esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The blades used to chop the straw from the 180 ha harvested area are shown in Figure 7. An arrow indicates the worn edge of the blades. The second edge was not worn (the blades were not turned over). During use, most of the protective coating on the blades (anti\(-\)corrosion coating) had been worn away. Some of the working surfaces of the blades were partially covered with deposits of organic material (resins), lignin, cellulose, hemicelluloses, or other straw components. Cleaning the blades with the organic solvent 2\(-\)propanol 70% (a pure solution from Carl Roth GmbH + Co KG, Karlsruhe, Germany) does not remove the thin coatings. The durability of the blades was also greatly affected by damage caused during use, which reduced the life of the blade and the quality of the chopping. The blades had been damaged by foreign objects (metal rods, stones, etc.) entering the thresher. Their wear was not typical and was not included in the calculation of the wear averages. The damage affected only the teeth (hardened zone) and was not the cause of the blade body breakage. The cutting\(-\)edge angle, average wear during testing, hardness, and chemical composition of the chopper blades are given in Table 1.  
The wear rates for all 80 chopper blades are shown graphically in Figure 8. Red color indicates wear significantly higher than average, and green--significantly lower. The minimal wear of the Alt. 2 chopping blades was due to the highest edge hardness (568 \(\pm\) 11 HV) obtained by induction hardening due to the highest carbon amount of the blade steel (0.42% C). The highest wear of Alt. 5 was due to the lowest carbon amount of the steel used (0.16% C), the low chromium content (only 0.49% Cr, compared to about 1.0% for the other blades), and the three to four times lower vanadium content (Table 1). \begin{table} \begin{tabular}{l c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Edge Angle**} & \multirow{2}{*}{**Average**} & \multicolumn{2}{c}{**Hardness H**} & \multicolumn{6}{c}{**Chemical Composition by Mass (\%)**} \\ \cline{3-14}  & \(\Theta\), Degree & & **Wear Off I, g** & **HV** & **HRC** & **Fe** & **C** & **Ni** & **Mn** & **Cu** & **V** & **Mo** & **Cr** & **Si** \\ \hline Original & 24.6 & 3.71 & 515 \(\pm\) 14 & 51 & 97.07 & 0.35 & 0.017 & 1.00 & 0.010 & 0.17 & 0.003 & 1.03 & 0.26 \\ Alt. 1 & 24.0 & 4.18 & 394 \(\pm\) 12 & 41 & 97.46 & 0.23 & 0.030 & 0.89 & 0.009 & 0.12 & 0.003 & 1.00 & 0.22 \\ Alt. 2 & 20.9 & 3.28 & 568 \(\pm\) 11 & 53 & 97.17 & 0.42 & 0.030 & 0.89 & 0.017 & 0.13 & 0.003 & 0.98 & 0.24 \\ Alt. 3 & 27.1 & 3.88 & 469 \(\pm\) 11 & 47 & 96.80 & 0.30 & 0.122 & 0.97 & 0.139 & 0.17 & 0.031 & 1.03 & 0.22 \\ Alt. 4 & 25.4 & 3.54 & 540 \(\pm\) 12 & 52 & 96.97 & 0.35 & 0.047 & 0.97 & 0.039 & 0.13 & 0.0166 & 0.93 & 0.34 \\ Alt. 5 & 29.2 & 4.77 & 349 \(\pm\) 9 & 37 & 97.95 & 0.16 & 0.030 & 0.90 & 0.005 & 0.04 & 0.0030 & 0.49 & 0.24 \\ \hline \hline \end{tabular}  \
## 2 Materials and Methods
### Machine Learning Algorithms Used in the Study
#### 2.5.3 Gradient Boosting Regression  
Gradient boosting regression (GBR) stands as a formidable ensemble-trained supervised machine learning model renowned for its predictive capabilities. This method operates by amalgamating numerous simple models into a singular composite model, a technique recognized as boosting. Boosting is often characterized as an additive model due to its incremental addition of basic models while maintaining the model's trees unchanged. The amalgamation of more fundamental models consistently enhances predictive accuracy. Central to gradient boosting is the reduction in losses through gradient descent, an iterative optimization procedure of the first order. In the realm of gradient boosting, decision trees function as weak learners, employing a squared error loss function. GBR orchestrates the training of a weak model to map features to the anticipated residuals of that weak model. These residuals are seamlessly integrated into the input of the current model, steering it toward the desired outcome. Through recurrent iterations of this process, the overall predictive accuracy of the GBR model steadily advances. The effectiveness of gradient boosting regression (GBR) is profoundly influenced by key hyperparameters that govern its behavior. The subsample parameter determines the fraction of samples used for fitting the individual base learners, playing a role in mitigating overfitting. The parameter n_estimators specifies the number of boosting stages or trees, directly impacting the complexity and performance of the model. The min_samples_split hyperparameter dictates the minimum number of samples required to split an internal node, influencing the model's tendency to partition the data. Simultaneously, min_samples_leaf sets the minimum number of samples needed in a terminal or leaf node, contributing to the prevention of overly specific nodes. The max_depth parameter controls the maximum depth of the individual decision trees, managing their capacity to capture complex relationships. Lastly, the learning_rate hyperparameter governs the contribution of each tree to the model, regulating the impact of new trees on the overall ensemble [75; 76].
