esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results & Discussion
### Total acidity and Production of Secondary Metabolites
Different research groups have proposed that acetic acid production is negatively correlated with nitrogen content; however, this was not observed for acetic acid content for the commercial and Sb strains [10; 45]. Considering, though, the strain-dependent manner of acetic acid production and the absence of statistically important differences between fermentations with discrete YAN concentrations, these results could be explained. Glycerol is a significant by-product of alcoholic fermentation, as it contributes to the organoleptic properties of wine, increasing viscosity and sweetness [46]. In the current study, the highest production of glycerol was observed in musts containing 150 mg N/L YAN, for all examined _S. cerevisiae_ strains (Table 3). These results are in agreement with previews studies, suggesting that nitrogen limitation increases glycerol production [45; 47]. Independently from the YAN level, glycerol concentrations ranged between 5.8-6.8 g/L, which was within the range of previously reported data (4.2-10.4 g/L) [45; 47; 48; 49; 50]. However, the Sb strain was characterized by the highest glycerol and lower ethanol production, although the differences observed were not statistically significant. Such differentiations between strains regarding glycerol production could be attributed to the differences between the two pathways that are linked to glycerol formation. The low and high glycerol producing strains differ in glycerol-3-phosphate dehydrogenase activity. The gapN gene that encodes non-phosphorylulating NADP\({}^{+}\) is responsible for the enzymatic activity, and in strains where this gene is suppressed, ethanol production is lower, while glycerol yield is higher. Higher production of glycerol by the Sb strain could also be ascribed to the suppressed expression of the mhpF gene encoding acetylating NAD+ linked to acetaldehyde dehydrogenase and overexpression of the frdA gene encoding NAD+ linked to fumarate reductase [51]. Furthermore, the three strains presented different production kinetics (Figure 5). Glycerol production could be related to glucose consumption, since glycerol reached a plateau when glucose was exhausted. Aerobic production of glycerol in the presence of glucose, which may result in respiro-fermentative growth, is a possible explanation for the results observed [52].
## 1 Introduction  
High energy, ultrafast laser in the mid-infrared (mid-IR) is desirable for applications in strong field physics [1], high-harmonic generation [2], and driving X-ray sources [3]. An optical parametric chirped pulse amplifier (OPCPA) is one of the most promising ways to achieve amplification for mid-IR laser pulses by applying proper nonlinear crystals and a phase matching condition. A kHz, mJ-level OPCPA at 2.1 \(\upmu\)m is reported with a high-energy cryogenic Yb:YAG pump laser [4]. The current limitation for a higher OPCPA output depends upon the development of high-energy picosecond pumping sources. Several investigations have been developed for the generation of high-energy OPCPA pump sources. A picosecond pump laser for IR OPCPA delivering 25 mJ at 3 kHz based on a Yb:YAG thin-disk amplifier has been reported [5]. A 100 mJ, 1 kHz laser output has been achieved as a pump for picosecond OPCPA based on Yb:YAG thin disk regenerative amplifier [6]. Additionally, an OPCPA pump laser based on rod-shaped Nd:YAG crystals has been reported, producing 130 mJ, 64 ps pulses with a repetition of 300 Hz [7].  
In this paper, we present a diode-pumped solid-state laser (DPSSL) amplification system based on a master oscillator power amplifier (MOPA) configuration, delivering 316.5 mJ, 50 ps pulse energy at a wavelength of 1064 nm and a repetition of 100 Hz. The amplification system works at room temperature. A thermal lens and thermally induced depolarization were studied and compensated. A reflecting volume Bragg grating was utilized in the regenerative amplifier to achieve temporal stretching of the laser pulses to suit the width of the signal laser pulses in the optical parametric amplifier (OPA) process. The output of the amplification system also features an approximately flat-top spatial distribution and a 0.53% energy stability (RMS).
## 2 Laryngeal Image Modalities
### Laryngeal Videostroboscopy  
During phonation, the vocal folds vibrate at a rate faster than can be perceived by the human eye. Therefore, the use of techniques to create an apparent slow-motion view of the periodic vibratory cycles has been necessary. The process to record an VS involves the use of a video camera attached to a rigid (transoral) or flexible (transnasal) endoscope where the illumination is provided by a strobe light that flashes at a rate that is synchronized with the patient's fundamental frequency during sustained vowel production. Therefore, VS is an estimated version of the vibration of the vocal folds that is acquired by sampling its motion.  
