esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Model for Prediction
#### 4.4.1 Polynomial Regression  
Polynomial regression, as a classic method for predicting building loads, primarily takes two forms: univariate and multivariate. Univariate polynomial regression focuses on historical data of building energy consumption, utilizing polynomial equations to forecast future energy consumption trends. Its basic form is:  
\[Y=a_{1}+a_{2}x+a_{3}x^{2}+\cdots+a_{n+1}x^{n} \tag{1}\]  
where, \(Y\) represents the predicted energy consumption value, \(x\) represents the historical energy consumption data or a time variable and \(a_{1}\) to \(a_{n}\) are the polynomial coefficients obtained through optimization algorithms such as least squares regression. It is noteworthy that the choice of model order \(n\) is crucial. While higher-order models can capture complex trends, they are prone to over-fitting. Conversely, lower-order models may miss important information due to under-fitting. Therefore, in practice, second- or third-order polynomials are often preferred as a balanced choice, with the second-order polynomial model particularly popular due to its moderate complexity and good generalization ability.  
Compared to the univariate model, the multivariate version further considers multiple factors that influence building energy consumption, such as temperature, humidity, wind speed, building size, and material properties. The model expression for the multivariate polynomial regression is:  
\[Y=a_{1}+a_{2}x_{1}+a_{3}x_{2}+\cdots+a_{n+1}x_{n} \tag{2}\]  
where, \(x_{1}\) to \(x_{n}\) denotes the data of \(n\) factors influencing energy consumption, and \(a_{1}\) to \(a_{n}\) are the corresponding coefficients. To enhance the prediction accuracy of the model, researchers also explore feature engineering techniques, such as feature multiplication, higher powers, etc, to capture the nonlinear relationships between variables. Similarly, the estimation of coefficients relies on statistical methods like least squares regression. [PERSON] et al. [156] proposed a physics-based multiple linear regression (PB-MLR) model specifically designed for predicting the hourly cooling loads of buildings. By integrating physical principles with statistical methods, this model demonstrates robust predictive capabilities. Additionally, [PERSON] et al. [157] employed multiple linear regression techniques to investigate the relationship between the geometric forms of residential buildings in India and their cooling loads, providing a scientific foundation for optimizingbuilding design. The advantage of such models lies in their high generalization ability even with small sample sizes, efficient training processes, and strong interpretability of results, which aid decision-makers in intuitively understanding the key factors influencing building energy consumption.
## Materials and Methods  
### Data Acquisition and Processing  
Seven male subjects (age = 23.6 \(\pm\) 3.4 years, height = 1.65 \(\pm\) 0.2 m; mean \(\pm\) sd) participated in the experiment with inclusion criteria of no history of physiological or nerve injury that may have affected gait. The surface EMG signals were detected by placing Ag/AgCl electrodes on the gastrocnemius medialis (mGas) muscle of the right leg together with a reference electrode at the patella in accordance with surface electromyography for the non-invasive assessment (SENIAM). The contact point of the electrodes was prepared by shaving and cleansing the skin with alcohol beforehand in order to reduce the impedance on the skin surface. The online processing of surface EMG signals was provided by EMG device ([PERSON], Saitama, Japan) with inter-electrode distances of 1 cm and amplified by a multichannel amplifier with bandwidth filter from 15 to 1000 Hz. Then, EMG signals were connected to 64 Ch analogue-to-digital converter as shown Figure 2. The experimental protocol was approved by the local ethical committee.  
The EMG signals were sampled at a rate of 1000 Hz using the Cortex software. Before conducting the experiment, each of the subjects was instructed to walk bare-footed at a self-selected comfortable pace on the treadmill for 3 s at a constant speed of 3 km/h. After the initial warm-up, the subjects underwent the task of walking on the treadmill for 5 s with the same speed as earlier. The experimental protocol was approved by the local ethical committee. The raw EMG signals then went through a high-pass and a low-pass filter with the second-order Butterworth at 20 Hz and 500 Hz, respectively, to minimise interference and unwanted line frequencies (50/60 Hz). An example recording of mGas muscle during 5 s is depicted in Figure 3.  
