esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Monitoring Fermentation Variables
#### 4.1.3 Yeast Activity  
Yeast activity is another vital parameter that influences the rate and quality of fermentation. In this context, AI-driven models can analyze patterns in yeast metabolism, identifying any anomalies that may indicate issues such as stuck fermentation or contamination. This predictive capability allows winemakers to address potential problems before they escalate, ensuring a smooth and efficient fermentation process [81].  
In their study, [PERSON], [PERSON], and [PERSON] demonstrated the use of a toolbox of biosensors for real-time monitoring of the yeast intracellular state during bioprocesses. This approach involves the integration of advanced biosensors with AI systems to monitor yeast viability, population dynamics, and metabolic by-products in real time. The biosensors provide detailed data on the physiological state of the yeast cells, which AI algorithms can analyze to detect and predict deviations from optimal fermentation conditions [81]  
The real-time monitoring facilitated by these biosensors is crucial for managing the fermentation process, as yeast health directly impacts fermentation kinetics and the development of flavor compounds. By maintaining optimal yeast activity, winemakers can ensure that the fermentation process produces wine with the desired characteristics. The AI systems, leveraging data from the biosensors, enable precise control and timely interventions, thus enhancing the overall efficiency and reliability of the fermentation process.
# A Study on Reactive Ultra-Fine Fly Ash and Sulfoaluminate Cement in Self-Leveling Mortar
## 3 Results
### Hardening Properties  
#### 3.2.1 Compressive Strength Test  
Each mix showed relatively weaker compressive strength results on day 1. The initial strength followed the order of F0 > F5 > F10 > F15. The early strength of mortar was mainly attributed to Type I Portland cement, and as the replacement ratio of PUFA increased, the amount of Type I Portland cement decreased, resulting in lower initial strength compared with the mix with no PUFA. However, as the curing period of the specimens increased, the strength began to increase, and so did the hydration degree of the cement mortar, leading to higher compressive strength. Based on the results in Figures 4 and 5, at 28 days, the strength was found to follow the order of F15 > F10 > F5 > F0. While the addition of PUFA affected the early strength, PUFA active pozzolanic reaction filled the pores and other voids, allowing the slurry to compact, which significantly improved compressive strength in the later stages [18; 21].  
Figure 3: Results of viscosity tests.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Group** & **Seconds** & **Group** & **Seconds** & **Group** & **Seconds** & **Group** & **Seconds** \\ \hline S0F5 & 42 & S10F5 & 39 & S20F5 & 39 & S30F5 & 38 \\ \hline S0F10 & 46 & S10F10 & 46 & S20F10 & 46 & S30F10 & 42 \\ \hline S0F15 & 58 & S10F15 & 58 & S20F15 & 50 & S30F15 & 46 \\ \hline \end{tabular}
\end{table}
Table 8: Marshall cone fluidity test results.  
In 28 days, the mix with a higher PUFA replacement ratio showed a positive trend in later-stage strength development. When the SAC volume percentage increased, both early- and later-stage strength were lower than those of the mix with a lower volume percentage. This is because when SAC was mixed with Type I Portland cement, an increase in SAC content resulted in the formation of a large amount of calcium aluminate, which caused expansion and cracking in later stages. This led to an increase in porosity, which in turn decreased the strength and increased the permeability coefficient [14]. Furthermore, when SAC was used alone, the hydration products were different, and mixed-use showed lower strength than the use of SAC alone. The result of S0F15 > S30F15 demonstrates this outcome.  
Figure 4: The influence of PUFA content on compressive strength for (**a**) 0% SAC (volume percentage of total cementitious materials), (**b**) 10% SAC (volume percentage of total cementitious materials), (**c**) 20% SAC (volume percentage of total cementitious materials), and (**d**) 30% SAC (volume percentage of total cementitious materials).
