esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 New Type of Solar-Thermal/Cold Gas Propulsion Description  
The operating principle of this new propulsion system is based on the focus of solar light. According to this concept, the working life of thrusters can be increased if additional energy is taken from Sun for the propellant's internal energy increase before its expansion in the nozzles.  
The new propulsion system is composed of a concave mirror having a small service propellant tank placed along its focal line (Figure 3).  
The cold gas (nitrogen, N\({}_{2}\)) is transferred from the main tank with a specific time before expansion. The service tank is a small cylinder made of stainless steel which is painted in black for a total absorption of solar light. When the solar light is focused on the service propellant tank, its temperature increases with several hundred degrees Celsius, thus heating the gas inside.  
During expansion, the propulsion force is higher than in the case of a cold gas because the expansion speed is higher. In this way, an important quantity of gas is saved at every gas expansion for attitude correction. The obtained reaction force is sufficient to change the satellite's attitude, so that it maintains its correct position in space. By heating up the propellant inside the service propellant tank, more corrections of attitude/gas expansions than in the case of classic cold gas propulsion system can be achieved.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 2 Related Works
### Dictionary Learning
#### 2.2.2 Discriminative Dictionary Learning Methods  
The discriminative dictionary learning methods consider the data's label information to enhance the discriminative and classification abilities of the representations. Since the learned dictionaries have a stronger discriminative ability, the discriminative dictionary learning classifiers have a stronger classification ability than sparse representation classifiers. The representative discriminative dictionary learning methods include the Discriminative KSVD (D-KSVD) [20] algorithm, analysis discriminative dictionary learning (ADDL) [21], Deep Dictionary Learning (DDL) [22], Self-expressive Latent Dictionary Pair Learning (SLatDPL) [23], and CEBSR [5] algorithms, among others. However, most of them are single-image-based methods, so they cannot be effectively used in image set classification or video recognition tasks. The CEBSR algorithm was used for image set classification, but it is essentially still a single-image-based method because it used a probe image matrix to model the image set. This leads to long computing time, and the classification performance cannot be guaranteed. In addition, most of these methods need to solve some time-consuming \(l_{1}\) optimization problems, resulting in high time complexity.
## 3 Experimental Results
### Coal Sample M2  
Coal sample M2 was under the experimental condition in which the gas pressure was 0.7 MPa and the confining pressure 3 MPa. The axial stress was loaded until the coal sample M2 was damaged. Then, the permeability evolution in the process of unloading the axial stress and confining pressure and loading the axial stress to the secondary damage of the coal sample M2 was measured. The following curves were obtained, as shown in Figure 5.  
Figure 4: Experimental results of coal sample M1. (**a**) Unloading axial stress; (**b**) unloading confining pressure; (**c**) loading to secondary damage; (**d**) fitting curve of permeability and axial stress during the secondary damage period.  
It can be seen from Figure 5a that after the coal sample M2 was damaged, the permeability increased when the axial stress was unloaded from 15 MPa to 1 MPa, and the increased rate of permeability was slow first and then fast. The permeability was lower than that of coal sample M1, which was under the confining pressure of 2 MPa. When the axial stress was reduced to 1 MPa, the permeability increased to about 0.9 mD, which was about 1.25 times that before unloading the axial stress.  
It can be seen from Figure 5b that when the confining pressure was unloaded from 3 MPa to 1 MPa, the radial restraint force of the coal sample decreased, and the permeability range increased approximately linearly. When the confining pressure was unloaded to 1 MPa, the permeability increased to about 1.15 mD, which was about 1.32 times that before unloading the confining pressure.  
It can be seen from Figure 5c that when the confining pressure of the M2 coal sample was loaded from 1 MPa to 2 MPa, the fractures of the coal sample M2 were closed again, and its permeability was approximately linearly reduced. When the confining pressure was increased by 1 MPa, the permeability was reduced by about 1.17 times.  
It can be seen from Figure 5d that when the axial stress was loaded from 1 MPa to 9 MPa, the coal sample underwent secondary damage. The peak intensity of the primary damage of the coal sample M2 was 2.33 times that of the secondary damage. During the secondary damage process, the change trend of permeability decreased first and then increased. Compared with Figure 5a, it can be seen that the permeability of the coal sample M2 after initial damage was about 0.72 mD, and the permeability minimum value in the secondary damage process was about 0.68 mD. The average permeability in the secondary damage process was higher than that during the initial damage process.
###### Abstract  
Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates.  