Figure 2 illustrates the complete procedure to record a VS. (1) and (3) represent the endoscope used to capture the vocal folds motion: rigid (90\({}^{\circ}\)) and flexible, respectively; (2) represents the vocal folds; (4) is the real vibratory pattern of the vocal folds; (5) is the stroboscopic light; and (6) is the estimated slow motion version of the vocal folds vibration. Some of the advantages and limitations of Laryngeal Videostroboscopy are mentioned [18] below:
_applied sciences_  
_Article_  
**Conversion of Waste Biomass into Activated Carbon and Evaluation of Environmental Consequences Using Life Cycle Assessment**  
**[PERSON] 1, [PERSON] 2, [PERSON] 3,4,*(c)**, [PERSON] 5,**  
**[PERSON] 6,*(c)** and [PERSON] 7(c)**  
**Abstract:** In this article, activated carbon was produced from _Lantana caman_ and olive trees by \(\mathrm{H_{3}PO_{4}}\) chemical activation. The prepared activated carbons were analyzed by characterizations such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, Brunauer-Emmett-Teller, X-ray diffraction, thermogravimetric analysis, and Fourier transform infrared spectroscopy. \(\mathrm{H_{3}PO_{4}}\) is used as an activator agent to create an abundant pore structure. According to EDX analysis, the crystalline structure destroys and increases the carbon content of the olive tree and _Lantana camara_ by 77.51 and 76.16%, respectively. SEM images reveal a porous structure formed as a result of \(\mathrm{H_{3}PO_{4}}\) activation. The Brunauer-Emmett-Teller (BET) surface area of the olive tree and _Lantana camara_ activated carbon was 611.21 \(\mathrm{m^{2}/g}\) and 167.47 \(\mathrm{m^{2}/g}\), respectively. The TGA analysis of both activated carbons shows their thermal degradation starts at 230 \({}^{\circ}\)C but fully degrades at temperatures above 450 \({}^{\circ}\)C. To quantify the potential environmental implications related to the production process of the activated carbon (AC) from olive trees, the life cycle assessment (LCA) environmental methodology was employed. For most of the tested indicators, chemical activation using \(\mathrm{H_{3}PO_{4}}\) showed the greatest ecological impacts: the ozone layer depletion potential (42.27%), the acidification potential (55.31%), human toxicity (57.00%), freshwater aquatic ecotoxicity (85.01%), terrestrial ecotoxicity (86.17%), and eutrophication (92.20%). The global warming potential (5.210 kg CO\({}_{2}\) eq), which was evenly weighted between the phases, was shown to be one of the most significant impacts. The total energy demand of the olive tree's AC producing process was 70.521 MJ per Kg.  
**Keywords:** activated carbon; life cycle assessment; \(\mathrm{H_{3}PO_{4}}\); chemical activation
## 4 Discussion
### Implications for Future Work  
Our newly built prism system (SIFprism) showed its consistency with commercial products (FLoX, SIV) and widely used systems (SI and SIII) for measuring reflectance and deriving SIF for a homogeneous canopy. Short IT enables SIFprism system to be used for changeable weather condition and to obtain high-precise SIF measurements [34]. However, the essential point of the prism system, whether it can be reliably used for long-term field operation, is the repeatability of the rotation of the prism to ensure the two optical paths are stable. As a newly built system, it still needs to be run in situ for a long time to investigate its stability. The prism system is suitable for measuring hemispherical canopy radiance taking the advantage of short IT, even CC-3 may dampen more than half of the light density.  
The bi-hemi configuration has an advantage in that it is able to capture signals from a large footprint area even the upwelling sensor is not mounted at high altitude. For monitoring photosynthesis derived from eddy covariance (EC) measurement using EC tower-based SIF measurement, mismatch between the footprints of these two measurements is a critical issue must be considered [34]. The footprint of EC measurement, which is usually tens of times the distance between the sensor and the canopy, is designed to represent the average condition of the canopy, no matter whether the canopy is homogeneous or heterogeneous. The SIF system of hemi-con configuration is probably suitable for homogeneous canopy but is hard to be representative for heterogeneous canopy when its diameter of FOV is still less than 10 m even the sensor is mounted 20 m above the canopy. In contrast, for upwelling channel, hemispherical sensor has a much larger FOV than conical sensor (almost 7 times) [43] and is better at matching an EC footprint. Therefore, the bi-hemi configuration can be well used above a heterogeneous canopy to acquire average information. But this configuration is non-preferable for a discontinuous canopy, such as small plots in the controlling experiment, needing to make sure the upwelling signal is not beyond the canopy. Another important thing is that proper atmospheric correction is necessary for SIF retrieval with the use of a cosine corrector to collect vegetation radiation due to long light path at large view angles [30].
