esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Melting Thresholds of Semiconductors under Nanosecond Pulse Laser Irradiation
## 3 Results and Discussion
### Indium Phosphide  
We have applied our model for the conditions of experiments [30], where InP was irradiated by a nanosecond laser at \(\lambda=532\) nm. In this paper, a laser fluence of 97 mJ/cm\({}^{2}\) was identified as the damage threshold. In our simulations, we have obtained a threshold value of 106 mJ/cm\({}^{2}\), which can be considered as a good agreement taking into account that there are no fitting parameters in our model. It should be mentioned that, although laser processing of InP is a common technique in its industrial applications, the thermophysical parameters at enhanced temperatures are still not well studied. Thus, several sets of data are available for the heat capacity of solid InP; see, e.g., [61]. The major problem is that measurements of the thermophysical properties at enhanced temperatures are affected by the high vapor pressure of phosphorous due to its high volatility. The thermal conductivity and the specific heat of molten InP are given in [53]. The reflectivity and absorption are calculated from data provided in [48]. Optical properties are taken as temperature independent and are considered the same for both solid and liquid state. In reference article [30], ablation of compound semiconductors is studied, and a model that takes into account evaporation of their components gives the melting threshold. Our result, which disregards this effect, gives \(F_{\text{th}}\) that is approximately 10% higher.
# Fluorescent Composite Cotton Fabric Modified with Crosslinked Chitosan for Theranostic Applications
## 4 Conclusions  
Chitosan was modified with 4-dimethylamino-1,8-naphthalimide fluorescent units and characterized by NMR and IR spectroscopy. In a solid state and a water-acid solution, the modified chitosan has an intense yellow color and emits a yellow-green fluorescence. This chitosan has been used to prepare cotton fabric composites in the presence of indomethacin and alginate. The indomethacin release was followed in phosphate buffer pH = 7.4 and found to proceed uniformly over 24 h from both CNI and CNIA materials. Coating with an additional alginate layer accelerates the release of indomethacin due to faster swelling of the layer due to the possibility of breaking some bonds from its interior. However, this does not affect the gel erosion as alginate remains on the material surface. The resulting CN, CNI, and CNIA composites possess very good antimicrobial activity against Gram-positive bacteria _B. cereus_ and Gram-negative bacteria _P. aeruginosa_. The sample containing only modified chitosan CN was found to almost completely inhibit the growth of both bacterial strains. The resulting three composite materials may find application as wound dressings as they possess antimicrobial properties. Two of the materials release an anti-inflammatory biologically active substance in a controlled manner.  
Conceptualization, D.S. and I.G.; methodology, D.S. and I.G.; formal analysis, D.S., D.A.; investigation, D.S., D.A. and I.G.; writing--original draft preparations, D.S. and I.G.; writing--review and editing, D.S. and I.G.; visualization, D.S. and D.A.; project administration, D.S. and I.G. All authors have read and agreed to the published version of the manuscript.  
This study is financed by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project No BG-RRP-2.004-0008-C01. This study is funded by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project No BG-RRP-2.004-0002-C01, \"BiOrgaMCT\".  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
Figure 12: Antimicrobial activity of cotton fabric (control) and CN, CNI, and CNIA materials on the growth of _P. aeruginosa_ and _B. cereus_.
## 2 Materials and Methods
### Data Collection Instruments
#### 2.3.3 Accelerometry  
Physical behaviors were quantified using direct measurements with an accelerometer (ActivPAL4, PAL Technologies Ltd., Glasgow, UK) positioned on the right thigh of the participants, using previously described procedures adapted to real-world settings [26]. The accelerometer measures triaxial acceleration at 20 or 40 Hz with 10-bit precision and stores data in a 64 MB internal memory. The output includes periods spent in work and non-work activities, where non-work consists of all activities outside the work environment. This study used only the thigh position to analyze and classify activities into lying down, sitting, standing, moving, and walking.  
