esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Energy Use  
Alongside the recordings of the IEQ parameters, a comprehensive energy audit was conducted to quantify and analyze the energy use patterns, enabling the identification of areas for improvement and optimization. Monthly electricity and natural gas consumption data were obtained from the utility companies' billing statements. Moreover, detailed data recordings capturing the hourly electricity consumption, provided by the Building Energy Management System (BEMS) installed in the educational settlement, were utilized in this assessment. The timeline of energy use data covered an academic year. This process entailed a meticulous examination of the consumption trends over an extended period, allowing for the identification of any significant fluctuations or irregularities in the usage patterns.  
The collected data were analyzed to assess total energy consumption and identify peak usage periods. The energy use intensity (EUI) was calculated by normalizing the total energy consumption against the building's square footage, facilitating comparisons with benchmark values for similar educational facilities. Additionally, an analysis of the energy performance index (EPI) was conducted to evaluate the building's efficiency relative to the established standards.
# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 4 Experimental Setup  
### Dataset  
In order to evaluate the proposed feature extraction technique, we used the RFAI dataset [31] which is publicly available. It is composed of volumetric synthetic textures of size 64 \(\times\) 64 \(\times\) 64 voxels. The authors in [31] considered several techniques for obtaining the 3D images: an interpolation strategy, a method based on the Fourier Transform, a random distribution of geometric shapes and a combination of all these methods. The first experiment was carried out by using the images built using the interpolation approach for which we selected 9 texture classes which are detailed in Table 3.  
As in [32], we opt to use the images constructed by interpolation and the nine texture categories. As shown in the original publication, those classes are more similar to biological tissue. We considered 40 texture samples per class: 10 regular images that are not affected by any transformation, 10 smoothed samples upon which the Gaussian blur was applied, 10 noisy textures altered by Gaussian noise and 10 textures affected by a subsampling distortion. Figure 10 illustrates a regular sample for each of the nine categories.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Texture Class Name** & **The Number of Texture Samples Per Class** \\ \hline Blobs & 40 \\ Blocks & 40 \\ PerlinAmp & 40 \\ PerlinNoise & 40 \\ RidgedPerlin & 40 \\ SinusSynthesis & 40 \\ Stone & 40 \\ Uwari & 40 \\ Veins & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Texture classes for the first experiment.  
Figure 11 presents some texture samples from category Blocks in order to exem-plify the applied transformations.  
For better observing the fact that the images constructed by interpolation are true 3D volumes, we show in Figure 12 a regular image sample from class SinusSynthesis. We can see that the 2D pattern is not translated along the depth axis since the slices of the 3D images are not identical.  
In the second experiment, we considered using all images from the RFAI database [31]: images built by interpolation, by using the Fourier Transform, by considering a random distribution of geometric shapes and a combination of all these methods. In total, there are 4745 images and 95 image categories. Figure 13 shows examples of images built by considering also the other strategies.  
Figure 10: Examples of normal texture images from each considered category: (**a**) Blobs (**b**) Blocks (**c**) PerlinAmp (**d**) PerlinNoise (**e**) RidgedPerlin (**f**) SinusSynthesis (**g**) Stone (**h**) Uwari (**i**) Veins.  
Figure 11: Examples of textures from category Blocks: (**a**) normal (**b**) noisy (**c**) smoothed (**d**) subsampled.  
Figure 12: Example of a regular image represented in slice view (slices 1, 32 and 64).
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### On-Chain Data Storage Methods
#### 2.1.1 Blockchain Database Structures  
Blockchain ensures secure data storage by providing a decentralized and immutable structure. Two fundamental techniques that optimize on-chain data management are Merkle Trees and Sharding:  
* _Merkle Tree_: A hash-based structure used to verify the integrity of transaction data. It is widely utilized in blockchain networks such as Bitcoin [21] and Ethereum [74] to enable fast and reliable transaction verification. While Merkle trees ensure data integrity, they have limitations when handling complex data structures. Besides maintaining data integrity, enhancing blockchain scalability is equally critical.
