esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Impact of Independent Variables on Risk Scores  
FAA PASER (surface deterioration): This factor has the strongest negative impact on the risk score for both runway and taxiway pavements, with coefficients of \(\beta 1=-55.78\) (runway) and \(\beta 1=-430.59\) (taxiway). As surface deterioration increases, the risk significantly rises, making surface quality the most critical factor in safety risks.  
Friction: Friction has a greater impact on the runway than the taxiway, with coefficients of \(\beta 2=-527.46\) (runway) and \(\beta 2=-43.68\) (taxiway) (\(p<0.05\)). This is expected, as aircraft on runways operate at much higher speeds (240-370 km/h) compared with taxiways (20-30 km/h), making friction essential for runway safety.  
IRI (roughness): Roughness levels, although important, have a lesser effect on risk compared with surface deterioration and friction. The coefficients were \(\beta 3=-27.37\) (runway) and \(\beta 3=-3.28\) (taxiway) (\(p>0.05\)). Roughness becomes a significant risk factor only when it exceeds critical thresholds (IRI \(>3.00\) m/km for runways and IRI \(>3.80\) m/km for taxiways).
## 1 Introduction  
Normal dimensions of the mitral annulus are important for maintenance of the valve's normal function. Enlargement of the mitral annulus (MA) produces abnormal coaptation of the valve leaflets and subsequent mitral regurgitation. Previous research has shown that left ventricular dilation and dysfunction are the most common causes of functional mitral regurgitation [1; 2]. In addition, in patients with ischemic or non-ischemic dilated cardiomyopathy, mitral regurgitation is frequent. Nonetheless, there have been no prospective data that could provide evidence of the association between LA enlargement and the severity of MA dilation. [PERSON] et al. reported annular dilation and severe mitral regurgitation in 6.5% of AF patients who showed a decrease in MR grade after cardioversion to sinus rhythm. Other studies, on the other hand, disputed these findings. According to [PERSON] et al. [3] and [PERSON] et al. [4], isolated AF does not result in annular dilation.  
The aim of our study was to investigate the effect of left atrial enlargement on the size of the mitral annulus, measured by multidetector computed tomography angiography (MDCT).  
The most common imaging approach for evaluating mitral valve function is echocardiography. However, the precise and comprehensive assessment of the MA given by MDCT makes this imaging method the preferable approach to measure both the MV and the LA with sub-millimeter spatial resolution.
## 2 Materials and Methods
### Characterisation
#### 2.4.4 Dynamic Light Scattering (DLS)  
DLS was employed to analyse the colloidal properties of the samples before and after functionalisation. The measurements were conducted using a Malvern Nano Zetasizer (UK) instrument. Pure water was used as the dispersion medium during the analysis, and the measurements were performed at room temperature. For the particle size measurements, a plastic cuvette was utilised, while the charge measurements of the samples were carried out using a capillary cell Zetasizer cuvette. Solutions of 1 mg/mL of both loaded and unloaded MOFs were prepared in water, and measurements were conducted in triplicate.  
Figure 2: UV–vis absorption spectrum for 50 \(\upmu\)M solution in water for (**A**) MTX, UiO-66, and UiO-66-MTX and (**B**) MTX, UiO-66-NH\({}_{2}\), and UiO-66-NH\({}_{2}\)-MTX.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 3 Wireless Standards at 60 GHz mmWave Band  
Different standardization efforts are being made for 60 GHz communication. Current standards include IEEE 802.11 ad, IEEE 802.11 ay, IEEE 802.15.3c, WirelessHD and ECMA387 [51; 59; 76; 77]. However, ECMA, WirelessHD, and IEEE 802.15.3c are wireless personal area network (WPAN) standards. The two most anticipated WLAN technologies for ultrahigh-speed 60 GHz communications are IEEE 802.11 ad and IEEE 802.11 ay. These are extended protocols of the IEEE 802.11 Wireless Fidelity (Wi-Fi) family. As opposed to the limited available bandwidth of sub-6 GHz ISM bands, such as 2.4 GHz and 5 GHz with 85 MHz and 500 MHz of bandwidth, respectively, the 60 GHz mmWave ISM band provides huge bandwidth of up to 8 GHz. IEEE 802.11 ad and IEEE 802.11 ay are networking standards for WiGig communication networks for supporting data rate of mGbps, which are anticipated as key enablers for indoor factory automation communication as well.  
Figure 4: Advantages of 60 GHz mmWave communication in Industry 4.0 and beyond.  