# GNSS Timing Performance Assessment and Results Analysis
## 2 The Key Indicators and Assessment Method of GNSS Timing Performance
### Assessment Method of GNSS Time Transfer Accuracy
#### 2.2.2 The Key Technology of GNSS Time Transfer Accuracy Assessment  
The key technology of GNSS time transfer accuracy assessment is to obtain a high-precision time offset of UTC(NTSC)-UTC\({}_{\text{GNSS}}\) through a GNSS monitoring receiver connected to UTC(NTSC). The GNSS monitoring receiver is the core equipment for obtaining  
Figure 2: Principle of GNSS time transfer accuracy assessment.  
UTC(NTSC)-UTC\({}_{\text{GNSS}}\). Additionally, the receiver delay calibration and receiver clock control strategy are crucial.  
The receiver delay refers to the delay from phase center of antenna to one pulse per second (1 PPS) reference signal input port. It is divided into two parts: one part is from the RF input port to the internal pseudo-range measurement latch point, and the other is from the latch point to the 1 PPS reference signal input port. It is also necessary to consider the inter-frequency bias (IFB) caused by different frequencies of GNSS signals at the receiver. The receiver delay and IFB can be absolutely calibrated by the GNSS signal simulator and high-speed oscilloscope [28; 29; 30].  
The frequency of the GNSS monitoring receiver clock is locked to the UTC(NTSC) 10 MHz frequency signal. The phase of the receiver clock is synchronized with the UTC(NTSC) 1 PPS signal when it is initially powered. During receiver operation, the UTC(NTSC) 10 MHz frequency is always synchronized with the 1 PPS within 1 ns. With such a receiver clock control strategy, the stabilities of the receiver clock and pseudo-range measurement can be guaranteed. Thus, the time offset UTC(NTSC)-UTC\({}_{\text{GNSS}}\) is obtained precisely.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.2 Biorebox-mt3 dms  
Bioredox was developed in 1998 by Carey et al. [655] based on the public domain code of MT3D. This model can be used to predict the results of enhanced bioremediation of petroleum hydrocarbons and chlorinated solvents. Bioredox allows the simulation of by-product and halogen transformations and coupled oxidation-reduction reactions. It has been used to evaluate the natural attenuation of BTEX and chlorinated ethenes at a former fire training site at Plattsburgh Air Force Base in New York [655].
###### Abstract  
Discontinuities exist widely in high rock slopes and threaten their stability at all times. To accurately characterize the information of rock mass discontinuities in high slopes, the layered rock slope on the north side of the Fushun West Open-pit Mine was taken as a typical sample, the high-definition images were collected using unmanned aerial vehicle (UAV) photogrammetry technology, and the Agisoft PhotoScan software was then used to generate the 3D rock slope model (i.e., 3D point cloud data) in this study. After the noise reduction processing of the generated rock slope model, the orientation and dominant grouping of the rock mass discontinuities were calculated by the KNN (K-nearest neighbor) search and the PCA (principal component analysis) algorithms, and the calculated results are calibrated with manual measurements in the local slope area. Moreover, the high-definition images containing different rock layers were selected to identify the detailed information of the rock discontinuities using the MATLAB program. This study shows that the orientation information of the rock discontinuities extracted from point cloud data are close to the corresponding result of manual measurements. There are great differences in the orientation of the discontinuities at different rock layers. The small-angle discontinuities are densely distributed in brown shale, which has a serious influence on the stability of the cracked rock slope. This research can provide a reference for the stability analysis of layered rock slopes and the prevention of landslide disasters in the Fushun West Open-Pit Mine.  
