esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Noise Programmable Voltage Source
## 3 Test of the Designed Voltage Source  
### Measurement Setup  
Since the main goal of this work is to demonstrate a very low-noise voltage source, the most important parameter to be characterize is, obviously, the level of noise that can be obtained at the output of the system in Figure 1. A block diagram of the measurement setup is reported in Figure 2. In order to reduce electro-magnetic interferences (EMI) during measurements, all devices and instrumentations were enclosed in a metal chamber. Moreover, in order to minimize the influence of low-frequency magnetic fields, mu-metal tape was used to completely cover the walls of the chamber. The instrumentation employed for measurements consists of a data acquisition card (National Instrument, model NI-6211 or simultaneously sampling NI-9215 in the case of cross-correlation measurements), a low-noise high input impedance amplifier (described in [34]), and a low-noise anti-aliasing filter (with a cutoff frequency of 20 kHz). Lead acid batteries (\(V_{BI}=6\) V; 5 Ah) were used to supply all devices and systems inside the chamber except the JFET drains, which were supplied by NiMH batteries. In the case of cross-correlation measurements, the low-noise amplifier was replaced by a two-channel low-noise amplifier. A dedicated software was developed to acquire signals and to calculate the voltage spectral density. The system software also allows one to monitor the charge state of all lead acid batteries in the measurement chamber. The connection between the PC and Low Noise (LN) source is made using and optically insulated link to reduce interferences. The Data Acquisition (DAQ) cards were connected to the PC using short cables and USB connectors placed at the chamber wall.
# Towards AI-Generated Essay Classification Using Numerical Text Representation
## 4 Research Methodology
### Model Evaluation  
The evaluation of classification models is crucial for understanding their performance and effectiveness in detecting AI-generated essays. While accuracy is a primary metric for assessing model performance, relying solely on accuracy can be misleading, especially in cases of class imbalance. Therefore, additional metrics such as precision, recall, specificity, F1-score, and Matthews correlation coefficient (MCC) are also considered to provide a comprehensive evaluation.  
Accuracy (_Acc_) measures the proportion of correctly classified instances among the total instances and is calculated as follows [8]:  
\[Acc=\frac{(TP+TN)}{(TP+FP+TN+FN)}\cdot 100 \tag{8}\]  
where  
* \((TP+TN)\)--the number of correctly classified AI- and student-written essays,
* \((TP+FP+TN+FN)\)--the total number of AI-generated essays and student-written essays together.  
In addition to the model accuracy, additional metrics were also calculated and compared [8].  
Sensitivity--\(TPR\) (true positive rate), calculated as follows:  
\[TPR=\frac{TP}{(TP+FN)}\cdot 100 \tag{9}\]  
where\(TP\)--the number of correctly classified AI-generated essays,  
\((TP+FN)\)--the total number of AI-generated essays.  
Specificity--\(TNR\) (true negative rate), calculated as follows:  
\[TNR=\frac{TN}{(TN+FP)}\cdot 100 \tag{10}\]  
where  
* \(TN\)--the number of correctly classified student-written essays,
* \((TN+FP)\)--the total number of student-written essays.  
Precision--\(P\), calculated as follows:  
\[P=\frac{TP}{(TP+FP)}\cdot 100 \tag{11}\]  
where  
* \(TP\)--the number of correctly classified AI-generated essays,
* \((TP+FP)\)--the total number of essays classified as AI generated.  
F1-Score--\(F1\), calculated as follows:  
\[F1=2\cdot\frac{P\cdot TPR}{P+TPR} \tag{12}\]  
Matthews Correlation Coefficient--\(MCC\), calculated as follows:  
\[MCC=\frac{TP\cdot TN-FP\cdot FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{13}\]  
This coefficient indicates the correlation between the predicted and actual classifications, ranging from \(-1\) to \(1\), where \(1\) indicates perfect classification.
# Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
## 3 Baseband Signal Generation METHOD
### Baseband Signal Generation  
The baseband array generated by Section 3.4 is calculated integer by integer, bit by bit, to determine whether it is 0 or 1, and then the signal is sent out through the transmitter. Figure 3 shows the signal flow diagram of the real COSPAS-SARSAT transmitter, and Figure 4 shows the program flow diagram of the COSPAS-SARSAT transmitter.
