esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Architecture
### ASTGC-LSTM Network
#### 3.3.1 Spatial-Temporal Graph Model Based on Human Skeleton Joints  
For the skeleton sequence, first a linear layer and LSTM layer were used to map the 3D coordinate of each joint onto a high-dimensional feature space. The first linear layer encodes the coordinates of the joint as a 256-dimensional vector, as the position features \(P_{t}\), and \(P_{ti}\) represent the position of joint i. Since it contains only position information, \(P_{ti}\) is useful for learning spatial structure features in graph models. The frame difference feature \(V_{ti}\) between two consecutive frames can facilitate the dynamic information for the ASTGC-LSTM. The series of two features can describe more abundant feature information, but the cascade of two features has the scale variance of the feature vector. Therefore, the LSTM layer was used to eliminate the scale variance between the two features. Equation (2) describes this principle.  
\[\begin{array}{ll}E_{ti}&=f_{lstm}(concat\big{(}P_{ti},V_{ti}\big{)})\\ &=f_{lstm}\Big{(}concat\Big{(}P_{ti},\Big{(}P_{ti}-P_{(t-1)i}\Big{)}\Big{)} \Big{)}\end{array} \tag{2}\]  
where \(E_{ti}\) is the augmented feature of joint \(i\) at time \(t\).
## 3 Method
### Mutually Supervised 3D Geometric Consistency Loss
#### 3.4.2 3D Flow Loss  
Figure 2 depicts how 3D flow loss is formed in our method. For the better understanding of scene geometry, we form an additional geometric enforcement to maintain the coherence between the 3D flow generated from estimated 2D flow and from point cloud, so that all the information we obtained from these three sub-tasks can be utilized as an unified constraint. As \(F_{t}^{f}(u_{f},v_{f})\) is the predicted dense 2D flow on X-Y image plane from frame \(t\) to \(t+1\), and \(D_{t}(x,y)\) is the predicted depth map, the 3D flow can be calculated as:  
\[F_{t}^{f}(z)=D_{t+1}((x,y)+F_{t^{f}}(u,v))-D_{t}(x,y) \tag{6}\]  
\[F_{3d}=C(F_{t^{f}}(u,v)+F_{t}^{f}(z)) \tag{7}\]  
where \(C\) denotes the concatenation operation. For the 3D flow computed from point cloud using both depth and pose prediction, is formulated as:  
\[F_{3d}^{{}^{\prime}}=KTP_{t}(x,y,z)-P_{t}(x,y,z) \tag{8}\]  
Thus, the complete mutually supervised geometric consistency loss is:  
\[L_{mu}=\sum_{(x,y,z)\in P}(1-O_{t})\|F_{3d}(x,y,z),F_{3d}^{{}^{\prime}}(x,y,z) \|_{1} \tag{9}\]
# Productivity Analysis of Documentation Based on 3D Model in Plant Facility Construction Project
## 3 Structural Steel Frame Documentation Process
### 3D-Model-Based Documentation  
3D-model-based documentation is a collaborative approach centered on 3D models. First of all, project participants jointly perform 3D modeling work. Once the master 3D model is created, the drawing work using the 3D model is performed as shown in Figure 1. Documentation method using 3D model can check design error such as logical error, redundancy and omission in 3D modeling process in advance. In addition, by performing the interference check using the 3D model, it is possible to ensure the integrity of the 3D model directly connected to the quality of the drawings. In this way, time for reviewing the quality of the calculated drawings can be saved. Once a 3D model with integrity is created, a shop drawing containing 2D or 3D information required for factory manufacturing can be parametrically created using the 3D model. The generated shop drawing adds a bit of information needed for production. In addition, quantity information can be quickly calculated based on information included in each member object of the 3D model. The calculated quantity information can be attached to the shop drawing.  
