esa-sceva/satcom-chunk-collection huggingface dataset:
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 3 Methodology
### Lossless EEG Compression Algorithm
#### 3.1.1 Dynamic Voting Prediction  
Biomedical signals are by nature chaotic and fluctuate rapidly. As such, there is no single generic prediction function that is capable of accurately predicting a biomedical signal while maintaining a low system complexity. As opposed to designing several prediction algorithms for each and every kind of biomedical signal, this study proposes a dynamic voting prediction algorithm that is independent to the type of body signal in order to enhance the overall performance of the signal prediction. The dynamic voting prediction scheme proposed in this study is composed of the following steps: the first-stage prediction, second-stage prediction, and the voting prediction stage.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 1 Introduction
### Notation  
The following notations are used in the rest of the paper. Let the bold fonts denote vectors or matrices. The transpose operations are denoted by \((\cdot)^{T}\), respectively. Let \(\left\|\cdot\right\|_{2}\) denote the (Euclidean) vector norms. If there are no special instructions, the inequality between two vectors refers to the element of the vectors. The inverse of matrix is denoted as \((\cdot)^{-1}\). \(\mathbf{I}_{N}\) denotes the identity matrix with the dimension \(N\times N\).
## 3 Results
### Protein retrieval  
To evaluate the capabilities of LaJolla in the field of protein retrieval we used the CATH protein classification as standard of truth and compared the results to two other popular algorithms in the field: CE [10] and TM-align [13]. CATH allows us to validate if the results produced by a method are \"true\" in terms of a similar classification. To this end, we used the topology level of CATH. Our reduced dataset CATH_1258 ensures that there is at least one other protein on the same topology level. The graphs in Figure 5 show how well the classification works in regard to the coverage at a certain scoring cutoff. To assess correct hits, we counted if the result with the best score was true (TOP 1) and also if a correct result was among the ten best hits (TOP 10). The results show that CE, despite its age, still is a very good method with good overall results. It ranks best when it comes to TOP 1 hits and second when it comes to TOP 10 hits. If one takes into account when the coverage line crosses the percentage of correct TOP1 and TOP 10 hits CE also ranks first. TM-align is faster than CE, and has the best characteristics regarding the TOP 10 hits in the field when considering a TM-Score between 0.0 and 0.5. LaJolla's coverage and sensitivity can be adjusted using the n-gram size. The TOP 1 hits with n-gram length 10 are equally good as the results produced by TM-align. The results of LaJolla show that a certain amount of chain gets lost when using longer n-gram sizes as they cannot be indexed. As LaJolla was used with standard parameters results below a TM-Score of 0.2 were neglected what also contributes to this. However, as the performance graph shows (4), this is a tradeoff between speed and coverage / precision. The performance is higher compared to CE and TM-align in all n-gram sizes except 10 where TM-align is faster. Figure 5: Evaluation of the coverage and precision of LaJolla (n-gram size 10, 20, 25, 30), CE and TM-align in a classification scenario. The red line indicates the percentage of the coverage of distinct topologies at a certain score cutoff. The black line represents the percentage of correct hits with the best score (TOP 1), the dashed black line represents the percentage of correct assignments with a true result being among the ten best hits (TOP 10).
# A Needs-Based Augmented Reality System
## 3 Theoretical Framework
### Need Analysis  
Capturing human needs in a measurable form is a complex endeavor. Therefore, it requires extensive study and analysis. In this research, [PERSON]'s human scale development theory and the proposed human needs matrix [36] (Table 5) are used as a guideline to explore the various needs and dissect them into measurable elements. The human needs matrix below presents examples of satisfiers for each need category, it is not exhaustive, further explanation and guidance on satisfiers are present in the human scale development book [36]. Furthermore, the Need Context Technology (NCT) framework (Table 6) [37] is used to map relations and tools in the analysis. The aim is to study the technology that supports need detection, starting with the most basic measurable needs (usually related to health readings, hunger, stress) and evolving into the most complex needs.  
The emphasis in this work is on human needs as triggers for an AR experience. Hence the goal is to provide the logic to respond to situations such as this example:  
* If a user has a subsistence need on a Being axis, trigger experience A. This statement leads to the following questions:
* What elements define a need?
* How to measure a need?  
Figure 4 describes the relationships between the main elements of the proposed model as follows:  
* A person has a need.
* A need has one of three triggers: homeostasis imbalance, incentive, or stimulation.