There were two main methods used for data windowing: adjacent and overlapping. As adjacent windowing techniques are widely adopted in the literature to analyse the stationarity of EMG signals [21; 24; 25; 26], only adjacent windowing techniques were considered in this study. Adjacent windowing is where disjointed segments with a predefined length, \(S_{i}\) use feature extraction and classification after a certain processing delay, \(\tau\) as shown in Figure 4. The \(\tau\) is the processing time  
Figure 2: Experimental setup during surface EMG data recording. A/D: analog-to-digital; mGas: gastrocnemius medialis.  
required to calculate the feature and classify the data. Adjacent windowing approach is commonly utilised to determine whether the EMG signals remain stationary or otherwise [21; 24; 25; 26]. In this study, the data were divided into adjacent segments with the length of window size 100, 200, 500, and 1000 (ms).
## 1 Introduction
Although the different image spatial resolutions can be converted to the same level using pixel resizing and re-sampling methods, the radiation received by the sensor at these two viewing angles reflect a different land surface contribution [2, 21]. Unfortunately, ATSR has been out of operation for over 10 years and its successor, the Advanced ATSR, provided data until 2012. Since then, dual-angle observations in thermal infrared have not been available. The multi-VNIR and TIR band optimization method usually applies the ancillary information provided by the VNIR remote sensing data to obtain component fractions of soil and vegetation. The soil and vegetation component temperatures are then simulated using an optimized algorithm or Bayesian theorem from TIR remote sensing data. However, the correlations between the neighboring TIR channels cannot be neglected when applying the optimization method, which always leads to ill-posed equations with solutions that are relatively unstable [16]. In addition, the utilization of spatial dependences among adjacent pixels decreases the spatial resolution of the initial TIR images [16], and the spatial resolution of the TIR channels is usually much coarser than the VNIR pixels. In recent years, the surface temperature-vegetation (_T-f_vg) and surface temperature-albedo (_T-_a_) spaces have been successfully applied to estimate surface evaporative fraction [22] and evapotranspiration [23] to disaggregate the land surface temperature and surface soil moisture [7, 24, 25, 26]. Moreover, in _T-f_vg and _T-_a_ space, surface soil wetness iso-lines can be generated [27, 28]. Assuming a relatively uniform soil texture over the image, the soil temperature values for all pixels along the soil moisture iso-line are presumed to be equivalent, as are the vegetation temperature values [29, 30, 31]. The soil and vegetation component temperatures are invariant, and the land surface temperatures thus vary only with the vegetation fraction cover or the surface albedo. Therefore, if one of the equivalent temperatures can be derived, the other then can be calculated using the Stefan-Boltzmann law for the soil and vegetation system while neglecting the scale affection in the non-isothermal pixels. The main objective of this study is to develop a practical method for separating the soil and vegetation component temperatures based on the _T-_a_ space according to the mono-surface energy balance (SEB-1S) model described below. ASTER data are used to test the method having a single viewing angle observation with high spatial resolution. The method is validated with ground component temperature measurements and evaluated based on the theory of equal soil and vegetation temperatures along the soil wetness iso-lines. Finally, some suggestions on how to validate retrieved soil and vegetation component temperatures from satellite observations are presented.
## 4 Visual-Inertial Odometry
### Optimization of Visual-Inertial Systems  
As shown in Figure 3, the poses of the keyframes, IMU bias, and velocity are combined as constraints to form a factor graph for global optimization of the visual-inertial system. The optimized keyframe poses are in the visual-inertial coordinate system and are not affected by environmental scale. The IMU factors are connected between two consecutive keyframes in the form of preintegration as described in Section 3.2. The time interval threshold between two consecutive keyframes is set to 0.5 s to avoid errors in preintegration results due to long time intervals.
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 4 Simulation's Results
### PLL Testbench
#### 4.3.2 Noise Simulations  
In Figure 21, the resulting post-layout Phase Noise for the whole PLL is shown. It is below \(-85\) dBc/Hz, which is in line with that of the VCO. The largest contribution is due to the CP/FFD at low and middle frequencies, while the VCO's noise is predominant at higher frequencies, as expected from the theory [19].  
