esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
Hence, a combiner matrix is typically used to combine the received signals from PD at the receiver. The combiner matrix \(\mathbf{F}^{T}\in\mathbb{R}^{N_{s}\times N_{r}}\) is used to combine the received signal across \(N_{r}\) PDs. Assuming independent numbers of received photons across all PDs, the combined signal \(\mathbf{F}^{T}y\) can be used to estimate the coded bit. This involves decoding the received signal and extracting the transmitted information from it.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology
### Hyperparameter Tuning on HDBSCAN
#### 3.5.1 Min_Samples  
_Min_samples_ is a parameter that is identical in both HDBSCAN and DBSCAN, as HDBSCAN is an extension of DBSCAN. The _min_samples_ parameter determines the algorithm's sensitivity to density variations within the data. In DBSCAN, it specifies the minimum number of points required to consider a point as a core point (i.e., a point surrounded by a minimum number of nearby points within a certain distance which the algorithm uses to identify clusters with high point density), effectively influencing how the algorithm differentiates between dense regions and sparse regions. This parameter indirectly controls the minimum-density threshold for forming clusters, with higher values resulting in stricter density requirements. Conversely, lower values relax the density criteria, enabling the detection of clusters in less dense areas.
## 3 Results and Discussion
### Energy Conversion of Two T-Section Prisms in Tandem
#### 3.2.2 Typical Energy Conversion of Soft Galloping (SG)  
In Figure 14, in the range of \(4.75\leq U_{r}\leq 12.25\), the four active power curves increased with an evident upward trend as the velocity increased. The energy conversion of UTP was similar to that of STP, but the active power and efficiency of UTP were a little lower than those of STP in the galloping branch. For \(U_{r}\leq 8.5\), the suppression of energy conversion of DTP was obvious, so the active power and efficiency of DTP were lower than those of UTP and STP due to the wake of UTP. In the galloping branch (\(10.375\leq U_{r}\leq 12.25\)), the active power and efficiency of DTP were also lower than those of STP, as shown in Table 6.  
Figure 14: Energy conversion curves of UTP and DTP for \(L\)/\(D=6\), \(R_{L}=21\)\(\Omega\) (\(\zeta_{total,UTP}=0.153\), \(\zeta_{total,DTP}=0.152\)) compared to STP: (**a**) Active power; (**b**) Energy conversion efficiency.  
Figure 13: The instantaneous voltage and active power of different reduced velocities for HG: (**a**) Upstream prism; (**b**) Downstream prism.  
In the VIV lower branch, the wake of UTP had a positive influence on DTP, the system of DTP output had more active power than that observed for STP and UTP, but the instantaneous voltage fluctuated, as shown in Figure 15. The local maximum active power of DTP reached 2.80 W, and the corresponding efficiency was 0.85%.  
In summary, the global energy conversion optimum branch was the galloping branch. The active power and efficiency of UTP and DTP were lower than those of STP with the same configuration. However, the active power of UTP + DTP was about 1.5 times that of STP. In the tests, mutual interference between UTP and DTP transformed from negative to positive as the velocity increased.
# Electricity Generation in LCA of Electric Vehicles: A Review
## 3 Spatial boundary.
### Data Quality
#### 3.3.2 Grid Databases  
National grid databases offer updated information on electricity generation. However, they are not intended primarily for LCA applications. They generally provide up-to-date and detailed  
\begin{table}
\begin{tabular}{l l l l} \hline  & **Sylised State** & **g CO\({}_{2}\)-eq/kWh** & **g CO\({}_{2}\)-eq/km** \\ \hline [PERSON] et al., 2014 [2] & Wind & 11 & 1.50 (WW) \\ [PERSON] et al., 2015 [57] & Wind & 11.2 & - \\ [PERSON] et al., 2017 [40] & Wind & - & 1.50 (WW) \\ Crossin and Doherty 2016 [25] & Wind & 24 & 76 \\ [PERSON] et al., 2015 [23] & Wind (average efficiency in 2012) & - & 70.4 \\ [PERSON] et al., 2015 [23] & Wind (average efficiency in 2030) & - & 58.7 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._information on electricity generation throughout the region they cover and updated and transparent stack emissions. Furthermore, depending on the dataset, they can provide also aggregated indicators (CO\({}_{2}\)-eq, etc.), but they do not provide upstream information about the electricity produced.  
