esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Geart4 Monte Carlo Calculations  
#### 3.4.1 Effect of Step Length (SL)  
The sensitivity of the Geant4 S factor calculations on the choice of the step-length (SL) parameter in the simulations is examined in Figures 8 and 9. Specifically, the S factors calculated by the convolution integral using the analytic relations of the present work (PW) are compared against Geant4 (version 10.5) simulations for three SL parameters (SL = default, SL = R, SL = R/10) and three target spheres (R = 0.1 mm, R = 1 mm, and R = 5 mm). The default Step Length is 1 mm for energies up to about 400 keV and varies from 1.2 to 5.6 mm for the energy range of 400 keV to 4 MeV according to the stepping function [46]. Unless otherwise stated, the Geant4 Standard Option 4 EM model is used in the simulations. In these simulations, the tracking and the production cut-off energies  
Figure 6: The mean (panel **a**) and maximum (panel **b**) percentage difference of the absorbed fraction (AF) values between the MC data and the results of the present work (PW), for the examined electron energies (100 keV–4 MeV) and for different sphere radii (1.3 mm–6.2 cm). For comparison, the (mean and maximum) differences between the MCNP and EGS4 codes are also included.  
Figure 7: Percentage difference of S factor between the analytic calculations of the present work (PW) and the MIRDcell Software for electron energies in the range 1 keV–4 MeV and different sphere radius (R).  
were set at 100 eV, because this value is generally considered as the lowest energy limit of validity of Geant4 (see Section 2.4).  
Figure 8: Geant4 calculated S factor values for spheres of radius R = 0.1 mm (panel **a**), R = 1 mm (panel **b**), and R = 5 mm (panel **c**) as a function of electron energy for different choices of the Step Length (SL) parameter. The tracking and production cut-off energies are set at 100 eV in these simulations.  
In Figure 9 (panels a to c), we present the S factor difference, \(100\frac{(\text{S}_{\text{Coant4}}-\text{S}_{\text{PW}})}{\text{S}_{\text{PW}}}\), between the Geant4 simulations (with different SL parameters) and the analytic calculations of the present work (PW) for monoenergetic electrons in the energy range 1 keV-4 MeV and different sphere radii (R = 0.1 mm, R = 1 mm, and R = 5 mm).  
Figure 9: Percentage difference of S factor between the analytic results of the present work (PW) and the Geant4 simulations for different Step Length (SL) parameters as a function of the electron energy for spheres of radius R = 0.1 mm (panel **a**), R = 1 mm (panel **b**), and R = 5 mm (panel **c**). The tracking and production cut-off energies are set at 100 eV in these simulations.
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 4 An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
### An Efficient Heuristic for EOS Integrated Scheduling
#### 4.3.4 The Perturbation Phase  
When the algorithm is trapped in a local optimum, a mechanism is needed to help the search escape from the trap and restart the search from a new starting point. To establish a more global form of diversification, and thereby reinforce the capacity of the algorithm to explore unexplored areas in the search space, we propose a dedicated perturbation mechanism.  
The general idea of the proposed perturbation is to remove a set of selected requests within the solution so that new insertions may be possible. To do this, we resort to a set of destroy operators coupled with a tabu mechanism. Specifically, we use four remove operators:  
* Random removal: Select \(q\) requests from a given solution (schedule) randomly and remove them;
* Priority removal: Iteratively removes the request with the lowest profit until the request bank is full;
* Opportunity removal: Iteratively removes the request with the largest number of VTWs; ties are first broken by profit (smaller profit requests are prioritized) and then randomly;
* Conflict removal: Iteratively removes the request with the largest conflict degree, which is provided by a conflicting measure proposed in [29].  
Removed requests are taboo and not allowed to be inserted into the solution in the next iteration. Each time when a perturbation is triggered, it is realized by the combined use of a removal operator randomly selected from the four candidates.
# Highly Doped Phosphate Glass Fibers for Compact Lasers and Amplifiers: A Review
## 3 CW Phosphate Fiber Lasers and Amplifiers
### Nd-Doped Phosphate Fiber Lasers and Amplifiers
An output power of 7.92 W and a slope efficiency of 38.1% were obtained but with unsatisfactory beam quality. In 2014 a single-mode multi-watt phosphate Nd\({}^{3+}\)-doped all-solid PCF with core diameters of 30, 35 and 40 \(\upmu\)m was reported [51]. An output power of 5.4 W and a slope efficiency of 31% were measured with a beam quality that is not degraded even at the highest power. Higher emitted power was registered by using a complex photonic crystal fiber (PCF) containing seven different single-mode cores. This allowed achieving 15.5 W of emitted power over a fiber length of 25 cm, featuring a slope efficiency of 57% over the absorbed power [52].
