esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 2 Background
### GNU Radio  
GNU radio is an open source SDR based on C++ language that enables communication among configurable blocks of different nodes, including the source, intermediate, and destination nodes. GNU radio companion (GRC) is an extended version of GNU radio that provides a user-friendly interface, and it has wider range of configurable blocks and functions [24]. GNU radio flow graphs, which consist of redconfigurable blocks, for the source, intermediate, and destination nodes of our testbed are shown in Figure 3. The functions and processes of the blocks that a data stream goes through from a source node to a destination node is shown in Table 1.  
Figure 3: GNU radio flow graph of the source, intermediate, and destination nodes.
## 3 Results and Discussion
### Model Validation and Heat Transfer Characteristics Analysis
decreasing trend until the end of the injection process. The evaporation mode in the core and peripheral regions successively transitions to the nucleate boiling regime. As indicated in Figure 10, the maximum heat flux moves radially from the core region to the peripheral region with time. The trend of wall heat flux is the result of the interaction of the local spray mass flux [34] and the evaporation mode at the liquid-solid interface. The maximum heat flux starts to decrease at a faster rate after the end of the injection process. The main reason is that the wall film stops increasing at the end of the injection cycle and evaporates rapidly due to the heat transfer with the air flow. The minor reason is that the evaporation mode at the liquid-solid interface transitioned to the free convection regime, which weakens the heat transfer between the wall film and the wall surface.
# A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
## 5 Results and Discussion
### Datasets and Parameter Setting  
In the VRP-CS, the instances are generated based on the VRPRDL dataset [28]. A psychological reference point related to delivery time is assigned to each optional delivery location. For location \(a\), the values of the psychological reference point are randomly assigned between \(e_{ia}\) and \(l_{ia}\) according to a Gaussian distribution. All instances are self-explanatory, i.e., they are named according to the number of customers and total number of locations. For example, for a dataset with 20 customers and 62 optional locations, the instance is named C21L62. Instances of four sizes are designed with 16, 21, 31, and 61 customers, respectively.  
This VRP-CS problem uses the risk attitude coefficient \(\alpha=\beta=0.88\), proposed by [PERSON] and [PERSON] [50], and, at the same time, sets the loss of aversion coefficient \(\lambda=2.25\). The value of the determined parameters is selected by means of a controlled experiment. The unit cost of delivery time is 1; the cost of using a single vehicle is 20; and the cost of unit customer satisfaction is 20. The MSALNS algorithm depends on the following parameters: maximum number of iterations \(I_{max}\), number of iterations without improvement \(U_{ui}\), number of iterations for updating the annealing temperature \(U_{gap}\), score attenuation coefficient \(\gamma\), cooling rate \(c_{T}\), initial score \(\phi_{0}\), score value of the reward \(\phi_{1}\), \(\phi_{2}\), \(\phi_{3}\), \(\phi_{4}\), satisfaction Shaw removal operator coefficient \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), \(\alpha_{4}\), \(\alpha_{5}\), and the scale coefficient of the removal operator \(\tau\). Through comparative experiments, all the MSALNS parameters set are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(I_{max}\) & 500 & \(c_{T}\) & 0.992 & \(\phi_{3}\) & 10 & \(\alpha_{3}\) & 0.5 \\ \(U_{ui}\) & 10 & \(\phi_{0}\) & 1 & \(\phi_{4}\) & 5 & \(\alpha_{4}\) & 0.1 \\ \(U_{gap}\) & 50 & \(\phi_{1}\) & 16 & \(\alpha_{1}\) & 0.2 & \(\alpha_{5}\) & 0.3 \\ \(\gamma\) & 0.3 & \(\phi_{2}\) & 8 & \(\alpha_{2}\) & 0.2 & \(\tau\) & 15\% \\ \hline \end{tabular}
\end{table}
Table 1: Algorithm parameter list.
