esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Evaluation Metrics  
In addition to the differences in the used dataset, the results are evaluated and presented in different ways in the reviewed papers. Moreover, there are no standard metrics for the segmentation evaluation, so different evaluation metrics are used to report the segmentation performance. For segmentation, the evaluation metrics are mainly divided into three categories: overlap-based metrics, distance-based metrics, and volume-based metrics. The metrics are summarized in Table 1. The overlap-based metrics indicate the difference in overlap measurement between automatic prediction and manual segmentation, which can be obtained by four indicators: true positive (TP), false positive (FP), false negative (FN) and true negative (TN). The TP value is the number of pixels segmented correctly as foreground. The count of pixels falsely classified as the foreground is given by the FP value. The total count of falsely classified as background pixels is represented by FN. The TN value represents the correctly classified background pixels. The most commonly used overlap-based metrics include the Dice similarity coefficient (Dice), Sensitivity (Sen), false positive volume fraction (FPVF) [35], false negative volume fraction (FNVF) [35], etc. To measure the contour difference between automatic and manual segmentation, the most commonly used metrics are distance-based metrics. In the context of mandibular segmentation, the following distance-based metrics have been frequently used: average symmetric surface distance (ASD), Hausdorff distance (HD), 95 th-percentile Hausdorff distance (95 HD), mean square error (MSE) [36], and root mean square error (RMSE) [36]. In some medical image segmentation tasks, the volume of the object is also very important for treatment planning, and the metric based on volume is helpful to evaluate the performance of the segmentation method. Volume overlap error and volume error are the common indices to evaluate the results of mandibular segmentation.
## 2 Materials and Methods
### Data Collection
#### Eddy Covariance (EC) Tower-Driven GPP Data  
To evaluate the response of AET, WUE, and GPP during an extreme drought event in the study area (e.g., the 2008-2009 drought event), we analyzed daily GPP data collected from the eddy covariance (EC) tower spanning 2001-2009 in the Yatir Forest, a semi-arid shrubland in the Eastern Mediterranean. The EC tower is located in the central part of the Yatir Forest, with coordinates of 35\({}^{\circ}\) 03\({}^{\prime}\) 07.01\({}^{\prime\prime}\)E longitude, 31\({}^{\circ}\) 20\({}^{\prime}\) 42.25\({}^{\prime\prime}\)N latitude, and an elevation of 658 m above sea level (m.a.s.l.). The EC method, widely recognized for its utility in computing carbon flux at the ecosystem level [69], has been consistently employed in the Yatir Forest since 2000. This methodology adheres to European standards and contributes to the EuroFlux and FLUXNET communities [70; 71; 72].  
For this study, we exclusively relied on GPP data from the Yatir Forest, accessible through the EuroFlux database ([[http://www.europe-fluxdata.eu/home](http://www.europe-fluxdata.eu/home)]([http://www.europe-fluxdata.eu/home](http://www.europe-fluxdata.eu/home)) (accessed on 24 September 2023)). The dataset covers the extensive timeframe of 2001-2020; however, our analysis specifically focused on the daily data from 2001 to 2009. To comprehensively examine the impact of the severe drought event, we integrated it with daily data of AET obtained from GLEAM, version 3.8a. Our preprocessing involved accumulating data over 6-month windows (180 days) using a daily-scale moving approach. This facilitated a comparative analysis between the SPEI-6 timescale and GPP, WUE as well as AET. Subsequently, we calculated the average of the moved-accumulated 6-month values for each day of the year (DoY) over the 9-year period. This allowed us to derive anomalies (%) for AET, GPP, and WUE in comparison to the long-term averages (see Appendix A for more details). The focus on the critical period of the severe drought event enhances our understanding of the nuanced responses of WUE and GPP to extreme water stress conditions.
## 3 5G Technology Enablers for V2X Use Cases
### Positioning Capabilities of 5G and Enablers  
As assisted driving technologies are evolving and spreading, the requirements for their positioning capabilities are expanding: higher accuracy, a faster response time, and a more robust operation are needed, even under severe and special conditions. Unfortunately, currently, available positioning technologies cannot fulfill these increased expectations, not to mention the rising demands due to the increasing number of (semi-)autonomous vehicles.  
The positioning technologies currently available to commercial vehicles can be categorized into three groups:  
1. Satellite-based positioning (GNSS);
2. Base-station-based (2G, 3G, 4G);
3. Hybrid solutions (DGPS).  
The limitations of these technologies are known: they focus on the control problem or the situation analysis regarding localization, but they assume ideal operating conditions of the positioning systems [21].  
