esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of HS to VM Consolidation
### Harmony Search for VM Consolidation  
VM placement allows decisions to select the source PM for power saving (or other purposes), selects the VM to migrate from the selected PM, and selects the destination PM to which the VM migrates. The proposed HS is described in this section and is designed to efficiently solve the VM placement model. The harmony considering rate (HMCR), which determines whether to create the new harmony based on the existing harmony or to generate randomly, pitch adjustment rate (PAR), which determines whether to consider the neighborhood values from the existing harmony, and bandwidth (BW), which determines the range of the neighborhood value, are the HS parameters that must be set separately.
## 4 Computational Simulations and Experimental Results
### System Simulation Design
#### 4.2.1 Frequency Detection Performance Analysis  
The analysis was conducted to evaluate the algorithm's capacity for frequency identification of photon pulse signals. Figure 6 illustrates the normalized spectral matching degree (NSM) against time \(t\), as the 00 codeword--corresponding to a carrier frequency of 40 kHz--energy distinct frequency detection windows at 40 kHz, 45 kHz, 50 kHz, and 55 kHz. Here, \(\lambda_{s}\) and \(\lambda_{b}\) maintain a 10:1 ratio, while the noise photons register at 2.5 \(\times\) 10\({}^{4}\) photons/s.  
It is evident that the spectral matching degree of the 40 kHz carrier signal, upon entering the 40 kHz frequency window, surpasses that of other frequency detection windows, peaking above 0.8. In contrast, the spectral matching degree of signals entering other frequency detection channels remains below 0.3. This substantiates the algorithm's efficacy in frequency detection for photon pulses, underscoring the effectiveness of segmenting photon pulses and subjecting them to frequency feature computation within frequency detection windows for accurate carrier frequency discrimination.
# Unraveling Patient Safety Culture Trends in U.S. Hospital Settings: A Yearly Retrospective Analysis
## 3 Results
### Patient Safety Culture Overview
Table 3 summarizes survey responses across 12 dimensions (D1-D12), categorized by region, hospital bed size, survey mode, staff surveyed, and hospital type. The results reveal notable variations in positive response rates, with ANOVA analysis indicating statistically significant differences (\(p<0.05\)) across most comparisons, particularly for region, bed size, and hospital type. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**PSC** & **Mean** & **SD** & **Min** & **10 th** & **25 th** & **50 th** & **75 th** & **90 th** & **Max** \\  & & & & **\%ille** & **\%ille** & **\%ille** & **\%ille** & **\%ille** & **\%ille** & **Max** \\ \hline D1 & 66\% & 9\% & 36\% & 56\% & 60\% & 66\% & 72\% & 77\% & 93\% \\ \hline D2 & 67\% & 7\% & 40\% & 58\% & 62\% & 67\% & 72\% & 76\% & 94\% \\ \hline D3 & 79\% & 6\% & 17\% & 72\% & 76\% & 80\% & 83\% & 87\% & 98\% \\ \hline D4 & 72\% & 7\% & 15\% & 63\% & 68\% & 73\% & 77\% & 81\% & 93\% \\ \hline D5 & 82\% & 6\% & 26\% & 76\% & 79\% & 82\% & 85\% & 88\% & 99\% \\ \hline D6 & 65\% & 7\% & 35\% & 56\% & 61\% & 65\% & 69\% & 73\% & 87\% \\ \hline D7 & 69\% & 8\% & 17\% & 59\% & 64\% & 69\% & 74\% & 78\% & 90\% \\ \hline D8 & 47\% & 9\% & 20\% & 36\% & 41\% & 46\% & 53\% & 58\% & 87\% \\ \hline D9 & 54\% & 9\% & 20\% & 42\% & 47\% & 53\% & 59\% & 66\% & 86\% \\ \hline D10 & 72\% & 9\% & 36\% & 60\% & 66\% & 73\% & 78\% & 83\% & 96\% \\ \hline D11 & 61\% & 10\% & 34\% & 50\% & 55\% & 61\% & 68\% & 74\% & 95\% \\ \hline D12 & 48\% & 11\% & 22\% & 35\% & 40\% & 46\% & 54\% & 62\% & 92\% \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \hline
**PSC** dimensions: D1: Overall Perceptions of Patient Safety; D2: Frequency of Events Reported; D3: Supervisor/Manager Expectations and Actions Promoting Patient Safety; D4: Organizational Learning; D5: Teamwork within Units; D6: Communication Openness; D7: Feedback and Communication about Error; D8: Nonpunitive Response to Error; D9: Staffing; D10: Management Support for Patient Safety; D11: Teamwork across Units; and D12: Handoffs and Transitions.  