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Vulnerabilities
#### 4.2.3 Equipment Failure  
This failure type includes the breakdown or degradation of operations and services, often caused by the malfunction of different parts of the smart grid, including generation plants, transformers, transmission lines, distribution networks, substations, smart meters, and control systems, causing power outages, voltage fluctuations, grid instability, the interruption of operations, and the disruption of services. This often occurs due to aging infrastructure, inadequate maintenance, manufacturing defects, environmental conditions, or electrical faults. This can be mitigated using proactive measures such as redundancy and backup systems, condition-monitoring techniques, regular and scheduled inspections, maintenance, and equipment testing. Electrical fires, overheating, and cascading outages can occur from equipment failures in transformers, circuit breakers, and power inverters in smart grids [104]. Furthermore, the deterioration of aging infrastructure components raises the possibility of mechanical failures that result in expensive repairs and extended service disruptions [105].
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 3 Methods
### EEG Preprocessing  
The raw EEG data were preprocessed using the EEGLAB toolbox in MATLAB (MathWorks, Natick, MA, USA). Initially, a basic Finite Impulse Response (FIR) filter in EEGLAB retained EEG signals within the 1-40 Hz range, suppressing extraneous frequencies. This range was chosen because biologically meaningful EEG information is predominantly found within 1-40 Hz, while frequencies outside this range often represent noise, such as electromyographic activity and electrical interference. A 50 Hz notch filter was also applied to eliminate environmental electrical noise. After filtering, Independent Component Analysis (ICA) was used to remove non-neural artifacts. Using expert knowledge and the ADJUST plugin [31], we reviewed each participant's ICA results to identify and remove components associated with eye movements, muscle activity, and other non-neural noises. The remaining independent components were then re-projected onto the channel level, resulting in clean EEG data, free from artifacts.  
To meet the requirements for Event-Related Potential (ERP) and spectral analyses, we segmented the purified EEG data. For ERP analysis, each trial's EEG data were divided into 1000 ms segments with a time window of [\(-\)200, 800] ms, using the first 200 ms as the baseline correction period. For spectral analysis, 3000-ms segments were selected with a time window of [\(-\)1000, 2000] ms, using the first 1000 ms as the baseline. Amplitude thresholds were set at \(\pm\)100 \(\upmu\)V to exclude trials with amplitudes outside this range, ensuring the analysis was not contaminated by extreme artifacts.  
After segmenting the EEG data by trial, they were regrouped into \"focused\" and \"distracted\" categories. Participants' attention during tasks was dynamic and varied  
Figure 4: Overview of proposed data-processing framework.  
individually. Noise interference typically increases the likelihood of distraction, but not all participants are equally affected. Some maintain high focus despite noise, while others are more sensitive to it. Based on task performance, EEG data were categorized: in the control group, trials where participants correctly identified hazardous images were marked as \"focused\"; in the noise group, trials where participants failed to identify hazards correctly were marked as \"distracted.\" We compiled a total of 840 epochs, with 571 correct trials in the control group and 269 incorrect trials in the noise group. After stringent quality control to exclude non-neuronal artifacts, 532 of the correct trials and 231 of the incorrect trials were deemed valid. Thus, we obtained 532 valid epochs of focused states and 231 valid epochs of distracted states.
# Biomass Fly Ash Self-Hardened Adsorbent Monoliths for Methylene Blue Removal from Aqueous Solutions
## 3 Results and Discussion
### MB Adsorption Tests  
#### 3.3.1 Influence of MB Initial Concentration  
The influence of MB initial concentration (1, 2.5, 5, 10 and 15 ppm) on the adsorption of the monoliths after 25 h of contact time was studied. Figure 7 shows the dye uptake (\(q_{e}\)), determined by Equation (4), as a function of initial MB concentration and AP content  
Figure 6: Influence of PLC and AP content on the compressive strength of the monoliths (14 curing days) (**a**). Detail of the microstructure of cured monoliths without AP and containing or not PLC (**b**). The red arrows t in evidence the cracks attenuation by the presence of PLC.  
in the monoliths. The uptake increases when the MB initial concentration rises, denoting the persistence of available active sites for adsorption (no saturation). Similar behaviour was observed for the AP amount variation since the higher the AP content the higher the uptake values are. This is explained by the increment of the sample's porosity caused by the AP addition. Moreover, the porogenic agent itself can generate active sites within the monoliths that result from the oxidation of the metallic Al (Al(OH)\({}_{4}^{-}\)) Chemical reaction (1). The addition of 5 wt.% of PLC does not significantly affect the uptake values, due to the meaningless microstructural changes obtained by its addition in terms of porosity values (see Figure 4). It should also be noted that some studies [17; 22] reported that pure cement shows negligible activity for MB adsorption.  
