esa-sceva/satcom-chunk-collection huggingface dataset:
# Swarm Intelligence Techniques for Mobile Wireless Charging
## 5 MCs Coordination and Charging Traversal Strategies
### Percentage of Energy Available to the MCs  
An important tradeoff to be investigated is the optimal amount of energy for the MCs, with respect to the overall network energy, \(\hat{\mathbf{f}}_{total}\). In order to maintain an open-mindedness, we assume that the total available network energy \(\hat{\mathbf{f}}_{total}\) is finite and remains the same for all cases. This will help to investigate any increase in the energy efficiency (and to what extent) with respect to the presence of the MCs in the network and the charging process. This specific tradeoff defines how much energy (with respect to the total available energy) should be initially provided to the MC. It may appear that increasing the energy of the MC will result in a better energy management in the network. However, this idea is contradicted by Equation (3), i.e., \(\hat{\mathsf{E}}_{total}=\hat{\mathsf{E}}_{sensors}+\hat{\mathsf{E}}_{\text{ MC}}^{init}\). If we assume that the total energy available to the network, \(\hat{\mathsf{E}}_{total}\), remains the same, then any increase in the energy of the MCs implies that the sensor-nodes will not be fully charged (i.e., will initially receive partial charging) and will, most likely, run out of energy shortly before being charged by the MC, thus leading to a possible operational network breakdown. We investigate the optimal amount of energy available to MCs as a percentage of \(\hat{\mathsf{E}}_{total}\), by selecting the initial energy of the MC to be 20, 40, 60, and 80% of the total energy of the network for the various ratios of \(\hat{\mathsf{E}}_{sensors}\) and \(\hat{\mathsf{E}}_{\text{MC}}^{init}\). Figure 5 shows the result of this investigation. From this result, it is evident that above 40% of the total energy provision to the MC will negatively affect the operations of the network. This result is predicated on two factors. Firstly, from Equation (3) above, an increase in the energy of the MC minimizes the initial energy of the nodes, thus leading to a quicker node death rate. Secondly, increasing the energy of the MC to a high amount may reduce the MC's rate of energy distribution in the entire network, resulting in an increase in the residual energy at the MC when the network is disconnected.  
A major conclusion shows that using a moderate percentage of energy at the MC is correct, such as the arbitrary value of 20%.
## 1 Introduction  
As the need for more sensitive and greener alternatives in analytical chemistry continues to grow [1; 2; 3; 4; 5], it is necessary for the further development of robust sample preparation technologies to meet these modern demands. Sample preparation, being the first step in any analytical procedure, is far-reaching since any and all steps in the workflow are consequently affected by the sampling and extraction method used. The sampling and clean-up of compounds before introduction into an instrument plays a critical role in the achieved sensitivity and quantitative capabilities of the method. Consequently, novel extraction techniques must be developed to enhance analytical performance, while meeting the newfound call for greener sample preparation methods. Microextraction, being characterized by a small amount of extraction phase compared to the volume of the sample [6], affords the opportunity of substantially reducing the amount of organic solvent used while still achieving similar or better results compared to more traditional extraction techniques such as solid phase extraction (SPE) [7; 8] and liquid-liquid extraction (LLE) [9]. The volume of the extraction phase, being inconsequential to the overall volume of the sample [6], allows rapid non-exhaustive extraction, in some cases non-depletive, that can easily be quantitated using a variety of calibration methods [10]. Moreover, analytes from the sample matrix are extracted in their \"free-form\" (non-boundor free-concentration), giving the opportunity for the analysis of bio-available analytes in various matrices. Among existing microextraction techniques, solid phase microextraction (SPME) is the most widely adopted as it allows solvent-less extraction that can be easily automated and adapted for in vivo and onsite applications [11]. The conventional configuration of SPME consists of an extraction phase coated on a solid, fiber-like support composed of fused silica, stainless steel or flexible metal alloys; this geometry allows ease of use and automated extraction and analysis [11]. As a sample preparation method, the use of SPME enables sampling and pre-concentration to be performed in one simple step, making the technique more versatile in its use and able to achieve better throughput compared to more laborious exhaustive methods such as SPE and LLE.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 2 Einstein Probe
