esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analyses
In our experiment, we did not observe the anti-Stokes lines corresponding to Stokes lines at 1203 nm and 1134 nm, since these two components were relatively weak compared with the Stokes line at 1273 nm. As shown in Figure 4a, the output pulse energy of the fundamental wave centered at 1030 nm is linearly dependent on the pump power. The pulse energies for both parametric components at 861 and 751 nm versus pump power increase with a higher rate from 10.85 to \(\approx\)12.6 W and with a lower rate from \(\approx\)12.6 to 15.24 W (see Figure 4b,c). The 1.9 ps pulses of 3.8 uJ at 1030 nm (peak power 2000 kW), 342 fs pulses of 241 nJ at 864 nm (peak power 701 kW), and 834 fs pulses of 21 nJ at 751 nm (peak power 25.1 kW) were obtained at 15.24-W pump power (see Figure 4d-f). We also tried to characterize the energy of Stokes lines at 1134, 1203, and 1273 nm at 15.24 W pump power by employing 40 nm bandpass filters. The pulse energy of 1273 nm component was measured to be 3.9 nJ. However, the pulse energies of the 1134 and 1203 nm components were too weak to access. Pulse compression along with the amplification is demonstrated in Figure 4e,f and Figure 5a. Under increased pump power, both parametric pulses are compressed monotonously. In our experiment, the highly intense fundamental (2000 kW), near-infrared (701 kW), and red (25.1 kW) waves imposed strong self-phase modulation (SPM) and cross-phase modulation (XPM) on the parametric gain, which extended the frequency coverage of the parametric components (see Figure 5b). This overcame the gain saturation, and resulted in compression of the pulse duration. Besides, in our experiment for the nonlinear LMA fiber, group velocity dispersion (GVD) in high-order modes could compensate for material group velocity mismatch, when fundamental and parametric waves were propagating with different modes [10; 20]. Thus, efficient nonlinear parametric amplification was achieved. Figure 4: The evolution of the output pulse energy with increased pump power for the fundamental wave at 1030 nm (**a**), and anti-Stokes lines at 864 nm (**b**) and 751 nm (**c**). The measured autocorrelation trace for the fundamental wave at 1030 nm (**d**), and anti-Stokes lines at 864 nm (**e**) and 751 nm (**f**). The fundamental wave and anti-Stokes lines were selected by bandpass filters. In Figure 4d–f, the solid curves resulted from experimental measurements, while the dashed curves were via hyperbolic secant fitting.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## ZigBee (IEEE 802.15.4)
### Free-Space Optical (FSO) Communications  
The demand for higher data rates requires broader bandwidth for communication system. Among different potential technologies, free-space optical (FSO) communication is one of the most promising technologies addressing the problem of large bandwidth and data rate requirements, as well as the \"last mile bottleneck\". The FSO system functions by transmitting modulated laser light through the air between the transmitter and receiver. More specifically, the signal is transmitted using a lens or parabolic mirror by narrowing the light and projecting it towards the receiver. The emitted light is then picked up at the receiver with a lens or mirror. Subsequently, the received light is focused on an optical detector and converted to electrical signals for further information extraction [98]. Besides the advantages of large data rate with unlicensed spectrum, the FSO communication is also considered to be a more secure technique than the RF communication [99; 100; 101; 102]. Thanks to the various advantages of FSO communications, FSO link can be part of the backhaul communication network for rural or remote substations monitoring applications. In [103], the FSO system based on a microring resonator (MRR) with the ability to deliver up to gigabit (line-of-sight) transmission per second is proposed for the two SG applications (AMI and DR). The experimental results demonstrate up to 10 times bandwidth improvement over the radius as large as 600 m and maintain receive power higher than the minimum threshold (\(-20\) dBm) at the controller/users, so the overall system is still able to detect the FSO signal and extract the original data without detection. The feasibility of FSO communications technology from the atmospheric context of Bangladesh has been analyzed for smart village energy autonomous systems in [104].