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 4 Discussion and Outlook
### Technological Implications  
The adoption of EO-based monitoring systems introduces significant technological implications for railway infrastructure management. Advanced SAR technologies, such as persistent scatterer interferometry (PSI) and differential InSAR, offer unparalleled accuracy in detecting structural deformations. By providing millimeter-scale resolution, these tools enable infrastructure managers to identify potential issues before they escalate into critical failures.  
Multispectral and thermal imaging data further enhance monitoring capabilities. For example, vegetation health assessments and flood risk analyses can be performed with higher precision, reducing operational disruptions caused by environmental factors. The integration of thermal data also enables the detection of heat-induced rail track deformations, an essential factor in maintaining safety during extreme weather conditions.  
The emergence of AI-powered analytics marks a transformative shift, enabling real-time processing of large EO datasets. Convolutional neural networks (CNNs) and other machine learning techniques can detect subtle anomalies and predict failure patterns with high reliability. However, the reliance on these advanced systems necessitates significant computational resources and expertise, highlighting the need for capacity-building initiatives within railway management organizations.  
Finally, the technological implications extend to the democratization of satellite data access. Collaborations between public and private entities can reduce costs and increase the availability of high-resolution data. These partnerships will enable smaller operators to adopt EO-based solutions, fostering widespread implementation and enhancing the overall resilience of global railway infrastructure.
# Value of Information Based Data Retrieval in UWSNs
## 4 Path Planning Algorithms
### Random Path Planner-RPP  
For evaluation purposes, we also implemented a random path planner (RPP) where the AUV randomly chooses the next sensor node for data collection. The route towards the selected node employs Euclidean shortest path. This path planner can be thought of as a planner which does not take into account Vol or time while scheduling visits to nodes, i.e., AUV schedules its visitation activity irrespective of the critical nature of the Vol in the UWSN.
## 3 RDE Cycles Description
### Rde 3, 4, 5, and 6
Figure 3 shows the fulfillment of the VA [95] and RPA criteria for all the proposed RDE cycles despite a great dispersion between them. In particular, higher RPA and VA [95] point out a more loaded cycle. The last calculation corresponds to the Moving Averaging Window (MAW). Such calculation starts from a previously performed WLTC, which serves to obtain the so-called CO\({}_{2}\) reference mass, defined as half of the CO\({}_{2}\) mass emitted by the vehicle during this WLTC reference test. The CO\({}_{2}\) reference mass is used to calculate the MAW as follows: CO\({}_{2}\) emissions are cumulated from the beginning of the RDE test (having a sampling frequency of 1 Hz) until reaching the CO\({}_{2}\) mass reference. The average speed and the average specific emissions of CO\({}_{2}\) in g/km in this period permit the definition of the first window. After resetting the CO\({}_{2}\) count to zero, the next window will start a second later and continue with the same calculation method until the end of the cycle. The RDE cycle is only considered valid when at least 50% of urban, rural, and motorway windows are within the tolerances defined by regulation calculated through the CO\({}_{2}\) characteristic curve. Figure 4 shows the MAWs of the cycles under study. As observed, all cycles have at least the 50% of MAWs within the tolerances of the CO\({}_{2}\) curve for every driving zone. The sixth RDE may seem invalid at first glance because many windows are below the lower limit. Nevertheless, it \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Min Value** & **Max Value** & **RDE 1** & **RDE 2** & **RDE 3** & **RDE 4** & **RDE 5\&6** \\ \hline \multicolumn{8}{l}{Route characteristics} \\ \hline Duration & min & 90 & 120 & 94.9 & 92.58 & 90.9 & 92.33 & 93.4 \\ Altitude & m a.s.l. & 0 & 700 & 6 & 6 & 6 & 6 \\ Temperature & \({}^{\circ}\)C & 0 & 30 & 20 & 20 & 20 & 20 \\ Altitude difference (start–end) & m a.s.l. & 100 & 0 & 0 & 0 & 0 & 0 \\ \hline Urban zone & & & & & & & \\ \hline Distance & km & 16 & - & 24.1 & 21.59 & 25.87 & 24.86 & 24.19 \\ Distance proportion & \% & 29 & 44 & 36.48 & 32.86 & 36.38 & 34.54 & 36.71 \\ Stop time (idle) & \% & 6 & 30 & 28.87 & 28.94 & 27.67 & 28.74 & 29.24 \\ Longer stop & \% & - & 80 & 7.25 & 5.67 & 4.08 & 4.23 & 4.05 \\ Average speed & km/h & 15 & 30 & 22.4 & 20.93 & 25.67 & 24.2 & 23.96 \\ \hline Rural zone & & & & & & & \\ \hline Distance & km & 16 & - & 24.25 & 23.64 & 27.89 & 26.61 & 22.31 \\ Distance proportion & \% & 23 & 43 & 36.71 & 35.98 & 39.22 & 36.97 & 33.86 \\ \hline Motorway zone & & & & & & & \\ \hline Distance & km & 16 & - & 17.71 & 20.47 & 17.36 & 20.5 & 19.39 \\ Distance proportion & \% & 23 & 43 & 26.81 & 35.98 & 39.22 & 36.97 & 33.86 \\ Time speed \textgreater{}100 km/h & min & 5 & - & 6.85 & 8.95 & 7.37 & 6.88 & 6.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RDE cycles characteristics.