\end{table} 
Table 2: Overall APR%, SD, and percentiles for the 12 PSC dimensions. Regionally, hospitals in the Central and South regions demonstrated the highest positive response rates across all dimensions, significantly exceeding those in the Northeast and West (\(p<0.001\)). Methodologically, the paper-based surveys yielded higher positive responses, especially in dimensions D9 and D12, where significant differences were observed.
# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 3 Proposed Approach
### Temporal Feature Extraction  
There remains temporal connectivity across sequential frames in sports videos. The recurrent neural network may play a crucial role in this regard. In RNN, not just the present input but also previously obtained inputs are taken into account to record the motion shifts in the successive frames. However, traditional RNN suffers from short term memory, i.e., it is incompetent in retaining information for longer periods. In this regard, LSTM, i.e., long short term memory, was developed as a remedy to short-term memory and vanishing gradient problems. LSTM was introduced to capture long term dependencies in sequential data with more controlling power on information flow. Moreover, LSTM resolves the vanishing gradient problem, as it possesses an additive gradient mechanism, which is contrary to the multiplicative gradient process of basic RNN that diminishes over the long sequence. However, it consists of a cell state and three gates, i.e., input gate, forget gate, and output gate. The cell state retains salient information over a time interval, and the gates control the information flow across the cell. The input gate determines which new information is going to be saved by the cell state. On the other hand, the forget gate elects which information will be eliminated from the cell state. Additionally, the output gate determines the outcome of the current LSTM cell, named as the hidden state. The equations used for the input gate, forget gate, cell state, and output gate are given below:  
\[i_{t}=\ \sigma\left(W_{l}\cdot\ cnt\ (H_{t-1},\ X_{t})\ +\ b_{l}\right) \tag{1}\]  
\[f_{t}\ =\ \sigma\left(W_{f}\cdot\ cnt\ (H_{t-1},\ X_{t})\ +\ b_{f}\right) \tag{2}\]  
\[C_{l}\ =\ f_{t}*C_{t-1}\ +\ i_{t-1}*tanh\ (W_{c}\cdot\ cnt\ (H_{t-1},\ X_{t})\ +\ b_{c}) \tag{3}\]  
\[o_{t}=\ \sigma\left(W_{o}\cdot\ cnt\ (H_{t-1},\ X_{t})\ +\ b_{o}\right)*tanh\ (C_{l}) \tag{4}\]  
In this paper, the extracted spatial features of sequential frames by CNN are fed to an LSTM layer for analyzing them in order.  
In the above equations, \(i_{t}\), \(f_{t}\), \(C_{t}\), and \(o_{t}\) refer to input gate, forget gate, cell state, and output gate, respectively. \(W_{l}\), \(W_{f}\), \(W_{c}\), and \(W_{o}\) are weight matrices, and \(b_{l}\), \(b_{f}\), and \(b_{c}\) are bias values of input gate, forget gate, cell state, and output gate, respectively. Additionally, \(H_{t-1}\) refers to the previous hidden state, \(X_{t}\) is the current input, \(*\) signifies pointwise multiplication, \(cnt\) stands for the concatenation operation, and \(\sigma\) and \(tanh\) represent activation functions.
To accomplish this task, the proposed method is divided into two differentiated stages: (i) dimensionality reduction, responsible for eliminating irrelevant individual features, and (ii) a group feature ranking by using random forest to assess the global importance of each feature cluster. This study demonstrates that the proposed method achieves competitive performance in machine learning tasks compared to existing methods. On the other hand, the work of [PERSON] [21] introduces MBHO, a novel feature selection method MBHO, based on a modified black hole algorithm. The proposed algorithm utilises a mutation technique (inversion) and an enhanced fitness function that considers feature relationships and relevance to classification labels. The experimental results were performed on 14 benchmark datasets, demonstrating the effectiveness of MBHO for improving classification performance and reducing feature set size. Finally, the authors of [22] propose a self-adaptive feature words (SAFW) method to enhance the extraction of relational quintuples from unstructured text for scientific knowledge base construction. Unlike prior approaches that rely on explicit entity clues, SAFW generates feature words dynamically for each sample, improving correlation information in the knowledge graph. The model creates new word representations, integrating them into the original sentence to determine relation types and locate entities. Evaluations conductedon four publicly available datasets demonstrate the superior performance of SAWF when compared to existing benchmarks. By effectively utilising BERT as an encoding mechanism and incorporating previously discarded words, SAFW not only enhances the interpretability of knowledge graphs but also strengthens the representation of relationships within these graphs. This methodology exhibits applicability to hidden knowledge discovery scenarios and diverse vertical domains, opening up avenues for novel insights. Despite the above-mentioned works in the context of the machine learning-based feature extraction and selection approaches, this field of computer science includes major challenges that have yet to be resolved. We sincerely hope that readers enjoy the Special Issue and find it worthy of understanding the real value of textual information compiled worldwide. We thank all authors for their contributions to this Special Issue and the reviewers for their efforts to improve the quality of the collected papers. SING research group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its information technology (IT) infrastructure. The author declares that he has no conflicts of interest regarding the publication of this paper.
