esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Micro-Tests after the Addition of B. pasteurii to CG and FA
#### 4.1.2 Urea Hydrolysis Test  
As is shown in Figure 4, the conductivity increases in all three groups of Table 2. In fact, the urease reaction involves the hydrolysis of the non-ionic substrate urea and, also in the absence of calcium ions, generates the NH4+ and CO32- ionic products. As a result, the corresponding increment in the electrical conductivity of the liquid can be measured [47]. At the same time, higher concentrations of ammonia produced from urea facilitate the production of adenosine triphosphate by ureolytic bacteria, speed up the metabolism and increase enzyme activity [48]. As the incubation time increases, _B. pasteurii_ hydrolyzes  
Figure 3: Effect of CG and FA environments on the biomass of _B. pasteurii_. The bacteria were cultivated in tubes and incubated for 60 h. FA group_1 and CG group_2 comprised _B. pasteurii_ cultivated in basic culture medium with FA and CG, respectively. Reference_0 was composed of _B. pasteurii_ cultivated in basic culture medium only. All of the tests were performed in triplicate. The vertical error bars indicate the standard deviation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Components** & **A—Spread** & **B—Spread** & **B—Strength** & **C—Strength** \\ \hline CG (g) & 2000 & 2000 & 1200 & 1200 \\ FA (g) & 0 & 600 & 360 & 360 \\ _B. pasteurii_ with mineralized solutions (mL) & 500 & 650 & 390 & 0 \\ Water (mL) & 0 & 0 & 0 & 390 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Composition of the mortars used for macro-tests.  
more urea, thus producing and accumulating more ions and increasing the conductivity of the liquid more rapidly.  
It was noted that the mineralization began to occur after approximately 12 h, 31 h and 43 h in CG group_2, FA group_1 and Reference_0, respectively. As the addition of _B. pasteurii_ to CG resulted in mineralization occurring 31 h earlier than that in Reference_0, it is possible to reduce the time when biomineralization begins to occur by approximately 72%, with respect to the systems without CG or FA. Likewise, the addition of _B. pasteurii_ to FA resulted in mineralization occurring 12 h earlier than that in the control group, which could decrease the time at which biomineralization begins by only 28%. In other words, the coexistence of CG (or FA) with _B. pasteurii_ not only facilitates _B. pasteurii_ multiplication but also boosts the production of urease by _B. pasteurii_, enabling a faster start to urea hydrolysis compared to the original culture method. Thus, with the addition of CG (or FA), the initiation of the MICP reaction process is accelerated.
# The Impact of GoLo Technology on the Quality Properties of Dealcoholised Wines
## 3 Results and Discussion  
### Oenological Parameters  
Table 2 displays the results obtained for the oenological parameters analysed before and after the dealcohlisation process using GoLo technology. Table 2 also shows that changes in alcohol content do not generally affect basic wine parameters such as pH, titratable acidity, and volatile acidity and other parameters not studied such as density [38]. However, among the oenological parameters studied, it is noted that the total polyphenol content increases by 12% for red wines, which have a higher polyphenol content, and by 8% for white wines.  
The parameters that exhibit significant differences (using Student's \(t\)-test to compare the means between the results before and after the process of dealcohlisation using GoLo technology) across all types of wines include total polyphenol content, turbidity, and both free and total sulphite contents. The variations in sulphite content have already been reported in relation to this type of dealcohlisation process in our previous study [5], as well as wine phenolic compounds and other dealcohlisation technology [39]. This confirms the effectiveness of the dealcohlisation process and the reduction of sulphites following the dealcohlisation process using GoLo technology.  
With respect to turbidity, it is noteworthy that it increased in all types of wine, with a more significant rise observed in wines with a higher polyphenol content. In this regard, turbidity is formed by suspended particles, such as proteins and phenolics, originating from the grape. Changes in the total content and type of polyphenols in the dealcohlisation process can influence the turbidity and colloidal stability of the final product by complex formation with polysaccharides [40].
# Tailored Quantum Alternating Operator Ansatzes for Circuit Fault Diagnostics
## 4 QAOA Approaches to CFD
### Approach 5: Bounded Fault Count with Oracle Circuit Simulator  
In this section, rather than restricting ourselves to the subspace of all fault configurations, we wish to restrict ourselves to the fault configurations up to a maximum number of faults. To accomplish this task, we can use the XY mixer also discussed in Section 4.2 by utilizing ancilla qubits in a complimentary way. Here, we have \(n-n_{o}\) non-output fault flag bits (the same as in Section 4.4) and \(n_{o}\) ancilla qubits to allow for representation of all state configurations with faults less than or equal to \(n_{o}\).  