## 2 Methodology for Selection of Studies
### Identification of the Studies  
The identification phase constitutes the cornerstone of the systematic review process, ensuring that the literature aligns comprehensively with the research scope. For this study, the timeframe was set to encompass publications from the last decade (2014-2024), including peer-reviewed journal articles and conference proceedings available in English and with full-text access. Publications such as editorials, review articles, letters, short communications, and policy briefs were excluded as they did not meet the requirements for addressing the study's specific research questions.  
The literature search utilized two prominent bibliographic databases: Scopus and Web of Science (WoS). Both platforms are renowned for their extensive and curated repositories of peer-reviewed literature:  
* Scopus: Offers advanced search functionalities, comprehensive indexing, and analytical tools for tracking trends and citations. Its content spans multiple disciplines and includes high-impact publishers like Elsevier, Springer, Wiley, and MDPI. The search string used for Scopus was as follows: TITLE-ABS-KEY (\"Energy management\") AND (\"Hierarchical\" OR \"Multi-agent\" OR \"Robust\") AND (\"peer to peer\") AND PUBYEAR > 2013 AND PUBYEAR < 2025 AND (LIMIT-TO (DOCTYPE, \"ar\") OR LIMIT-TO (DOCTYPE, \"cp\")) AND (LIMIT-TO (LANGUAGE, \"English\")).
* Web of Science: Known for its rigorous curation, WoS provides a multidisciplinary collection of high-quality publications, with robust citation tracking and analysis capabilities. For Web of Science, the following query was applied: ALL = (Energy management) AND (ALL = (Hierarchical) OR ALL = (Multi-agent) OR ALL = (Robust)) AND (ALL = (peer to peer)).  
The search queries were designed meticulously to align with the review's objectives. Refinements were applied to both queries to limit results by publication year (2014-2024), document type (articles and conference proceedings), and language (English). The initial search yielded 82 records from Scopus and 215 records from Web of Science.  
Given the substantial overlap between Scopus and WoS, duplicate entries were anticipated. To address this, all retrieved records were imported into a bibliographic management tool for deduplication. This process identified and removed 53 duplicate records, resulting in a final dataset of 244 unique entries.  
This refined dataset provides a robust foundation for the subsequent Screening, Eligibility, and Inclusion phases.
## 2 Materials and Methods
### Data Collection
#### 2.3.2 Timber Harvest Site Testing  
To establish effective data collection parameters at the timber harvest sites, flights were conducted at each harvest site and optimized by the third flight (post-cut) at Volz. At first, Volz was flown _before_ harvest, with the same parameters that were established for the PWA site. However, when these settings were used in the following flights at other timber harvest sites, images displayed motion blur, overexposure, and low depth of field. Because of this, the flight altitude, shutter speed, and number of images outside the study area were adjusted for the mid-cut flight at Volz (Table 8). The flight was conducted at 500 ft (150 m) AGL to broaden the depth of field and field of view, as well as to reduce the motion blur and the overall number of images [57]. Since the canopy heights were around 100 ft (30 m) tall, this increase in flight altitude was permittable by the 14 CFR Part 107 regulations at the time--which allowed for the operation of UASs no more than 400 ft above the ground or above the tallest object in open airspaces [60]. Additionally, the shutter speed was increased to 1/4000 s to further reduce the likelihood of overexposure and motion blur in images (Table 8).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **Takeoff** & **Landing** & **Total Duration** & **Number of Images** & **Clouds/Brightness 1** & **Wind 1** \\ \hline
4/01/20* & 1500 & 1520 & 20 min & 303 & Scattered, sunny & Variable \(\sim\)8 MPH \\
4/03/20 & 1113 & 1130 & 17 min & 378 & Overcast, diffuse & Calm \(<\) 3 MPH \\
4/11/20 & 1409 & 1428 & 19 min & 389 & Clear, sunny & Low \(<\) 5 MPH \\
5/01/20 & 1332 & 1347 & 15 min & 310 & Clear, sunny & Variable \(\sim\)5 MPH \\ \hline \end{tabular}
\end{table}
Table 7: Flight information for each UAS survey surrounding PWA prescribed burn. NOTE: * =pre-burn, 1 =unstandardized data collection parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
Every flight at the Volz site used the same mission plan, which covered the same 10-acre area in 15 to 20 min by flying an east-to-west pattern. Since the flight height changed between the first and last three flights (400 ft AGL to 500 ft AGL), the number of images was reduced, but each image captured a wider field of view and greater depth of field.  