In accordance with the variation of \((q_{e})\) with \(C_{0}\) (Figure 7), the MB removal efficiency \((E)\), determined by Equation (5), increases with the increment of the open porosity of the monoliths and decreases with the rise of the MB concentration (Figure 8). This decrease is due to fact that the number of active sites in each monolith is constant and the higher the MB concentration, the higher the number of molecules of the dye. For example, the increase in the MB concentration from 1 to 15 ppm induces a \(E\) reduction of \(\sim\)53% for the monoliths with the higher AP content with (5C:9A) and without (0C:9A) PLC. For each MB concentration, it is observed that the uptake and the removal efficiency tend to increase with increasing porosity, specifically the open porosity. The existence of more open pores enhances the exposed area of the sample and, consequently, the number of active sites available increases.  
Figure 8: Effect of the initial concentration on the removal efficiency \((E)\) of MB by the different samples (contact time: 25 h): (**a**) samples without and (**b**) with PLC.  
Figure 7: Effect of the initial concentration on the uptake of MB by the different samples (contact time: 25 h): samples prepared without (**a**) and with PLC (**b**).
## 1 Introduction  
Rice is one of the most important cereal crops related to China's national economy and people's livelihood. Furthermore, it is the staple food for more than half of the world's population [1, 2]. Brown rice is de-hulled rice grain, which consists of bran, embryo, and endosperm layers [3, 4]. The germ and bran layers on brown rice's surface are removed in sand roller rice mills with a whitening roller's high-speed rotation to achieve white rice milling [5]. The rice bran and broken rice are produced during rice milling. A lower suction-type method is used to collect the rice bran and broken rice mixture in traditional horizontal rice mills [6]. The collected mixture needs to be sorted again due to the different uses of rice bran and broken rice. High investment costs and energy consumption are generated with the lower suction-type method [7]. A large amount of rice is deposited at the bottom of a milling chamber with the lower suction-type method, resulting in uneven rice polishing and an increased broken rice rate [8]. The mixing of rice bran and broken rice is not conducive to the development of the rice processing by-product industry. The separation of rice bran and broken rice can be achieved with the upper suction-type rice mill during polishing. The phenomenon of material accumulation at the bottom of the milling chamber can be alleviated with the upper suction air method [9; 10].  
In previous studies, most of the work focused on researching the broken rice separation process and its characteristics in rice milling. [PERSON] et al. researched the broken rice separation characteristics through broken rice separation experiments using a laboratory indented cylinder separator [11]. [PERSON] et al. studied the effect of cylinder rotational speed on grain losses and broken germination through rice husking tests [12]. The coupling of computational fluid dynamics (CFD) and discrete element method (DEM) has been successfully applied to various fields [13; 14; 15]. [PERSON] et al. investigated the aerodynamic separation mechanism of rice husks from brown rice following paddy hulling using coupled CFD-DEM [16]. [PERSON] et al. researched the impact of airflow velocity, aperture and cylinder sieve deflectors on threshed rice separation in an airflow cylinder sieve device using CFD-DEM [17]. However, few studies have focused on the movement behavior of rice particles in milling. The influence of air speed and jet speed on the separation of rice bran and broken rice remains unclear.  
In this study, the upper suction-type rice mill working principle, rice particle movement laws in the air duct and the air duct's structure are analyzed. The upper suction-type rice mill model is established using the CFD-DEM coupling method, and the movement behavior of rice particles in milling is investigated. The separation test of rice bran and broken rice is conducted with a prototype, simulation reliability is verified, and optimal working parameters are determined.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### BackCom in Textile/Clothes  
[PERSON] et al. made the connectivity of regular clothes by transforming them into frequency modulation radio wave stations [96]. This prototype is fabricated on the chest of the cotton T-shirt by making an antenna of a conductive thread. This T-shirt can transmit information to nearby smart devices using BackCom. Project Jacquard proposes BackCom for interactive digital textiles. Figure 45 shows the movement detection on smart devices by interactive clothes. Project Jacquard suggests that interactive clothing materials be made more economical, while utilizing existing textile weaving equipment and technology [203]. Similarly, a battery-free platform is suggested for wearable devices [204]. This platform works on harvested energy generated from feet movement, in addition to Amb-BackCom.  