The requirements for the positioning of driving-assisted vehicles are easily comparable to those in industrial applications, which are gathered in [16]. Moreover, there are well-defined overlapping test scenarios for the future challenges around positioning, among others:  
* Valet parking using indoor positioning [35];
* Handling maneuvers in urban canyons (tunnels, dense cities);
* Asymmetric visibility scenarios [43].  
There is a need to discover the possibilities of the application of improved localization provided by 5G to overcome these challenges.  
Possibilities in 5G for localization are presented in [38], proving that it is worth trying to satisfy these requirements using 5G. It is worth noting that 5G does not have unlimited possibilities, as 5G is not used only for localization. Ref. [42] examined the trade-off between a millimeter-wave-based communication speed and localization precision in a multi-user scenario.  
In this section, the currently used 5G positioning technologies are compared, and then the possibilities in hybrid solutions with 5G are put next to each other. Finally, cooperative localization techniques are examined, with further possibilities to improve 5G localization techniques.
## 5 Conclusions  
In this study, we try to understand which parameters control large scale hydropetrophysical properties of carbonate reservoirs. For this purpose, plug samples of three rock types have been characterized from pore to core scale, with laboratory and imaging techniques. Samples petrophysical properties have then been compared with same parameters from large scale studies. Similar results in the same orders of magnitude were found.  
Nevertheless, we highlight that microstructure is the most essential parameter in carbonates rocks characterization. Indeed, micropores control rocks properties. A change in their quantity and shape is responsible for considerable changes in petrophysical properties. For example, in samples with a low proportion of micropores, acoustic wave velocities are more likely to be high, inducing quite cohesive rocks. Similarities found with large scale reservoirs enable us to say that all scales are controlled by the same properties, inducing that microstructure study is essential in reservoir characterization.  
Conceptualization, M.L. and L.L.; methodology, M.L. and L.L.; validation, M.L. and L.L.; formal analysis, M.L.; investigation, M.L.; resources, L.L.; writing--original draft preparation, M.L.; writing--review and editing, L.L.; visualization, M.L.; supervision, L.L.; project administration, L.L.; funding acquisition, L.L. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by two projects: EC2 CO (INSU-CNRS) StarTrek project and the JPI-Water UrbanWat project from WaterWorks2017; and by a CIFRE PhD fellowship provided by Voxaya and ANRT.  
Not applicable.  
We acknowledge the MRI platform member of the national infrastructure France-BioImaging supported by the French National Research Agency (ANR-10-INBS-04, <<Investments for the future>>), the labex CEMEB (ANR-10-LABX-0004) and NUMEV (ANR-10-LABX-0020). We also acknowledge the petrophysics plateform members of Geosciences Montpellier Laboratory. We would like to acknowledge the 4 anonymous reviewers for their comments that allow improving the final version of this article.  
The authors declare no conflict of interest.  
Figure 13: On the **left**: evolution of formation factor depending on porosity; On the **right**: evolution of P waves velocities depending on porosity (modified from [9; 63]).
## 3 Results and Discussion
### Analysis of Relationship SM-VI-CO\({}_{2}\) under Drought Condition  
#### 3.2.1 Selecting Drought Periods Using Agricultural Drought Indices  
As per the statistical analysis of past hydrometeorological records, extreme droughts have been occurring in South Korea since the year 2000, namely in 2001, 2007 to 2009, and 2014 to 2015 [5; 39; 40]. [PERSON] et al. [39] and [PERSON] et al. [5] analyzed droughts in Korea from 2013 to 2015. Particularly, in 2014, not only did the drought occur severely, but the recorded rainfall was less than 50% the normal. Additionally, in 2015, the region experienced only 873 mm of precipitation, which is 64% of the historical average of 1,366 mm. Variations in the monsoon climate had resulted in a dry rainy season with no precipitation [41]. Figure 6 shows the results of the temporal variation analysis of SSMI and VCI from 2001 to 2015. As with the results presented in a previous research, drought status occurred in 2001, 2007-2009, and 2014-2015. Based on the results of previous studies and Figure 6, this study chose 2014 to 2015 as the drought period.  
Figure 6: Temporal variations of drought indices and precipitation from 2001 to 2015 at (**a**) SMC and (**b**) CMC site.