Once all flights for the prescribed burn at PWA and the selective timber harvest at Volz were completed, the next step was to correct the image coordinates that were collected by the PPK and engage in photogrammetric processing (see Figure 5 for the workflow).
## 2 Software-Defined GNSS Receiver
### BeiDou Signal Tracking  
The acquisition approaches mentioned earlier in Section 2.1 provide the initial estimates of the carrier Doppler and the code offset. After the acquisition, the control will be handed over to the tracking loops to track the variations of carrier phase and code offset due to the line of sight movement between the satellites and the receiver.
## 2 Materials and Methods
### Dose Conversion and Ventilation Models
#### 2.4.4 Modeling the Effect of Air Ventilation  
Models were developed to simulate the effectiveness of ventilation rates in reducing exposure to radon and thoron gas for samples where the estimated annual dose from radon and thoron (in the worst-case scenario from Section 2.4.2) exceeded 0.3 mSv. Three ventilation scenarios were considered: low (0.5 m\({}^{3}\)/h), medium (2 m\({}^{3}\)/h), and high (10 m\({}^{3}\)/h) ventilation rates. Mathematical expressions were then derived to calculate the dose after ventilation, considering variables, such as ventilation time, house condition, ventilation rate, and the amount of stone surface area used. The formulae developed here are useful for adjusting ventilation rates or the amount of radon/thoron-emitting surface area in various housing conditions to keep the total annual effective dose, including the effect of gamma radiation, below the recommended limit of 1 mSv [23].  
\[\begin{split}\text{Dose after Ventilation (mSv)}=\\ \text{T}\times\Big{(}\frac{\text{S}\times\text{C}_{\text{Rn220}} \times\text{Eq}_{\text{Rn220}}\times\text{DCF}_{\text{Rn220}}}{\text{Q}^{ \text{X}}\text{V}_{\text{eff}}}+\frac{\text{S}\times\text{C}_{\text{Rn222}} \times\text{Eq}_{\text{Rn222}}\times\text{DCF}_{\text{Rn222}}}{\text{Q}^{ \text{X}}\text{V}_{\text{eff}}}\Big{)}\end{split} \tag{5}\]  
In Equation (5), Q is the ventilation rate per hour, T is the system running time (assuming 8 h per day), EqRn220 and EqRn222 are Equilibrium Factors = 0.4 for radon and 0.01 thoron.  
Also, dose conversion factors (DCFs) for radon are equal to \(9\times 10^{-6}\) and \(40\times 10^{-6}\) mSv per Bqm\({}^{-3}\) per h, respectively.  
The theoretical framework for the model integrates radon/thoron exhalation dynamics with gamma dose calculations, assuming steady-state conditions for gas diffusion ([PERSON]'s law) and equilibrium between parent isotopes (U-238, Th-232) and their decay products. Key limitations include the exclusion of seasonal humidity effects on material exhalation rates and the assumption of homogeneous material composition, which may oversimplify real-world heterogeneity. Ventilation rates were modeled as constant, though actual airflow varies diurnally. Additionally, gamma dose estimates rely on mass activity concentrations without granular spatial resolution. Recent work by [PERSON] et al. (2014) [24] highlights similar challenges in reconciling lab-measured exhalation rates with field observations. This emphasizes the need for probabilistic modeling in future studies to account for environmental variability.
## 3 Results and Discussion
### Accuracy Simulation Experiment of Fault Diagnosis  
The fault data obtained by the simulation was used as the test set. The simulated sensor faults were tested by the PCA fault diagnosis model. The test results are shown in Figure 6 below.  
The simulation results showed that when the temperature of the transformer was in a stable state, its SPE statistic (Q statistic) also remained in a stable state, and the projection on the residual space was smaller than the SPE threshold. This showed that the normal steady-state signal could be accurately identified by the PCA model. When the dry-type transformer temperature jumped normally, the sensor output value of the monitor also changed, and the projection in the residual space also jumped. However, the SPE value  
To verify the diagnostic accuracy of the PCA model through the above fault simulation method, five typical faults were added to the whole-day data at random times. Each fault was tested 50 times, and the experimental results are shown in Table 1.
