esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Design and Analysis of Results
### Determination of the Optimal Operating Point for FP-ECDL
Since the optical wavelength measurement device used for simultaneous wavelength and power readings may introduce some error at higher power levels, a more precise optical power meter (selecting the 1550 nm wavelength range) is separately employed for power readings at these points. The actual powers are all above 13 mW and show minimal differences. Therefore, in the \(H33\) channel, at an operating current of 200 mA, point \(A\) is considered the optimal operating point. In addition, due to the relative motion between satellites, there is the Doppler effect, resulting in a Doppler frequency shift [29]. Therefore, it is necessary to tune the laser wavelength according to the actual situation. In the context of space coherent laser communication, the tuning range of the laser is generally required to be greater than 50 pm [28]. The FP-ECDL studied in this paper has a tunable range of approximately 160 pm for a single mode. For example, in the above test results, the optimal operating point is at point \(A\). Through heating, it can be tuned to point \(B\) with a range of about 94 pm, and through cooling, it can be tuned to point \(C\) with a range of about 68 pm. These results indicate that by employing the bidirectional temperature-scanning method for FP-ECDL, an ideal optimal operating point can be obtained. Furthermore, based on the obtained wavelength-temperature change rate, the precise wavelength tuning of FP-ECDL can be achieved. This suggests significant potential applications for FP-ECDL in the field of inter-satellite laser communication.
## 3 Results
### MGD Characterization with Continuous Proton Beams at Bern Cyclotron Laboratory  
At the Bern cyclotron laboratory, the MGD was irradiated with the beam parameters listed in Table 1. The corresponding charge measured by the beam stopper was used to compensate for small fluctuations in the beam intensity. Figure 7 shows the counts measured by the three chambers as a function of the bias voltage for two beam intensities, 50 and 550 pA. It can be noticed that the chamber with the smallest gap is the only one that reaches the saturation at 50 pA with a bias voltage larger than 150 V.  
The counts per unit of thickness as a function of V/d\({}^{2}\) are shown in Figure 8a and depend on the beam intensity. The measurements from different chambers reach the saturation at around 600 V/cm\({}^{2}\) for 50 pA, while more than 1600 V/cm\({}^{2}\) would be needed for 350 pA and 550 pA. The integrated charge for each test increases with the beam intensities because the MGD was irradiated for a fixed time. Using Equation (5) for fit and Equation (1) for f, the fits to the points were calculated and shown in the same figure. The rate of volumetric ionization (\(S\)) as a function of the measured beam current is evaluated from fits, and the good linearity is shown in Figure 8b.  
Figure 8: (**a**) Total counts per unit of thickness as a function of V/d\({}^{2}\) for three beam intensities (50, 350, 550 pA) and fits. (**b**) Rate of volumetric ionization as a function of the measured beam current.  
Figure 7: Integral counts measured by the three chambers as a function of the bias voltage for two beam intensities: (**a**) 50 pA and (**b**) 550 pA.  
In Figure 9, we report the relevant curves, which can be used to extract the corrected integral counts of IC1. The IC1 efficiencies (f\({}_{1}\)), computed as the ratio between the actual IC1 readouts and the C\({}_{\rm sat}\), are plotted as a function of f\({}_{\rm n}\)/f\({}_{\rm 1}\) = Q\({}^{\prime}_{\rm n}\)d\({}_{\rm 1}\)/Q\({}^{\prime}_{\rm 1}\)d\({}_{\rm n}\) where n = 2 and n = 3. Data are then fitted with Equation (6), providing two calibration curves (fit A and B in Figure 9), each one being specific of the beam and detector characteristics used. The fit parameters are summarized in Table 2.  
The measurement performed with a bias voltage of 160 V and beam intensity of 550 pA had provided as relative efficiency Q\({}^{\prime}_{\rm 1}\)d\({}_{\rm 2}\)/Q\({}^{\prime}_{\rm 2}\)d\({}_{\rm 1}\) = 0.321 and Q\({}_{\rm 3}\)d\({}_{\rm 1}\)/Q\({}_{\rm 1}\)d\({}_{\rm 3}\) = 0.147. As a correction factor for the IC1 counts, one can compute f1 either using either fit A either B with the parameters listed in Table 2, and the resulting f\({}_{\rm 1A}\) = 0.61258 and f\({}_{\rm 1B}\) = 0.61007 deviate by less than 0.3%.  
