esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress and Applications of 2D Antimonene
## 3 Practical Applications
### Cancer Therapy  
Due to the high specific surface area and good photothermal effect, antimonene quantum dots show good ability of drug delivery or photothermal therapy (converting light energy into heat energy under light irradiation can induce the thermal ablation of cancer cells, thus killing malignant tumors) in the field of cancer therapy.  
[PERSON] et al. [67] proposed ultra-thin antimonene nanoparticles as a novel radiosensitizing agent that achieves effective chemoradiotherapy effects by inducing a strong oxidative stress response and significant in vivo radiotoxicity.  
The practical use of metal radiosensitizers is hampered by a number of practical limitations. First, some nanoparticles with low clearance rates, such as silver nanoparticles, can cause neurotoxicity, genotoxicity or cellular dysfunction in vivo. Second, the manufacture of multifunctional therapeutic radiosensitizers always requires the addition of materials in multiple steps, leading to unpredictable safety issues in vivo, such as undefined biotransformations. Antimonene quantum dots and nanoparticles were prepared using ethanol as solvent by conventional liquid-phase separation method, and PLGA (lactic co-glycolic acid) was prepared by emulsifying solvent of oil in water to be used as the carrier of antimonene quantum dots and nanoparticles. The carrier improves the dispersibility of antimonene quantum dots in physiological environment and the stability of antimonene nanosheets. While antimonene quantum dots and antimonene nanosheets can be oxidized to Sb\({}_{2}\)O\({}_{3}\) by X-ray as shown in Figure 14a, Sb\({}_{2}\)O\({}_{3}\) produces ROS with specific cytotoxicity to cancer cells as shown in Figure 14b. Under X-ray irradiation, AMQD and AMNSs with more surface defects promote electron transfer, produce ROS and mitochondrial rupture in cancer cells, and inhibit tumor formation through hypoxia regulation. The excellent absorption of the antimonene material in the near infrared region (808 nm) makes itself an ideal photoacoustic (PA) reagent for the detection of deep tumors.  
In addition, antimonene [68] can also be used as a contrast agent to monitor somatic cells and image tumors in vivo. In the near infrared window of 800 nm, the molar extinction coefficient of antimonene nanoplate is 2.24 \(\times\) 10\({}^{9}\), which is higher than that of other small molecules. The results show that antimonene nanosheets are excretable and biocompatible in nude mice, that is, antimonene nanosheets can be used as an excellent photoacoustic contrast agent.  
Figure 14: Mechanism of antimonene in the treatment of cancer. Reprinted with permission from Ref. [67]. Copyright 2019, John Wiley and Sons.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### SADRL Applied to Distributed Agents in a Multi-Agent Environment
#### 6.1.6. [PERSON]'s SADRL Approach in a Multi-Agent Environment  
In [6], [PERSON] et al. embedded SADRL in distributed agents, which are the source and intermediate routers, to find the optimal route to the destination router independently based on routing metrics (i.e., the number of hops and reliability of a route) (A.3). The agents maximize their individual rewards in a competitive (X.2.1) manner. The key feature is that distributed agents observe local states and learn from historical knowledge (e.g., previous actions), rather than exchanging knowledge with neighboring agents, which reduces the signaling overhead (O.4), and hence increasing scalability (O.5). The proposed approach addresses the challenges of: (a) ultra-densification (C.1) caused by a large number of node pairs; (b) high dynamicity (C.2) due to the dynamic condition of the operating channels (i.e., the changes of the traffic patterns); and (c) high dimension (C.3) with large state and action spaces.  
At each transmission opportunity, the source router \(i\) represents state \(s_{t}^{i}\) with a two-tuple information: (a) the destination router of the current packet and the next packet; and (b) the historical actions (i.e., the selected action executed right before the current action) of the source router \(i\). The action \(a_{t}^{i}\) represents a selected neighboring (or next-hop) router \(j\). The reward \(r_{t+1}^{i}(s_{t+1}^{i})\) represents the total queuing time at router \(i\) and the transmission time to the selected next-hop router. Each agent \(i\) find the optimal route to minimize the average packet delivery time. The traditional SADRL approach has been shown to reduce end-to-end delay (P.3).