* A need has a satisfier, which has an AR experience.
* A context describes the state of a person and can be an external state (environmental), or the internal state of a user.
* An external environmental state can be an incentive that triggers a need.
* An internal state can reflect a homeostasis imbalance that triggers a need.  
Figure 3: Layers of functionality in the proposed application.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Fundamental Human Needs** & **Being** & **Having** & **Doing** & **Interacting** \\
**Existential Categories** & **(Qualities)** & **(Things)** & **(Actions)** & **(Settings)** \\ \hline Context-Aware & User, Who (Identity) & Things & What (Activity) & Where (Location), Weather, Social, Networking \\ Categorization & Emotion Sensors & IoT systems and Sensors & Activity recognition through motion sensors & Location awareness, nearby user device (for proximity with other users) \\ Sensors and Technology & & & & \\ \end{tabular}
\end{table}
Table 5: Human needs matrix [38].  
A trigger in psychology is a factor that activates a need. There are three types of needs triggers [40]:  
* Homeostasis Imbalance: is the internal state that reflects a malfunction in the body processes resulting in a rise of a need. (internal)
* Incentive: is an external positive or negative environmental stimulus that motivates a person. (external)
* Stimulation: is an activity that causes excitement or pleasure.  
Connecting these triggers (Figure 5) with the basic needs and the related technology (Table 6) forms the foundation for detecting a human need and establishing a need as a trigger for an AR experience.
## 3 Model and Dataset
### Model
#### 3.1.1 BertGCN: Statistical Graph with BERT Features  
The BertGCN consists of three graph convolution layers, namely the input layer, the hidden layer, and the output layers, along with a dropout layer. After initializing the graph network, it is subsequently trained using the 'RoBERTa-based' model specification, as previously reported [22]. The feature information of nodes is transferred from the BERT embedding, and the pre-trained 'RoBERTa-based' model is imported for this purpose. In the next step, the pre-trained BERT model is fine-tuned using our own dataset, which is labeled as in vivo, in vitro, and other. Once the fine-tuning process for BERT is complete, the feature is updated, and the extracted BERT features are transferred back to the BertGCN to construct node features (Figure 1a). The BertGCN follows the same structure as the TextGCN, comprising node features (i.e., words and documents) and weighted edges, as per a previous study [10].  
The multiplier lambda adjusts the trade-off between the two objectives, where \(\lambda=1\) corresponds to the full BertGCN model and \(\lambda=0\) corresponds to using only the BERT module [19]:  
\[Z\ =\ \lambda\ Z_{GCN}\ +\ (1-\lambda)\ Z_{BERT} \tag{1}\]  
The terms \(Z_{GCN}\) and \(Z_{BERT}\) represent the classification output of each model, with the aggregated \(Z\) reflecting the combined output that incorporates both models. In this case, the performance of the full BERT model with a composition parameter of 0 (\(\lambda=0\)) serves as the baseline for comparison.
# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
## 2 Hybrid Link Resource Prediction and Scheduling Model
### Communication Networking Model
From a model perspective, it is required to search for the topology architecture when is the maximum in the communicable node architecture. In addition, it is also necessary to ensure that at any time, at least one pair of node channels in the four communication domains can connect for communication and interaction, as shown below. \[\left\{ \begin{array}{l}I_{(T_{e,i}T_{e,j})}(t)=\bigcup C_{(q_{0},q_{1},q_{0}^{ \prime},q_{1}^{\prime})}(t),q_{0},q_{1}\in T_{e,i},q_{0}^{\prime},q_{1}^{ \prime}\in T_{a,j}\\ I_{(T_{e,i}T_{e,k})}(t)=\bigcup C_{(q_{2},q_{3},q_{2}^{\prime},q_{3}^{\prime})} (t),q_{2},q_{3}\in T_{e,i},q_{2}^{\prime},q_{3}^{\prime}\in T_{s,k}\\ I_{(T_{e,i}T_{m,p})}(t)=\bigcup C_{(q_{4},q_{5},q_{4}^{\prime},q_{5}^{\prime})} (t),q_{4},q_{5}\in T_{e,i},q_{4}^{\prime},q_{5}^{\prime}\in T_{m,p}\\ I_{(T_{a,j}T_{e,k})}(t)=\bigcup C_{(q_{6},q_{7},q_{6}^{\prime},q_{7}^{\prime})} (t),q_{6},q_{7}\in T_{a,j},q_{6}^{\prime},q_{7}^{\prime}\in T_{s,k}\\ I_{(T_{a,j}T_{m,p})}(t)=\bigcup C_{(q_{8},q_{9},q_{8}^{\prime},q_{9}^{\prime})} (t),q_{8},q_{9}\in T_{a,j},q_{8}^{\prime},q_{9}^{\prime}\in T_{m,p}\\ I_{(T_{s,k}T_{m,p})}(t)=\bigcup C_{(q_{10},q_{11},q_{1},q_{1}^{\prime})}(t),q_{ 1},q_{11}\in T_{s,k},q_{1}0^{\prime},q_{1}1^{\prime}\in T_{m,p}\\ \forall q_{\bar{q}}\forall q_{\bar{q}_{\bar{q}_{\bar{q}_{\bar{q}_{\bar{q}_{\bar{ q}_{\bar{q}_{\bar{q}_{\bar{q}_{\bar{q}_{\bar{q}}_{\bar{q}}_{\bar{q}}_{\bar{q}} _{\bar{q}}}}}}}}}}}}}(t)=C_{(q_{\bar{q}+\lambda},q_{\bar{\