Aiming at the current research status of Tibetan SBD, the need for downstream task dataset establishment, and Tibetan grammar semantic analysis, this study explores the automatic recognition of Tibetan sentence boundaries based on the BERT pre-trained language model combined with the classical deep learning model through which the computer learns the meaning of the sequence composed of each shad (\"[\"]) and the text preceeding it, which transforms the SBD problem into a binary classification problem to determine whether the current shad (\"[\"]) is an actual sentence end marker. Since the current publicly available pre-trained language models for Tibetan are generally effective on the SBD task we first train a BERT pre-trained language model for Tibetan in this study based on BPE and Sentencepiece and verify the performance of the model by combining CNN [24], RNN [25], RCNN [26], and DPCNN [27] on the two pre-trained language models. Meanwhile, to verify the effectiveness of the data in this study, experiments are also conducted on the publicly available TiBERT [28] model and Multilingual BERT(Multi-BERT) [29], which proves the effectiveness of the SBD data in this study on multiple pre-trained language models, as well as the suitability of Tibetan SBD tasks for the BERT pre-trained language model based on the BPE word cutting approach. Meanwhile, to reduce the model's computational complexity and training cost, the experiment selects the number of token sequences in the window before the punctuation mark to participate in the training and testing. Finally, it proves the high efficiency of the BERT(BPE) deep learning-based model in the Tibetan SBD task.
# Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction
## 2 Quantum Dots-Based Photocatalyst for Hydrogen Evolution
### Metal-QD-Based Hybrid Photocatalyst  
Recently, hybrid photocatalysts with noble metal QDs as cocatalysts are being widely investigated by researchers [102]. Noble metal QDs such as Au, Ag, and Pt QDs can serve as antennae to capture photogenerated electrons from the CB of semiconductor photocatalysts, accelerate the migration of charge carriers, and consequently improve photocatalytic efficiency [103; 104; 105]. Moreover, due to the unique localized SPR effect, noble-metal QDs can efficiently improve the solar light absorption ability, then contribute to the improvement of photocatalytic performance [54; 55; 102; 103; 104; 105; 106; 107]. For instance, [PERSON] et al. [54] have reported on the use of a simple hydrothermal process, followed by photoreduction, to synthesize hybridized photocatalysts with gold quantum dots embedded in cadmium sulfide nanospheres. The fabricated Au-CNS hybrids show an improved photocatalytic hydrogen evolution rate. In addition, their stability is also enhanced because of the chemical stability and the rapid migration of photogenerated electrons from CNSs to Au QDs. Here, a rough surface and irregular fissures are crucial to anchoring Au QDs onto the CNSs, and then the strong interaction between Au QDs and CNSs lead to the rapid separation of charge carriers and further improve the photocatalytic efficiency. [PERSON] et al. [55] have reported that Ag QDs/g-C\({}_{3}\)N\({}_{4}\) (Ag QDs/g-C\({}_{3}\)N\({}_{4}\)) photocatalysts exhibit a higher photocatalytic H\({}_{2}\) evolution rate than blank g-C\({}_{3}\)N\({}_{4}\) due to the extension of the solar light absorption by coupled Ag QDs, which can act as a g-C\({}_{3}\)N\({}_{4}\) electron reservoir for the effective separation of photoexcited electron-hole pairs, further increasing the photocatalytic efficiency of Ag QDs/g-C\({}_{3}\)N\({}_{4}\).
while limiting the visibility of obstacles and reducing reaction time, potentially creating hazardous situations [11, 12].  
Prolonged smartphone use can alter spinal posture across various age groups, significantly elevating the risk of spinal musculoskeletal disorders [5, 13]. Particularly, it negatively affects posture throughout the entire spine, including cervical, thoracic, and lumbar regions [5, 6, 10, 14, 15, 16, 17, 18, 19, 20, 21]. For instance, text messaging reduces cervical rotation angles by approximately 2-4 degrees on average and increases thoracic and lumbar flexion, potentially causing pain and discomfort in the neck and shoulders [20]. Increased cervical flexion due to smartphone use also leads to the sustained contraction of the upper trapezius, elevating muscle activity and fatigue, which can result in discomfort and pain [14, 17, 18]. Short-term spinal posture alterations will progressively intensify with the prolonged duration and increased frequency of smartphone use, ultimately leading to diverse spinal joint issues.  
