esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 3 Smart Building Monitoring Applications
### HVAC Systems Management  
Heating, ventilation, and air-conditioning (HVAC) systems are essential elements of any building. They manage the building's climate control to ensure occupant comfort and safety (H  
* Presence sensors like passive infrared (PIR);
* Radio-frequency identification (RFID) tags, which must be worn by all people using the building;
* Optical cameras;
* Interior parameter measurements like air temperature, CO\({}_{2}\), humidity, and acoustics, which are dependent on human presence.  
Correct sensor placement significantly affects the operation of an HVAC system. For example, the temperature distribution profile is not uniform within one location, HVAC system operation can be substantially affected by sensor layout, and, as the study by [PERSON] et al. [27] shows, the optimal sensor placement can also differ with time. In general, there is no universal methodology for optimal sensor location or count and this is usually decided based on engineering judgment or heuristic methods [26].
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 3 A New Risk Measurement Method Considering Multiple Driving Behaviors  
### Proposal of a New SSM  
To comprehensively evaluate the collision risk of a vehicle in mixed traffic flow, it is necessary to consider both longitudinal car-following behavior and lateral lane-changing behavior simultaneously. Based on the previous discussion, we integrate the concepts of safe lane-changing distance and available lane-changing space to reorganize the total risk assessment metrics, providing relevant formulas and parameter definitions, ensuring logical consistency and coherence.
# Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
###### Abstract  
Multiscale entropy (MSE) was used to analyze electroencephalography (EEG) signals to differentiate patients with Alzheimer's disease (AD) from healthy subjects. It was found that the MSE values of the EEG signals from the healthy subjects are higher than those of the AD ones at small time scale factors in the MSE algorithm, while lower than those of the AD patients at large time scale factors. Based on the finding, we applied the linear discriminant analysis (LDA) to optimize the differentiating performance by comparing the resulting weighted sum of the MSE values under some specific time scales of each subject. The EEG data from 15 healthy subjects, 69 patients with mild AD, and 15 patients with moderate to severe AD were recorded. As a result, the weighted sum values are significantly higher for the healthy than the patients with moderate to severe AD groups. The optimal testing accuracy under five specific scales is 100% based on the EEG signals acquired from the T4 electrode. The resulting weighted sum value for the mild AD group is in the middle of those for the healthy and the moderate to severe AD groups. Therefore, the MSE-based weighted sum value can potentially be an index of severity of Alzheimer's disease.  
e 2020 10 1244 10.3390/app10041244
# Microstructure-Based Prediction of the Elastic Behaviour of Hydrating Cement Pastes
## 4 Mechanical Model
### Lattice Model
#### 4.1.2 Boundary Conditions and Effective Elastic Modulus  
The constructed lattice model is imported to DIANA FEA computer software (released version 10.1), which is the numerical solver chosen in the present study. The configuration of a uniaxial tensile test setup is considered for evaluation of elastic modulus in DIANA FEA. Therefore, the uniaxial test is configured for the REV microstructure by fixing all the nodes on the bottom surface of the specimen (plane \(Z=0\)), and imposing a uniform surface displacement on the top surface (\(Z=n\cdot l\), where \(n\) is the number of voxels along the \(Z\) direction). [PERSON]'s modulus of the different cement paste phases is assumed to equal in both compression and tension. Thus the loading sign has no relevance for the present simulations. The lattice model is solved in tension by imposing a prescribed displacement of \(10^{-4}\)\(\upmu\)m to the top surface nodes (plane \(Z=n\cdot l\)). The effective elastic modulus is derived from the following equation:  
\[E_{eff}=\frac{R\cdot L_{eff}}{A_{eff}\cdot\Delta_{long}} \tag{1}\]  
where \(R\) is the total reaction force in the supports at \(Z=0\) on a direct tensile load test and \(A_{eff}\) is the effective area (equal to the area of each external surface of the cubic REV). \(L_{eff}\) is the effective length (equal to the distance between the two opposite loaded REV faces) and \(\Delta_{long}\) is the total longitudinal displacement in REV (\(10^{-4}\)\(\upmu\)m). It should be noted that the differences between the results when loading along direction \(Z\) or along directions \(X\) or \(Y\) were negligible (less than 1%), meaning that the effective elastic modulus is not influenced by the loading direction in the REV.  