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail
### Modeling  
The term model is used with double meaning throughout the compressor. At first, it is used to denote the unit of the algorithm that outputs a probability that will participate in the mixing phase. One can interpret this as an \"elementary\" model. The second meaning is the collection of units that are modeling a given type of data. The output of such models is, evidently, a collection of probabilities. Example models are TextModel (for language-specific language stemming and word modeling), MatchModel (for repeatable long matches of data), RecordModel (for data structured in records), SparseModel, JpegModel (for specific jpeg data), WavModel, ExeModel, DmcForest (a collection of dynamic Markov coding models), XmlModel, PpmModel (various order prediction by partial matching), ImageModels (for different bit depth image data), and many more. One or more of this type of model is selected according to the input stream type and compression parameters.  
It is outside of the scope of this paper to explain models unrelated to image compression. These can be further detailed in a general compression paper.  
Figure 1: PAQ8 Image compression diagram.
## 3 Knowledge-Guided Parallel Hybrid Local Search Algorithm
### Knowledge-Guided Initial Solution Construction  
The quality of the initial solution significantly influences the subsequent performance of the algorithm. A knowledge-based initial solution construction method is employed to speed up the search. This knowledge-guided initialization method integrates multiple heuristic rules derived from classical satellite scheduling problems to reduce ineffective searches in local search algorithms.  
In constructing the initial solution guided by knowledge, four heuristic rules are used to generate the initial solution: visible time windows (VTW), task start times, task execution times, and task profits. Tasks are sorted according to the sequence of these four heuristic rules, and the scheduled tasks are inserted into the empty schedule individually. If the task does not violate the constraints, the task is started. Otherwise, the task is canceled.
## 3 Experimental Results and Analyses
### Horizontal Deformation
#### 3.3.2 Horizontal Deformation along the Depth
In Figure 23a, the horizontal deformation of pile reaches the maximum negative value at pile head when the displacement load is less than \(-10\) mm, and gradually diminishes as the pile depth increases. The distribution of deformation is a little similar to the test and traditional theory of soil-pile. However, when the displacement load rises to \(-10\) mm, a turning point appears at the pile depth of \(0.4\) m (\(2.58b\)), as shown in Figure 23b. With the increase of displacement load, the horizontal deformation at the depth of \(0.4\) m declines and changes from the negative to the positive, and finally goes up to \(+11.83\) mm when the displacement load reaches \(-16\) mm. The phenomenon in Figure 23b is caused by the accumulative deformation. Figure 21: Distribution of horizontal deformation of specimen under positive displacement loads. Figure 22: Void formation. (**a**) Process of void formation. (**b**) Void behind the abutment.
## 3 Methodology
### TimeGAN
#### 3.3.2 Generators and Discriminators  
The TimeGAN model's generator randomly selects vectors from the vector spaces \(Z_{S}\) and \(Z_{X}\) of known distributions rather than producing synthetic outputs directly in the feature space, and inputs them into the embedding space, then generates the latent distributions \(\mathrm{H_{S}}\) and \(\mathrm{H_{X}}\). Here, \(\mathrm{g_{S}}:Z_{S}\to\mathrm{H_{s}}\) and \(\mathrm{g_{X}}:H_{S}\times H_{X}\times Z_{S}\to H_{X}\) represent the generator networks for generating latent states of static features \(\mathrm{H_{S}}\) and latent states of time features \(\mathrm{H_{X}}\), respectively. The generator \(\mathrm{g}:Z_{S}\times\Pi_{\mathrm{t}}\mathrm{Z_{X}}\to\mathrm{H_{s}}\times \Pi_{\mathrm{t}}\mathrm{H_{X}}\) is implemented through a recursive network that transforms static and temporal random vectors into feature latent encodings \(\mathrm{\hat{h}_{S}}\) and \(\mathrm{\hat{h}_{1:T}}=\mathrm{g}(Z_{S},Z_{1:T})\):  
\[\hat{h}_{S}=g_{S}(z_{s}) \tag{13}\]  
Figure 7: Structure of TimeGAN ([PERSON] et al. [39]).  
\[\hat{h}_{t}=g_{X}\left(\hat{h}_{S},\hat{h}_{t-1},z_{t}\right) \tag{14}\]  
The random vectors \(z_{S}\) are sampled from known vector spaces \(z_{S}\), and \(z_{t}\) is generated stochastically using random sampling. In this model, it is assumed that \(z_{S}\) and \(z_{t}\) follow Gaussian distributions and a Wiener process, respectively.  