However, 60 GHz communication is supposed to be directional, supported by beamforming techniques as opposed to sub-6 GHz omnidirectional communication. The complete 60 GHz band covers the frequency range from 57 to 71 GHz, according to a new release by FCC [78]. There has been a release of 14 GHz spectrum in the United States, Europe, and Canada. However, the 9 GHz spectrum (57-66 GHz) is being used in other regions such as Singapore, Australia, and Russia. China uses a 59-64 GHz band, whereas 57-64 GHz is being used in South Korea. A worldwide spectrum allocation of the 60 GHz unlicensed band is depicted in Figure 5[78;79]. A summary of 60 GHz wireless standards is given in Table 2[59].
## 3 Results and Discussion
### Results of Laboratory Tests  
During the laboratory tests, a total of six structural systems were tested (Figures 7-16), three each of 3 m and 6 m lengths. The averaged deflection-force relationship in determining the load-bearing capacity from all tested structural systems is shown in Figure 17.  
Material tests were conducted on a total of 18 samples, with 6 each cut from the top flange, web of the beam, and connectors. The material sample tests showed that the average longitudinal modulus of elasticity for the steel of the beams was 205.526 GPa and for the steel of the connectors 199.603 MPa, which is within the typical range of values for steel ranging from 200 to 210 GPa. The average yield strength for the steel from which the beams were made was 490.000 MPa, while for the steel of the connectors it was 362.000 MPa.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 3 Methodology
### Train, Validation, and Test Splits  
Following the creation of windows from the dataset, three distinct splitting strategies were employed:  
**Random (window) splitting**: The windows were randomly divided into train, validation, and test sets, adhering to a stratified sampling approach based on the SoC label. The distribution of the SoC labels was maintained at approximately 70% (train), 15% (validation), and 15% (test) sets.  
**Intra-session splitting**: With this splitting method, windows from the train and validation sets were combined and subsequently shuffled and randomly assigned to form new train and validation sets at a 70/30 ratio. However, the test set remained separate; hence, the testing was performed on \"unseen\" data.  
**Session splitting**: In this strategy, windows originating from the same session were not allowed to be allocated across multiple sets, which means windows from the same session were only present in one of the train, validation, or test sets. The objective was to prevent any data leakage between sets and maintain the integrity of session-wise information. Like the random splitting strategy, the train, validation, and test sets were formed using stratified sampling on the SoC label, which also ensured approximate distributions of 70%, 15%, and 15% respectively. By utilizing the stratification measure, we ensured the desired distribution based on the session size.  
Such splitting strategies were used in order to investigate issues of data imbalance, overfitting and underfitting, data leakage, etc.
## 2 Approximated Lowpass Elliptic Response
### Stability  
Prior to designing hardware realizations of the fractional-order transfer functions, it is necessary to analyze them with the determined coefficients to ensure that they realize stable responses. Analyzing the stability of fractional-order systems is often accomplished by converting the \(s\)-domain transfer function to the \(W\)-plane defined in [25]. This transforms the transfer function from fractional order to integer order. This reduces the complexity of analyzing the stability by allowing traditional analysis methods to be employed. Applying this process to the denominator of Equation (7) yields the characteristic equation in the W-plane given by:  
\[a_{2}W^{m+k}+a_{3}W^{k}+1 = 0 \tag{10}\]  
From this characteristic equation, the roots of Equation (10) for \(\alpha=0.1\) to 0.9 were calculated with \(k=10\) to 90, respectively, when \(m=100\) using all sets of coefficients from both the symmetrical and asymmetrical fittings. The minimum root angles, \(|\theta_{W}|_{\min}\), for each case of the symmetrical and asymmetrical fittings are given in Figures 6 and 7. The minimum root angles are all greater than the minimum required angle for stability, \(|\theta_{W}|>\frac{\pi}{2m}=0.9^{\circ}\). This confirms that the fractional-order filters using the determined coefficients are stable for all fitted frequency ranges. It is interesting to note that the minimum phase angle for all fitted frequency ranges approaches a similar value (\(1.07^{\circ}\)) as \(\alpha\) approaches 0.9 and the \(\omega_{B1}\) case has the largest stability margin compared to the other cases.  
Figure 5: DC and high-frequency gain of fractional-order transfer function using coefficients from asymmetrical fittings using frequency ranges with: **(a)** lower limits of \(\omega=10^{-5}\) rad/s; and **(b)** upper limits of \(\omega=10^{5}\) to approximate elliptic characteristics.  
