esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### IFA Design Using DH-BPSO
Furthermore, the base IFA with all patches shows a smaller gain over all azimuth and elevation angles. Additionally, the effect of the optimal patch configuration in the realized gain is minimal and the peak realized gain is 2.7 dBi. Therefore, the DH-BPSO-optimized IFA significantly outperforms the reference IFA in bandwidth. In addition to the 14 mm ground clearance, the bandwidth effect was further investigated by reducing the size of the ground clearance in the base and optimized IFAs. In our simulations, the ground clearance was varied from 9 mm to 14 mm with an increment of 1 mm. Figure 6 shows the optimized antenna geometries for the different ground clearance. Active parasitic patches are clustered around the feed, ground strip, and IFA arm. This is because of the strong, high-frequency current flow in this area. The patch configurations in Figure 6d,e appear identical, however the size of the parasities differs between the two models, leading to a difference in bandwidth. Figure 7 compares the simulated bandwidths of the DH-BPSO optimized and base IFAs for each ground clearance length. At each clearance length, the optimized IFAs show a marked improvement in bandwidth over the base IFA. The magnitude of this improvement increases with increasing ground clearance length. In addition, the rate of bandwidth increase is higher in the optimized IFAs than in the base IFA. The minimum simulated bandwidth increase is 20.3% at 9 mm. The maximum simulated bandwidth increase is 74.3% at 14 mm, as mentioned previously. Figure 6: Optimized patch configurations for ground clearances of (**a**) 9 mm, (**b**) 10 mm, (**c**) 11 mm, (**d**) 12 mm, (**e**) 13 mm, and (**f**) 14 mm. Figure 7: Comparison of the measured bandwidths with the simulated bandwidths for both the optimized and base IFA’s.
###### Abstract  
Magnetic carbon materials as adsorbents for dye removing have attracted increasing attention because of their magnetic separation feature. However, the immobilization of large magnetic particles on a carbon matrix greatly decreases the available sites for adsorption, resulting in a low adsorption capacity. The synthesis of magnetic carbon materials as adsorbents for dye adsorption with high adsorption capacity remains challenging. Herein, porous carbon (PC) was firstly synthesized through the calcination of macroporous acrylic type cation exchange resin. The as-prepared PC was applied as a matrix to deposit nano-sized Fe\({}_{3}\)O\({}_{4}\) nanoparticles (MPC) via a facile one-pot solvothermal strategy. The nano-sized Fe\({}_{3}\)O\({}_{4}\) nanoparticles (5.19 nm in diameter) are uniformly distributed on the PC surface. The MPC possesses an exceptional performance for methylene blue removal (\(q_{e}\) = 214.4 mg g\({}^{-1}\)) at room temperature, outperforming most previous magnetic carbon adsorbents. The large surface area of the MPC originated from the combined advantages of PC and nano-sized Fe\({}_{3}\)O\({}_{4}\) must be ascribed to the high performance of MPC composite toward methylene blue adsorption.  
Keywords:methylene blue; adsorbent; magnetic composite; porous carbon +
Footnote †: journal: Applied
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Data Normalizing  
The LUCIA dataset from Valladolid consists of 8760 hourly measurements of energy demand from an HVAC system throughout the year. Therefore, it is necessary to compare this dataset over time and under different conditions. Applying min-max scaling will help standardize the values of energy demand, allowing for clear observation of trends, peak usage times, and consumption patterns. This process is crucial for understanding how the HVAC system performs under various circumstances and can inform strategies for energy efficiency improvements. The FUHEM building in Madrid provides a dataset of 100 random data points related to heating demands. Like the LUCIA dataset, normalization through min-max scaling is essential to account for variations arising from occupancy and outdoor temperatures. This technique adjusts the values to make them more comparable, enabling better analysis of heating performance over time. Consequently, any insights drawn from the data will be reliable and relevant for optimizing the building's heating processes. In the EII building, the dataset includes 260 random combined data points for heating and cooling needs. Normalization using min-max scaling facilitates the analysis of interactions between heating and cooling demands across various seasons. By standardizing the measurements, it becomes easier to identify relationships between the two demands, which will further assist in uncovering trends and efficiencies [37; 38; 39]. This holistic approach to normalization supports better decision-making for energy management and contributes to more sustainable building operations. The energy demand forecasting workflow in shown in Figure 2.