### Instrument Design  
The EP carries a wide-field X-ray telescope (WXT) with a large instantaneous FoV, which adopts a novel lobster-eye MPO technology. Complementary to this wide-field instrument is a follow-up X-ray telescope (FXT) with a large effective area and a narrow FoV. Figure 1 shows the configuration of the EP payload:  
* Wide-field X-ray telescope (WXT): There are 12 almost identical WXT modules, each with an FoV of \(\sim\)300 sq. deg. Each WXT module includes a lobster-eye MPO mirror assembly with a focal length of 375 mm, a focal plane detector array, and an electronics unit. The mirror assembly of each module comprises 36 MPOs mounted on a supporting alloy frame. An MPO is made of a thin plate with millions of square micro-pores perpendicular to the surface, slumped into a spherical shape. Incoming X-rays at a grazing-incidence angle are reflected off the walls of the pores and are brought onto a focal sphere with a radius of half the curvature of the optic. It produces true imaging with a characteristic cruciform point-spread function. The detector array of each module is composed of four black-illuminated CMOS sensors, each 6 cm by 6 cm in size and 4 k by 4 k pixels. An aluminum layer of a 200 nm thickness is coated on the surface to block incident optical and UV light. The nominal detection bandpass of the WXT is 0.5-4 keV.
* Follow-up X-ray telescope (FXT): The FXT is a set of two telescopes of the Wolter-I optics, which have almost the same design and are co-aligned. The design of the mirror assembly is similar to that of the eROSITA telescopes, which has a focal length of 1.6 m. The focal plane detectors are built from pn-CCDs, and a set of thin and thick filters of aluminum layers are mounted on a filter wheel.
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 5 Experiments
### Experimental Setup
#### 5.1.2 Baselines  
We compare the proposed DTGCF with the following baselines:  
* CCF [5]: CCF is a clustering-based CF model, which adopts hierarchical clustering to aggregate redundant tags. User and item representations based on these tag clusters are generated and used to provide recommendations;
* ACF [3]: ACF leverages the deep autoencoders to extract tag-based low-dimensional dense features for users and employs user-based collaborative filtering to generate recommendations according to those extracted features;
* DSPR [9]: It is a tag-aware deep recommendation model which adopts deep neural networks to learn nonlinear latent features for users and items from the tag space;
* TGCN [13]: It constructs a tag graph convolutional network, which employs a type-aware neighbor sampling strategy and an attentive CNN-based information updating method for improved tag-aware recommendations;
* LFGCN [14]: It establishes a tag-aware light graph convolutional network to integrate topological structure for learning more accurate representation of users and items;
* MMR [20]: MMR is a diversity-based re-ranking method by maximizing the marginal relevance. This method is initially used to reorder documents and generate summaries and is further extended into item recommendation [21];
* DPP [22]: DPP is an elegant probabilistic model of repulsion, in which higher probability is assigned to those item sets that are more diverse. In this way, it can balance the recommendation accuracy and diversity, providing re-ranked recommendation lists;
* DGCN [23]: It is a GCN-based diversified recommendation model which performs three diversity-based strategies: rebalanced neighbor discovering, category boosted negative sampling, and adversarial learning.  
To summarize, these baseline methods can be divided into two types. The first type (from CCF to LFGCN) belongs to the accuracy-focused tag-aware recommendation model. Among them, CCF, ACF, and DSPR are feature-based tag-aware models, while TGCN and LFGCN are GCN-based models. The second type (from MMR to DGCN) is the diversity-boosted recommendation model. Note that MMR and DPP are post-processing methods, while DGCN is an end-to-end model for diversified recommendation.
## 3 Datasets
### Small-Scale Laboratory Dataset  
The laboratory dataset was acquired during a controlled experiment at Hydrogeosite Laboratory of CNR-IMAA. The aim was to image salt plume migration under almost natural gradient conditions by means of cross-hole electrical resistivity monitoring for quantifying hydraulic conductivity and transport parameters of a homogeneous porous aquifer [64; 65].  