Figure 6: Minimum phase angle of roots of Equation (10) using coefficients from symmetrical frequency fittings.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 3 Proposed Method
### Extraction of Spatial Proximity Information Between Connected and Disconnected Nodes  
As an initial preprocessing step, our method iteratively removes nodes with the lowest degree from the graph's adjacency matrix until the average degree no longer increases. This simplifies the network structure while preserving core connectivity. Unlike traditional adjacency matrices that capture only direct links, our method constructs a spatial proximity matrix that encodes both direct and indirect node relationships. This allows for more effective clustering and optimal DBSCAN parameter selection.  
Our proximity-based feature extraction algorithm (Algorithm 1) initializes node features based on the adjacency matrix and iteratively refines them using a smoothing approach over five iterations. This ensures that nodes with strong network relevance share similar feature vectors, while weakly connected or isolated nodes have distinct representations. Instead of computing similarity in a single step, we iteratively update node features based on the influence of neighboring nodes.  
Input: G (Graph), ADJ (Adjacency Matrix), \(\alpha\) (Weighting Factor), maxIter (Iterations)  
Output: F(z) (Proximity Feature Vectors)  
1. For each node z in G:  
2. F(z) \(\leftarrow\) ADJ[z] // Initialize with adjacency row  
3. For t = 1 to maxIter:  
4. For each node z in G:  
5. For each node m in G:  
6. \(\Delta\_\text{mz}\leftarrow\text{ShortestPath(G, z, m)}\)  
7. \(\omega\_\text{mz}\leftarrow\exp(\Delta\_\text{mz})\)  
8. F_[t+1](z) \(\leftarrow\)\(\alpha\times\) F_t(z) + (1 \(-\)\(\alpha)\times\)\(\omega\_\text{mz}\)  
9. Return F(z) for all z  
**Algorithm 1** Extract proximity features of nodes.  
The feature update equation follows:  
\[F_{t+1}(z)=\alpha F_{t}(z)+(1-\alpha)\underset{m\in\text{neighbors}(z)}{\sum} \omega_{mz}\cdot F_{t}(m) \tag{1}\]  
where \(\omega_{mz}=e^{-\Delta\omega_{mz}}\) is an exponential weighting function that captures distance-based similarity between node zz and its neighbor mm. This iterative process ensures feature smoothness while retaining critical network structures, improving clustering performance.
# Intuitionistic and Interval-Valued Fuzzy Set Representations for Data Mining
## 4 Generalization Involving Interval-Valued and Intuitionistic Fuzzy Information
### Generalization with Ordinary Hierarchies
#### 4.1.2 IVF Concepts in Hierarchies  
Next, we consider the case where the concept sets, Qi,s, in the hierarchy have associated uncertainty. In this case, for interval-valued fuzzy memberships, \(I\) (c\({}_{i}\)'),  
\[\text{Q}_{1}:\;\;\;<\text{c}_{1}\text{'},I\;(\text{c}_{1}\text{'})\textgreater,\,\text{Q}_{2}:\;\;<\text{c}_{2}\text{'},I\;(\text{c}_{2}\text{'})\textgreater,\,\ldots\;\text{Q}_{m}:\;\;<\text{c}_{m}\text{'},I\;(\text{c}_{m}\text{'})\textgreater\]  
For cases where n of the data values a\({}_{i}\), a\({}_{j}\),... in tuples generalize to some c\({}^{\prime}\), we have n tuples of the form t (c\({}_{i}\)', \(I\) (c\({}_{i}\)')), and these can be merged as in the previous section. Now, if the data values themselves have interval-valued fuzzy memberships, then a fusion of the intervals from the hierarchy and in the data must be considered. Depending on the semantics of the application, there are several possibilities. Either interval-valued fuzzy memberships may be used in the generalized and combined values by applying Favg or Fmd as discussed above, or a weighted combination may be used. Note that the resultant generalized tuples must still be combined based on their similarity. Again, we must consider the possible interval relationships:  
\[I\;(\text{c}^{\prime})\subseteq\mathbf{I}\;(\text{a}^{\prime})\;\text{or}\;I \;(\text{c}^{\prime})\cap\mathbf{I}\;(\text{a}^{\prime})\
eq\emptyset\]  
If a concept interval, \(I\;(\text{c}^{\prime})\), does not overlap a particular data interval, \(\mathbf{I}\;(\text{a}^{\prime})\), then a reasonable semantic decision is that the tuple t (a\({}^{\prime}\)) should not be generalized. For the other cases, Favg or Fmd can be used. The information about the interval-valued membership of c\({}^{\prime}\) is likely more significant, since the value from the hierarchy, c\({}^{\prime}\), is the value in the generalized tuple. Therefore, for example, in Favg, we can weigh \(I\;(\text{c}^{\prime})\) appropriately.  