# A Multi-Band LNA Covering 17-38 GHz in 45 nm CMOS SOI
###### Abstract  
This paper presents a multi-band low-noise amplifier (LNA) in the 45-nm CMOS silicon-on-insulator (SOI) process. The LNA consists of three stages, with the differential cascode amplifier as the core structure. The first stage is mainly responsible for input matching to ensure favourable noise characteristics and bandwidth, while the subsequent stages increase the gain. Moreover, the LNA utilizes balnus for input/output and interstage impedance matching. Switch capacitances are added to switch the three operating bands of the LNA, which cover 17-38 GHz overall. Measurement results show that the proposed LNA achieves a gain (S21) of 23.0 dB and a noise figure (NF) of 4.0 dB.  
b  
alun; cascode; low-noise amplifier (LNA); multi-band; switch capacitance
## 3 Results  
### Optimization of Wine Yeast Identification by MALDI-TOF MS  
1.1 Comparative Analysis of Species Identification by MALDI-TOF MS Biotyper Database and 26S rDNA Sequencing  
In a first study, 623 yeast clones isolated from the prefermentary stages and beginning of alcoholic fermentation were first identified using 26S rDNA sequencing. All the yeast isolates were identified successfully by sequencing at species level, resulting in 17 yeast species among traditional enological ones (Figure 1). A majority of the yeast isolates (66%) were identified correctly at species level (score > 2) by the MALDI-TOF MS with Biotyper database and according to the 26S rDNA sequencing identification, whereas 6% were identified only at genus level (score between 1.8-2). Finally, 28% of yeast isolates were not identified (176 clones out of 623) using the MALDI-TOF MS Biotyper database, probably because the colony extraction and/or identification scores were not correct. Although the _S. cerevisiae_ species is well represented in the Biotyper database (13 strains), only 54% of isolates identified by 26S rDNA sequencing (191/353 clones) were correctly identified using the MALDI-TOF MS Biotyper database (score >2) (Figure 1). For the 162 S. _cerevisiae_ isolates not correctly identified by the MALDI-TOF MS Biotyper database, the scores were lower than 1.8 (134 isolates), probably due to the non-wine origin of the _S. cerevisiae_ in the Biotyper database. For _H. uvarum_, representing 20% of the isolates by 26S rDNA sequencing, 90% of total _H. uvarum_ isolates were identified correctly by the MALDI-TOF MS Biotyper database that contains eight strains.
## 2 Materials and Methods
### Influence of the Dielectric Material on the Antenna Characteristics  
Four different dielectric materials were analyzed, FR4 with the relative permittivity of 4.4, RO3003 with \(\varepsilon_{r}=3\), RO4003C with \(\varepsilon_{r}=3.55\) and RO4350B (\(\varepsilon_{r}=3.66\)) with two of their standard thicknesses, namely 0.51 mm and 1.51 mm. The S parameters were obtained for the 8 different structures, and the results were represented on 2 different graphs: one for the dielectrics with 0.51 mm thickness (Figure 7a) and the other for the same dielectrics with 1.51 mm thickness (Figure 7b). Even if for the original structure the measurements were made in the frequency range of 1-3 GHz due to the fact that our measurements device is functioning up to 3 GHz, the rest of the numerical modeled structures were analyzed in the frequency range of 1-5 GHz.  
It can be observed that all the analyzed antenna structures have their bandwidth and operating frequency range influenced by their substrate dielectric. The values of S11 are higher for the thinner dielectric in the case of RO3003 and RO4003C, while for a thicker dielectric, S11 has a higher value for RO4350B and RO4003C. Two questions arise after considering these results: the increase in the dielectric thickness influences in the same way the S parameters in all the antennas considered and which structure has the widest bandwidth. In order to answer these questions, the graphs in Figure 8 contain the results for the two dielectric dimensions interpreted for each dielectric type. For each study, it can be stated that the higher S11 values are present for the thicker structures. In addition, the antenna with the dielectric RO3003 of 1.51 mm functions in 2 distinctive bandwidths.  
Figure 7: S11 values obtained for the structure with different dielectrics with thicknesses: (**a**) 0.51 mm; (**b**) 1.51 mm.  
Figure 8: S11 values obtained for the structure with different dielectric thicknesses for: (**a**) FR4; (**b**) RO3003; (**c**) RO4003C; (**b**) RO4350B.  
The next step is to determine the best configuration from the directivity, front to back ratio and operating frequency point of view, keeping in mind the fact that the frequency for which the antenna should function is 2.4 GHz. The best directivity is obtained for a RO3003 dielectric with 0.51 mm thickness (Table 2). It can be stated that the directivity and gain values decrease for the higher values of the dielectric thicknesses.  
