esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Analysis of Gas Reduction in Multiphase Flow with a KMS Helical Static Mixer
## 2 KMS Static Mixer Preliminary Considerations  
A static mixer was designed to generate a homogeneous mixture of miscible components without mobile pieces [15]. Multiple geometries are described in [19], where the K, G, R, and M mixers are studied for particular cases and fluid properties such as viscosity; however, it can be also used to produce phase dispersion, chemical reactions, and heat transfer, among other applications [16]. It is verified in [20; 21] that, with a gaseous component as a non-miscible phase in the mixture, the mixture fails and dispersion arises along the pipeline where the mixer is hosted. Then, it is possible to take advantage of this effect by producing a phase dispersion because of the tension between the phases. For KMS mixers, if the dispersion phase is a gas into a liquid, the densities' difference and the produced centrifugal effect concentrate the lightest phase at the center of the pipe, while the heaviest is distributed towards the walls [17]. Figure 1 shows the typical configuration of a KMS, used in this work. Along length \(L\), the mixer is composed by \(n\) helical elements with diameter \(D\) and length \(1\) with relation \(l/D=1\), discontinuously connected by an angle \(\alpha=90^{\circ}\) and walls with a torsion angle \(\beta=180^{\circ}\)[16].  
For the jet-pump ALS, by using the KMS as the inlet device, it is imperative to define a minimum pressure drop in the horizontal separator to ensure the extraction of water as the power fluid for the injection pump. With a KMS, the drop in pressure \(\Delta P_{sm}\) is then calculated as [16]  
\[\Delta P_{sm}=2K_{T}f\rho\frac{Lv^{2}}{D} \tag{1}\]  
where \(K_{T}\) is the drop coefficient for static mixers in turbulent flow (\(K_{T}=150\)) with the Fanning factor \(f\) defined as \(f=0.079/Re^{0.25}\), \(\rho\) being the mean density of the mixture; \(v\), is the velocity of the fluid; \(Re\), is the Reynolds number and \(L=nD\). The dynamics conditions of the fluid then determine the drop in pressure; however, the \(n\) elements of  
Figure 1: KMS static mixer configuration for \(n=4\).  
the KMS should be set to avoid a low dispersion according to the coefficient of variation \(C_{oV}\)[16], defined as:  
\[C_{oV}=K_{i}^{L/D}=K_{i}^{n} \tag{2}\]  
where \(K_{i}\) is the coefficient of the mixture, defined for a KMS mixer as \(K_{i}=0.5\) for turbulent flows. Notice that the phase dispersion becomes lower as \(n\) increases, producing a high quality of the mixture.
# Tailorable Brillouin Light Scattering in a Lithium Niobate Waveguide
## 4 Conclusions  
In this paper, a new type of hybrid photon-phonon waveguide system is designed by using a new inorganic material, lithium niobate, as the propagation medium, and the tuning of the SBS sound field signal frequency is achieved by adjusting the width of the waveguide. According to the PE effect and the MB effect, the gain of the suspended structure SBS laser reaches 0.31 W\({}^{-1}\)m\({}^{-1}\), and the high SBS gain excites higher-power phonons, realizing the non-uniformity of the sound field with a signal frequency of 1-15 GHz given its linear class tuning characteristics. Therefore, this suspended waveguide width tuning solution can realize the photon-phonon conversion based on the Brillouin nonlinear effect, and can guide and manipulate phonons emitted by the Brillouin on the chip. Therefore, the Brillouin device can provide an opportunity for the mixing of physics and photonics, CMOS and MEMS.  
Conceptualization, W.W. and Y.Y.; methodology, Y.L. ([PERSON]) and G.W.; software, W.W.; validation, W.W., Y.Y. and Y.L. ([PERSON]); formal analysis, G.W., Z.W., C.S. and K.L.; investigation, W.W.; resources, Z.L., Y.W. and Z.B.; data curation, Y.Y.; writing--original draft preparation, W.W. and Y.Y.; writing--review and editing, W.W. and Y.Y.; visualization, S.L., Y.L. ([PERSON]), T.L. and X.Y.; supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.L. ([PERSON]), T.L., X.Y. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 62005074, No. 61927815, No. 62075056, and No. 62004059) and Key Laboratory Fund Project (No. 61421070302).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: The relationship between the coupling efficiency of the Brillouin-suspended laser and the frequency change with the waveguide width. Where m represents the order of the acoustic mode, the number in the table represents the coupling efficiency and its unit is kHz.