# Characterization of Dolomite Stone Broken Under Axial Impact
###### Abstract  
As the extraction of oil and gas progresses into deeper and ultra-deep geological formations, the enhancement of rock-breaking efficiency in drill bits has emerged as a critical factor in ensuring energy security. Among the various techniques employed, vibratory percussion drilling technology is widely recognized for its ability to improve both the efficiency and speed of penetrating hard rock formations. This study examined the effects of varying loading conditions on the characteristics of rock fracture and damage, maintaining a constant cutting speed and lead angle. By designing a small polycrystalline diamond compact (PDC) drill bit and incorporating simulation results, the research sought to analyze the influence of axial impact components on the efficiency of breaking dolomite samples, as well as the effects of impact frequency and amplitude on drilling pressure and rock-breaking energy. The findings revealed that an increase in the axial impact amplitude significantly enhanced rock-breaking efficiency, elevated von Mises stress, and increased principal compressive stress. An increase in impact frequency effectively reduced the overall stress and frictional work. These results underscored that the stress analysis revealed that the peak stress increased at lower impact amplitudes, with notable changes occurring at an amplitude of 1.5, leading to a 100% increase in Mises peak stress compared with an amplitude of 1.0. Axial impact drilling promoted deep crack formation and the development of a tensile damage zone beneath the cutter, indicating its effective rock-breaking capabilities. Axial impact drilling significantly reduced the threshold drilling pressure compared with conventional rotation, with an impact amplitude of 0.3 mm decreasing the static load by 44.1%. Additionally, increasing the axial impact amplitude enhanced the rate of penetration (ROP) while maintaining a constant static load, resulting in remarkable efficiency improvements. The results of the study are expected to provide theoretical guidance for the mechanism of impact rock breaking and the design of impact rock-breaking tool parameters.  
axial impact; dolomite stone; impact amplitude; impact frequency; torque Article
## 3 Simulations and Results
### Observation Simulations
Also, a prolonged observational gap lasting about 15 h is consistently identified within each daily cycle. These findings suggest that, when initiating observations from randomly selected epochs, an observation window exceeding 24 h is recommended to ensure sufficient access coverage and to support robust OD performance.
# Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
## 3 Problem Formulation  
In this paper, we focus on _explainable product recommendation_, where the objective of the recommender system is to recommend products to users and explain why the products are recommended.  
Formally, we first construct a knowledge-base as a set of triplets \(S=\{(e_{h},e_{t},r)\}\) for recommendation, where \(e_{h}\) is a head entity, \(e_{t}\) is a tail entity, and \(r\) is the relationship from \(e_{h}\) to \(e_{t}\). Then the goal of explainable recommendation is twofold, 1) for each user \(u\), find one or a set of items \(i\) that are most likely to be purchased by the user, and 2) for each retrieved user-item pair, construct a natural language sentence based on \(S\) to explain why the user should purchase the item.  
For simplicity, we consider 5 types of entities (i.e., \(e_{h}\) or \(e_{t}\)) for explainable recommendation:  
* _user_: the users of the recommender system.
* _item_: the products in the system to be recommended.
* _word_: the words in product names, descriptions or reviews.
* _brand_: the brand/producers of the product.
* _category_: the categories that a product belongs to.  
Also, we consider 6 types of relationships (i.e., \(r\)) between entities:  
* _Purchase_: the relation from a user to an item, which means that the user has bought the item.
* _Mention_: the relation from a user or an item to a word, which means the word is mentioned in the user's or item's reviews.
* _Belongs_to_: the relation from an item to a category, which means that the item belongs to the category.
* _Produced_by_: the relation from an item to a brand, which means that the item is produced by the brand.
* _Bought_together_: the relation from an item to another item, which means that the items have been purchased together in a single transaction.
* _Also_bought_: the relation from an item to another item, which means the items have been purchased by same users.  