The protocol involved recording direct measures for at least four consecutive days, including at least two workdays and two non-workdays (leisure) [23]. Work and non-work hours were cross-referenced with self-reported data collected via an activity diary provided to participants, which recorded routine activities such as waking up, starting and finishing work, and bedtime. To minimize the potential Hawthorne effect, participants were instructed to maintain their normal routines during the accelerometer wear period. The accelerometers were initialized for data recording and download using PAL Software Suite version 8 (PALconnect, PALbatch, and PALanalysis). Data was processed using ActiPASS software version 2024.06 (The National Research Centre for the Working Environment, Copenhagen, Denmark, and Occupational and Environmental Medicine, Department of Medical Sciences, Uppsala University, Sweden). This software identifies and classifies physical behaviors, differentiates physical activities and behaviors, and measures time spent in each position based on the inclination and maximum standard deviation of thigh accelerations. The data show excellent sensitivity (\(\geq\)93%) and specificity (\(\geq\)93%) for activities such as sitting, standing, moving, walking, climbing stairs, and cycling based on a semi-standardized protocol [27].  
Direct measurement variables express participants' physical behaviors in absolute time (minutes) and relative time (percentage of a 24-h day) during work and non-working days. These variables include: 1. time lying down; 2. time sitting; 3. time standing; 4. time moving; 5. time walking; 6. and sleep time. These variables were categorized as \"Low\" and \"High\" based on their median.
## 4 Experimental Results
### Time Domain and Frequency Domain Analysis  
In order to reduce the space of this paper, the time domain and frequency domain signal analysis only gives signals for the three typical loading stages of the SA sensor. During the experiment, the three typical loading stages of the specimen are as follows--(1) When the specimen is not loaded, it is in a completely healthy state; (2) The specimen is loaded to the state where cracks begin to appear. (3) The specimen is loaded until the crack developed into failure state. The time domain signals from OS1 and OS2 of beam 1 are shown in Figure 12. The time domain signals from OS1 and OS2 of beam 2 are shown in Figure 13. Each figure reflects the sensor signal response of a period from the swept sine wave signal. The results show that the amplitudes of the signal by the sensors decreases with the increase of the applied load. The reason is that cracks occur at the OS1-4 region of the specimen with the increase of load. The crack causes the stress wave signal reflect and attenuate. The initial crack and failure state of the specimen can be found by time domain analysis. In this test, the SA actuators generated swept sine wave signal to the sensors and the received signal is highly sensitive to the debonding crack condition between the CFRP and concrete. Fourier transform is also used for further frequency analysis of received signals. Compared with time domain signals, the downward trend of power spectral density (PSD) energy can be seen more easily observed in frequency domain. The frequency domain signals received by OS1 and OS2 of beam 1 are shown in Figures 14 and 15, respectively. The frequency domain signals by OS1 and OS2 of beam 2 are shown in Figures 16 and 17, respectively. By comparing the amplitude of the signal in the frequency domain, it is clear that the amplitude of the signal decreases with the increase of the applied load. It can be seen from the time domain and frequency domain analysis that the tension end region cracks earlier than the bond end region with the increase of load. Stress concentration at the end of CFRP leads to debonding damage at the early loading stage. This indicates the starting point of structural damage. The tensioned end region and the bonded end region of the beam 1 are cracked earlier than the beam 2. It is shown that increasing the length of the non-pre-stressed CFRP bond can slow down the appearance and expansion of the crack.  
Figure 16: Frequency domain signal of OS1 in the beam 2.  
Figure 14: Frequency domain signal of OS1 in the beam 1.  
Figure 13: Time domain signals of OS1 and OS2 in the beam 2.  
Figure 15: Frequency domain signal of OS2 in the beam 1.
## 4 Results and Discussion
### Comparative Methods  
The performance of FP-SFOA-DQN-FL was analyzed and compared with former techniques, like the federated object-detection algorithm [1], FedCV [9], DRFL [19], and active learning [20].  
