esa-sceva/satcom-chunk-collection huggingface dataset:
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 2 Background and System Model
### Improper Random Variables  
This section introduces some preliminaries about improper random variables that will be used throughout the paper. The interested reader is referred to [9] for a comprehensive treatment of the topic.  
**Definition 1** ([9]).: _A complex-valued random variable \(X\) is said to be circular (or circularly symmetric) if \(e^{\mathfrak{j}\mathfrak{j}}X\) has the same probability distribution as \(X\) for all real \(\mathfrak{j}\)._  
**Definition 2** ([27]).: _A complex-valued random variable \(X\) is said to be proper if \(X\) is uncorrelated with its complex conjugate, i.e., \(E[(X-E[X])^{2}]=0\)._  
A circular \(X\) is proper, but the converse is not necessarily true. However, for complex Gaussian random variables, propriety implies circularity, so both terms can be used interchangeably. The next definitions characterize an improper random variable.  
**Definition 3** ([9]).: _The complementary variance of a zero-mean complex random variable \(X\) is defined as \(\tilde{\sigma}_{X}^{2}=\operatorname{E}[X^{2}]\)._  
Furthermore, \(\sigma_{X}^{2}\) and \(\tilde{\sigma}_{X}^{2}\) are a valid pair of variance and complementary variance if and only if \(\sigma_{X}^{2}\geq 0\) and \(|\tilde{\sigma}_{X}^{2}|\leq\sigma_{X}^{2}\).  
**Definition 4** ([9]).: _The circularity coefficient of a complex random variable \(X\), which measures the degree of impropriety, is defined as:_  
\[\kappa=\frac{|\tilde{\sigma}_{X}^{2}|}{\sigma_{X}^{2}}. \tag{1}\]  
The circularity coefficient satisfies \(0\leq\kappa\leq 1\). If \(\kappa=0\), then \(X\) is proper, otherwise improper. If \(\kappa=1\), we call \(X\) maximally improper.  
There are two models that are typically used to express an improper complex random variable \(X=X_{l}+jX_{Q}\): the augmented complex model and the real composite model. The former defines the augmented random variable \(\mathbf{x}=\begin{bmatrix}X&X^{*}\end{bmatrix}^{T}\). This way, its covariance matrix (called the augmented covariance matrix) contains all the second-order information of \(X\), i.e.,  
\[\mathbf{R}_{X}=\begin{bmatrix}\sigma_{X}^{2}&\tilde{\sigma}_{X}^{2}\\ (\tilde{\sigma}_{X}^{2})^{*}&\sigma_{X}^{2}\end{bmatrix}. \tag{2}\]  
Alternatively, the real composite model defines the real random vector \(\tilde{\mathbf{x}}=\begin{bmatrix}X_{I}&X_{Q}\end{bmatrix}^{T}\), and its covariance matrix also contains the complete second-order information of \(X\). We now present some well-known results about the differential entropy of a complex Gaussian random variable.  
**Theorem 1** ([9]).: _The differential entropy of an improper complex Gaussian random variable \(X\) is:_  
\[h(X)=\frac{1}{2}\log\left[(\tau e)^{2}\det(\mathbf{R}_{X})\right]=\log\left( \tau e\sigma_{X}^{2}\right)+\frac{1}{2}\log\left(1-\kappa^{2}\right). \tag{3}\]  
Taking \(\kappa=0\) in the above expression yields the entropy of a proper Gaussian random variable [27]. Indeed, it is well known that the entropy of a complex random variable is maximized if it follows a proper complex Gaussian distribution [27].
# Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies
## 3 IoT Technologies in Smart Cities of the Future  
Smart cities are continuously embracing the IoT technological evolution, combined with relevant advances in the areas of 5G networks and Mobile Edge Computing (MEC). Under this perspective, smart cities of the future can be considered as a microcosms of interconnected (physical and virtual) \"objects\" where advanced and human-centric services can be provided to citizens in the form of cyber physical systems.  