## 1 Introduction
Moreover, due to interference from the cloudy and rainy climate, the limited data availability made the exploration of glacial lake distribution and evolution patterns even more challenging. Synthetic aperture radar (SAR) data has the advantages of all-weather and full-time capabilities. This study utilizes the free availability and short revisit cycle (six days) of Sentinel-1A/1B SAR data, and aimed to develop an effective, automated, and high-temporal-resolution methodology for extracting the glacial lake outlines. We combined the full time series of the Sentinel-1A/1B data over the southeastern Tibetan Plateau using a pixel-compositing approach involving derived backscattering coefficient stacks, examined medians to robustly estimate the glacial lake extent for each date using an improved level-set method, and demonstrated the ability of this median-based compositing approach to deal effectively with data quality, abnormal values, and other issues that commonly affect single-scene methodologies.
## 4 Cluster Analysis Based on K-Means Algorithm
### Route Distribution Clustering Method  
The observed value is a vector of cumulative distribution data for different road segments in the road network. For example, the cumulative distribution vector of the road segment number \(i\) is \(s_{i}=[p_{i-1},p_{i-2},\ldots,p_{i-q}]\), \(q\) is the number of values taken in order from the continuous cumulative distribution function, and \(p_{i-q}=1\). A road network with \(n\) routes contains \(n\) cumulative distribution vectors, \(s_{1},\ldots,s_{i},\ldots,s_{n}\). The specific implementation steps of clustering are as follows.  
1. For each road section, based on Equations (4) and (5), the discrete cumulative frequency distribution data of road performance indexes are continuous to obtain a continuous equation.
2. For each road section, based on the value of the road performance index from small to large, that is, the independent variable of the function, the cumulative distribution value is calculated to obtain a total of \(q\) independent variables, so when the independent variable is finally taken to be 100, the function value must be 1; and the cumulative distribution values of each road segment form a \(q\)-dimensional vector.
3. Determine the value of K by calculating the sum of squares of errors after clustering, and draw a graph of the relationship between the sum of squares of errors and the number of classes.
4. After setting the initial K value, perform K-means algorithm clustering, and classify the road sections with similar road performance level distributions into one category.
## 5 Performance Analyses
### Closed-Form Expressions and Monte Carlo Simulation Comparison  
This section compares the results obtained with the closed-form expressions and the Monte Carlo simulation. The numerical results related to the closed-form expressions are due to obvious reasons reported based on \(\mathbb{P}_{\text{e}}\times P_{\text{T}}\) (dBm) plots. In addition, the Monte Carlo simulations are reported in terms of \(\text{BER}\times P_{\text{T}}\) (dBm) plots. As the lack of CSI at the transmitter is assumed, we apply UA together with adaptive modulation, meaning that all data communication schemes allocate the same amount of power and bits. Moreover, the adaptive modulation configuration is set only to perform the squared 16-QAM constellation, the frequency domain equalizer C-ZF is adopted, \(L_{\text{CP}}=14\), \(\Delta=7\), \(P_{\text{T}}\in[-10,40]\) (dBm), and the PLC channel is corrupted by AWGN. To perform the Monte Carlo simulation, we assumed the transmission of \(10^{7}\)-equiprobable \(2N\)-length symbol blocks, which are constituted by the points of the square 16-QAM constellation, resulting in the transmission of \(1.024\times 10^{10}\) bits.  
Figure 8 shows the numerical results for this comparison. There is a perfect agreement between the numerical results attained by the closed-form expressions (i.e., the \(\mathbb{P}_{\text{e}}\) curve) and Monte Carlo simulation (i.e., the BER curve). Moreover, after a certain value of \(P_{\text{T}}\), its increase does not manage to improve the values of \(\mathbb{P}_{\text{e}}\) and BER. This behavior  
is expected since we consider the presence of the CP length violation and the STO. As a result, the SINR will reach the \(\mathbb{P}_{\rm e}^{\rm LB}\) as \(P_{\rm T}\) increases; see (20). Consequently, both \(\mathbb{P}_{\rm e}\) and BEP will reach a lower bound, as illustrated in Figure 8 by the horizontal lines. Since the numerical results obtained with the closed-form expressions and Monte Carlo simulation are in agreement, the following sections present only the numerical results obtained using the closed-form expressions.