## 3 Results and Discussion
### Volatile Compound Analysis
In the FCC group, the relative content of esters decreased to 7.33%, which may be related to the CCR effect triggered by glucose, degraded from corn starch. This effect is suggested to further inhibit metabolic pathways in \(D\). _hansenii_ associated with the synthesis of higher alcohols and the expression of alcohol acetyltransferase, thereby limiting the generation and conversion of ester precursors and reducing ester accumulation, as reported in previous studies [7]. In contrast, the FNP group, by alleviating the NCR effect, may have enhanced the activity of metabolic pathways related to ester synthesis, leading to a significant increase in ester content, which reached 22.38%. The FNP group showed the substantial accumulation of volatile compounds such as ethyl hexanoate and ethyl benzoate, which are commonly associated with \"fruity\" and \"floral\" aromas in previous studies. The relative content of ketones in the FCC and FNP groups increased to 2.89% and 6.35%, respectively. Ketones, which have a low sensory threshold, contribute to fruity, floral, and creamy aromas. They primarily originate from \(\beta\)-keto acid decarboxylation, incomplete \(\beta\)-oxidation of free fatty acids, and amino acid degradation. Some studies have found that carbon sources are converted into pyruvate through glycolysis, which can then be transformed into acetyl-CoA, and under specific metabolic conditions, short-chain ketones may be produced via an atypical pathway [52]. In the FNP group, the abundant nitrogen source may have maintained metabolic balance in \(D\). _hansenii_, reducing its dependence on fat for energy. This increase in incomplete \(\beta\)-oxidation flux could have promoted the accumulation of medium-chain ketones, such as 2-heptanone, which have contributed to fruity aromas [53]. The FNP group also exhibited a significant accumulation of 2-pentylfuran (2.95%), a low-threshold compound known to contribute to plant-like aromas such as beamy and green notes. Additionally, pyrazine compounds (2.13%) were detected exclusively in the FNP group. Pyrazines require carbohydrates as carbon source precursors and amino acids as nitrogen source precursors [54]. The elevated FAA levels in the FNP group may have provided sufficient substrates for non-enzymatic pyrazine synthesis, promoting the early accumulation of pyrazines during fermentation. Pyrazines have an extremely low flavor threshold and can contribute unique roasted and nutty aromas at low concentrations. In summary, the FCC group generated aldehydes and ketones under conditions of low free fatty acid levels, contributing fresh, grassy flavor characteristics to the sausages. The relative content of esters, ketones, and pyrazines increased in the FNP group, with reduced aldehyde contents, leading to a complex and rich flavor profile with fruity, nutty, and other distinctive characteristics.
# Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs
## 3 Case Studies
### Simulation Results of the Day-Ahead Control Stage  
According to the day-ahead forecasting of preliminary load consumptions, PV generations, and EV charging demands (Figures 11-13), the grid voltage distribution patterns before the control activation can be estimated via the time series power flow simulations, as illustrated in Figure 15. When the day-ahead control is not activated, all OLTC transformers have their taps set at nominal positions, and none of the capacitor banks is switched on. It is observed from Figure 15 that a great portion of buses has their terminal voltages drop below 0.95 pu during the period of 7 p.m. to 11 p.m. due to the concurrence of high EV charging demand and peak power consumptions of basic loads. Significant voltage deviation (up to 0.09 pu) is also observed during the EV peak charging period.  
By applying the modeling procedure of the day-ahead control given in Section 2.4.1, the optimal operating curves for the OLTC transformers and CBs can be derived, as illustrated in Figure 16. According to [44], the switching action costs of OLTCs and CBs are assumed to be 1.4 USD/time and 0.24 USD/time, respectively, while the marginal cost of the power losses is set at 80 USD/MWh. The grid voltage distribution pattern with the day-ahead control being applied is shown in Figure 17. Comparing the grid voltages before and after the day-ahead control, it turns out that the day-ahead control successfully brings the bus voltages back to the safe operation margin (i.e., 0.95\(\sim\)1.05 pu) during the period of peak power demand, even though certain bus voltages are close to the boundaries.