layered rock slope; UAV; rock discontinuities; rock mass stability; landslide risk assessment Article
## 3 Case Study Application
### Data and Characteristics Used within the Vulnerability Conceptual Framework
#### 3.2.3 Sociodemographic Vulnerability  
For this mapping, data were collected from the database of the National Statistics Institute of Portugal [31]. The data included information on the age of buildings within the Oliveira do Castelo district (in which Guimaraes is located) and demographic data for the case study area in Guimaraes. Since the data were not geolocated and represented general statistics for the district, an interpolation was performed based on the number of existing buildings in the case study area. For the map depicting the Fire Risk Index (FRI) and buildings' age, the new data were randomly assigned to the buildings using the \"Select Randomly\" tool, and values were allocated accordingly. It is important to note that the results of this map do not perfectly reflect reality due to the randomization of data. However, given the limitations of obtaining real-time data, this approach was deemed the most optimal, utilizing the 2021 national database. Furthermore, an extensive manual grouping of the buildings was conducted based on the year of construction and the assigned Fire Risk Index. In the second map, the focus was on portraying the FRI, the number of inhabitants within each building, and whether the buildings with inhabitants accommodated individuals with disabilities. This analysis was based on categorizing the buildings as residential, as established in previous studies [4; 10; 19; 20; 21], and incorporating the newly interpolated data obtained from the 2021 census. Similar to the previous map, a comprehensive manual grouping of the buildings was carried out based on the number of inhabitants, the number of people with a disability, and the assigned Fire Risk Index.
## 3 Methodological Approach
### Evaluation Metrics
#### 3.2.3 Confusion Matrix  
Accuracy assessment is an integrated part of most mapping projects and calculation of the confusion matrix, and related metrics such as the overall, producer's, and user's accuracy and the kappa statistic are required to present quantitative value regarding the performance of the proposed approach [22, 23]. The confusion matrix assists in expressing the classifiers' ability to discriminate the classes. This indicator is especially useful for diagnosing the classifier in a class-wise fashion, not only for the inference phase but also for the pretraining phase.
# On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
## 6 Simulation Results
### Validating the Baseline Model  
Validation of the baseline model is an important step for this work, since it normalises the simulation with real statistics on hateful users. Regarding the first phase of network construction, multiple metrics could be satisfactorily reproduced in accordance to the state-of-the-art. However, runs resulted in extremely high network density ratios of hateful users over normal users which turned out to be ca. 11 times more than reported by [22]. Additionally, the amount of followers as well as the ratio between followers and followees of haters were too high compared to normal users. Subsequently, during the second phase of opinion diffusion, these metrics decreased until being very close to the reported values for higher network sizes. Table 1 details a subset of the resulting average values for simulations with varying number of ticks. As signaled in red, only the reciprocity among hateful users were too low compared to normal users. Although this might be repaired by introducing additional rewiring rules for users who switch from normal to hateful state, we advocate for the simplicity of the model and leave this for future work.  
Simulation results also show an interesting fact in comparison between the phases of network growth without and with opinion diffusion. The switch from one phase to another demarcates a structural change in the sub-network of hateful users. It allows hate cores to disseminate hatred in strains to normal users with lesser network densities, showing that true hate cores might be even more densely connected than reported by statistics about real social networks. Overall, from these results, we can argue that our simulation represents hateful behaviours in a reasonable way.  
Figure 7: Representation of the network during the opinion diffusion by content. Blue and red dots represent content originally authored by normal and hateful users, respectively. In the center we can clearly see a densely connected hateful network component, which is infecting an influential normal user with hateful content. Infected influential users serves then as a proxy to spread content.
## 2 Space-Variant PSF of OAP System  
The OAP is usually designed as the intersection of a symmetric parabolic, which is called the parent parabolic, with a circular object, as shown in Figure 2. So, the research on PSF of OAP can be carried out in two steps: the diffraction limited effect of parent parabolic without aberration and the inherent aberration caused by the off-axis characteristic. In the next two subsections, we give a detailed explanation of how these two factors cause the space-variant blur.
