esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Tested Cases
### Numerical Examples
For function SA, EW-LCB always ranks first, while the ranks of EI, WEI, LCB, PLCB change a lot. For instance, PLCB ranks second when the initial sample sizes are \(5d\) and \(15d\), while when it comes to the size of \(10d\), the rank changes to fourth. As for function L3, the numbers of the sample size of EI, WEI, LCB change little, while the numbers of the sample size of PLCB and EW- LCB change significantly when the initial sample size transforms from \(5d\) to \(10d\). This represents that PLCB and EW-LCB may perform well with a small sample size in the case of a quite complex function while EI, WEI, and LCB only represent this feature when the function is simple. This is attributed to the objective weighting factors in PLCB and EW-LCB, which are able to allocate factors to balance global exploration and local exploitation. In summary, the EW-LCB method shows the greater ability in balancing between global exploration and local exploitation compared to the other four AKBDO methods.
## 2 Materials and Methods
### Data and Preprocessing  
Four Landsat TM (Thematic Mapper) images acquired in 1996, 2001, 2006, and 2011 were selected as the main data sources to analyze the effects before and after the implementation of the two rangeland protection policies; the images were selected for their cloud-free high quality and similar climate condition (Table 1). These images, downloaded from the United States Geological Survey (USGS, [[http://glovis.usgs.gov](http://glovis.usgs.gov)]([http://glovis.usgs.gov](http://glovis.usgs.gov))), were selected from images obtained during summer and autumn (July-August) when vegetation generally reaches their maximum growth during the region's growing season [6]. All the study images were rectified to a UTM (Universal Transverse Mercator) projection (Zone 48N, Datum: WGS-84) using at least 45 well-distributed ground control points (GCPs) for each image and cubic convolution resampling [10] with the root mean square errors (RMSEs) less than 0.25 pixels (7.5 m). In addition, the land use map of 1995 and 2000 covering the study region, together with the China-Brazil Earth Resources Satellite CCD (Charge-coupled Device) image on 23 August 2007 (19.5 m spatial resolution) and recorded data in field survey in July 2012 were selected as auxiliary sources information for the four years, respectively.
## 4 Improvement for Energy Equation  
### Wall Temperature Rise Issue  
After solving the mixing plane issues, the KARI uncooled turbine was analyzed using the steadyUniversalMRFFoam solver.  
Figure 6 shows that the results from OpenFOAM are consistent with the ones from the commercial code in terms of pressure and velocity, but not for the temperature. It turned out that the compressible MRF solver of OpenFOAM had calculated the wall temperature to be high, affecting the temperature of the flow field. Since the viscous heating term was included in this solver's energy equation, the wall temperature rose. But, even when the term was excluded, the wall temperature was still higher than that of the commercial code's result, as shown in Figure 7. In addition, the wall temperature in the rotor's rotating area was high, so the wall boundary condition was suspected, but the temperature of the non-rotating area of the stator was also high, indicating that the reason was not a boundary condition, but the energy equation.  
Figure 6: Comparison of KARI uncooled turbine flow analysis results mid span, with the viscous heating term by CFX and the OpenFoam (OF) compressible MRF solver: (**a**) CFX, pressure; (**b**) CFX, relative velocity; (**c**) CFX, temperature; (**d**) OF, pressure; (**e**) OF, relative velocity; and (**f**) OF, temperature.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 6 Experimental Setup  
### Datasets  
As introduced in Section 3, due to the nature of our proposed study we only considered the speaker-dependent partition of the LIP-RTVE database in our experiments. However, in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Adapter Size** & 8 & 16 & 32 & 64 & 128 & 256 \\ \hline
**Learnable** & & & & & & & \\
**Parameters**\({}^{\dagger}\) & 0.15\% & 0.27\% & 0.52\% & 1.02\% & 2.02\% & 4.02\% \\ \hline \hline \end{tabular} \({}^{\dagger}\) Percentages w.r.t. the entire end-to-end system, which comprises around 52.5 million parameters.  