u}+\lambda},q_{\bar{ \
u}+\lambda}^{\prime},q_{\bar{\
u}+\lambda}^{\prime})}(t),\lambda\in[2,4\cdots,10]\\ \max\{E_{C}\}\end{array} \right. \tag{3}\]
## 3 Model Verification  
Since there is no relevant literature on partially penetrated well in composite naturally fractured gas reservoir, to validate the accuracy of the proposed model, the comparison of this model with an analytical solution of completely penetrated well in composite reservoir is implemented.  
Although [PERSON] and [PERSON] (2019) mainly aimed at analyzing transient pressure, the dimensionless production rate solution can also be obtained based on the relationship of Equation (14). Therefore, we consider the circumstance of \(h_{\mathrm{w}}=h\) in our model, which means the well is penetrated completely. We compare the results obtained from our model with [PERSON] and [PERSON]'s. The dimensionless parameters used for validation are presented in Table 1. All of the dimensionless parameters are defined in Appendix A.  
Figure 3 shows the comparison of production rate curves. The lines represent the results obtained from the proposed model, and the dots represent the results obtained from [PERSON] and [PERSON]'s method. As we can see, there is a good agreement between these two models, which indicates that our proposed model for partially penetrated well in composite naturally fractured reservoir is accurate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Dimensionless reservoir radius, \(R_{\mathrm{eD}}\) & 6000 \\ Dimension inner region radius, \(R_{\mathrm{1D}}\) & 2000 \\ Dimensionless reservoir thickness, \(h_{D}\) & 300 \\ Storativity ratio of natural fracture, \(\omega\) & 0.05 \\ Interporosity flow coefficient between fractures and matrix, \(\lambda\) & 0.0001 \\ Mobility ratio between outer and inner regions, \(M\) & 0.025 \\ Diffusivity ration between outer and inner regions, \(\delta\) & 0.05 \\ Permeability anisotropy factor for fracture, \(\kappa_{1}\) & 1 \\ Permeability anisotropy factor for matrix in outer region, \(\kappa_{2}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dimensionless parameters for model validation.  
Figure 3: A comparison of the dimensionless production rate curves between this model and literature’s method.
## 3 Results
### Using the MODIS LST Product as Input for the RS-CFLUX Model  
LST input for the RS-CFLUX model can be replaced with the MODIS LST product. The average of daytime and nighttime LSTs of the MODIS daily LST product MOD11A1 was used as input of RS-CFLUX, and it is compared with the daily average _in-situ_ LST (see left column of Figure 7 and Table 4). MODIS LST has a large positive bias comparing with _in-situ_ LST at the Zhangye wetland station, but little bias at the Daman superstation. This is attributed to the strong land surface heterogeneity at Zhangye wetland station. When the average daytime and nighttime LSTs of MOD11A1 were used as input for RS-CFLUX at the Daman superstation and Zhangye wetland station, the RS-CFLUX model performed well at both stations (see middle and right columns of Figure 8 and Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stations** & **Fluxes** & **R2** & **RMSE** & **NSE** & **Slope** & **Residue** \\ \hline \multirow{2}{*}{Daman} & GPP & 0.92 & 1.96 (gC/m\({}^{2}\)/day) & 0.87 & 0.75 & 0.33 (gC/m\({}^{2}\)/day) \\  & NEP & 0.88 & 1.31 (gC/m\({}^{2}\)/day) & 0.87 & 0.94 & 0.07 (gC/m\({}^{2}\)/day) \\ \hline \multirow{2}{*}{Wetland} & GPP & 0.94 & 1.00 (gC/m\({}^{2}\)/day) & 0.85 & 0.97 & \(-\)0.43 (gC/m\({}^{2}\)/day) \\  & NEP & 0.78 & 1.02 (gC/m\({}^{2}\)/day) & 0.61 & 1.05 & \(-\)0.04 (gC/m\({}^{2}\)/day) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the RS-CFLUX modeled GPP with the observed GPP at the Daman superstation (Daman) and Zhangye wetland station (Wetland).  