* _Also_viewed_: the relation from an item to another item, which means that the second item was viewed before or after the purchase of the first item in a single session.  
Therefore, for explainable product recommendation, the first goal is to retrieve item \(i\) that are likely to have the _Purchase_ relationship with user \(u\), and the second goal is to provide explanations for the \((u,i)\) pair based on the relations and entities related to them.
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
## 4 Asymptotic Performance Analysis for Long-Distance Short-Packet Transmission  
In this section, we focus on the solution given by Theorem 3, i.e., \(\epsilon=LQ\left(\mathcal{G}\left(\frac{E^{th}}{N^{th}},\frac{N^{th}}{L}\right)\right)\). Let \(\bar{\gamma}\) and \(\bar{n}\) denote \(\frac{E^{th}}{N^{th}}\) and \(\frac{N^{th}}{L}\), respectively. To give more insight, we develop an asymptotic analysis of the error probability for large \(\bar{n}\) or \(\bar{\gamma}\).  
To start with, we present Theorem 4 to approximate the overall error probability \(\epsilon\) when \(\mathcal{G}\left(\frac{E^{th}}{N^{th}},\frac{N^{th}}{L}\right)\to\infty\).  
Theorem 4: _If \(E^{th}\) or \(N^{th}\) is large enough, the overall error probability is approximated by:_  
\[\epsilon\approx\frac{L^{\frac{3}{2}}\sqrt{\frac{E^{th}}{N^{th}}( \frac{E^{th}}{N^{th}}+2)}\exp\left(-\frac{N^{th}\left(\ln\left(1+\frac{E^{th}} {N^{th}}\right)-\frac{L\ln 2}{N^{th}}\right)^{2}\left(1+\frac{E^{th}}{N^{th}} \right)^{2}}{2L\frac{E^{th}}{N^{th}}\left(\frac{E^{th}}{N^{th}}+2\right)} \right)}{\sqrt{2\pi N^{th}}\left(\ln\left(1+\frac{E^{th}}{N^{th}}\right)-\frac{ L\ln 2}{N^{th}}\right)\left(1+\frac{E^{th}}{N^{th}}\right)}. \tag{40}\]  
Proof: We notice that for any \(x>0\), the following inequation holds:  
\[\frac{x}{1+x^{2}}\exp\left(-\frac{x^{2}}{2}\right)<\int_{x}^{+\infty}\exp \left(-\frac{u^{2}}{2}\right)du<\frac{1}{x}\exp\left(-\frac{x^{2}}{2}\right). \tag{41}\]  
Substitute \(x=\mathcal{G}\left(\frac{E^{th}}{N^{th}},\frac{N^{th}}{L}\right)\) into Equation (41), and the proof is completed.  
From Theorem 4, we also express \(\epsilon\) with \(\bar{\gamma}\) and \(\bar{n}\) as:  
\[\epsilon\approx\frac{L\sqrt{\bar{\gamma}(\bar{\gamma}+2)}\exp\left(-\frac{ \bar{n}\left(\ln\left(1+\bar{\gamma}\right)-\frac{\ln 2}{\bar{n}}\right)^{2} \left(1+\bar{\gamma}\right)^{2}}{2\bar{\gamma}(\bar{\gamma}+2)}\right)}{\sqrt {2\pi\bar{n}}\left(\ln(1+\bar{\gamma})-\frac{k\ln 2}{\bar{n}}\right)\left(1+ \bar{\gamma}\right)}, \tag{42}\]  
in which we recall that \(\bar{n}=\frac{N^{th}}{L}\) and \(\bar{\gamma}=\frac{E^{th}}{N^{th}}\).
## 3 DFIG System with DVR-Based SMES  
The DVR can assist the DFIG in achieving the fault ride through by compensating the voltage dips at the outlet of the DFIG. Additionally, raising the fault voltage at the DFIG outlet during the fault can also improve the frequency offset of the fault current, which can significantly impact the distance protection of the power systems. The DVR-based SMES mainly consists of a VSC converter and a SMES system composed of a DC/DC converter, superconducting coil, and a refrigeration device [31; 32], as shown in Figure 4.  
