esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hs-Spm-Gc-Ms-O Analysis
#### 2.2.3 Identification and Quantitation Analysis of Volatile Compounds  
The identification of the volatiles was performed via the mass spectrometry library (MS), odor qualities (O), retention indices (RI), and compared with authentic flavor standards (S). The volatile compounds were identified according to the NIST 17 database and confirmed by RI. The RI was determined following the same chromatographic conditions with the standard C6-C30 n-alkane series. The formula used to calculate the RI was as follows: \(\mathrm{RI}=100n+100(t_{x}-t_{n})/(t_{n+1}-t_{n})\), where \(t_{x}\), \(t_{n}\), and \(t_{n+1}\) represented the retention times of compound x, alkane n, and alkane n + 1, respectively (\(t_{n}<t_{x}<t_{n+1}\)). Some odorants can be validated by comparing their odor characteristics and retention times to those of authentic aroma standards under identical chromatographic conditions. The aroma compounds in non-smoked bacon were semi-quantitated via a mass concentration of an internal standard. The concentration of flavor compounds was determined by computing the peak area ratio of the internal standard and the target substances. To further quantify the key aroma compounds in bacon, the volatile compounds with an FD factor \(\geq\)27 were screened to construct the calibration curves. The process for establishing the calibration curves was as follows: mixing different concentrations of authentic aroma compounds with an artificial odorless matrix, which were then analyzed through gas chromatography-selected ion monitoring (GC-SIM). The calibration curves represent the concentration ratio of volatile compounds to the internal standard and the peak area ratio of standard peak area to the internal standard as x and y, respectively. The preparation of the artificial odorless matrix followed earlier research [19]. Briefly, the mixture of diethyl ether and pentane (2:1, \(v\)/\(v\)) was added to the minced bacon samples and agitated for 8 h. After that, the samples were freeze-dried for 48 h at \(-\)60 \({}^{\circ}\)C in a freeze dryer. The operation was repeated at least three times until no volatiles were detected by GC-MS-O. The internal standard's recovery yield (\(R\)) was calculated to ensure accurate results. \(R\) is the ratio of the peak area of the internal standard added to the sample (Padded) to the peak area of the single internal standard (Preal). The recovery yield for 2-methyl-3-heptanone was 96.22%.  
The odor activity value (OAV) can assess the impact of volatile compounds on the overall aroma. It is calculated as the concentration ratio in the samples to those in the water's odor threshold.
## 2 Materials and Methods
### Statistical Analyses  
Prior to recruiting participants for our study, to ensure adequate statistical power for our reliability study with 8 repeated measurements, we calculated the required sample size using G*Power 3.1. We conducted an a priori power analysis for repeated measures ANOVA within factors. The analysis parameters included a moderate effect size (\(f\) = 0.25), a significance level (\(\alpha\)) of 0.05, a desired power (1-\(\beta\)) of 0.8, a single group, 8 measurements, an assumed correlation of 0.5 among repeated measures, and a nonsphericity correction (\(\epsilon\)) of 1.0. Based on these settings, the analysis determined that a total sample size of 16 subjects would achieve an actual power of 0.819. This sample size is sufficient to detect a moderate effect while minimizing the risk of Type II errors, thereby ensuring the reliability of the results in our repeated measures design.  
Descriptive data are presented as mean \(\pm\) standard deviation (SD). The normality of the data was assessed using the Shapiro-Wilk test. The relative reliability of the three different starting block positions was investigated using a \(t\)-test and intraclass correlation coefficient (ICC). Following [PERSON] et al. [19], we classified the magnitude of the ICC values through a qualitative scale: values close to 0.1 are considered low reliability; 0.3, moderate; 0.5, high; 0.7, very high; and those close to 0.9, extremely high [19]. We also examined the differences between the test and retest using different error measures. The sum of squared errors (SSE), the mean sum of squared errors (MSE), the root mean sum of squared errors (RMSE), and the percentage error was calculated.  
The following methods were used to study absolute reliability: standard error of measurement (SEM) as a percentage of the mean value of the measurements, standard error of estimate (SEE), the coefficient of variation (CV), and Bland-Altman plots [20; 21], with a value of SEM \(\leq\) 15% and CV \(\leq\) 10% considered acceptable [22]. The heteroscedasticity of errors was also identified in the Bland-Altman plots and defined as a coefficient of determination (r2) > 0.1. To estimate the smallest change in score that indicates a \"real change\" in 90% of the participants, the minimal detectable change (MDC\({}_{90}\)) was calculated.  