The bandwidth on which the analyzed structures are functioning was also determined, and the results can be seen in Table 3.  
Thus, from the gain and bandwidth point of view, the antenna constructed on a RO3003 dielectric of 0.51 mm thickness is the best, which is followed by the antenna with the same dielectric substrate with a thickness of 1.51 mm. As a compromise between the results, the optimization went further considering this antenna.
## 3 Proposed Method
### Pts  
In the conventional PTS method, an input block is divided into V sub-blocks. Each sub-block is zero-padded to create a vector of length N. IFFT is performed separately for each sub-block, significantly increasing the computation complexity. Adjacent, interleaving, and pseudo-random are some of the widely used partitioning schemes [19]. The adjacent method is used in the proposed method, owing to its simplicity and effectiveness. From Figure 3, it can be observed that the resulting sequences are optimized by phase rotation factors \(b=\left[b^{1},b^{2},\dots,b^{V}\right]\), where \(b^{v}=e^{j2 mv/W}\) and \(v=0,\,1,\dots,\,W-1\), to create symbol candidates referred to as partial transmit sequences. This operation results in a variation ofthe peak values for the signal candidates, and the one with the minimum PAPR is chosen for transmission.  
The total number of signal candidates depends on V and W, where W is the number of the phase factors allowed for a single sub-block.  
The process of producing the optimum phase factor vector that reduces the PAPR is as follows [6; 19]:  
\[\left\{\widetilde{\mathrm{b}^{1}},\widetilde{\mathrm{b}^{2}},\ldots\widetilde{ \mathrm{b}^{V}}\right\}=\arg\min_{[\mathrm{b}^{1},\;\ldots,\;\mathrm{b}^{V}] \subseteq\mathrm{L}\leq\mathrm{L}}\left|\sum_{\
u=1}^{\
u}\mathrm{s}_{\
u}^{ \
u}\mathrm{b}^{\
u}\right|^{2} \tag{11}\]  
The transmit signal after reducing the PAPR can be expressed as follows:  
\[\widetilde{\mathrm{s}_{\mathrm{m}}}(\mathrm{t})=\sum_{\
u=1}^{\
u}\mathrm{s} _{\mathrm{m}}^{\
u}\widetilde{\mathrm{b}^{\
u}} \tag{12}\]  
Direct application of this method to each FBMC symbol separately is not effective. As the symbols overlap, the parts where the symbols overlap will have a peak regrowth, increasing the PAPR again.
# Security Performance Analysis of Full-Duplex UAV Assisted Relay System Based on SWIPT Technology
## 3 Performance Analysis  
It is assumed that the channel coefficients remain constant for all nodes during each communication frame. For computational simplicity, the connection probability (CP) and secrecy outage probability (SOP) of the UAV-assisted relay system are derived for the airborne eavesdropping scenario, assuming that the system operates under medium- to high-SINR conditions. For simplicity of calculation, it is assumed that \(\epsilon=0\) in Equations (13) and (15), and this assumption is valid for medium- to high-SINR conditions. According to the Shannon channel formula, the channel capacity of the legitimate channel in the system is defined as \(C_{M}=\frac{1}{2}{\rm log}_{2}(1+\gamma_{AB})\) and the channel capacity of the eavesdropping channel about the drone eavesdropping node E is defined as \(C_{E}=\frac{1}{2}{\rm log}_{2}(1+\gamma_{E})\), where \(\gamma_{AB}\) and \(\gamma_{E}\) are the signal-to-interference plus noise ratios at node B and at node E in the communication system. Based on the first type of Marcu-Q and Bessel functions [22; 23] and the literature [24], a series of complex calculations and simplifications are performed to finally derive closed expressions for the system's probability of connectivity and the probability of safety interruption.
