esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatial Compounding Method for Non-Delayed Sequential Beamforming
## 2 Materials and Methods
### Evaluation  
Two proposed compound images are present, denoted as Comp1 and Comp2. To provide a fair comparison, both NDSB and DAS images are shown.  
The reconstructed images are evaluated in terms of resolution and image contrast. The resolution is quantified by imaging a point target. We define the resolution as the full-width-at-half-maximum (FWHM) in the lateral and axial direction. The performance of image contrast is evaluated in terms of contrast ratio (CR) which is defined as  
\[\text{CR}=20\times\text{log}(\frac{I_{out}-I_{in}}{0.5*(I_{out}+I_{in})}), \tag{10}\]  
where \(I_{in}\) denotes the mean intensities of the measured cyst and \(I_{out}\) denotes the mean intensities of the measured background.  
The contrast-to-noise ratio (CNR) value is also used which is defined as  
\[\text{CNR}=20\times\text{log}(\frac{I_{out}-I_{in}}{\sqrt{\frac{I_{out}^{2}+I _{in}}{2}}}). \tag{11}\]  
The unit of CR and CNR is decibel (dB).
# Analytic Form Fitting in Poor Triangular Meshes
## 3 Methodology
### Primitive Fitting from Triangle Set
#### 3.2.2 Cone Fitting  
The geometrical parameters of the cone are:  
1. \(p_{0}\): cone apex.
2. \(\vartheta\): axis direction.
3. \(\gamma\): opening angle.  
**Cone axis direction \(\vartheta\)**  
As in the cylinder case (Section 3.2.1), the vectors normal to the cone triangles allow us to estimate the direction of the cone axis.  
The unitary vectors \(n_{i}\) normal to the cone triangles are translated to the origin (Figure 10). Their tips form a planar circle which is the base of a dual cone whose apex is at the origin. Observe that this dual cone _is not_ the one contained in triangle input \(S\). This _dual_ cone in Figure 10 is obtained by using the normal vectors in the triangle set \(S\) as cone generatrices, but it has the same axis direction vector as the sough cone \(g()\).  
**Cone apex \(p_{0}\)**  
The straight line \([b_{i},\hat{e_{i}}]\) (Figure 11) tangent to triangle \(t_{i}\) passes through the triangle baricenter \(b_{i}\) and has direction \(\hat{e_{i}}\) (i.e., direction of the cone generatrix at \(b_{i}\)). The point at which the lines \([b_{i},\hat{e_{i}}]\) are nearest to (or intersect) each other are the elements of \(C_{p}\). The cone apex \(p_{0}\) is the center of gravity of \(C_{p}\).  
Each set of vectors \([n_{i},\hat{e_{i}},\vartheta]\) lies in a plane that intersects the cone and contains the apex \(p_{0}\). We calculate a temporal vector \(temp\) which is normal to such a plane as the cross product between \(n_{i}\) and \(\vartheta\). The cross product between the \(temp\) vector and \(n_{i}\) guarantees that \(\hat{e_{i}}\) is perpendicular to \(n_{i}\) and lies in the aforementioned plane. Equation (2) summarizes the process.  
\[\begin{split}& temp=n_{i}\times\hat{v}\\ & e_{i}=temp\times n_{i}\end{split} \tag{2}\]  
Figure 10: Cone fitting. Dual cone formed with Origin as apex. Unitary vectors normal to \(S\) are generatrices for the dual cone. Origin-located tails of \(n_{i}\) vectors normal to triangles \(t_{i}\) at Origin. Normal vector tips contained in a plane form a flat disk \(d\). Minimal dispersion direction (principal component analysis) of disk \(d\) is the direction of cone axis \(\hat{v}\).  
**Opening Angle \(\gamma\)**  
The opening angle of the cone is computed as the average of the angles \(\gamma_{i}\) (Equation (3)) between the cone axis direction \(\vartheta\) and the calculated vectors \(\hat{e}_{i}\).  
\[\gamma=\frac{1}{m}\sum_{i}\gamma_{i}. \tag{3}\]
## 3 Research Design
### Questionnaire Development  
As part of this study, a questionnaire was developed to gather feedback on the effectiveness of each explanation method used in the experiment. The objective was to collect qualitative data and gain insight into the features that make an explanation method more effective according to the radiologist cohort. As no validated survey for explanation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Value** \\ \hline Number of Radiologists & 10 \\ \hline Average Experience & 12.1 \\ \hline Standard Deviation (years) & 4.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics on the radiologist cohort used in this study.  