Finally, [PERSON]'s d was computed to quantify the magnitude of the difference between the test and retest. The scale used for the magnitude of the ES was specific to training research: negligible (<0.2), small (0.2-0.5), medium (0.5-0.8), and large (>0.8) [23].  
Separate analyses were performed for each position and strength manifestation. To interpret the observed magnitude of differences in the coefficients of variation of the two-strength manifestation, the mean of the CV of all conditions was made, and a default for the smallest important ratio of 1.15 was used [24].  
All analyses were performed using the Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, version 26.0; Armonk, NY, USA), and the level of significance was set at \(p<0.05\).
# Optimal Crew Scheduling in an Intensive Care Unit: A Case Study in a University Hospital
## 1 Introduction
[PERSON] (2020) focused on the analytical evaluation of waiting times using the Markov chain and uniformization method. Despite the vast research on hospital workforce scheduling, an integrated approach that simultaneously optimizes nurses, doctors, and caregivers remains largely unexplored. [PERSON] (1963) reviewed nurse and doctor scheduling but noted that most studies treat these as distinct problems with unique objectives and constraints. Existing models often lack flexibility, require significant computational time, or fail to account for interdependencies between different personnel groups. For instance, nurse scheduling models frequently disregard physician shift constraints, leading to inefficiencies and potential staff dissatisfaction. To address these gaps, this study proposes a unified Mixed-Integer Programming (MIP) model that optimizes shift scheduling for nurses, doctors, and caregivers within a single framework. By integrating these roles, the model ensures a balanced workload, fair shift distribution, and improved operational efficiency in Pediatric Intensive Care Units (PICUs). Unlike previous studies that focus solely on either nurse or doctor scheduling, this model accounts for interdisciplinary interactions and enhances hospital-wide scheduling efficiency. Furthermore, the proposed model is adaptable to different hospital settings, including emergency units, operating theaters, and general wards. By modifying its constraints and parameters, the model can accommodate various hospital policies and workforce structures. This study demonstrates that the MIP-based scheduling system significantly reduces scheduling conflicts, improves fairness, and enhances overall hospital efficiency compared to traditional manual methods. The model's scalability ensures its applicability in both small-scale hospitals with limited staff and large healthcare facilities with complex scheduling requirements. The remainder of this paper is structured as follows: Section 2 describes the methodology, including the problem description and limitations, mathematical model, and optimization approach. Section 3 presents the results, while Section 4 discusses the findings in relation to existing research. Finally, Section 5 concludes with recommendations for future research and potential applications of the model in different healthcare settings.
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
###### Abstract  
Orthogonal frequency division multiplexing (OFDM) is the key technology used in high-speed communication systems. One of the major drawbacks of OFDM systems is the high peak-to-average power ratio (PAPR) of the transmitted signal. The transmitted signal with a high PAPR requires a very large linear range of the Power Amplifier (PA) on the transmitter side. In this paper, we propose and study a new clipping method named Palm Clipping (Palm date leaf) based on hyperbolic cosine. To evaluate and analyze its performance in terms of the PAPR and Bit Error Rate (BER), we performed some computer simulations by varying the Clipping Ratio (CR) and modulation schemes. The obtained results show that it is possible to achieve a gain of between 7 and 9 dB in terms of PAPR reduction depending on the type of modulation. In addition, comparison with several techniques in terms of PAPR and BER shows that our method is a strong alternative that can be adopted as a PAPR reduction technique for OFDM-based communication systems.  
