esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mortar Proprieties
#### 3.1.4 Mechanical Properties  
The flexural and compressive strengths were determined after 28 days of hydration to analyze the effects of SS-PCM incorporation on the mechanical properties of mortars. Figure 8 shows the results.  
The flexural and compressive strengths of mortars incorporating SS-PCM were lower than those obtained for the reference mortar, and this reduction increased with the content of SS-PCM. There was a compressive strength drop of about 50% for the mortars M5 and M10, and 70% for M15%. These results are in agreement with the increase in mortar porosity  
Figure 8: Mechanical strength of 28 days mortars as a function of SS-PCM content.  
Figure 7: Density of mortars as a function of SS-PCM content of mortars.  
previously observed (Figures 5 and 6). It should be noted that the compressive strength of the M5 mortar was similar to that of the M10 mortar. As well as the total porosity, the M5 compressive strength (26 MPa) is very close to that of M10 (27 MPa). The Figure 8 shows a reduction in flexural strength of 50% between the reference mortar (M0) and SS-PCM composite mortars (M5, M10 and M15), and a similar compressive strength (around 5 MPa) between SS-PCM composite mortars (M5, M10 and M15).  
The mechanical strength drop is a disadvantage for construction materials. However, the mechanical performance of SS-PCM composite mortars remains sufficient for most building applications (26 MPa for M5, 27 MPa for M10 and 18 MPa for M15). For building applications, in which higher mechanical is not needed (bricks, masonry wall, coating wall, roof panels) [2], the SS-PCM could be added to cementitious materials to improve the thermal performance of the building.  
Thanks to review articles [2; 30; 31; 29], the mechanical resistance of lightweight cementitious material and PCM cementitious material according to its density was provided in the Figure 9. The results of the present work were added to the figure as red squares. All results in the literature show a trend for the compressive strength to increase with the material density. However, for a given density, the mechanical strength differs according to the binder composition, foaming method and the hydration cure. This explains the wide dispersal of compressive strength according to the studies of lightweight materials. Our results (red squares) remain in the range of compressive strengths for PCM materials. The mechanical strength of PCM composites was lower than that of lightweight material for a given density. This is due to the presence of PCM molecules without binding properties in the PCM composite matrix. The advantage of PCM over lightweight aggregates is its latent enthalpy (heat storage capacity) providing high thermal inertia of building.
## 2 Data Quality Parameters and Related Work in OSM  
The quality issue in VGI data is a clear challenge. Rigorous and longitudinal studies are required for creating trust in OSM data. There have been numerous research studies in recent years. Figure 1 presents the data of 485 papers collected from various bibliographic databases, such as the IEEE explore, Springer, MDPI, Mendeley and Zotero repositories. A review of these articles showsthat many of them reported on the assessment of OSM data. In these studies, the quality of data has been judged based on the context of their application, which is a function of intangible properties represented using quality indicators [32; 33]. Quantitative dimensions shared with GI review the completeness, logical consistency, positional, temporal and semantic consistency. Non-quantitative dimensions review lineage, purpose, usage and constraints. Further, dimensions exclusive for VGI review the believability, compliance and convergence [34]. The following sections discuss the related work by researchers to assess the completeness, attribute accuracy and semantic accuracy using intrinsic data quality indicators.
# Broadband Spectral Domain Interferometry for Optical Characterization of Nematic Liquid Crystals
## 2 Materials and Methods  
### Liquid-Crystal Cells  
The transmissive LC cells employed in the experiment are home-made with the nematic LC mixture \(E7\) or _MLC_2132, inserted in between two 0.7 mm thick BK7 substrates. Both of them support a conductive layer of ITO (Indium-Tin-Oxide) as a transparent electrode. A thin film of polyvinyl alcohol (PVA) is spin-coated on the ITO layer and then rubbed with subsequent strong anchoring to align the molecules in a plane parallel to the substrate. The anchoring conditions determine the average orientation of the director. Two different thicknesses are considered in the following experiments: 205 \(\upmu\)m to analyze the accumulated chromatic phase (Section 3) and 25 \(\upmu\)m to investigate the temperature influence (Section 4). The LC thicknesses are measured with an error of \(\pm 2\)\(\upmu\)m.