* _Sharding_: A technique that partitions blockchain data into smaller fragments, reducing network load and improving transaction throughput. This approach is particularly advantageous for high-transaction-volume networks, as it enables parallel processing across shards. While sharding enhances scalability, it also requires additional security measures to prevent data vulnerabilities [17; 18; 22].  
These two fundamental techniques form the backbone of blockchain-based data management. However, blockchain technology extends beyond database structures. Smart contracts play a crucial role in making data storage processes more flexible and user-friendly, further enriching blockchain-based data management.
## 3 Experimental Implementation
### Accommodation of Unknown Receivers  
The concept of SCC was first proposed by [PERSON] et al. in [23]; its structure and typical waveform are shown in Figure 12, in which \(\varphi\) is the control angle. The current flowing  
Figure 11: Identified parameters and corresponding differences from set values (dotted line) in experiments; (**a**) identified \(M_{ps}\), (**b**) identified \(R_{L}\); and (**c**) identified \(f_{s}\).  
Figure 10: Waveforms of input voltage \(u_{p}\), input current \(i_{p}\), ZCD signal of \(i_{p}\) and RMS output of the circuit during parameter identification process. Three cases are given, each with different \(f_{s}\) and \(R_{L}\).  
through SCC is taken as a reference. After capturing its ZCD signal, complementary PWM signals are sent to two MOSFETs set up in parallel with the capacitor. Since the SCC has the characteristics of continuously adjusting the equivalent capacitance, the WPT system can use SCC on the primary side to adjust the resonant frequency equivalently, which in turn can accommodate unknown receivers with different resonant frequencies.  
\[C_{scc}=\frac{\pi C_{1}}{2\pi-2\varphi+sin2\varphi} \tag{16}\]  
\[\omega_{s}^{2}\left(\frac{1}{\frac{1}{C_{1}}+\frac{1}{C_{scc}}}\right)L_{p} \tag{17}\]  
With a radian-format control angle \(\varphi\), the SCC equivalent capacitance is given in (16). The calculation is based on the fundamental harmonic analysis (FHA) and the assumption that a sinusoidal current flows through the SCC. For detailed derivation, please refer to [23]. Theoretically, the SCC in this paper can adjust its capacitance from \(C_{2}\) to infinity. However, \(\varphi\) ranges from \(\pi/2\) to \(\pi\), which means that when the value of the control angle is close to \(\pi\), a slight difference will lead to a significant change in the equivalent capacitance, as shown in Figure 13a. Once the SCC is series-connected to \(C_{1}\), not only does the overall capacitor voltage \(u_{cp}\) become more sinusoidal, as shown in Figure 12, but it also creates a more linear relationship between the control angle and the equivalent capacitance, as presented in Figure 13b. This configuration enables the transmitter to operate in the ZPA state with receivers that have different resonant frequencies by calculating the corresponding value of \(C_{scc}\) by (17) and obtaining the control angle via a look-up table. To show the accommodation capability of the receivers with different resonant frequencies, three identification cases with the actual resonant frequencies of the receivers, 81.45 kHz, 86.9 kHz, and 96.9 kHz, respectively, are conducted as illustrated in Figure 14. The input voltage and load resistance are kept at 90 Vpp and 30 \(\Omega\), respectively. It can be seen that the \(u_{p}\) and \(i_{p}\) are in phase and the system efficiency can be maintained above 80% for all cases.  
Figure 12: The SCC with a series-connected capacitor and typical waveform of SCC.  
Figure 13: Graph of equivalent capacitance as a function of control angle. (**a**) Equivalent capacitance of SCC; and (**b**) equivalent capacitance of SCC connected with \(C_{1}\).