OFDM; high peak-to-average power ratio (PAPR); bit error rate (BER); high power amplifier (HPA); solid-state power amplifier (SSPA); clipping; palm clipping +
Footnote †: journal: Information Information
## 1 Introduction
**(e)** Magnitude and **(f)** phase profiles of the Fresnel reflection coefficients, and **(g)** magnitude and **(h)** phase profiles of the Fresnel transmission coefficients at the interface between air and ITO. All coefficients are calculated for normally incident light at visible frequencies. shown in Figure 5. The absorption band is centered at 470 nm, which is primarily caused by optical loss within the coated lossy dielectric, and the band starts to shift slightly from its original position as the angle of incidence increases beyond 20\({}^{\circ}\). For incident angles greater than 40\({}^{\circ}\), this absorption band is overlaid by new absorption modes that emerge for incident angles greater than 20\({}^{\circ}\). These modes redshift as \(\theta\) increases, which are denoted by the dashed oval and green-colored arrows in Figure 6a,b, respectively. On the other hand, the absorption band centered at 585 nm maintains its profile at normal incidence up to approximately 60\({}^{\circ}\). These results suggest that light absorption based on strong localization of GSPs in the grating exhibits a wider acceptance angle. Figure 4: (**a**) Spectral absorption in the visible region with respect to the thickness of the upper dielectric medium deposited on the Al subwavelength grating (\(p\) = 160 nm, \(w\) = 40 nm, \(t_{\rm g}\) = 30 nm), and (**b**) corresponding map for a flat Al reflector (\(w\) = 0). Spectral absorption profiles when \(t_{\rm d}\) = 180 nm (**c**) with and (**d**) without the grating structure. Figure 5: Distributions of the magnetic field within the structure when (**a**) 470 and (**b**) 585 nm transverse magnetic (TM) polarized plane waves illuminate the structure at normal incidence. The structural parameters are \(p\) = 160 nm, \(w\) = 40 nm, \(t_{\rm g}\) = 30 nm, and \(t_{\rm d}\) = 270 nm. The calculation domain includes two unit cell elements, and the color scale is properly normalized to compare the field magnitudes. From an experimental perspective, fabrication of the investigated structures would demand sophisticated techniques. The subwavelength grooves on the metal surfaces can be patterned by electron-beam lithography or, alternatively, by exploiting the spacer patterning technique, which is compatible with CMOS (complementary metal-oxide-semiconductor) processes. The ITO coating, including filling of ITO in very tiny spaces with a size of several tens of nanometers, would be possible via atomic layer deposition. Deposition should be followed by planarization of the top surface by chemical mechanical polishing.
## 3 DR and AGC Unit Capabilities
### Modeling of Residential Smart Appliances
#### 3.1.1 AC Model  
An AC system with a thermostat works in an \"on-off\" manner and the AC will simply run at its rated power when turned on. In general, a thermostat control is set so that the room temperature will fluctuate around the thermostat set point \(T_{\rm AC}^{\rm s}\) within the dead band of \(\pm T_{\rm AC}^{d}/2\).  
Controlling the AC load is carried out by adjusting cooling set points. The AC status is related to the current room temperature, cooling set point, and the thermostat dead band. The relationship is presented in Equation (3):  
\[p_{\rm AC}(t)=\left\{\begin{array}{ccc}P_{\rm AC},&T_{\rm AC}(t)\geq T_{\rm AC }^{\rm s}(t)+T_{\rm AC}^{d}/2\\ 0,&T_{\rm AC}(t)\leq T_{\rm AC}^{\rm s}(t)-T_{\rm AC}^{d}/2\\ p_{\rm AC}(t-1),&T_{\rm AC}^{\rm s}(t)-T_{\rm AC}^{d}/2\leq T_{\rm AC}(t)\leq T _{\rm AC}^{\rm s}(t)+T_{\rm AC}^{d}/2\end{array}\right. \tag{3}\]  
where \(p_{\rm AC}(t)\) is the working power of the air conditioner in time interval \(t\) (kW); \(P_{\rm AC}\) is the rated power of AC (kW); \(T_{\rm AC}(t)\) is the room temperature in time interval \(t\) (\({}^{\circ}\)F); \(T_{\rm AC}^{\rm s}(t)\) is the cooling set point in time interval \(t\) (\({}^{\circ}\)F); and \(T_{\rm AC}^{d}\) is the thermostat dead band (\({}^{\circ}\)F).  
The AC is controlled by changing the cooling set point \(T_{\rm AC}^{\rm s}(t)\). Increasing the cooling set point to some value can stop the AC working. The controlled formula is presented in Equation (4):  
\[T_{\rm AC}^{\rm s}(t)=s_{\rm AC}(t)\cdot T_{\rm AC}^{set} \tag{4}\]  
where \(s_{\rm AC}(t)\) is the DR control signal which is received from the in-home controller, and \(T_{\rm AC}^{\rm set}\) is the cooling set point (\({}^{\circ}\)F).  
For each time interval \(t\), the room temperature is calculated in Equation (5):  
\[T_{\rm AC}(t+1)=T_{\rm AC}(t)-\Delta t\cdot\frac{G(t)}{\Delta c}+\Delta t\cdot \frac{C_{\rm AC}}{\Delta c}\cdot\frac{p_{\rm AC}(t)}{P_{\rm AC}} \tag{5}\]where \(\Delta t\) is the length of time interval \(t\) (hour); \(G(t)\) is the heat gain rate of the house during time interval \(t\), positive for heat gain and negative for heat loss (Bth/h); \(C_{\text{AC}}\) is the cooling capacity (Bth/h); and \(\Delta c\) is the energy needed to change the temperature of the air in the room by \(1\)\({}^{\circ}\)F (Bth/\({}^{\circ}\)F).