Figure 44: BackCom in logistics.  
Figure 43: BackCom in the bio-medical field.
## 2 Literature Review
[12] focuses on eliminating indicators that are highly correlated with each other through correlation analysis and retaining only four indicators, namely, maximum tangential stress, uniaxial compressive strength, tensile strength, and the elasticity-strain-energy index of the surrounding rock, which optimizes the input features by reducing redundant information. Similarly, [PERSON] et al. [13] considered the correlation between the indicators and selected only four indicators, namely, maximum tangential stress, uniaxial compressive strength, tensile strength, and elastic energy index of the surrounding rock, in order to prevent the effect of multiple covariance on the modeling process and complexity of the final model. [PERSON] et al. [14] still use the correlation between indicators as the criterion for selecting indicators. It can be seen that most of the studies on rockburst prediction tend to use the correlation between indicators as a criterion to obtain a set of evaluation index systems with weak correlation. However, a comparison of several studies in practice reveals that the correlation between evaluation metrics does not always directly affect the performance of predictive models. For example, the variable autocoder-natural gradient boost model (VAE-NGBoost) proposed by [PERSON] et al. [12] has the highest correlation coefficient between the input metrics of 0.582, which is a relatively low correlation coefficient. Finally, the VAE-NGBoost model achieved an accuracy of 0.921, while the deep forest model of [PERSON] et al. [7] achieved an accuracy of 0.924 despite the highest correlation of 0.9 between its input variables. In addition, the correlation between the indicators selected by [PERSON] et al. [15] was 0.788, and the accuracy of the final model was 0.946. These results show that the correlation between the evaluation indicators is not a decisive factor for the model's effectiveness. On the other hand, the correlation between the evaluation indicators and the rockburst grade also plays a non-negligible role in the model effect. Generally speaking, choosing indicators that are highly correlated with the rockburst grade as inputs can improve the predictive ability of the model. However, current research on this aspect is still weak, and most existing studies do not use the correlation between the indicators and the rockburst grade as a criterion to select evaluation indicators.
## 4 Direct Aylation of 1,3,4-Oxadiazoles with the Use of Organometallics  
Another interesting solution to the synthesis of conjugated 1,3,4-oxadiazole derivatives is the use of organometallic compounds. In 2010, three approaches dedicated to this subject were published by independent research teams. [PERSON] et al. described a procedure using organosilcon compounds for the direct aylation of 2-substituted 1,3,4-oxadiazol. The useof a nickel catalyst and a suitable bipyridinium ligand (bpy) at elevated temperature in a relatively short reaction time led to 2,5-disubstituted products in good yields (Scheme 7; **18a-d**; 54-63%) [57]. The authors demonstrated an efficient nickel catalyst system for the direct Hiyama-type cross-coupling of 1,3,4-oxadiazoles with organosilanes through C-H bond cleavage. The elaborated methodology making use of the nickel catalytical system can constitute an alternative to palladium catalyzed reactions and broaden the spectrum of synthetically useful organosilicon moieties. Then, [PERSON] and co-workers documented a method based on organo-boron compounds as effective reagents for the direct arylation of oxadiazoles. The substrate, 2-phenyl-1,3,4-oxadiazole, was combined with palladium and copper catalysts and subjected to long-term heating to form a new carbon-carbon bond in high yields (Scheme 7; **19a,b**; 73-75%) [58]. This synthesis was the first example of Suzuki-Miyaura-type direct C-H arylation of azoles, in which azoles instead of their halides or pseudohalides were used in reactions with boronic acids. The palladium-copper catalyst allowed the direct arylation of the substituted 1,3,4-oxadiazoles with some organoboron acids in mild conditions. The same year, [PERSON] also described a universal method for the synthesis of coupled systems by the direct arylation of 2-aryl-1,3,4-oxadiazoles with organoboron compounds. This reaction was based on a nickel catalyst with a suitable 2,2\({}^{\prime}\)-bipyridine ligand. In the presence of only oxygen and relatively short heating, this reaction led to the formation of arylation products in satisfactory yields (Scheme 7; **20a-h**; 32-84%) [59]. In 2013, [PERSON] and co-workers modified two previously published methods [58,59] and showed that the use of aromatic boronic acids, Pd(OAc)\({}_{2}\) as a catalyst, Cu(OAc)\({}_{2}\) as an oxidant, and an extensive 2,2\({}^{\prime}\)-bipyridine ligand (bip) led to products in high yields (Scheme 7; **21a-j**; 83-92%) [60]. This methodology has been applied for the direct coupling of these 2-phenyl-1,3,4-oxadiazole derivatives with a range of arylboronic acids to prepare final compounds.  