## 3 Results and Discussion
### XRD Phase and TG Analyses of Layered Sandstone After High-Temperature Treatment  
XRD phase analysis results of the layered sandstone after high-temperature treatment are shown in Figure 6. At room temperature, the crystal diffraction peaks of layered sandstone primarily correspond to quartz, microcline, and a small amount of illite. As the heat treatment temperature rises, except for a slight decrease in diffraction peak intensity of illite, the phase compositions of the specimens do not exhibit any significant patterns of change. The TG and derivative thermogravimetry (DTG) curves obtained from TG are shown in Figure 7. As depicted in this figure, the first noticeable weight loss peak appears in the DTG curve between 25 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, indicating that the sandstone mass loss is mainly due to the gradual removal of adsorbed and interlayer water from clay minerals. Between 400 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, the sandstone mass continues to decrease, and multiple small weight loss peaks appear in the DTG curve, with a peak shape that is broad and gradual in steepness. This result indicates that clay minerals continue to lose hydroxyl water, albeit at a rate significantly lower than that when the temperature is below 400 \({}^{\circ}\)C. This is because as the dehydration process continues, most of the water within the sandstone core evaporates, thus reducing the impact of dehydration on the specimen mass when the temperature rises above 400 \({}^{\circ}\)C. In addition, the fracturing of clay minerals weakens the cementing force between the sandstone grains, possibly causing fine sand grains to detach, thus reducing the sandstone mass.  
Figure 5: Mass loss rate (**a**) and longitudinal wave velocity reduction rate (**b**) in layered sandstone under different temperature conditions.  
Figure 6: XRD analysis of layered sandstone after high-temperature treatment.
## 2 Methodology
### Target Decomposition for DCP Data
#### 2.2.2 Three-Component Decomposition  
[PERSON] et al. proposed compact three-component decomposition theory using the Stokes vector by considering the relation between the Stokes vector and the 3 \(\times\) 3 coherency matrix [20]. This method extracts three components; namely, surface, double-bounce, and volume scattering power as a function of dominant \(\alpha\) and degree of polarization (DoP) which represents the polarized wave ratio of the total receiving power (similar value with entropy).  
The Stokes vector for the DCP mode is defined as (LHCP transmit case):  
\[\mathbf{g}=\left[\begin{array}{c}\left\langle g_{0}\right\rangle\\ \left\langle g_{1}\right\rangle\\ \left\langle g_{2}\right\rangle\\ \left\langle g_{3}\right\rangle\end{array}\right]=\left[\begin{array}{c} \left\langle\left|S_{KL}\right|^{2}+\left|S_{LL}\right|^{2}\right\rangle\\ -2 Im\left\langle S_{RL}S_{LL}^{*}\right\rangle\\ 2 Re\left\langle S_{RL}S_{LL}^{*}\right\rangle\\ \left\langle\left|S_{RL}\right|^{2}-\left|S_{LL}\right|^{2}\right\rangle\end{array} \right]. \tag{9}\]  
Three components--the surface (\(P_{s}\)), double-bounce (\(P_{d}\)), and volume (\(P_{v}\)) scattering power--are obtained as:  
\[\left[\begin{array}{c}P_{d}\\ P_{v}\\ P_{s}\end{array}\right]=\left[\begin{array}{c}\frac{1}{2}g_{0}m(1-\cos 2\alpha_{s })\\ g_{0}(1-m)\\ \frac{1}{2}g_{0}m(1+\cos 2\alpha_{s})\end{array}\right]. \tag{10}\]  
Figure 2: The \(H/\bar{\alpha}\) plane. (**a**) FCP and FLP; (**b**) DCP.  