\end{table}
Table 1: Percentage (%) of learnable parameters with respect to the entire end-to-end system (see Section 4.1) for each adapter size considered in our experiments. Adapter sizes are represented by the hidden units that compose its inner projection. Percentages were computed considering all the injected Adapter modules, i.e., one Adapter for each layer of the encoder and decoder (see Figure 3).  
Figure 3: Scheme of how adapters are injected to adapt an end-to-end VSR system to a specific speaker. It should be noted that only the Adapter modules are estimated during the adapting process, while the rest of the model is kept frozen.  
order to estimate our speaker-adapted VSR systems and adequately interpret the obtained results, the twenty most talkative speakers were selected. Therefore, depending on the adaptation method appl (see Section 6.3), two different partitions used to estimate our VSR models in our work can be distinguished:  
* **The entire speaker-dependent partition**. This corresponds to the official speaker-dependent partition of the LIP-RTVE database. The training and development sets offer around 9 h and 2 h of data, respectively. As suggested in Section 5, using one of these data sets to estimate the model can be seen as a task adaptation. Concretely, this partition is used when applying the fine-tuning MST strategy, providing a model used as initialisation for the TS-SAT strategy and the Adapters-based adaptation method.
* **The speaker-specific partition**. Only the data corresponding to the aforementioned twenty speakers are considered in this case. On average, the training and development sets offer around 15 min and 4 min of data per speaker, respectively. As suggested in Section 5, when seeking to estimate a VSR system adapted to a specific speaker, only data associated with that speaker is used. Concretely, this partition is considered in all of our proposed methods with the exception of the fine-tuning MST strategy.  
It should be noted that, regardless of whether a dataset from one partition or the other is used to estimate the VSR models, the speaker-specific test set (composed of around 4 min of data per speaker) was used to assess the effectiveness of all the proposed adaptation methods. In addition, as described in Section 6.2, the development set of each partition was used to analyse how the different proposed adaptation methods behaved when only a significantly reduced amount of data was available for their estimation.
###### Abstract  
To address the limitations of existing wind turbine airfoil databases, the high computational cost, and low efficiency of noise prediction, this paper proposes a wind turbine airfoil noise prediction method based on generalized airfoil sets and residual neural networks. Firstly, taking a database of 31 commonly used wind turbine airfoils as a reference, a generalized airfoil set with diverse geometric contours was generated. This was achieved by employing airfoil functional integration theory, B-spline curves, and the Class function/Shape function Transformation (CST) method while varying coefficients and control vector parameters. Secondly, the BPM semi-empirical model was used to compute the noise for the generalized airfoil set, which served as the data labels for deep learning. Finally, classical machine learning models were utilized to construct the airfoil noise prediction model. The results demonstrate that the airfoil noise prediction model constructed with the residual neural network (ResNet-18) achieved the highest prediction accuracy, with a mean squared error (MSE) of 0.0282 and a coefficient of determination (\(R^{2}\)) of 0.99972. Additionally, the trained model exhibited computational efficiency that was 17.5 times higher than the BPM model.  
wind turbine; airfoil noise; deep learning; residual neural network Article
## 2 Materials and Methods
### Subject-Specific Model Generation  
From the segmented geometries (i.e., bones, soft tissues, muscles), a single leg subject-specific MSK dynamics model for each participant was developed following the pipeline proposed by [PERSON] and colleagues [26].  