Figure 8: Performance analysis based on MyNursingHome dataset. (**a**) Accuracy, (**b**) Loss, (**c**) MSE, (**d**) RMSE, (**e**) FPR, (**f**) Mean average precision.
# Development of Rockburst Research: A Comprehensive Review
## 5 Rockburst Intensity Classification and Prediction
### Mathematical Model Approaches for Rockburst Classification and Prediction
#### 5.3.1 Uncertainty Theory Techniques
A multidimensional connection cloud model based on set pair analysis and cloud model theory for rockburst strength prediction was proposed [134], which is simpler and more convenient for practical application than a one-dimensional cloud model. An extended multiattribute boundary approximate area comparison method (MABAC) based on a triangular fuzzy number was used to evaluate the rockburst tendency in four key areas of the Kaiyang Phosphate Mine [140]. Three distance-based multicriteria decision making methods combining hesitant fuzzy sets were used to evaluate the rockburst risk of the Xincheng Gold Mine; the results were consistent with the measured results [141]. [PERSON] et al. established a two-step comprehensive evaluation model via the rough set theory and technique for order preference by similarity to an ideal solution based on five empirical criteria (the elastic energy index criterion, the Russenes criterion, the Tao criterion, the rock brittleness coefficient criterion and the rock mass integrity coefficient criterion), and 20 rockburst samples were collected to evaluate the effectiveness of the two-step model. The results showed that compared to the existing artificial intelligence methods, the minimum error rate of the two-step model was significantly reduced [142].
## 2 Theoretical Models
### Comb-Line Noise and Carrier-Envelope Phase Noise
For the Fourier frequency below 1 kHz, it is mainly introduced by the cavity loss; for the Fourier frequency range of 1 kHz to 100 kHz, it is mainly introduced by pump noise; for the Fourier frequency range higher than 100 kHz, it is mainly introduced by quantum noise generated by amplified spontaneous emission. A figure depicting the technical noise contribution to comb modes and carrier-envelope phase noise could be found in Ref. [47]. What's more, a discussion on the excess carrier-envelope phase noise in saturable absorbers was presented in the literature [48].
## 5 Experimental Result and Analysis
### Comparison with Other Methods  
To illustrate the superiority of the proposed methodology, comparative analysis was carried out among the proposed method and three other methods. The first comparison method is the EMD-based CFBEE method. In this method the rotor vibration signal was decomposed by EMD to derive the sub-components and the EMD-HT TFS was used to extract the time-frequency information of different rotor states. Then the CFBEE of the EMD-HT TFS was calculated as the fault features. The Second comparison method is the EMD-based time-frequency entropy (TFE) method which was developed in [17]. The entropy of the whole EMD-HT TFS was calculated in this method. The third comparison method is the ISSD-based TFE method. In this method, the fault signatures were extracted by calculating the TFE of the ISSD-HT TFS.  
Figure 20 displays the curve of the CFBEE of the simulated rotor states (each state includes 40 samples) derived by using the EMD-based CFBEE method. The EMD-based CFBEE curves of the four states show a similar trend of change. Hence, it is difficult to distinguish these four faults by referring to the EMD-based CFBEE. The EMD-based CFBEE was then input to SVM as the feature vector for the automatic classification of rotor sates and the results were given in Figure 21. 19 samples of out of balance state are misadvised, and the classification accuracy is 76.25%, which is lower than the proposed method result. It can be concluded that ISSD is more suitable for processing rotor vibration signals compared to EMD.  
Figure 19: The classification results of SVM using the fault features shown in Figure 18.  
Figure 18: The characteristic frequency band energy entropy of the four rotor states obtained using the proposed method.  
Figures 22 and 23 display the classification results of SVM by using the EMD-based TFE and ISSD-based TFE methods, respectively. For the classification results shown in Figure 22, 28 samples of four rotor states are misidentified, so the identification accuracy of the EMD-based TFE method is only 56.25%. Three samples were mistakenly by using the ISSD-based TFE method as Figure 23 shows, therefore the identification accuracy of the ISSD-based TFE method is only 96.25%. The contrast analysis results show that the CFBEE index can more accurately evaluate the characteristic information of the TFS compared the TFE index.  