The [PERSON]'s ratio is evaluated based on the transverse contraction strain \(\epsilon_{trans}\) and the longitudinal strain \(\epsilon_{long}\):  
\[v_{t}\cong-\frac{\epsilon_{trans}}{\epsilon_{long}}\cong-\frac{\Delta_{ trans}}{\Delta_{long}} \tag{2}\]  
where the transverse displacement \(\Delta_{trans}\) is calculated by drawing the REV deformed configuration, and taking a linear regression along the faces parallel to the direction of loading, as suggested in [3; 26].
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments
### Performance across Different Databases  
Table 6 presents the SROCC results of the cross-database test on LIVE, CSIQ, TID2013, and LIVEC to test the generalization performance of MT-IRN and compare it with the state-of-the-art methods.  
Overall, in 12 tests, MT-IRN achieves the top two performances in 11 of them, outperforming the other methods. When a cross-database test is conducted on the synthetically distorted databases of LIVE, CSIQ, and TID2013, most methods achieve good performance as the distortion types are relatively similar among the databases. However, TID2013 contains more complex distortion types, and the performance of the model will decline when it is tested on this dataset. Nevertheless, MT-IRN still achieves the highest SROCC, demonstrating its good generalization performance. When a cross-database test between synthetically and authentically distorted databases is conducted, many methods struggle to achieve good performance. However, MT-IRN achieves the top two performances in all the tests, surpassing other deep learning-based and GAN-based methods.
## 4 Materials and Methods
### Decomposition of the HRV Signals  
#### 4.2.1 Emd  
The decomposition of the signals using EMD is a relatively new nonlinear signal processing method. It is employed for the processing of nonlinear and non-stationary signals [11]. Herein, the basis functions for decomposition are derived from the signal itself, unlike the Fourier or wavelet transforms where the basis signal is pre-fixed. The EMD method decomposes a given signal into several intrinsic mode functions (IMFs) and a residue (Equation (1)) using the process regarded as sifting [50]. The IMFs are a set of amplitude and frequency (AM-FM) modulated signal components. They satisfy two criteria: (i) the number of maxima and minima should be either the same or vary only by one to the number of zero crossings, and (ii) the average value of the envelopes formed by connecting local maxima and minima of the signal is zero [8]. As the HRV signals are nonlinear and non-stationary [38], the decomposition of the HRV signals was carried out by EMD. The implementation of the decomposition process was carried out in MATLAB software (2015a, MathWorks Inc., Natick, MA, USA).  
\[x(t)=\sum_{k=1}^{M}f_{k}(t)+r_{M}(t) \tag{1}\]  
where \(M\) represents the number of IMFs,\(f_{k}(t)\) indicates the \(k\)th IMF, and \(r_{M}\) corresponds to the residue.
# A New Sum-Channel Radiating Element for a Patch-Monopole Monopulse Feed
###### Abstract  
This article describes a new design for the sum-channel, circularly-polarized, radiating patch element of a multimode patch-monopole monopulse feed. Such feeds are suitable for the prime-focus illumination of a deep symmetric reflector. A relatively simple and compact feed design allows for a compact single-mirror antenna with monopulse tracking. The feed is about a wavelength in diameter, also making it suitable for illuminating smaller antennas, for example, in LEO-satellite ground (client) stations. The design applies a slotted circular patch as a circularly-polarized sum-channel element. The design variables are optimized mainly for the sum-channel aperture illumination efficiency in an S-band satellite ground station. After a few cut-and-try iterations, the final feed prototype was produced and measured. In addition to an additional degree of freedom in the geometry and potentially easier fabrication, the new sum-channel radiating-element shape allows for a slightly better monopulse channel isolation, and a higher radiation-pattern symmetry compared to the previous design.  
monopulse antenna; multimode antenna; prime-focus feed; circularly-polarized patch +
Footnote †: journal: Electronics Letters  
1
Footnote 1: email: [EMAIL_ADDRESS]  
2
Footnote 2: email: [EMAIL_ADDRESS]
# Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
## 4 Methodology
### Dataset Construction  
To demonstrate the validity and improvements of our approach, we installed a Davis Vantage PRO 2 weather station sensor [42] in each of the four GHs to collect temperature and humidity data with an accuracy of \(\pm 0.3\,\mathrm{\SIUnitSymbolCelsius}\) and \(\pm 2\%\), respectively. The four GHs contain cultivated tomato plants and are located in different areas of Pisa, Italy, which reduces the dependency between the collected data compared to having all GHs in the same location. Specifically, data were sampled every 15 min throughout the entirety of 2024, resulting in a dataset consisting of 35,136 samples per GH and monitored parameter, for a total of 281,088 recorded values.  