Fit parameters are reported in Table 2, together with the results obtained at the other accelerator facilities.
## 2 Related Work
### Revocable Keyword Search  
Revocable keyword search [31] is implemented in Scheme [32]; however, it requires time synchronization. To enhance flexibility and security, [PERSON] et al. [33] proposed MD-AKS, which enabled revocable keyword search without trusted third parties or attribute-related overhead. This flexibility is critical in applications like video anomaly detection, where access to video segments needs rapid adjustment in response to real-time events. The techniques in [34,35] also involve re-encrypting the index, which requires synchronization between the owner and the user. [PERSON] et al. [36] introduced a public-key encryption scheme with ephemeral keyword search. This is especially relevant in video-based crowd analysis, where fast keyword updates are essential for monitoring crowd behaviors and detecting disruptive events. [PERSON] et al. [37] and others developed a time-controlled ABKS scheme for index encryption across multiple time intervals, though it does not prevent cloud servers from accessing the index without time information. [PERSON] et al. [38] introduced a DSSE scheme to enhance keyword search revocation with flexible shielding capabilities, ensuring robust privacy. [PERSON] et al. [39] further enhanced keyword search revocation with a time-controlled scheme, which is vital for maintaining security in video surveillance systems.
## 3 Results and Discussion
### Thermal Properties  
The thermal conductivity was measured using a surface probe at room temperature. The specimens were stored in a drying chamber for five days before measurement to ensure that the samples were water-free.  
Figure 8 shows the results of the measurements. Sample 100C exhibited the highest thermal conductivity of 0.83 W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\). Since the specimen contained an open porosity of about 26%, the thermal conductivity of the sample should include contributions from the CSA cement and air. With the addition of HGMSs, a third contribution had to be accounted for. Consequently, the thermal conductivity decreased with an increasing amount of HGMSs and accordingly a decreasing amount of cement. The lowest thermal conductivity was achieved for sample 30C. Its conductivity was \(\lambda\) = 0.043 W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\) (Table 4). Given the fact that the open porosity for the specimens from 70C to 30C did not vary much (only 3.2% variation), the decrease in thermal conductivity can be exclusively explained by the insulating effect of the HGMSs. The HGMSs exhibit low-pressure conditions inside, which result in a very low internal heat transfer by means of convection, and heat conduction is limited due to their thin glass shell. In accordance, the thermal conductivity of the pure HGMSs was measured to be 0.0401 W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\) using a needle probe. As described, the spheres arrange themselves close to the densest sphere packing. It is therefore expected that the thermal conductivity of the HGMS/CSA composites would approach that of pure HGMSs with an increasing amount of HGMSs. According to the performed data fit in Figure 8, the infinite limit is \(a_{0}\) = 0.0404 W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\), which is in good agreement with the above value.  
In order to achieve even lower thermal conductivity values (the conductivity of air is 0.026 W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\)[23]), an increase in open porosity would be required without losing the structural cohesion that is mainly provided by solid bridges between HGMSs and the cement's crystalline phases.  
Figure 7: Three-point bending tests of the hydrated specimens. The specimens were tested after 14 days of hydration in a wet state. For each sample composition, six prisms were tested (\(N\) = 6). Data fit to \(f(\phi)=a_{0}+a\cdot\phi^{o}\), with \(a_{0}\) = 0, \(a\) = 4.892, and \(v\) = 0.638.
# Review on Rail Damage Detection Technologies for High-Speed Trains
## 3 Non-Destructive Testing Methods for High-Speed Train Rails
### Artificial Intelligence Detection Algorithms
To address the drawbacks of traditional image processing methods such as long processing time and low defect recognition accuracy,a new convolution kernel combined with a cascade-based feature fusion strategy was chosen to design an improved Faster R-CNN rail defect detection method for implementing detection tasks [99].
# Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters
## 3 Numerical Results
### Urban Areas  
The trends observed in the urban environment are similar to those in the suburban environment (Figure 10). All interference values when using a 150 MHz guard band meet the protection threshold, regardless of the antenna array configuration (4-by-4, 8-by-8, or 16-by-16). The maximum received interference is \(-20.7453\) dB, \(-16.9017\) dB, and \(-14.2304\) dB for the 4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. This indicates that a 150 MHz guard band provides robust interference mitigation, even when using larger antenna arrays in densely built environments. Notably, the curve for the 50 MHz guard band with the 4-by-4 antenna array approaches but does not exceed the \(-6\) dB threshold, with a maximum received interference of \(-6.7601\) dB. This confirms that a 50 MHz guard band is sufficient for the urban environment with the 4-by-4 antenna array. With a 50 MHz guard band, the maximum received interference is \(-0.0126\) dB and 6.1357 dB for the 8-by-8 and 16-by-16 antenna arrays, respectively. The corresponding probabilities of meeting the protection threshold are 99.8% and 98.7%, respectively. Without protection, the maximum received interference is 11.1755 dB, 17.8717 dB, and 22.7011 dB with the 4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. These values match those outlined in Table 4. The probabilities of meeting the protection threshold are 87.3%, 74.2%, and 55%, respectively. Urban environments typically involve more physical obstructions, such as skyscrapers, compared to suburban and rural areas. These obstacles can absorb, reflect, and scatter 5G signals, resulting in higher path loss and reducing direct interference with radar altimeters. This natural attenuation explains why narrower guard bands will still be effective with smaller antenna arrays in urban areas.  
Regarding practical implementation, the variability of the simulation results underscores the challenge of establishing a fixed guard band that effectively balances spectrum efficiency and interference mitigation. A one-size-fits-all approach could either under-protect radar altimeters or lead to inefficient spectrum use. Static guard band assignments do not account for dynamic factors such as weather conditions and varying network loads. To address these limitations, sensors and monitoring systems could be deployed in strategic locations, such as areas near flight paths, to provide real-time data on interference levels. Network management systems could then use this real-time feedback to dynamically adjust the guard band width.  
Figure 9: CDF of interference-to-noise ratio in the suburban environment with: (**a**) 4-by-4; (**b**) 8-by-8; and (**c**) 16-by-16 antenna arrays.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 4 Lunar Orbital Filter
### Orbital Propagation
A solution to the propagation matrix is \(\Phi=e^{F\mathbf{x}t}\), where \(\mathbf{\Delta}t\) is the time between step \(k\) and \(k+1\)[28]. To calculate the dynamic matrix \(F\), the derivative for the gravitational acceleration for the Sun in terms of \(\mathbf{r}_{s}\), should be calculated  
\[\frac{\partial g_{s}}{\partial\mathbf{r}_{s}}=\frac{-Gm_{s}}{|\mathbf{r}_{s}|^{3}}- \frac{3 Gm_{s}\mathbf{r}_{s}\mathbf{r}_{s}^{\intercal}}{|\mathbf{r}_{s}|^{5}}. \tag{26}\]  
The chain rule may then be applied to find the derivative in terms of the state vector term \(\mathbf{r}\),  
\[\frac{\partial g_{s}}{\partial\mathbf{r}}=\frac{\partial g_{s}}{\partial\mathbf{r}_{s }}\frac{\partial\mathbf{r}_{s}}{\partial\mathbf{r}}=-\frac{\partial g_{s}}{\partial \mathbf{r}_{s}}, \tag{27}\]  
which uses the relation \(\mathbf{r}_{s}=\mathbf{p}_{s}-\mathbf{r}\). Similar forms may be computed for the Moon \(g_{m}\) and Earth \(g_{e}\) gravitational equations. For spherical harmonics, the solution is more complex. The reader is directed to [66] for the non-spherical gravitational Jacobian. The remaining terms forming the dynamic matrix are  
\[F=\left[ \begin{matrix}0_{3\times 3}&I_{3}\\ \frac{\partial g_{e}}{\partial\mathbf{r}}+\frac{\partial g_{m}}{\partial\mathbf{r}}+ \frac{\partial g_{e}}{\partial\mathbf{r}}&0_{3\times 3}\end{matrix} \right]. \tag{28}\]
## 2 Raw Materials and Experimental Methods
### Effect of Coupled Mechanical-Chemical Activation on Hydration Activity of Copper Slag Powder  
Considering the copper slag grinding efficiency and actual energy consumption, the copper slag powder milled for 90 min was selected to replace cement with 20%, 30%, and 40% mass fractions. The chemical activator CaO was employed to stimulate the hydration activity of the copper slag powder by external mixing, and the mechanical-chemical coupling-activated copper slag powder composite cement was prepared. The CS series specimens were expressed as composite cement specimens mixed with copper slag powder, and the CA series specimens represent the mechanical-chemical coupling-activated copper slag powder composite cement. The CaO content in CA series samples was set to 10% of the mass of the copper slag powder. The first number in the sample code represents the amount of copper slag powder, and the