esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Data-Driven Models  
Data-driven models learn pedestrian movement characteristics from the real world, and the model optimizes parameters based on real-world datasets to improve simulation performance. [PERSON] et al. [9] proposed a data-driven method that fills a virtual road network with real traffic flow and calculates the speed of each agent in each frame based on real data. [PERSON] et al. [10] introduced a data-driven model that defines an energy function based on the needs of traffic interaction, where a smaller energy function indicates better simulation results. The model selects the optimal speed and behavior from real datasets to minimize the energy function, which can better replicate real traffic interactions. However, this method's simulated trajectories are overly dependent on the optimized dataset, and due to the limited data on direct pedestrian-vehicle interactions, the model features fewer pedestrian-vehicle interactions in its simulations. The pedestrian trajectories lack validation and cannot be directly applied to pedestrian-vehicle interaction behavior.  
With the development of artificial intelligence, deep learning-based trajectory prediction has begun to be applied in pedestrian-vehicle interaction models. [PERSON] et al. [11] and [PERSON] et al. [12] applied optimized LSTM to mixed-interaction simulations, using the LSTM model to extract latent features of trajectories during forward propagation and optimizing the prediction of the next position during backward propagation, achieving better trajectory prediction capabilities than expert models. However, such methods require large datasets and significant computational power, meaning the model cannot run in real time and cannot meet the requirements of autonomous driving simulation tests.  
Compared to expert models, data-driven models have strong capabilities in pedestrian trajectory prediction but require large datasets for learning and have high demands on data optimization. However, collecting mixed-traffic data that include pedestrian-vehicle interactions remains a challenging and costly task. Furthermore, data-driven models generally cannot run in real time, which makes them unsuitable for autonomous driving testing needs. Expert models, on the other hand, do not require large datasets for learning, have lower computational demands, and are more easily run in real time, making them better suited for autonomous driving platform testing.
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 3 Data Acquisition and Preprocessing
### Remote Sensing Data Acquisition and Preprocessing
#### 3.1.1 Vegetation Indices  
Vegetation indices were extracted from the clipped Landsat 9 imagery of Xiwuzhumuqin Banner. First, reflectance values from each band, including red, green, blue, near-infrared, and shortwave infrared, were obtained as canopy reflectance data. Vegetation indices (as shown in Table 1) were then calculated.  
A total of twelve vegetation indices were initially calculated (Table 1), but seven were selected for model development based on Pearson correlation analysis and ecological relevance. Specifically, indices with weak correlations (R \(<\) 0.53, \(p\)\(>\) 0.01) such as RGBVI (R = 0.482) were excluded, while those with significant positive correlations (R \(\geq\) 0.53, \(p\)\(<\) 0.01)--including MVI (R = 0.637), Clg (R = 0.577), and GNDVI (R = 0.558)--were retained. This selection ensures that the input variables are both statistically significant and ecologically meaningful, reflecting key aspects of vegetation photosynthesis, canopy structure, and stress responses.
## 7 Discussion  
This section aims to examine the constraints of our methodology and provide insights into potential directions for future development. During the operation of FLuaScan, it leverages existing software tools like Semgrep and Neo4j. However, the methodology employed in FLuaScan is entirely innovative, and no method described in existing literature can do the same. At present, FLuaScan exclusively focuses on supporting the Lua programming language. However, the fundamental theories discussed in this paper regarding vulnerability mining are applicable to various programming languages. Furthermore, the semgrep we employ demonstrates the capability to support multiple programming languages, facilitating seamless portability to other script languages. After conducting surveys, we discovered that many IoT manufacturers also employ languages such as ASP and PHP in their product development. Therefore, enhancing FLuaScan to support multiple scripting languages is our next research direction. Meanwhile, some manufacturers are developing products by packaging compiled Lua bytecode as firmware rather than directly using Lua source files. The absence of symbol information in this scenario poses numerous challenges for static analysis. The next step of our research is to find ways to improve FLuaScan to adapt to Lua programs without sources.
