esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] (2018) generated various flight candidate schedules randomly, and their respective robustness was measured by taking quality robustness criteria into consideration, which included timeliness of delivery, amount of cargo moved, and cost. [PERSON] (2018) took the quality robustness criterion of total completion times as the optimization objective and adopted a heuristic algorithm to solve the robust scheduling problem of a job shop. Additionally, some researchers proposed criteria concerning both solution robustness and quality robustness, while these criteria were not adopted comprehensively in measuring robustness. [PERSON] (2018) proposed two criteria to measure the robustness of job shop production management--the expectation value of makespan as the quality robustness criterion and the expectation value of the total start time delay of all procedures as the solution robustness criterion. [PERSON] (2018) defined the efficiency emphasized measurement as a solution robustness criterion and a cumulative distribution function inspired measurement as a quality robustness criterion, which were respectively applied in the optimization model. [PERSON] (2018) established a proactive management schedule in resource-restricted projects, where the start time deviation was adopted to measure the solution robustness, and the completion probability was considered as the criterion for quality robustness. [PERSON] [21] and [PERSON] [22] proposed a linearly composite criterion of both solution robustness and quality robustness in job shop management concerning random malfunctions, but its weight was assigned as a subject to decision-makers' preferences. In summary, current research concerning robustness measures only focuses on solution robustness or quality robustness, which cannot measure the schedule robustness from both the project execution and the project result. Some criteria include both solution and quality robustness criteria, but their weights neglect the bounded rationality of subjective weights and the inherent importance and nonlinear intercriteria correlations of objective weights. In this paper, the composite robustness criterion containing both solution robustness and quality robustness is proposed from both the project execution and the project result, considering the bounded rationality of subjective weights and the inherent importance and nonlinear intercriteria correlations of objective weights. The remainder of this paper is organized as follows: Section 1 provides an overview of the related work on weighting methods. Section 2 describes the framework for this paper. Section 3 describes the methodology of the construction schedule robustness measure. In Section 4, a case study of an underground power station in China is presented to verify the consistency, representativeness, and the advantage of the proposed criterion and methods. The conclusion of this paper is highlighted at the end of the paper.
# Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
## 3 Methods
### AMF Calculation  
The AMFs for the EMI series TOCs were determined using a look-up table (LUT) calculated with the SCIATRAN model [49]. The VCDs were derived from SCD/AMF values. These AMFs depend on observational geometry factors such as, relative azimuth angle (RAA), viewing zenith angle (VZA), surface albedo, solar zenith angle (SZA), cloud characteristics, and a priori ozone profile. TOMS V8 Climatology [50] provided the column-dependent ozone profile used as the a priori information.  
In this study, the AMF was determined using multidimensional linear interpolation from a pre-calculated look-up table (LUT). For the EMI TOC, a two-step AMF calculation was employed. First, initial VCDs were derived from SCDs and rough AMFs, calculated using interpolation from a preliminary AMF LUT that excluded VCD as a parameter. The ozone profiles for this LUT were selected by month and latitude from the SCIATRAN database. Next, precise AMFs were calculated using interpolation from a detailed AMF LUT, which included the parameters listed in Table 3[39].
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 7 Conclusions  
We have presented a complete theoretical investigation of filesystem synchronization. Our definition of a filesystem is arguably simplistic, but it captures the important features of real-word implementations. The changes between the original and the updated filesystems are encoded using a sequence of virtual filesystem commands. The synchronizer receives two sequences corresponding to the two replicas to be synchronized, and produces a third sequence which describes the synchronized, or merged state. Definition 6 gives an intuitively correct and convincing description of when the resulting sequence leads to a meaningful merged filesystem. In Section 5 we proved an operational characterization of the possible merged filesystems. The main result of Section 6 is a conflict-based non-deterministic synchronization algorithm which creates exactly those merger sequences which are allowed by Definition 6. The simplicity of this algorithm and the complexity of proving its correctness was a surprise to us. Its success underlines the power of the algebraic framework of file synchronization developed in [18].  
Multiple questions and possible extensions of the current work remain which are the subject of future research. Some of them are discussed in the next subsections.