second number is the grinding time of the copper slag. For example, CS20-30 denotes the composite cement system mixed with copper slag powder after grinding for 30 min, and that the content of copper slag powder was 20%. The  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Composition** & **CaO** & **MgO** & **Al2O3** & **SiO2** & **Fe2O3** & **SO3** & **P2O5** & **Na2O** & **K2O** & **CuO** & **MnO** & **LOSS** \\ \hline Copper slag & 2.46 & 0.75 & 4.98 & 30.44 & 52.78 & 0.52 & 0.08 & 1.38 & 1.24 & 0.32 & 0.12 & 4.93 \\ OPC & 59.74 & 0.92 & 6.57 & 21.35 & 3.79 & 3.24 & 0.15 & 0.13 & 0.78 & 0.04 & 0.02 & 3.27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of OPC and copper slag (wt/%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**OPC/\%**} & \multicolumn{3}{c}{**Copper Slag**} \\ \cline{2-5}  & & **Ball-Milling Time/Min** & **Content/\%** \\ \hline Cement & 100 & - & - & 0.35 \\ CS20-0 & 80 & 0 & 20 & 0.35 \\ CS20-30 & 80 & 30 & 20 & 0.35 \\ CS20-60 & 80 & 60 & 20 & 0.35 \\ CS20-90 & 80 & 90 & 20 & 0.35 \\ CS20-120 & 80 & 120 & 20 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mixing ratio of copper slag powder-based composite cementitious system with different grinding times.  
CA20-30 sample represents the composite cement system mixed with copper slag powder after grinding for 30 min. The content of copper slag powder was 20%, and the content of CaO was 2%.  
The compressive strength and compressive strength activity index of copper slag powder-based composite cement paste samples were the same as in Section 2.3.
## 2 Regulatory Aspects
### Rest of the World  
The international regulation of BPA for using in food contact substances is not harmonized. However, several other countries also have legislation designed to either restrict or prohibit the use of BPA in food contact products, especially those designed and intended for use by young children. These include Argentina, Australia (voluntary phase out), Brazil, China, Ecuador, Japan, South Korea, and Turkey.
## 1 Introduction
### Self-Healing Networks
#### 1.5.3 Methods Based on Data Mining
Information about the various types of faults, variables that influence the various types of faults, the relationship between the types of faults and the influencing variables, the threshold for each variable, the likelihood that each variable influences faults, and, finally, comparison of the information obtained were all related to the data collection stage. Finally, this method constructed the model via the Bayesian network approach. It was proved that when parameter values were calculated using statistical features, rather than expert judgment, systems to detect the causes of faults performed more accurately. In [26] a strategy for locating the fault's root cause, that combined neural network and law-based techniques, was offered. The rules matrix and a hierarchical and distributed multi-dimensional structure were employed to gain the capacity for parallel reasoning. However, the author concentrated on the Internet network and did not estimate the accuracy of this method on cellular networks. Due to the neural network's high power in detecting data behavior, it was also employed in research in [27] in such a way that the criteria for determining the cause of a fault were applied to each type of fault that the neural network specified. These rules could use other available data to check network efficiency and deviation of neighboring cells to ensure that one did not affect the other. * **Unsupervised Methods** Among the unsupervised techniques, the Self-Organization Map (SOM) is the most popular one. This neural network-based technique is usually utilized for data presentation. This process transforms high-dimensional data into two- or three-dimensional data, preparing it for presentation [28]. According to [29], the SOM algorithm was given a predetermined set of features, and after clustering the data, each cluster was utilized to identify the various fault types, their sources, and the times when they occurred. This technique was only used in regard to the difficulties of signaling channel capacity and traffic flow analysis. The study's researcher in [29] began by using SOM to decrease the dimensions of the data to two or three dimensions. After applying clustering techniques, such as hierarchical approaches, and division methods, such as k-means, to the data, the clusters were then assessed using the Davies-Bouldin index.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 6 Future Directions and Challenges
### Environmental Impact on VLC Systems  
Another key area of research lies in the assessment of the impact of environmental conditions on the performance of VLC systems. Although VLC is generally robust against many forms of interference that affect RF communication, certain environmental factors, such as fog, thermal turbulence, and precipitation, can still degrade VLC performance, particularly in outdoor or automotive applications. A multistage quadrature amplitude modulation (M-QAM) approach was employed in prior studies, to test the impact of these environmental conditions on the performance of VLC systems using ceiling lights and automotive lamps.  