## 3 Results and Discussion
### Residual Stresses  
For values ranging between 2.5 and 5 um in depth, residual stresses play a predominant role in improving fatigue life [51]. Based on the results of the residual stress and residual strain measurements, shown in Tables 2 and 3, respectively, the success of the UIT can be attributed to high compressive stresses at the surface of the specimens.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Component**} & \multicolumn{2}{c}{**Treated—Plane 114**} & \multicolumn{2}{c}{**Untreated—Plane 211**} \\  & **Stress (MPa)** & **Error (MPa)** & **Stress (MPa)** & **Error (MPa)** \\ \hline \(\sigma_{\mathbf{11}}\) & \(-1.9\times 10^{3}\) & 45 & \(-3.0\times 10^{2}\) & 20 \\ \(\sigma_{22}\) & \(-1.8\times 10^{3}\) & 45 & \(-2.8\times 10^{2}\) & 22 \\ \(\sigma_{33}\) & \(-1.2\times 10^{3}\) & 26 & \(-2.1\times 10^{2}\) & 12 \\ \(\sigma_{12}\) & 27 & 6.0 & \(-0.9\) & 2.7 \\ \(\sigma_{13}\) & \(-1.1\) & 6.0 & 1.1 & 2.5 \\ \(\sigma_{23}\) & 37 & 22 & 19 & 9.3 \\ \(\sigma_{VM}\) & \(6.0\times 10^{2}\) & 48 & 84 & 21 \\ \(I_{1}/3\) & \(-1.6\times 10^{3}\) & 68 & \(-2.7\times 10^{2}\) & 32 \\ \hline \end{tabular}
\end{table}
Table 2: Residual stress results (MPa).  
Figure 12: The average hardness of treated and untreated specimens from the Rockwell C test.  
These stresses help minimize the generation of tensile stresses at the surface due to cyclic loadings thereby suppressing crack formation and nucleation [49].  
The high compressive stresses help minimize the damage caused during cyclic loading by shifting the mean stress downwards. This eventually leads to longer life in the part as compressive stresses will lower the stress ratio. Experimental data has shown that as the stress ratio becomes increasingly negative, longer lives were measured. This can be explained by compressive surface stresses preventing dislocations from moving within the material. The hydrostatic stresses for each tensor reveal the same trend; the untreated sample had a hydrostatic stress of \(-265\) MPa while the treated sample had \(-1644\) MPa. A high compressive hydrostatic stress could also represent an increase in fatigue resistance due to UIT.
# A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers
## 5 Statistical Tests  
To demonstrate the randomness of the keys generated, we applied the NIST statistical tests [22]; in particular, we calculated the \(p\)-value parameter for four different tests.  
* Frequency: This calculates the number of 0-bits and 1-bits of the key, and it is established using a formula if their proportion is compatible with a random number.
* Block frequency: This calculates the proportion of 0-bits and 1-bits that is present in M-bit-size blocks extracted from the sequence.
* Cumulative sum: The parameter of the cumulative sum is calculated in this way: An M-bit size is considered and, replacing the 0-bits with \(-1\)-bits, the bit-by-bit sum is executed, obtaining the maximum distance from 0.
* Longest run: This is a parameter that indicates the maximum length of consecutive 1-bits inside an M-bit-size block inside the sequence.  
In all the tests run, all the \(p\)-values of the parameters were greater than 0.01, and this indicated the cryptographic key is actually random. In Table 1, the \(p\)-values obtained from the tests performed for each fractal are reported.  
Figure 10: Fragment interaction.  
Figure 11 below shows a graph that provides a visual representation of the statistical values found for each fractal:
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Cryptography Authentication
#### 2.5.1 ID-Based PKC (Public Key Cryptography)  
Traditional public-key cryptography (TPKC) was introduced based on PKI. Several protocols using the TPKC have been proposed, where each user will have a certificate to bind their identity and public key. Those certificates are produced by a trusted third party called the certificate authority (CA) [8]. However, the system has an overhead to be borne when the number of users increases.  
To overcome the weaknesses in these TPKC protocols, identity-based public-key cryptography (ID-based PKC) has been proposed in the last several years. The participant's identity itself is taken as the public key so that no certificate is needed to bind its identity and public key [8]. Figure 4 shows the flow of ID-based PKC authentication.  
Figure 3: Architecture of system for trip trajectory authentication.