The models, built with the nmsBuilder software (v.2.1) [11], included seven bone complexes linked by idealized joints (i.e., hip, knee, patellofemoral, ankle, subtalar, and metatarsophalangeal joints) for a total of 13 degrees-of-freedom. Joint centers and axes of the reference systems were identified by fitting analytical shapes (i.e., spheres and cylinders) to the articular surfaces, in MeshLab [46]. The patellofemoral joint was defined as in [27]. The inertial properties of each segment (e.g., thigh) were computed from the segmented volumes, assuming the densities of bones and soft tissues to be 1.42 g/cm\({}^{3}\) and 1.02 g/cm\({}^{3}\), respectively [47; 48]. Muscle origin and insertion points were taken from a generic atlas (Full Body Model [27]) and mapped onto the subject-specific bony geometries through affine transformations. Wrapping geometries were included to ensure physiological muscle behavior (e.g., to avoid in-bone penetrations) and the line of action of each muscle was checked to fall inside the scanned muscle volume. Minimal manual adjustments were performed where deemed necessary. Each muscle-tendon unit was modeled as a Hill-type actuator [49], described by [PERSON] et al. [50]. The optimal fiber length (OFL) and tendon slack length (TSL) parameters were extracted from the generic Full Body Model and anthropometrically scaled [51]; the maximal isometric force (MIF) values of each muscle were recalculated using the physiological cross-sectional area (PCSA), as per (1) and (2):  
\[PCSA_{i}=V_{m}^{i}\mathrm{cos}\,\theta/l_{o}, \tag{1}\]  
\[MIF_{i}=\sigma PCSA_{i}, \tag{2}\]  
where \(V_{m}\) is the \(i\)-th muscle volume, \(\theta\) is the pennation angle, \(l_{o}\) the OFL, and \(\sigma\) the specific tension, set at 60 N/cm\({}^{2}\) and in line with literature data [23].
# Research on the Impact of COVID-19 on Micromobility Using Statistical Methods
## 2 Literature Review  
Since the outbreak of COVID-19, several studies have described how human mobility responds to an epidemic. It has been necessary to prevent the spread of the virus since March 2020. The governments of many countries tried to slow down the spread with travel and work restrictions, quarantines, curfews, cancellations and postponements of events, and facility closures [13]. All of these measures were aimed to reduce the contact between infected and non-infected persons [14].  
COVID-19 influenced human mobility and changed typical human activities. According to [15; 16], mobility data are crucial for urban planning, traffic forecasting, network applications, and epidemic control [17; 18; 19]. Therefore, the topic of human mobility during a pandemic is critical. The authors of [20] stated that not only did government restrictions decrease mobility, but economic impoverishment also negatively affected mobility. For example, international tourism has plummeted. The pandemic has also resulted in changes in transport behavior, where measures and restrictions play an important role in reducing preferences for public transport and promoting shared mobility services [21].  
There are many options for how mobility during a specific period can be researched. The authors of [22] used privacy-protected mobile device location data, which were integrated with COVID-19 case data and census population data. With this dataset, they made a COVID-19 impact analysis platform that can inform users about the effects of the spread of COVID-19 and government orders on mobility and social distancing.  
Another opportunity is questionnaires that are fulfilled face to face, online, or during a telephone interview. For example, the authors of [23] used several unified questionnaires to examine the quality of life after social and mobility restrictions. Reference [24] used a qualitative telephone interview and a quantitative household survey. The results showed that a high share of respondents (62.6%) experienced no changes in their mobility behavior due to the COVID-19 pandemic.  
In this article, we focused on micromobility during the pandemic period. Many studies have examined and analyzed patterns of human micromobility by using GPS trajectory data. Most of these studies focused on understanding bike-sharing mobility models in docked and undocked bike-sharing systems. For example, the authors of [25] examined the travel behaviors of two types of bicycle users (short term and long term) by analyzing bicycle routes between docking stations.  
In the field of micromobility, the authors of [26] proposed an algorithm that returns a multi-criteria route modeled on COVID-19-modified parameters of micromobility while considering avoidance of COVID-19, as well as the shortest available safe route for user ease and shortened time of exposure to the outside environment.