Since in digital design, a temporal characterization of noise is usually preferred, the absolute Jitter has been measured, resulting in a peak-to-peak value of 8.8 ps and an RMS (root mean square) value of 2.03 ps (typical corner, considering 12,500 cycles, which in terms of Phase Noise means a bandwidth between 500 kHz and 1 GHz). Absolute Jitter is really important for communications systems, since it measures how much an edge of the clock is in a different position from the ideal one. For other applications, Period Jitter is of most interest, since it measures the difference between a clock  
Figure 19: Highlight of the output nodes of the CP.  
period and the average one. Therefore, Period Jitter has been also measured, in the typical corner as well, resulting in a peak-to-peak of 96 fs and an RMS value of 14.744 fs.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Static** & & & & & & \\
**Dielectric** & **Dipole** & **Donsity [\(g\)/cm\({}^{3}\)]** & **Viscosity [mP=s]** & **Boiling Point [\({}^{\circ}\)C]** & **Melting Point [\({}^{\circ}\)C]** & **Refractive** & **Molecular** \\  & **Constant** & **Momeni [D]** & & & & & & **Index** & **Weight** \\ \hline water & 80.2 & 1.85 & 0.998 & 1.00 & 100 & 0 & 1.333 & 18.015 \\ MeOH & 33.0 & 1.66 & 0.792 & 0.59 & 64.7 & \(-\)97.6 & 1.329 & 32.05 \\ EtOH & 25.3 & 1.44 & 0.789 & 1.2 & 78.37 & \(-\)114.1 & 1.361 & 46.07 \\ FA & 109.5 [22; 23] & 3.37 & 1.133 & 3.23 (25 \({}^{\circ}\)C) & 210 & 2–3 & 1.447 & 45.04 \\ NMF & 186.0 [22; 23] & 3.83 & 1.011 & 1.72 (25 \({}^{\circ}\)C) & 198–199 & \(-\)4 & 1.43 & 59.07 \\ DMF & 37.4 [22; 23] & 3.82 & 0.944 & 0.71 (25 \({}^{\circ}\)C) & 153 & \(-\)61 & 1.43 & 73.09 \\ DMSO & 47.2 & 3.96 & 1.10 & 1.99 & 189 & 19 & 1.479 & 78.14 \\ [BMIM] & 13.9 [29] & & 1.21 (20 \({}^{\circ}\)C) & 132 (20 \({}^{\circ}\)C) [25] & & & 1.41 & 226.02 \\ \hline \end{tabular}
\end{table} 
Table 1: Physical properties of solvents: values at 20 \({}^{\circ}\)C for water, MeOH, EtOH, DMSO [16]. white light source and the spectral sensitivity of the measurement system, the experimental results were plotted as \(\Delta T/T\), normalized by the transmitted light intensity of the probe light, \(T\), which was also measured with the above multi-lock-in measurement system by chopper modulation. Next, the AC impedance method was used to measure the voltage actually applied to the electrode interface. The AC impedance method was performed by using two identical ITO electrodes as two terminals by a two-terminal method, using a potentiometer/galvanostat SI1287 (Solartron, Victorville, CA, USA) and an impedance analyzer SI1260 (Solartron, Victorville, CA, USA). The electrode onto which the light was irradiated used as the working electrode, and the other electrode was used as the counter electrode. The voltage applied during the measurement was 50 mV AC, and the measurement frequency range was 10\({}^{-1}\)-10\({}^{7}\) Hz. However, the data in the high-frequency region is cut off in Figure 11 in Appendix A because it fluctuates significantly.
# Biopurification of Oligosaccharides by Immobilized _Kluyveromyces Lactis_
###### Abstract  
Oligosaccharides with diverse and complex structures such as milk oligosaccharides \"have physiological functions including modulating intestinal microbiota\" or stimulating immune cell responses. However, milk carbohydrates include about 40-50% of lactose which requires a cost-effective method to separate. We developed a new method to purify the oligosaccharides \"from carbohydrate mixtures\" such as human milk oligosaccharides (HMOs) and galactooligosaccharides (GOSs) by exploiting immobilized _Kluyveromyces lactis_ as microbial catalysts. Evaluation of media components exhibited no significant differences in the lactose removal efficiency when nutrient-rich media, minimal salt media, and distilled water without any media components were used. With the immobilization on alginate beads, the lactose removal efficiency was increased 3.4 fold compared to that of suspension culture. When the immobilized cells were reused to design a continuous process, 4 h of pre-activation enhanced the lactose eliminating performance 2.5 fold. Finally, immobilized _K. lactis_ was used as microbial catalysts for the biopurification of HMOs and GOSs, and lactose was effectively removed without altering the overall distribution of oligosaccharides.  