## 4 Simulation Results
### A Single Congested Link
CSFQ has to estimate the MAR of each flow; therefore, traffic variations can influence fairness. However, exponential flow rate estimation methods can relieve such an effect. The fairness of RPQ is close to that of CSFQ, but it can also enhance fairness by increasing the number of output queues. As mentioned previously, this also reduces larger queueing delays. AFQ repeatedly outperforms the other schemes. In Figures 4-7, we conclude that AFQ is able to achieve robust and optimal fairness under a single congested link, where various traffic conditions are considered.
# Dual Stimuli-Responsive Polysaccharide Hydrogels Manufactured by Radiation Technique
## 1 Introduction
Those are: (a) using CCl\({}_{4}\) crosslinker in a mixed water-alcohol solvent [10]; (b) using alkyne (gas) as a crosslinking mediator during the irradiation of polysaccharides in solid phase [11]; (c) the irradiation of modified polysaccharides in an aqueous environment, where those substituted moieties or side chains participate in crosslinking [12]; (d) the utilization of crosslinkers--multifunctional monomers or macromers to link the macromolecules [13; 14; 15]; and (e) the utilization of chemically modified polysaccharides with polymerizable side groups, i.e., having unsaturated bonds [16]. Characteristically, the macroscopic products of (a) and (b) are suspensions--due to a significant increase in polymer molecular weight, without achieving a continuous gel--whilst those of (c)-(e) are firm, continuous gels, typically maintaining spatial integrity. Hydrogels are used or under development as biomaterials in various medical applications, such as, for instance, wound treatment, including chronic wounds, systems for the delivery of pharmaceuticals and biologically active compounds, scaffolds in tissue engineering and for soft tissue, as well as cartilage and bone defect treatment; the products more often include polysaccharide hydrogels [17; 18; 19]. The properties of hydrogels chiefly result from used polymers, whilst irradiation conditions and additives may be diverse, and should respond to the requirements of the anticipated application. An interesting feature of some polymers used to construct biomaterials is their stimuli-responsive character. The hydrogels made of such polymers are expected to retain this attribute. One typical example is that of a synthetic, temperature-responsive poly(N-isopropyl acryl amide) (PNIPAAm) with the earliest advanced application for cell-layer engineering [20; 21]. Polysaccharides are used in medical applications, including hydrogels, due to their biocompatibility and some times unique bioactivity, e.g., the hemostatic and bacteriatic properties of chitosan [22]. The reports indicate the stimuli-responsive characteristics of polysaccharide-based gels and materials. [23; 24]. In this work, the formation and properties of hydrogels made of hydroxypropylcellulose and carboxymethylcellulose and the mixture of these two biopolymers in various compositions, with or without the addition of PEGDA as a crosslink, were investigated. Hydrogels were produced by irradiating these solutions with accelerated electrons at various doses. The possibility of the crosslinking of various polysaccharides initiated by ionizing radiation has been demonstrated before [6], however, whether combinations of CMC and HPC with the crosslinking agent PEGDA can obtain hydrogels with dual responses to stimuli has not been studied to date. The swelling characteristics in pH and a temperature altering environment were explored.
###### Abstract  
This study aimed to compare the agreement of ball speed in throwing and kicking between the Bushnell and the Stalker radar guns. Sixty-four healthy young adults (21.8 \(\pm\) 3.5 years) performed a total of 320 throws and 320 kicks during the study. Ball speed was measured using both the Bushnell and the Stalker radar guns for each trial. The mean difference in throwing speeds was found to be 1.39 km/h (95% CI: 1.20; 1.59), while the mean difference in kicking speeds was 1.38 km/h (95% CI: 1.20; 1.56). The agreement, assessed through intradass correlation tests, revealed an excellent level of consistency for both ball speeds in throwing (ICC: 0.986 [95% CI: 0.986; 0.991]) and kicking (ICC: 0.986 [95% CI: 0.983; 0.989]). An analysis of variance showed significant differences for both throwing (\(p\)! 0.001; d = 0.799) and kicking (\(p\)! 0.001; d = 0.851), accompanied by a percentage difference of 2.17% in throwing and 1.91% in kicking. In conclusion, this study suggests that the correlation between both radar guns is excellent. However, Bushnell consistently underestimates the ball speeds of throws and kicks, with differences that are statistically significant and practically relevant. Despite this, the high correlation between the devices indicates that practitioners can use either device, provided they adjust normative values accordingly.  