methods existed in the literature, we developed our own questionnaire. To ensure its validity and comprehensibility, a team consisting of a machine learning researcher, a clinical radiologist with an MD, and a radiology researcher with both a PhD and an MD developed the questionnaire. We used a previously published study on questionnaire design for inspiration [23]. After the questionnaire was created, it was reviewed and approved by two radiologists to confirm its clarity and effectiveness. We ensured that the questionnaire was easy for the radiologist cohort to understand. Following multiple rounds of feedback and revisions, the questionnaire was ultimately approved by each member of our multidisciplinary team.  
Our team evaluated the effectiveness of the explanation methods using a 5-point Likert scale. During the experiment, a 5-point Likert scale ranging from 1 to 5 was employed to rate each method, where 1 indicated \"strongly disagree\" and 5 indicated \"strongly agree\". Subsequently, each radiologist was asked to elucidate their rationale for their rating in a text field. This was to determine if perceived effectiveness is related to actual effectiveness. For each MRI image and its interpretation visualization, radiologists were asked the following questions: Is there a lesion present (Yes/No)? How helpful is this interpretation method at showing why the model made its decision on a scale from 1 to 5? They were also asked to provide reasoning for their rating. For interpretation and uncertainty visualization, the additional questions included the following: Does the visualization help understand the model's classification? Does it inspire confidence in the classification? Does it justify the classification? Radiologists were also asked to list strengths and weaknesses of the visualizations and suggest improvements. Finally, additional comments were requested, including lesion presence in further images and optional feedback.
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
## 4 System-on-Chip Verification  
A complete dual-frequency single-chip GNSS receiver including radio was implemented in a 110 nm CMOS technology (see Figures 24 and 25). A two-wideband of 52 MHz high gain RF AFE  
Figure 23: Phase noise of the L5/E5 digitally controlled oscillator.  
Figure 21: Phase noise of the L1/E1 digitally controlled oscillator.  
Figure 22: Tuning characteristics of the L5/E5 digitally controlled oscillator.  
of 131 dB with NF 2.3 dB was integrated with a triple-core 32-bit RISC navigation and application processor with user accessible eFlash. The radio part includes the LNAs, active baluns, mixers, ADPLLs and IF with I/Q ADCs. The total chip area is 34 mm\({}^{2}\). The chip used has been manufactured and is currently in the measurement phase (see Figure 26). The manufactured SoC has been successfully used to acquire the GNSS signals using an in-house developed antenna (see Figure 27). The acquisition time used was 1 ms and the SNR (signal-to-noise ratio) obtained was 16.4 dB. The CCNV1_A1 was also used to develop positioning software.  
Figure 24: CCNV1-A1 integrated circuit die microphotography.  
Figure 25: CCNV1-A1 packaged dies.  
All the presented results are post-layout simulation-based. Table 1 summarizes the performance of the presented dual-frequency receiver and a comparison with the state-of-the-art parameters. The comparison indicates that this receiver supports most of the GNSS systems, simultaneously working with only two RF channels. Additionally, it is important to mention that ultra-low leakage technology with metal stack on aluminum was chosen. Its parameters, such as connection resistance, inductance quality factor, and a high Vt of transistors, are unfavorable for analog and RF design in particular, which should be taken into account during the comparison.  
Figure 27: GPS L1 acquisition results obtained with a CCNV1-A1 and in-house developed antenna with a 1 ms acquisition time.  
Figure 26: CCNV1-A1 measurement environment with the evaluation board. The photo presents the CCNV1-A1 measurement board connected to the LabSat 3 GNSS Simulator and DSO90254A Digital Storage Oscilloscope.
## 1 Introduction  
Non-contact boxing training, defined here as boxing, is popular amongst people with Parkinson's disease (PD) for management of the condition and improvement in general fitness [1, 2, 3]. It incorporates high-intensity interval training (HIIT), which has been shown to improve aerobic capacity in the general population and people with PD [4, 5, 6, 7, 8, 9, 10]. Boxing also requires intricate footwork and punches (exercise complexity) that challenge cognition and dynamic balance, which are particularly relevant to people with PD [11, 12, 13, 14].  