## 2 Materials and Methods
### External Workload Quantification  
The players were provided a GPS device (WIMU PRO, RealTrack Systems, Almeria, Spain) to monitor their external workload during the training sessions and friendly matches that took place during the pre-season. These WIMU PRO devices include a 10-Hz GPS and 100-Hz triaxial accelerometer [20]. To reduce interunit variability, each player wore their own unit on the centre of their upper back, and each player used the same GPS device during the entire season [11]. Following the manufacturer's recommendations, all WIMU devices were activated 15 min before the start of each training session and friendly match to properly acquire satellite signals and facilitate synchronisation. The recorded data were downloaded after each register and analysed using a customised software package (WIMU SPRO, Almeria, Spain). To quantify the external workload demands, the following variables were selected: total distance (TD); jogging (distance covered from 6 to 12 km\(\cdot\)h\({}^{-1}\)); cruising (distance covered from 12 to 18 km\(\cdot\)h\({}^{-1}\)); high-intensity running (distance covered from 18 to 21 km\(\cdot\)h\({}^{-1}\)); high-speed running (distance covered from 21 to 24 km\(\cdot\)h\({}^{-1}\)); and sprinting (distance covered above 24 km\(\cdot\)h\({}^{-1}\)). Also, the number of the following variables were recorded: low-intensity accelerations (LACCs; from 3.0 to 4.0 m\(\cdot\)s\({}^{-2}\)); high-intensity accelerations (HACCs; above 4.0 m\(\cdot\)s\({}^{-2}\)); low-intensity accelerations (LDECs; from \(-\)4.0 to \(-\)3.0 m\(\cdot\)s\({}^{-2}\)); and high-intensity accelerations (HDECs; below to \(-\)4.0 m\(\cdot\)s\({}^{-2}\)). The reliability and validity of the WIMU devices for the selected variables have been previously proved [20].
## 3 Evaluation Criterion
### Anti-Jamming Performance  
The narrowband-jamming and matched-spectrum-jamming are the main threats to the pseudo code and carrier tracking as well as the demodulation process. In order to effectively evaluate the anti-jamming ability of navigation signals against the above interferences, the paper introduces four parameters, including anti-narrowband-jamming merit factor \(Q_{\text{\tiny{Dem=L/NWF}}}\) and anti-matched-spectrum-jamming merit factor \(Q_{\text{\tiny{Dem=L/NMS}}}\) for the demodulation process, and the anti-narrowband-jamming merit factors \(Q_{\text{\tiny{CTAL/NWF}}}\) and anti-matched-spectrum-jamming merit factor \(Q_{\text{\tiny{CTAL/NLS}}}\) for the code tracking process.  
The demodulation performance mainly lies in the coding properties and effective signal-to-noise ratio (\(E_{b}\) / \(N_{0}\)) of received signals. When there exists noise and one non-white interfering signal with high power, the effective (\(E_{b}\) / \(N_{0}\)) is approximately expressed by:  
\[(E_{b}\) / \(N_{0}\))\({}_{df}=\frac{C}{R(N_{0}+J\gamma_{s,J})}=\frac{C}{J}\frac{1}{R\gamma_{s,J}} \propto\frac{1}{R\gamma_{s,J}} \tag{20}\]  
where \(R\) denotes the message rate and \(J\) is the received power of the interfering signal. Neglecting the constant term of \(C\) / \(J\), a quantity called anti-jamming merit factor for the demodulation process can be obtained by converting Equation (20) into a dB level version, _i.e._:  
\[Q_{Dom,U}=10\times\log_{10}\Bigg{(}\frac{1}{R\gamma_{s,J}}\Bigg{)}[\text{dB}] \tag{21}\]  
As for narrowband-jamming, we take a delta function to describe the PSD of the interfering signal as:  
\[G_{J}(f)=\delta(f-f_{J}) \tag{22}\]  
where \(f_{J}\) is the carrier frequency offset. Through Equations (19), (21) and (22), we find that the demodulation performance would be deteriorated to a great extent when \(f_{J}\) corresponds to the peak of the desired signal PSD. Thus Equation (21) can be further modified as follows:  
\[Q_{Dom,U,N  
\[Q_{CTL,U}=10\log_{10}\left(\frac{\int_{-B/2}^{B/2}G_{s}(f)f^{2}df}{\int_{-B/2}^{B/ 2}G_{s}(f)G_{J}(f)f^{2}df}\right)\left[\text{dB}\right] \tag{28}\]  
Like the analysis of the demodulation process above, by substituting \(G_{J}(f)=\delta(f-f_{J})\) and \(G_{J}(f)=G_{s}(f)\) into Equation (28), the anti-narrowband-jamming merit factors and anti-matched-spectrum-jamming merit factors for the code tracking process can be derived separately as follows:  
\[Q_{CTL,NNF}=10\times\log_{10}\left[\frac{\int_{-B/2}^{B/2}f^{2}G_{s}(f)df}{ \max[f^{2}G_{s}(f)]}\right]\left[\text{dB}\right] \tag{29}\]  
\[Q_{CTL,NMS}=10\times\log_{10}\left[\frac{\int_{-B/2}^{B/2}f^{2}G_{s}(f)df}{ \int_{-B/2}^{B/2}f^{2}G_{s}^{2}(f)df}\right]\left[\text{dB}\right] \tag{30}\]