## 3 Proposed Methods
### Loss Function  
The loss function of YOLOv4 includes three parts: confidence, classification and bounding box regression loss. YOLOv4 employs the complete intersection over union (_IoU_) loss (_CloU_) [46], replacing the mean squared error loss adopted in YOLOv3 with the bounding box regression loss. _CloU_ takes the overlap area, center point distance and aspect ratio into consideration simultaneously, and the convergence speed and detection accuracy were improved. _CloU_ introduces a penalty item \(\alpha\
u\) based on the distance _IoU_ loss to impose the consistency of the aspect ratio for the ground truth (_bbst_) and bounding box (_bbb_). The loss of _CloU_ can be defined as Equation (9).  
\[\begin{split} Loss_{CloU}&=1-\left(IoU-\frac{\rho^ {2}\left(\frac{\rho^{4}}{\rho^{2}},\frac{\rho^{4}}{\rho^{2}}\right)}{c^{2}}- \alpha\
u\right)\\ \alpha&=\frac{\
u}{1-IoU+\
u}\,\ \
u=\frac{4}{\pi^{2}} \left(\arctan\frac{\
u\delta^{tt}}{\rho^{2}}-\arctan\frac{\
u\delta^{tt}}{ \rho^{2}}\right)^{2}\end{split} \tag{9}\]  
Figure 8: The image is divided into \(52\times 52\), \(26\times 26\) and \(13\times 13\) by \(P_{1}\), \(P_{2}\) and \(P_{3}\), respectively.  
Figure 9: The process of converting anchor to bounding box.  
where \(b^{gt}\), \(b^{b}\) are the center of \(bb^{gt}\), \(bb^{b}\), respectively, \(\rho\) denotes the Euclidean distance, \(c\) represents the diagonal length of the smallest enclosing rectangle covering \(bb^{gt}\), \(bb^{b}\), \(\alpha\) is a positive trade-off value and \(\
u\) means the consistency of the aspect ratio. \(w^{gt}\), \(w^{b}\) are the width of the \(bb^{gt}\), \(bb^{b}\), respectively. \(h^{gt}\), \(h^{b}\) are the height of the \(bb^{gt}\), \(bb^{b}\), respectively.  
_CIOU_ can directly minimize the distance between the bounding box and ground truth and accelerate the model convergence. Previous works [47; 48; 49] have proved that _CIOU_ can perform better in detecting objects with diverse sizes, which can match well with the characteristics of remote sensing object detection tasks.
## 5 Climatology (2002-2022) of Longitudinal Structures in the Ionosphere
### TEC WN2 Structure
Unfortunately, there is a paucity of information on the nonmigrating tides discussed above, and to what degree they are capable to penetrating into higher regions of the IT  
Figure 14: (**a**) LT variability of the climatologically mean (2002–2022) TEC WN2 structure at different latitudes, shown in the upper side of each plot, during October (**left** column of plots) and April (**right** column of plots); (**b**) latitudinal dependence of the tidal and SPW2 contribution in months when the TEC WN2 structure has large amplitudes: October (**left** plot) and April (**right** plot); and (**c**) latitude-time climatological structure of the TEC SPW2 amplitude (**upper left** plot), the TEC DE1 amplitude (**upper right** plot), the TEC DW3 amplitude (**bottom left** plot), and the TEC SW4 amplitude (**bottom right** plot). system. [PERSON] et al., 2014 [91], reported that the observed SW4 tide in the IT system is a combination of the in situ generated and propagating from below tide, while [PERSON] et al., 2013 [95], obtained that the DE1 is in situ excited tide because the magnetic control of the ion-neutral interactions in the IT system. In summary of Section 5, it should be noted that it examines in detail the climatological (2002-2022) features of the longitudinal TEC WN4, WN3, and WN2 structures in a way as they are present in the global TEC maps. The main reason is to quickly recognize these structures among the other ionospheric variations. However, this approach makes the longitudinal TEC structures dependent on the semiannual variability of the IT system, and to some extent this is a weak point of the present study. The only TEC structure that has not been affected by the semiannual IT variability is the WN4 one; most probably this is because the seasonal variability of the main contributor, the DE3, is similar to the IT semiannual one. It is worth noting that the above TEC longitudinal structures were successfully predicted for the first time by the empirical global TEC model. The TEC WN3 structure is the strongest one with a maximum magnitude of 5.9 TECU. It shows a clear hemispheric asymmetry as the SH structure is stronger than the NH one, but only at latitudes of \(\pm\)(30-50\({}^{\circ}\)). The main contributors to this TEC structure are the TEC SPW3 followed by TEC DE2. The second strongest TEC structure is the WN2 one with a maximum magnitude of 5.4 TECU. Generally, this structure also reveals a hemispheric asymmetry, but in this case, the NH structure is stronger than the SH one, with an exception at \(\pm\)20\({}^{\circ}\). The TEC SPW2 and TEC DE1 contribute the most for the production this structure. The weakest TEC structure is the WN4 one that has the largest amplitude of 3.7 TECU. It displays a clear hemispheric asymmetry, as the SH structure is stronger than the NH one at all latitudes. The TEC DE3 followed by the TEC SPW4 are the main contributors for this TEC structure. It is important to underline that all these magnitude maxima are located at the latitude of the EIA that is \(\pm\)30\({}^{\circ}\) modip latitude.