During a period of normal operation, the DVR-based SMES cannot compensate for the voltage of the 35 kV line. Additionally, there is no power exchange between the DVR-based SMES system and the power system. During a period where voltage dips occur, the DVR-based SMES system, in series with the 35 kV line, starts to compensate for the voltage dips, so that the voltage of the DFIG can resume at a normal value. Additionally, the method can mitigate the frequency offset of the fault current by increasing the fault voltage at the DFIG outlet.
# A Ku-Band Fully Differential Low-Power High-Input TdB Low-Noise Amplifier
###### Abstract  
This paper introduces a Ku-band fully differential low-power high-input \(1\) dB compression point (P1 dB) low-noise amplifier (LNA). A fully differential structure is employed to enhance the input P1 dB, common-mode noise rejection, and second harmonic cancellation. The first stage adopts large transistors and is optimized for power consumption and noise figure (NF). The output stage is designed with class AB bias, resulting in improved P1 dB, power consumption, and linearity. The proposed two-stage fully differential common-source (CS) LNA was implemented using 65 nm bulk complementary metal oxide semiconductor (CMOS) technology. The fabricated LNA achieved a minimum NF of 2.7 dB at 13.6 GHz. Furthermore, it achieved a maximum gain of 19.92 dB at 12.2 GHz. Additionally, the LNA has an input P1 dB of \(-\)7.45 dBm and an output power 1 dB compression point (OP1 dB) of 10.09 dBm, both measured at 15.6 GHz. The LNA operates with a power consumption of 11 mW at a 1 V supply, and occupies a core size of 0.75 mm \(\times\) 0.35 mm.  
Ku-band; fully differential; low-noise amplifier; input P1 dB; class AB; CMOS; low power
# Lower-Body Positive Pressure Treadmill Training for Pediatric Gait Disorders: A Scoping Review
## 3 Results
### Training Protocols  
Protocol descriptions are presented in Table 3. In three studies, anti-gravity treadmill training was combined with conventional therapy (e.g., stretching and strengthening exercise, physical therapy or occupational therapy focused on walking capacity) [11; 18; 19].  
In the other studies, participants performed training with the anti-gravity treadmill only. Ten studies compared the effect of anti-gravity training to a control intervention, such as conventional physical therapy [11; 18; 19; 21], occupational therapy focused on walking capacity [22; 23; 24; 25; 26] and robotic and BWS treadmill training [20]. The training protocols presented across studies varied in intensity and duration (see Table 2). Training was typically conducted with a frequency of two or five times a week in sessions lasting 20-45 min each, for a total intervention duration ranging from 2 to 12 weeks. The settings of the anti-gravity treadmill were similar across the different studies: body weight support was initially set at 30-50%, then decreased progressively; gait speed was initially set at 0.7-1.5 km/h, then gradually increased. However, none of the studies reported individual data to describe how settings were progressed.
## 4 Discussion
### Toxicity and Macro and Micro Nutrients  
The quality properties of the analyzed composts were greatly influenced by the different techniques and raw materials used. The levels of toxicity and the content of macro and micro nutrients identified in all the samples fell within the Hungarian parameters for quality and safe compost. Moreover, the determined macronutrient (Ca, Mg) and micronutrient (Fe, Mn) content of the examined composts have similar and, in some cases, more favorable nutrient-supplying capacity in crop production than the frequently used cow manure-based composts. Despite the positive results, it is important to highlight the higher levels of zinc that fell close to the limit of toxicity and the low presence of magnesium, the latter being an important component in a high-quality compost. This tendency was found in samples with no treatments such as turning and watering, which would have sped up the composting process. The statistical analysis of our examinations, in part, supports [PERSON] results [25], according to which the initial raw materials of composts primarily determined their macro and microelement content and thus the quality of the finished compost. This is evidenced by the fact that in the case of untreated compost, wherein the initial raw materials included the slowly degradable sycamore and grape leaves, not enough magnesium was revealed even after 3-4 years without intervention. This can be explained in part by the low magnesium content of the initial raw materials, but also by the lack of composting treatments that slowed down the mineralization. Similarly, the higher zinc content in the non-treated composts can be explained by the initial raw materials (wood and ash, which can be even higher in the case of preservative-treatedwood or surface-treated wood waste), which occurred mainly in compost samples with no treatments. Another important consideration is regarding the higher levels of cadmium content, mainly in samples that contained wood ash (samples E and F), dishwashing water (sample K), and/or kitchen waste as raw materials. This examination confirms what other authors have already described [65]: that the release of cadmium into soil and groundwater is largely attributed to anthropogenic activities, including the use of synthetic phosphorus fertilizers (which may be contaminated with cadmium depending on the location of extraction). Other potential sources of the cadmium pollution identified in the literature are drinking water and recycled wastewater [66; 67]. Given the above, samples from organic farming resulted in zero cadmium content.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 3 Materials and Methods
### Synthetic Data Generation  
In this section, we develop a process dedicated to the generation of synthetic iPPG video streams. A suitable amount of synthetic data ensures proper training and validation of machine learning models that contain a very large number of intrinsic variables. This kind of procedure has already been employed in astronomy for the determination of galaxy morphology [75] and the detection of gravitational waves [76], and in bioinformatics for automatic genetic variant annotation [77] and feature extraction from functional magnetic resonance images [78].  