G 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Dehydration/Hydration Stability
Calcium oxide, which was present in the initial material, had not been identified anymore; it is very likely that the contact with atmospheric carbon dioxide led to a total conversion of the latter into calcium carbonate (Equation (12)), CaCO\({}_{3}\), already detected on the pristine material. \[\text{Ca}\text{O}_{(\text{s})}+\text{CO}_{2}\text{(g)}\rightarrow\text{Ca} \text{CO}_{3(\text{s})} \tag{12}\]  
The aluminum hydroxide phase, previously detected in CA-SH sample, was no longer observable because of the decomposition during the cycles; in fact, as mentioned above, boehmite (AlO(OH)) was formed, and subsequently, it is possible to reason that this phase was slowly converted into alumina. This suggestion is also supported by the slow hydration kinetic, which aluminum oxides and oxides hydroxides present [40]. SEM images shown in Figure 12 (magnification = 100 k) lead to further considerations regarding the investigated materials. Comparing samples' morphologies before (Figure 6) and after (Figure 11) cycling, some differences were observable. In particular, CA-32H was characterized by rounded nanoparticles around 100-200 nm (calcium hydroxide),  
Figure 11: Diffraction pattern after the five-cycle experiment for CA-32H (red), CA-11H (blue) and CA-SH (orange). (JCPDS reference cards: Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\cdot\)6H\({}_{2}\)O: 24-0217; Ca\({}_{12}\)Al\({}_{14}\)O\({}_{33}\): 09-0413; Ca(OH)\({}_{2}\): 44-1481; Al\({}_{2}\)O\({}_{3}\): 04-2852; CaCO\({}_{3}\): 12-0489). homogeneously distributed, covering hexagonal plates (katoite). In contrast, CA-11H was characterized by nanoparticles, probably mayenite and calcium hydroxide (red arrows), deposited on coalesced hexagonal plates of katoite (black arrows). Again, the smaller particle size after five cycles could be related to the less crystallinity evidenced in Figure 10. CA-SH still showed less uniformity. Large particles were predominantly observed (white arrows) and different structures, as confirmed by XRD analysis (Figure 11), were present. The detected morphological and structural materials' modifications can address the different behavior of the two co-precipitated materials. The absence of decomposition processes of katoite phase in CA-3 EH could be related to the presence of calcium hydroxide impurities: this phase could play a fundamental role in terms of surface basicity by promoting a better thermodynamic stability of katoite, avoiding the formation of mayenite, as described in Equation (11) for CA-11H and CA-SH, thus guaranteeing a larger conversion. At the same time, it is reasonable to assume that the surface basicity imposed by calcium hydroxide is also responsible for the stabilization observed for CA-11H (in which calcium hydroxide is formed during the cycling), and the difference in percentage conversion is due to the formation of mayenite in this second case, which involves a loss of the active portion of the material.
## 3 Proposed Distributed Secondary Control Technique
### Case One: Control Objectives Realization
It should also be noted that using Topology \(X\) will also reduce the communication burden within the DC microgrid, thereby reducing the cost of operation and system implementation. Hence, the cyber layer of our proposed FDSC utilizes Topology \(X\) for efficient data exchange. Finally, in assessing the effectiveness of our proposed FDSC, we present Table 4, demonstrating the superior performance of our controller in attaining the control objectives, as compared to the previously suggested approaches found in the existing literature: conventional secondary control, Q1, which involves tuning the control parameters by trial and error method, secondary supervisory control, Q2, proposed in [8], and distributed consensus secondary control, Q3, suggested in [31]. The results shown in Table 4 indicate that our proposed FDSC, when compared with Q3, has a 96.7% reduction in response time to regulate the bus voltage and a 56% reduction in response time to distribute the current accurately. In comparison to Q2, our proposed FDSC exhibits a significant 94.2% decrease in response time for restoring the bus voltage to its nominal value and a 25% reduction in response time for proper current sharing. Furthermore, in comparison with the conventional secondary control technique, our proposed FDSC outperforms with a noteworthy 95% reduction in response time for voltage restoration to its rated value and a 46.4% decrease in response time for optimal current distribution. Hence, the FDSC proposed in this study is reliable and robust, achieving the fastest convergence for both voltage regulation and current sharing objectives compared to the other controllers.
# uDMA: An Efficient User-Level DMA for NVMe SSDs +
## 5 Experiments and Results
### Experimental Setup  
Table 1 shows the hardware and software environment of the experiment. We conducted a comparative experiment on a physical server with an Intel Xeon (R) Gold 5115 CPU. The CentOS 7.4 operating system and 20.01 SPDK were installed on the server. All performance evaluations were conducted on a commercial Intel Optane 900P Series NVMe SSD with a 280 GB capacity.  