# New Results for the Error Rate Performance of LoRa Systems over Fading Channels
## 1 Introduction  
In recent years, the exponential growth in the number of inexpensive, Internet-connected devices has given birth to the Internet of things (IoT) and its numerous applications, including autonomous farming, wearable health monitoring, smart homes and cities. Nevertheless, the increasing number of connected devices in conjunction with memory, bandwidth and energy availability constraints has revealed the limits of traditional connectivity technologies, namely ZigBee, Bluetooth and WiFi, in terms of energy consumption, scalability and throughput [1]. In order to fulfill the communication requirements of the IoT, the so-called Low-Power Wide Area Networks (LPWAN) have recently attracted significant attention within the research community as well as for commercial use, due to their ability to complement traditional cellular and short-range wireless technologies in an efficient manner [2; 3; 4; 5]. Among all available LPWAN protocols, the so-called LoRa (Long Range) technology [6] has emerged as a promising candidate for smart sensing technology for civil (e.g., environment and health monitoring, smart metering, precision agriculture) and industrial applications, in urban and rural environments, due to its long-range and low-power capabilities. LoRa modulation is a 3 GPP standard based on the chirp spread-spectrum (CSS) technology [7; 8; 9; 10] and uses the industrial, scientific and medical (ISM) frequency bands at 433 MHz, 868 MHz or 915 MHz with data rates of up to 50 kbps. Although the LoRa technology is well documented in [6], there are still relatively few studies on its theoretical performance. A summary of related works is presented in Table 1.  
Specifically, the mathematical representation of the LoRa modulation/demodulation process and its performance in terms of the symbol and bit error rates for additive white Gaussian noise (AWGN) and frequency selective fading channels were addressed in [7]. In [21], a moment matching method was employed to obtain accurate closed-form approximations for AWGN and Rayleigh fading channels. Multi-antenna LoRa systems were addressed in [11; 24]. The performance of relay-based LoRa networks was addressed in [25]. A first attempt to provide exact BER expressions for Rayleigh, Rice and Nakagami-_m_ fading is available in [22]. Nevertheless, as was pointed out in [21; 23], the proposed methodology for channels other than Rayleigh may suffer from numerical stability issues, due to the computation of large values of binomial coefficients. To this end, ref [23] leveraged the properties of the Marcum Q-function to provide accurate approximations for the BER of LoRa systems over Rice and Nakagami-_m_ fading channels. However, several results obtained using this method, i.e., for Nakagami-_m_ fading, require the computation of hypergeometric functions with two arguments [23], which in turn are not available as built-in functions in standard mathematical software packages such
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## Glossary  
* [1] EVI Enhanced Vegetation Index
* [2] GEE Google Earth Engine
* [3] GRVI Green-Red Vegetation Index
* [4] HSV Hue-Saturation-Value
* [5] LULC Land use and land cover
* [6] MSAVI Modified Soil-adjusted Vegetation Index
* [7] MNDWI Modified Normalized Difference Water Index
* [8] MODIS Moderate Resolution Imaging Spectroradiometer
* [9] NIR Near-infrared
* [10] NDVI Normalized Difference Vegetation Index
* [11] NDBI Normalized Difference Built-up Index
* [12] NDWI Normalized Difference Water Index
* [13] OLI Operational Land Imager
* [14] RGB Red, green, blue bands
* [15] SWIR Short-wave infrared
## 3 Results and Discussion  
### Removal of Cr(VI) in Relation to the Dose of Fe(II)  
In these experiments the factor that affects the system is the concentration of ferrous iron that is available for reaction with hexavalent chromium after the mixing of the water with the reducing agent. Figure 2 shows that as the concentration of ferrous iron is increased, Cr(VI) percentage removal is also increased. The applied doses were 0.25, 0.5, 0.75 and 1 mg/L at an initial chromium concentration of 50 \(\mu\)g/L, corresponding to molar ratios of 4.8, 9.6, 14.4 and 19.2. For these molar ratios, Cr(VI) removal was in the order of 45%, 85%, 88% and 92% respectively. These results corresponded to residual Cr(VI) concentrations of about 24, 8, 6 and 3 \(\mu\)g/L respectively.  