## 4 Results and Discussion
### Analysis of Questionnaire Results and Travel Trajectory  
Fifty-seven route selection logs created by 31 participants were analyzed in the previous section. In addition, a post-experiment questionnaire, and the corresponding travel trajectories from 12 participants were also obtained and examined. Table 3 summarizes their route selection logs and actual behavior obtained from the questionnaires and travel trajectory. Data for \"Shortened travel time\" and \"Chosen option\" were obtained from the operational logs. \"Actual behavior\" was confirmed by the results of the questionnaire and the travel trajectory analysis.  
The chosen option in the route selection log for 10 of the 12 participants was consistent with their actual behavior, which was a match rate of approximately 83%. This suggests that it is possible to estimate actual stopover behavior from the operational logs.  
Five participants, as listed in Table 3, made additional stopovers, for a stopover rate of approximately 41%. In addition, the operational logs showed that 45% of the participants (14 of 31) chose the \"Depart later\" option. People who chose the \"Depart later\" option were more likely to stopover. Considering these results, the stopover rate was estimated to be approximately 40%.  
Figure 7 summarizes when the participants searched for return routes using the application; the results were obtained using the questionnaire. Ninety-two percent of all participants used the application just before departing for returning home or on the way home. Therefore, if they had not searched for a route at that time, they were likely to depart when the route search was conducted. A stopover performed after the search was carried out was considered as an additional stopover because of the information on estimated traffic congestion and, as such, it was not a planned stopover.  
Figure 6: Selection ratio of “Depart now” and the other departure options based on estimated shortened travel time.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 4 Discussion
### The Effect of Terrain Correction on Classification Results  
For hilly areas, variations in topographic relief can cause differences in backscatter coefficients and affect the accuracy of land-cover classification. Many studies have shown that topographic correction is the key to Sentinel-1 imagery processing [76; 77; 78]. In this study, we used the Sentinel-1 data processing process developed based on GEE, which included topographic correction [37]. The method depended on the angular relationships between the SAR imagery and the terrain geometry. We selected the NASA SRTM Digital Elevation 30 m as auxiliary data for terrain correction (data available at: [[https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003?hl=en](https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003?hl=en)]([https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003?hl=en](https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003?hl=en)), accessed on 22 June 2022). The topography-corrected Sentinel-1 imagery improved the classification results by 0.006 to 0.024 in overall accuracy. For the coverage category, the extraction was most pronounced for irrigated cropland, where the Sentinel-1 responding to water bodies was most significant during the irrigation period.
## 3 Engine and DOC Response
Firstly, the maximum temperature at the DOC outlet can be calculated for each operating point as the one that should be reached if all the heat released by the oxidation of the post-injected fuel was used to increase the exhaust gas temperature at the DOC outlet:  
\[T_{\text{DOC,}out_{\text{max}}}=T_{\text{DOC,}out_{\text{base}}}+\frac{\mu_{f, PI}LHV}{\mu_{exh}c_{p}}. \tag{19}\]  
The maximum temperature is represented by the continuous series in plots (a) and (b) of Figure 10 for each operating point and post-injected fuel mass rate. It is compared against the predicted temperature at the DOC outlet. The difference between theoretical and modelled values gets higher in both points as the post-injected fuel mass rate increases. Additionally, the predicted DOC outlet temperature is increasing as the post-injection is delayed. Therefore, the efficiency of the post-injection even increases as the post-injection is delayed. It is calculated according Equation (20) and represented in Figure 10c and d for points #A and #B respectively. In addition, these plots also make it evident that the lower the post-injected fuel mass rate is, the higher the post-injection efficiency is. \[E_{PI}=\frac{T_{\text{DOC,}out}-T_{\text{DOC,}out_{\text{base}}}}{T_{\text{DOC, }out_{\text{max}}}-T_{\text{DOC,}out_{\text{base}}}} \tag{20}\]  
Figure 9: Effect of the post-injection timing and the post-injected fuel mass rate on the DOC gas temperature and the O\({}_{2}\) concentration at the DOC outlet. The trends in post-injection efficiency as a function of the timing and the fuel mass rate are mainly governed, as previously described, by the partial combustion of this fuel into the cylinders. If such an oxidation takes places, part of the energy available in the post-injection fuel mass contributes to useful work, thus increasing the torque; other second order effects on the post-injection efficiency are related to the VGT (variable-geometry turbocharger), which opens as the exhaust gas temperature increases and to the heat transfer until the DOC outlet. It is interesting to note that the lowest efficiency is obtained in point #A, characterised by high load without EGR, when the post-injection delay is minimum. However, the efficiency sharply increases with a slight delay (from 30 to 40 CAD aTDC) and a monotonous increase is kept till 60 CAD aTDC. With this timing, both operating points show the same post-injection efficiency, i.e., the dependence of this parameter on the operating point disappears.