Articles that rely on this kind of database, thus, either integrate the analysis with other databases providing upstream emissions for each generation technology (e.g., [PERSON] et al. [46] rely on eGRID data (see later on) and integrate them with GREET model [92]), or neglect upstream emissions, focusing only on combustion emissions.  
eGRID is the most used database by articles assessing EVs introduction in the American grid. For every power plant in the United States, eGRID supplies detailed emissions profiles and Generation resource mix.  
It also provides updated average generation emissions [35; 37; 46], but due to its level of detail on power plants information it is the source for studies that require input for their model to detect the marginal technologies affected by BEVs introduction [33; 34].  
EPA's Continuous Emissions Monitoring Systems (CEMS) dataset provides hourly gross generation load, consumed fuel, and CO\({}_{2}\) emissions from direct combustion for all grid-connected electricity generating units with a capacity of 50 MWh or more in the continental United States from 1997 through the present. For this reason, it has been widely used by American studies applying regression models to identify the short term marginal mixes.  
Other studies look at national TSO and DSO in order to gather the most updated data available on national electricity mix ([PERSON] et al., 2017 [15] use data from Entso-e for European countries, [PERSON] from ELIA for Belgium [41], [PERSON] and [PERSON] from REN for Portugal [27], etc.) or to obtain non aggregated data (usually 1 h time resolution) to elaborate linear regression in order to determine marginal emission factors [9; 25; 28].  
[PERSON] et al. [51] draw the EU electricity mix for 2009 from European Environment Agency (EEA) and the upstream emissions are filled from the literature [93; 94; 95; 96]
## 4 Reliability Analysis of Electric Power Network
### Observations  
Based on the test calculation results the following observations have been made:  
* Looking at the reliability indices calculated for UDN and RDN one can certainly state that location and type of DG source does not directly affect the power supply reliability. The APR application in UDN definitely improves all the investigated reliability indices, i.e., SAIFI, CAIFI, SAIDI, CAIDI, EENS, ASAI, ASUI).
* Three considered variants of DG source location in AMDN have been compared. Looking at ASAI index, the highest value is for \"AMDN_1\" and the lowest one is for \"AMDN_2\". There is also an impact of DG types on both the interruption frequency and interruption duration.
* An impact of DG type on reliability indices is also noticeable for DNMG. For all considered DNMG variants, the lowest ASAI value is observed in \"Case_3\" with WTPP and PVPP connected to a microgrid. Analyzing three variants of microgrid locations, the highest ASAI value is observed for \"DNMG_1\". In addition, \"DNMG_3\" characterizes the lowest value of ASAI index.
* Both the interruption frequency (SAIFI, CAIFI) and interruption duration (SAIDI, CAIDI) for UDN are lower than for RDN.  
* The interruption frequency (SAIFI, CAIFI) calculated for RDN is about tenfold higher than for AMDN with DG unit connected (except the variant AMDN_3 in which the ratio is about four). An interesting observation about interruption durations is made. The values of CAIDI index are almost comparable to one another, but the SAIDI for AMDN is over twelvefold lower than for RDN structure. This observation shows the strongly irregular distribution of the repair duration in the analyzed AMDN structure.
* DNMG with connected LV microgrids does not significantly improve the power supply reliability in comparison to UDN with APR.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 2 System Model and Problem Formulation
### System Model
Therefore, it is significant to apply a compressed sensing technique for underwater data sampling.
## 2 Materials and Methods
### Decision Tree Method for Phenology Retrieval  
The decision tree method is a classification strategy to process non-linear relationships between features and classes without any assumptions about the data probability distributions and to handle data measured on different scales [40]. The decision tree or similar methods like the decision plane is effective in rice monitoring with compact polarimetric (CP) parameters or target decomposition parameters [5; 6; 9]. The decision tree is of a treelike structure. The leaves are class labels, and intermediate nodes are criteria with several possible outcome branches. The classification processes begin at the root note and intermediate notes until encountering a leaf. A typical binary decision tree is shown in Figure 5.  
Figure 4: A RADARSAT-2 image acquired on 2 July 2016. VH, VV, and HH bands were assigned as red, green, and blue colors, respectively. Study area is within the yellow box.  