Future research should expand on this work, to develop more resilient modulation schemes capable of adapting to changing environmental conditions [88]. This will be particularly important for healthcare systems that rely on VLC in both indoor and outdoor environments, such as for ambulance-to-hospital communication or emergency response scenarios.
# Trends in Performance Limits of the HOT Infrared Photodetectors
## 6 Colloidal Quantum Dot Infrared Photodetectors
### Brief View
The lead chalcogenides CQDs (primarily PbS) are the materials for SWIR photodetectors with detection to 2 \(\upmu\)m. The peak can be adjusted using smaller dots by adding NIR bands to hyperspectral visible image sensors or using larger dots to include the InGaAs spectrum of image sensors [75]. From a performance standpoint, SWIR photodetectors based on PbS CQDs have reached electricity, D\({}^{*}\) comparable to commercial InGaAs photodiodes, with a values of \(>\)10\({}^{12}\) Jones at 300 K. HgTe CQDs have opened the MWIR spectral range. Detectivity, D\({}^{*}\) between 10\({}^{10}\) to 10\({}^{9}\) Jones at 5-\(\upmu\)m was demonstrated for HgTe CQD devices while maintaining a fast response time at thermoelectric cooling temperatures. It is unlikely that CQD IR detectors will ever reach the performance of currently popular InGaAs, HgCdTe, InSb and T2 SL photodiodes. Recent demonstrations of low-cost SWIR and MWIR CQD imaging arrays have heightened the interest in these devices. For both PbS and HgTe CQD photodetectors integration in camera imaging have been demonstrated [76]. It is expected that the successful implementation of this new class of IR technology may match the broad impact of cheap CMOS cameras that are widely used today. First SWIR cameras built on CQD thin film photodiodes fabricated monolithically on silicon ROICs have been launched [77; 78]. The Acuros camera has resolution 1920 \(\times\) 1080 (2.1 megapixels, 15-\(\upmu\)m pixel pitch) and uses 0.4 to 1.7 \(\upmu\)m broadband spectral response [77]. The IMEC's prototype imager has resolution of 758 \(\times\) 512 and 5 \(\upmu\)m pixel pitch. The CQD photodiodes on silicon substrate reach an external QE above 60% at 940 nm wavelength, and above 20% at 1450 nm, allowing uncooled operation with dark current comparable to commercial InGaAs photodetectors [78]. At present, CQD cameras are used in newer applications that require high-definition low cost imaging on smaller pixels without extreme sensitivity. It can be predicted that increasing the dot size while maintaining a good mono-dispersion, carrier transport and QE will improve maintaining low noise levels. Due to continuous development of deposition and synthesis techniques, much higher performance will be reached in the future.
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 4 Results  
### Parameterization of the Random Forest Classifiers  
The RF grid search with the tenfold cross validation method indicated that _ntree_ value of 1,000 combined with _mtry_ value of 5 was optimal for classifying general land use and land cover (LULC)classes (Figure 3a) to separate cropland from non-cropland. On the other hand, the _mtry_ value of 15 combined with _ntree_ value of 2000 resulted in the lowest OOB error rate of 0.18% for classifying the mono- and mixed maize cropping systems as shown (Figure 3b).
## 2 Related Work
### Transformer-Based Advances in Propaganda Detection  
Transformer models like BERT and RoBERTa have revolutionized NLP, including propaganda detection. Recent studies have shown promising results using these models supplemented with engineered features. [PERSON], et al. [33] demonstrated how transformers capture persistent word connections for nuanced propaganda detection.  
[PERSON] and [PERSON] [22] enhanced BERT by integrating BiLSTM architectures and employing a joint loss function, effectively combining syntactic and pragmatic signals at multiple text levels. [PERSON], et al. [34] incorporated Conditional Random Fields (CRFs) and a customized dense layer atop BERT for token-level propaganda identification. Additionally, hybrid approaches using Logistic Regression classifiers with RoBERTa tokenization have been explored for span identification tasks [35]. These efforts underline the power of transformer-based embeddings in capturing subtle linguistic cues.  
However, technical deficiencies persist. Many models are highly dependent on domain-specific fine-tuning and struggle with cross-domain generalization. Moreover, while token-level classification improves granularity, it often sacrifices coherence at the document level.