The cost function remains the same as that used in Section 4.4, and the same procedure with the simulation of the circuit to find the faults on the end bits can be used, except for the aforementioned replacement of the mixing operator.  
The initial state is then \(|\,\psi\,\rangle=\binom{n}{n_{o}}^{-1/2}\sum_{x\in\{0,1\}^{n-n_{o}}\text{ s.t. }|x |_{x}\leq n_{o}}\sum_{y\in\{0,1\}^{n_{o}}\text{ s.t. }|y|_{1}=n_{o}-|x|_{1}\] \[|\,x\,\rangle\,|\,y\,\rangle\), similar to the state mentioned in Section 4.2. Note that since we also have implied faults, the maximum number of faults that can be expressed is between \(n_{o}\) and \(2\,n_{o}\). The initialization of this state is discussed in more detail in Section 4.6.
## 4 Evaluation
### Application Evaluation
#### 4.4.3 Output Success Criteria  
Success Criterion SC-13 Reusable Database  
As a basis for evaluation, output data of the method and the software prototype are examined and compared with the required input data. The input data builds the core of the database and is enriched by expert knowledge from the application (for instance, evaluation of the correctness of determined dependencies). For evaluation of reusability, it is examined whether the output data generated from application are sufficient to cover all data-based input data apart from project-specific inputs (requirement data) (cf. Section 3). Other expert knowledge that is not valid for subsequent projects (for instance, impact dimensions of a specific requirement change) is not considered. SC-13 is considered fulfilled if all input data, with the exception of project-specific data (requirements), can be generated by output data from previous applications.  
Evaluation result: The following input data were deemed relevant for reuse:  
* Likelihood analysis: Number of changes triggered by an initiator in past projects.
* Dependency analysis: Correctness of dependencies between requirements.
* Proactive action selection: Suitability of the proposed proactive measures.  
The number of changes triggered by an initiator is documented during the course of the project and stored in the project file. The change history can be evaluated by subsequent projects by means of reference project selection. This ensures reusability and no separate data needs for likelihood analysis in a subsequent project.  
Manually evaluated correctness of the dependencies between requirements is automatically stored in the project database and can also be exported as a separate file. This  
Figure 17: Questionnaire results on area “application effort”.  
allows one to train an improved dependency model. For the dependency analysis, just the dependency model as well as project-specific data are required, so that the requirement of reuse for the dependency analysis is fulfilled.  
The suitability of the proposed proactive measures is captured by counting each selected measure for the recommender mechanism in subsequent projects. Thus, opportunity to reuse is given. Since all three relevant input data can be generated by previous projects, the reusability is rated as fulfilled.  
Validation result: SC-13 is fulfilled.
## 2 Theoretical Background
### Fuzzy Theory
#### 2.1.4 Creating the Single-Factor Assessment Matrix  
A single-factor fuzzy evaluation is conducted to judge one factor separately and confirm the degree of membership for the target of evaluation toward evaluation-set elements. The evaluation target is carried out by the \(i\)th factor U\({}_{i}\) and the membership grade of the \(j\)th element \(V_{i}\) in the evaluation set is \(r_{ij}\). Therefore, the evaluation result of the \(i\)th factor \(U_{i}\) can be determined as follows:  
\[R_{i}=\frac{r_{i1}}{V_{1}}+\frac{r_{i2}}{V_{2}}+\ldots+\frac{r_{in}}{V_{n}} \tag{5}\]  
where \(Ri\) is called the single factor evaluation set, which is the fuzzy subset of the evaluation set. It can be expressed as \(R_{i}\) = (r\({}_{i1}\), r\({}_{i2}\),..., r\({}_{in}\)). Similarly, the single factor evaluation set of each factor can be determined as follows:  
\[\begin{split} R_{1}&=(r_{11},r_{12},\ldots,r_{1n}) \\ R_{2}&=(r_{21},r_{22},\ldots,r_{2n})\\ &\quad\quad\vdots\\ R_{m}&=(r_{m1},r_{m2},\ldots,r_{mn})\end{split} \tag{6}\]  
The fuzzy matrix that is composed of the membership grade of each single factor evaluation set is called the single-factor assessment matrix, \(R\), as shown in Equation (7). R is a fuzzy matrix and can also be viewed as the fuzzy relational matrix from U to V, or so-called fuzzy mapping.  
