esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Properties and Crystallinity of Micromolded Specimens  
Micromolded specimens produced by both conventional microinjection and ultrasonic micromolding (USM) exhibited similar thermal properties, as shown in Figure 7. During the initial heating run, the main melting peak occurred at approximately 171-172 \({}^{\circ}\)C. Notably, the melting enthalpy for such samples was around 43-44 J/g, indicating a similar degree of crystallinity. This crystallinity was close to 29%, as calculated from the reported enthalpy of 143 J/g for a fully crystalline P3 HB sample. During the cooling phase, both types of specimens displayed a consistent exothermic peak, with an enthalpy between 42 and 43 J/g, occurring at temperatures ranging from 115 to 117 \({}^{\circ}\)C.  
The behavior observed in the second heating run differed from the first as a consequence of different cooling rates of the sample in the mold and within the DSC equipment.  
Figure 6: (**a**) X-ray diffraction profile of a USM-processed specimen of P3 HB; (**b**) X-ray diffraction profiles showing the region between 13 and 18 nm\({}^{-1}\), which is highly susceptible to the aging process. Melt extruded sample (blue), USM-processed sample (black), and USM-processed sample after an aging process (orange).  
Figure 7: (**a**) DSC first heating; (**b**) cooling; and (**c**) second heating runs for extruded P3 HB specimens (red lines) and micromolded P3 HB specimens obtained by conventional microinjection (blue lines) and by the USM technology (green lines).  
Consequently, the temperature of the primary peak slightly decreased, from 171 \({}^{\circ}\)C to 165 \({}^{\circ}\)C, likely due to a reduction in lamellar thickness. Despite this, a small shoulder at around 171 \({}^{\circ}\)C remained visible (see dashed red box in Figure 7). This shoulder could possibly be attributed to a typical reorganization of crystals during the second heating phase.
## 3 Methodology
### Challenges of Developed Method  
Firstly, the system presented in this study is dependent on CAD design. The system requires a certain amount of manual input for the initial design phase. While automation assists execution, significant human intervention is still required during the early design stages. Secondly, there is the issue of boundary setting. Site boundaries must be manually specified, highlighting the system's limitations in autonomously adapting to new environments without preset parameters. Thirdly, manual input is needed in task operations. Specific task sequences involving dump trucks still require manual adjustments. Designating loading zones and ensuring proper alignment between excavators and trucks fall under this category. Fourthly, operational inefficiency is another limitation. The case study revealed that occasional manual intervention is necessary, which can lead to potential delays. Additionally, the system lacks the capability for dynamic adjustment based on real-time feedback, making it challenging to maintain a continuous workflow. Construction planning personnel must assess the usability and areas for improvement of this system. This will help establish empirical validation standards for what to measure and verify through the task planning system and provide a clear direction for future research and development efforts. So, in future work, practical validation will be conducted with results and more data support. These limitations indicate areas for system improvement and provide clear directions for future research and development efforts. They contribute to the achievement of a fully autonomous excavation process. Addressing these issues can significantly enhance the system's efficiency, adaptability, and applicability across various construction scenarios.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Ionic Liquids
#### 3.6.3 The Impact of Anion in Ionic Liquid on Cellulose Dissolution Effectiveness  
The anion in ionic liquid has a major impact on cellulose dissolution. Generally, anions that are strong hydrogen bond acceptors, e.g., chloride, acetate, formate, and sulphate, are considered the most effective [100,110]. In order to estimate the anion effect on cellulose dissolution, Kamlet-Taft parameters describing the three independent polarity parameters (the hydrogen-bond-donating ability (\(\alpha\)), the hydrogen-bond-accepting ability (\(\beta\)), and the polarizability/dipolarity (\(\pi^{\circ}\))) of a solvent are used. The most important seems to be the \(\beta\) parameter, related to the hydrogen bond basicity of the anion. The higher the \(\beta\) value of the anion, the better the dissolution properties of ionic liquid [111,112]. For example, 1-allyl-3-methylimidazolium formate [amim][HCOO]\({}^{-}\) is more effective than 1-allyl-3-methylimidazolium chloride ([amim]Cl) because of the higher basicity of hydrogen bonding of the [HCOO]\({}^{-}\) anion [96]. It has been also shown that replacing a hydrogen atom in the [CH3 COO]\({}^{-}\) anion with an electron-withdrawing group (e.g., [HOCH2 COO]\({}^{-}\), [H2 NCH2 COO]\({}^{-}\)) decreases the solubility of cellulose [104].