**Scheme 7. Direct arylation of 1,3,4-oxadiazole reactions using organometallic compounds.**
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
### Background
#### 1.1.2 Time-of-Arrival Estimation  
For most TWR systems, time-of-arrival (ToA) estimation is the primary measurement mechanism that drives the rest of the signal processing chain. Naturally, the precision of these measurements will dictate the overall ranging precision of the system and is the subject of numerous studies in different applications. In indoor environments, researchers have characterized the performance of both wideband [30] and ultra-wideband [31] ToA estimators in dense multipath channels. Similarly for outdoor environments, several ToA estimation techniques were explored in [32; 33], whose corresponding lower bounds were investigated in [34]. Other researchers have also investigated the use of existing communications infrastructure to enable these measurements, including LTE [35], 5G [36], and Wi-Fi [37]. Neural networks have also been considered for large networks of ToA-based localization sensors [38].  
ToA estimation is well characterized by different types of bounds. Ziv-Zakai and Weiss-Weinstein lower bounds on ToA estimation were derived and discussed in [39] and [40], respectively, while a Cramer-Rao lower bound on hybrid time-of-arrival/received signal strength (ToA/RSS) was derived in [41]. Notably, 2D Cramer-Rao lower bounds on localization were presented in [42], and optimal sensor placement for time-difference-of-arrival (TDoA) systems is discussed in [43]. The CRLBs proposed in this work extend this2D positioning bound to three dimensions, explicitly includes the number and placement of antennas in the bound, and additionally considers orientation estimation.
## 2 Material and Methods
### Sentinel-2 Sea-Ice Concentration  
We classify the Sentinel-2 reflectances according to their frequency distribution (see the example in Figure 6b). The three bands from the visible spectrum (bands 2-4) are investigated. Histograms of all 3 bands (red/640 nm, green/560 nm and blue/490 nm) are evaluated for all 79 Sentinel-2 scenes. They show no notable difference for our purpose; all three bands enable a clear distinction between the thin and thick-ice classes if both classes are present and a clear distinction between ice and water if only thick ice is present. We decide to use band 4 (red/640 nm). The reflectances are classified into three classes: open water, thin ice and thick ice. As the ice peaks differ from scene to scene, we select specific thresholds for each scene instead of using a global threshold for each scene. If there are two peaks at the high end of the reflectance spectrum, both are treated as ice. We create two reference SIC datasets: One for which the thin-ice class is treated as ice and one for which it is treated as water. The SIC dataset where the thin ice is treated as water will be called thick-ice SIC as it comprises only thick ice. Consequently, the SIC dataset for which also thin ice is treated as ice will be called thin-ice dataset as it comprises thin and thick ice. The resolution of the binarised ice/water maps is 10 m and the desired resolution of the SIC dataset is 1 km. Thus, we assume that one SIC pixel (1 km) comprises 100 by 100 reflectance pixels. We select each pixel in the SIC array and assign it with the average of the surrounding 100 by 100 reflectance pixels, assuming that the SIC pixel is in the centre of the box. In this way, we obtain a SIC dataset at 1 km resolution from the 10 m reflectance dataset.