Excessive smartphone use also alters gait patterns [9, 12, 22]. Specifically, biomechanical gait changes, including reduced stride length, decreased cadence, and slower gait speed, are commonly observed [23]. Additionally, smartphone use while walking constitutes a dual-task scenario, temporarily diminishing attention and cognitive function [24, 25, 26]. Particularly, texting while walking heightens cognitive demands, leading to decreased overall gait speed, reduced text comprehension, and increased gait variability [26].  
The primary objective of this study is to systematically review and summarize individual studies investigating the effects of smartphone use on spinal posture and gait alterations. Although reviews published since 2020 have examined either spinal posture [27] or gait alterations [27, 28] due to smartphone use in healthy individuals, reviews addressing both simultaneously remain limited [27].
## 5 Simulation Results and Analyses
Finally, due to the similar main mechanism of soft LDPC metric usage in timing recovery, we do not expand these contents more for purpose of concise manuscript. However, they can be simply derived and constructed for practical applications in satellite and wireless communications.
# Value of Information Based Data Retrieval in UWSNs
## 2 Related Work
### Using Mobile Sinks in Underwater Sensor Networks  
Mobile sinks have been used as data mules in both sensor networks [5; 42; 43; 44; 45] and UWSNs [2; 4; 6; 7; 10]. Mobile sinks in UWSNs have been used in the form of AUVs [6; 7] as well as dolphins [46]. In [46] it has been proposed to use a DDD (delay-tolerant data dolphin) to harvest the data from sensor nodes in the region of interest. The data collection event can be triggered by both the DDD or the sensor node. The DDD uses a bi-directional acoustic communication medium. As the movement of DDDs is random, therefore, there is a randomness associated with the event data collection, and this inhibits optimal performance.  
Vol-based transmission scheduling of sensor nodes via acoustic links to the sink has been explored in [47]. While Vol-based path planning of an AUV to collect data from sensor nodes has also been explored in [12; 48; 49; 50].
## 3 Experiment Validation and Analysis
### Experiment and Analysis of Glare Suppression for Real-Time Positioning Information  
The experimental setup for real-time positioning information glare suppression is shown in Figure 9, consisting of a visible light monochrome camera and a rotatable polarizer. The experiment was conducted outdoors in Changchun, simulating a calm sea area (coordinates: N 43\({}^{\circ}\)50\({}^{\prime}\)55\({}^{\prime\prime}\), E 125\({}^{\circ}\)23\({}^{\prime}\)55\({}^{\prime\prime}\)), aiming to verify the effectiveness of the real-time positioning information glare suppression method. According to the real-time positioning information acquisition and calculation method described in Section 2.2.1, the light incidence angle at different times is calculated. During the experiment, the polarizer in front of the camera lens is rotated until the image grayscale is minimized, at which point the rotation angle of the polarizer differs by 90 degrees from the glare polarization angle. By measuring the rotation angle of the polarizer, the corresponding glare polarization angle is calculated. The experimental results demonstrate the relationship between the incidence angle and the glare polarization angle, with the relationship curve shown in Figure 10 and the fitted experimental data presented in Table 2.  
Figure 10: Real-time positioning information glare suppression relationship curve between incidence angle and polarization angle.  
Figure 9: Real-time positioning information glare suppression experimental apparatus.  
We compared the polarization angles for the same incidence angles in Table 2 with those in Table 1, and calculated the relative error of the experimental data in Table 2 with respect to Table 1. The results of the relative error calculation are shown in Table 3.  
From Table 2 and Figure 10, it can be observed that the relationship curve between the incidence angles and polarization angles in the real-time positioning information glare suppression experiment is approximately consistent with the incident-polarization correlation shown in Figure 8. The results of the relative error calculation indicate that the discrepancies between the experimental data and the benchmark data are relatively small, with an average relative error of 5.91%. Hence, there exists an incident-polarization correlation in the real-time positioning information method, confirming the effectiveness of the glare suppression method based on real-time positioning information calculation.
# Measurement of Unit Weight of Dry Sand Using Piezoelectric Sensor
## 3 Results of EMI Sensing
### Relationship between Unit Weight and Frequency and Conductance  
Table 2 summarizes the results of unit weight change caused by an increase in surcharge levels. As shown in Figure 10, the peak frequency demonstrated an increase although the decrease in peak conductance was observed with an increase in surcharge levels regardless of the sand size. This effect was most dominant for the small-sized Nakdong river sand.  