We compared the performance of uDMA with the latest user-level DMA methods implemented in SPDK [13]. We tested the effectiveness of uDMA under two benchmarks: a microbenchmark, perf [27, 28], and a real-word benchmark, RocksDB [29]. perf is a benchmarking tool that can be used for performance tests. It has been widely used to test I/O performance [13, 14]. RocksDB is a key-value and high-performance embedded database. It implements an LSM-tree storage engine to provide a key-value store and reading/writing functions. The configuration of perf and RocksDB were consistent with the official test of SPDK.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 8 Applications
### Medicine  
In cancer research, biosensors are used to analyze changes in the protein contents of cells [99]. Cancer cells use the body's energy and cause a change in how food energy is utilized in the human body. When these cancer cells are multiplied, they give impulse to adjoining organs, nerves and blood vessels, etc., [100]. In biomedical treatment, human fluids such as blood, tears, saliva, and urine can be used as an analyte in which different components of the chosen analyte can be analyzed [101]. In diabetic patients, monitoring the glucose level with blood as a sample causes uneasiness. As an alternative, tears can be used as an analyte in which the glucose level is 50 to 100 times lower than that of blood [102]. For every disease, earlier detection significantly improves the chances of a cure. Conventional techniques for detecting diseases are time-consuming. Additionally, to detect the disease requires a lot of expensive equipment. In recent years, researchers have found that these current biosensors are cost-effective and take less time compared with the conventional techniques. These biosensors are defining nano- or micro-fabrication technologies and the use of optical, electrical, and mechanical transducers.
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 2 Fluid Transient Dynamics Model  
In this study, a transient flow model for fracturing fluid in horizontal wells has been established, taking into account the influence of non-constant friction on pressure transmission. Figure 2 illustrates a variable-flow horizontal well.  
The model is based on the following assumptions:  
1. [label=()]
2. Axial flow only: it is assumed that the fracturing fluid exists only in the axial direction within the wellbore pipeline and does not have any radial flow.
3. Adiabatic flow: the flow of fracturing fluid in the wellbore pipeline does not involve any heat exchange with the external environment, indicating an adiabatic process.  
Figure 2: Diagram of a variable-flow horizontal well.  
1. Ignoring reservoir seepage effects: the influence of reservoir seepage on the fluid flow is neglected in this study.
2. Homogeneous and compressible fluid: The fluid within the pipeline is assumed to be homogeneous and compressible, with the viscosity remaining constant. The non-Newtonian characteristics of the fluid are neglected.
3. Constant cross-sectional area and rigid wall: the cross-sectional area of the pipeline remains constant along its length, and the pipeline wall is considered to be rigid.
4. Ignoring the impact of azimuth: the azimuth remains constant, and only the influence of wellbore inclination on pressure transmission is considered.
## 2 Materials and Methods
### Reference UM-VLC Channel Model
#### 2.3.4 Reference Scattering Model  
The NLOS scattering component \(H_{\mathrm{scatter}}\) is defined in [7; 18] as  
\[H_{\mathrm{scatter}}=\lim_{N\to\infty}\frac{(m+1)A_{R}\,\rho_{s}}{2N\pi}\sum_{ n=1}^{N}\frac{p_{n}}{d_{n}^{2}}\,\Omega_{\mathrm{scatter}}^{(n)}\,\Pi(\theta_{n})\,, \tag{42}\]  
where \(N\) is the number of dust particles or scatterers, which tends to infinity; \(d_{n}\) is the total distance the light travels in its trajectory from the LED up to the \(n\)th scatterer and  
Figure 2: Tilt and rotation angles for the \(w\)th reflector.  
then to the PD; \(\rho_{s}\) is the reflectance of the dust particles; and \(p_{n}\) is the scattering phase function for the \(n\)th scatterer. \(\Omega_{\text{scatter}}^{(n)}\in[0,1]\) is a trigonometric expression which depends on the \(n\)th incident angle at the PD, \(\theta_{n}\), on the relative position, rotation, and tilt of the LED, the \(n\)-scatterer, and the PD and on the scattering angle of the \(n\)th scatterer, \(\phi_{n}\). The function \(\Pi(\cdot)\) accounts for the validity of the \(n\)th light beam, by checking whether it falls in the PD's field of view or not. Namely,  
\[\Pi(\theta_{n})=\begin{cases}1&0\leq\theta_{n}\leq\Theta_{\text{FoV}}\\ 0&\theta_{n}>\Theta_{\text{FoV}}.\end{cases} \tag{43}\]  
Note that \(p_{n}\) depends implicitly on the scattering angle \(\phi_{n}\), and can be expressed as [7; 18]  
\[p_{n}=\frac{k_{s}^{\text{mie}}}{k_{s}}p_{\text{mie}}(\phi_{n})+\frac{k_{s}^{ \text{ray}}}{k_{s}}p_{\text{ray}}(\phi_{n})\,, \tag{44}\]  
where \(k_{s}^{\text{mie}}\) is the scattering coefficient of the Mie scattering dust particles, \(k_{s}^{\text{ray}}\) is the scattering coefficient of the Rayleigh scattering air molecules, \(k_{s}=k_{s}^{\text{mie}}+k_{s}^{\text{ray}}\) is the total scattering coefficient, \(p_{\text{mie}}(\cdot)\) is the Mie scattering phase function, and \(p_{\text{ray}}(\cdot)\) is the Rayleigh scattering phase function.  