\[\text{Favg}\;(\mathbf{I}\;(\text{a}^{\prime}),\,\text{w}(\text{c}^{\prime}) ^{*}\;I\;(\text{c}^{\prime})) \tag{21}\]  
This captures the general trend of the case of the relationships between the intervals. When there is no overlap, w = 0 corresponds to there being no generalization. As the overlap increases, w increases to 1 when \(I\;(\text{c}^{\prime})\) is totally contained in \(\mathbf{I}\;(\text{a}^{\prime})\).
## 4 Overview of DSRC, LTE C-V2X, and 5G NR V2X
### Dsrc  
Over the past two decades, dedicated short-range communication (DSRC) has been one of the primary radio access technologies (RATs) used for V2X communications. The IEEE 802.11p standard, the basis of DSRC, was the first V2X technology used worldwide. It operates in the 5.9 GHz frequency band and is an extension of the IEEE 802.11 standard (commonly known as Wi-Fi) tailored for fast and reliable communication between vehicles and roadside infrastructure. Its design prioritizes safety applications, such as collision avoidance and traffic management, by delivering low-latency communication, high data rates, and robust connectivity in dynamic vehicular environments.  
While DSRC 802.11p has been a pioneering standard for vehicular communication, it exhibits certain limitations compared to cellular vehicle-to-everything (C-V2X) technology. DSRC operates in a single dedicated frequency band (5.9 GHz), which may lead to potential congestion and interference in densely populated areas. Additionally, DSRC lacks the inherent support for diverse communication modes that C-V2X offers, encompassing both direct short-range communication (similar to DSRC) and wide-area communication facilitated by cellular networks. C-V2X also boasts an improved non-line-of-sight (NLOS) performance, enabling communication around obstacles.  
To narrow the performance gap, IEEE 802.11 bd [30; 31; 32] has been developed as the next-generation DSRC protocol to improve V2X performance. It allows up to three repetitionsper packet, aiming to increase time diversity and enable maximum ratio combining at the receiver, thus improving the probability of correct decoding [32]. Additionally, it adds extra modes of operation to enhance throughput and extend communication ranges by reducing the noise sensitivity level [26; 33].
# Uni-Planar MIMO Antenna for Sub-6 GHz 5G Mobile Phone Applications
## 2 Proposed Uni-Planar MIMO Antenna
### Characteristics of Single-Antenna Elements  
The single MIMO element is designed and simulated in the commercially available electromagnetic software CST Microwave Studio. For excitation of the antenna element, a 50 \(\Omega\) discrete port is utilized. Figure 2 shows the simulated reflection coefficient (\(S_{11}\)) of a single antenna element. It is observed that the antenna element resonates well in the 3.5 GHz frequency band and provides a wideband response. The \(-6\) dB and \(-10\) dB impedance bandwidths are noted to be 1.28 GHz (3-4.28 GHz) and 720 MHz (3.18-3.9 GHz), respectively.  
The \(S_{11}\) of the proposed antenna can be adjusted by changing some parameters of the antenna. For a particular design, the parameters \(W_{slot}\) and \(L_{slot}\) have a great impact on the performance. The parameter \(W_{slot}\) plays a major role in achieving impedance matching in the band of interest, while the parameter \(L_{slot}\) can be tuned to obtain resonance for the desired frequency band. This effect is clearly visible in the results of Figure 3, which shows the \(S_{11}\) characteristics of the designed antenna for varying \(W_{slot}\) and \(L_{slot}\).  
Figure 3a illustrates the \(S_{11}\) characteristics of the proposed antenna for varying values of parameter \(W_{slot}\). It can be noted from the figure that the parameter \(W_{slot}\) has a significant effect on impedance matching. The maximum impedance matching is observed for  
Figure 2: Simulated \(S_{11}\) characteristics of a single-antenna element.  
\(W_{slot}\) = 14.5 mm, as shown in Figure 3a. On the other hand, when the value of parameter \(L_{slot}\) is changed in the range from 11.5 mm to 14.5 mm, a shift in the resonant frequency is observed, as shown in Figure 3b. It is important to mention here that these values have a negligible effect on the isolation performance of MIMO antennas.
