esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### BVOC Identification and Qualitative Analysis  
Table 1 summarizes terpen identified in the chromatograms (Figure S1) obtained from the analyses of samples collected at the three sampling points. In all three sampling points, the only terpen identified were monoterpenes and monoterpenoids (MTs), while no sesquiterpenes or other BVOCs were detected. Indeed, the _Q. alex_ BVOC emission pattern is mainly characterized by monoterpenes [45; 46; 47; 48; 49; 50]. In the first sampling point, there were no differences in MTs collected using the two sampling techniques (DBSS and static sampling). In both cases, the identified MTs were: monocyclic monoterpene hydrocarbons (d-limonene and _p_-cymene), one oxygenated derivative of monocyclic monoterpenes (1,8-cineole), and bicyclic monoterpene hydrocarbons (\(\alpha\)-pinene, \(\alpha\)-thujene, camphene, \(\beta\)-pinene, sabinene, car-3-ene). Similarly, qualitative differences between the two sampling techniques were not found in the second sampling point. In particular, the following acyclic monoterpene hydrocarbons were detected: myrcene and \(\beta\)-cis-ocimene. \(\beta\)-phellandrene, \(\alpha\)-phellandrene, \(\alpha\)-terpinene, d-limonene, \(\gamma\)-terpinene, \(p\)-cymene and terpinolene were detected among monocyclic monoterpene hydrocarbons. Finally, among the bicyclic monoterpene hydrocarbons, the following compounds were identified: \(\alpha\)-pinene, \(\alpha\)-thujene, camphene, \(\beta\)-pinene, sabinene and car-3-ene. Some qualitative differences were found during the third sampling point between DBSS and static sampling. Indeed, a higher number of MTs were identified using the DBSS compared to the static sampling. Among the acyclic monoterpene hydrocarbons, \(\beta\)-cis-ocimene were identified in both cases, while myrcene and \(\beta\)-trans-ocimene were found only using the DBSS. Among the monocyclic monoterpene hydrocarbons, d-limonene and \(p\)-cymene were detected using both sampling techniques, while only applying DBSS, \(\alpha\)-terpinene, \(\gamma\)-terpinene and terpinolene were found. Finally, among the bicyclic monoterpene hydrocarbons, the following were found: \(\alpha\)-pinene, \(\alpha\)-thujene, camphene, \(\beta\)-pinene and sabinene in both cases, while car-3-ene only using DBSS.
## 3 Results and Discussions
### Consistency Analysis between GNSS, GRACE and GGFC
#### 3.1.2 Quantitative Evaluation with Correlation Coefficient and WRMS Reduction
Both the original loading data and interpolation can introduce errors, especially for stations on ocean islands or small peninsulas where the physically expected loading signals are very small. Figure 5: Histogram of statistical results for the correlation coefficients (the red text above the histogram represents the average of the correlation coefficients).
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Adaptive
#### 5.1.3 Enhancing Routing Performance Using Swarm-Based Clustering  
[PERSON] et al. [128] propose an adaptive routing scheme that minimizes the issue of unstable routes caused by limited battery and high mobility of UAVs. The proposed scheme uses the glowworm swarm optimization algorithm in an energy-aware cluster formation, which includes CH election. The main objective is to enhance route stability (O.1) as well as to enhance routing performance and QoS (O.3) in FANETs. The routing scheme addresses three challenges, namely high dynamicity (X.1) of network topology and cost (X.2) as well as low residual energy (X.3) as a result of high energy consumption and computational cost since flying nodes are spaced far apart in the sky. The routing scheme is designed for both hop-by-hop (C.1.1) and source (C.1.2) routing approaches.  
During the neighbor and route discoveries stage, each node sends control messages that contain residual energy (M.4). Each node forms a neighbor set, and the neighbors are sorted according to a fitness value, which is calculated based on their respective residual energy levels. During the route selection stage, in order to reduce energy consumption, the underlying network is clustered based on mobility metrics (M.1), whereby nodes join a cluster in which its speed is near the speed of the CH of the cluster. The CH collects data from CMs and then aggregates and forwards the data. Therefore, selecting CHs with higher residual energy level and node density reduces energy consumption, and hence, it prolongs route lifetime. For route selection, a route is selected based on geographical location (M.3) and residual energy (M.4) of nodes. During the route maintenance stage, which is required when the residual energy level of CHs or CMs reaches the minimum threshold, the CHs and CMs initiate the route discovery mechanism, which is the first stage.  