To make a detailed comparison, the classification accuracy for each rotor state corresponding to the proposed method and its three comparison methods was listed in Table 1. The superiority of the proposed method can be clearly demonstrated.
## 6 Results
### Evaluation of Performance Metrics  
Accuracy, precision, recall, and F1 score--the most informative and frequently used metrics to evaluate classifiers--were used as the performance metrics. Accuracy is a performance metric that serves as a fundamental metric in classification tasks, representing the proportion of correctly classified instances among the total number of instances, as given in Equation (4) [80]. Precision, given in Equation (5), is a measure of the exactness of a classifier, reflecting the proportion of true positive predictions among all instances predicted as positive. Recall assesses the completeness of a classifier, indicating the proportion of true positive predictions among all actual positive instances as given in Equation (6). F1 score harmonizes precision and recall, offering a comprehensive assessment of a classifier's performance by considering the balance between the two metrics, as given in Equation (7).  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}; \tag{4}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}; \tag{5}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}; \tag{6}\]  
\[\text{F1 Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}. \tag{7}\]  
The confusion matrices presented in Figures 12 and 13 provide an integrated view of correctly and incorrectly predicted classes, highlighting patterns of overlap among empty debris, respiration, and movement signals. In particular, respiration and slight body movements can produce similar low-amplitude micro-motion signatures, making them prone to misclassification under noisy conditions. For example, minor torso shifts or interference from external vibrations may resemble breathing patterns, thereby confusing the model's learned features.  
In examining our available data in Figures 12 and 13, we notice that respiration and movement signals occasionally exhibit overlapping characteristics, especially in the low-amplitude range. Slight variations in breathing or minor body movements can appear highly similar, leading to misclassification even in controlled tests. This observation underscores the difficulty of reliably distinguishing these signals in real-world scenarios, where environmental noise and subtle human motions can blur the boundary between respiration and movement signatures. Table 6 presents the performance metrics of different DL models across various classification tasks involving empty, respiration, and movement classes at three different frequencies.
## 3 Analysis  
A circular harmonic filter is one component from the circular harmonic expansion [7]:  
\[f(r,\theta)=\sum\limits_{m=-\infty}^{+\infty}f_{m}(r)exp(im\theta) \tag{1}\]  
with  
\[f_{m}(r)=\int\limits_{0}^{2\pi}f(r,\theta)exp(-im\theta)d\theta \tag{2}\]  
The zero-order CHC is merely the sum of information over each ring with radius \(r\):  
\[\widetilde{f}(r)=f_{0}(r)=\int\limits_{0}^{2\pi}f(r,\theta)d\theta \tag{3}\]Rotated images have the same zero-order CHC. When we apply the correlation operator between the zero-order CHCs of the image \(g\) and the reference \(f\), a correlation peak is obtained if the input scene image is a rotated version of the reference, including rotation with zero degrees. We then produce a new input scene image \(g\) and a new reference \(\widetilde{f}\), which are the zero-order CHCs of \(g\) and \(f\). The expected correlation peak must be sharp because, in contrast to the conventional method, we do not select one component among an infinite expansion. Apart from the fact that both the input scene image and reference are substituted by two derived images, conventional correlation is applied which should give a sharp correlation peak. Moreover, various correlation methods can be applied in conjunction with this image compression.  
What is really performed is the following operation, \(\widetilde{g}\otimes\widetilde{f}\), where \(g\) and\(f\) is the original input scene image and reference, and \(\otimes\) denotes the correlation operator. The new reference \(\widetilde{f}\) is calculated numerically, whereas \(\widetilde{g}\) is provided optically. The expected correlation peak arises on the optical axis because both \(g\) and \(f\) are projected onto the zero-order CHC. In practice, this peak is likely to be hidden by the zero order of diffraction, a useless bright spot on the optical axis. This diffraction order generally results from uncertainty in the fabrication process. To overcome this serious problem, we can laterally shift \(\widetilde{f}\) by a certain amount \((x_{0},y_{0})\). The correlation peak is therefore laterally shifted by the same distance \((x_{0},y_{0})\), and the detector can be fixed in the same place for all, because its position is known a priori.  