This evolution moves in parallel with the increase in the heterogeneity of IoT technologies [25; 26] in terms of the production of different types of intelligent IoT devices, the support of various communication protocols, the release of IoT platforms tackling deployment in various parts of the available infrastructure (e.g., edge and cloud), the tackling of diverse requirements stemming from various use cases and the conceptualization of various information models for semantically representing entities in a smart city.  
As the number of IoT devices and solutions expands, any proprietary approach does not scale and, thus, slows the growth of the IoT ecosystem. Such heterogeneity and multiplicity make inherent the need for the design of architectural approaches that are able to support a high level of convergence and integration among existing and evolving IoT technologies. To do so, as stated at the World Economic Forum report for \"Realizing the Internet of Things\" [27], the arisen unique and impending IoT ecosystem challenges must be addressed not in an ad hoc and piecemeal manner, but with a holistic view.  
Toward the design of next generation CPS systems for Smart Cities, novel schemes must be developed that are able to tackle convergence, openness, and interoperability. Next-generation CPS will enable and will be enabled by current and future advances in several emerging technologies, such as 5G, cloud/edge-native computing, tactile internet, and artificial intelligence (AI), presenting a tremendous potential for the development of solutions applicable to Smart Cities.
## 2 System Model
### The General Framework
#### 2.1.2 Joint Power Allocation  
This study separated the joint power allocation of I2V and V2V from the reinforcement learning strategy and optimized it using a Stackelberg game. Specifically, after completing step (a), the Stackelberg game is used to split the joint power allocation problem into two subproblems of lower complexity. Iterative optimization is then used to obtain the approximate optimal power allocation \(\mathbf{p}_{k,n}\) and \(p_{m,n}\) for V2V and I2V. This method reduces the action space size and improves the learning efficiency of the agents.
## 2 Experimental Setup
### Test Gap  
The spark gap is constructed using 2 hemisphere electrodes, see Figure 2. The ground shield has 4 vent holes with a diameter of 6.3 mm spaced 90\({}^{\circ}\) apart around the center of the spark gap. The vent holes in the ground shield permit the gases under test to fill the spark gap while maintaining a coaxial transmission line structure across the gap. The ground shield is coated in a layer of epoxy to prevent the breakdown of the external ground shield instead of across the gap. The electrodes are mounted in PEEK sleeves, see Figure 2, to prevent breakdown from initiating at the junction between the electrode and the 2121 cable. Once the system is fully mounted, the gap distance between the electrodes is 2.2 mm.  
CST simulations of the spark gap calculated the capacitance across the gap, \(C_{gap}\), to be 1.6 pF, see Figure 2. CST simulation calculated the field enchatment factor of the gap to be 1.315. Thus, the macroscopic electric field is multiplied by 1.315 to determine the peak  
Figure 2: A cross-section view of the spark gap within the pressure chamber to scale. The gap capacitance \(C_{gap}\) was calculated to be 1.6 pF. The vent holes allow for insulating gas to fill the gap. The PEEK tubes help mitigate tracking from the junction between the electrodes and the 2121 cable. The ground shield is made from a single cylinder cut in half lengthwise. These halves are potted in epoxy to prevent breakdown to them. This cross-section does not show the ground electrically connected across the gap.  
electric field at breakdown. The macroscopic electric field is the maximum voltage, \(V_{max}\), prior to breakdown divided by the gap distance, \(d\).