\[R=\left[\begin{array}{c}R_{1}\\ R_{2}\\ \vdots\\ R_{i}\\ \vdots\\ R_{n}\end{array}\right]=\left[\begin{array}{cccccccc}r_{11}&r_{12}&\ldots&r_{ 1j}&\ldots&r_{1m}\\ r_{21}&r_{22}&\ldots&r_{2j}&\ldots&r_{2m}\\ \vdots&\vdots&\ddots&\vdots&\ddots&\vdots\\ r_{i1}&r_{i2}&\ldots&r_{ij}&\ldots&r_{im}\\ \vdots&\vdots&\ddots&\vdots&\ddots&\vdots\\ r_{n1}&r_{n2}&\ldots&r_{nj}&\ldots&r_{nm}\end{array}\right]. \tag{7}\]  
However, in this study, there are numerous factors to be considered and each factor can have different levels. It is difficult to resolve a problem by using single-factor fuzzy evaluation. It is also difficult to obtain reasonable assessment results. As a result, this study is based on fuzzy comprehensive evaluation, and then the fuzzy comprehensive evaluation is implemented. This is because there are too many factors to consider when making a selection from complicated schemes. There are levels between factors, therefore we must adopt the multifactor assessment matrix. The procedure is to divide the factor set into several levels according to its characteristics, carry out comprehensive assessment on each level, and then conduct in-depth combined evaluations on the evaluation results.
## 3 The Proposed Meshless Numerical Scheme
### Time-Marching Approach  
In the previous subsections, the numerical process of the proposed ST-GFDM scheme has actually been introduced, but those processes only work in a single ST domain, \(\Omega=[a,b]\times[0,\mathrm{dt}]\). To deal with numerical problems with unpredictable end-times or long temporal scales, a simple technique, the time-marching approach, is used in the proposed numerical scheme. The schematics of the time-marching approach are demonstrated in Figure 2. In Figure 2a, a small-scale ST domain, \(\mathrm{D1}=[a,b]\times[0,\mathrm{dt}]\), is formed when the simulation starts and the distribution of nodes is set up like in Figure 1a. In this ST domain, the initial conditions \(U_{0}(x)\) are satisfied along \(\Gamma_{1}^{1}\), \(\Gamma_{2}^{1}\), and \(\Gamma_{3}^{1}\), which satisfy the boundary conditions \(f_{1}(a,t)\) and \(f_{2}(b,t)\), respectively. The governing equation (Equation (1)) is satisfied on \(\Gamma_{4}^{1}\) and \(\Omega\). Then, the numerical processes in Sections 3.1 and 3.3 are applied to obtain the numerical solutions in the D1 domain. Once the numerical procedures within D1 are completed, the D1 will be shifted by distance dt along the \(t\)-axis, and the 2 nd small-scale ST domain \(\mathrm{D2}=[a,b]\times[\mathrm{dt},2\mathrm{dt}]\) is formed as shown in Figure 2b.  
The numerical processes within D2 are the same as the D1, but the boundary conditions along \(\Gamma_{1}^{2}\) are different. In \(\Gamma_{1}^{2}\), the numerical solutions at \(\Gamma_{4}^{1}\) in D1 are treated as the initial conditions for D2. Thus, the numerical solutions in D2 can be solved by the proposed ST-GFDM and the two-step Newton's method. It should be noted that the coordinates between D1 and D2 are relative locations. Thus, the weighting coefficients of the proposed ST-GFDM in D1 are the same as those of each ST domain generated by the  
Figure 2: The schematic diagrams of the time-marching approach in the proposed ST-GFDM. (**a**) 1 st domain, (**b**) 2 nd domain, and (**c**) n-th domain.  
time-marching approach. After moving the ST domain within n times, the n-th ST domain, \(\text{Dn}=[a,b]\times[(\text{n}-1)\text{dt},\text{n}\times\text{dt}]\), is generated (see Figure 2c), and the numerical solutions are solved. The simulation will finish when n \(\times\) dt reaches the specified end-time determined by the numerical example. To summarize the above numerical process, the flowchart is shown in Figure 3. In the next section, four numerical examples are provided to verify the proposed ST-GFDM scheme.
## 3 Results and Discussion  
### Gas-Liquid Interface Characteristics in the Mixing Section  
Figure 6 presents the time-averaged liquid volume fraction, pressure, and velocity contours on the central plane of the model at a nozzle exit velocity of 16.78 m/s. It shows that the working fluid is ejected from the nozzle, creating a low-pressure zone that entrains gas from the suction chamber into the mixing section. The solid boundaries of the mixing section cause the entrained gas to form an annular gas ring around the liquid, which suppresses the diffusion of the liquid jet. As a result, the gas-liquid flow in the entire system exhibits a coaxial flow pattern, resulting in an annular flow regime.  