## 4 Limitations and Future Work  
One limitation of this study is the reliance on the DSI-24 EEG headset, which, while advanced, may not capture the full spectrum of neurophysiological responses associated with cognitive stress due to its limited number of electrodes. In relation to the previous literature, a review was made in [38], addressing methods of assessing mental stress with EEG. It showed that most of the reported studies addressed the prefrontal cortex, prefrontal and frontal lobes as brain regions with a number of channels that did not exceed 9 in 15 studies out of 33 reported ones. The reported accuracies of these methods were between 50% and 99.94%, and they relied on a variety of classifiers like linear discriminant analysis (LDA), SVM, K-nearest neighbors (KNN), random forest (RF), artificial neural network (ANN) and convolutional neural network (CNN). It is important yo highlight that each study was made on a separate set of conditions, using different features and classifiers, which could be more or less complex. While the accuracy reported in this work competes with many of the ones reported in [38], many have been reported to be more accurate. However, this work relies on features and a classification scheme that are computationally competitive and adds an important feature, which is the interaction with the social robot that can help to make the level of stress evolve with time, affecting the accuracy results. We add that the interaction dynamics between the player and the Furhat robot were not extensively explored, particularly in terms of the robot's influence on player behavior and stress levels. Future research could expand on these aspects by integrating more comprehensive EEG systems with a higher number of channels for finer brain activity mapping and conducting in-depth analyses of player-robot interactions. Exploring the use of advanced machine learning algorithms for more nuanced interpretation of EEG data and extending this research to diverse gaming genres and settings would also be beneficial in understanding the broader applications of this technology in cognitive stress monitoring. However, the combination of an EEG-based stress monitoring system in conjunction with a social robot interacting with the use of a language model is a new way of using these technologies which can be built on for a better exploitation of their abilities. This can help in the enhancement of user experiences not only in video gaming, but also in other domains where stress can be managed through social interaction and conversation.
## 4 Results and Analysis
### Yield Estimation Performance for Different Combinations of Inputs  
To evaluate the performance of yield estimation from different data sources, we put different combinations of input variables in a BO-LSTM model for analysis. Figure 4 shows the yield estimation performance of BO-LSTM for winter wheat with five different input variables. We found that yield estimates using GPP (R\({}^{2}\) = 0.72, RMSE = 186.13 kg/ha) alone were more accurate than those using LAI (R\({}^{2}\) = 0.67, RMSE = 221.32 kg/ha) alone. When combined with meteorological data, the accuracy of GPP combined with meteorological data was higher (R\({}^{2}\) = 0.81, RMSE = 180.66 kg/ha) compared to the remotely sensed vegetation index (R\({}^{2}\) = 0.78, RMSE = 190.96 kg/ha), because GPP more directly reflects the process of organic matter accumulation by vegetation photosynthesis. The yield estimation accuracy of winter wheat gradually increased as the input data increased, indicating that the estimation accuracy of fusing multiple sources of data was better than the input from a single data source. The highest estimation accuracy was achieved by integrating all data together (R\({}^{2}\) = 0.83, RMSE = 177.84 kg/ha). The addition of the data improved the model's ability to capture spatial heterogeneity in yield, capturing more features associated with winter wheat yield. In particular, the yield estimation accuracy of the LSTM model improved significantly when combined with meteorological data, suggesting that meteorological data provide unique and irreplaceable information.