The routing scheme is compared with a state-of-the-art, which is a swarm-based routing scheme that provides an optimized number of clusters for routing. Random direction mobility model (A.3) is used in simulation. The routing scheme has been shown to increase QoS (P.1) (i.e., lower cluster formation time) and route setup success rate (P.4) as well as to reduce energy consumption (P.2).
## 2 Data and Methodology
### SRO Methodology and Approaches to Collecting Coplanar Spire-RO Data  
The procedures for obtaining the simultaneous limb overpass RO (SRO) events from two receivers on different LEOs with the same GNSS satellite are illustrated in Figure 6 and detailed in [32]. The SRO-based RO data quality intercomparing helps to address the needs of quick inter-calibration, validation, and quality assurance of Small Satellite-based multi-RO sensors, particularly in the era of an increasing number of RO sensors being paired with augmented GNSS satellites. The first step in the SRO prediction module is using the Simplified orbital Perturbation Model (SGP4) with the satellite orbital Two-Line Element (TLE) as an input to predict the positions and velocities of LEOs or GNSS satellites. By establishing a functional relationship between the RO profile impact height and the direct height learned from past RO sounding events, the prediction algorithm solves four coupled equations (derived from the locations of a given pair of LEO and GNSS satellites) at each instant to predict the time and location (longitude and latitude) of the rising or setting RO limb-sounding events. The prediction module is validated with COSMIC-2 RO observations regarding the ground track, RO events number, and RO event distribution as a function of the antenna viewing angle [32]. In the SRO prediction, the TLEs from three satellites (two LEOs, LEO1 and LEO2, and one GNSS) for a given period are used as inputs for the RO prediction method. The RO prediction method generates two datasets, one for the receiver LEO1 and specific GNSS transmitter (i.e., GNSS_A hereafter), and the other for the receiver LEO2 and the same transmitter GNSS_A. These two datasets contain potential RO events, each comprising the calculated RO occurrence times and positions. The criteria for pairing RO events include a time window of less than 20 min and a distance of less than 200 km between RO event positions. The predicted SRO times are then used to identify the SRO events between LEO1 and LEO2 by searching and matching the time tag in the filenames of the RO atmospheric Profile (atmPrf) data products. The main advantage of SRO comparisons of RO data products is that the method significantly reduces collocation uncertainties due to a much shorter time (20 min) and minor atmospheric path differences in comparison to traditional RO comparisons (within a 3 h time window). The SRO events are very close to the coplanar. The UCAR-processed Spire data are collected and assessed in this study. We collected over 16,000 Spire-COSMIC-2 SRO pairs and 8000 Spire-Metop GRAS coplanar pairs since 2020 during the DO3 and DO4 periods.  
The Spire-RO pairs are collected within 20 min and 200 km using the latitude and longitude information at a 5 km altitude for each RO profile. We specified the approach to generate the error covariance matrix in the DA system in Section 5.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 2 Computer Vision
### Monocular Camera  
A monocular camera is a visual system that uses a single camera for image acquisition. It can perceive color and texture information in the environment and has the advantages of simple structure and low cost. However, monocular cameras cannot directly calculate depth information using multi-view geometry principles, and their accuracy is limited. As an easy-to-implement and easy-to-deploy visual sensor, monocular cameras play an important role in various stages of agriculture [23; 24]. For example, in [25; 26], monocular cameras are used to capture target images as initial image data, which are then processed through deep learning models for training and inference to address irrigation and crop grading issues in agriculture. In [27,28], computer vision is combined with electronic nose technology to achieve practical and efficient solutions in crop storage and quality inspection.