In this study, four concerned phenological phases (i.e., transplanting, vegetative, reproductive, and maturity) need to be separated. Therefore, at least three thresholds are required. In order to distinguish rice phenology with the thresholds, one needs to ensure that backscattering coefficients or their combinations must have reliable separability among four critical phases. Since rice plants develop rapidly during the growing season and biophysical variables vary with phenological phases, the development phases are separable from each other. For example, distributions of the phases vs. VH backscattering coefficients are shown in Figure 6 using a boxplot. A division line near \(-20\) dB can divide the transplanting phase from the other three. Similar division lines between other phenological phases were also observed on other polarizations (as shown in Figure 7). Once segmental thresholds to delineate four phenological phases were determined, a decision tree classifier could be developed to separate each development phase using backscattering coefficients.
## 2 Materials and Methods  
### Models  
This work accumulated the performance and computational attributes of predictive modeling algorithms, categorized distinctly based on computational hardware requirements and model types. The implemented methods were grouped into two primary categories: DL-based and classical machine learning-based (ML-based). Within each category, further classification was applied to distinguish the sub-types of algorithms clearly. These models were executed across differing hardware configurations, including CPU-bound implementations and GPU-accelerated counterparts, thereby facilitating a comparative analysis.  
DL architectures comprised recurrent structures, feedforward designs, and GPU-enhanced adaptations. Recurrent structures, which incorporate temporal dependencies, were constrained to CPU execution. These encompassed Long Short-Term Memory (LSTM) [17], Recurrent Neural Networks (RNNs) [18; 19; 20], and Gated Recurrent Units (GRUs) [21], all of which retained sequence retention capabilities. Feedforward architectures [22], operating without explicit temporal dependencies, were similarly confined to CPU-based evaluation. In contrast, GPU-enhanced architectures exhibited a definable augmentation in computational throughput. These encompassed convolutional frameworks such as CNN-VGG16-TL (Transfer Learning) [23], alongside transformer-based structures with ViT-FT [24; 25] for a patch of 16 pixels. Additionally, recurrent architectures (LSTM, RNN, and GRU) and feedforward designs (Neural Net) were deployed in GPU settings, demonstrating heightened computational efficiency. Also, the architecture, CNN VGG-16 [26; 27], was exclusively designated for GPU execution, broadening the spectrum of neural configurations under investigation. The complete roster of algorithms, together with their category, GPU availability, and model type, is summarised in Table 1.  
The classical machine learning models were systematically classified into ensemble methodologies, discriminant-based techniques, statistical regressions, Bayesian probabilistic models, decision tree classifiers, instance-based learning, and baseline metrics. Ensemble methods, containing Random Forest [28], Stacking [29], Bagging [30], AdaBoost [31], Gradient Boost [32], and Histogram-Based Gradient Boosting (HGBT) [33; 34], were collectively executed using CPU. Notably, three models from the ensemble boosting family, CatBoost [35], LightGBM [36], and XGBoost [37], were implemented across both CPU and GPU environments, ensuring a circumscribed comparative assessment. Discriminant analysis comprised Regularized Discriminant Analysis (RDA) [38], Quadratic Discriminant Analysis (QDA) [39], and Linear Discriminant Analysis (LDA) [40], each employing distinct covariance estimation techniques. Linear methodologies incorporated Support Vector Machines (SVMs) [41]. Probabilistic classifiers followed a Bayesian framework, featuring GaussianNB [42], BernoulliNB [43], and MultinomialNB [44]. Additionally, Decision Trees (DT/C4.5) [45] adhered to recursive partitioning principles, while K-Nearest Neighbors (KNN) [46; 47] maintained an instance-based classification approach.