## 5 Conclusions and Future Work  
This paper proposes an error-controlled lossy compression scheme and designs a multi-level indexing strategy based on a quadtree to enhance data management and query efficiency. This method first uses the traditional JPEG compression algorithm for multi-level compression, then manages the compressed data using a quadtree structure, and finally uses the ConvNeXt model to enhance the restoration of compressed data, thereby reducing reconstruction errors. Through evaluation of compression ratio, PSNR, and practical application scenarios, it is found that compared to traditional lossless compression methods,  
Figure 11: The results of eddy detection before and after compression.  
this method achieves a higher compression ratio while maintaining the same reconstruction quality. The experimental results also show that the compressed and reconstructed data perform well in subsequent analysis and visualization, validating the effectiveness of the data compression method. In terms of multi-level indexing, the experiments demonstrate that the proposed indexing strategy is more efficient than direct queries, greatly improving data management and query efficiency. Notably, at lower compression ratios, the data reconstruction quality of our proposed method is inferior to existing methods (e.g., SZ). This is because the uniform partitioning of the quadtree leads to compression being significantly affected by data similarity. Therefore, in the future, we plan to expand the dataset to encompass more diverse and complex data types. Based on the characteristics of the data, we aim to use other tree structures for indexing to achieve dynamic partitioning and compression.  
Conceptualization, Y.T.; methodology, B.S. and Y.F.; validation, X.P. and Y.F.; formal analysis, R.G.; investigation, R.Z.; data curation, Y.F. and R.Z.; writing--original draft preparation, B.S. and Y.F.; writing--review and editing, R.G. and Y.T.; visualization, X.P. and R.Z.; supervision, B.S.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Project No. 62302211). All training and testing data we used are from \"Copernicus Marine Environment Monitoring Service\" ([[https://data.marine.copernicus.eu/products](https://data.marine.copernicus.eu/products)]([https://data.marine.copernicus.eu/products](https://data.marine.copernicus.eu/products)) (accessed on 3 July 2023)). The authors declare no conflicts of interest.
## 3 Kernel Density Estimation based on Non-Uniform Quantization
### Kernel Density Estimation based K-means Quantization (KDE-KM)  
The proposed kernel density estimation based k-means quantization (KDE-KM) obtains the probability density function by performing the kernel density estimation from the original weights. The KDE-KM is then performed using sampled data from the probability density function that has the same characteristics of the original data, and it is possible to quantize more efficiently because it performs calculations on a much smaller number of data. Below is the pseudocode of the proposed KDE-KM methodology.  
Algorithm 1 shows the proposed KDE-KM process. The proposed method estimates the \(PDF^{0}\) from the original weights \(W^{0}\) and then \(PDF^{0}\) samples a set of sampled data \(D^{s}\) by using the number of samples \(N\), which is less than the number of original weights \(W^{0}\). The proposed method can reduce the time complexity of quantization by using sampled data \(D^{s}\). In this case, \(K\) is set to \(2^{\text{bit}}\). After the clustering is finished, it assigns all the original weights \(W^{0}\) to a specific cluster \(c^{i}\), and assigns \(\mu^{i}\), which is the cluster centroid of \(c^{i}\) as the quantization value to the original weights to get \(W^{q}\).  
```
0:\(W^{0}\), \(q_{bit}\), \(N\), \(E\)
0: original weights \(W^{0}\), target quantization bit \(q_{bit}\), the number of sampling data \(N\), the number of maximum iteration \(E\)
0:\(W^{q}\)
0:quantized weights \(W^{q}\)
0:Initialization:\(K\), \(C\), \(M\)
0: The number of cluster \(K=2^{\text{bit}}\), clusters \(C=\left\{c^{1},\ c^{2},\ \dots,\ c^{k}\right\}\), cluster centroids \(M=\left\{\mu^{1},\mu^{2},\ \dots,\ \mu^{k}\right\}\)
1:\(PDF^{0}\gets KDE(W^{0})\) // kernel density estimation using original weights
2:\(D^{s}\gets PDF^{0}(N)\) //sampling \(N\) data using probability density function
3:for\(\text{e}=1\) to \(E\)do
4:\(c^{i}\leftarrow\operatorname*{argmin}\limits_{c^{i}}\lVert\mu^{j}-d_{i} \rVert^{2}\), for \(d_{i}\in D^{s}\) // assign \(D^{s}\) to clusters in \(C\)
5:for\(\text{j}=1\) to \(K\)do
6:\(\mu^{j}\leftarrow\frac{1}{|c^{i}|}\sum\limits_{i=1}^{|c^{i}|}d_{i}^{j}\), where \(d_{i}^{j}\in D^{s},c^{j}\) // update cluster centroids \(M\)
7:\(c^{i}\leftarrow\operatorname*{argmin}\limits_{c^{i}}\lVert\mu^{j}-w_{i}^{q} \rVert^{2}\), for all \(w_{i}^{q}\in W^{q}\) // assign all original weights \(W^{0}\) to a cluster
8:for\(\text{j}=1\) to \(K\)do
9:for\(\text{i}=1\) to \(c^{k}\)do
10:\(w_{i}^{j}\leftarrow\ \mu^{j}\), where \(w_{i}^{j}\in W^{q}\), \(c^{j}\) // quantize the original weights \(W^{0}\)
11:\(W^{q}\gets W^{0}\)// set quantized weights \(W^{q}\)
12:return\(W^{q}\)
```  
**Algorithm 1** KDE-KM  
Figure 2: Overview of weights quantization using the kernel density estimation.