[PERSON] and colleagues pioneered the feasibility and safety of boxing in PD through a case series and a randomized clinical trial, reporting enhanced balance, gait, and quality-of-life outcomes [15, 16]. Subsequent studies showed that boxing interventions led to improvements in these outcomes, as well as cognitive function and depression [3; 15; 16; 17; 18; 19; 20; 21]. The literature often refers to boxing as vigorous to high-intensity aerobic exercise for people with PD. However, studies do not always report the intensity zone achieved during boxing combinations or the effect of other non-boxing-related drills and activities often included in protocols that incorporate measures of exercise intensity [1; 10; 22; 23; 24; 25; 26; 27; 28]. Exercise intensity is an essential training variable for HIIT protocols and also a determinant of energy expenditure (EE) [6].  
An important feature that may impact on exercise performance is the presence of an autonomic dysfunction, particularly chronotropic incompetence (CI). This feature is characterized by the incapacity to achieve a targeted threshold of 85% maximum age-predicted heart rate (MA-PHR) during peak exercise, even though clinical and physiological indicators suggest that maximal effort has been achieved [29; 30]. CI affects between 10 and 50% of people with PD. Interest in its effect on exercise performance is growing, particularly during incremental maximal exercise testing and continuous aerobic protocols [31; 32; 33; 34; 35]. Studies to date have not examined physiological responses and the level of exercise intensity in people with PD with CI during boxing. While this activity is considered an alternative form of HIIT for the general population, this has yet to be examined systematically in people with PD.  
This exploratory study had three key aims. The first was to examine the effect of CI on physiological and clinical responses during boxing sessions in people with PD. The second was to evaluate the percentage of time spent in the high-intensity training zone based on individualized physiological measures of exercise intensity. The last aim was to evaluate the energy expended during boxing sessions.
## 2 Literature Review  
### Concrete Carbonation Model  
Concrete carbonation is a phenomenon where calcium carbonate (CaCO\({}_{3}\)) is produced by the reaction of calcium hydroxide Ca(OH)\({}_{2}\) and C-S-H in a hydrated cement paste. This action gradually lowers the concrete pH from 12 to 9. Although carbonation can increase the strength of concrete, the properties of steel change when carbonation reaches the steel reinforcement. A corroded rebar has a larger volume than steel, which leads to concrete damage, spalling, and cracking [24]. Despite maintaining a certain covering depth of rebars to prevent this deterioration, carbonation continues over time. Several studies have focused on short-term carbonation (within a year) to predict carbonation rates. Until 1980, the carbonation depth was predicted using a linear regression method based on compounding variables, such as material ratio, binder type, and certain environmental variables [25; 26]. Subsequent studies have proposed mathematical and analytical models for predicting the carbonation depth of concrete [27]. Most studies are based on the theory of diffusion based on [PERSON]'s second law, which states that the depth of concrete carbonization is proportional to the square root of time, as indicated in Equation (1) [28; 29; 30].  
\[C_{t}=k\sqrt{t}, \tag{1}\]  
where \(C_{t}\) denotes the carbonation depth, \(k\) indicates the coefficient of carbonation rate, and \(t\) represents the exposure time. Here, \(k\) is the most important coefficient that determines the carbonation rate. [PERSON] et al. [29] expressed the coefficient as an equation for carbon dioxide concentration and relative humidity. [PERSON] et al. [31] suggested an equation that included temperature and concrete quality. [PERSON] and [PERSON] [32] proposed a carbonation model including the cement type, hydration degree, water-cement ratio, and diffusivity of CO\({}_{2}\), considering reactivity as a parameter. This coefficient is primarily influenced by various factors, such as cement or binder, water-binder ratio, curing time, CO\({}_{2}\) concentration, and relative humidity. Because these equations are based on laboratory conditions, they exhibit a relatively high error in field conditions owing to changes in the influencing factors. [PERSON] and [PERSON] [33], [PERSON] et al. [34], and [PERSON] et al. [35] proposed equations to modify the exponent of \(t\) according to environmental changes. Furthermore, a fib model [36; 37] has previously been applied to bridge abutment and pier carbonation data using an equation based on a robust prediction model.
# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 2 Data
### Radiation and Weather Data  
For the measurements of solar radiation, temperature, and wind speed, we used the data offered by the PVGIS portal [34]. According to the resolution of the PVGIS-SARAH2 solar radiation database [40; 41], we identified the 0.05\({}^{\circ}\) resolution sectors that overlap the territory of Sardinia (Figure 4) and collected the time series data via a series of application programming interface (API) calls to [[https://re.jrc.ece.europa.eu/api/v5_2](https://re.jrc.ece.europa.eu/api/v5_2)]([https://re.jrc.ece.europa.eu/api/v5_2](https://re.jrc.ece.europa.eu/api/v5_2)) /seriescalc (accessed on 13 November 2022).  