Keywords:_Kluyveromyces lactis; human milk oligosaccharides; galactooligosaccharides; lactose; immobilization; microbial catalysts +
Footnote †: journal: Applied Sciences
## 3 Outage Analysis of Blind-RIS-NOMA Uplink System
### Outage Probability of the Blind-RIS-SR-NOMA System for Uplink Transmission
#### 3.1.3 Outage Analysis for User 2  
After decoding the signal of user 1, its impact is removed from the received superposed signal.  
\[y_{2}=\Bigg{(}\sum_{i=1}^{N}h_{i}g_{2i}\Bigg{)}\sqrt{\mu_{2}\;\kappa}x_{2}+n_ {BS} \tag{15}\]  
The SNR of user 2 is given by [18]  
\[v^{x_{2}}=\frac{\mu_{2}\;\kappa_{5}|M|^{2}}{N_{0}}{=}\;\mu_{2}\;\kappa|M|^{2} \tag{16}\]  
The outage does not occur for user 2, when \(x_{1}\) and \(x_{2}\) are decoded successfully. The system should satisfy the following QoS requirements of both users [18].  
\[\log_{2}(1+v^{x_{1}})\geq\widetilde{D}_{1} \tag{17}\]  
\[\log_{2}(1+v^{x_{2}})\geq\widetilde{D}_{2} \tag{18}\]  
where \(\widetilde{D}_{2}\) is the desired rate demand of user 2. The following conditions for no outage of user 2 are obtained by simplifying (17) and (18).  
\[\alpha_{1}\geq\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\;\kappa} \tag{19}\]  
\[\alpha_{2}\geq\frac{D_{2}}{\mu_{2}\;\kappa} \tag{20}\]  
where \(D_{2}=2^{\widetilde{D}_{2}}\;-1\). The expression in (19) can be obtained by using steps similar to Appendix A. The detailed derivation of (20) is highlighted in Appendix D.  
The probability of no outage is obtained using  
\[P\bigg{(}\alpha_{1}\geq\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\;\kappa },\alpha_{2}\geq\frac{D_{2}}{\mu_{2}\;\kappa}\bigg{)}=\frac{exp\Big{(}-\Big{(} \frac{D_{1}}{\mu_{1}\;\kappa{N}{v_{1}}^{2}}+\frac{D_{2}}{\mu_{2}\;\kappa{N}{v _{2}}^{2}}+\frac{D_{1}D_{2}}{\mu_{1}\;\kappa{N}{v_{1}}^{2}}\Big{)}\Big{)}}{1+ \frac{\mu_{2}D_{1}{v_{2}}^{2}}{\mu_{1}\;{v_{1}}^{2}}} \tag{21}\]  
The detailed derivation of (21) is highlighted in Appendix E. The probability of outage of user 2 is given by  
\[P_{out}^{Uker72}=1-\frac{exp\Big{(}-\Big{(}\frac{D_{1}}{\mu_{1}\;\kappa{N}{v _{1}}^{2}}+\frac{D_{2}}{\mu_{2}\;\kappa{N}{v_{2}}^{2}}+\frac{D_{1}D_{2}}{\mu_{ 1}\;\kappa{N}{v_{1}}^{2}}\Big{)}\Big{)}}{1+\frac{\mu_{2}D_{1}{v_{2}}^{2}}{\mu_ {1}\;{v_{1}}^{2}}} \tag{22}\]
## 3 Results and Discussion
### Health Risk Assessment

Table 3: Selected studies on health risk assessments via inhalation of PAHs and heavy metals reported in the literature. Conversely, exposure assessments are subject to inherent uncertainty because they rely on several assumptions. These include the exposure duration and frequency, inhalation rate, average body weight, and annual mean air pollutant concentrations. These parameters can vary considerably depending on individual characteristics, such as physical activity and lifestyle [72]. In particular, the inhalation rate can vary considerably. In the present study, however, these variables were not explicitly incorporated. Furthermore, the cancer slope factors applied in the risk assessment also entail a degree of uncertainty. Using annual average pollutant concentrations fails to capture temporal exposure variability, which is particularly relevant in Poland. Here, both particulate matter levels and composition undergo significant seasonal fluctuations, with the most pronounced changes observed during the winter months [16; 86; 126]. In addition to this long-term variability, short-term smog episodes, especially in winter, can lead to acute spikes in pollutant concentrations that often exceed daily limit values several times over. Immediate health impacts are associated with these peaks, especially for vulnerable groups like children, the elderly, and individuals with pre-existing respiratory or cardiovascular conditions. Even brief exposure to such episodes has been associated with hospital admissions, asthma exacerbations, and elevated cardiovascular stress [127; 128]. Given these fluctuations, the _ILCR_ values estimated in this study should be interpreted as approximate indicators of potential carcinogenic risk. To better account for uncertainties and individual variability, probabilistic approaches such as Monte Carlo simulations should be employed [118; 129]. Furthermore, incorporating a wider range of environmental variables and demographic characteristics would enhance the robustness and credibility of health risk assessments.