Experiments were carried out at pH 7.3. At this pH value, total chromium was also very low (similar to Cr(VI) values). These results are in agreement with batch studies [15] and are quite improved from results obtained in continuous mode operation units, which instead of a pipe flocculation reactor, used a Cr(VI) reduction tank [14]. In the study of [PERSON] et al., 2005 [14], it was shown that a molar ratio of Fe(II)/Cr(VI) of 9 to 56 was required to achieve a more than 90% total chromium removal for water containing 3-4 mg/L dissolved oxygen. In our experiments, a ratio of about 15 was needed for water containing dissolved oxygen of about 8 mg/L. Given the fact that increased dissolved oxygen in water competes with Cr(VI) for Fe(II) oxidation, it is expected that Fe(II) oxidation takes place also by the dissolved oxygen, according to the kinetics of bivalent iron oxidation by oxygen [22]. Therefore, the results obtained provide evidence that the use of pipe flocculation reactors can be a more efficient reactor for reducing hexavalent to trivalent chromium than the use of Cr(VI) reduction tanks, since under fully oxic conditions, the unit achieved more than 90% Cr(VI) removal by a molar ratio Fe/Cr of about 15, for a linear velocity of about 8 m/h.  
To maintain the stable operation of the unit, the filters were backwashed once a day with clean drinking water and the sludge was collected and disposed. This helped to avoid replacing the sand very frequently. The sand is progressively coated with iron oxides, which as the literature studies  
Figure 2: Removal of Cr(VI) as reacted to the molar ratio of ferrous iron to hexavalent chromium. Experimental conditions: Cr(VI) initial concentration: 50 \(\mu\)g/L, Fe(II) doses: 0.25, 0.5, 0.75, and 1 mg/L, linear velocity: 8 m/h, value of pH: 7.3.  
suggest can enhance the rate of Cr(VI) reduction by Fe(II) and also oxidation of Fe(II) to Fe(III), when the dosing is over stoichiometric levels [20; 22].
## 4 Results and Discussion  
### Suppression of the Twin-Image Effect  
#### 4.1.1 Digital Filtering  
A numerical holography experiment was performed according to the in-line scheme of two model opaque square particles with a side of 89.7 \(\upmu\)m, located in one plane on a transparent homogeneous background. The numerical reconstruction of particle images from the modeled digital hologram (Figure 6a) shows a band pattern in particle images associated with the twin-image effect. After digital filtering using a filter described in Section 3.2 (Formula (15)) [12], we can see that the mutual influence of the real and virtual images has significantly decreased (Figure 6b).  
It should be noted that the twin-image effect in general and especially for closely spaced adjacent particles affects the energy distribution in the image regions used to determine K and \(\Delta\)I (Section 2.2) and, thus, the accuracy of determining the best image plane of a particle, which is determined by K and \(\Delta\)I maximum values. In field experiments, the position of the best image plane is taken as the longitudinal coordinate of a particle, therefore, the accuracy of its determination plays a key role in the study of the particle dynamics in space.  
For the described experiment, Figure 7 shows the dependence of the determination error of the best image plane \(\Delta=|z-z_{P}|\) of one of the square particles on the distance between the centers of mass of images of adjacent particles (Figure 6), where z--distance from particles to the recording plane, z\({}_{P}\)--distance from the plane of the digital hologram to the best image plane determined by the maximum boundary intensity jump \(\Delta\)I.  
The figure shows that digital filtering of a virtual image at small distances between particles reduces the determination error of the best image plane approximately twice and then reduces it to zero. The oscillating nature of the best image plane error is explained by the influence of interference bands caused by the twin-image effect on the quality indicators used to determine the best image plane. This demonstrates the need to suppress a twin image in digital particle holography measurement systems, as well as to determine the boundary conditions for the relevant algorithms.