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **NumOfUnits** & **MaxEpochs** & **MinBatchSize** & **DropoutLayer** & **Time/min** & **RMSE** \\ \hline \multirow{3}{*}{Bayesian optimization} & 202 & 314 & 9 & 0.1 & 14 & 149.51 \\  & 335 & 501 & 10 & 0.1 & 14 & 181.57 \\  & 354 & 510 & 11 & 0.025 & 14 & 182.80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Output results of Bayesian optimized LSTM hyperparameters on the training set.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & _Anisophyllea_ & _Anmonia_ & _Canarium_ & _Caenorhabditis_ & _Caenorhabditis_ & _Eugenia_ & _Landolphia_ & _Raphionacme_ & _Trisetamma_ & _Vitex_ \\  & _quangensis_ & _subsp. cuneata_ & _furthii_ & _Langensis_ & _Lanccolata_ & _maliensis_ & _maliensis_ & _tianum_ & _tianum_ & _subsp._ \\ \hline C [g/kg] & 502 & 490 & 501 & 522 & 526 & 486 & 448 & 460 \\ \hline N [g/kg] & 9.5 & 20.3 & 20.7 & 11.8 & 11.7 & 14.6 & 11.9 & 12.0 \\ \hline P [g/kg] & 0.42 & 1.26 & 1.23 & 0.37 & 0.86 & 1.83 & 1.33 & 0.76 \\ \hline K [g/kg] & 2.92 & 12.40 & 6.78 & 2.03 & 4.54 & 15.63 & 11.72 & 3.19 \\ \hline S [g/kg] & 8.32 & 1.58 & 1.66 & 6.11 & 1.87 & 1.28 & 0.87 & 1.23 \\ \hline CN & 53:1 & 24:1 & 24:1 & 44:1 & 45:1 & 33:1 & 38:1 & 38:1 \\ \hline N:P & 23:1 & 16:1 & 17:1 & 32:1 & 14:1 & 8:1 & 9:1 & 16:1 \\ \hline NS & 1:1 & 13:1 & 12:1 & 2:1 & 6:1 & 11:1 & 14:1 & 10:1 \\ \hline Ca & 3.53 & 12.10 & 6.98 & 3.56 & 4.96 & 5.74 & 16.44 & 4.91 \\ \([\)g/kg\(]\) & (0.11) & (0.14) & (0.06) & (0.04) & (0.22) & (0.01) & (0.08) & (0.05) \\ \hline Mg & 1.57 & 2.28 & 1.69 & 1.45 & 2.65 & 3.04 & 3.52 & 1.38 \\ \([\)g/kg\(]\) & (0.03) & (0.03) & (0.01) & (0.01) & (0.05) & (0.01) & (0.01) & (0.01) \\ \hline Mn & **331** & 49.5 & 108 & **222** & **578** & 67.6 & **286** & 63.2 \\ \([\)mg/kg\(]\) & (13.2) & (0.4) & (1.0) & (0.8) & (3.3) & (0.7) & (4.6) & (0.7) \\ \hline Fe & 64.7 & 171 & 160 & 56.3 & 334 & **1086** & **1307** & **1643** \\ \([\)mg/kg\(]\) & (13.0) & (78.5) & (2.3) & (1.2) & (3.5) & (31.4) & (2.1) & (95.1) \\ \hline Na & 23.3 & 120 & 39.4 & 30.7 & 29.6 & 28.4 & 18.9 & 16.4 \\ \([\)mg/kg\(]\) & (7.0) & (73.2) & (5.7) & (5.3) & (14.5) & (9.5) & (0.5) & (1.9) \\ \hline Zn & 14.1 & 10.6 & 8.4 & 10.3 & 13.6 & 21.8 & 19.0 & 9.7 \\ \([\)mg/kg\(]\) & (1.5) & (1.8) & (0.9) & (0.9) & (1.4) & (3.0) & (0.8) & (0.6) \\ \hline B & 10.2 & 29.4 & 7.8 & 14.6 & 23.7 & 26.8 & 36.4 & 35.7 \\ \([\)mg/kg\(]\) & (2.4) & (0.8) & (0.6) & (0.6) & (1.1) & (0.4) & (0.2) & (0.8) \\ \hline Cu & 1.8 & 0.7 & 0.8 & 1.3 & 4.0 & 3.5 & 5.7 & 4.3 \\ \([\)mg/kg\(]\) & (0.7) & (0.1) & (0.1) & (0.1) & (0.1) & (0.1) & (0.2) & (0.2) \\ \hline Al & **7884** & 196 & 120 & **13,989** & **6809** & 722 & **4674** & 179 \\ \([\)mg/kg\(]\) & (181) & (51.4) & (27.2) & (6.5) & (122) & (29.2) & (35.7) & (12.5) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Element contents in leaves (dry mass) in g/kg or mg/kg; respective standard deviation in brackets behind the mean element content; contents in bold highlight particular high values. Africa. Therefore, preserving the existing populations seems to be a lucrative opportunity to utilize the inconvenient soil conditions, while cultivating fruits of nutritional relevance. Furthermore, an expansion of cultivation is conceivable for an introduction and marketing as new food crops.