## 2 Block-ISAI Preconditioning  
The idea of incomplete sparse approximate inverses (ISAI) was proposed in [17] to avoid solving least squared problems which is the time consuming procedure in SAI methods [19; 20; 21; 22; 23]. To some extent, it sacrifices the accuracy of approximating the inverse of a matrix to seek sufficient parallelism on parallel architectures, but it offers efficient solutions in some applications. In the context of LU factorization based preconditioning, the ISAI method in the block format (Block-ISAI) [18] approximates the inverse of the lower factor by  
\[\min_{L_{inv}(R_{L},icol)}||L(R_{L},R_{L})L^{inv}(R_{L},icol)-I_{icol}^{R_{L}}|| _{2}^{2},\quad(icol=1,2,3,\cdots) \tag{1}\]  
where \(L\) is the lower factor, \(L^{inv}\) the estimation of the inverse of \(L\), \(R_{L}\) the block-row indices of the \(icol^{th}\) block-column of \(L^{inv}\), and \(I_{icol}^{R_{L}}\) the block-vector extracted from the \(R_{L}^{th}\) rows of the \(icol^{th}\) column of the identity matrix. The sparsity pattern for \(L^{inv}\), expressed as \(S(L^{inv})\)  
\begin{table}
\begin{tabular}{c c} \hline \(L\), \(U\) & lower and upper factors from the Block–ILU factorization \\ \(S(L,U)\) & sparsity pattern for the lower or upper factor \\ \(|L|,|U|\) & factors with their absolute values \\ \(L^{inv},U^{inv}\) & estimation of the inverse of \(L\), \(U\) \\ \(R_{L}\) & block-row indices of a block-column of \(L^{inv}\) \\ \(R_{L}^{j}\) & block-row indices of the \(j\)th block-column of \(L^{inv}\) \\ \(I_{icol}^{R_{L}}\) & block vector formed by \(R_{L}\) block-rows \\  & and \(icol\) block-column of the identity matrix \\ ISAI & Incomplete Sparse Approximate Inverses \\ BJPB-ISAI & Block–Jacobi preconditioning with Block–ISAI \\ BCSR, BCSC & block compressed sparse row, block compressed sparse column \\ SBTS & small block triangular systems \\ \hline \end{tabular}
\end{table}
Table 1: Mathematical symbols and abbreviations. (\(L\), \(L^{inv}\) with subscript \(i\) represents the corresponding part in \(i\)th sub-domain).  
is estimated by multiplying \(|L|\) for \(k\) times, i.e., \(S(L^{inv})=S(|L|^{k})\), where \(|L|\) is the lower factor consisting of the absolute values in \(L\). Because \(L(R_{L},R_{L})\) is a square matrix, (1) is equivalent to the solve of  
\[L(R_{L},R_{L})L^{inv}(R_{L},icol)=I_{ical}^{R_{L}},\quad(icol=1,2,3,\cdots) \tag{2}\]  
When \(L_{inv}\) is solved in block-columns, the equations in (2) can be calculated in parallel, which fits the fine-grained feature on the GPU architecture. The inverse of the upper factor, denoted \(U^{inv}\), can be obtained in a similar way.
## 4 Discussion
The relations between two sensors can be described by several ways such as neural networks, statistical analysis and hidden markov models [15]. Our proposed model involves new findings on space physics to the prediction. For instance, a correlation between two observed data is revealed, this achievement is applicable to the prediction model by generating profiles. The physics-based fact is also applicable to the our proposed model, if the fact is available between the two kinds of quantitate values. Furthermore, the proposed model is adaptive for changing a structure of the satellite network. The satellite network for the prediction is robust when one satellite leaves from the satellite network because the two satellites only change their links. The satellites need to change their locations according to their mission progresses. Rewiring between the two satellites can change the logical structure of the satellite network. Therefore, our proposed model is suitable for space weather forecasting with _in-situ_ sensing. Two issues must be discussed in order to apply the proposed model to satellite networks. The first issue is the location of the satellites making up the satellite network for the space weather forecasting. The communication for exchanging data between satellites would be difficult due to their distance or location. For this case, an arrangement of simple transponders for relaying the observed data is addressed or observation satellites transfer the data for the destination of the satellite. The storage space of the observation satellites is extremely limited due to their purpose. The transponders also provide storage space for the observation satellites. The observation satellites push or pull the data depending on their demand for use. The second issue is coverage of the observation. The ACE, STEREO-A and STEREO-B record space environmental data and transfer them to the ground stations. However, the ground stations do not necessarily receive all data submitted by the observation satellites. The operation for the observation also would be suspended due to some technical or managing reasons. In this case, the interpolation of the observed data by other satellites addresses the missing data. The new techniques for processing the observed data are also need to be considered for automation of space weather forecasting.