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
###### Abstract
Chaos-based image encryption techniques are highly efficient in the case of multimedia data [17], using chaos game for encryption [18], making them ideal in terms of ease of implementation for cybersecurity applications [19]. 1D chaotic maps are known for their limited number and range of control parameters, in addition to their tendency to converge under finite precision implementation. To address these issues, studies have proposed using 2D chaotic maps [20] that offer a balance between hardware complexity [21] and chaotic performance [22]. Despite this, due to their simple mathematical representation and low cost in terms of hardware implementation, a series of studies have been conducted to test the response of 1D chaotic systems under finite precision implementation [23; 24], introducing novel 1D chaotic functions to expand the number and range of control parameters used [25; 26]. The systems proposed in these studies were proven to be robust and secure when used in cryptographic systems. This paper further explores the trend of expanding the number and range of control parameters of 1D maps [25] to achieve a wide range of dynamic behavior [26] by introducing a new piecewise 1D map with chaotic behavior across a wide range of its control parameters. Furthermore, the newly introduced map is employed as the pseudorandom number generator (PRNG) within a novel image encryption scheme to assess its resilience and suitability in encryption systems. The scheme successfully withstood all rigorous testing it was subjected to, demonstrating its robustness against well-known statistical and differential attacks. The paper is organized as follows: Section 2 provides a literature overview of the well-known 1D maps, and Section 3 introduces the new 1D map and presents the results of verifying its chaotic behavior. In Section 4, a new image encryption algorithm is suggested, which utilizes the map as a pseudorandom number generator (PRNG). The robustness of the proposed encryption scheme is demonstrated by applying known attacks in Section 5 before concluding the paper in Section 6. The effectiveness of the proposed algorithm was proven through rigorous testing, showing that it possesses robustness with remarkable confusion-diffusion properties.
## 3 Results  
### Effects of VOCs Released by BF06 on Plant Growth and Photosynthesis  
To investigate whether the release of VOCs by BF06 could regulate plant growth, the composition of mVOCs was initially extracted and identified using headspace SPME-GC-MS. Our results revealed that the strain BF06 could release 21 different types of volatile compounds (Table S2). The effects of BF06 VOCs on plant growth were further evaluated. The _Arabidopsis_ seedlings were first cultured on 1/2 MS medium for seven days. Then, the plants were transferred to two separate petri dishes with or without exposure to BF06 VOCs. After 10 days of BF06 VOCs exposure, the growth phenotype and several physiological parameters were analyzed in both the non-exposed (control) and VOCs-exposed plants. The VOCs-exposed plants appeared to show better growth performance than the controls (Figure 1A,B). Shoot and root fresh weights were notably higher in the VOCs-exposed plants than in the controls (Figure 1C). Similar results were observed for dry weights (Figure 1D). In addition, the number of rosette leaves was greater in the VOCs-exposed plants than the controls (Figure 1E). The VOCs-exposed plants also exhibited a larger size of rosette leaves compared with the controls (Figure 1F).  
BF06 VOCs exposure markedly increased total chlorophyll content to over 28% as compared to the controls with 1.55 \(\pm\) 0.16 mg-Chl:g\({}^{-1}\) FW (Figure 2A). We further examined net photosynthetic rate (\(Pn\)) and chlorophyll fluorescence parameters in leaves of both the control and VOCs-exposed plants. The VOCs-exposed plants exhibited higher values of \(Pn\) tha  
Figure 1: The release of VOCs by _B. amyloliquefaciens_ (BF06) improved the growth of _Arabidopsis_ plants. Seven-day-old seedlings were treated with or without exposure to BF06 VOCs for 10 days, then these plants were selected to analyze their growth phenotypes (**A,B**); fresh weight (**C**); dry weight (**D**); rosette leaf number (**E**); and total leaf areas (**F**). Different letters represent significant difference between the control and VOCs-exposed plants at \(p<\) 0.05.  