## 1 Introduction  
A _phylogenetic tree_ is a graphical depiction of the evolutionary history of a collection of taxa (typically species or genes). The leaves are in one-to-one correspondence with the taxa, the internal nodes correspond to hypothetical ancestral taxa, while edges represent ancestor-descendant relationships. Here, we consider two problems in computational phylogenetics.  
* The input to the _tree compatibility problem_ is a collection \(\mathcal{P}=\{\mathcal{T}_{1},\ldots,\mathcal{T}_{k}\}\) of rooted phylogenetic trees with partially-overlapping taxon sets. \(\mathcal{P}\) and the trees within it are called, respectively, a _profile_ and the _input trees_. The problem is to find a tree \(\mathcal{T}\) whose taxon set is the union of the taxon sets of the input trees, such that each input tree \(\mathcal{T}_{i}\) can be obtained from the restriction of \(\mathcal{T}\) to the leaf set of \(\mathcal{T}_{i}\) through edge contraction. If such a tree \(\mathcal{T}\) exists, then \(\mathcal{P}\) is said to be _compatible_; otherwise, \(\mathcal{P}\) is _incompatible_.
* The input to the _incomplete directed perfect phylogeny problem_ (IDPP) is an \(n\times k\)_character matrix_\(A=[a_{ij}]\), where each row corresponds to a taxon and each column to a _character_. The _state_\(a_{ij}\) of taxon \(i\) on character \(j\) is zero, one, or?, depending on whether, for species \(i\), character \(j\) is absent, present, or the state is unknown. A _completion_ of \(A\) is a matrix obtained from \(A\) by replacing each ? by either zero or one. IDPP asks if \(A\) has a completion \(B\) with the following property. There exists a phylogenetic tree that explains the evolution of the taxa described by \(B\) with at most one \(0\to 1\) state transition on each character.  
It is well known that testing the compatibility of a collection of _unrooted_ trees--an NP-complete problem [1]--is equivalent to the undirected version of IDPP, namely the problem of testing the compatibility of a collection of \"partial binary characters\" (bipartitions of a subset of a set of species) [2]. Since a profile of rooted trees is effectively a collection of unrooted trees that have a common root taxon, the preceding observation establishes the connection between rooted tree compatibility and IDPP. We should also note that a reduction from rooted tree compatibility to IDPP is implicit in the work of [PERSON] et al. [3].  
Here, for completeness, we provide a short and direct proof of the equivalence of tree compatibility and IDPP. We go further and argue that a previous algorithm for IDPP is effectively a variation of a recent tree compatibility algorithm. We then present a computational study of algorithms for these problems. This study provides an opportunity to analyze the engineering issues that arise when applying dynamic graph connectivity data structures in a highly-specific context. Our empirical results show that, in this setting, simple data structures perform better than more sophisticated ones with better asymptotic bounds.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 3 Key Aspects of the BSI Technical Guideline with Relevance for Energy Management
### Configuration of the SMGW via Processing Profiles  
The SMGW is configured by the _SMGW Administrator_[15] using so-called _Processing Profiles_[16]. The following types of profiles determine how the SMGW collects and processes measurements from meters:  
_Meter Profile_: The _Meter Profiles_ specify how an SMGW interacts with a _Smart Meter_. Among others, it configures the unique meter identifier, which protocol to use, which measurement registers to collect, information regarding encryption, and whether the communication is unidirectional or bidirectional. It also configures the time interval in which an SMGW must receive or request the measurements and update the internally saved latest meter reading. _Examination Profile_: The _Examination Profiles_ specify which data need to be derived from the meter measurements. This is done by configuring a tariff use case (see Section 3.3) and the respective parameters. Each examination profile contains a list of _Communication Profiles_ determining the recipient of the generated data. _Communication Profiles_: There are different _Communication Profiles_ for the HAN and the WAN. In both cases, the profiles define connection details that specify how to reach a communication partner based on uniform resource identifiers (URIs).  