The simulated phreatic aquifer consists of 1 m\({}^{3}\) of homogeneous silica sand (95% SiO\({}_{2}\)) characterized by an average diameter equal to 0.09 mm (very fine sand), porosity of about 45-50%, and hydraulic conductivity in the order of 10\({}^{-5}\) ms\({}^{-1}\)[66]. An external hydraulic system was used to impose a constant hydraulic gradient during all the experiment while contamination occurs by continuously injecting 10 liters of salt water (NaCl solution with a concentration of 100 g/L) for 10 days. The source of contamination was at \(-\)0.15 m of depth (Figure 4).  
The geophysical system used to monitor the plume migration consisted of six piezometers (A\(-\)F in Figure 4) instrumented for CH-ERT as shown in Figure 4. In detail, 12 steel electrodes (plate of 0.5 cm) were installed along the six piezometers and used to perform the 2D cross-borehole resistivity measures within \(-\)0.1\(--\)0.65 m in depth. In addition, pH, Eh, temperature, and electrical conductivity of water were monitored for 31 days both in the piezometer A and at the outlet [64; 65].  
The entire geophysical dataset consists of 210 CH-ERTs with 504 measurements with reciprocal for errors assessment. Recurrent Neural Networks (RNN) were applied to time-lapse geoelectrical measurements for predicting the displacement of the contaminant over time in the small-scale laboratory experimental setup. In this work only measurements between piezometers C and D (transversal section respect to the plume migration) were considered. In detail, the first 15 daily surveys as the historical dataset (7560 data) were used for training the recurrent network. Then, the data belonging to the remaining five sets of measurements (2520 data) were used as \"test data\" for checking the predictive capabilities of implemented RNN. Figure 5 shows a subset of data (in terms of apparent resistivity) taken at several measurement points.  
Figure 4: Laboratory experimental set-up. A–F are the piezometers instrumented for CH-ERT. Distances are in cm. Modified from [65].
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Gaussian Process Regression (GPR)
#### 2.4.2 Dataset Partitioning (Training, Validation, and Testing)  
After data pre-processing, the machine learning dataset can be divided based on three building processes of the machine learning model [14; 32]. The first is the training process, where a mathematical model that represents the training set is created from the iterative learning process of the training set by the MLA. The second is the validation process. The k-fold cross validation as one of the most common techniques for machine learning validation is selected for this study. k = 5, which is usually used for validation [33], is chosen for each machine learning model in this study. The third is the testing process, where the previously trained and validated model is then tested on previously unseen data. The test dataset is usually drawn from the entire dataset by 20-30%. In this study, the test data were assembled by randomly drawing 30% of the sample dataset.
###### Abstract  
We fabricated a core-cladding Ge-Sb-Se glass fiber with a Ge\({}_{125}\)Sb\({}_{125}\)Se\({}_{72.5}\) core and Ge\({}_{15}\)Sb\({}_{10}\)Se\({}_{75}\) cladding, achieved a supercontinuum spectrum spanning from 2 \(\upmu\)m to 9 \(\upmu\)m by pumping the Ge-Sb-Se fiber with a core diameter of 11 \(\upmu\)m using a femtosecond laser pump at 3.8 \(\upmu\)m, and numerically simulated the supercontinuum generation using the generalized nonlinear Schrodinger equation. In particular, we investigate the effect of the different Raman response functions that were calculated using the traditional single Lorentzian model and a multiple vibrational mode model on the evolution of the supercontinuum by comparing the supercontinua obtained from simulation and experimental results. We demonstrate that the Raman response function generated by the multiple vibrational mode model captures the actual response behavior of the material, and the supercontinuum generated using this model has more accuracy. To the best of our knowledge, this is the first reported study on supercontinuum generation in Ge-Sb-Se fiber utilizing a Raman response function calculated using the multiple vibrational mode model. This significant advancement enables more accurate simulation of supercontinuum generation in fibers with a multi-peaked structured Raman gain spectrum and holds great potential for optimizing the performance of various mid-infrared supercontinuum sources.  
chalcogenide; Raman response function; mid-infrared; supercontinuum +
Footnote †: journal:  
Article
# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 2 Materials and Methods
### Case Study  
The verification of the designed system was realized in a company that manufactured roof windows. This was a custom production system where each product that enters production is assigned to a specific customer and can be configured in any way when ordering.  