In the manufacturing stage, the information contained in the 3D model can be used for manufacturing using a CNC (computerized numerical control) machine. In other words, data necessary for cutting, drilling, welding, and automatic assembly position of the member can be obtained. It can also be used as nesting data that automatically places members in the factory. The 3D-model-based documentation approach saves time and money by integrating site and factory processes compared to the 2D-drawing-based documentation approach. In addition, by utilizing a 3D model with integrity,accuracy and productivity can be improved. Finally, raw materials can be saved through automation (e.g., nesting data). On the other hand, if the complexity of the building is high or large, there may be a limit in constructing a 3D model with integrity. Because of this, few studies analyze the productivity of the micro task of extracting documentation from 3D model. In particular, very few studies compare the 2D-drawing-based documentation approach to the 3D-model-based documentation approach for the same project. The purpose of this study is to analyze the productivity of 3D-model-based documentation method through case analysis of structural steel frame.
# Properties and Functionality of Cereal Non-Starch Polysaccharides in Breadmaking
###### Abstract  
Non-starch polysaccharides (NSPs) are biopolymers included in the fiber of cereal grains and seeds. Among NSPs, arabinoxylans and \(\beta\)-glucans are considered to play the most important role in breadmaking. In the literature to date, there is a lack of studies that summarize the current information on the properties and role of NSPs in this process. This review summarizes the up-to-date knowledge concerning the properties and functionality of the most common cereal NSPs in bread baking. In studies conducted to date, it has been shown that the addition of both arabinoxylans and \(\beta\)-glucans in amounts up to about 6% causes an increase in the water absorption of flour, and as a consequence, it forces the use of a larger addition of water to achieve the desired consistency of the dough. Even a small addition of NSPs can have a positive effect on the dough yield; making more bread from the same amount of flour and such bread is less caloric since neither NSPs nor water contributes to additional calories. Moreover, it has been shown that even a small addition of arabinoxylans or \(\beta\)-glucans has a positive effect on bread volume, moisture, and texture of the bread crumb as well as the preservation of bread freshness. The knowledge concerning cereal NSPs, especially modified ones and of defined structure, is incomplete and further research is needed to characterize their functionality in breadmaking.  
non-starch polysaccharides; arabinoxylans; dough; bread; bread quality +
Footnote †: journal: Accepted: 8 February 2023  
and molar mass of polysaccharides and the mechanisms of their effect in shaping bread quality are still unexplained.  
In order to study the role of NSPs in bread baking, there is a need to extract them from various raw materials in significant quantities. On a laboratory scale, this is a complicated and time-consuming process; hence many researchers, in order to study the role of individual hydrocolloids in baking, choose to purchase commercially available preparations. Industrially obtained hydrocolloids may be poorly purified and therefore contaminated with associated substances, or as a result of purification and the technological process used, they often undergo significant modifications, including partial degradation in the process of obtaining them, so that they often do not reflect the structure and properties of native hydrocolloids. There is also frequently a lack of adequate characterization of commercial preparations performed by defined and recognized methods. The variety of commercially available preparations of non-starch polysaccharides is limited, which also hinders research on their potential application in bread baking.  
In the literature to date, there is a noticeable lack of reports that summarize current information on the properties and role of NSPs in bread baking. The purpose of this review is to present the properties and function of NSPs in bread baking, with particular emphasis on AXs and \(\beta\)-glucans.
## 4 Conclusions  
Nat-CLI, HCLI-0.1 M, HCL-0.2 M, HCLI-0.5 M, and HCLI-1.0 M are mainly composed of clinoptilolite; however, their crystallinity decreases with acid treatment.  
FT-IR spectra also are in good agreement with the about reduction of crystallinity of HCL due to the dealumination of Nat-CLI that take place after its acid treatment.  
The ions from natural zeolite (Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), and Mg\({}^{2+}\)) decrease significantly with increasing H\({}_{2}\)SO\({}_{4}\) concentration to obtain acid-modified natural zeolite which promoted an increase in Si/Al ratios.  
The p\(\mathrm{H}_{\mathrm{PZC}}\) value is affected by acid treatment, which decrease for all the acid-modified clinoptilolite-rich tuffs in comparison with Nat-CLI.  
Sorption of Pb(II) is achieved after a short contact time (200 min) for the samples with a Si/Al ratio between 4.31 to 7.91; whereas the equilibrium time for the samples with Si/Al ratios from 9.05 to 9.51 is approximately 700 min. The Pb(II) sorption kinetic for Nat-CLI, HCLI-0.1 M, and HCL-0.2 M well fits to the pseudo-second order model, while for HCLI-0.5 M and HCLI-1.0 M the Pb(II) sorption is better described applying the pseudo-first order model.  