Figure 4: Schematic diagram of the experimental system.  
Figure 5: Relationship between gas entrainment rate and nozzle outlet velocity.  
Figure 7 illustrates the time-averaged radial velocity contours at the entrance, middle, and exit sections of the mixing section, highlighting the gas disturbances affecting the interface. The contours reveal non-uniform radial velocity distributions at the inlet, middle, and outlet of the mixing section, with the velocity on the gas inlet side typically being higher. This indicates that the interface within the mixing section is subjected to non-uniform radial gas disturbances.  
Figure 8 shows three types of instantaneous gas-liquid interfaces (isosurfaces where the gas volume fraction equals 0.5) for different mixing section sizes. It reveals that the gas-liquid interface gradually transitions from a smooth cylindrical shape to a lobed structure along the jet direction. This transition occurs due to the non-uniform gas disturbances within the mixing section, as shown in Figure 7. The gas-liquid interface types in the mixing section are classified according to the interface shape at the outlet of the mixing section. Figure 8 displays a smooth interface characterized by consistently smooth lobed surfaces within the mixing section. Figure 8 shows a slug interface, where pronounced surface waves form on the concave surface near the exit of the mixing section, leading to irregular pits. Figure 8 presents a punctiform interface, characterized by distinct surface waves on the convex surface near the exit of the mixing section, causing irregular protrusions.  
Figure 6: The time-averaged (**a**) liquid volume fraction, (**b**) pressure, and (**c**) velocity contours on the central section.  
Figure 7: The time-averaged radial velocity contours of the (**a**) entrance, (**b**) middle, and (**c**) exit sections of the mixing section.
# Mechanical Focal Vibration Therapy for Muscle Injury Recovery in a Runner
## 2 Materials and Methods
### Vibratory Stimulation Technique  
The mechanical perturbation was applied by a ViSS One Evolution device v5.2.13 (Manufactured by Vissman S.r.l., Rome, Italy), a device equipped with a source that releases square wave mechano-sonic vibrations. The device further features an electromechanical probe and incorporates a transducer, a structural support, and a control unit for square-modulated power wave generation at a frequency of 100 Hz and a force ranging from 4 to 6 Newtons, thus resulting in a minimal displacement of 5 to 15 \(\upmu\)m peak-to-peak [24; 25]. Two probes were administered--one for the rectus femoris muscle and the other for the vastus lateralis--through 80 mm diameter, 140 mm rectangular cups made of acrylonitrile butadiene styrene (ABS).  
The patient was instructed to sustain an isometric contraction of the targeted muscle while the force sequences were applied. This contraction was maintained at about 50% of the patient's Maximum Voluntary Contraction (MVC). The experimental approach involved delivering vibratory stimulation to the skin covering the middle third of the injured quadriceps muscle, as illustrated in Figure 1.  
In this scenario, [PERSON] followed a conventional two-week rehabilitation protocol for a quadriceps injury to restore function and minimize pain. The first week involved rest, ice application, and lower limb elevation. As active rest, we recommended light, pain-free movements to maintain mobility. The second week introduced active exercises (isometric contractions) to help preserve strength. Proprioceptive and stretching exercises improve flexibility and prevent tightness.  
Ten sessions (5 applications per week) of mechanical vibration were performed across 2 weeks (duration of each application was 20 min, pause time interval between two consecutive applications was approximately 30 s). The quadriceps was maintained in isometric contraction throughout the stimulation. Measurements were performed at baseline (T0) one day after the injury, at the end of treatment (T1), and four weeks after the end of the treatment (T2). This particular vibratory stimulation protocol was designed taking into account the results obtained in our preliminary study on normal subjects, where the extensor torque was measured after vibration.