The production system consists of 17 workplaces, and each product passes through the process as one piece, which is mounted on the underrun automated guided vehicle (AGV). After each operation, based on the current conditions, the subsequent workplace is chosen for job processing.  
To monitor and collect data from production, it was necessary to cover two production halls. This monitoring system uses direct ultra-wideband (UWB) anchors, personal RTLS tags, tags for the tracking of AGV trucks, and tags for forklift pallet trucks.  
The proposed production system does not contain common machines, and the measurement of the real cycle times of the workplaces was realized by identifying the time remaining for the job at a specific workplace using the RTLS. In this way, accurate real-time localization and data collection was achieved, and all data were integrated with the Track and Trace (TaT) and Tecnomatix Plant Simulation 2201 software [2]. In addition to the data from the monitoring system, a connection to the SAP corporate ERP information system was set up. The whole data system works with the support of the digital twin software platform. A more detailed description of the case study was published in [16]. A demonstration of the system in practice can be seen in Figure 5.
# Test Method for Single Satellite's Inter-Satellite Link Pointing and Tracking via Ground Station
## 2 Models of Inter-Satellite Link Pointing and Tracking
### Beam Pointing Angle  
Beam pointing is the instantaneous vector of a microwave antenna or a laser link. As the relative position of the constellation evolves, the pointing is time-varying. In this situation, in order to maintain the inter-satellite link, the line of sight (LOS) of the beam point should track the target satellite. The beam-pointing vector in the antenna coordinate frame \([X_{a},\,Y_{a},\,Z_{a}]\) is converted by two beam-pointing angles, the rotation angle and the off-axis angle in Figure 1 and Equations (7) and (8). The corresponding range of the off-axis angle is \([0,\,\,\pi/2]\), and the range of the rotation angle is \([0,\,\,2\pi]\).  
\[\gamma_{oa}=\arccos\left(\frac{z_{a}}{\left\|\mathbf{r}_{link}^{a}\right\|} \right),\mathbf{r}_{link}^{a}=\begin{bmatrix}x_{a}&y_{a}&z_{a}\end{bmatrix}^{T}, \gamma_{oa}\in[0,\,\,\pi/2] \tag{7}\]  
\[\gamma_{ra}=\left\{\begin{array}{ll}\arctan 2(y_{a},x_{a}),&y_{a}\geq 0\\ \arctan 2(y_{a},x_{a})+2\pi,&y_{a}<0\end{array},\,\,\gamma_{ra}\in[0,\,\,2\pi]\right. \tag{8}\]
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 5 Fiber-Optic Hydrogen Sensors  
With the recent development of optical testing technology and fiber-optic communication technology, optical sensing technology, which uses optical fibers as a carrier to sense and transmit environmental parameters through light wave signals, was also applied to hydrogen concentration detection, which can also be generally referred to as fiber-optic type sensors [58; 59; 60; 61]. Catalytic and electrical hydrogen sensors can respond quickly and accurately to hydrogen concentration at room temperature and pressure and are currently used in; however, these sensors, which are based on electrical characteristics, may generate sparks during use and have the potential to cause hydrogen explosions, which are a safety hazard. Fiber-optic sensors detect hydrogen through sensitive materials and optical signals, which are intrinsically safe and immune to electromagnetic interference, and have the advantages of small size, wide measurement range, and resistance to high temperatures and pressures. Fiber-optic hydrogen sensors are divided into interference modulation type, intensity modulation type, and wavelength modulation type.
## 3 Experimental Study
### Evaluation Metrics  
The performance is evaluated from four indicators, including the root mean squared error (\(RMSE\)), mean absolute error (\(MAE\)), symmetric mean absolute percentage error (\(SMAPE\)), and score function (\(Score\)). These indicators are defined as follows  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(y_{pred_{i}}-y_{true_{i}})^{2}} \tag{13}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}|y_{pred_{i}}-y_{true_{i}}| \tag{14}\]  
\[SMAPE=\frac{100\%}{n}\sum\limits_{i=1}^{n}\frac{|y_{pred_{i}}-y_{true_{i}}|}{(| y_{pred_{i}}|+|y_{true_{i}}|)/2} \tag{15}\]  
\[Score=\left\{\begin{array}{l}\sum\limits_{i=1}^{n}e^{-\frac{y_{pred_{i}}-y_ {true_{i}}}{13}}-1,y_{pred_{i}}-y_{true_{i}}<0\\ \sum\limits_{i=1}^{n}e^{\frac{y_{pred_{i}}-y_{true_{i}}}{10}}-1,y_{pred_{i}}-y _{true_{i}}\geq 0.\end{array}\right. \tag{16}\]  
where \(n\) is the number of test samples, \(y_{pred}\) denotes the predicted RUL, and \(y_{true}\) denotes the real RUL. \(RMSE\) is selected to measure the deviation between the prediction RUL and the real RUL. \(MAE\) is more robust to outliers. \(SMAPE\) can represent the quality of the model. \(Score\) is a scoring used in the 2008 PHM management data challenge.