The maximum photosystem II (PSII) quantum ration of variable to maximum fluorescence (\(Fv/Fm\)) in the VOCs-exposed plants was remarkably higher than that in the controls (Figure 2C). A similar trend of the effective quantum yields of PSII photochemistry (\(\Phi\)PSII) was seen in the tested plants (Figure 2D). In addition, the values of NPQ were notably higher in the VOCs-exposed plants than in the controls (Figure 2E), indicating that mVOCs exposure reduced excitation pressure on PSII by thermal energy dissipation.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 3 Methodology
### Datasets  
For this study, three publicly available datasets containing temperature and humidity measurements from enclosed environments were selected. These datasets were chosen because they offer a variety of environmental conditions and span multiple settings, providing a diverse range of data points for analysis.  
1. **Correlating Indoor and Outdoor Temperature and Humidity in a Sample of Buildings in Tropical Climates**[22]--This dataset includes absolute humidity, relative humidity, and temperature measurements for indoor and outdoor environments across 35 buildings in eight tropical cities. We extracted Tmp and RH data from each building to create a consolidated dataset, referred to as _dataset1_, for our subsequent analyses.
2. **Indoor Temperature and Relative Humidity Dataset**[23]--This dataset comprises indoor temperature and relative humidity measurements collected over a month using twelve Xiaomi Mijia sensors in Medellin, Colombia. We consolidated all sensor data into _dataset2_ for further analysis.
3. **Temperature and Humidity Dataset**[24]--This dataset includes temperature and humidity measurements for three regions, including Shenyang, Beijing, and Guangzhou. The data from all three cities have been combined into _dataset3_ for subsequent analysis.  
Each dataset contains time-stamped records of temperature and humidity levels, which were merged into a unified database for analysis. The primary objective was to analyse the relationship between these environmental variables and the emotional well-being of individuals within these environments.
## 3 Results and Discussion
### Cation Content in Yeast Biomass  
Although literature data show that Mg\({}^{2+}\) and Ca\({}^{2+}\) play an important role in the biotechnological physiological properties of yeast cells [48], there is no information regarding the effect of these metal ions on the immunity of yeast to dehydration and subsequent rehydration. Therefore, we investigated whether rehydration with different rehydration media (PTWAir, PTWN, CW) could affect the quantity of selected ions in the obtained yeast biomass. Among the many cations present in yeast, magnesium, calcium and zinc are involved in the regulation of cell structure and metabolic activity during growth and fermentation [49]. The highest levels of these ions were found in fresh culture. The water used for rehydration had no effect on the amount of cations in the cells. Control samples (CW) of yeast strains _Sc_ T58 and _Sc_ US-05 with Ca\({}^{2+}\) content 0.3 mg/g of dry mass and 0.35 mg/g of dry mass, respectively, were statistically different from _Sc_ S33 and _Sp_ W30/70 strains with Ca\({}^{2+}\) content 0.15 mg/g of dry mass and 0.11 mg/g of dry mass, respectively. Ca\({}^{2+}\) has been considered as an important factor affecting flocculation, during which they accumulate in yeast cells [50]. Recent brewing publications show an inverse correlation between Mg\({}^{2+}\) and Ca\({}^{2+}\). The capacity to dominate one of the two metals is largely dependent on their concentration levels. A high Mg-Ca ratio has the best effect on yeast physiology as well as on the fermentation processes [51]. Magnesium exerts a protective effect on the dehydration of stationary cells in the growth phase by acting as a charge stabilizer of the cell membranes. Deterioration in magnesium bioavailability may lead to unfavorable changes in the physiology of yeast cells; in particular, their ability to withstand the dehydration-rehydration process [52]. Yeast cells were characterized by relatively high concentrations of Mg\({}^{2+}\), compared to the amounts of other divalent ions, i.e., Ca\({}^{2+}\) and Zn\({}^{2+}\) (Table 2). This dependence has been confirmed by numerous literature reports [49; 53]. In the water used in this research (Zywiec Zdroj), the magnesium content was 5.37 mg/L (a concentration much higher than that recorded in cells), which means that the yeast absorbed only a small part of this element from the rehydration medium. Zn\({}^{2+}\) in yeast biomass must be present for cell multiplication and an efficient fermentation process [54]. It can be concluded that the water used to rehydrate the analyzed strains did not contribute to the increase in the content of individual elements in the yeast biomass.