## 3 The End-to-End Instrument Performance Simulation System (EIPS) Framework
### EIPS Components
#### 3.2.4.2 Retrievals and performance diagnostics  
For testing and analysing the performance of an atmospheric remote sensing instrument, the information and error analysis model has been integrated with various useful OE-based retrieval diagnostics:  
1. _Jacobian Matrix:_ The elements of the Jacobian matrix provides the sensitivity of the satellite measurements with respect to the target atmospheric parameters. These Jacobians play an important role in the spectral channels design and placement in the EM spectrum. The mathematical expression for the Jacobian matrix is given by the following Equation (5): \[\mathbf{H}=\frac{\partial\mathbf{y}}{\partial\mathbf{x}}\] (5)
2. _Retrieval Errors:_ Retrieval errors are given by the square root of diagonal elements of analysis error covariance matrix \(\mathbf{A}\). Comparison of analysis errors with the background errors gives the reduction in the retrieval error, and which is also a measure of retrieval error performance of an instrument. Retrieval and background errors can be calculated by the mathematical expressions (6) and (7) respectively: \[\mathrm{a_{ii}}=\sqrt{\mathrm{diag}(\mathbf{A})}\] (6) \[\mathrm{b_{ii}}=\sqrt{\mathrm{diag}(\mathbf{B})}\] (7) where \(\mathrm{a_{ii}}\) and \(\mathrm{b_{ii}}\) are the elements on the ith row and ith column of \(\mathbf{A}\) and \(\mathbf{B}\) matrix respectively and \(\mathrm{diag}(.)\) denotes the leading diagonal of the matrix.
3. _Averaging Kernels:_ Averaging kernels are defined as the sensitivity of the retrieval state with respect to the true state of the atmosphere. Mathematically, these averaging kernels can be formulated as: \[\mathbf{U}=\frac{\partial\mathbf{\hat{x}}}{\partial\mathbf{x}}=\left(\mathbf{ H}^{\mathrm{T}}\mathbf{R}^{-1}\mathbf{H}+\mathbf{B}^{-1}\right)^{-1}\mathbf{H}^{ \mathrm{T}}\mathbf{R}^{-1}\mathbf{H}\] (8) where \(\mathbf{U}\) is the averaging kernel matrix,\(\mathbf{\hat{x}}\) is the retrieved atmospheric parameter and \(\mathbf{x}\) is true state of the atmospheric parameter.
4. _Degrees of Freedom for Signal (DFS):_ Information content is another important diagnostic for measuring the performance of an atmospheric observing system. Information content of satellite measurements can be estimated by comparing retrieval error covariance matrix \(\mathbf{A}\) with the background error covariance matrix \(\mathbf{B}\). In the context of EIPS framework, the Degrees of Freedom for Signal (DFS) has been incorporated as the measure of information content, and it is given by the Equation (9): \[\mathrm{d_{s}}=\mathrm{tr}\left(\mathbf{I}-\mathbf{A}\mathbf{B}^{-1}\right)\] (9) where \(\mathrm{d_{s}}\) is the degrees of freedom for signal, and \(\mathrm{tr}\left(.\right)\) denotes the trace of a matrix.
# Analysis and Experimental Research of a Multilayer Linear Piezoelectric Actuator
## 2 Structure and Working Principle  
### The Structure of the Piezo-Vibrator  
The structure of the piezo-vibrator in the MLPA is shown in Figure 1. The piezo-vibrator consists of a block of multilayer piezoelectric ceramic (PZT-81), a contactor made from alumina ceramic (99% Al\({}_{2}\)O\({}_{3}\)), and some Teflon wires. The multilayer piezoelectric ceramic is adhered with the contactor using epoxy glue. Then cross-wires are welded to connect the cross-zone of the electrode and signal wires and a GND (ground) wire are welded to connect the electric signal applied by a power supply.  
The structure of the multilayer piezoelectric ceramic is shown in Figure 2. It consists of two pieces of outer electrode, several pieces of inner electrode and thin slices of PZT-81. The structure is a sandwich one and it is fabricated with the method of low-temperature co-sintering. The electrodes can be divided into two groups based on their shapes and roles. One group, which is used as signal electrode, contains the electrodes with four separate electrode portions, and each portion has a finger  
Figure 1: Structure of the piezo-vibrator in the MLPA (multilayer linear piezoelectric actuator), A’, B’, C’, D’, E’ and F’ are silver.  