# A Convenient Way to Determine the Optimum Angle of Incidence of Fizeau Interferometer
## 2 Analysis of Spatial Light Intensity Distribution
The optical path difference between the incident wavefront and the mth outgoing wavefront to point P is  
\[\Delta L\ =\ L_{m}-L_{0} \tag{3}\]  
when the beam exits from a parallel plate (\(z_{0}\geq d\)), the optical path from the incident wavefront and the mth exit wavefront to point P satisfies  
\[L_{0}\ =\ (z_{0}-d)\cdot cos\theta+y_{0}\cdot sin\theta+d\cdot\sqrt{n^{2}- sin^{2}\theta} \tag{4}\]  
\[L_{m}\ =\ (z_{0}-d)\cdot cos(\theta+2 ma)+y_{0}\cdot sin(\theta+2 ma)+d \tag{5}\]  
At this time, the optical path difference between the incident wavefront and the mth outgoing wavefront to point P is  
\[\Delta L\ =\ L_{m}-L_{0}= (z_{0}-d)\cdot(cos(\theta+2 ma)-cos\theta)+y_{0} \tag{6}\] \[\cdot(sin(\theta+2 ma)-sin\theta)+d\] \[\cdot\Big{(}\sqrt{n^{2}-sin^{2}(\theta+2 ma)}-\sqrt{n^{2}-sin^{2} \theta}\Big{)}\]  
when m\(\alpha\) approaching zero, Equation (6) can be expressed in a simplified form by use of the Taylor-series expansion  
\[sin(2m\alpha)\ =\ 2 ma-\frac{(2m\alpha)^{3}}{6}+(2 ma)^{5} \tag{7}\]  
Figure 2: The plane wave is incident into the wedge-shaped cavity and forms multiple new plane wavefronts after multiple reflections by the wedge-shaped cavity, and produces coherent amplitude superposition at a point P in space: (**a**) amplitude coherent superposition when point P is inside the parallel plate; (**b**) amplitude coherent superposition in the air after the beam exits through the parallel plate. \[L_{m}\ =\ z_{0}\Big{(}n^{2}-sin^{2}(\theta+2 ma)\Big{)}^{\frac{1}{2}}+y_{0}sin( \theta+2m\alpha) \tag{2}\]0  
In the approximation we collect all the term in Equations (7) and (8) up to the second power of \(m\alpha\). The optical path difference between the incident wavefront and the mth outgoing wavefront to point P becomes  
\[\Delta L\ =\ \left\{ \begin{array}{l}2 ma\cdot y_{0}-\left(\frac{2m\theta \alpha}{n}+\frac{2m^{2}\alpha^{2}}{n}\right)\cdot z_{0}\ \ \ \ \ \ \ \ \ \ \ z_{0}\in[0,d]\\ 2 ma\cdot y_{0}-\left(2m\alpha\theta+2m^{2}\alpha^{2}\right)\cdot\left(z_{0}+ \left(\frac{1}{n}-1\right)\cdot d\right)z_{0}\in(d,\infty)\end{array} \right. \tag{9}\]  
If the amplitude transmission coefficient of the parallel plate surface close to the focal plane array is t, the amplitude spatial distribution is  
\[E(y_{0},z_{0})=\]  
\[\left\{ \begin{array}{l}E_{0}\sum\limits_{m=0}^{M}R^{m}exp\left\{ik\left[2m \alpha\cdot y_{0}-\left(\frac{2m\theta\alpha}{n}+\frac{2m^{2}\alpha^{2}}{n} \right)\cdot z_{0}\right]\right\}z_{0}\in[0,d]\\ E_{0}\sum\limits_{m=0}^{M}tR^{m}exp\left\{ik\left[2m\alpha\cdot y_{0}-\left(2m \alpha\theta+2m^{2}\alpha^{2}\right)\cdot\left(z_{0}+\varepsilon_{z}\cdot d \right)\right]\right\}z_{0}\in(d,\infty)\end{array} \right. \tag{10}\]  
Among \(\varepsilon_{z}\ =\ \frac{1}{n}-1\). Under the paraxial condition, the spatial distribution of amplitude is  
\[E(y_{0},z_{0})\ =\ \left\{\begin{array}{l}E_{0}\sum\limits_{m=0}^{M}R^{m} exp\left\{ik\left[2m\alpha\cdot y_{0}-\frac{2n^{2}\alpha^{2}}{n}\cdot z_{0} \right]\right\}\
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Building-Integrated Photovoltaics and Rooftop Photovoltaics
#### 3.3.2 PV Systems on Rooftops  
PV on rooftops offer grid integration opportunities due to their proximity to the point of consumption. The utilization of solar PV electricity on rooftops can also reduce the distribution losses, which in 2021 accounted for 6% or 170 TWh [28; 29].  