Isotherm suggests that the Pb(II) sorption process for Nat-CLI, HCLI-0.1 M, and HCL-0.2 M are well described by the Langmuir model. The maximum sorption capacity of samples with Si/Al ratios of 4.37, 5.31, and 7.91 are 48.54, 37.04, and 14.99 mg g\({}^{-1}\), respectively. The experimental data fit better to the Freundlich model for the sorbents with Si/Al ratio of 9.05 and 9.53.  
The Nat-CLI, HCLI-0.1M and HCL-0.2M with a dosage of 6 g L\({}^{-1}\) show an excellent and similar ability to remove Pb(II). Sorption efficiency is also dependent on the dosage of HCLI-0.5 M and HCLI-1.0 M. At 10 mg L\({}^{-1}\), the maximum sorption capacity reaches approximately 92.36% and 36%, respectively.  
Maximum removal of Pb(II) by Nat-CLI, HCLI-0.1 M, and HCL-0.2 M occur in a range of 6-8; whereas for HCLI-0.5 M and HCLI-1.0 M the Pb(II) uptake also takes place at higher pH values.  
Acid modified clinoptilolite with Si/Al ratio values of 9.05 and 9.51 may be used as a low-cost and abundant resource for Pb(II) removal from basic wastewaters such as those produced by the textile industry and may provide an alternative to more costly materials.  
[PERSON] and [PERSON] conceived and planned the experiments; [PERSON] wrote the manuscript with input from all authors. [PERSON] contributed to the analysis and interpretation of the results. A.V.C.Q., A.R.V.-O., J.V., F.A.-F. and G.G.-V. carried out the characterization of the samples and the interpretation of the results. All authors provided critical feedback and helped shape the research, analysis and manuscript.  
This research was funded by National Council of Science and Technology of Mexico grant numbers 169133 and 254665.  
The authors declare no conflict of interest.
# Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
## 5 Projection  
We now consider how to project our Born machine \(|\Psi\rangle\) onto the state \(|\Psi_{x}\rangle\) spanning only those signals \(y\) consistent with a given measurement outcome \(x=Ay\), where \(A\) is some given \(m\times n\) sensing matrix. Mathematically, projection is achieved via an idempotent (\(\Pi^{2}=\Pi\)) but non-unitary projection operator \(\Pi\). Non-unitary transformations are highly non-trivial to implement on a quantum computer, and this step, in addition to the sheer number of qubits, poses one of the main obstacles to implementing TNCS in a quantum computer. We _may_ achieve a projection by measuring \(m\) of the \(n\) qubits, collapsing each one to a basis state 0 or 1. Entanglement between qubits ensures that the un-measured qubits have also collapsed to a corresponding state. However, in order to prepare the _desired_ state \(|\Psi_{x}\rangle\), we must repeat the training and measurement steps until the measured qubits' values are consistent with the classically observed measurement outcome \(x\).  
Consider the following straightforward example, in which the sensing matrix \(A\) measures three individual pixels of the six-dimensional signal \(y\).  
\[A=\begin{bmatrix}0&0&1&0&0&0\\ 1&0&0&0&0&0\\ 0&0&0&0&1&0\end{bmatrix} \tag{8}\]  
Let us assume that the result of our classical measurement yields \(x=\begin{bmatrix}1&0&1\end{bmatrix}\), meaning the original signal \(y\) is consistent with the vector \(y=\begin{bmatrix}0&?&1&?&1&?\end{bmatrix}\). Now we measure the first, third, and fifth qubits of the trained Born machine \(|\Psi\rangle\). In order to implement the required projection, we must measure 0, 1, and 1 respectively. If we measure any other combination of 0's and 1's on these three qubits, we must discard this \(|\Psi\rangle\), train another, and try again. However, once we have successfully measured 0, 1, and 1, we may then measure the remaining three qubits to fill in the question marks in \(y\).  