to connect with the other electrodes with the same structure (namely all the fingers labeled with A are connected together using silver A', as shown in Figure 1, and B, C, D can be done in the same manner). The other group, which is used as the GND electrode, contains the electrodes with a single piece, and each piece has two fingers to connect with other electrodes with the same structure (namely all the fingers labeled with E are connected together using silver E', as shown in Figure 1, and F can be done in the same manner). The polarization directions of each PZT-81 layer are parallel to the thickness direction and the adjacent layers possess the opposite directions. The multilayer piezoelectric ceramic is \(30\times 7.5\times 3\) mm\({}^{3}\) in size with 15 PZT-81 layers after optimizing the design, and the thickness of each layer is 200 \(\upmu\)m. For the piezoelectric ceramic chip, the relationship between the strain and electric field intensity can be described using piezoelectric Equations (1) [20]:  
\[x_{1}=d_{31}E_{3} \tag{1}\] \[x_{1}=\Delta L/L\] (2) \[E_{3}=V/t \tag{3}\]  
where \(x_{1}\) is the strain of the piezoelectric ceramic, \(L\) is the length of the piezoelectric ceramic, \(\Delta L\) is the deformation along the direction of the length when the voltage \(V\) is applied, \(t\) is the thickness of the piezoelectric ceramic, \(E_{3}\) is the electric field intensity applied parallel to the polarization direction.  
Then \(\Delta L\) can be described as the following Equations (4):  
\[\Delta L=d_{31}LV/t \tag{4}\]  
It can be concluded that when the thickness \(t\) decreases, the \(V\) needed decreases to get the same \(\Delta L\). This is one of the main reasons to configure the piezoelectric ceramic in a multilayer form.
## 5 Results and Discussion  
### Benchmark Test Functions  
In this section, the developed hybrid algorithm (PSOGSA) is benchmarked using ten standard test functions to verify its efficiency. The characteristics of these benchmark test functions are summarized in Table 1[40; 41]. The functions \(F_{1}(x)\) to \(F_{7}(x)\) are unimodal functions that are mainly used to investigate the convergence characteristics and optimiza  
Figure 5: The hybrid PSOGSA algorithm flowchart.  
tion accuracy of a given algorithm. Moreover, the functions \(F_{8}(x)\) to \(F_{10}(x)\) are multimodal functions that are mainly used to check whether an algorithm can avoid precocity and find global optimal solutions [40,41]. Each function dimension (n) is set to 30 to avoid random interference.  
For performance verification, the hybrid PSOGSA algorithm is compared with other optimization algorithms such as MVO, PSO, SCA, HBO, PO and GSA. The results of the benchmark functions are summarized in Table 2 based on the mean value and standard deviation (std) values for each function. It can be observed from Table 2 that the hybrid PSOGSA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability.
## 5 Results and Discussion
### Results of Factor Analysis  
Factor analysis was carried out to further explore the barriers of BIM implementation in this study. The KMO value of this study was 0.512, which is acceptable as it satisfies the threshold of 0.50. Values below 0.50 should lead the researcher \"to either collect more data or rethink which variables to include\" [120]. These results are illustrated in Table 5. The detailed picture of factor analysis of barriers to BIM implementation in sustainable building projects was composed of five groups. These groups were Group 1--Government-related barriers, Group 2--Market-related barriers, Group 3--Personal-related barriers, Group 4--Construction environment-related barriers, and Group 5--Cost-risk barriers.
###### Abstract
The proposed system is entirely RF-free, energy-efficient, and flexible. For making the system energy-efficient, a well-collimated RGB downlight to the vegetables is utilized. This downlight provides a well-defined photon flux for growing leafy vegetables and also allows the reflected light to be reused for communication. In this work, leafy vegetables such as lettuce are chosen to investigate since it is among the most studied species in indoor smart-farming using LEDs [16]. For growing lettuce, although the conventional red-blue LED combination is still effective, additional 24% green LED light can enhance the growth of the vegetable [17]. Vegetables have their own reflectance towards each color. Considering that the green light has the highest reflectance, here we propose to use the modularized concentrated green reflected light for the uplink communication. For a complete RF-free environment, a downlink constructed from the combination of white LEDs (ceiling lights) for workers in the farm and a photodiode installed in each smart-farming unit is investigated. The modularized and relocatable smart-farming units allow various types of vegetables to grow in the same unit farm. In Section 2, a description of the proposed smart-farming architecture and fundamentals of the photosynthetic photon flux density and its connection with communication parameters are presented. Section 3 describes the findings from a series of simulations, followed by proof-of-concept demonstrations showing the feasibility of the proposed architecture. Finally, discussions and conclusions are drawn in Section 4.