Figure 2: (a) Coverage of the 1 m resolution DSM (in black) over the territory of Sardinia and (**b**,**c**) a clipping of the DSM within the Municipality of Cagliari.  
Figure 4: The island of Sardinia represented by the territorial boundaries of its municipalities (in black), (**left**) superimposed on the grid of sectors of size 0.05\({}^{\circ}\) (in blue) of the PVGIS-SARAH2 database and (**right**) by the average annual global irradiance on a 1 m\({}^{2}\) surface oriented to the south and inclined by 20\({}^{\circ}\).  
Figure 3: (a) Percentage of municipality area and (b) percentage of rooftop surface covered by the DSM.  
Each individual call to the API service returned a 16-year long (2005-2020) hourly time series of the values \(G_{b}\) (DNI\({}_{h}\), direct irradiance on the plane of the array, POA), \(G_{d}\) (DHI\({}_{h}\), diffuse irradiance on the POA), \(G_{r}\) (reflected irradiance on the POA), \(H_{\text{sun}}\) (elevation of the sun), \(T_{\text{2m}}\) (air temperature measured at 2 m above the surface) and \(WS_{\text{10m}}\) (wind speed measured at 10 m above the surface). As a result, each time series contains over 140,000 values.
## 2 System Design  
### System Introduction  
The RWMS consists of the field sensor module, the base station module, the data center module and the WEB releasing module (Figure 1). The field sensor module acquires the real-time data of the water-level and sends the converted data to the MCU (Micro Control Unit). The base station module controls the field sensor module and transfers the data to the data center module by GPRS/GSM.  
The data center module receives and stores data from the GPRS/GSM module and then transfers the data to the WEB releasing module where the data will be published.
## 2 Data and Methods
### Model Comparison: Nested Restraint Tests  
We used nested model comparison tests to determine the loss in accuracy by constraining certain models, _i.e._, holding some parameters constant. In general, we assume that we have an inverse problem for the model observations \(f(t,\theta)\) and are given \(n\) observations with the cost function described above in Equation (5). We are interested in using data to question whether the \"true\" parameter \(\theta_{0}\) can be found in a subset \(\Omega_{H}\subset\Omega\), for which we make the same assumptions as [PERSON], [PERSON], and [PERSON] [10]. Thus, we want to test the null hypothesis \(H_{0}\):\(\theta_{0}\in\Omega_{H}\), or that the constrained model provides an adequate fit to the data. We then define  
\[\Theta_{H}^{n}(Y)=\operatorname*{argmin}_{\theta\in\Omega_{H}}S^{n}(Y;\theta) \tag{19}\]  
and  
\[\hat{\theta}_{H}^{n}(y)=\operatorname*{argmin}_{\theta\in\Omega_{H}}S^{n}(y; \theta), \tag{20}\]  
where  
We refer to [10] for a description of asymptotic convergence as \(n\to\infty\), which yields the model comparison result  
\[U_{n}(Y)=\frac{nT_{n}(Y)}{S^{n}(Y;\theta^{n})} \tag{23}\]  
with the corresponding realizations  
\[\hat{u}_{n}=U_{n}(y) \tag{24}\]  
which can be compared to a \(\chi^{2}\) distribution with \(r\) degrees of freedom. In this project we use a \(\chi^{2}(1)\) table when comparing the results from the Logistic model to those from the Bernoulli model.
## 3 Test Results and Analysis
### Load-Displacement/Strain Analysis  
The load-displacement curves of the axially compressed specimens are shown in Figure 4. In the elastic stage, the specimen is slightly deformed, the displacement changes linearly with increasing load, and only a few cracks appear at this time. When the load reaches 75% P\({}_{\mathrm{max}}\), the specimen enters the elastoplastic state, the stiffness of the specimen decreases, the displacement increases faster and the load-displacement relationship becomes nonlinear. When the load reaches P\({}_{\mathrm{max}}\), the specimen enters the damage phase, the section yields, and the load carrying capacity of the specimen begins to decrease. Figure 5 shows the relationship between the load and the strains in the section flange, section web, RPC and hoop. The section flange and web reach their yield stress before P\({}_{\mathrm{max}}\), and the hoop bar yields close to P\({}_{\mathrm{max}}\), indicating that the hoop bar plays an important role in the ultimate stage. The load-strain relationship curves for the section flange, section web and RPC are approximately the same, indicating that the section and RPC may act in concert.  