For the medium and large-sized sand grains used in this study, changes in peak frequency and conductance were small when the different levels of surcharge were applied on top of the specimen. This is because the amount of surcharge itself was low and the transferred load to sand particles was also small. This resulted in only a small change in the unit weight, which had a marginal influence on the measured frequency and conductance of the 20 mm PZT sensor.  
Figure 8: Conductance versus frequency for three sand types: (**a**) Signatures of EMI for the small-sized sand; (**b**) signatures of EMI for the medium-sized sand; (**c**) signatures of EMI for the large-sized sand.  
Figure 9: Effect of surcharge on peak frequency and conductance for three sand types: (**a**) Peak frequency–surcharge curve; (**b**) peak conductance–surcharge curve.
## Materials and Methods
### Quartz Crystal Microbalance with Dissipation (QCM-D) Monitoring  
QCM-D experiments were performed in a Q-Sense E4 instrument (Q-Sense, Biolin Scientific, Vastra Frolunda, Sweden) with the use of a QEM 401 electrochemistry module, externally controlled by means of a Ch1660 C Potentiostat (CH Instruments Inc., Austin, TX, USA). Prior to their use in the experiments, gold coated quartz sensors (QSX 301, Q-Sense, Biolin Scientific) were sonicated in 2% (_w/w_) SDS solution for 20 min and thoroughly rinsed with ultrapure water and ethanol. The crystals were dried in a stream of nitrogen, subsequently treated with UV/Ozone (BioForce Nanosciences, Ames, IA, USA) for 30 min and incubated for 1 h in a 1 mM solution of octanethiol in ethanol to ensure their hydrophobicity. Afterwards, the octanethiol-coated sensors were sonicated in ethanol and finally mounted into the QCM-D chamber. Experiments were performed at 25 \({}^{\circ}\)C. Real time variations of Frequency (\(\Delta\)f) and dissipation (\(\Delta\)D) parameters were observed at several overtones (_n_ = 3, 5, 7...,13) throughout the experiments. Injection of SbpA protein (50 \(\upmu\)g/mL), all washing steps as well as the addition of the different buffers were performed by means of a peristaltic Pump (Ismatec, Cole-Parmer GmbH, Wertheim, Germany) operating at a flow rate of 0.3 mL/min. Conditions for the electrochemical running protocols (i-t Amperometry, Cyclic Voltammetry) and the corresponding applied values were defined by means of the CH660C software (version 14.08, CH Instruments, Inc., Austin, TX, USA).
# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
###### Abstract  
Reducing the uncertainties in carbon balance assessment is essential for better pastureland management in arid areas. Climate forcing data are some of the major uncertainty sources. In this study, a modified Biome-BGC grazing model was driven by an ensemble of reanalysis data of the Climate Forecast System Reanalysis data (CFSR), the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), to study the effect of climate change and grazing on the net ecosystem exchange (NEE) of the pasturelands in Central Asia. Afterwards, we evaluated the performance of corresponding climate datasets over four major pasteured types, and quantified the modeling uncertainty induced by climate forcing data. Our results suggest that (1) a significant positive trend in temperature and a negative trend in precipitation were obtained from the three climate datasets. The average precipitation is apparently higher in the CFSR and MERRA data, showing the highest temperature value among the data sets; (2) pasturelands in Central Asia released 2.10 \(\pm\) 1.60 Pg C in the past 36 years. The highest values were obtained with the CFSR (\(-\)1.53 Pg C) and the lowest with the MERRA (\(-\)2.35 Pg C) data set; (3) without grazing effects, pasturelands in Central Asia assimilated 0.13 \(\pm\) 0.06 Pg C from 1981-2014. Grazing activities dominated carbon release (100%), whereas climate changes dominated carbon assimilation (offset 6.22% of all the carbon release). This study offered possible implications to the policy makers and local herdsmen of sustainable management of pastureland and the adaptation of climate change in Central Asia.  
carbon dynamics; Central Asia; reanalysis data; pasturelands; Biome-BGC model 2022  
1
## 3 Materials and Methods
### Processing the Images  
When taking images in the defined scenario, every captured image is saved as a single file into the currently defined directory, being identified with any name pattern. Then, the employed camera operates with a frequency \(f(s)\) when creating a set \(s\) of images, resulting in each image \(I_{(s,c)}\) being taken every \(1/f(s)\) second and saved to be further processed.  