### Review of Existing Methods and Approaches  
In recent years, various optimization techniques have been employed to enhance the efficiency of solar-powered UAVs, focusing on both energy management and aerodynamic performance. Traditional methods, such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), have been widely used to optimize UAV flight paths by dynamically adjusting to environmental conditions. These heuristic approaches are effective at finding solutions to complex problems and have been extensively discussed in the literature [1; 16].  
For instance, Genetic Algorithms mimic the process of natural selection to iteratively refine potential solutions, making them suitable for real-time UAV path optimization in dynamic environments [8]. Similarly, Particle Swarm Optimization is inspired by the social behavior of birds flocking together and has been successfully applied to enhance the adaptive capabilities of UAVs, particularly in uncertain and fluctuating environmental conditions [15; 20].  
Despite the effectiveness of these methods, they often require significant computational resources and may struggle with real-time adaptation when environmental variables change rapidly [14; 18]. Furthermore, conventional approaches tend to focus either on energy efficiency or aerodynamic optimization, rather than integrating both aspects holistically [19; 21].  
In contrast, this study leverages the Slime Mold Optimization (SMO) algorithm, a novel heuristic inspired by the foraging behavior of slime molds, to optimize the neural networks guiding UAV flight paths. The SMO algorithm not only balances exploration and exploitation effectively, but also adapts in real time to dynamic environmental changes, offering a more comprehensive solution for solar UAVs that must operate efficiently in varying conditions [36; 44].  
This approach represents advancement over traditional methods by integrating aerodynamic calculations and energy management strategies into a unified framework, thereby enhancing the operational duration and reliability of solar-powered UAVs [4; 17; 45].
## 5 Results and Analyses
### Prediction Models
#### 5.3.6 Statistical Tests  
We apply a one-way ANOVA to answer **RQ4** by comparing all prediction models across four scenarios using AUC, where Factor A in the ANOVA test is the prediction model (NB, SVM, KNN, and RF). The results are shown in Table 16. We rejected H0 because all \(p\)-values in the table are less than \(\alpha\) (0.05). According to the standards classifications of [PERSON] proposed in Section 4.3.2, the results of eta squared reveal that the effect size was large [57].  
Additionally, multiple comparisons were performed using [PERSON]'s confidence intervals [53] (see Figure 5). In the chart, it is possible to identify which pairs of Factor A (prediction models) significantly differ across scenarios. If a confidence interval does not include 0, then the pair is significantly different. The results obtained from Figure 5 revealed that there were significant differences between ensemble models (RF) and all individual models (NB, SVM, and KNN). Similarly, there were significant differences between NB-SVM and SVM-KNN.
## 2 Background
### TIBWB-OFDM Waveform  
The building process of the TIBWB-OFDM transmitted block \(\mathbf{X}\) consists of packing together a set of \(N_{s}\)-windowed OFDM symbols by a time-domain square-root-raised cosine (SRRC) window [23] of roll-off \(\beta\), followed by a time-interleaving (permutation) operation.  
Let \(\mathbf{S}_{i}=[S_{i,0},S_{i,1},\dots,S_{i,(N-1)}]^{T}\) denote the \(i\)th conventional \(N\)-carrier OFDM symbol packed within \(\mathbf{X}\), with \(i=0,\dots,N_{s-1}\), and let \(\mathbf{S}=[\mathbf{S}_{0},\mathbf{S}_{1},\dots,\mathbf{S}_{N_{s-1}}]\). The construction of block \(\mathbf{X}\) can be represented in matrix format as  
\[\mathbf{X}=\Pi^{(N_{k})}vect\left[\mathbf{A}\left((\mathbf{1}_{N_{s}}^{T} \otimes\mathbf{h}_{SRRC})\odot(\mathbf{1}_{2}\otimes\mathbf{FS})\right) \right], \tag{1}\]  
where  
* \(\mathbf{F}\) is the inverse discrete Fourier transform (IDFT) matrix with size \(N\times N\) ;
* \(\mathbf{h}_{SRRC}=[h_{-N},\dots,h_{-1},h_{0},\dots,h_{N-1}]^{T}\) is the SRRC is the SRRC of roll-off \(\beta\) where \[\mathbf{h}_{n}=\begin{cases}\begin{array}{cc}1,&|n|\leq\frac{N}{2}(1-\beta) \\ cos(\frac{\pi}{4\beta}[\frac{2n}{N}-(1-\beta)]),&\frac{N}{2}(1-\beta)\leq|n|< \frac{N}{2}(1+\beta)\end{array};\\ 0,&|n|\geq\frac{N}{2}(1+\beta)\end{array}\] (2)
* \(\mathbf{A}=[\mathbf{0}_{N(1+\beta)\times\frac{N}{2}(1-\beta)}\quad\mathbf{I}_ {N(1-\beta)}\quad\mathbf{0}_{N(1+\beta)\times\frac{N}{2}(1-\beta)}]\) is a truncation matrix that removes the trailing and ending rows of zeros that result from the windowing operation;
* \(\mathbf{1}_{\ell}\) is an \(\ell\)-length column vector of \(1\)s, \(\mathbf{0}_{m\times n}\) is \((m\times n)\)-size matrix of \(0\)s, and \(\mathbf{I}_{n}\) is an \((n\times n)\)-size identity matrix;
* \(vect()\) is the matrix-vectorization function that reshapes a matrix into a column by reading the matrix column by column;
* \(\odot,\otimes,\times\) denote, respectively, the Hadamard, Kronecker, and conventional matrix multiplications;
* \(\Pi^{(N_{k})}\) is the time-interleave permutation matrix [7].  