For each of the three profile types, multiple instances may be configured, e.g., multiple _Meter Profiles_ to collect measurements from multiple meters.
# Example usage calculate_urban_resilience('input_file_name')
#### 5.4.2 Exposure  
For the exposure analysis in our study, we assess the potential impact based on two primary factors: the area in square metres and the population of each region, categorized by the HASC_2 coding system. The exposure score is calculated using an entropy weighting method, where 'area sqm' is given a weight of approximately 0.421 and 'population' a weight of approximately 0.579. This weighted approach allows us to account for both the geographical size and the number of people potentially affected by seismic events, providing a balanced measure of exposure for each municipality.  
The pattern of diverse exposure levels across various areas indicates the heterogeneity of seismic risk potential in Portugal, offering vital insights for region-specific disaster preparedness and resilience planning (Figure 5). The data analysis highlights the varying degrees of exposure to earthquake hazards across different regions and sub-regions in Portugal, with area (in square metres) and population identified as key indicators. For instance, in the Aveiro region, Santa Maria da Feira stands out, having the highest exposure at 0.236 and contrasting sharply with Murtosa's much lower exposure of 0.022. In the Azores, Ponta Delgada faces a higher exposure level at 0.146, while Corvo is notably less exposed at 0.002. In the Beja region, both Odemira and Mertola register the highest exposure value at 0.324, in stark contrast to Barrancos, which has the lowest exposure at 0.041. Moving to Braga, the city itself shows a significant exposure level of 0.279, compared to Vizela's minimal exposure of 0.036. Similarly, in Braganca, the city of Braganca experiences the highest exposure at 0.331, while Carrazeda de Ansaies has the lowest at 0.074.
## 3 Methodology  
As depicted in Figure 5, DSC is a technique consisting in measuring a heat-flow rate when the plates containing the reference and sample cells have an imposed temperature [4]. The device maintains this temperature equality by heating the two cells in a differential way. The difference in power dissipation to the measurement cell and the reference cell eliminates the influence of the container and thus determines the net heat flux \(\Phi\) exchanged with the sample. It should be noted that the sign convention may change depending on the manufacturer. For our experiments we use the convention of our calorimeter where the endothermic phenomena are represented towards positive ordinates.  
Thus, the DSC curve records the variation of the heat-flow rate \(\Phi\) during time, i.e., when the plate temperature is evolving with time.  
Figure 4: Example of a thermogram with the corresponding characteristic temperatures.  
As a rule of thumb, the thermal request is based on the setting of a heating/cooling curve generally due to a linear variation of the temperature of the plate \(T_{\rm plt}\):  
\[T_{\rm plt}=T_{0}+\beta\,t \tag{2}\]  
where \(\beta>0\) in case of heating and \(\beta<0\) in case of cooling and \(T_{0}\) the temperature of the plate at \(t=0\).  
With respect to the previous remarks, when simulating the DSC experiment [7,8], one has to consider the heat equation inside the sample:  
\[\rho\frac{\mathrm{d}h}{\mathrm{d}t}=\overrightarrow{\
abla}\cdot(\lambda \overrightarrow{\
abla}T) \tag{3}\]  
From a practical point of view with the PCM in question with a relatively low thermal conductivity, the temperature of the metallic pan equals the temperature of the plate. Therefore, the boundary conditions corresponding to the present system are (see Figure 6):  
\[-\lambda\left(\frac{\partial T}{\partial n}\right)_{\mathcal{S}_{i}}=\alpha_{ i}\left[T-T_{\rm plt}\right]_{\mathcal{S}_{i}}\qquad\forall i=p\,\mathrm{or}\,t \tag{4}\]  
where \(\alpha_{i}=\alpha_{p}\) or \(\alpha_{t}\) according to the location of the interface \(i\) are the heat transfer coefficients between the sample and the metallic pan.  
Thus, when adding the various contributions for each boundary, the total heat flow rate \(\Phi\) can be computed at every instant:  
\[\Phi=\sum_{i}\alpha_{i}\mathcal{S}_{i}\left[T_{\rm plt}-T\right]_{\mathcal{S} _{i}} \tag{5}\]  
Finally, it means that if one incorporates the proper form for the enthalpy function in Equation (3), then the response of the sample to the thermal stress Equation (2) can be calculated thanks to the sum Equation (5).  
