effective frequencies in the given frequency bandwidth will be densely distributed so that the effect of the MTMD can be fully exerted. The maximum control efficiencies of the lateral and vertical MTMDs in this round can be 17.9% and 30.4%, respectively.  
Except the control efficiency, the robustness is another consideration in the performance of the MTMD. The parameter sensitivity of the number of TMDs is also investigated with the frequency ratio ranging from 0.90 to 1.10. As is apparent from Figure 15, the difference of the control efficiency for different frequency ratios will be nearly constant when the number of TMDs is large enough. This means the attenuation factor will not deviate too much from the prediction even if there is a big shift captured in the modal frequency of the main structure.  
Hence, to keep the MTMD with a stable performance, the number of TMDs should lie in the aforementioned steady range. However, considering the increasing construction difficulty of more TMDs, the lower limit of the range is a preferable selection of this number. In this round, the numbers of TMDs in the lateral and vertical MTMD system are set as 13 and 11, respectively.
## 1 Introduction
In addition to these cardiovascular benefits, exercise plays a critical role in modulating key biomechanical parameters, such as cadence [17], which are essential for maintaining functional independence in patients with HFpEF [18]. Cadence, defined as the number of steps per minute, is considered a biomechanical parameter because it directly reflects the efficiency of gait and the coordination of the musculoskeletal and nervous systems during movement. As a measure of gait dynamics, cadence is influenced by factors such as stride length, joint mobility, and neuromuscular control, making it a key indicator of overall functional capacity [19]. Cadence is closely related to overall mobility and the ability to perform daily activities [20]. In HFpEF patients, reduced cadence is associated with impaired exercise tolerance and a greater risk of adverse cardiovascular outcomes [21]. This relationship between cadence and functional capacity highlights its role as a biomechanical marker, as it integrates the mechanical and neuromuscular components necessary for effective movement. Interventions that improve these biomechanical parameters can increase exercise tolerance and improve functional outcomes in HFpEF patients [22; 23]. Previous studies have shown that structured exercise programs, which include both combined aerobic and strength training and HIIT, can produce substantial benefits in patients with HFpEF, particularly in improving cardiovascular function and exercise capacity [11]. However, evidence specifically addressing the impact of these exercise modalities on key biomechanical parameters such as cadence (steps per minute) during the 6-minute walk test (6 MWT) in this population is limited [24]. The hypothesis of this study, based on the \"Ejercicio en Insuticiencia Cardiac con Fraccion de Eyeccion Preservada\" (ExIC-FEp) trial, is that incorporating these targeted exercise interventions will result in significant improvements in these biomechanical parameters, further improving functional capacity and physical performance in patients with HFpEF. Thus, the aims of this study were to assess the impact of a CE (aerobic and strength training) program and a HIIT program on 6 MWT dynamics (cadence, distance, maximum heart rate [HRmax], and exercise intensity) in patients with HFpEF; compare changes before and after the intervention; and assess how the change in cadence is associated with the changes in distance, HRmax, and intensity in the 6 MWT.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 3 Methodology
### Random Forest Classification  
Random Forests (RF) is a learning method for classification based on generating a large number of decision trees, where each is constructed using a different subset of the training set. These subsets are usually selected by sampling at random and with replacement from the original data set. The decision  
Figure 7: Distribution of the (a) position of the maximum of the VV/VH Gaussian fitting; (b) variance of the VV/VH time series; and (c) slope of the VH linear fitting for 11 crop types represented by the normal distribution function.  
trees are then used to identify a classification consensus by selecting the most common output. It has demonstrated its ability to yield high quality classification with a high computational time. In our study, we classified rice and non-rice plots by introducing the extracted metrics into the random forest classifier available via the \"R\" open statistical computing software (Package \"randomForest\"). The random forest is trained using the values of the position of the maximum (\(b\)), the standard deviation (\(c\)), and the correlation coefficient (\(R^{2}\)) of the VV/VH Gaussian fitting, the variance of VV/VH, and the slope \(S\) of the VH linear fitting for the 832 reference plots for the two classes (Rice and other crops). In fact, 5 folds cross-validation is first performed using the 832 reference plot. To do so, the dataset is randomly divided into 5 folds of equal size. Four folds are used to train the model, and the fifth fold is used for the validation phase. This operation is repeated five times, so that each fold will be a possible validation set. The number of trees chosen is 300 and the number of features used in the construction of each tree is 2. Finally, the map obtained using the trained RF classifier was further compared to the available national data. The overall accuracy, Kappa, and F\({}_{1}\) score were calculated for the obtained confusion matrix [28; 29]:  
\[\text{Overall Accuracy ``P}_{0}\text{''}=\frac{\text{TP}+\text{TN}}{\text{TP}+ \text{TN}+\text{FN}+\text{FP}} \tag{6}\]  
\[\text{Expected Accuracy ``P}_{e}\text{''}=\frac{\left(\text{TP}+\text{FP} \right)\times\left(\text{TP}+\text{FN}\right)+\left(\text{FN}+\text{TN} \right)\times\left(\text{FP}+\text{TN}\right)}{\left(\text{TP}+\text{TN}+ \text{FN}+\text{FP}\right)^{2}} \tag{7}\]  
\[\text{ Kappa }=\frac{P_{0}-P_{e}}{1-P_{e}} \tag{8}\]  
\[\text{F}_{1}\text{ score }=\frac{2\text{TP}}{2\text{TP}+\text{FP}+\text{ FN}} \tag{9}\]  
where TP is the number of the rice plots truly classified as rice plots, TN is the number non-rice plots truly classified as non-rice plots, FP is the number of non-rice plots falsely classified as rice, FN is the number of rice plots falsely classified as non-rice plots, N is the total number of plots, P\({}_{0}\) is the observed accuracy and P\({}_{e}\) is the expected accuracy.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 5 Simulation Test
### Typical Response  
The typical response characteristics are from the simulation case where the active power imbalance between SM and local load is \(-10\%\) after the unintentional opening of CB3 in Figure 6, which causes the loss of the main grid. In this simulation, the reactive power was exactly balanced. This simulation was based on the default settings: \(\Delta Ang\_set=1.0^{\circ}\), \(\sum Ang\_set=18^{\circ}\)\((df/dt)\_set=0.5\) Hz/s. CB3 was open at the instant 0 s, which means the main grid was lost. The islanding condition wa detected by the proposed composite PAD solution at 0.42 s.  