## 3 Results and Discussion
### Role of Hydrogen Bonds  
It is known that hydrogen bonds help stabilize the protein molecule and, especially in bR, hydrogen bonds formed in the neighborhood of the retinal have a significant impact on the interface between bR and graphene. This paper is concerned with how this effect has a positive influence on the yield of the photocycle and its branched state. The criteria used for the hydrogen bonds involved a cut-off distance of 3.0 A between the atoms and a cut-off angle of 20 degrees. The total number of H bonds formed by bR and within 5 A of retinal with respect to time were analyzed. More H bonds were formed in bR-graphene (Figure 3b) compared to the bR control (Figure 3a) during the first 20 ns. However, for the rest of the simulation, the average number of H bonds formed were similar in both cases. The considerable variation of H bonds in bR-graphene during the initial period of interactions indicated the rapid conformational changes within bR exposing the non-polar amino acid residues to be adsorbed on the surface of graphene. To understand the spatial distribution of hydrogen bonds around the retinal site within the adsorption distance of 5 A, the number of hydrogen bonds around the retinal active site were analyzed and found to be twice as much in the presence of graphene (Figure 3d) compared to bR control (Figure 3c) specifically after 40 ns. This reveals the time it took the adsorption onto graphene to affect the dihedral changes in the retinal. It is also known that water is a mediator in the effective  
Figure 2: Stability analysis of bR and graphene during the 100 ns simulations: (**a**) Root Mean Square Deviation of bR with and without graphene; (**b**) Root Mean Square Deviation of graphene. (**c**) Root Mean Square Deviation of Retinal in the presence and absence of graphene; (**d**) Distance between the center of masses of bR and graphene.  
function of the bR protein, and as shown in Section 3.7, these hydrogen bonds around the retinal are also regulated by the number of water molecules in the vicinity of the retinal. A detailed 3D spatial distribution of the hydrogen bonds for the entire bR molecule in the presence and absence of graphene is presented in the supplementary Figure S7.
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
###### Abstract  
A new off-axis optical design alternative to that of the GRACE Follow-on mission for future NGGM missions is considered. In place of the triple-mirror assembly of the GRACE Follow-on mission, a laser retro-reflector is instead generated by means of lens systems. The receiving (RX) beam and transmitting (TX) beam are enforced to be anti-parallel by a control loop with differential wavefront sensing (DWS) signals as readout, and a fast-steering mirror is employed to actuate the pointing of the local beam. The tilt-to-length (TTL) coupling noise of the new off-axis optical bench layout is carefully studied in the present work. Local TTL originated from piston noise as well as assembly and alignment errors of optical components are studied. Effort is also made to have an in depth understanding of global TTL due to relative attitude jitter between spacecraft. The margin of TTL noise in the position noise budget for laser ranging is examined. With an open loop control of the offset between the reference point of the optical bench and the centre of mass of a satellite, the TTL noise of the new off-axis optical bench design may be suppressed efficiently.  
off-axis layout; tilt-to-length coupling; NGGM +
Footnote †: journal: remote sensing  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]-K.
## 3 Models
### For Specific Task  
In this section, we introduce the specific model implementation for multi-choice MRC tasks such as RACE [2], Dream [43] and ReCO [3]. For each task, we simply plug the task-specific input and decision modules into our multi-decision-based transformer architecture.  
**RACE & Dream.** In this task, each sample contains a passage \(P\) of text, a question sentence \(Q\) and \(k\) candidate answer sequences \(\{C_{1},\cdots,C_{k}\}\). We concatenate each candidate answer with the corresponding question and passage as the inputs: \"\([[CLS],Q,C_{i},[SEP],P]\)\".  