From a numerical point of view, this has been done here using a finite volume method with an explicit temporal integration and a first-order flux scheme on a structured mesh.  
Figure 5: Schematic representation of a DSC system.  
Figure 6: Idealized geometry used in the model.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 3 Results
### On-Body Analysis  
In this section, the proposed dual band conventional antenna is placed over human body and an analysis is conducted on the basis of the return loss, radiation patterns and specific absorption rate (SAR) values.
## 4 Simulations on the Trapping System
### Electrical Simulations
#### 4.1.1 Paul Trap Stability Diagram
Therefore, in order to ascertain if the trap is stable for a pair of parameters \(\{q,a\}\), one has to solve Equation (6) numerically and check the absolute value of the \(Z(\pi)\) eigenvalues. The disadvantage of this procedure is that the stability boundaries are not well defined. To overcome this inconvenience and find an approximated expression for the stability boundaries, one can use the multiple-scale perturbation theory [32]. Basically, this approach consists of substituting an independent variable with a pair of fast-scale and slow-scale independent variables. Then, the additional degree of freedom is exploited to remove long-term and non-periodic variations in the approximated solutions, which usually limit their validity only in a short time range when standard perturbation techniques are used instead. For a system of coupled Mathieu equations, the approximated boundaries of the first stability region are:  
\[a_{1} = -\frac{1}{2}q^{2}\] \[a_{2} = \frac{1}{2\alpha}q^{2}\] \[a_{3} = 1-\cos(\theta)q-\bigg{(}\frac{\cos(\theta)^{2}}{8}+\frac{2\sin( \theta)^{2}(5+\alpha)}{(1+\alpha)(9+\alpha)}\bigg{)}q^{2}\] \[a_{4} = -\frac{1}{\alpha}\bigg{(}1-\cos(\theta)q-\bigg{(}\frac{\cos( \theta)^{2}}{8}+\frac{2\sin(\theta)^{2}(5+1/\alpha)}{(1+1/\alpha)(9+1/\alpha) }\bigg{)}\,q^{2}\bigg{)}\]  
The stability diagram of the Paul trap evaluated by using both numerical solutions and multiple-scale perturbation theory is reported in Figure 7. The green area represents the first stability region for the uncoupled Mathieu equations (\(\theta=0\)) and the geometry coefficients of our trap. The first stability region evaluated with the numerical method (black thick dots) is well approximated by the boundaries obtained with the multiple-scale perturbation theory (blue curves) only for small values of \(q\). For \(q>0.7\), the boundary curves enclose a region of \(\{q,a\}\) pairs (depicted in blue) for which the numerical method predicts instability. For small \(q\), both coupled and uncoupled systems have stability regions characterized by the same boundaries; nevertheless, the coupled system has in general a wider stability region. However, while the primary stability region does not appreciably vary for small \(q\) with nonzero mixing angles \(\theta\), the presence of a coupling term strongly affects the secondary stability region. When \(\theta=45^{\circ}\), as in our trap, the maximum coupling is reached: the result is that the secondary stability regions shift towards the primary one and join it, forming an exceptionally wider triangular-shaped stability region [32], as shown by the black dots in the graph of Figure 7. There is a discrepancy between the numerical and the analytic calculations. Nevertheless, Paul traps are usually operated in the lower part of the stability diagram, where both methods predict a stable operation of the trap. For the coefficients of our trap geometry, a potential difference on the blade electrodes of \(200\,\mathrm{V}\) with \(\omega_{RF}=3.2\,\mathrm{MHz}\) corresponds to \(q\approx 0.15\)[33].