## 4 Flat-Panel Beam-Steering Antennas with Low-Profile Phase Shifters with Fast Response
### Fast Tuning Low-Profile Planar Delay Line Phase Shifter
It is separated from the top substrate (glass) with the strip line and alignment layer by spacers, where the thin cavity between the NaM and glass of few \(\upmu\)m only is filled later with LC. The schematic is shown in Figure 23.6 While the magnetic field is able to pass through the nanowires almost unperturbed, the electric field is confined in between the signal line and the nanowires. Hence, an increase of the equivalent capacitance \(C^{\prime}\) of the transmission line is obtained, while the inductance \(L^{\prime}\) remains unchanged, thus providing a slow-wave effect characterized by a decrease of the phase velocity \(v_{ph}=1/\sqrt{L^{\prime}\cdot C^{\prime}}\). This slow-wave effect strongly enhances when the strip line is narrow to the end caps of the nanowires, i.e., when the LC layer height \(h_{LC}\) is very thin. The NaM technology allows the realization of tunable microstrip lines for 50 \(\Omega\) design with an LC layer height of \(h_{LC}\leq\) 4 \(\upmu\)m. This enables a significant reduction of the switch-off response time \(\tau_{\text{off}}\). Figure 23.7 Cross-section of a slow-wave microstrip line and its working principle: unbiased, quasi-perpendicular orientation (**above**) and full-biased, parallel orientation (**below**). It is built up by a nanowire membrane (NaM) on top of the ground plane, where the space in between the NaM and the top substrate (glass) with the strip line is filled with LC. The parameters are \(h_{NaM}\) = 50 \(\upmu\)m, \(h_{Gold}\) = 2 \(\upmu\)m, \(h_{LC}\) = 4 \(\upmu\)m, and \(w_{LC}\) = 500 \(\upmu\)m [84]. For the realization [81; 84], the microstrip line has been processed on a glass substrate by means of photolithography. Additionally, the alignment layer as well as cavity walls made of SU8 have been processed on the same substrate. The SU8 cavity walls are processed in a way that the LC layer height of \(h_{LC}\) = 4 \(\upmu\)m is ensured. Afterwards, the membrane with \(h_{NaM}\) = 50 \(\upmu\)m is glued to the already processed substrate. LC filling is done under vacuum conditions, and the parallel orientation is achieved by the application of an AC biasing voltage of 1 kHz to the strip line itself. Recent results in [84] looks very promising, since it shows a FoM of up to 70\({}^{\circ}\)/dB in a frequency range between 30 and 60 GHz, which is accompanied with \(|S_{11}|<-15\) dB and a miniaturization factor of more than 2 compared to an IMSL phase shifter, providing the same differential phase shift. The switch-on response time are 3.1 and 9.4 ms, and most decisively, the switch-off response time are 116 and 125 ms for GT3-23001 and GT7-29001, respectively. The switch-off response time is less than expected, which is due to just one alignment layer on the top substrate only. Hence, it can be reduced by even thinner LC layers \(h_{\text{LC}}\leq 2\upmu\)m and by more effective alignment layers on both inner surfaces of the cavity.
## 2 Material and Method
### RNA Extraction and qRT-PCR  
The total RNA for each berry sample was extracted using the HipPure Plant RNA Mini Kit (Magen, Guangzhou, China) and first-strand cDNA was synthesized using First-Strand cDNA Synthesis SuperMix (Novoprotein, Suzhou, China). Six genes, including _VvDXR_ (VIT_17s0000g08390) [(20)], _VvDXS_ (VIT_05s0020g02130) [(27)], _VvDDR_ (VIT_03s0063g02030) [(26)], _VvCCD_ (VIT_13s0064g00840) [(28)], _VvGPPS_ (VIT_15s0024g00850) [(26)] and _VvCSLinNer_ (VIT_00s0271g00060) [(26)], were determined using CFA96 (Bio-Rad, Hercules, CA, USA) and SYBR qPCR SuperMix Plus (Novoprotein). The primers were designed using Primer 5 software (Premier Biosoft, Palo Alto, CA, USA), and the sequences of each primer are listed in Table S2. The \(2^{-\Delta\text{ACT}}\) method was used to calculate the relative expression levels.