## 4 Experiments and Discussions
### Real Data Experiments
#### 4.2.2 Adregcdb  
In this section, 10 s of time was selected for the FECG extraction experiment. Table 2 shows the correlation coefficients of the FECG extraction results by our method. Figures 12-16 show the experimental results of all 20 abdominal channels of the ADFECGDB database. The results show that, except for Channel 1 of r04, Channel 1 of r07, and Channel 3 of r10 having no observable FECG signals, FECGs were well extracted from all other channels with good correlation coefficients. The FECGs extracted successfully by our method have complete QRS waveforms and accurate R peak positions, P waves and T waves are clear, and the trend is reasonable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ch1** & **ch2** & **ch3** & **ch4** \\ \hline r01 & 0.798 & 0.764 & 0.756 & 0.847 \\ \hline r04 & & 0.795 & 0.785 & 0.808 \\ \hline r07 & & 0.821 & 0.767 & 0.802 \\ \hline r08 & 0.737 & 0.659 & 0.701 & 0.703 \\ \hline r10 & 0.781 & 0.742 & & 0.571 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficients between the extracted FECGs and the clean scalp FECGs.  
Figure 12: Extraction results for r01 in the ADFECGDB database.  
Figure 14: Extraction results for r07 in the ADFECGDB database.  
Figure 13: Extraction results for r04 in the ADFECGDB database.  
Figure 16: Extraction results for r10 in the ADFECGDB database.  
Figure 15: Extraction results for r08 in the ADFECGDB database.
## 2 Some Existing Methods of Estimating the Lithium-Ion Battery State of Charge
### Experimental Methods  
Experimental methods refer to the methods to explore battery characteristics through battery experiments. The first experimental method is referred to as the Coulomb counting method [36]. This technique estimates the SOC in the battery by integrating the current transferred to the battery over time, to determine the remaining charge relative to the maximum battery state of charge. Coulomb counting is often utilized in combination with model-based methods due to its relatively simple implementation. However, this method suffers from inaccuracies due to sensor error accumulation and initial value errors; moreover, it is only suitable for online applications [37; 38]. The second experimental method is the so-called Hybrid Pulse Power Characterization (HPPC) [39]. This method consists of monitoring voltage variations during short-/high-current charge/discharge pulses with relaxation periods. In this method, the cell voltage response is affected by the electrochemical kinetics of the battery, such as ohmic losses, double-layer capacity, and lithium-ion diffusion [40; 41]. The third existing experimental method is Electrochemical Impedance Spectroscopy (EIS), which measures the current and voltage relationship over a frequency range and can be used to model kinetic battery processes through the impedance spectrum and estimate physical properties like diffusion coefficients and reaction rates. Impedance Spectroscopy (EIS) requires linearity, stability, and causality, which makes it challenging to implement as an online method [42]. The experimental methods alone are more suitable for offline state-of-charge estimation due to the required experimental conditions.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Assessing Labeling Heuristics  
Although our dataset, UWF-ZeekDataFall22, has several different types of attacks, as shown in Table 1, this clustering is performed on the attack versus non-attack data. That is, the analysis is not at the level of the attack, but whether a data point is an attack or not. Hence, after K-means clustering was performed on the data, the labels from the labeled dataset were used to determine which cluster points were attacks. A new column, \"Prediction,\" was added to the DataFrame to hold the data and predict the class of the cluster. Experimentation was performed with three distinct heuristics for labeling attack clusters:  
First Method: Clusters with At Least One Attack labeled as Attack Clusters  
Second Method: Clusters with at Least 25% Attack Data labeled as Attack Clusters  
Third Method: Clusters with at Least 50% Attack Data labeled as Attack Clusters  
For each labeling method, the twelve previously mentioned connection-based features were used: duration, proto, conn_state, src_port_zeek, dest_port_zeek, orig_pkts, resp_pkts, orig_ip_bytes, resp_ip_bytes, orig_bytes, resp_bytes, resp_bytes, and missed_bytes.  
The threshold of 1, 25%, and 50% were selected to explore the trade-offs between high recall and low false positives when labeling clusters. The threshold of 1 assumes that a single attack in a cluster should raise concern, following the principle of maximum sensitivity, often applied in early-stage detection within the cyber kill chain framework. The 25% and 50% thresholds reflect increasing confidence in the cluster's identity as primarily malicious, corresponding to mid-stage and late-stage actions within the MITRE ATT&CK lifecycle such as lateral movement or exfiltration. These thresholds also simulate differing tolerance levels in real-world intrusion detection, ranging from aggressive (1 single attack) to conservative (50%) strategies. Future work could include an adaptive threshold model based on the specific attack class or tactic.