# Multiresponsive Hybrid Microparticles for Stimuli-Responsive Delivery of Bioactive Compounds
## 2 Materials and Methods
### Cytotoxicity and Hemolysis Assay  
Cytotoxicity was determined by MTT assay using a fibroblast NIH/3T3 cell line. The cells were cultured in the medium (\(2.5\times 10^{5}\) cells/mL) containing the samples for 24, 48 and 72 h. Culture medium without microparticles, bare microparticles and DOX were used as controls to evaluate the benefits of the microcarriers. The microparticle suspensions were prepared in the cell growth medium at 50 \(\upmu\)g/mL concentration. The cells were washed with 200 \(\upmu\)L of PBS per well, and then 50 \(\upmu\)L of 1 mg/mL MTT solution was added to each well. After 2 h incubation, the MTT solution was removed by aspiration, and 50 \(\upmu\)L of isopropanol was added to dissolve the formazan crystals. A Tecan multiplate reader was used to evaluate the optical density at 595 nm. Cell viability percentage was calculated as the ratio of mean absorbance of triplicate readings of sample wells (\(I_{sample}\)) compared to the mean absorbance of control wells (\(I_{control}\)):  
\[\textit{Cell viability}\ (\%)\ =\ \frac{I_{sample}}{I_{control}}\times 100 \tag{3}\]  
The cytotoxicity, in combination with ultrasound, was evaluated. The formulations were seeded in 96-well plate containing \(2.5\times 10^{5}\) cells/mL and exposed to 1 MHz ultrasound (2 and 20 W/cm\({}^{2}\)) using the same timing adopted in the in vitro release studies. Evaluation after 48 and 72 h was conducted to understand the effect of ultrasound treatment over time.  
Hemocompatibility of bare microparticles was evaluated by the hemolysis assay, following a reported protocol [27,28], where first 1.5 mL of acid citrate dextrose (ACD) was added to 10 mL of a human blood sample. Afterwards, 100 \(\upmu\)L of the formulation containing particles (concentration ranging from 5 to 100 \(\upmu\)g/mL) was added to 1 mL of the treated sample and incubated for 2 h at 37 \({}^{\circ}\)C under orbital shaking. The samples were centrifuged at 6000 rpm for 15 min; the plasma was collected (100 \(\upmu\)L plasma + 1 mL Na\({}_{2}\)CO\({}_{3}\)); and the OD values at 450, 380 and 415 nm were recorded. The obtained values were compared with the controls. In order to achieve 100% hemolysis, the cells were lysed using the nonionic detergent Triton X-100 as positive control, while PEG (1000 \(\upmu\)g/mL) was used as a negative control. Each assay was performed in triplicate.
## 1 Introduction
The use of PSI requires advanced imaging modalities, such as high-resolution computed tomography (CT) images to design and print the implant according to each patient's unique anatomy and defect characteristics [19]. This approach allows for a more precise fit and alignment of the implant with the surrounding bone and soft tissue structures, which can improve the functional and aesthetic outcomes of the surgery. Moreover, PSI can reduce operative time and blood loss by minimizing the need for intraoperative adjustments and bone grafting. PSI also enables the creation of complex geometries and porous structures that can enhance the biomechanical stability and osseointegration of the implant [20]. However, PSI also poses some challenges such as higher costs, longer lead times, regulatory issues, and ethical concerns. Therefore, careful patient selection and multidisciplinary collaboration are essential for the successful application of PSI in orthopedic surgery [21]. Compared to previous studies performed in the same context, this study introduces a novel approach to reconstructing the zygomatic arch and enhancing facial appearance. Specifically, the use of virtual planning of the surgery is a relatively new technique that allows for more precise pre-surgical planning compared to traditional methods. Additionally, assessing clinical matching in terms of orbital measurements and numerical models' registration is a more comprehensive and accurate way of evaluating the success of the surgical intervention. Previous studies have focused on one or the other, rather than combining both. By utilizing both virtual planning and clinical matching assessment, this study provides a more holistic approach to reconstructing the zygomatic arch and improving the cosmetic outcome for patients with related facial injuries.