After all images are saved, the algorithm moves to the second phase. The directory containing all images of set \(s\) is read, with each image being processed sequentially according to their creation timestamp. In this phase, the processing of the images is performed as follows:  
* For each image \(I_{(s,c)}\), the disc is identified, resulting in the cropping of the image in order to comprise only this visual part, reducing the computational cost when compared to the processing of entire images. In this phase, the center of the cropped image is saved, \((Px,Py)\), which is the center of the disc (valid for all detected spots in the considered images set);
* The cropped image is transformed into a grayscale image, facilitating the detection of (any) spots. In the sequence, the image contrast is increased (we assume a standard factor of 50%). Although different image processing techniques could be employed, we consider a Gaussian low-pass filter as reference to reduce the high-frequency components of the grayscale image, since it has a reasonable performance with low computational cost. Then, one or more spots can be identified through a threshold-based technique settled to small objects. In fact, since the center of the disc is a regular screw, imperfections can be wrongly detected as a visual target. In order to overcome this, spots detected very close to the center of the disc are ignored, as well as \"imperfections\" processed as black spots outside the disc (the disc's radius is used to compute the maximum allowed distance);
* Every processed image will result in the computation of the (\(x\),\(y\)) coordinates of the center of the detected spot. Then, this information, along with the name of the processed image and the disc's center, is inserted into a text file. At the end of the processing of the set \(s\), a result file will contain all coordinates of the (correctly) detected spots in each processed image.  
Figure 1: The schematic configuration for the disc in the considered scenario, highlighting the four quadrants of the disc’s circumference.  
When all images are processed, the algorithm moves to the third phase, described in the next subsection.
## 2 Literature Analysis about the Life Cycle Assessment of Carbon Supply Chains
### Life Cycle Assessment of Carbon Capture and Storage Supply Chains
Through a mathematical optimization evaluating the cost and environmental impact of the supply chain, the authors find that increasing carbon dioxide storage beyond the minimum amount significantly lowers cost and environmental impacts in up to 13 out of 16 impact categories (only resource use minerals and metals, land use, and ozone depletion increase while climate change, particulate matter, ionizing radiation, photochemical ozone formation, acidification, terrestrial eutrophication, aquatic freshwater eutrophication, aquatic marine eutrophication, human toxicity cancer effects, human toxicity noncancer effects, ecotoxicity freshwater, water scarcity, and resource use energy carriers decrease). A first more detailed life cycle assessment for these supply chains is proposed by [PERSON] et al. [57], considering a cradle-to-grave analysis with Gabi software. Carbon dioxide is captured with different systems from a supercritical pulverized coal process, using amine, aqueous ammonia, and calcium looping. Then, the considered supply chains are compared. The system boundaries include (a) power plant feed by coal; (b) upstream processes such as extraction and processing of coal, limestone, and solvents used in capture technology, as well as power plant, coal mine, and carbon dioxide pipeline construction and commissioning; (c) downstream processes: carbon dioxide compression, transport, and storage as well as power plant, carbon capture and storage units, coal mine, and carbon dioxide pipeline decommissioning. The results show that amine technology has a lower value for global warming potential but not for all environmental categories: acidification potential, eutrophication potential, or other categories related to human toxicology are better for aqueous ammonia technology. Other impact categories such as ozone depletion potentials are better for calcium looping. [PERSON] and [PERSON] [35] (2015) propose a review of the life cycle assessment works for these kinds of systems. The results suggest that carbon dioxide emissions in a power plant can be decreased by 63-82% per unit of produced electricity. However, there is an increase in acidification and human toxicity. A reduction in global warming potential is also reported by [PERSON] et al. [58]: it is 68-92% for fossil power plants and 39-78% for cement plants. From the above analyses, it is evident that LCAs of CCS supply chains have been conducted in the literature considering different impact categories and not only the global warming potential. However, in this case, a CCS is not able to ensure a reduction in all impact categories and a trade-off should be achieved.