## 3 Mathematical Approach
### Localisation  
The linear, elastic, microscopic problem in the absence of body loads reads as:  
\[\begin{array}{c}\
abla\cdot\sigma^{\varphi}=0\\ \epsilon^{\varphi}=\frac{1}{2}\big{(}\
abla u^{\varphi}+(\
abla u^{\varphi})^{ T}\big{)}\\ \sigma^{\varphi}=\mathbb{C}^{\varphi}\epsilon^{\varphi}\\ \langle\epsilon^{\varphi}\rangle=\epsilon^{0}\end{array} \tag{3}\]  
There is an absence of boundary conditions in Equation (3). These boundary conditions must reproduce, as closely as possible, the in situ state of the RVE inside the material. Therefore, they strongly depend on the choice of the RVE itself, and particularly on its size. Amongst the classical boundary conditions considered in the literature (see [PERSON] [56] for further reading), Dirichlet boundary conditions are used in our problem:  
\[\mathbf{u}^{\varphi}=\mathbf{u}^{0}\;\;\text{on}\;\;\partial\Omega_{S}^{\varphi} \tag{4}\]  
Then, the local strain \(\epsilon^{\varphi}(\mathbf{u}^{\varphi})\) is split into its average and fluctuating term so that:  
\[\begin{array}{c}\epsilon^{\varphi}(\mathbf{u}^{\varphi})=\epsilon^{0}+\epsilon ^{\varphi}(\mathbf{u}^{\star})\\ \langle\epsilon^{\varphi}(\mathbf{u}^{\star})\rangle=0\end{array} \tag{5}\]  
Let Equation (5) be substituted into the equilibrium (divergence free) equation of the stress tensor (see Equation (3)), namely:  
\[\begin{array}{c}\
abla\cdot(\mathbb{C}^{\varphi}\epsilon^{\varphi}(\mathbf{u}^ {\star}))=-\
abla\big{(}\mathbb{C}^{\varphi}\epsilon^{0}\big{)}\;\;\text{in} \;\;\;\Omega_{S}^{\varphi}(\mathbf{y})\\ +\text{boundary conditions}\end{array} \tag{6}\]  
where the boundary conditions are related to Equation (4). By virtue of the linearity of the problem, the solution of \(\epsilon^{\varphi}(\mathbf{u}^{\star})\) in Equation (3) for a general macro strain \(\epsilon^{0}\) may be expressed as the superposition of elementary unit strain solutions \(\epsilon^{\varphi}(\mathbf{\chi}_{kh})\)[56], such that:  
\[\epsilon^{\varphi}(\mathbf{u}^{\star})=\epsilon_{kh}^{0}\epsilon^{\varphi}(\mathbf{ \chi}_{kh}) \tag{7}\]  
where \(\mathbf{\chi}_{kh}\) are the displacements associated to those elementary strain states denoted by indices \(kh\) resulting from the solution of Equation (3).  
By substitution of Equation (7) into Equation (5), the micro-strains are expressed as:  
\[\epsilon^{\varphi}(\mathbf{u}^{\varphi})=\epsilon_{kh}^{0}(\mathbf{I}_{kh}+\epsilon ^{\varphi}(\mathbf{\chi}_{kh})) \tag{8}\]  
where \(\mathbf{I}_{kh}\) is the identity fourth order tensor with components \(\mathbf{I}_{kh}=\big{(}I_{ij}\big{)}_{kh}=1/2\big{(}\delta_{ik}\delta_{ih}+\delta _{ih}\delta_{jk}\big{)}\). Equation (8) allows us to obtain the microstructural distribution of the strain field, under the aforementioned hypothesis and once the boundary conditions are established through the macroscopic ones.
## 3 Experimental Results
### Effect of Different Factors on Blockage Type
#### 3.3.3 Effect of the Landslide Velocity Entering the River  
The movement and deposition processes are affected by factors related to the landslide material and slope conditions. It is difficult to identify a complete blockage from only one condition because a complete blockage is a consequence of many different factors. However, from these tests, it was found that landslide velocity plays an important role in identifying the type of river blockage. Although the same blockage may be induced by different materials and slope conditions, the velocities of landslides when entering the river are almost the same. When the velocity varies, the type of river blockage also changes. The greater the difference in landslide flow velocity, the more the dam cross-section changes. As shown in Figure 15, different velocities yield completely different dam cross-sections and blockage types, and a roughly equivalent velocity yields almost the same blockage type under the same landslide volume.  