## 2 Background Concept and Problem Modeling  
### Path Planning for Robot Manipulator and Configuration Space  
In this paper, configuration space \(\mathbf{Q}\) is defined as the set of all joint angles of the manipulator. \(\mathbf{Q}\) is a subset of \(\mathbb{R}^{n}\), where \(n\) denotes the number of joints of the manipulator. Therefore, the position of the robot can be expressed as a point \(q\in\mathbb{R}^{n}\) using values of \(n\) joint angle. \(q_{i}\) represents the \(i\)th joint angle value, and it holds \(q=(q_{1},q_{2}\cdots q_{n})\). Configuration space \(\mathbf{Q}\) can be represented by two subsets. The first is defined as \(\mathbf{Q}_{\text{\tiny{min}}}\) in which the robot does not collide with obstacles, itself, or other robots. The second is the space that is the complement of \(\mathbf{Q}_{\text{\tiny{min}}}\), and it is denoted by \(\mathbf{Q}_{\text{\tiny{min}}}\) in which collision occurs. In other words, if the robot's position exists in \(\mathbf{Q}_{\text{\tiny{min}}}\), it means that the robot collides with an obstacle, a wall, another robot, or itself [5; 25; 26]. The robot manipulator does not collide if the joint angle belongs to \(\mathbf{Q}_{\text{\tiny{min}}}\).  
In the existing sampling-based method, random sampling points at \(\mathbf{Q}_{\text{\tiny{min}}}\) are made and connected to form the shortest line, and this process is repeated to construct a path from a starting point to a goal point. Such lines and points constructed in this way are collision-free paths because they are in \(\mathbf{Q}_{\text{\tiny{min}}}\). In addition, in this paper, \(q_{t}\in\mathbf{Q}\in\mathbb{R}^{n}\) represents the joint angle of the robot manipulator at the \(t\)th time step, and \(T\) represents the maximum number of time steps during one episode of the algorithm. Therefore, when the episode starts, the algorithm must start at the starting point \(q_{\text{\tiny{min}}}\in\mathbf{Q}_{\text{\tiny{min}}}\) and find the shortest possible collision-free path within the maximum number of steps, \(T\), to arrive at the goal point \(q_{\text{\tiny{goal}}}\in\mathbf{Q}_{\text{\tiny{min}}}\).  
Finally, in the proposed algorithm, when the starting point \(q_{\text{\tiny{min}}}\in\mathbf{Q}_{\text{\tiny{min}}}\) and goal point \(q_{\text{\tiny{goal}}}\in\mathbf{Q}_{\text{\tiny{min}}}\) are input, the trained RL-based path planning algorithm calculates the shortestpath without collision. Hence, the path is defined as a sequence of states using \(q_{t}\). In this case, for any \(T_{1}\leq T\), all elements in the path including the sequence \(q_{0}=q_{\text{\tiny ini}}\), \(\cdots\), \(q_{T_{1}}=q_{\text{\tiny pol}}\) must belong to \(\mathbf{Q}_{\text{\tiny ini}}\) because there must be no collision.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 2 Materials and Methods
### Satellite-Based NDVI  
We incorporated two common satellite-based NDVI products into our analysis to provide a baseline to large-scale phenological studies: MODIS and Landsat 8 [30]. For each, we extracted NDVI from analysis-ready composite datasets available on Google Earth Engine, specifically the MOD13Q1 V6 Terra Vegetation Indices 16-Day Global 250 m product and the Landsat 8 Collection 1 Tier 1 8-Day NDVI Composite. We compiled all available scenes for both products as a time series of means and standard deviations of pixel values within the study extent. Statistics were computed for edge pixels with an area-based weighting of pixel values partially within the study extent.
## 3 Results and Discussion
### Spatial-Temporal Distribution Characteristics Analysis
#### 3.1.1 Spatial Distribution Characteristics of Pavement Runoff Pollutants  
Box charts of different pollutants are plotted in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sites** & **Rainfall Duration** & **TN** & **TP** & **COD** & **Zn** & **Pb** & **SS** \\  & **(min)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** \\ \hline  & 0 & 12.333 & 2.019 & 616 &  
The pollutants SS, Zn and Pb showed very clear spatial distribution characteristics. In Figure 6a, the concentrations of SS at Sites 3 and 1 were much higher than those at the other two sites. These two sites were near the intersection and the west gate of Yangtze Campus, respectively. Large amounts of particle dusts or other suspended pollutants brought by vehicles and pedestrians passing by may be the reason for this characteristic. SS is a typical quality index for surface water, the maximum concentration of which was up to 48 times more than the limitation value. Particles of suspended matter are usually visible to human eyes, unstable in water and easy to be removed by porous adsorptive materials. As to the heavy metals Pb and Zn (Figure 6b,d), high concentrations existed in the pavement runoff near the intersection (Site 1). This is due to exhaust emission and frequent braking of vehicles at such sites.  
Figure 6: Box charts of different pollutants.  
However, COD showed the same concentration level at all four sites, which indicated that there were many organic pollutants in this area. In Figure 6e,f, the spatial distributions of TN and TP were similar to that of SS because dissolved pollutants attach to particles.