Future Internet_**2010**, \(2\), 469-504; doi:10.3390/fi2040469  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Node Coloring and Color Conflict Detection in Wireless Sensor Networks**  
**[PERSON]**\({}^{1}\)**, **[PERSON]**\({}^{2}\)**, **[PERSON]**\({}^{1,\star}\)**, **[PERSON]**\({}^{2}\)  
**and [PERSON]**\({}^{1}\)  
\({}^{1}\) INRIA, Rocquencourt, 78153 Le Chesnay Cedex, France; E-Mails: [EMAIL_ADDRESS]  
(S.M.); [EMAIL_ADDRESS] (I.A.)  
\({}^{2}\) LIMOS-CNRS, Clermont Universite, Les Ceseaux, 63177 Aubiere Cedex, France;  
E-Mails: [PERSON]DDRESS] (G.C.); [PERSON]_ADDRESS] (M.M.)  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +33139635233; Fax: +33139635566.  
_Received: 20 September 2010; in revised form: 1 October 2010 / Accepted: 8 October 2010 / Published: 13 October 2010_  
**Abstract:** In wireless sensor networks, energy efficiency is mainly achieved by making nodes sleep. In this paper, we present the combination of SERENA, a new node activity scheduling algorithm based on node coloring, with TDMA/CA, a collision avoidance MAC protocol. We show that the combination of these two protocols enables substantial bandwidth and energy benefits for both general and data gathering applications. As a first contribution, we prove that the three-hop node coloring problem is NP-complete. As a second contribution, the overhead induced by SERENA during network coloring is reduced, making possible the use of these protocols even in dense networks with limited bandwidth. The third contribution of this paper is to show that applying any slot assignment algorithm with spatial reuse based on node neighborhood without taking into account link quality can lead to poor performances because of collisions. The use of good quality links will prevent this phenomenon. The fourth contribution consists of optimizing end-to-end delays for data gathering applications, by means of cross-layering with the application. However, color conflicts resulting from topology changes, mobility and late node arrivals can give rise to collisions. As a fifth contribution, we show how the MAC layer can detect color conflicts, and cope with them at the cost of a slightly reduced throughput. Then, we discuss the tradeoff between requesting SERENA to solve the color conflicts and dealing with them at the MAC layer, our third contribution. The combination of SERENA and TDMA/CA is evaluated through simulations on realistic topologies.  
keywords:wireless sensor network; bandwidth efficiency; energy efficiency; data gathering application; spatial reuse; MAC protocol; node coloring; capture effect; collision avoidance
## Data Used in this Study
### Raster Data
#### 2.1.3 Meteorological Data  
We used temperature and solar radiation data derived from the European Centre for Medium Range Weather Forecasts (ECMWF). PAR was calculated from the ECMWF shortwave solar radiation by multiplying by a coefficient of 0.48 [47]. Original ECMWF data with 0.25\({}^{\circ}\) and 10-day spatiotemporal resolution were aggregated to 0.5\({}^{\circ}\) monthly data by nearest-neighbor sampling and temporal averaging assuming each month consists 30 days.  
The ratio of actual evapotranspiration to potential evapotranspiration (ET/PET) was used as an indicator of water availability [48]. ET and PET were derived from the MOD16A2 product (8-day interval, 500 m), which estimates ET and PET by the Penman-Monteith equation [49]. The MODIS ET/PET data were quality filtered by good quality flags using a QC layer. Maximum ET/PET value of the 8-day interval estimates within the month of interest was used to indicate water availability during the period and 500 m pixels within the 0.5\({}^{\circ}\) grid were averaged to match the coarse resolution datasets.
## 5 Conclusions  
In this study, no significant differences were found in the ultrasonographic assessment of venous congestion using the VExUS protocol between patients with acute heart failure (AHF) and preserved (HFpEF) versus reduced ejection fraction (HFrEF), despite notable differences in biomarkers and clinical signs of congestion. These findings support the applicability of the VExUS protocol as an objective and standardized tool for evaluating systemic congestion in AHF patients, regardless of LVEF phenotype. Its implementation may contribute to improved risk stratification and a more precise tailoring of decongestive therapy.  
VExUS may represent a valuable addition to the clinical toolkit for the personalized management of acute heart failure.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15158157/s1](https://www.mdpi.com/article/10.3390/app15158157/s1)]([https://www.mdpi.com/article/10.3390/app15158157/s1](https://www.mdpi.com/article/10.3390/app15158157/s1)), VExUS Scoring Reproducibility analysis.  