## 2 Materials and Methods
### Medical Images and DNN Models  
To evaluate the performance of black-box UAPs generated using our algorithm, we used the medical image datasets and DNN models in our previous studies on DNN-based medical image classifications [14; 21]. The datasets contain the skin lesion images (in seven classes) for skin cancer classification, OCT images (in four classes) for referable diabetic retinopathy classification, and chest X-ray images (in binary classes) for pneumonia classification (also see Table S1 for the details of the class labels). All images had a pixel resolution of \(299\times 299\) pixels. The skin lesion images are red-green-blue, whereas the OCT and chest X-ray images are in grayscale. We used the test images to generate UAPs and evaluate the UAP performance because the black-box condition assumes that adversaries cannot access the training data. The skin lesion, OCT, and chest X-ray image datasets contained 3015, 3360, and 540 test images, respectively. Note that the skin lesion image dataset was only class-imbalanced (also see Table S1).  
To investigate the relationship between the model architecture and UAP performance, following [14] and [21], we used the Inception V3 architecture [25], Visual Geometry Group-16 (VGG16) [26], and Residual Network with 50 layers (ResNet50) [27] architectures. The DNN models were obtained using transfer learning from the ImageNet dataset [28] (see [14] for the test accuracies of the DNN models).
## 4 Interactive Compression
### Interactive compression of books.
With the above settings we have a very limited communication error: _i.e._, only a very few words (often less than ten) are mismatched in a whole book which therefore maintains its readability. Table 2 shows the communication error percentage for each book in the previous experiment when the hash value is [PERSON] and [PERSON]'s hash of length 12 bits. There is a strong relationship between the length of the hash value, the compression results, and the compression errors. With a longer hash we have less compression errors but a worst compression, with a shorter hash we improve compression but we pay the price of more compression errors. Figure 3 depicts the relationship between the length of the hash value, the size of the compressed file and the error percentage for the book \"Uomini che oodiano le donne\". In the left part of the picture we have on the x-axis the lengths of the hash value in bits and on the y-axis the corresponding sizes in Kb of the compressed file. In the right part of the picture on the x-axis we have again the lengths of the hash value in bits but on the y-axis we have the corresponding error percentages. The figure shows that the compression results are better for a hash value that has a length of 9 bits, but also that this length is not good enough to cope with transmission errors. Instead a hash of length 15 bits gives a compression that is almost lossless but the compression obtained is not competitive. The right compromise for the hash length here might be 12 bits. The behavior for the other books is very similar to the one in Figure 3.  
Figure 3: Compression and hash length for “Uomini che oodiano le donne”.
# Microwave Photonic Signal Processing and Sensing Based on Optical Filtering
###### Abstract  
Microwave photonics, based on optical filtering techniques, are attractive for wideband signal processing and high-performance sensing applications, since it brings significant benefits to the fields by overcoming inherent limitations in electronic approaches and by providing immunity to electromagnetic interference. Recent developments in optical filtering based microwave photonics techniques are presented in this paper. We present single sideband modulation schemes to eliminate dispersion induced power fading in microwave optical links and to provide high-resolution spectral characterization functions, single passband microwave photonic filters to address the challenges of eliminating the spectral periodicity in microwave photonic signal processors, and review the approaches for high-performance sensing through implementing microwave photonics filters or optoelectronic oscillators to enhance measurement resolution.  
Microwave photonics; photonic signal processing; photonic filtering; single sideband modulation; single passband microwave photonic filter; optical sensors +
Footnote †: journal: Applied Sciences
# Rethinking LEO Mega-Constellation Routing to Provide Fast Internet Access Services
## 3 Our Proposed Algorithm
### Forwarding Strategy  
When the satellite set is determined, each non-landing satellite in the set will forward data to each landing satellite in a certain proportion. Meanwhile, two reference directions for data forwarding have been given by the theoretical minimum hops between satellites. Each satellite flips a coin, to randomly select one of two reference directions as the forwarding direction of the next hop. In addition, in order to limit the forwarding path to a rectangular range of a spherical surface, with the source satellite and the destination satellite as two endpoints, the rest of the path is forced to select the horizontal hops to complete after the packet has passed through all the vertical hops.  
By adopting such a probabilistic forwarding mode, the forwarding path of data packets can have enough randomness so that the ISL's capacity resources can be fully utilized, and the load balancing of network resources can be realized without the need for centralized routing calculation.