# Functionality Investigation of the UAV Arranged FMCW Solid-State Marine Radar
## 5 Results of the Secondary Radar
As was mentioned above, one of the possible reasons why vessels in the open sea were invisible by AIS was the frequency drift of the reference oscillator. This issue was fixed in the second experiment, during which the pulse compression solid-state radar was lifted into low altitude. The SDR receiver was replaced with one with a more stable oscillator. During this experiment we obtained AIS targets at a greater range and the number of targets increased several times as presented in Figure 21.  
Figure 19: The structure of the Quarter-wavelength choke-sleeve antenna. Figure 14:0 The noise in the receiver when the hanging half-wave dipole antenna is connected: (**a**) the batteries of the drone are disconnected, (**b**) the batteries of the drone are connected. Additionally, we created and investigated a large 2 m length antenna array using the drone when wind speed was up to 15 m/s. Maximal altitude was limited to 7 m. During the trials, only a few vessels were in the nearest sea area. Thus, the diversity of the detected targets was not great. Due to wider antenna dimensions, the angular resolution of the radar was increased 4 times. Radar scanning was implemented by rotating the drone's antenna while the drone stayed in place. We did not observe radio frequency interferences when used this radar. Due to the lower weight and single antenna, this radar is attractive for UAV's applications.
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 4 Proposed Risk-Assessment Approach
### RS Derivation from NIST  
The concept for RS (Risk Score) computation is based on the NIST model, which defines risk as the product of Threat, Vulnerability, and Impact as illustrated in Equation (10), where:  
* Threats considered are DoV, DoC, and DoS--known attacks that are consistently exploited along the OT ICS/CPS kill chain [8; 9; 10; 11; 12].
* Vulnerability refers to the risks associated with using a WAN device to extend OT infrastructure and the likelihood of its mitigation features successfully addressing DoV, DoC, and DoS. In other words, it is equal to the probability of risk and probability of mitigation to succeed
* Impact is represented by a weight that prioritizes the OT infrastructure, ranking the risks and mitigations based on their current relevance to the specific OT environment. Precisely, the impact equals the weight of risk and the weight of mitigation.  
\[R_{NIST}=Threat\;*\;Vulnerability\;*\;Impact \tag{10}\]  
By substituting the relevant values, Equation (1) will be formulated, representing the average risk from both the risk associated with each device used in the OT WAN and the probability of mitigation failure.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 5 Discussion
### Accuracy Discussion
An improved and higher safeguard can be provided against these two attacks, while providing efficient services and boosting user confidence.
# A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks
## 6 Summary  
In view of the fact that non-adjacent nodes may contribute greater importance than adjacent nodes, this paper considers the influence of all adjacent nodes within the second order. First, the importance transfer probability model is proposed. At the same time, it draws on the idea of the node deletion method and uses the node importance transfer correlation matrix to determine the importance of network nodes. This method not only considers the contribution of adjacent nodes and non-adjacent nodes to the importance of the evaluated node but also considers the global importance of the node and combines the global and local ideas to make the node importance evaluation result more accurate. In addition, combined with the concept of network structure entropy, a satellite network anti-destroy performance evaluation algorithm is obtained. Simulation experiments show that the method in this paper is more accurate in evaluating the importance of nodes, and when the network topology changes, the index can effectively reflect the change. The method in this paper is also more reasonable and effective when evaluating the anti-destructive performance of the network. Therefore, the method is feasible, effective and accurate, and can be widely used in the evaluation of the importance of nodes and the invulnerability of satellite networks.  
Conceptualization, P.Y. and S.H.; methodology, P.Y. and S.H.; software, S.H.; validation, P.Y.; formal analysis, S.H.; investigation and J.Z.; resources, S.Z.; data curation, P.Y.; writing--original draft preparation, S.H. and P.Y.; writing--review and editing, P.Y. and S.H.; supervision, S.Z.; project administration, S.Z.; funding acquisition, S.Z. and P.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by research supported by Science and Technology Department of Sichuan Province, P.R. China, grant number 2023 YFG0155.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.