As shown in Figure 16, the velocity of landslides entering rivers is mainly affected by the slide angle, and the other two factors, including landslide volume and particle size, have relatively small influences on the velocity of landslides entering rivers. The fluctuation range of the velocity is within 1 m/s when the slide angle is the same but becomes 2 m/s if the slide angle changes. As the slide angle increases, not only does the initial potential energy of landslide materials increase directly, but the dissipated friction energy is also reduced during the movement process, which results in a significant improvement in the transformation of potential energy into kinetic energy. Therefore, the increase in slide angle makes the landslide velocity rise significantly, and the range of the velocity for different slide angles is much larger than that for the same slide angle. In addition, landslide volume and particle size are two other factors that affect the velocity of landslides entering rivers, and the influence of landslide volume on the landslide velocity is less than the influence of particle size. In actuality, the velocity of landslides entering rivers does not change significantly and even decreases slightly when the landslide volume increases. Landslides with smaller volumes are more likely to roll freely without too much influence from other particles. Following this perspective, as the particle size increases, landslide materials with the same volume could consist of fewer particles, and the interactions among particles decrease; as a result, landslide materials are more easily accelerated in the movement process and present a higher velocity, as observed in the experiments.  
Figure 15: Photographs of river blockages for different velocities of landslides entering rivers.
## 4 Discussion
Future research should aim to include a larger sample size to ensure adequate power and provide more definitive conclusions on the efficacy of the shaking device for upper limb rehabilitation in stroke patients. Second, our findings indicated the variable efficacy of the intervention across different levels of severity of impairment. The intervention showed significant benefits in patients with moderate severity but not in those with mild or severe impairments. This suggests that the effectiveness of the intervention is contingent on the patient's initial functional status, necessitating further investigation of tailored therapeutic approaches for different severity levels. Third, all the participants received BoNT-A treatment, which may have confounded the effects of the shaking device on motor recovery. Future studies should consider separate groups for those receiving BoNT-A and those who do not to isolate the effects of the shaking device. Fourth, variability in adherence to the intervention protocol owing to the home-based nature of the therapy may have influenced the outcomes. Ensuring the consistent application of the intervention in such settings remains a challenge, and methods for monitoring and improving home-based intervention fidelity should be integrated into future studies. Fifth, the primary outcome measure, the FMA-UE, may not have captured all relevant aspects of functional improvement owing to its susceptibility to ceiling effects [49], particularly in patients with mild impairment. Including additional functional and patient-centered outcome measures could provide a more comprehensive assessment of the intervention's impact. Continued BoNT-A treatment combined with daily training in the chronic phase can improve shoulder and arm motor function, even in patients with moderate-to-severe paralysis. The findings of this study support this hypothesis. Additionally, patients who underwent shaking training exhibited reduced chronic pain, a previously unreported benefit. Although the motor function improvements were small, they were clear, leading to greater patient motivation and activity, which, in turn, can further enhance motor function. The stratified analysis indicated that the intervention had significant effects, particularly in patients with moderate impairment. For patients capable of isolated movement, treatments such as transcranial magnetic stimulation may further enhance functional recovery. In patients with severe or moderate impairment, implementing a home-based training regimen using a shaking device can reduce pain and improve motor function. Conducting this training for 10 min, three times a day, while engaging in other activities, such as watching TV or talking, can be beneficial. This method offers a safe and sustainable treatment option that allows patients to continue treatment without excessive strain, thereby potentially reducing the economic burden on families and healthcare systems.
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 4 Methodology
### XGBoost  
The decision tree is selected by XGBoost as its based learner [23]. The error between the predictive value and the target is reduced by adding more new based learners. Summation of all the base learners achieves the final predictive values. The XGBoost algorithm can be considered as an additive model consisting of M decision trees, which are given by:  
\[Y_{i}=\sum_{m=1}^{M}f_{m}(x_{i}),\ f_{m}\ \in F \tag{7}\]where\(f\) is a decision tree, and \(F\) represents the function of all decision trees. In the regression process, the object function of the additive model becomes  
\[bj(\theta)=\sum_{i=1}^{n}l(y_{i},\ \gamma_{i})\ +\ \sum_{m=1}^{M}\Omega(f_{m})\,\ \ \theta=(f_{1},\ f_{2},\ \ldots,\ f_{M}) \tag{8}\]  
where \(l\) denotes loss function and \(\Omega\) is the regularization term.  