In Figure 3, we illustrate the average temperature and humidity for the four distinct GHs (represented by different colors) across the four seasons.  
From Figure 3, it can be observed that the mean temperature varies significantly across seasons compared to the variation in mean humidity. Moreover, among the different GHs, the temperature variation is more pronounced during summer and winter, whereas the variability in humidity remains more uniform across the GHs.  
Conversely, Figure 4 shows the standard deviation of temperature and humidity across the GHs and throughout the seasons.  
Regarding the standard deviation of temperature, it can be noted that the four GHs exhibit different behaviors. On the other hand, the humidity trend appears more uniform across the GHs, except for a peak during winter in GH3. Indeed, the fact that they are located in different areas reinforces the idea that the data from the various greenhouses are independent of one another.  
Additionally, Table 2 presents the minimum and maximum temperature and humidity values for each GH, as well as the global values for the dataset, which are later utilized in Section 4.3.
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
## 3 Results and Discussion
### Chromatographic and Mass Spectrometric Conditions Optimization
A majority of the IsoP were completely depleted from the charcoal-stripped plasma, however, trace residual peaks for some IsoPs remained even after stripping with charcoal. For these IsoPs, the background peak area was subtracted from the peak area of the calibration curve standards in order to construct the calibration curves with acceptable accuracy and precision. Using analyte/IS peak area ratios, the recoveries of IsoPs in the charcoal-stripped serum were similar to those in unstripped plasma (data not shown), indicating a similar matrix effect for the study samples (unstripped plasma) and calibration curve (stripped plasma). IsoPs were first isolated from the matrices by solid-phase extractions (SPE) and evaluated using three different SPE cartridges, namely the silica-based C8 and C18 phase cartridge and an Oasis HLB cartridge. The extraction efficiency was improved using an elution combination of NH\({}_{4}\)OH:2-Propanol: \(n\)-Hexane (1:7:12, \(v/v/v\)). We found the stability of the analytes to be influenced by pH, being most stable in basic conditions. Both the desorbing solvent and its volume were investigated to ensure effective elution of the analytes from the sorbent and extraction stability. The RAM-1147 (structural analogue) was selected as the IS in this method [28]. The IS selected for this method is an isoprenoid triazole bisphosphonate, and therefore serves as a structural analogue to IsoP. The IS had similar chromatographic behavior without prolonging the analysis time, and had a similar ionization response in the ESI mass spectrometry to that of analytes. In addition, the extraction recovery of the IS (\(>\)70%) was satisfactory and was stable during the analytical process. Figure 3: Representative MRM ion-chromatograms of (**a**) blank charcoal-stripped plasma (GPP), (**b**) GPP spiked in charcoal-stripped plasma (rt, 3.6 min,10 ng/mL), (**c**) Blank human plasma sample of GPP, (**d**) blank charcoal-stripped plasma (FPP), (**e**) FPP spiked in charcoal-stripped plasma (rt, 5.8 min,10 ng/mL), (**f**) Blank human plasma sample of FPP, (**g**) blank charcoal-stripped plasma (GGPP), (**h**) GGPP spiked in charcoal-stripped plasma (rt, 7.5 min,10 ng/mL), (**i**) Blank human plasma sample of GGPP, (**j**) blank charcoal-stripped plasma (RAM1147), (**k**) RAM114 spiked in charcoal-stripped plasma (rt, 4.7 min,10 ng/mL), (**l**) Blank human plasma sample spiked with RAM1147.