## 2 AI Techniques in Communication Networks
### Machine Learning and Deep Learning in Network Applications
#### 2.1.2 Unsupervised Learning
Their parallel processing ability allows for faster detection in real-time network monitoring scenarios. Although still relatively new in the context of network security, early studies have demonstrated that Transformer-based models can outperform traditional RNNs and other models in terms of accuracy and efficiency, especially for large and high-dimensional datasets. **Analysis:** Unsupervised learning methods like these are especially valuable in scenarios where labeled data are sparse or when network administrators need to identify previously unknown threats. K-means clustering facilitates the establishment of baseline traffic patterns, making deviations more noticeable and enabling early detection of suspicious activity. Meanwhile, PCA's dimensionality reduction capability streamlines the process, ensuring that critical insights are obtained from vast datasets quickly and efficiently. RNNs and Transformer networks further enhance the detection capabilities by capturing temporal dependencies and long-range correlations, which are critical for analyzing time-series data in real-time [47]. Unlike traditional rule-based systems, which rely on preset signatures to identify threats, unsupervised models adaptively recognize novel attack types or behavioral changes, providing a dynamic advantage in evolving network environments. In summary, unsupervised learning approaches add an essential layer of intelligence to network security by facilitating scalable, real-time anomaly detection. Their capacity to analyze unlabeled data and detect unknown threats makes unsupervised learning indispensable in modern cybersecurity strategies, particularly as network architectures continue to grow in complexity [48].
## Materials and Methods  
### Study Area and Sampling  
Honghu City (113\({}^{\circ}\)07\({}^{\prime}\)-114\({}^{\circ}\)05\({}^{\prime}\)E, 113\({}^{\circ}\)07\({}^{\prime}\)-114\({}^{\circ}\)05\({}^{\prime}\)N), Hubei Province, which is located at the center of Jianghan Plain, was selected as the study area. Honghu has an approximate area of 2519 km\({}^{2}\) and an average altitude ranging mostly from 23 m to 28 m. The study area is an alluvial plain. Thus, the terrain has gentle slopes, with an average slope of approximately 0.3%. The main soil parent materials of this area are river alluvium and lacustrine deposits. The soils are diverse and composed of various Chinese soil taxonomy classifications, including paddy and moisture soils [22]. Their approximate classifications from the World Reference Base of Soil Resources are Typic Haplaquet and Dystrochrept [51]. The soil styles are simple, but the soil nutrient varies. The main land use can be classified as cultivated, forest, construction land, water, and other lands. The cultivated land area accounts for 66.49% of the entire region, with rice, oilseed rape, lotus root, and wheat as staple crops. Generally, the rice in Hubei Province matures two or three times a year, and oilseed rape matures once a year, which is own in September or October, and harvest time is in April or May the following year. Honghu City experiences subtropical humid monsoon climate with four distinct seasons. Rainfall is abundant and varies between 1060.5 and 1331.1 mm. The mean annual temperature is approximately 16 \({}^{\circ}\)C, with the coldest temperature of 3.8 \({}^{\circ}\)C in January and hottest temperature of 28.9 \({}^{\circ}\)C in July.  
A total of 787 samples (0-30 cm) were collected through random sampling on the main study area. The distances between each sampling site ranged from 100 m to 1492 m. The spatial distribution of these soil sampling points was mainly concentrated in the central and northwestern parts of Honghu City (Figure 1). For each location, five soil samples were taken from the four corners and the center of a 1 m \(\times\) 1 m square area. Approximately 1 kg subsample was collected from the composite sample for laboratory analysis after plants and debris covering the soil surface were removed. First, all samples were air dried in the laboratory at 20 \({}^{\circ}\)C-25 \({}^{\circ}\)C for 14 days. Subsequently, these soils were crushed using a porcelain mortar to break down large aggregates and passed through a 0.25 mm sieve. This step can remove the effect of soil texture and moisture and reflect the authentic SOC characteristics of soil samples. Subsequently, the SOC content was measured using potassium dichronate [52]. All these methods can be found in the studies of [PERSON] et al. [53] and [PERSON] et al. [22].
# Target Speaker Extraction by Fusing Voiceprint Features
## 3 Experiments and Results  
### Experiment Settings  
**Data generation** The train-clean-100 subset and test-clean subset of the LibriSpeech [35] were used to generate the dataset for the performance evaluation experiments. There were 251 speakers and 40 speakers in the train-clean-100 subset and the test-clean subset, respectively. Tse-FV requires two inputs: (1) the noisy audio \(\mathbf{a}_{mix}\) and (2) the reference speech \(\mathbf{s}\) of the target speaker.  