Lastly, previous to transmission, a zero pad (ZP) with length \(N_{ZP}\) greater than the propagation delay is added at the end of each TIBWB-OFDM block in order to avoid any intersymbol interference (ISI) [8].
## 3 Research Stream Analysis
### RS2: MC Technologies and Methods
#### 3.2.3 RS2.3: MC Technologies and Methods for Production Architecture
 } \\ \hline
[134] & 2017 & & & & & & & \(\checkmarkmark\) \\
[169] & 2017 & & & & & & \(\checkmarkmark\) \\
[23] & 2017 & & & & & & \(\checkmarkmark\) & \(\checkmarkmark\) \\
[149] & 2018 & &  
Advanced manufacturing technology (AMT) refers to modern technologies, including flexible manufacturing systems (FMS), computer integrated manufacturing (CIM), and cyber-physical systems (CPS), to improve productivity, flexibility, and cost efficiency [175; 202]. CIM systems evolved into CPS, integrating physical processes with software and global networks, enabling real-time communication and information exchange [150; 161]. CPS technology requires multidisciplinary collaboration and is essential in various fields, including manufacturing, healthcare, and autonomous systems [23]. FMS has been critical in achieving high flexibility and low cost in MC, offering systems that can quickly adapt to customer needs [14]. FMS adoption is still prevalent in industries like automotive manufacturing due to its ability to manage high variability with cost efficiency [156]. However, its flexibility is limited by the predefined boundaries of the system [160], leading to the rise of reconfigurable manufacturing systems (RMS), which offer greater adaptability to changing market demands [170]. RMS combines flexibility, modularity, and scalability, making it suitable for high-demand, MC environments [149]. RMS and modular product design are closely linked, with approaches for optimizing product-family configurations and manufacturing systems to handle variable customer demands efficiently [195]. Technologies like IoT and cloud computing enhance the adaptability of RMS and other manufacturing systems by facilitating real-time data exchange and predictive maintenance [23; 162]. The advent of I4.0 technologies is transforming manufacturing systems into \"smart\" factories, where the integration of IoT, CPS, and cloud computing enables dynamic production scheduling and improves system efficiency [184]. These systems also incorporate advanced robotics for flexible automation and human-robot collaboration, which is pivotal for future manufacturing flexibility and customization [167; 176] towards the establishment of the Industry 5.0 paradigm [198]. Despite high automation levels in systems like RMS, human involvement remains crucial, especially in areas like material handling and tool setup. Human-robot interaction is being enhanced with intelligent systems, such as those using machine learning, computer vision, and collaborative robots, which improve both safety and flexibility [23; 190].
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 4 FlexBFT Design
### Performance Analysis  
As an optimistic asynchronous consensus algorithm, FlexBFT needs to analyze its performance from a variety of network conditions. The main things that need to be paid attention to are communication complexity, message complexity, and block generation delay.  
* When network conditions are good, nw-ABC in the fast mode protocol is mainly run. Its message complexity is \(O(n^{2})\), and its communication complexity is \(O(nB)\). It requires four rounds of communication to output a block.
* When there are some network fluctuations, what promotes consensus is nw-ABC and pace synchronization. The ABA in pace synchronization here is implemented by the work of [PERSON] et al. [36], which is almost the most advanced ABA at present. Each ABA requires seven rounds of communication.
* When there are strong network fluctuations, except for the first fallback from the fast mode protocol to the back-up mode protocol, which requires all the above overhead plus the communication complexity of MVBA \(O(n^{2}|m|+\lambda n^{3}\log n)\) and \(O(n^{3})\) message complexity.