## 2 Preliminary
### Grammar-Based Compression  
A _context-free grammar_ (_CFG_) is a quadruple \(G=(\Sigma,N,\mathcal{D},S)\) of disjoint finite alphabets \(\Sigma\) and \(N\), a finite set (a dictionary) \(\mathcal{D}\subset N\times(N\cup\Sigma)^{*}\) of _production rules_, and the _start symbol_\(S\in N\). Symbols in \(N\) are called _nonterminals_. A production rule \(A\to b_{1},\ldots,b_{k}\) in \(\mathcal{D}\)_derives_\(\beta\in(\Sigma\cup N)^{*}\)_from_\(\alpha\in(\Sigma\cup N)^{*}\) by replacing an occurrence of \(A\in N\) in \(\alpha\) with \(b_{1},\ldots,b_{k}\), denoted by \(\alpha\Rightarrow\beta\). Similarly we say that \(\mathcal{D}\) derives \(\beta\) from \(\alpha\) provided \(\alpha\Rightarrow^{*}\beta\), where \(\Rightarrow^{*}\) is the reflexive, transitive closure of \(\Rightarrow\). If a string is derived from the start symbol, we also say that the CFG derives the string. In this paper, we assume that any CFG is _admissible_[1]; that is \(G\) derives just one string in \(\Sigma^{*}\) and for each nonterminal \(A\in N\), exactly one production rule \(A\rightarrow\alpha\) is defined in \(\mathcal{D}\). We also assume that any \(A\in N\) is _appropriate_, that is, \(A\rightarrow\alpha,B\rightarrow\alpha\in\mathcal{D}\) implies \(A=B\). The _size_ of \(G\) is the total length of strings on the right hand sides of all production rules, and is denoted by \(|G|\). The aim of grammar-based compression is formalized as a combinatorial optimization problem as follows:  
**Problem 1**.: Grammar-Based Compression  
_Input: A string \(w\in\Sigma^{*}\). Output: An admissible CFG \(G\) that derives \(w\). Measure: The size of \(G\)._  
In the following, we assume that every admissible CFG is restricted such that the length of right hand side of any production rule is two. Note that for any CFG \(G\), there is an equivalent restricted CFG \(G^{\prime}\) whose size is at most \(2|G|\). Thus this restriction is reasonable.  
An important relation is known to exist between an admissible CFG and the following factorization. The _LZ-factorization_\(LZ(w)\) of \(w\) is the decomposition of \(w\) into \(f_{1},\ldots,f_{k}\), where \(f_{1}=w[1]\), and for each \(1<\ell\leq k\), \(f_{\ell}\) is the longest prefix of \(\mathit{suf}_{\ell}=f_{\ell},\ldots,f_{k}\) that appears in \(f_{1},\ldots,f_{\ell-1}\), and otherwise \(f_{\ell}=\mathit{suf}_{\ell}[1]\). Each \(f_{\ell}\) is called a _factor_. The size \(|LZ(w)|\) of \(LZ(w)\) is the number of its factors. The following result is used in the analysis of the approximation ratio of our algorithm.  