In the stage of dataset generation, two speakers marked as speaker A and speaker B were selected and speaker A was regarded as the target speaker. Then, the reference speech and target speech of the target speaker were randomly selected from the speech library of speaker A and the interfering speech was randomly selected from the speech library of speaker B. After that, silent segments in the speech were removed. The signal-to-noise (SNR) value was used to adjust the amplitude of the target speech and the interfering speech, which is a common way of controlling the relative level of two segments of speech [6; 8; 9; 36]. Denote the selected SNR value as \(\alpha\). The amplitude of the target speech was enhanced by multiplying a coefficient \(10^{\frac{\text{dB}}{2}}\) to its time-domain signal. By contrast, the amplitude of the interfering speech was reduced by multiplying a coefficient \(10^{-\frac{\text{dB}}{2}}\) to its time-domain signal. In order to balance the SNR distribution in the training set and the test set, the selected SNR values obeyed the uniform distribution. The values of SNR were uniformly distributed over the half-open interval \([0\text{ dB},5\text{ dB})\) in this work. Then, \(\mathbf{a}_{mix}\) was generated by adding the adjusted speech together. Moreover, to accelerate the speed of training, the length of each speech was limited to 3 s. In this way, a dataset consisting of 50,000 noisy audios together with 50,000 clean segments of speech of the target speakers were constructed for training. Similarly, a test set contain 1000 noisy audios and 1000 clean segments of speech of the target speaker were constructed. The sampling rate of the speech was 16 kHz.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 3 Liquid-Assisted Antennas
### Microfluidic Channels
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref.** & **Antenna Type** & **Pla-Nar Dimensions mm\({}^{2}(\lambda_{0})\)** & **Reconfig. mm\({}^{2}(\lambda_{0})\)** & **Liquid Type** & **Tuning Type** & **Frequency Range (GHz)** & **TR Equation (1)** & **TX Equation (3)** & **Radiation Efficiency** \\ \hline
[81] & mono-pole & yes & \(30\ \times\ (0.78\lambda_{0})\) & 40 & frequency & pure water & discrete (N = 4) & 4.44–5.82 & 27\% & 1.3 & \textgreater{}90.4\% \\ \hline
[82] & patch & yes & \(90\ \times\ (0.56\lambda_{0})\) & 60 & frequency & pure water & discrete (N = 4) & 1.39–1.86 & 29\% & 1.34 & 68.8–86.3\% \\ \hline
[83] & patch & yes & \(3.32\ \times\ (0.31\lambda_{0})\) & 2.45 & frequency & various \({}^{a}\) & discrete (N = 4) & 16.8–28.4 & 51\% & 1.7 & 27\% \\ \hline
[84] & patch \({}^{b}\) & yes & \(135\ \times\ (0.52\lambda_{0})\) & 135 & freq, polarization & pure water & contin. & 0.700–1.150 & 49\% & 1.64 & 77–82\% \\ \hline
[85] & annular slot & yes & \(14.8\times 23.05\) & frequency & various \({}^{c}\) & discrete (N = 6) & 3.3–4.2 and 5.2–8 & 24\% & 1.27 1.54 & 73.4\% & 39.6\% \\ \hline
[87] & mono-pole & yes & \(90\ \times\ (1.05\lambda_{0})\) & 45 & frequency & FC-40 & contin. & 1.7–3.5 & 69\% & 2.06 & 87.4–92.4\% \\ \hline
[89] & patch & yes & \(142.1\ \times\ 83\) & frequency & mineral oil & contin. & 2.22–2.53 \({}^{d}\) & 13\% & 1.14 & 27–38\% \\ \hline
[90] & Vivaldi & yes & \(250\ \times\ (3.75\lambda_{0})\) & 150 & frequency & KCl solution & discrete (N = 2) & 2.5–3.25 and 3.25–4.50 & 26\% 32\% & 1.30 1.39 & 70–87\% \\ \hline
[86] & patch & yes & \(95\ \times\ (0.76\lambda)\) & 95 & polarization– (0.76\(\lambda)) & water \& air & discrete (N = 2) & \(\sim 2.4\) & - & - & 22\% \\ \hline \end{tabular}  
* water, acetone, methanol, ethanol, \({}^{b}\) the data shown here is for the design III in LP. \({}^{c}\) air, water, acetone, \({}^{d}\) for the first mode.  
\end{table} 
Table 5: Planar antennas with microfluidic channels.