The position of the \(N\) scatterers is described by a probability distribution function. The reference channel used in this work uses a distribution contained in a two-dimensional disk around the receiver [7]. Other distributions have been proposed, such as in [18], where a distribution contained in a three-dimensional sphere around the receiver is put forward.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis
### Write Delay Comparison  
Write delay, also referred to as write access time (WAT), is defined as the time interval between the moment the word line voltage reaches 50% of its full swing and the point at which the storage nodes Q and QB intersect [15]. Figure 6 compares the WAT of various SRAM cells, including QCCS, SCCS, SAW16T, HP16T, RHSCC16T, S8P8N, and the proposed EWS-16T cell. As illustrated in the figure, the EWS-16T cell exhibits the shortest write delay among all the evaluated designs, demonstrating superior performance in write operations.  
The proposed EWS-16T employs a dual-access transistor structure composed of both PMOS and NMOS transistors, enabling simultaneous and fast control of the storage nodes (Q, QB) and the internal nodes (S0, S1). This bidirectional conduction structure facilitates rapid voltage inversion during write operations, effectively reducing the WAT. Furthermore, the cell is initialized with a weak '1' at the storage node and a weak '0' at the internal node, allowing reliable switching with minimal charge disturbance. In addition, the PMOS pull-up transistors connected to the storage node are sized to be 1.7\(\times\) wider than the access transistor, making the storage node weaker and thus allowing the access transistor to discharge the '1'-storing node more effectively. According to experimental results, the proposed EWS-16T achieves a WAT of 22.11 ps, which is 1.89\(\times\), 1.95\(\times\), 1.54\(\times\), 2.17\(\times\), 1.69\(\times\), and 1.29\(\times\) faster than QCCS, SCCS, SAW16T, HP16T, RHSCC16T, and S8P8N cells, respectively.
# Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale X-ray Security Images
## 6 Conclusions  
In this paper, we evaluated the impact of GAN-based image augmentation, also called image synthesis, in the detection performance on a practical X-ray security image dataset. We described the details of our image synthesis approach, which takes into consideration the internal distribution of threats in the existing training set such that we do not introduce bias during the learning phase.  
We found that image synthesis significantly improves the FPR by up to 15.3% on the SIXray100 subset, which closely resembles a practical X-ray security dataset. This performance gain was due to the model gaining more information about the differences between normal and threat objects from the synthesized images. The FPR is further improved by up to 19.9% on the SIXray100 subset by combining image synthesis with image transformation, which enlarged the dataset even more and generated new perspectives of both the normal and threat objects. On the other hand, we observed that using any image augmentation approach does not significantly impact the TPR, especially when the model already achieves relatively high TPR. Overall, the best performance is achieved when image transformation is used simultaneously with any image synthesis approach.  
In conclusion, GAN-based image augmentation is an effective approach to combat the class-imbalance problem. When used as a supplementary automation tool, the significant reduction of the FPR enables human screeners to trust more on the predictions of the detection algorithm, which in turn increases the efficiency of the security procedures. Inadvertently, this improvement did not come at the expense of the TPR, which remained relatively high, making threat detection models more suitable in practical applications. The potential benefits can be further maximized by using GAN-based augmentation together with other approaches.  
Future research in this field includes developing an automatic and more realistic threat image projection models that also considers the differences in the orientation, color scheme, and other properties of threat objects with respect to the background, which could significantly improve not only the FPR but also TPR; hence, the overall performance of a detection model.  
Conceptualization, J.K.D. and Y.-J.J.; methodology, J.K.D. and Y.-J.J.; software, J.K.D.; validation, Y.-J.J.; formal analysis, J.K.D. and Y.-[PERSON]; investigation, J.K.D. and Y.-[PERSON]; resources, J.K.D.; data curation, J.K.D.; writing--original draft preparation, J.K.D.; writing--review and editing, J.K.D. and [PERSON]-[PERSON]; visualization, J.K.D.; supervision, Y.-J.J.; project administration, Y.-J.J.; funding acquisition, Y.-J.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Kwangwoon University and by the MISP Korea, under the National Program for Excellence in SW (2017-0-00096) supervised by IITP.  
The authors declare no conflict of interest.