## 2 Related Work
### Original Sine Cosine Algorithm (SCA)  
A recent development of the SCA metaheuristic algorithm was made according to the mathematical sine cosine functions, followed by its implementation for the utilization and investigation of an optimized use on the global level. This algorithm was created by [PERSON] in 2016 [13]. The SCA also corresponds to other population-based metaheuristic algorithms, in which it begins with a random distribution of a category of solutions. The placement improvement in every search agent in this algorithm is conducted through Equations (1) and (2).  
\[X_{i}^{t}(j+1)=\left\{X_{i}^{t}(j)+r_{1}\times\sin(r_{2})\times \left|r_{3}P_{i}^{t}-X_{i}^{t}(j)\right|\right\},r_{4}<0.5 \tag{1}\] \[X_{i}^{t}(j+1)=\left\{X_{i}^{t}(j)+r_{1}\times\cos(r_{2})\times \left|r_{3}P_{i}^{t}-X_{i}^{t}(j)\right|\right\},r_{4}\ \geq 0.5 \tag{2}\]  
where, based on (1) and (2), \(X_{i}^{t}(j)\) refers to the placement of \(j^{th}\) search agent along the \(i^{th}\) dimension after the \(t^{th}\) iteration, while \(P_{i}\) refers to the placement of the destination point along the \(i^{th}\) dimension.  
Furthermore, \(r_{1},r_{2},r_{3}\) and \(r_{4}\) refer to the major four parameters in the SCA, with parameter \(r_{1}\) indicating the direction of movement inside or outside the area between the destination and resolution, as shown in Figure 1. Parameter \(r_{2}\) represents the random number in \([0,2\pi]\) as the outward movement represents positive sine and cosine of the investigation, while the ingoing movement represents negative sine and cosine. Parameter \(r_{3}\) denotes the weight to highlight (\(r_{3}>1\)) or reduce the highlight \(r_{3}<1\) on a random impact of location on the definition of distance. As a random value in \([0,1]\), parameter \(r_{4}\) identifies Equation (3), signaling the subsequent location. The calculation of the \(r_{1}\) value is based on Equation (3), in which the existing iteration is represented by \(t\), while \(T\) refers to the highest iterations, and the constant is indicated by \(a\), given as  
\[r_{1}=a-ta/T \tag{3}\]  
To utilize the search space in the SCA, the search agent relocation can be made at another resolution. In the search space investigation, the resolution can also be found outside of the equivalent locations.
# Bond Fatigue of TRC with Epoxy Impregnated Carbon Textiles
## 3 Results
### S-N Diagram  
The most common way of evaluating fatigue tests is to create S-N diagrams. Here, the number of cycles to failure \(N\) at a defined load level \(S\) can be read off. Figure 13 shows the S-N diagram for the present investigations. The two different minimum stresses are marked by two different colors. The related maximum stresses can be seen at the y-axis. The different anchorage lengths are defined by different symbols. Runouts are edged by a black line, and marked by an arrow with the number of non-failed specimens. For a better understanding, the S-N diagram is broken down by the anchorage lengths in Figure 10, and the results are listed in Table 11.  
As one can see in Figure 13, there is only a very small number of failed specimens. Only at high loads (\(\sigma_{\mathrm{max}}\geq\)85%) and with \(1a\) as the anchorage length did failure occur rapidly and assuredly. Specimens with anchorage lengths of \(2a\) or longer failed seldom, and even at high loads (e.g., \(\sigma_{\mathrm{max}}=95\%\)), runouts occurred.  
In the S-N diagram in Figure 14, the maximum stresses are shown as absolute stresses, ignoring the different minimum stresses. Furthermore, the mean tensile strength and its range of variation and the reference stresses of the different anchorage lengths are shown. It can clearly be seen that with  
Figure 12: Comparison of the normalized development of the secant modulus of different anchorage lengths at similar load level (here: \(\sigma_{\mathrm{min}}/\sigma_{\mathrm{max}}=50\%/85\%\)).  
Figure 13: S-N diagram with relative maximum stresses for DPO tests with different anchorage lengths and two different minimum stresses.  
shorter anchorage lengths (\(1a\) and \(2a\)), the transmittable load is quite low. Only with anchorage lengths of \(3a\) or \(4a\) can loads close to the tensile strength be reached. However, from \(3a\) to \(4a\), no significant increase in transmittable load can be noticed.