where \(m\) and \(\alpha_{s}\) represent the DoP and dominant \(\alpha\) respectively, defined as:  
\[m=\frac{1}{g_{0}}\sqrt{\sum_{i=1}^{3}g_{i}^{2}}, \tag{11}\]  
\[\alpha_{s}=\frac{1}{2}\text{tan}^{-1}\left(\frac{\sqrt{g_{1}^{2}+g_{2}^{2}}}{ \pm g_{3}}\right)\ \ (+:\text{LHCP transmit}\ \ -:\text{RHCP transmit}). \tag{12}\]  
As \(\alpha_{s}\) indicates the dominant \(\alpha\), the same \(\alpha\) provided by full polarimetric (FP) data can be recovered only when there is a dominant eigenvector in the coherency matrix of FP and a reflection symmetric medium.
Such a 'plug-n-play' configuration increases the risk of cyber-misebehavior. Compounding factors include the typical mixture of multitudes of wired or wireless communications that employ cloud-connected embedded systems used by consumers to interconnect with each other [9; 10]. Intrusion detection systems (IDS) are widely used to improve security posture in an IT infrastructure. An IDS is considered a suitable and practical approach to detect attacks and assure network security by safeguarding against intrusive hackers [3]. Anomaly-based IDS approaches can efficiently detect zero-day (unknown) attacks [11; 12]. An intrusion can be defined as a sequence of unexpected activities locally or globally, harming network confidentiality, integrity, and/or availability (i.e., the CIA triad) [13; 14]. The network traffic consists of packets associated with packet header fields. Features related to those instances are important to define the purpose of detecting anomalies. The purpose of an IDS is to detect and/or prevent abnormal misbehavior (i.e., unauthorized use), both passive and active network intruder activities, and thus improve CIA. In recent times, machine learning (ML)-based approaches have been employed for intrusion detection in IoTs IDSs [3; 15; 16; 17; 18; 19; 20; 21]. Existing IDSs assume that the IoT devices have the same feature pattern and packet types. However, IoT devices vary in some respects, such as hardware characteristics and functionality, computational capability, and different abilities for generating various features [11; 22]. The features become sparse when nodes are aggregated to create data, and the irrelevant features (attributes) are set to either nulls or zeros. Data sparsity is one of the disadvantages that affect the accuracy and efficiency of data modeling. Feature selection, an important part of a machine learning-based solution, plays an important role in increasing detection accuracy and speed of the training phase. Several feature selection techniques have been proposed to improve detection of anomalous behavior variants such as Flexible Mutual Information-based Feature Selection (FMIFS) [23], Modified Mutual Information-Based Feature Selection (MMIFS) with Support Vector Machine (SVM) [24], and SVM with Neural Networks (NN) [25]. Those approaches/models and other recent state-of-the-art studies have been presented in the related work section. _Detection accuracy of anomaly-based IDSs is considered the main challenge in the IoT ecosystem due to the constantly evolving nature of the IoT environment_[26; 27]. This paper proposes a novel feature selection approach for machine learning-based IDS towards obtaining a resilient performance within the diverse IoT ecosystem.
# Optimized Architecture for Efficient OFDMA Network Design
## 4 Validation and Robustness Study of the Analytical Model
### Channel Model
#### 4.3.1 Average Channel Model  
All MS devices share the same channel memory but with stationary probabilities identical to those of the memoryless model.
# Acoustic TDOA Measurement and Accurate Indoor Positioning for Smartphone
## 4 Experiments and Results
### Experimental Results and Analysis
Each test point was tested 100 times, and the average positioning error of the 20 test points was shown in Figure 16:0  
According to Figure 23, the method proposed in this article is still applicable to Scenario 2. In Region 1, the average positioning error of the test points is 0.36 m; in Region 2, the average positioning error of the test points is 0.34 m; in Region 3, the average positioning error of the test points is 0.32 m; in region 4, the average positioning error of  
Figure 16:1 Average positioning error in scenario 2 (using Proposed-ML algorithm): (**a**) Average positioning error of test points 1–10; (**b**) Average positioning error of test points 11–20. Figure 16:2 Box diagram of positioning error at different test points in scenario 1 (using Proposed-ML algorithm). the test points is 0.40 m; and in region 5, the average positioning error of the test points is 0.29 m. Therefore, sub-meter-level positioning accuracy can still be achieved in Scenario 2. Our system and method provide a good solution for indoor positioning.