## 5 Experiment III: Listening Experience Rating  
As a final assessment of the optimal SBR levels derived from Expt. II, a subjective rating task was undertaken to evaluate listening experiences when the audio scenes were created with a variety of SBR levels. Instead of general audio quality, in this experiment the balance between _intelligibility_ and _sound effect_ is considered as the major aspect that affect a listener's experience.
# Perceptual Characteristics of Voice Identification in Noisy Environments
## 3 Perceptual Characteristics for Different Frequency Distributions  
The results of experiment 1 show that the perception of voice identity will be affected by the type of noise in the low SNR (<0 dB) environment. Compared with white noise and pink noise, speech noise has a more significant impact. The source of this experimental phenomenon may be that the energy of speech noise is concentrated in the low-frequency  
Figure 4: Scatter diagram of measured and predicted values of voice identity perception.  
and medium-frequency bands, which are close to the frequency areas of voice, so it has a more significant impact on voice identity perception. In order to research the impact of the frequency distribution on voice identity perception, a listening experiment was designed and implemented under speech noise environments, in which different frequency bands were removed.
## 3 Results and Discussion
### Artificial Neural Network Results  
We utilized the parameters listed in Table 1 to construct predictive models using an ANN. Our model employed two activation functions, namely Tahn and ReLu, and five further different structures: 5-S-T, 5-S-R, 7-S-R, 10-S-R, and 5 \(\times\) 2-S-R. The ANN structure learning parameters can be found in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test No.** & **\(n\), [rev/min]** & **\(V_{p}\), [mm/min]** & **\(a_{p}\), [mm]** & **\(R_{dr}\), [\(\mu\)m]** \\ \hline  
& 2.856 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The training parameters.  
After the network was successfully trained, the coefficient of determination (R\({}^{2}\)) was used as a criterion for comparing results, as represented in Equation (5).  
\[R^{2}=1-\big{(}\frac{\sum i\;(y_{i}-\hat{\mathbf{y}}_{j})^{2}}{\sum i\;(\hat{ \mathbf{y}}_{j})^{2}}\big{)} \tag{5}\]  
where \(y\) presents the measured (exact) value and \(\hat{\mathbf{y}}\) presents the obtained values. The functions of activation in this study were ReLU and Tanh. The ANN model was developed using Python 3.10 and the backpropagation algorithm.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 4 Methodology
### Masked Language Model  
The operating mechanism of LogEDL proposed in this study is shown in Figure 2. Since LogEDL performs anomaly detection through the pre-training method of MLM of BERT and the EDL method, a detailed description of MLM is given in this section, and a detailed description of the EDL method in the log anomaly detection process is given in Section 4.2.  
The reasons for using MLM for system log anomaly detection are as follows. Due to the existence of a large amount of log data, there are enough data to effectively pre-train BERT using the MLM approach, resulting in a large number of contextual and structural features of the log sequences. MLM is inspired by the completion task, which replaces certain tokens of the input sentence with [MASK], and then, predicts the token in the [MASK] tokens. Bert is a large-scale bidirectional (fully contextual) trained model, which means that the model takes into account the contextual information of the entire input sequence when generating the representation of each word, unlike the pre-training of left- or right-directed language models. The LogEDL model uses the MLM-trained model for anomaly detection, which is expected to improve the generalization performance of the logs for effective anomaly detection.  
Figure 1: Open-world learning process.  
Second, the task of labeling log sequences is very difficult, and the number of normal log sequences is much larger than the abnormal log data. Anomaly detection is a method based on unsupervised learning that uses only normal data for training to detect anomalies. While MLM is a self-training task, it does not need to label the data, and it is suitable for cases where the normal data are much larger than the abnormal data, which meets the purpose of anomaly detection. Therefore, it is more suitable than supervised binary classification-based methods for situations where there is a large amount of normal data.  
Finally, from a practical operations perspective, experts detect anomalies by comparing the actual log keys with the generated log keys. While MLM methods can focus on patterns in context, they tend to have lower prediction probabilities for abnormal patterns [20]. However, MLM training methods cannot yet express anomalies.