###### Abstract
[22], thanks to a multiciteria decision method for concrete optimization (CONCRETOP), have shown that the best concrete mixes in terms of both concrete properties and cost and environmental impact, are those manufactured with both FA and Rec additions, rather than with only FA or Rec. In particular, the Global Warming Potential (GWP) of concrete mixes depends on the FA and Rec dosage ratio rather than the dosage of the single materials [23]. Moreover, the GWP of Rec strongly depends on the transportation scenario, but this effect significantly decreases with FA addition [24]. Therefore, it has been already widely proved that replacement of Nat with Rec and the replacement of cement with HVFA, given a little bit of compromise towards strength and durability aspect, can give great benefits to both economic and ecological aspects. As reported above, many researchers have already studied the different properties of Rec concrete with HVFA. However, durability, which is a key property to ensure sustainable application of these materials in the construction sector, still needs more research to be fully investigated. In this field, in particular, the literature still reports very few works on the protection offered by HVFA and Rec concrete to the corrosion of reinforcing bars. [PERSON] et al. [25], thanks to the theoretical development, validation, and implementation of a 1D numerical service-life prediction model for RCA, have affirmed that the use of either FA or slag allows the achievement of a 50-year service life for Rec concrete in chloride-laden environments. By the salt ponding test, [PERSON] et al. [26] have stated that impermeable and high-quality Rec concrete, able to give high corrosion resistance to reinforcements, can be produced by replacing 20-30% cement with FA. In [PERSON] et al. [27], no significant differences in the corrosion resistance of reinforced Rec concretes, manufactured with 15% of spent fluid catalytic cracking catalyst and 35% of FA, and Nat concretes under a natural chloride attack have been observed. Moreover, in our knowledge, the corrosion behavior of galvanized steel reinforcements in reinforced RCA in HVFA has never been investigated. Therefore, the purpose of this work is to determine whether the sustainability issue introduced in concrete design by Rec and HVFA would have any adverse effect on the durability of reinforced concrete in terms of penetration speed of chloride and carbon dioxide, and in terms of corrosion of bare or galvanized steel reinforcement embedded in concrete, if cracked. To investigate the single and combined effect of Rec and HVFA addition on concrete properties, four different concrete mixes were prepared and compared:  
1. Nat, as reference;
2. Nat + FA with HVFA added at equal amounts as cement;
3. Rec, by replacing the 100% of Nat with Rec;
4. Rec + FA with HVFA. The different mixtures were compared in terms of mechanical performances, carbonation and chlorides penetration, and corrosion behavior of embedded bare and galvanized steel reinforcements.
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 4 Evaluation Results and Discussion
### Evaluation Metrics  
Evaluation metrics include metric to evaluate the accuracy of the IDS and the level of overhead to compare with the network operating without IDS. Metrics to evaluate the accuracy of an IDS is mainly the True Positive Rate and the False Positive Rate. The True Positive rate can also be called the Detection Rate as it shows how good the IDS is at detecting the malicious behaviors. On the other hand, the False Positive rate represents the falsification of the IDS as it shows how bad the IDS can be at falsifying to detect a legitimate behaviors. The formula to calculate these two metrics are:  
\[\textbf{True Positive Rate (TPR): }\text{TP}_{\text{rate}}=\frac{\text{TP}}{\text{TP}+ \text{FN}}\] \[\textbf{False Positive Rate (FPR): }\text{FN}_{\text{rate}}=\frac{\text{FP}}{\text{FP}+ \text{TN}}\]  
where  
* True Positive (TP): is the total correctly detected malicious behaviours. This happens when the IDS correctly raise alert on a malicious event.
* False Positive (FP): happens when the IDS erroneously raises a false alarm over a legitimate behaviour in the network.
* True Negative (TN): happens when the IDS correctly judging a legitimate behaviour as normal.
* False Negative (FN): happens when the IDS erroneously judging a malicious event as normal.  
Figure 6: Topology set up.  
The impacts to the resource consumption are evaluated utilizing the network energy usage and node power consumption as presented in [6]. The formula to calculate these metrics are as follow:  
\[\textbf{Energy usage}\left(\textbf{mJ}\right)\] \[=\left(19.5\text{mA}\times\text{transmit}+21.8\text{mA}\times \text{listen}+1.8\text{mA}\times\text{CPU}\right. \tag{1}\] \[+0.0545\times\text{LPM}\right)\times 3\;\text{V}/4096\times 8\] \[\textbf{Power Consumption}\;\left(\textbf{mW}\right)=\frac{\text{Energyusage }\left(\text{mJ}\right)}{\text{Time}\left(\text{s}\right)} \tag{2}\]  
The more energy and power the network uses, the shorter its lifetime is.