## 5 Evaluation
### Experimental Results
#### 5.3.4. Gains with OT  
Figures 9 and 10 show the frequency and mean gains using the OT protocol. The gains from poisoning attacks on the proposed protocol could be estimated as the maximum gains for RMA and RKVA attacks. We summarize the results of this frequency and mean gains in Tables 3-5. Our experiments using the MovieLens dataset, with the ratio of fake users to genuine users being 1 to 10, demonstrated that the proposed emPrivKV improved the frequency and mean gains by 17.1% and 25.9% of those of PrivKV, respectively, where the number of fake users was 0.1 of genuine users (Table 3). When the privacy budget \(\epsilon\) was 0.1 to 5, our experiments demonstrated that the proposed emPrivKV improved the frequency and mean gains by 0.6% and 1.6% of those of PrivKV, respectively, where \(\epsilon\) was 0.1 (Table 4). When the number of target keys was 1 to 15, our experiments demonstrated that the proposed emPrivKV improved the frequency and mean gains by 0.7% and 20.8% of those of PrivK  
We conclude that our approach is effective for data that are perturbed using the LDP algorithm.
# Application of Modern Research Methods for the Physicochemical Characterization of Ion Exchangers
## 1 Introduction
### Forms of Ion Exchangers  
For industrial purposes, the ion exchangers can be obtained in the form of beads (IEB) or fibres (IEF) (Figure 3a-f). In the purification industry, where ion exchangers are used in the form of beads, the mesh size is often used to determine the particle size distribution. However, fibres can simultaneously work as mechanical filters and ion exchangers. They are mostly promising for continuous counter flow process technologies due to a more developed surface than beads, thus providing higher ion exchange rates [23]. As examples of fibrous ion exchangers, FIBANs (Figure 3d-f) (from Physical and Organic Chemistry Institute, Minsk, Belarus) and Smopex(r) (from Johnson Matthey, London, UK) should be mentioned. Now, IEF are manufactured by Ecofil-Deco Company, Minsk, Belarus).  
These types of ion exchangers are used, among others, for deep air purification from ammonia. These materials are characterized by the chemical properties comparable to those of the ion exchange resins such as mechanical strength and flexibility [24; 25]. Small sizes of their monofibres (5-50 \(\upmu\)m) and thickness uniformity allow for high speed and efficiency of the ion exchange process. They are also characterized by an extremely great rate of ion exchange, high osmotic stability allowing for their repeated drying and wetting as well as transformation from one ionic form into another without damaging the fibres [26; 27]. The typical half-life of ion exchange processes for fibres is 2-10 s, while for the traditional ion exchangers it is 30-200 s. High process speed is achievable due to the short diffusion path. As a result, they can be used in the form of a thin filter layer of 7-20 mm at a high solution or gas flow rate for cleaning exhaust gases (from SO\({}_{2}\), HCl, NOx, HF), removal of fumes (NH\({}_{3}\), RNH\({}_{2}\)), odorant (RSH, RNH\({}_{2}\)) or air purification from tobacco smoke, e.g., in smokers' cabins.  
Figure 2: Gel (microporous) and macroporous ion exchangers.
# Sensitivity Analysis of Numerical Coherency Model for Rock Sites
## 4 Comparison with Empirical Function  
Based on the sensitivity analysis results, a comparison was performed with [PERSON]'s [1] empirical coherency function. The parameters with minimal impact on plane-wave coherency at hard rock site were fixed as follows: CLv = 10 m, CLh = 20 m, and analysis depth = 150 m. The comparison focused on adjusting the CV, which has the most significant influence on plane-wave coherency. Figure 9 shows the numerical coherency functions derived for the CV range (15-30%) alongside the empirical coherency function. Notably, for CV = 30%, the numerical results closely matched the empirical function across most separation distances and frequency ranges (Table 2). This suggests that if the spatial variability parameters of a target site can be estimated through site investigations, and numerical coherency functions for various hard rock sites can be developed through numerical analysis. However, it is important to note that the results were obtained using spatial variability parameters derived from other hard rock sites, rather than parameters estimated for this target site. Because the spatial variability parameter is inherently site-specific and may differ even within the same rock class, caution is warranted. Further validation of  
Figure 8: Evaluation of sensitivity analysis regarding effect of analysis depth on plane-wave coherency.  
the numerical functions against empirical functions using spatial variability parameters derived from rigorous site investigations is necessary.