At each block, we extract the encoded \"\([CLS]\)\" representations of all sequences and concatenate them as a matrix \(H_{n}^{C}\in R^{k\times d}\) to represent the \(k\) candidates. Then \(H_{n}^{C}\) is fed into the decision module, which is a Feed-Forward Network (FFN), to predict the probability distribution,  
\[\begin{split} P_{n}(Y|S)&=g_{n}(H_{n},\theta_{n}^{d} )\\ &=\text{FFN}(H_{n}^{C},\theta_{n}^{d})\\ &=softmax(\sigma(H_{n}^{C}\cdot W_{1}+b_{1})\cdot W_{2}+b_{2}) \end{split} \tag{9}\]  
where \(\sigma\) is a tanh function, \(W_{1}\in R^{d\times d}\), \(W_{2}\in R^{d\times 1}\), \(b_{1}\in R^{d}\), \(b_{2}\in R\) are trainable parameters and \(\theta_{n}^{d}=\{W_{1},W_{2},b_{1},b_{2}\}\).  
**ReCO.** ReCO is also a multi-choice task with a passage \(P\), a question \(Q\) and three candidate answers \(\{C_{1},C_{2},C_{3}\}\). Compared with RACE, the candidate answer in ReCO is shorter. Therefore, we directly concatenate all candidates with question and passage as the input sequence:  
\[[CLS],C_{1},[CLS],C_{2},[CLS],C_{3},[SEP],Q,[SEP],P]. \tag{10}\]  
At each block, the three \([CLS]\) representations \(\widetilde{H}_{n}^{C}\in R^{3\times d}\) are passed through an FFN to obtain the predicted probability.  
\[P_{n}(Y|S)=g_{n}(H_{n},\theta_{n}^{d})=\text{FFN}(\widetilde{H}_{n}^{C},\theta _{n}^{d}) \tag{11}\]
## 5 _Salmonella_ Mediates Tumor Cell Self-Destruction  
Programmed cell death, and apoptosis in particular, is crucial in tumor clearance involving the activation of caspase cascade in response to cancer therapy; however, in a state of high-grade malignancy, apoptosis remains attenuated [2]. The efficiency of _Salmonella_ in combating tumor malignancy primarily involves the activation of cell death pathways (Figure 1 and Table 1) by nutrient competition and an enhanced stimulation of tumor-specific immune responses [5; 17]. To highlight the mechanism, _Salmonella_ treatment increased the expression of cleaved caspase-3 required in the activation of apoptosis via caspase cascade system in two melanoma models, K1735 and B16F10 [46]. Apoptotic response of tumor was only reversed in pan-caspase inhibitor Z-VAD-FMK-treated tumor cells which verified the theory that apoptosis is involved in _Salmonella_-mediated tumor cell death [46]. Furthermore, autophagy adds up to the layer of responses following _Salmonella_ treatment as indicated by the upregulation of Beclin-1 along with an enhanced conversion of LC3-I to LC3-II, an autophagosomal marker [46]. The activation of autophagic signaling pathway is mediated by downregulation of AKT/mTOR/p70S6K being a crucial target of _Salmonella_ in tumor cells [46]. In another study, _Salmonella_ was used as a vector to carry second mitochondria-derived activator of caspases (Smac) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) genes which enhance apoptotic cell death and resulted to almost 90% regression of tumor growth in murine melanoma, lung carcinoma, and mammary carcinoma models [47; 48].  
Figure 1: _Salmonella_-mediated tumor immunotherapy. _Salmonella_ triggers immune cells infiltration into tumor tissue and coax tumor cells self-destruction while preventing tumor microvascularization and delaying tumor cell migration.
## 2 Materials and Methods
### Synthesis of MXene and MQDs  
Ti\({}_{2}\)N MXene was prepared by chemical etching of the Al layers from the Ti\({}_{2}\)AlN MAX phase by following a previously reported method [10]. During the chemical etching process, the etchant was prepared by dissolving 6 g KF in 5M HCl then 1 g of the MAX phase was added to 20 mL of the prepared etchant solution and kept undisturbed for 3 h. Subsequently, the etching solution with the MAX phase was bath-soniacted for 1 h at 40 \({}^{\circ}\)C to accelerate intercalation, which further assisted the exfoliation process. Subsequently, the solution was centrifuged and washed with DI water until the pH became neutral. Finally, the MXene sheets were filtered and dried overnight at 40 \({}^{\circ}\)C. The yield of MXene formation was found to be approximately 70%, which is comparable to the previously reported yield [10].  
MQDs were synthesized using a hydrothermal method followed by centrifugation [35]. The obtained Ti\({}_{2}\)N MXene after Al etching from Ti\({}_{2}\)AlN MAX was dispersed in DI water, and the pH of the solution was adjusted to 10 by the addition of ammonium hydroxide (NH\({}_{4}\)OH) solution. This solution was poured into a Teflon container that was placed in an autoclave. The autoclave was then placed in an oven at 100 \({}^{\circ}\)C for 6 h and cooled to room temperature. After cooling, the solution was centrifuged (10,000 rpm) for 20 min, and the MQDs were collected as dispersed in the supernatant. The supernatant was carefully separated and stored in a refrigerator for frequent use for further characterization.  