**Theorem 1** ([PERSON] [23]).: _For any string \(w\) and its admissible CFG \(G\), the inequality \(|LZ(w)|\leq|G|\) holds._
# Analytical Approach in the Development of RF MEMS Switches
## 1 Introduction
However, research conducted in the field of RF MEMS switches is largely limited only by the RF characteristics of these devices, although taking into account the interdisciplinary nature of MEMS technologies, it is necessary to take into account the relationship of electrical parameters with the mechanical parameters of MEMS structures. For example, residual stresses in deposited metal layers of RF MEMS switches have a much greater impact on the performance and life expectancy of the device than expected. In addition, the vast majority of RF MEMS switches require large values of control voltage (40-100 V for pull-down voltage and 15-30 V for hold-down voltage, respectively), along with high switching time, which significantly reduces the scope of their application, excluding wireless communication devices, which involve the use of low-voltage power supplies given the growing demand for RF MEMS switches in this area [3; 4; 29]. In [30] is also shown that the life expectancy of MEMS switches with an electrostatic activation mechanism strongly depends on the magnitude of the applied voltage. In addition, achieving a high switching speed remains a major limitation; few studies have been devoted to improving the switching speed, except that proposed in [31], by miniaturizing the MEMS switch. In [32], the authors demonstrate the switching time at the level of nanoseconds through the use of the membrane dielectrics as a structural material. It is shown in [33] that the addition of curved sides to miniature elastic suspension elements increases the stiffness coefficient, causing a further increase in the resonance frequency, which leads to a low switching time. Consequently, improving the switching speed has a negative effect on the control voltage. Increasing the stiffness of the elastic suspension elements will inevitably lead to an increase in the value of the control voltage. In general, our review presents new design solutions and an analytical approach to the development of RF MEMS switches. The analytical approach is based on the knowledge of materials, key performance indices and failure mechanisms of RF MEMS switches, and parametric analysis is based on a mathematical model.
# Scan to BIM Mapping Process Description for Building Representation in 3D GIS
## 3 Related Works
This study classified urban structures such as buildings to develop a digital twin model and investigated and analyzed technologies that connect information. There was a study on BIM-GIS data integration to implement a smart city information model [28]. This study proposed the necessary steps to map IFC to GIS. The research topics investigated focus on generating geometries from scan data, and they are classified as follows. 1. Develop the mapping process using existing commercial tools such as ESRI. 2. PCD processing algorithms such as point feature calculation and denoising. 3. Development of a segmentation algorithm for PCD classification, such as ground surface and building. 4. Development of a Vectorization Algorithm for Building Footprint Extraction in Polygon Format such as SHP. 5. Development of a building height extraction algorithm. 6. A geometry generation algorithm as a 3D object format. As a result of the investigation, it was identified that the algorithm model that creates geometric objects from scan data varies depending on the characteristics of the scan data and user requirements. For example, extracting the building footprint obtains various results, depending on whether input scan data and algorithm model parameters are used. Since user requirements determine the output quality, the Scan to BIM mapping process without the defined use case inevitably produces undesirable results. The backgrounds and needs of the investigated studies range from the purpose of GIS analysis to the implementation of digital twin models. Most studies are limited to mapping scan data to GIS without generating BIM objects with fixed models and parameters. The scope of this study proposes a customizable SBMD method with a process and structure that performs Scan to BIM using ALS data in 3D GIS.
# Automated Exploratory Clustering to Democratize Clustering Analysis
## 4 Experimental Works
### Usage Scenarios  
In order to gain insights on the effects of different strategies for different users, we create an application cost \(ac\) for every clustering set. This application cost is dependent on \(|C|\), the number of clusterings in the set; \(ec\), an exploratory cost to model the effort to manually evaluate the clusterings for the practitioner; \(v\), the value of the clustering, measured as the minimal distance of a clustering in the set to the optimal clustering (measured by the ground truth); and \(uc\), a usability cost modelling the negative impact of a suboptimal clustering.  
**Definition 10** (Application Cost).: _Let \(\mathbb{C}\) be a set of clusterings on a dataset, \(\mathbb{U}\) the set of all clusterings on the dataset, \(\mathbb{gt}\) be the known best clustering for this dataset, and \(\operatorname{sim}\) a similarity measure. We can define the application cost \(ac\) dependent on the exploratory cost \(ec\) and the usability cost \(uc\) as follows:_  
\[ac(C,gt,ec,uc)=\frac{|C|}{|U|}\cdot ec+\left(1-\max_{c\in\mathbb{C}}sim(c,gt) \right)\cdot\,uc \tag{11}\]  
Using this cost function, we can define different user scenarios, where the costs are distributed differently. This should make it possible to compare the methods under a variety of settings. Our parametrisations are shown in Table 5.  
In our scenario, we set \(ec+uc=1\) to ensure \(ac\in[0,1]\). However, this is not a fixed property of the application cost. We can see that our method CU only evaluates the quality of the best clustering, disregarding the size of the clustering set. On the other hand, CE only evaluates the size of the clustering set, without any regard to the quality reached by the set.