Vector mapping is used to improve the decision tree for each regularization term \(\Omega\)_(f)_. \(\Omega\)_(f)_ can be represented by  
\[\Omega(f)\ =\gamma T+\frac{1}{2}\lambda\sum_{j=1}^{T}\omega_{j}^{2} \tag{9}\]  
where \(T\) indicates the number of a decision tree's leaf nodes; \(\omega\) represents the vector of score, and both \(\gamma\) and \(\lambda\) express the penalty factor.  
XGBoost uses the forward stage-wise algorithm to simplify the complex of the model. Every time the model adds a decision tree, it learns a new function and its coefficients to fit the last step predicted residuals. Therefore, when the \(t\)th step learning happens, the predictive value of \(x_{i}\) is \(Y_{i}^{t}=Y_{i}^{t-1}+f_{t}(x_{i})\). Therefore, the object function should be expressed by  
\[Obj(\theta)^{t}=\sum_{i=1}^{n}l\big{(}y_{i},Y_{i}^{t}\big{)}\ +\ \sum_{m=1}^{t}\Omega(f_{m})\ =\sum_{i=1}^{n}l\Big{(}y_{i},Y_{i}^{t-1}+f_{t}(x_{i})\Big{)}+\sum_{m=1}^{t} \Omega(f_{m}). \tag{10}\]  
The greedy algorithm is applied in the XGBoost model to build the decision tree; by building decision trees continually, a complete XGBoost model is established. Furthermore, the randomization technique is also implemented in XGBoost to reduce overfitting and increase training speed, and the sparsity-aware algorithm is used to effectively eliminate missing values from the computation of the loss gain of split candidates. Numerous parameters have been tuned for XGBoost in this work, which include the learning rate, the minimum sum of weights of all observations required in a child, the maximum depth of the tree, and the subsampling rate. These important parameters will be mentioned in Chapter 7. The process of tuning is to select the best parameters for an algorithm to optimize the performance.
## 4 Experimental Results and Discussion
Additionally, the SGs used in the experiments were specifically designed for research purposes rather than commercial applications. Given the superior stability, efficiency, and optimized design of commercial SGs, even better frequency regulation can be expected when implementing the proposed controller in real-world applications. The comparison of the results obtained from the use of PSO-Fuzzy PID in loading and unloading tests reveals that the system performance is significantly enhanced system performance compared to traditional PLC-PID and Fuzzy PID methods. Specifically,  
Reduction in settling time:  
* In loading experiments, PSO-Fuzzy PID reduced settling time by 30% compared to Fuzzy PID and 50% compared to PLC-PID. * In unloading experiments, PSO-Fuzzy PID reduced settling time by 30% compared to Fuzzy PID and 42% compared to PLC-PID. Reduction in maximum undershoot:  
* PSO-Fuzzy PID reduced the maximum undershoot by 10% compared to Fuzzy PID and 56% compared to PLC-PID. Reduction in maximum overshoot:  
In unloading experiments, PSO-Fuzzy PID reduced the maximum overshoot by 20% compared with Fuzzy PID and 43% compared with PLC-PID. These results emphasize that PSO-Fuzzy PID is superior in performance and has a faster response time when compared to other control methods. Applying this technique to optimize the parameters of the Fuzzy PID controller can enhance the stability and performance of the system under changing loading and unloading conditions.
# A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome
## 3 Materials and Methods
### Feature Selection
#### 3.3.3 Mutual Information (MI)  
The Mutual Information algorithm is a widely known filter method of feature selection. This filter method considers the statistical characteristics of the dataset. Mutual Information is based on the entropy measure; entropy quantifies the uncertainty in the features [40].  
Mutual information helps quantify the information shared between two features \(F\) and \(S\) and is denoted by \(MI(F;S)\) is defined by Equation (1).  