According to conservative estimates, the EU's rooftop solar potential stands at a minimum of 560 GW, capable of generating 680 TWh of electricity annually [30]. Harnessing this vast potential, a significant portion of the installations will need to be undertaken by individuals, either through rooftop systems on single-family homes or collectively owned systems on multi-unit apartment buildings [31; 32]. Moreover, there is growing interest among citizens in harnessing solar energy for self-consumption, both with and without energy storage, as well as in installing solar systems on shared apartment buildings [33].  
The revised Energy Performance of Buildings Directive, which includes the concept of Nearly Zero-Energy Buildings (NZEBs), was published in May 2024 [34]. Once implemented by EU Member States, it is expected to further boost the adoption of rooftop solar energy systems. Furthermore, various municipalities are either considering or have already introduced regulations mandating the installation of renewable energy systems in new constructions. If each of the 2 million new residential buildings constructed annually in the EU were equipped with 4 kWp of rooftop PV, approximately 8 GWp of additional capacity could be added every year [35]. This could lead to an additional 48 GWp of rooftop PV capacity by 2030.  
The full upscaling of the building-integrated photovoltaics market requires actions related to PV module and Balance of System (BoS) technology development, business models, design, and energy integration (TRL 4-8) [19]. In addition, clarity regarding PV in building regulations at the regional, national, and EU level are essential to avoid fragmentation (TRL 8-9).  
As of 12 March 2024, the European Parliament has implemented the EU Solar Standard [36]. This regulation requires the installation of rooftop solar energy systems in all new public and commercial buildings exceeding a floor area 250 m\({}^{2}\) by 2026. In addition, this includes the installation of rooftop PV in existing buildings of the same size undergoing renovations by 2027, and all new residential buildings by 2029.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 4 Design of Pido
### Delay Modeling in Intertidal WSN
Therefore, \(ld_{ij}(t)\) makes an important part of delays and needs to be taken into consideration for route selection in IT-WSNs. **(2) Node Delay:** The node delay \(nd_{i}(t)\) is associated with node conditions such as buffer occupancy and node status. For a newly arriving packet, the node delay \(nd_{i}(t)\) is the waiting time for the packet to be sent. \(nd_{i}(t)\) is time-varying and is related to the number of already buffered packets in the node, the processing capability of the current node as well as the node's future status. The delay of node \(i\) (\(nd_{i}(t)\)) can be defined as:  
\[nd_{i}(t)=\left\{ \begin{array}{ll}\frac{B_{i}(t)}{C_{i}(t)}*T,&S_{i}^{{}^{ \prime}}(t+\tau)=1,\\ \infty\,&S_{i}^{{}^{\prime}}(t+\tau)=0,\end{array} \right. \tag{4}\]  
where \(B_{i}(t)\) and \(C_{i}(t)\) are the buffer condition and process capability of \(Node\)\(i\) at time \(t\). Note that nodes in IT-WSN keep generating and storing packets locally during the under-water period, which lead to the number of buffered packets considerable. Both \(B_{i}(t)\) and \(C_{i}(t)\) are time variant. **Buffer condition (\(B_{i}(t)\)):** Buffer condition is the \"waiting list\" of a node, and can be read from registers. When there are too many packets in the buffer, the node will set a buffer alarm to avoid buffer overflow. **Processing capability (\(C_{i}(t)\)):**\(C_{i}(t)\) is the average process capability during the last \(n\) periods. We define:  
\[C_{i}(t)=Avg.\{C_{i}(t-\lambda)\},\lambda=1,...,n \tag{5}\]  
and we set \(n=10\) in IT-WSN. \(S_{i}^{{}^{\prime}}(t+\tau)\):** In the definition of \(nd_{i}(t)\), we introduce \(S_{i}^{{}^{\prime}}(t+\tau)\) as the predicted status of \(Node\)\(i\) at time \(t+\tau\). Here, \(\tau\) is a parameter that can be set by users, which reveals the tolerance of waiting. If a node is still available at time \(t+\tau\), we consider it as a valid neighbor for packet forwarding, otherwise the \(nd_{i}(t)\) will be set as infinite. The average \(ld_{ij}(t)\) is nine cycles in IT-WSN as shown in Figure 3, thus we set \(\tau=10\) in our system. We illustrate how to obtain \(S_{i}^{{}^{\prime}}(t+\tau)\) in Section 4.3.