Similarly, the discriminator processes from the embedding space. The discriminant function \(d:H_{S}\times\Pi_{t}H_{X}\rightarrow[0,1]\times\Pi_{t}[0,1]\) takes in the potential encoding of static features and temporal features and returns categories \(\overset{\sim}{y}_{S}\), \(\overset{\sim}{y}_{1:T}=d\left(\overset{\sim}{h}_{S},\overset{\sim}{h}_{1:T}\right)\), with h- representing real states and \(\overset{\sim}{h}\)- representing generated states. These are divided into output layer classification functions:  
\[\overset{\sim}{y}_{S}=d_{S}\left(\overset{\sim}{h}_{S}\right) \tag{15}\]  
\[\overset{\sim}{y}_{t}=d_{X}\left(\overset{\sim}{u}_{t},\overset{\sim}{u}_{t}\right) \tag{16}\]  
where \(\overset{\rightarrow}{u}_{t}=\overset{\rightarrow}{c}_{X}\left(\overset{ \sim}{h}_{S},\overset{\sim}{h}_{t},\overset{\rightarrow}{u}_{t-1}\right)\) and \(\overset{\leftarrow}{u}_{t}=\overset{\leftarrow}{c}_{X}\left(\overset{ \sim}{h}_{S},\overset{\sim}{h}_{t},\overset{\leftarrow}{u}_{t+1}\right)\) represent forward and backward latent state sequences, respectively, while \(\overset{\rightarrow}{c}_{X}\) and \(\overset{\leftarrow}{c}_{X}\) are the recursive functions. There is no restriction on the structure of the discriminator, and the standard recursive formulation was used in this study.
## 2 Materials and Methods
### _f_APAR\({}_{\text{green}}\) _and_ APAR\({}_{\text{green}}\) _Calibration_  
At each of the 12 calibration points (Section 2.2 above; numbered circles, Figure 2), we also measured incident, transmitted and reflected PAR (photosynthetically active radiation) using a light bar (AccuPAR LP-80, Decagon, Pullman, WA, USA) as well as NDVI\({}_{\text{680,800}}\) from spectrometer measurements (Figure 2). At each site, light bar measurements consisted of downwelling PAR above the canopy (S), upwelling PAR reflected from the canopy (R), downwelling PAR below the canopy (T), and upwelling PAR below the canopy (U). Ten measurements were made at each calibration point and averaged, then _f_APAR was calculated, following the procedure described in [PERSON] _et al._ [44] and the equation:  
\[f\text{APAR}\,=\,1-t-r\,+\,tr_{s} \tag{3}\]  
where \(t\) is the fraction of radiation transmitted through the canopy (\(t\) = T/S), \(r\) is the fraction of radiation reflected by the canopy (\(r\) = R/S), and \(r_{s}\) is the reflectance of the soil surface (\(r_{s}\) = U/T).  
These _f_APAR measurements were multiplied by the green:total biomass fraction to calculate a _f_APAR\({}_{\text{green}}\) value (fraction of PAR absorbed by green canopy material) for comparison with the proxy NDVI values (phenology stations) and NDVI\({}_{\text{680,800}}\) values collected at each harvest site (Figure 2). To calculate the APAR\({}_{\text{green}}\) term of the LUE model (Equation (2)), we derived the following NDVI- _f_APAR\({}_{\text{green}}\) relationships from the _f_APAR calibration sites:  
\[f\text{APAR}_{\text{green}}\,=\,(0.8799\times\text{NDVI}_{\text{680,800}})-0.139 4\,(R^{2}\,=\,0.79) \tag{4}\]  
\[f\text{APAR}_{\text{green}}\,=\,(1.3916\times\text{proxy NDVI})-0.3603\,(R^{2} \,=\,0.82) \tag{5}\]  
\[f\text{APAR}_{\text{green}}\,=\,(0.9879\times\text{MODIS\,NDVI})-0.2131\,(R^{2} \,=\,0.79) \tag{6}\]  
In these equations, proxy NDVI and MODIS NDVI were simulated from narrowband reflectance (as described in Section 2.4 below). In the final model, we used Equation (5) (with proxy NDVI) since it yielded the best calibration (highest \(R^{2}\)). In accordance with Equation (2), continuous APAR\({}_{\text{green}}\), was calculated as the product of PAR (from phenology station) and _f_APAR\({}_{\text{green}}\) values.