\[MI(F;S)=\sum_{s\in S}\sum_{f\in F}p(f,s)\log\frac{p(f,s)}{p(f)p(s)} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sr. No.**} & **Feature Selection** & **Number of** & **Features** \\  & **Algorithm** & **Features Selected** & \\ \hline \multirow{3}{*}{1} & [PERSON] & & Weight (Kg), Pulse rate (bpm), RR (breaths/min), Hb (g/dL), Pregnant (Y/N), FSH (mIU/mL), LH (mIU/mL), Waist (inch), AMH (ng/mL), PRG(ng/mL), Weight gain (Y/N), hair growth(Y/N), BP \_Systolic (mmHg), FD follicle No. (R), Avg. F size (L) (mm) \\ \hline \multirow{3}{*}{2} & & & Age (yrs), Weight (Kg), Height (cm), Pulse rate (bpm), RR (breaths/min), Cycle (R/I), Cycle length (days), Pregnant (Y/N), FSH (mIU/mL), FSH/LH, Waist (inch), Waist-HP Ratio, PRG (ng/mL), Weight gain (Y/N), Hair loss (Y/N), Pimples (Y/N), FD follicle No. (L), FD follicle No. (R), Avg. F size (R) (mm) \\ \hline \multirow{3}{*}{3} & & & Follicle No. (L), Follicle No. (R), Skin darkening (Y/N), Fast food (Y/N), hair growth (Y/N), Cycle (R/I), FSH/LH, Cycle length (days), Weight gain (Y/N), AMH (ng/mL), PRL (ng/mL), Pimples (Y/N), BP \_Systolic (mmHg), Waist (inch), Age (yrs) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the feature selected by three algorithms.  
If the Mutual information value is very high \(F\), \(S\) are closely related, and if zero they are completely unrelated.  
This principle of filtering method of feature selection, when applied to the data, gives how closely related the individual features are to the target [41]. The features are then ranked according to their individual contribution toward the target variable, as seen in Figure 1. Out of these, the top 15 features (10 non-invasive and 5 invasive features) were selected.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 8 Discussions
Using the confusion matrix, we observed that the predicted accuracy of the MP condition significantly dropped in all classifiers when different environments of training and test were used in the data. This condition cannot be clearly evident using other above-mentioned metrics. This incident also proved that the identification of the MP condition was more challenging than the other two classes (NLOS and LOS). Moreover, we observed that the predicted accuracy of the NLOS condition was generally quite good in all of the evaluations regardless of the classifiers. At the same time, the misclassification rate of NLOS as either an LOS or MP condition was also moderately low. This criterion is crucial because the highest error rate in UWB measurements is usually caused by the NLOS condition (Section 2). In contrast, the misidentification of LOS as an NLOS condition in a certain model did not hurt the overall system performance of the UWB application.
# Support Group as a Management Modality for Burning Mouth Syndrome: A Randomized Prospective Study
## 4 Discussion  
Burning mouth syndrome is a diagnosis of exclusion. The exact pathophysiology is quite elusive, and so is the etiology. Hence, the management is challenging, and no definitive gold standard has been introduced. The aim of the present study was to evaluate the utility of support group sessions in the management of BMS. Persistent psychological stress triggers physiological responses in the nervous and immune systems, leading to disruptions in neuroendocrine, neurotransmitter, and neuroanatomical processes. These responses have been elicited under laboratory conditions. The implication of these changes for future health has become an important topic of psychoneuroimmunological research [28]. Studies have shown that cognitive behavioral therapy can lead to normalization of stress-induced neural activity in individuals with a range of anxiety disorders already very early in treatment and relative to a control group [29; 30; 31; 32]. According to emerging evidence, the outburst of the COVID-19 pandemic has triggered worsening of anxiety, sleep quality, and pain intensity in BMS patients [33]. It should be mentioned that our study took place shortly before the curfews caused by the COVID-19 eruption in the country. Ongoing pain can lead to poor sleep quality, mood disturbances, maladaptive behaviors, and ineffective coping strategies, making psychological interventions a logical management modality [34]. Support groups serve an important function in patients affected by a disease, especially a rare disease [35]. Positive effects have been reported in patients with Wilson disease [35], utiligo [36], lumbar spine fusion [37], and cancer [38; 39; 40; 41]. To the best of our knowledge, no opposing evidence has been reported. Support group participants are more often female [41], as demonstrated in our study as well. One of the main benefits of group therapy is the opportunity it provides for social comparison and delivery of targeted education and consistent counselling. Patients report an increase in knowledge, understanding, and confidence in self-managing their pain [42]. In the present study, one of the most prevalent themes discussed by the group members was their unawareness that BMS was a recognized phenomenon. Consequently, they felt exceptional for experiencing BMS symptoms. The association between traumatic life events and BMS is not well established [20; 43]. In the study of [PERSON] et al. [20], all patients who took part in psychological interviews reported family or personal losses just before the onset of BMS symptoms. This was true