XRD, PL, Raman Spectroscopy, UV-Vis Absorption, PL Excitation (PLE), Quantum Yield (QY), FE-SEM, XPS and HR-TEM Measurements  
The XRD patterns of the Ti\({}_{2}\)AlN MAX phase and corresponding Ti\({}_{2}\)N MXene were recorded using a Bruker AXS diffractometer (D2 PHASER A26-X1-A2B0B2A Karlsruhe, Germany) at room temperature. Raman spectra were recorded using a WITec confocal microscope system (Alpha-300 SR, WITec GmbH, Ulm, Germany) equipped with a 100\(\times\) objective lens (N.A. = 0.9) with an excitation wavelength of 532 nm. UV-Vis absorption spectra of the Ti\({}_{2}\)N MQDs were recorded in deionized water (Optizen UV-Vis spectrometer). PL and PLE spectra were measured using a Cary Eclipse spectrometer. The quantum yield of the Rhodamine 6G (R6G) solution was measured using a Quantaurus-QY, Hamamatsu Photonics spectrometer. FE-SEM (JSM-7600F, JEOL Corp., Tokyo, Japan) images of the Ti\({}_{2}\)AlN MAX phase and Ti\({}_{2}\)N MXene were captured at field voltages ranging from 5 kV to 10 kV. XPS of MQDs was recorded using X-ray photon spectrometer (ESCALAB250). The HR-TEM (JEM-3010, JEOL Corp.) images of the MQDs were obtained at 300 kV.
# Low-Cost Biochar Adsorbents for Water Purification Including Microplastics Removal
## 2 Materials and Methods
### Adsorption Tests  
The biochars and selected AC samples were tested for their organic contaminant adsorption capacity using phenol. Sub-samples of 0.1 g biochar or AC were agitated in 15 mL of phenol solution (100, 200, 500, 1000, and 2000 mg/L) for 24 h, after which the suspensions were filtered to 0.45 \(\upmu\)m and analyzed spectrophotometrically (Shimadzu UV-1800) at 271 nm. The ACs were tested using two replicate samples and the calculated relative standard deviations (SD/mean*100, RSD) ranged from 0.1 to 18.1%. The highest RSDs (\(>\)10%) were found with the low 100 and 200 mg/L concentrations. The biochars were tested as single determinations.  
The cation exchange capacity (CEC) and the exchangeable cations were determined for selected activated biochars as described in [29]. In addition, the concentration of released phosphorus was measured. Briefly, AC cations were exchanged for NH\({}_{4}\)\({}^{+}\) by an overnight extraction (1:10 \(uvb\) ratio) using 0.5 M NH\({}_{4}\)OAc (pH 7). After extraction, the biochars were centrifuged and resuspended twice with equal amounts of 0.5 M NH\({}_{4}\)OAc (pH 7) to ensure saturation of exchange sites with NH\({}_{4}\)\({}^{+}\). The three supernatants were combined and analyzed. Excess NH\({}_{4}\)\({}^{+}\) was rinsed using deionized water. The adsorbed NH\({}_{4}\)\({}^{+}\) was then exchanged by an overnight extraction using 1 M KCl. Concentrations of Al, Ca, Fe, K, Mg, Mn, Na, and P were determined in NH\({}_{4}\)OAC extracts and the CEC from the quantity of exchangeable NH\({}_{4}\)\({}^{+}\) in the KCl extracts. The standard deviations of the CEC measurements ranged from 0.3 to 1.1 mmol/kg.  
For the microplastics experiments, three activated biochars with increasing surface areas and different pore size distributions were selected. A glass column was filled with 20 g of the respective biochar material. The filled column was washed with 5 L of tap water to remove fine biochar particles. Microplastic particles of various sizes and shapes were simulated using 2 g of spherical polyethylene (PE) microbeads (10 \(\upmu\)m), 2 g of cylindrical, smooth PE pieces (2-3 mm) as well as 2 g fleece shirt fibers. Each column was eluated with 30 fractions of 50-mL tap water each. The fractions were filtered using pre-weighed glass fiber filters that were weighted again after drying for 3 d in a heated 25 \({}^{\circ}\)C closed cabinet. Each experiment was performed in triplicate. The biochar material was recycled by intensive washing with tap water and ultrasonication prior to the next use. The MP material recovered was assessed on glass fiber filters using a microscope ([PERSON] et al. in prep).