# Erosion Rate Measurements for DART Spacecraft Ion Propulsion System +
## 3 Results
### Relative Erosion Rates at Grid Center
The Ia variation between these throttle conditions was several percent at most, with facility background pressure fluctuation determined to be the primary cause. Linear density dependence on \(|V_{a}|\) is anticipated if most moly density results from ion accel impingement with energy driven by the magnitude of the accel voltage. The plot is consistent with that. In Figure 8, we present a collection of results. Among the findings are that average xenon neutral density (proportional to \((1-\eta)/\eta\)) extrapolates to zero long before Ia approaches it, background pressure similarly extrapolates to zero with nonzero Ia intercept, QCM signal extrapolates to zero concurrently with background pressure, and extrapolation to zero of moly density (LIF signal) at grid center correlates much better with \((1-\eta)/\eta\) than with Ia. Figure 8d shows that the moly density at grid center is highly sensitive  
Figure 6: Relative peak density at grid center as a function of nominal total voltage for the throttle conditions included in Figure 5. The data were fit to an exponential function. Most of the plot scatter is due to variations in propellant mass utilization and accel voltage. Figure 7: Effect of accelerator grid voltage on LIF signal at grid center for DTL28-H-H and DTLNEW throttle points (see Figure 5). Discharge chamber flow rates were identical. to the relative change of Ia. In addition, LIF data were obtained while facility pumping speed was reduced due to fewer pumps operating, whereas two additional cryopumps were online during the subsequent QCM work. Ia changed proportionally to pumping speed despite equivalent background pressure. The source flow is identical, but with reduced pumping speed, lower bleed rate of background gas produced the same elevated background pressure. Because the background gas was fed in through a port that views the thruster, some of the gas collided directly with the accel grid and this may explain the slope change with pumping speed for the accel current vs. background pressure plot.
## 4 Experiments and Analysis
### Experiment 2: Measuring the Effectiveness of the Correction System in Real Life
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{1}{c}{**Average CVA Immediately**} & \multicolumn{1}{c}{**Average of CVA Values**} & \\  & \multicolumn{1}{c}{**after the Start of the Experiment (\({}^{\circ}\))**} & \multicolumn{1}{c}{**Dropped Due to Screen**} & \multicolumn{1}{c}{**Difference (\({}^{\circ}\))**} \\ \hline Person#1 & 53 & 48 & 5.0 \\ Person#2 & 43.2 & 39.8 & 3.4 \\ Person#3 & 53.4 & 46.8 & 6.6 \\ Person#4 & 46.4 & 43.6 & 2.8 \\ Person#5 & 43.8 & 39.2 & 4.6 \\ Person#6 & 55.7 & 51.2 & 4.5 \\ Person#7 & 47.0 & 41.0 & 6.0 \\ Person#8 & 41.0 & 34.7 & 6.3 \\ Person#9 & 40.2 & 38.8 & 1.4 \\ Person#10 & 38.5 & 32.9 & 5.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average CVA value without personalized feedback up to 3 min after starting the experiment. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{1}{c}{**Baseline**} & \multicolumn{1}{c}{**FHP Time**} & \multicolumn{1}{c}{**FHP Time**} & \multicolumn{1}{c}{**Average CVA**} & \multicolumn{1}{c}{**Average CVA**} \\  & \multicolumn{1}{c}{**CVA (\({}^{\circ}\))**} & \multicolumn{1}{c}{**without**} & \multicolumn{1}{c}{**with**} & \multicolumn{1}{c}{**without**} & \multicolumn{1}{c}{**with**} \\  & \multicolumn{1}{c}{**Feedback (s)**} & \multicolumn{1}{c}{**Feedback (s)**} & \multicolumn{1}{c}{**Feedback (\({}^{\circ}\))**} & \multicolumn{1}{c}{**Feedback (\({}^{\circ}\))**} & \multicolumn{1}{c}{**Feedback (\({}^{\circ}\))**} \\ \hline Person#1 & 53 & 660 & 230 & 51.3 & 53.7 \\ Person#2 & 43 & 620 & 280 & 41.7 & 44.4 \\ Person#3 & 51 & 450 & 0 & 50.7 & 54.5 \\ Person#4 & 45 & 540 & 300 & 44.8 & 46.7 \\ Person#5 & 41 & 340 & 160 & 43.7 & 44.1 \\ Person#6 & 53 & 490 & 150 & 53.3 & 55.9 \\ Person#7 & 44 & 550 & 110 & 43.5 & 46.9 \\ Person#8 & 40 & 1394 & 504 & 37.8 & 41.1 \\ Person#9 & 41 & 1336 & 714 & 40.1 & 42.2 \\ Person#10 & 38 & 1438 & 287 & 33.4 & 40.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average CVA value in a 15 min experiment. In addition, existing research [11; 12; 15; 18; 20; 21] on posture determination has focused on detecting incorrect posture and has not been concerned with correcting it. Meanwhile, the research on correcting bad posture [26; 27] has focused on correcting people who are already in the FHP state and not on detecting whether a person is or not in the FHP state. However, the proposed system detects the FHP condition in a personalized way using different thresholds for each person. The proposed system is convenient as it employs only one wearable sensor, and it uses feedback to increase the average CVA of participants by 3-5\({}^{\circ}\). Participants in a correction system using exercise or therapy for more than four weeks [25; 26] exhibited an average increase in CVA of \(\sim\)4\({}^{\circ}\). Therefore, the correction effect of the proposed system is similar to that of a sustained commitment to exercise and therapy.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 3 Atomic Clocks and Time Scale Metrics
### Performance Metrics for Time Scale
#### 3.2.1 Accuracy  
The frequency accuracy \(A\) of a time scale measures the correspondence between a unit second defined by the time scale and the one in the international system of units. It is defined as  
\[A=\frac{f-f_{0}}{f_{0}}, \tag{13}\]  
where \(f\) is the actual frequency of the measured frequency standard, and \(f_{0}\) is its nominal frequency. Accuracy is crucial in comparing local atomic time with international atomic time.
# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 2 Related Work
This can lead to higher localization errors and, in a real-world application, can cause the position of the user to jump between different rooms even when the user is static. Our proposed solution tries to solve this problem by carefully analyzing the quality of the positioning before passing it to the application. In these works, even though they try to improve accuracy using probabilities and other filters, most of them are based on refining the training database and/or combining information from several samples for statistical analysis, making them harder to be used by energy-constrained mobile devices. In this work, we are proposing a different approach by combining a novelty detection system with the prediction probability to filter out these unreliable position estimations without discarding correct, useful information from mobile devices.
## 3 Results and Discussion
### Comparison of Optimal Conditions with Conventional Extraction  
Moreover, the results obtained after applying the optimal conditions were also compared to those obtained after using a conventional treatment (stirring from 0 to 180 min) in head side streams extracts (as a model matrix). As can be observed in Figure 6, the protein recovery was very similar after employing both treatments, around 32%. However, higher values of ABTS and ORAC were reached under UAE optimal conditions, with levels ranging from 149.64 to 377.54 uM TE and from 319.29 to 974.52 uM TE, respectively. In this sense, UAE treatment could improve the extraction of antioxidant compounds.  
Figure 5: Comparison of the optimal condition with the lowest treatment of UAE (0.5 min): (**A**) protein recovery (%), (**B**) ABTS values (uM TE), and (**C**) ORAC values (uM TE).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Optimal Conditions for UAE**} & \multicolumn{2}{c}{**Protein Recovery (\%)**} & \multicolumn{2}{c}{**ABTS (\#M TE)**} & \multicolumn{2}{c}{**ORAC (\#M TE)**} \\ \cline{2-10}  & **Time** & **Temperature** & **PHT** & **Predicted** & **Experimental** & **Predicted** & **Experimental** & **Predicted** & **Experimental** \\  & **(min)** & **(C’O)** & **pH** & **Values** & **Values** & **Values** & **Values** & **Values** & **Values** \\ \hline Head & 25 & 20 & 5.5 & 32.19 & 31.7 \(\pm\) 0.1 & 90.91 & 142.6 \(\pm\) 25 & 260.60 & 327.71 \(\pm\) 12.15 \\ Skin & 30 & 32 & 5.5 & 24.63 & 33.7 \(\pm\) 0.7 & 189.73 & 240.9 \(\pm\) 26 & 384.48 & 359.08 \(\pm\) 13.01 \\ Bone & 30 & 20 & 8.5 & 66.00 & 54.2 \(\pm\) 0.0 & 292.92 & 139.5 \(\pm\) 22 & 673.43 & 584.68 \(\pm\) 67.09 \\ Viscera & 26 & 50 & 8.5 & 94.52 & 94.6 \(\pm\) 1.0 & 516.02 & 412.3 \(\pm\) 32 & 5705.61 & 5475.65 \(\pm\) 357.50 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimal conditions, predicted values, and experimental responses of protein recovery and antioxidant activities (ABTS and ORAC) for different fish side streams.  
Figure 6: Optimal condition of UAE vs. conventional extraction (Control) for sea bass head: (**A**) protein recovery (%), (**B**) ABTS values (uM TE), and (**C**) ORAC values (uM TE).