## 3 Interference Power Output by the Short-Code Systems  
The power of the interference output by the baseband low-pass filter is  
\[P_{j}=\int\limits_{-R_{b}}^{R_{b}}S_{j}(f)*S_{c}(f)|H(f)|^{2}df \tag{12}\]  
Here, \(\left|H(f)\right|^{2}\) is the transfer function of the baseband low-pass filter. In order to ensure that the useful signal passes through the low-pass filter without distortion, the passband bandwidth of the baseband low-pass filter should be equal to the bit bandwidth, that is,  
\[H(f)=\left\{\begin{array}{ll}\frac{1}{\sqrt{2}}&|f|\leq R_{b}\\ 0&|f|>R_{b}\end{array}\right. \tag{13}\]  
\(S_{j}(f)\) is the power spectral density after demodulation of single-frequency interference. Here, the high frequency components are filtered out. Without loss of generality, if \(\Delta\varphi\) is uniformly distributed from 0 to 2\(\pi\), then \(S_{j}(f)\) can be written as  
\[S_{j}(f)=\frac{I}{2}[\delta(f-\Delta f)+\delta(f+\Delta f)] \tag{14}\]  
Therefore, the output interference power is  
\[\begin{split} P_{j}&=\frac{I}{4}\int\limits_{-R_{b}}^{R _{b}}[\delta(f-\Delta f)+\delta(f+\Delta f)]*S_{c}(f)df\\ &=\frac{I}{4}\int\limits_{-R_{b}}^{R_{b}}S_{c}(f-\Delta f)+S_{c}(f+ \Delta f)df\end{split} \tag{15}\]  
It can be seen that the power spectral density curve interval of single-frequency interference after despreading is \(1/T_{b}\).  
After processing by the receiver, the output power of the useful signal is  
\[P_{s}=\int\limits_{-R_{b}}^{R_{b}}\left(\sqrt{2P}\right)^{2}S_{d}(f)|H(f)|^{2 }df \tag{16}\]  
In (16), \(S_{d}(f)\) is the power spectral density function of the data signal \(d(t)\). Because \(d(t)\) is a binary waveform function taking +1 or \(-1\) with equal probability, the integral is equal to 1. Therefore, the useful signal output power is \(P_{s}\), equal to \(P\).  
When the interference frequency offset is an integer multiple of the bit bandwidth, that is, when \(\Delta f=kR_{b}\) (\(k\) is an integer, \(\left\lvert\,k\,\right\rvert\leq N\)), the power of the interference output by the baseband low-pass filter is  
\[P_{j}=\left\{\begin{array}{ll}\frac{I}{2N^{2}}&\left\lvert\,k\,\right\rvert=0 \\ \frac{(N+1)I}{2N^{2}}\sin\mathrm{c}^{2}\left(\frac{k}{N}\right)&\left\lvert\,k \,\right\rvert\
eq 0\end{array}\right. \tag{17}\]  
Because the \(\mathrm{sinc}^{2}\) function decreases monotonically within a given range, the power of the interference output by the DSSS system is at maximum when \(\left\lvert\,k\,\right\rvert=1\), i.e., when the absolute value of frequency offset is equal to the bit bandwidth. At this point, the performance of the system is severely reduced.
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## Dimensionality Reduction of HSIs Based on Information Measures  
Band selection refers to the selection of band images which have relevant or irrelevant information [23]. The entropy and mutual information were generally used as the criterion for the band selection [21]. The entropy of each spectral band was calculated to evaluate the amount of contained information in this band. A threshold was set to exclude the irrelevant spectral bands. Then CMF was used for spectrum segmentation and the mutual information was used for the final band selection. The process can be mainly divided into the following two steps.