Conceptualization, A.A.-O. and J.R.-G.; Data curation, L.K.E.-S., A.C.-S.d.S., Z.S.A.F. and S.C.-A.; Formal analysis, A.C.-S.d.S., M.S.-M., V.G.-H. and A.A.-O.; Investigation, L.K.E.-S., A.C.-S.d.S., Z.S.A.F. and S.C.-A.; Resources, M.S.-M., V.G.-H., A.A.-O. and I.G.-L.; Software, A.A.-O.; Supervision, J.R.-G.; Visualization, L.K.E.-S.; Writing--original draft, L.K.E.-S.; Writing--review and editing, [PERSON]-L. and [PERSON]-G. All authors have read and agreed to the published version of the manuscript.  
Project PID2022-1391430A-I00 was funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Aragon (CEICA) (protocol code COL21/003; date of approval: February 2023).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The data that support the findings of this study are available from the corresponding author upon reasonable request. Due to privacy and ethical restrictions, raw data are not publicly available.  
The authors thank the Internal Medicine Department of Hospital Clinico Universitario Lozano Blesa for their support and the patients who participated in this study.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HF & Heart failure \\ LVEF & Left ventricular ejection fraction \\ VExUS & Venous Excess Ultrasound \\ HFpEF & Heart failure with preserved ejection fraction \\ HFrEF & Heart failure with reduced ejection fraction \\ CA125 & Carbohydrate antigen 125 \\ NTproBNP & N-terminal pro-B-type natriuretic peptide \\ POCUS & Point-of-care ultrasonography \\ LUS & Lung ultrasound \\ IVC & Inferior vena cava \\ AHF & Acute heart failure \\ CKD & Chronic kidney disease \\ NYHA & New York Heart Association \\ \end{tabular}
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 4 Results  
### TEC Results  
To confirm that our model produced reliable values for ionospheric electron content, we compared the TEC values generated by our IAMM model with those generated by IGS. A snapshot of this comparison is shown in Figure 5(a) (IAMM) and Figure 5(b) (IGS) for a period of high solar activity, which happened, for example, to be on 5 August 2023 at around 22:00, according to a report by the National Oceanic and Atmospheric Administration (NOAA) [58]. We chose this date as an illustrative example to show that our model is able to generate reliable TEC values even during periods of intense solar activity. As seen in Figure 6, there is a high similarity between the TEC values generated between the two models.  
In order to further verify that our values are close to the real values produced by IGS, we also adopted a statistical approach over several points and computed the correlation coefficients between the two TEC maps, as well as the mean and standard deviation of errors between the two TEC maps (our model versus the IGS model). The results are presented in Table 3. Even with 31098 points, the correlation coefficient between the generated TEC values from the two models is around 96%, which indicates that the TEC map produced by our model follows the same pattern as the TEC map from IGS. Further validation is carried out based on GPS raw measurements in Section 4.2.  
Figure 5: Example for VTEC calculation using IAMM for User A located at Tampere University.  
Figure 6: A snapshot world TEC map as predicted by the two models for 5 August 2023 at 22:00 UTC. (**a**) Data from our IAMM model. (**b**) Data from IGS.
# Matching Quality Detection System of Synchronizer Ring and Cone
###### Abstract  
A synchronizer is a key component in automotive transmission. It is necessary to detect the matching quality between a synchronizer ring and cone. For this purpose, a friction torque based detection system of matching quality between a synchronizer ring and cone is designed in this paper. In the system, the acceptance criteria are established by the residual sum of squares (RSS), and the quality of the synchronizer is determined by measuring the friction torque and backup gap. This synchronizer ring and cone matching quality detection system has been implemented. The system is mainly used for quality detection of the synchronizer ring and cone in the automobile gearbox before packing. It improves the consistency of the synchronizer ring and synchronizer cone, which makes the synchronizer lighter and more reliable during shifting of the gearbox. According to market research, the system designed and implemented in this paper is advanced and original.  
matching quality; synchronizer ring; synchronizer cone; friction torque +
Footnote †: journal: Applied Sciences