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 3 Antenna Design and Simulation
### Antenna Silicone Layer Design  
Implantable antennas differ from most antennas operating in free space in that they function within the human body, a complex dielectric environment composed of skin, fat, muscle, and other tissues. Each tissue has different propagation and absorption characteristics for electromagnetic waves, with varying dielectric constants and conductivities. Therefore, the addition of a covering layer is of significant importance in the design of implantable antennas. With a covering layer, direct contact between the antenna's radiator and human tissue can be avoided. Due to the special working environment, implantable antennas have strict requirements for safety and reliability during the design process. Thus, to meet the safety requirements of implantable antennas, the material used for the covering layer must exhibit biocompatibility characteristics, meaning it can exist within the human body for extended periods without causing adverse reactions, ensuring compatibility with the human body, and further optimizing antenna performance.  
Silicone is a material with high biocompatibility, similar in viscosity to glue, and is suitable for the design of implantable antennas. Its excellent flexibility allows it to adapt well to the deformation of human tissues, reducing potential damage to surrounding tissues and enhancing the antenna's safety and comfort, thereby providing robust characteristics. Additionally, silicone has good corrosion resistance, ensuring safety and extending the antenna's lifespan. At 2.45 GHz, the relative dielectric constant of silicone is 3.02 [17]. When using silicone as the covering layer material, it effectively isolates the antenna from muscle tissue while ensuring the antenna retains biocompatibility, thereby achieving the minimization of the antenna to the greatest extent possible.  
To determine the thickness of the silicone layer used, the reflection coefficient of the antenna under different thicknesses was simulated using simulation software. As shown in Figure 6, as the thickness of the silicone layer increases, the antenna's resonant frequency shifts toward the high-frequency end, and the bandwidth of the antenna is also expanded. When the thickness h\({}_{1}\) is increased to 0.4 mm, the antenna's operating frequency band is 2.08-2.64 GHz. Further increasing the thickness of the silicone layer does not significantly change the antenna's resonant frequency or operating frequency band. Therefore, a 0.4 mm silicone layer is used to minimize the antenna's volume while ensuring antenna biocompatibility.  
Figure 5: Reflection coefficients corresponding to each design step.
## 3 Monte Carlo Simulation Methodology
Given the source parameters and tolerances, the QBER is a well-defined quantity for an infinitely long sifted key, and can be estimated to the desired precision by averaging the QBERs obtained from repeated simulations of finite key length. Random bit errors will obey Poisson statistics, so we expect the sample standard deviation of their rate to scale as \(\sqrt{1/N_{\text{sifted}}}\).
# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
## 3 Results
### Analysis of Time Series Data: Incorporating Periodic Data Gathering for Long-Term Impact Assessment  
#### 3.5.1 Current Research Limitations  
Many studies focus on the short-term benefits of Industry 4.0 technologies on corporate profits, often neglecting the long-term effects on sustainability. Implementing periodic data gathering over time is essential to gain a deeper understanding of how Industry 4.0 technologies influence sustainability metrics such as resource efficiency, environmental impact, and social benefits. Regular data collection at intervals--such as 3, 5, and 10 years--can help monitor changes in carbon emissions, energy consumption, and operational efficiency, providing a comprehensive view of the technologies' long-term sustainability contributions.  
Importance of Time Series Data: to establish a well-rounded view of the long-term benefits of Industry 4.0, periodic data gathering should be incorporated into research methodologies. Repeated assessments at set intervals will allow for the observation of trends in key sustainability indicators, including reductions in emissions, energy savings, and improvements in social welfare metrics. This approach will not only demonstrate the immediate gains but also the cumulative sustainability advantages over extended periods, offering crucial insights for policy-making and industrial strategy.  
Practical Case Analysis: for instance, a selected manufacturing company that gradually adopts MES, IIoT, and AM technologies over a five-year period can serve as a case study to monitor the trends in profitability, production costs, carbon footprint, and resource efficiency. This method would help distinguish the short-term benefits from the long-term impacts, aiding other companies in making informed decisions about their own sustainability efforts.
# Compact Photonic-Crystals Based Isolator Using Ni-Zn Gyromagnetic Ferrite Posts
## 3 Photonic-Crystals Isolator
### Operating Principle of Faraday Rotation Isolator
Furthermore, we can adjust the angle \(\theta\) mentioned above in order to achieve the superior isolation for W1 of the isolator.