The following from Figure 7a,b and Figure 8a,b present the change of the related electrical quantities before and after loss of main grid. From Figure 7a, at the instant of loss of main grid, these three voltage phase angles changed in the same direction with the values larger than the setting \(\Delta Ang\_set=1.0^{\circ}\). So, the calculation of accumulated phase angle drift based on the real-time frequency and the frequency value at 0 s was initiated. Figure 7b shows the accumulated phase angle drift. At 0.42 s, accumulated phase angle drift reached the threshold \(18^{\circ}\). At 0.52 s, the maximum absolute  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Synchronous Machine** & **Gas Turbine Inertial** \\ \hline Rated Apparent Power: 30 MVA & H = 2.525 s \\ Rated voltage: 10.5 kV & **110/10 kV Transformer (YD1)** \\ Rated Field Circuit Current: 200 A & Rated Apparent Power: 100 MVA \\ \(X_{d}=1.81\)\(X_{q}=1.76\) & Leakage reactance: 0.001 pu \\ \(X_{d^{\prime}}=0.3\)\(X_{q^{\prime}}=0.65\) & Leakage resistance: 0.01 pu \\ \(X_{d^{\prime\prime}}=0.23\)\(X_{q^{\prime\prime}}^{\prime}=0.25\) & **Fault** \\ Rated Power Factor: 0.8 & Fault type: AG, AB, ABC, ABCG \\  & Fault Resistance: 0.1 Ohm, 0.5 Ohm, 0.8 Ohm \\ \hline
**Cable 1** & **Cable 2** \\ Resistance R = 0.033 Ohm & Resistance R = 0.06 Ohm \\ Inductance L = 0.825 mH & Inductance L = 1.5 mH \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 6: Simulation model.  
ROCOF value in five power cycles was below reset setting 0.5 Hz/s. Then the calculation of phase angle drift was terminated.
## 1 Introduction  
The prediction of lymph node involvement in breast cancer represents an important task which could reduce unnecessary surgery and improve the definition of oncological therapies [1; 2; 3; 4; 5; 6]. The research of a trade-off strategy among time-consuming, expensive and invasive methodologies is the scientific goal of several research studies [7; 8], especially with reference to breast cancer patients with clinically negative sentinel lymph nodes.  
For patients with clinically negative lymph nodes at the clinical or radiological exam [9], the current guidelines provide the removal of sentinel lymph node biopsy (SLNB). These are the first axillary draining lymph nodes, known as \"sentinel\" lymph nodes [10; 11; 12]. However, the SLNB is time-consuming and expensive, and may lead to complications such as wound infection, seroma, paraesthesia, lymphedema and hematoma [7; 13; 14; 15; 16; 17]. In patients with early stage breast cancer for whom there is a low incidence of axillary metastases (about 10-25%) it may be an unnecessary invasive procedure [7; 12; 13; 14; 18].  
The aim of our work is to develop other less invasive and preferably cheaper diagnostic tools without compromising the diagnosis of patient care. In clinically negative patients, the availability of a new tool able to provide an accurate probabilistic estimation makes surgical actions unnecessary, and this would result in an immediate improvement in both the effectiveness and the quality of care [19].  
In our previous work [20] we had generalized an open-source classification algorithm adding other prognostic factors with respect to those used in the original work [21; 22; 23]. In this study, we report the results of a multivariate analysis aimed at developing a sentinel lymph nodes status predictive model for patients with and characterized by a clinically negative lymph node status. Particularly, we evaluated the predictive power of different breast cancers' clinical and immunohistochemical features. With respect to previous work, we introduced new prognostic factors such as Her2/neu, multiplicity and an in situ component and trained three different state-of-the-art machine learning algorithms on the retrospective collected data. Moreover, we implemented a sequential forward procedure to select an optimal subset of features, and we evaluated the results obtained in hold-out cross-validation.