Figure 4: Load–axial displacement curve.  
Figure 5: \(Cont\).  
Figure 6 shows the load-transverse displacement curves for each point of the deflected specimen, from which the transverse displacements at each point are symmetrically distributed along the cross section in the column towards the ends. The greater the eccentricity is, the greater the transverse displacement of the specimen. Figure 7 shows the relationship between the eccentric load and the transverse displacement in the middle of the specimen. The higher the steel content is, the higher the ultimate displacement of the specimen. The eccentricity has a significant effect on the ultimate load capacity of the specimen; the higher the eccentricity is, the lower the ultimate load of the specimen. For II-PSRPC-1 and II-PSRPC-4, II-PSRPC-2 and II-PSRPC-5, and II-PSRPC-3 and II-PSRPC-6, the steel content and RPC strength. The eccentricity increases from 0.4 to 0.6 with the same grade, and the ultimate load decreases by 27.07%, 25.14%, and 24.55%, respectively.  
Figure 6: _Cont._  
Figure 6: Specim load–transverse displacement curve. (**a**) II-PSRPC-1; (**b**) II-PSRPC-2; (**c**) II-PSRPC-3; (**d**) II-PSRPC-4; (**e**) II-PSRPC-5; (**f**) II-PSRPC-6.  
Figure 7: Load–transverse displacement curve of the middle part of the specimen.
## 2 Related Work on Diacritics Restoration
### Classical Approaches
#### 2.1.1 Rule-Based Approaches  
The oldest practicable solutions achieving an acceptable accuracy for the diacritics' restoration problem are based on a set of rules. The creation of the rules typically requires human intervention and linguistic skills. They also often employ external language resources, i.e., morphological analyzers, syntactic analyzers, and/or morpho-phonological processing tools [9; 10]. The authors in [11] use the lemmatization technique to restore diacritics for the Czech language. Their method contains the set of if-then rules that consider prefixes and suffixes.  
As presented in [12], different language resources (i.e., a word-based language tri-gram model with the back-off strategy, augmented with the letter-based language model and the extremely simple morphological model) can be integrated into a cascade of finite-state transducers to restore diacritics on the Arabic texts. Changing diacritics changes not only the syntax, but the semantics of a target (ambiguous) word.  
The authors in [13] use a rule-based algorithm to determine the implication of relationships between undiacritized and diacritized words by computing distances and conflicts between them based on a distance-map tuned over a long domain experience. Despite the fact that handcrafted rules are less flexible to include all aspects of the language and are harder to transfer to new domains, they are still in use today (1) when the solving task and domain are very specific; (2) if there is no possibility to get the training corpus of a sufficient size and diversity; and (3) as the baseline approach or for comparison purposes.
## 4 Numerical Results and Sensitivity Analysis
### Sensitivity Analyses According to the Sales Commission Co
However, the break-even point rose from \(\text{C}_{o}{{}^{*}}=4.1\%\) for \(\rho=0.6\) to \(\text{C}{{}^{*}}=8\%\) for \(\rho=0.7\) until reaching \(\text{C}{{}^{*}}=11.2\%\) for \(\rho=0.8\). As already discussed in Section 4.2, the increase in the value of the customer online preference \(\rho\) caused a shift in the demands of the supply chain towards the online platform, resulting in an increase in the online optimal total profit and a decrease in the pharmacy's optimal total profit. Thus, for a high customer preference towards purchasing products online \(\rho\), the online channel paid a higher unit sales commission to the pharmacy in order to achieve equal total profits for both channels. For example, in the case of the break-even point \(\text{C}_{o}{{}^{*}}\), the total paid commission represented 46% and 64% of the optimal total profits generated by the pharmacy during the planning horizon \(H\) for a customer online preference \(\rho\) equal to 0.7 and 0.8, respectively. Hence, in-depth analyses of the market shares and the unit sales commission is considered essential activity for managers in order to allow both distribution channels to coexist, share profits and revenues and to establish a dynamic of cooperation profitable for everyone. Figure 7: Optimal total profits of both channels according to the unit sales commission \(\text{C}_{o}\) for different customer preferences towards purchasing via the online channel \(\rho\).