The procedure has five steps: A waveform model, fitted to real iPPG pulse waves using Fourier series, is employed to construct a generic wave (see Figures 2a and 3). A two-second signal is produced from this waveform (Figure 2b), and a linear, quadratic, or cubic tendency is added (Figure 2c). Note that both amplitude and frequency are controlled. The unidimensional pulse signal is then transformed to a video using vector repetition (Figure 2d). Random noise is independently added to each image of the video stream (Figure 2e). This step reproduces natural fluctuations due to camera noise that randomly appear in images.  
Figure 2: Flowchart of the synthetic imaging photoplethysmography (iPPG) video generator approach. (**a**) A realistic pulse model approximated with Fourier series (sum of sine and cosine functions, see Figure 3 and Table 1) serves as the base waveform. (**b**) A two-second signal is produced from this waveform. (**c**) A linear, quadratic, or cubic tendency is added to the signal. (**d**) Videos are generated by repeating the signal for each pixel. (**e**) Random noise is independently added to each image of the video stream. This step reproduces natural fluctuations (due to camera noise) that randomly appear on images. Note that illustrations below blocks (**d**,**e**) have been magnified.
## 1 Introduction
In particular, day-ahead load forecasting (DALF) is crucial for power system operations, as it enables grid operators to schedule power generation efficiently, minimizing energy waste, ensuring complete supply coverage, and preventing economic losses at first glance. Furthermore, day-ahead load forecasting significantly aids in the integration of renewable energy by facilitating the management of variable renewable sources and improving demand-side management strategies. Moreover, forecasting electricity demand for the entire following day allows power grid operators to optimize power generation dispatch, manage reserve capacity to accommodate changing demand, and it provides power market traders with the necessary information to make informed decisions about buying and selling electricity in the day-ahead market, based on anticipated consumption over the next 24 h.
## 5 Simulation Results and Analysis
### Simulation Results for Complex Scenes  
To further verify the effectiveness of the proposed method, the imaging processing simulation for complex scenes is carried out using real TerraSAR-X image data. Here, the resolution of TerraSAR-X image data is 1 m and the scene is 5 km (azimuth) \(\times\) 10 km (range). Specifically, the complex image data are used as the scattering coefficients of scenes, and then the multichannel SAR echo data of the hypersonic platform can be simulated according to the imaging geometry shown in Figure 1 and the simulation parameters listed in Table 1. Furthermore, the imaging processing is performed using the proposed ETF algorithm, which is shown in Figure 7. Finally, the imaging results of partial scenes are shown in Figure 12b. Meanwhile, as a comparison, the imaging result obtained by the first sub-aperture is also provided in Figure 12a. It can be clearly seen that the defocus and azimuth ambiguity are obvious in SAR images because the PRF is lower than the Doppler bandwidth of echo signal. However, in Figure 12b, we can see that the azimuth ambiguity is effectively suppressed using the proposed multichannel HRWS imaging algorithm, and that the SAR image for complex scenes is well focused.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Targets**} & **IRW (m)** & **PLSR (dB)** & **ISLR (dB)** \\  & **Range/Azimuth** & **Range/Azimuth** & **Range/Azimuth** \\ \hline \(T_{1}\) & 0.883/0.67 & \(-13.22/-13.17\) & \(-9.81/-9.10\) \\ \(T_{3}\) & 0.883/0.66 & \(-13.24/-13.21\) & \(-9.82/-9.12\) \\ \(T_{5}\