## 6 Discussion
### Limitations of the Study and Future Directions  
Despite its demonstrated effectiveness, the proposed synchronous sampling method has several limitations. First, its performance is contingent on the proper functioning of both sensors; failure of one sensor renders the method inoperable. Second, the method assumes a relatively symmetrical runout pattern where the errors induced at opposite sensors are equal and opposite. In cases of highly complex or asymmetrical runout motion, the cancellation might be incomplete. Third, the accuracy is dependent on the precise symmetrical mounting of the sensors and assumes that both sensors have closely matched characteristics (e.g., sensitivity, phase response). Significant discrepancies could introduce residual errors. Finally, the random runout was simulated using elastic pads; applying and validating this method on in-service diesel engines under real-world load and thermal conditions would be a crucial next step.
# Object-Independent Grasping in Heavy Clutter
## 3 Experiments  
Several experiments were conducted to determine whether the proposed DBP is effective for grasping objects that have a complex piling structure. For this purpose, the performance of DBP was compared with that of three grasping algorithms. The first one used a random method. In this, the grasp position was the central coordinate of the cluster found by the grasp candidate generator, and the grasp angle was determined randomly. Thus, compared to the DBP method, the grasp position was the same, and the grasp angle was different. The second algorithm was based on principal component analysis (PCA) [17]. In this method, the grasp center position was set to one of the center points of the clusters in the clustered depth image, and the grasp angle was obtained by PCA. Thus, a narrow part of the target object was used for grasping. The third algorithm was based on an ANN [18]. In this algorithm, the ANN received the depth image and used it to estimate the grasp pose. Note that the algorithm did not previously learn the objects to be used in the experiments.  
The three algorithms and DBP were tested with different objects both in a 2D cluttered environment and in 3D bin picking. In the 2D clutter, grasping was performed for 20 types of objects in an area delimited by white lines. In 3D bin picking, the target objects were placed in a 390 mm \(\times\) 480 mm \(\times\) 250 mm box. In this case, the parameters of the DBP algorithm were \(p=0.1\), \(n=20\), \(c=80\), and \(k=0.5\).  
Figure 8 shows the experimental setup, which comprised a UR5 robot, a RealSense D435 RGB-D sensor mounted on the robot arm, and a Robotiq two-finger gripper. The main central processing unit (CPU) was an Intel Core i9-7940X, and the GPU was a GeForce GTX 1080 Ti. Figure 8a shows the 45 different objects used in the experiments. In the 2D cluttered environment shown in Figure 8b,  
Figure 7: Grasp pose modification of (**a**) the gripper width and (**b**) the gripper center.  
20 objects were randomly selected among the 45 objects and stacked in the outlined area. In bin picking, as shown in Figure 8c, the 45 objects were used. The objects comprised the Australian Center for Robotic Vision (ACRV, Brisbane, Australia) picking benchmark (APB) [19], the Yale-CMU-Berkeley (YCB) benchmark [20], the World Robot Summit (WRS) 2018 set, and household items.  
To test the DBP scheme in a heavy clutter, a 330 mm \(\times\) 450 mm \(\times\) 260 mm box was filled with small cosmetic containers, as shown in Figure 9a. Without proper consideration of the space around the object, grasping is difficult. In this experiment, the parameters of the DBP algorithm were \(p\) = 0.05, \(n\) = 20, and \(c\) = 80. In contrast to the previous experiments, the width of the gripper was fixed, because only one type of object was targeted.