## 4 Discussion
### Buckling
#### 4.3.3 Summary of Buckling Analysis  
The computational analysis of \(\mathrm{Eln}\)+/\(+\) and \(\mathrm{Fbln5}\)\(-\)/\(-\) reveals that both analyzed material laws are capable of buckling predictions, while FEM can be efficiently used to simulate this phenomenon.  
The Holzapfel law and the used constant sets seem promising regarding the analysis of buckling, as acceptable accuracy has been achieved in five out of six analyzed cases (\(\mathrm{Eln}\)+/\(+\) with all the analyzed \(\lambda_{z}\) values and \(\mathrm{Fbln5}\)\(-\)/\(-\) with \(\lambda_{z}=1.2\) and \(\lambda_{z}=1.4\)). The accuracy of critical buckling pressures' estimation, when the Fung law was used, was acceptable only in two out of six analyzed cases (\(\mathrm{Eln}\)+/\(+\) with \(\lambda_{z}=1.4\) and \(\mathrm{Fbln5}\)\(-\)/\(-\) with \(\lambda_{z}=1.2\)).  
The results of the buckling analysis are especially important for arteries which have higher stretches, because the real (in vivo) \(\lambda_{z}\) is rather high, as reported in [19; 23]. It is worth mentioning that for the highest analyzed stretch value \(\lambda_{z}=1.4\), the Holzapfel law was able to detect buckling for both mouse models under consideration (\(\mathrm{Eln}\)+/\(+\) and \(\mathrm{Fbln5}\)\(-\)/\(-\)), while the Fung law prediction was successful only for the \(\mathrm{Fbln5}\)\(-\)/\(-\) model.  
Therefore, the Holzapfel constitutive relation, in the version studied in this paper (the carotid artery is built of two layers of Holzapfel material), appears to be better suited for the analysis of artery buckling.  
Carotid arteries of mouse models of human disease (following [19]) were analyzed here. Such loss of stability is known to cause arterial tortuosity, which may lead to, for example, stroke [46]. Therefore, analysis of carotid arteries buckling is desirable.
###### Abstract  
During the last few decades, considerable advances in quantum information theory have shown deep existing connections between quantum correlation effects (like entanglement and quantum discord) and thermodynamics. Here the concept of conditional entropy plays a considerable role. In contrast to the classical case, quantum conditional entropy can take negative values. This counter-intuitive feature, already well understood in the context of information theory, was recently shown theoretically to also have a physical meaning in quantum thermodynamics [[PERSON] et al. Nature **2011**, 474, 61]. Extending this existing work, here we provide evidence of the significance of negative conditional entropy in a concrete experimental context: Incoherent Neutron Scattering (INS) from protons of H\({}_{2}\) in nano-scale environments; e.g., in INS from H\({}_{2}\) in C-nanotubes, the data of the H\({}_{2}\) translational motion along the nanotube axis seems to show that the neutron apparently scatters from a fictitious particle with mass of 0.64 atomic mass units (a.m.u.)--instead of the value of 2 a.m.u. as conventionally expected. An independent second experiment confirms this finding. However, taking into account the possible negativity of conditional entropy, we explain that this effect has a natural interpretation in terms of quantum thermodynamics. Moreover, it is intrinsically related to the number of qubits capturing the interaction of the two quantum systems H\({}_{2}\) and C-nanotube. The considered effect may have technological applications (e.g., in H-storage materials and fuel cells).  
quantum thermodynamics; quantum information; negative conditional entropy; quantum correlations in condensed matter; nanoscale quantum confinement; proton quantum mobility; incoherent neutron scattering; H storage materials; fuel cells; quantum materials +
Footnote †: journal: Applied Physics Letters