###### Abstract  
Precise point positioning (PPP) has received much attention in recent years for its low cost, high accuracy, and global coverage. Nowadays, PPP with ambiguity resolution and atmospheric augmentation is widely regarded as PPP-RTK (real-time kinematic), which weakens the influence of the long convergence time in PPP and regional service coverage in RTK. However, PPP-RTK cannot work well in urban areas due to limitations of non-line-of-sight (NLOS) conditions. Inertial navigation systems (INS) and vision can realize continuous navigation but suffer from error accumulation. Accordingly, the integration model of multi-GNSS (global navigation satellite system) and PPP-RTK/INS/vision with a cascading Kalman filter and dynamic object removal model was proposed to improve the performance of vehicle navigation in urban areas. Two vehicular tests denoted T01 and T02 were conducted in urban areas to evaluate the navigation performance of the proposed model. T01 was conducted in a relatively open-sky environment and T02 was collected in a GNSS-challenged environment with many obstacles blocking the GNSS signals. The positioning results show that the dynamic object removal model can work well in T02. The results indicate that multi-GNSS PPP-RTK/INS/vision with a cascading Kalman filter can achieve a positioning accuracy of 0.08 m and 0.09 m for T01 in the horizontal and vertical directions and 0.83 m and 0.91 m for T02 in the horizontal and vertical directions, respectively. The accuracy of the velocity and attitude estimations is greatly improved by the introduction of vision.  
PPP; multi-GNSS; PPP-RTK/INS/vision integration; cascading Kalman filter; urban vehicle navigation; dynamic object removal
## 3 Results
#### 3.1.4 Comparison of Realized Test Times with Proposed Execution Times in PASm-12 (Q3)
The effect size for each test, specifically the differences between the planned times and the actual times achieved, is as follows: for the 10 km test in Zarnesti, [PERSON]'s d was 0.44, indicating a small to moderate difference; for the 21 km test in Cluj-Napoca, [PERSON]'s d was 1.68, indicating a large difference, favorable to the actual times achieved; for the 21 km test in Oradea, [PERSON]'s d was 0.41, indicating a small difference, with actual times close to those planned; for the 21 km test in Brasov, [PERSON]'s d was 1.39, showing a large difference, favorable to the actual times achieved, which were shorter; and for the 21 km test in Grosseto, [PERSON]'s d was \(-0.53\), suggesting a moderate difference, with the actual times being longer than those planned. After executing the main test--the objective (EMACNS--Grosseto), the entire group agreed to continue monitoring the training process through PASm-12 until its full completion without participating in common competitions imposed by PASm-12. This request was largely due to family and professional responsibilities and financial constraints. However, all runners agreed to participate in the study until the full execution of PASm-12 contents and to report continuously via smartwatches both the requested training parameters and competition participation within the competition period defined by PASm-12, even if these were not always exclusively related to the half marathon event. Data collected from S1-S6 show that female runners continued competitive activity (except SI--due to professional and academic reasons), participating in both national and international competitions (Table 11). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test No.**} & \multirow{2}{*}{\begin{tabular}{c} **Sports Event/Test** \\ **km/Location** \\ \end{tabular} } & \begin{tabular}{c} **Proposed** \\ **PASm-12 Average** \\ **Time s (I)** \\ \end{tabular} & \begin{tabular}{c} **Realized Average** \\ **Time S1–S6 s (J)** \\ \end{tabular} & \begin{tabular}{c} **Difference I–J** \\ **Time S1–S6 s (J)** \\ \end{tabular} & \
\begin{tabular}{c} **SD** \\ \end{tabular} &
\begin{tabular}{c} **t** \\ \end{tabular} & \(p\) \\ \hline
1 & Zámresid 10 km test & 3000 & 2951.6 & 48.40 & 110.97 & 0.975 & 0.39 \\
2 & Cluj-Napoca 21 km test & 7035 & 6458 & 576.20 & 344.297 & 3.742 & 0.02 \\
3 & Oradea 21 km & 7035 & 6858 & 177.00 & 433.06 & 0.914 & 0.41 \\
4 & Brasov 21 km & 7035 & 6446.8 & 588.20 & 422.96 & 3.110 & 0.04 \\
5 & Grosseto 21 km & 6300 & 6535.2 & \(-\)235.20 & 441.19 & \(-\)1.192 & 0.3 \\ \hline \hline \end{tabular} Note: N = 6, bootstrapping—1000; execution time averages are described in seconds; SD—standard deviation.  
\end{table} 
Table 10: Differences between the average realized test times by runners S1–S6 and the proposed average times in PASm-12 (H3.b). From these quantitative data, it is observed that the participation of each subject varies in number (between 0-10), with the predominant ones being at nationally scheduled competitions.