# Rosemary Leaf Extract Inhibits Glycation, Breast Cancer Proliferation, and Diabetes Risks
## Materials and Methods
### Animal Experiment and Blood Sample Analysis  
Healthy male Sprague-Dawley (origin) rats weighing 190 \(\pm\) 10 g were used in the animal experiment. Twenty-four rats were divided into three groups with eight in each group as normal control (NC), diabetic control (DC), and diabetic group with rosemary extract treatment (DRE) groups. All the rats were caged under the conditions of humidity (55 \(\pm\) 5%), temperature (at 23 \({}^{\circ}\)C), and light (12/12 h light/dark cycle) with free access to food and water. After being fed with a basic diet for one week, the rats were fasted for 12 h. The rats in the DC and RE groups were treated by intraperitoneal injection of streptozotocin (STZ) at a dose of 45 mg/kg body weight to induce hyperglycemia stress [18]. The rats in the NC and DC groups were still fed with the basal diet for 6 weeks. The rats in the RE group were administered for receiving rosemary extract based on 0.2 g/kg of body weight through oral gavage each day for the 6 consecutive weeks.  
Blood was then collected from the rat's tail vein in order to measure the concentration of glucose weekly by using a glucose assay kit (GlucoDr, Germany). After 6 weeks, the animals were all sacrificed and blood samples were drawn by heart puncture. After being centrifuged at 1500 g for 10 min, theserum of the blood sample was separated and stored for further analysis. The concentrations of low density lipoprotein (LDL), high density lipoprotein (HDL), triglycerides (TG), cholesterol (CHO), and the activities of liver enzymes including alkaline phosphatase (ALP), glutamic oxaloacetic transaminase (GOT) and glutamic pyruvate transaminase (GPT), as well as antioxidant enzyme superoxide dismutase (SOD), anti-oxidation capacity (AOC), malonaldehyde were determined by commercial assay kits. The procedures in the animal experiment were approved and complied with the Chinese Code of Practice for the Care and Use of Animals for Scientific Purposes.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### End-to-End Assessment
#### 4.3.3 Applicability Considerations  
On a final note, as referred in [49; 50], various use cases have defined specific requirements for maximum latencies. The most stringent among them are emergency services, such as pre-crash warnings, which require a 50 ms maximum latency. In comparison, most other use cases require a maximum latency of 100 ms. With this understanding and examining the obtained results, significant conclusions can be drawn.  
The median E2E values, as shown in Table 11, do not surpass \(\sim\)47 ms. This indicates that the developed approach aligns well with the requirements of many use cases. However, focusing solely on median values does not provide a complete picture. Therefore, the highest E2E time was also calculated, representing the worst-case scenario regarding latency and computational measurements.  
The highest E2E time was encountered in the communication flow smartphone X \(\rightarrow\) OBU using the DLAPP protocol, reaching an E2E time of approximately 190 ms. Nonetheless, it is important to note that these values are considered outliers. Outliers were identified using the interquartile range (IQR) method, specifically, values above \(Q3+1.5~{}\times~{}IQR\) or below \(Q1-1.5~{}\times~{}IQR\), as illustrated in the box plot in Figure 19.  
Lastly, the same analysis was repeated, i.e., considering maximum values but now excluding outliers. In this case, the maximum E2E time observed was 86 ms in the smartphone X \(\rightarrow\) OBU communication flow using the MFSPV. This means that, when assuming the worst-case scenario while excluding outliers, the results obtained in this study still remain at 14% below the maximum latency requirements for many use cases [50], such as automated shuttle remote driving [49].
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
###### Abstract  
The amount of waste generated by society is constantly increasing. Consequently, there is a need to develop new and better methods of treating it. A significant part of municipal waste is biowaste, which can be treated as a source of valuable resources such as nutrients, organic matter, and energy. The present work aims to determine the properties of the tested household biowaste and the possibility of using it as feedstock in slow pyrolysis to obtain biochar. The slow pyrolysis process of the biowaste was carried out in an electrically heated Horizontal Tube Furnace (HTF) at temperatures of 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C in a nitrogen atmosphere. The analysis showed that depending on the type and composition of the biowaste, its properties are different. All the biowaste tested has a high moisture content (between 63.51% and 81.53%), which means that the biowaste needs to be dried before the slow pyrolysis process. The characteristics of kitchen biowaste are similar to those of food waste studied by other researchers in different regions of the world. In addition, the properties of kitchen biowaste are similar to those of the typical biomasses used to produce biochar via slow pyrolysis, such as wood, almond shells, and rice husks. Both kinds of garden biowaste tested may have been contaminated (soil, rocks) during collection, which affected the high ash content of spring (17.75%) and autumn (43.83%) biowaste. This, in turn, affected all the properties of the garden biowaste, which differed significantly from both the literature data of other garden wastes and from the properties of typical biomass feedstocks used to produce biochar in slow pyrolysis. For all biowaste tested, it was shown that as the pyrolysis temperature increases, the yield of biochar decreases. The maximum mass yield of biochar for kitchen, spring garden, and autumn garden biowaste was 36.64%, 66.53%, and 66.99%, respectively. Comparing the characteristics of biowaste before slow pyrolysis, biochar obtained from kitchen biowaste had a high carbon content, fixed carbon, and a higher HHV. In contrast, biochar obtained from garden biowaste had a lower carbon content and a lower HHV.  