In the most general case, the zero-order CHC of the reference is numerically calculated. The lateral shift is also integrated in the numerical procedure. The main difficulty lies in the optical implementation of the projection of the scene image onto its zero-order CHC.  
It is worth noting that image data looks squeezed or collapsed into a line that contains all the information of the image scene. This is different from the conventional concept of image compression. This is a result of the fact that rotation invariance is combined with image compression.
# Corrosion Behavior and Conductivity of TiNb and TiNb Coated Steel for Metallic Bipolar Plates
## 3 Results and Discussion  
### Structural Characterization  
To avoid the possible effects from rough SS substrates, TiNb and TiNbN surface films for structural characterization were deposited on well-polished silicon wafers. The XRD patterns are shown in Figure 1.  
The vertical hatched lines represent peaks recorded in PDF cards No. 38-1155 (cubic NbN), No. 38-1420 (cubic TiN), No. 34-0370 (cubic Nb), and No. 44-1288 (cubic Ti). The diffraction peaks of cubic Nb and Ti are very close and consequently indicated by the same lines. The TiNb film follows the  
Figure 1: X-ray diffraction (XRD) patterns for TiNb and TiNbN coatings on silicon wafers. The hatched lines indicate peak positions of selected materials. The peaks of cubic Ti and Nb are showed by coinciding lines.  
same patterns suggesting that the alloy has the same cubic structure. It is reasonable to assume a solid solution of Ti and Nb. The angles are shifted to lower values indicating some stress. For the TiNbN coating, the Bragg angles of diffraction peaks are between those of TiN and NbN, which denotes that TiN and NbN are likewise a solid solution [26]. Moreover, diffraction peaks arising from (111), (200), (220), (311), and (222) plane reflections are clearly identified, confirming the face centered cubic (fcc) crystalline structure.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 1 Introduction  
Tracking devices based on GNSS (Global Navigation Satellite System) are now commonplace, their many uses include wildlife tracking [1] and vehicle tracking [2]. Wildlife applications, in particular, are constrained by size and weight, and, until recently, GNSS devices have been limited to tracking animals such as moose [3] and marine species [4] capable of carrying a large receiver, limiting the range of species that can be tagged [5]. Improvements in semiconductor fabrication have steadily reduced power consumption [6], and improvement in battery technology [7] have reduced the weight of such tracking devices to the point that GNSS tracking devices can be deployed to track small mammals and medium-sized birds [8; 9]. With each technical development allowing smaller and lower-power tracking devices, the range of species that can be tracked, and the duration of the deployment is increased.  
The most widely used GNSS is the GPS (global Positioning System) network [10]. GNSS tracking devices operate by using codephase and navigation message data transmitted from the satellites to determine the time of transmission of a signal. This can be used to determine the distance (ranges) from the current position to each satellite as well as the precise local time (see for example Kaplan [10]).  
Navigation messages transmitted from the receiver contain sufficient information to determine orbital parameters (ephemerides) of the satellites and the time of propagation of the signals from the satellite, and hence the ranges. Using the measured ranges from four or more satellites, the position of the receiver is determined--commonly using a least-squares procedure [11]. The time to first fix typically exceeds 30 s, as this is the time required for all of the satellite ephemerides to be transmitted. As a result, traditional GPS receivers are required to operate from cold-start for more than 30 s--typically consuming tens of milliamps--in order to measure their position. GNSS positionaccuracy varies, and in the case of GPS, the distribution of positioning errors is not Gaussian [12] and depends sensitively on the antenna characteristics [10]; typically, the first fix from a single-frequency commercial GPS has a horizontal positioning error of \(\sim\)10 m [13] in open skies.