s 2024 14 10476 10.3390/app142210476
## 2 Methodology
### Simulation Properties
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{Orbit Simulation} \\ \hline Galileo-like constellation & Galileo FOC box-wing model \\ Walker definition & \(56^{\circ}\): \(24/3/1\) \\ Orbit radius & 29,600 km \\ Numerical integrator & Runge-Kutta 4 th order \\ \multicolumn{3}{c}{Force models} \\ Earth gravity field & EGM2008 16 \(\times\) 16 [53] \\ Gravitational perturbation & Sun, Moon, and planets [54] \\ Relativistic perturbations & Schwarzschild Term, Lense-Thirring Precession, \\ \multicolumn{3}{c}{Geodesic Precession [54]} \\ Solar flux & Constant \\ Earth’s albedo & Analytical \\ Satellite surface properties & Box-wing model (EUSPA metadata [55]) \\ \hline \multicolumn{3}{c}{Data simulation} \\ \hline Data time span & 1 day \\ Sampling interval & GNSS observations \\ Observation noise & 30 s \\ Zenith wet delays & Harmonic function with horizontal variations \\  & Observation weight \(P_{GNSS}=\frac{\cos^{2}(z)}{\sigma_{\text{SNS}}^{2}}\), where \(z\) is \\ Observation weight & satellite zenith angle and \(\sigma_{GNSS}\) is GNSS \\  & observation noise \\ Station clock errors-observation noise & 1 ns \\ Satellite clock errors-observation noise & 0.1 ns \\ \multicolumn{3}{c}{ISL observations} \\ Four connectivity schemes & \\ Sampling interval & 30 s \\ Observation noise & 0.5 cm \\ Observation weight & \(P_{ISL}=\frac{1}{\sigma_{ISL}^{2}}\), \(\sigma_{ISL}\) is ISL observation noise \\ Satellite clock errors-observation noise & 0.1 ns \\ \hline \hline \multicolumn{3}{c}{Estimation with weighted least squares adjustment} \\ \hline Satellite positions and velocities & \\ ECOM2–9 parameters [56] & \\ Epoch-wise satellite clocks & \\ Epoch-wise station clocks (one station clock is fixed) & \\ Zenith wet delays-piecewise linear model & \\ Variance component estimation & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation and estimation settings.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
###### Abstract  
Thanks to advancements in silicon photomultiplier sensors (SiPMs) and system-on-chip (SoC) technology, our INFN Roma1 group developed ArduSiPM in 2012, the first all-in-one scintillator particle detector in the literature. It used a custom Arduino Due shield to process fast signals, utilizing the Microchip Sam3X8E SoC's internal peripherals to control and acquire SiPM signals. The availability of radiation-tolerant SoCs, combined with the goal of reducing system space and weight, led to the development of an innovative second-generation board, a better-performing device called Cosmo ArduSiPM, suitable for space missions. The architecture of the new detector is based on the Microchip SAMV71 300 MHz, 32-bit ARM\({}^{\circled{\otimes}}\)-M\(\mathcal{T}\) (Microchip Technology Inc., Chandler, AZ, USA). While the analog front-end is essentially identical to the ArduSiPM, it utilizes components with the smallest possible package. The board fits in a CubeSat module. Thanks to the compact design, the board has two independent channels, with a total weight of only 40 grams within a CubeSat form factor. The ArduSiPM architecture is based on a single microcontroller and fast discrete analog electronics. It benefits from the continued development of SoCs related to the IoT (Internet of Things) market. Compared with a system with a custom ASIC, this architecture based on software and SoC capabilities offers considerable advantages in terms of cost and development time. The ability to incorporate new commercial SoCs, continuously emerging from advancements in the aerospace and automotive industries, provides the system with a robust foundation for sustained growth over the years. A detailed characterization of the hardware and the system's response to different photon fluxes is presented in this article. Additionally, coupling the device with a scintillator was tested at the end of this article as a preliminary trial for future measurements, showing potential for further enhancement of the detector's capabilities.  
silicon photomultiplier; SiPM; radiation-tolerant; scintillator; SoC; microcontroller; IoT; CubeSat; space +
Footnote †: journal: Sensors  
00185 Rome, Italy; [EMAIL_ADDRESS] (G.C.); [EMAIL_ADDRESS] (F.L.); [EMAIL_ADDRESS] (L.M.); [EMAIL_ADDRESS] (L.R.)