household biowaste; pyrolysis; biochar; biomass; waste; garden waste +
Footnote †: journal: Applied sciences
## Appendix A Explanations of Top Intrusion Features in XAI-IDS  
_Appendix A.1. Detailed Explainability of Top Intrusion Features in RoEduNet-SIMARGL2021 Dataset_  
We can explain the sample analyzed by LIME using the top five features. We first give detailed definitions of each of these features below.  
**Explaining Top Features from a Security Point of View:**  
1. [label=(0)]
2. **TCP_WIN_SCALE_IN:** This feature is relevant because certain port scanning techniques involve manipulating TCP window scaling factors to gather information about open ports or available services on a target network. A high or unusual value of TCP_WIN_SCALE_IN could indicate such activity.
3. **TCP_FLAGS:** The TCP flags can provide insights into the nature of network traffic. In the case of PortScan or Network Service Discovery, specific flag combinations (such as SYN-only packets) are commonly used to scan for open ports. The presence of specific TCP_FLAGS patterns can indicate port scanning behavior.
4. **TCP_WIN_MIN_OUT:** The minimum window size advertised by the sender (source) can provide information about the behavior of the scanning activity. Port scanners may use specific window sizes to optimize their scanning, and unusual values of TCP_WIN_MIN_OUT suggest scanning behavior.
5. **TCP_WIN_MS_IN:** The Maximum Segment Size (MSS) used by the receiver in a TCP connection can offer insights into the scanning activity. Port scanners may manipulate the MSS value to gather information about the target network's characteristics. Unusual or unexpected values of TCP_WIN_MSS_IN could indicate scanning behavior.
6. **TOTAL_FLOW_EXP:** This refers to the number of flows exported so far. A high number of TOTAL_FLOW_EXP can be used as an explanation feature to indicate DoS attacks in which the attacker floods the network with many packets. It can also be used as an error troubleshooting tool to catch wrong biases in the AI model.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 1 Introduction
However, as the physiological study of the human eye is still an open problem, such methods sometimes lead to image degradation and color anomalies. The color consistency of remote sensing images needs to be achieved according to the image features. Since the HY-1C satellite was recently launched and the Antarctic images feature a high dynamic range and high contrast, the direct application of previous methods cannot obtain good processing results. For previous methods, much manual operation is still necessary. In this paper, according to the characteristics of the HY-1C CZI instrument for Antarctica, a novel color consistency processing framework is proposed to address the problems of large color differences and uneven lighting in the image mosaics. There are two steps in the proposed framework: single-view consistency processing and multi-view consistency processing. Firstly, the individual images are processed to achieve color consistency, based on mask theory. Auxiliary masks are obtained by simple clustering and used as semantic information to adjust the filter functions adaptively. The low-frequency information extraction of the scene segmentation is obtained by the use of a Gaussian filter with an auxiliary mask. After the color consistency processing of each image, a color transfer method is introduced for adjacent images to be mosaicked, to achieve color consistency for multiple images. An evaluation method for the color consistency of remote sensing images is also introduced, which includes both subjective and objective evaluations. In summary, the proposed method takes into account the consistency in both a single image and between different images.
# Defect Detection for Wear Debris Based on Few-Shot Contrastive Learning
## 3 Method
### Overall Architecture  
Compared with Baseline, we jointly optimize model with Aligned Contrastive Loss and Multi-Scale Fusion, as shown in Figure 1. Specifically, positive pairs which query image and support image are in the same category, and negative pairs which query image and support image are in the different category were constructed. We build a weight-shared framework that consists of two branches. The query branch of the weight-shared framework is a Faster RCNN network, which contains RPN and detector. The Aligned Contrastive Loss promotes instance level intra-class compactness and inter-class variance. The Multi-Scale Fusion fuses the features of query branch in different resolutions with the information from support branch. We utilize this framework to train the matching relationship between support and query features, so as to make the network learn general knowledge among the positive/negative pairs.