In the common case where the sensing matrix \(A\) does _not_ measure individual pixels, we must generalize our procedure. In fact, we offer four distinct projection protocols, each with its own technical challenges. As a reference, a visual overview of the protocol as a whole can be found in Figure 4.  
Figure 4: General workflow diagram of the proposed protocol where it can be seen that the Pixel–Qubit mapping (e.g., Figure 2) occurs before undertaking training (e.g., Figure 3). The projection (e.g., Figures 5 and 6) can then utilize the training and measurements which ultimately leads to the sampling.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 7 Conclusions  
In this paper, we study the multi-satellite autonomous imaging mission planning problem and propose the fully distributed algorithm c-CBBA. The algorithm is improved on the basis of the basic CBBA, mainly addressing its drawbacks of high communication volume. The Task Preemption Strategy (TPS) and Single-Chain Strategy (SCS) are proposed to improve the operation efficiency of the algorithm during the actual on-board scheduling. By analyzing the operation process of CBBA under the background of satellite imaging mission planning, TPS, an acceleration convergence mechanism, is designed to address the hidden drawbacks in its convergence process. The SCS policy is used to prune communication links that have redundant information interactions during satellite communication. The experimental results show that c-CBBA can achieve an average communication volume reduction of 46.2% of the original algorithm while ensuring that the average profit is not less than 97.8%, effectively improving the actual operational efficiency of the algorithm. In further work, the application of c-CBBA to large-scale scenarios will be explored. Task allocation modes such as dividing satellite clusters and multi-stage allocation will be investigated, as well as designing the operation mechanism of c-CBBA under the corresponding modes.  
Conceptualization, Q.Y. and B.S.; methodology, Q.Y. and B.S.; software, Q.Y. and B.S.; validation, Q.Y. and B.S.; formal analysis, Q.Y. and B.S.; investigation, Q.Y. and B.S.; resources, Q.Y. and B.S.; data curation, Q.Y.; writing--original draft preparation, Q.Y.; writing--review and editing, L.H. and Y.C.; visualization, Q.Y.; supervision, Y.C. and P.W.; project administration, P.W.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 72001212; and Young Elite Scientists's Sponsorship Program by CAST, grant number 2022 QNRC001.  
not applicable.  
The authors declare no conflict of interest.
# Decentralized Inner-Product Encryption with Constant-Size Ciphertext
## 6 Conclusions  
Thus far, there is only one decentralized inner product encryption, proposed by [PERSON] et al. in 2018. In their scheme, however, the length of ciphertexts are dependent on the number of authorities, which may become a bottleneck in the system. Therefore, we would like to solve this problem. In this manuscript, we present a novel decentralized inner product encryption which achieves constant-size ciphertexts. In addition, our scheme is proven to be selectively secure under the \(\ell\)-DBDHE assumption. We further implement our scheme and the scheme of [25] to analyze the execution time. Except for the KeyGen algorithm, our work has better performance in the remaining four algorithms (Setup, AuthSetup, Encrypt, Decrypt). Yet, our scheme is the first DIPE scheme achieving constant-size ciphertext, and there are several potential improvements. One direction could be to upgrade the security to chosen-ciphertext security. Several generic methods [31; 32; 33; 34; 35] have been proposed in the literature, however, constructing a DIPE scheme with direct chosen-ciphertext security is an open problem. In addition, the security of our scheme is proven under the random oracle model. How to construct a DIPE scheme that is secure in the standard model is also a worth-fighting goal.  
Figure 2: The time cost for C Implementation of (**a**) Setup, (**b**) Authsetup, (**c**) KeyGen, (**d**) Encrypt, (**e**) Decrypt algorithm. (Pairing group: Type a1, \(|GID|=10\), # of authorities \(=|\bar{X}|=|\bar{Y}|=[1,\ldots,25]\), \(k=1\)).  