## 1 Introduction
In this paper, we present a highly sensitive low-cost CSRR-based microwave sensor for adulteration detection in, and characterization of, edible oil. The sensor was simulated on Computer Simulation Technology (CST) Microwave Studio and fabricated on commercially available low-cost FR-4 substrate. The sensor could measure the complex permittivity of an unknown oil by the resonance frequency and peak attenuation, which can also be used to identify the unknown oil. Two empirical equations were developed to calculate the permittivity and loss tangent of unknown oils from the shift in resonance frequency and peak attenuation. The sensor operated at a frequency of 5.25 GHz with maximum sensitivity and quality factor of 4.6 and 39, respectively. The proposed sensor possessed advantages such as low cost, compact design, ease of fabrication and high sensitivity. Later sections will extensively discuss the design, simulation, experimental results, and validation.
# Special Issue on eHealth Innovative Approaches and Applications
## 2 Published Articles
Currently, critical healthcare infrastructures are very vulnerable to cyber attacks [40], as demonstrated by the several successful cyber attacks in recent years. Many of these are caused by a lack of awareness and incorrect behaviours on the part of end-users, who do not adopt the required level of cyber hygiene, i.e, regularly and promptly applying software updates, or adopting unique and strong passwords. Thus, the issues faced in this paper are of great importance and urgency, considering the large and increasing number of cyber attacks in the healthcare domain; these are very often caused by human errors. Therefore, cyber education can prevent many of the issues caused by poor cyber hygiene. On the other hand, cyber security and hygiene reduction for non-expert users (such as in the case of the healthcare domain) should adopt human-centric approaches to be more effective. Therefore, this paper firstly reports the best practices that should be adopted by healthcare organisationsand professionals to achieve a high level of cyber hygiene. Moreover, it explains how these best practises could be applied in a use-case scenario with the aim of improving awareness about privacy and cyber security. Finally, the authors presented their long-term vision based on human-centric approaches, which aims to facilitate the development of efficient practices and education associated with cyber hygiene, leveraging a flexible, adaptable and practical framework. The article from [PERSON] et al. (contribution 13) is a review of the more recent advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery. The combined use of AI and IoT in healthcare allows for defining frameworks and solutions for the smart analysis of health and clinical data, improving the work of physicians and providing additional evidence for clinical decisions. Therefore, the authors highlighted how the application of AI and the IoT has changed healthcare service delivery from the traditional hospital-centred model to a personal portable device-centred model. In detail, this literature review highlights how recent research describes the advantages of embracing AI techniques and wearable sensors in tasks related to innovating and optimising the analysis of health and clinical data. Moreover, the review also discusses some challenges related to the issues of using AI and the IoT applied to healthcare data analysis. Finally, it identifies some issues and opportunities for future work.
## 5 Simulation Results and Discussion
### Parameter of Damping Controller  
The rotor angle between Generators 3 and 1 (\(\delta_{13}\)) is selected as the controller input signal. The damping controllers are designed separately so that the LFO suppression effect of RPM, APM, and DCPM on the inter-area oscillation and its influence on the shafting oscillation of the doubly-fed wind turbine can be examined. The parameters of different modulation obtained by the PSO are shown in Table 1.
# Comparison of Measurement Possibilities by Non-Invasive Reflectometric Sensors and Invasive Probes
###### Abstract  
The measurement of the moisture content of building materials is of key importance both in the process of building structures and in their subsequent operation. In engineering practice, indirect techniques of moisture measurement, mainly, resistance and capacitive, are the most popular. The main objective of this research work was to compare the classic TDR measurement technique to the non-invasive, surface TDR sensors. Moisture measurements were carried out on samples made of cellular concrete with density class of 400 and 600. These samples were moist to various degrees, from 0 to 69% (400 c.c.) and from 0 to 55 (600 c.c.). For each sample, five measurements were carried out. Both the RMSE and the expanded uncertainty values were more favorable for the TDR FP/mts probe and were consistent with the literature data. Compared to them, the measurement result for the 400 c.c. samples with the S1 probe was 154.6%, and that with the S2 probe was 87.03% of the values obtained with the invasive probe. When measuring the 600 c.c. samples, we found values of 122.16% for S1 and of 120.1% for S2 of those obtained with the invasive probe. The use of surface TDR sensors provided an easy and quick measurement without damaging the surface and structure of the tested material, as there was no need to introduce the probe actuators inside the tested material.  
TDR sensor; moisture; porous materials; calibration model 2023