## 3 Fe-MOF Synthesis
### Sonochemical Synthesis  
Sonochemical synthesis is based on the application of high ultrasonic energy. It consists of cyclic mechanical vibration and possesses a frequency between 20 kHz and 1 MHz that allows the chemical reaction to occur (Figure 4d) [120]. In fact, the key part of this synthesis is the generation, growth and collapse of a bubble that occurs in the aquatic environment [121]. This process is known as acoustic cavitation and features very high local temperatures and pressures with a very large cooling and heating rate [121]. As for their characteristics, similar to other synthesis methods, they are also able to provide homogeneous nucleation, decrease crystallisation time and have low energy consumption [120,121]. In addition, another advantage of this method is that the preparation of the components uses ambient conditions and is energy efficient, making it a process that can be considered environmentally friendly [122]. Because of all these advantages and its extensive use in the synthesis of nanoparticles, it has started to be used in the synthesis of MOFs [122].  
Although it is considered an environmentally sustainable method, it should not be forgotten that some syntheses use toxic solvents such as DMF; an example of this is the work of [PERSON] et al. [123], who carried out the synthesis of a novel MIL-100(Fe)@MIL-53(Fe) photocatalyst. This catalyst has very favourable properties as it allows the degradation of a dye such as methyl orange and its reuse with a degradation efficiency remained around 92% in the fifth cycle [123]. However, there is evidence of the influence of the solvents used in the synthesis and this fact is reflected, for example, in the work of [PERSON] et. al. [124]. They studied the photocatalytic properties of their synthesized MOF, which was Fe-benzentricularboxylic (Fe-BTC), depending on the combination of solvents used (DMF, distilled water and ethanol). From this study [124], they concluded the existence and importance of this influence and the need to use DMF.  
Nevertheless, other studies have looked for alternatives to having free Fe-MOF to see if they could improve the existing Fe-MOF or use no-toxic dissolvent. Indeed, this was achieved and a clear example was the work of [PERSON] et al. [125], who immobilized MIL-100(Fe) in carboxymethyl cellulose (CMC). A great advantage of their synthesis was that the solvent used was water, so proving non-toxic solvents can be used, and it operated under non-severe conditions. The only drawback was the long drying time of the final product which was 7 h at a temperature of 60 \({}^{\circ}\)C. Despite this drawback, this synthesis is promising as it achieved better adsorption rates and was more efficient than MIL-100 (Fe) which was synthesized by the solvothermal method [125].  
Finally, similar to the microwave synthesis route, this method has also been widely used in combination with other methods to enhance the advantages provided by both methods combined. Examples include sonochemical-assisted hydrothermal synthesis [126] and the combination of microwave and sonochemical [127].
# A 3D Printed Membrane-Based Gas Microflow Regulator for On-Chip Cell Culture
## 3 Results and Discussion
### Application of the Regulator for Culturing of E. Gracilis  
On the basis of the flow characteristics, the regulator was used to deliver carbon dioxide to the lab-on-a-chip containing _E. gracilis_. The authors decided to culture this species because _E. gracilis_ is an interesting model microorganism [34]. It has recently been widely investigated in the field of microfluidics as a result of, amongst other things, the ease of obtaining it from the environment, as well as its high sensitivity to different external factors [17; 35]. The mentioned sensitivity of _E. gracilis_ may provide vital information on, for instance, the toxicology of freshwater environments, as indicated in [36].  
In this study, the photosynthetic growth of these microorganisms was investigated in a two-week period with CO\({}_{2}\) flow rate equal to 0.3 \(\upmu\)L/min (the regulator with 400 \(\upmu\)m thick PDMS membrane, pressure equal to 0.1 bar, was applied, Figure 7). The construction of the regulator--tight with no gas leakage--provided an equal and stable flow rate during the measurements. As a result, a notable population growth of _E. gracilis_ was observed through optical detection methods. Both the qualitative and quantitative analysis of the culture was conducted using real-time tracking software (Figure 8). The on-chip cell culture increased five times from beginning to end (Figure 9). Such a result indicates appropriate culturing conditions in the lab-on-a-chip chamber, enhanced by the CO\({}_{2}\) microflow from the regulator.  
Figure 7: The comparison of flow values of the regulator for different gases but fixed input pressure and PDMS membrane.