**Author Contributions:** Conceptualization, Y.-F.T. and S.-J.G.; methodology, Y.-F.T.; formal analysis, Y.-F.T.; investigation, Y.-F.T. and S.-J.G.; writing--original draft preparation, S.-J.G.; writing--review and editing, Y.-F.T.; supervision, Y.-F.T.; project administration, Y.-F.T.; funding acquisition, Y.-F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan (ROC), under grant numbers MOST 110-2221-E-004 -003-, MOST 110-2218-E-004-001-MBK, MOST 109-2221-E-004 -011 -MY3 and MOST 109-3111-8-004-001.  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 5 Experiment and Result  
In this section, several research questions on the BLSTM neural network for malicious JavaScript detection are proposed. This can help us objectively evaluate the effectiveness of the method proposed in this paper. To this end, we propose comprehensive experiments to answer these questions:  
RQ1: Is the BLSTM neural network effective for malicious JavaScript detection?  
RQ2: How effective is the BLSTM neural network compared to other machine learning-based detection methods?  
RQ3: What is the influence of obfuscation on the BLSTM neural network and other learning-based detection methods?
## 2 Study Area and Datasets
### Remote Sensing Data  
The ASTER is a sensor that collects multispectral visible, near-infrared and thermal infrared images. It is a special remote sensing tool intended to monitor land surface energy balance, hydrological processes and climate [30]. ASTER images with a high spatial resolution of 15 m in both the visible and near-infrared wave bands (0.52-0.86 \(\upmu\)m) were used in this study to estimate the vegetation fraction and the surface emissivity. The LST products, which had a spatial resolution of 90 m, were acquired from the thermal infrared wave band (8.125-11.65 \(\upmu\)m) of ASTER images and provided by the Heihe Plan Science Data Center [31; 32]. The albedo in the study area was calculated from images acquired from the HJ-1A/B, which is a small satellite constellation that is used for monitoring and forecasting environmental changes and disasters. The HJ-1A/B was launched by China in 2008. Two CCDs (0.45-0.89 \(\upmu\)m) with a spatial resolution of 30 m are carried by each satellite.
## 3 Calibration Status of Sentinel-2  
### Sensor Description  
As part of the Copernicus program of the European Union (EU), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission acquiring high spatial resolution (10 to 60 m) optical imagery. The Sentinel-2 mission concept draws in large part from the heritage of the Landsat program, as well as from the French Satellite Pour l' Observation de la Terre (SPOT) series.  
The Sentinel-2 mission is performed by 2 identical satellites (S2A, launched in June 2015, and S2B, launched in March 2017) each carrying a single imaging payload named MSI (the Multi-Spectral Instrument). Thanks to a wide imaging swath of 295 km, the two satellites ensure a revisit time of 5 days over a large part of global emerged lands and coastal waters, producing some 4 Terabytes of data every day.  
The MSI is a pushbroom imager with a three-mirror anastigmat design utilizing 10 focal plane modules forming 10 bands in the visible and near-infrared (VNIR) and 3 bands in the SWIR (see [12] for a complete description). A solar diffuser can be positioned at the entry of the telescope for radiometric calibration purposes. Unlike OLI, the MSI does not accommodate an additional reference diffuser or calibration lamps.  
Figure 3: Distribution of Sentinel-2/Landsat-8 registration test sites.
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 3 Background
### Task Allocation
#### 4.4.1 Energy Modeler  
The energy modeler is in charge of calculating the sensing cost based on the user's profile and bid. Once the social activity rate and sociability factor of a user are received from the user profiling module, Equation (5) is used by the energy module to assess the energy cost of task \(j\) by using Equation (5):  
\[E_{wj}^{u_{i}}=P_{t_{j}}^{u_{i}}*T_{j}+P_{O_{j}}^{u_{i}}*T_{prep}^{j} \tag{5}\]  
As noted in Section 4.2, \(P_{O_{j}}^{u_{i}}\) stands for the overhead for user \(i\), which is simply the power cost for user \(i\) to prepare his/her device to start sensing the task \(j\). This includes unlocking the phone, which involves the screen backlight, and turning on the sensor(s). The overhead contributed to the energy consumption of user \(i\) to sense the task \(j\) itself (\(P_{t_{j}}^{u_{i}}*T_{j}\)). In the equation, \(T_{prep}^{j}\) is the time needed to prepare the smartphone to sense task \(j\). Relying on the fact that users with a higher sociability factor are expected to be more active during the task assignment, the built-in sensors in their smartphones are likely to be already in use. Thus, the higher the sociability factor, the lesser the energy overhead for sensing.