Figure 7: Validation of the NEP estimated by the RS-CFLUX model at the Daman superstation (Daman) and Zhangye wetland station (Wetland). The slope and residual of the linear fit are shown in Table 3.
## 5 Conclusions  
As key components of the transmission train, planetary gearboxes play an important role in guaranteeing the normal operation of the satellite communication antenna. With their special gear transmission structure, planetary gearboxes exhibit complicated dynamic responses, which increase the difficulty of fault feature extractions for planetary gearboxes. It is proved that the customized multiwavelets which are similar to fault features and have excellent properties can achieve a good result in fault feature detections. The redundant symmetric lifting scheme is applied to produce customized multiwavelet functions. Moreover, the quotient of kurtosis and entropy is proposed to choose the optimal multiwavelets. On the basis of the local concentrated energy, the improved neighboring coefficients choose variant thresholds and sizes of neighbors at different decomposition levels. The proposed method incorporated customized multiwavelets and INC threshold. It was applied to the planetary gearbox fault detections. Experimental results showed that the proposed method could detect the pitting fault features on two neighboring teeth of the sun gear in a planetary gearbox.
## Materials and Methods  
### The Calculation Model of Building Whole Life Cycle Carbon Emission  
The whole life cycle of a building can be divided into the building materials production phase, materials transportation phase, building construction phase, building operational phase, and demolition phase. The whole life cycle carbon emission is the sum total of carbon emissions of the five phases, which can be expressed by the following equation [30].  
\[\text{C}\left(\text{Whole Life cycle}\right)=\text{C}_{\text{Production}}+ \text{C}_{\text{Transportation}}+\text{C}_{\text{Construction}}+\text{C}_{\text{ Operation}}+\text{C}_{\text{Demolition}}\]  
Meanwhile, the data source varies among different life cycle phases of building. The most ideal situation is that all the data used in the calculation are from actual engineering record documents. However, the building may be in the phases of design, construction, or early operation, which means varying degrees of data deficiency. Different data sources and paths can be chosen according to the actual situation. Figure 1 shows the calculation frame model, and optional data sources and corresponding data processing solutions [31].  
The carbon emission factor method is used to calculate the carbon emission of each phase. There are three main forms in practical applications.  
1. For the carbon emissions generated directly from energy consumption: \[\text{C}_{\text{Energy}}=\text{E}_{\text{i}}\;\text{F}_{\text{E}\text{i}}\] \[\text{C}_{\text{Energy}}\text{: The carbon emissions generated by energy consumption }\text{E}_{\text{i}}\text{: Energy consumption }\text{F}_{\text{i}}\text{: Carbon emission factor (carbon emission per unit of energy consumption) }\]
2. For the carbon emissions generated from materials production: \[\text{C}_{\text{Materials}}=\text{M}_{\text{i}}\;\text{F}_{\text{Mi}}\] \[\text{C}_{\text{Materials}}\text{: The carbon emissions generated by materials production }\text{M}_{\text{i}}\text{: Amount of material weight, length, surface area, volume, etc.}\] \[\text{F}_{\text{Mi}}\text{: Carbon emission factor (carbon emission per unit amount of material) }\]  
Figure 1: Frame of building whole life cycle carbon emission calculation model.  
For the carbon emissions generated from engineering processes:  
\[\text{C}_{\text{Engineering process}}=\text{Q}_{\text{i}}\;\text{F}_{\text{Pi}}\]  
\[\text{C}_{\text{Engineering process}}\]  
The carbon emissions generated during the engineering process  
\(\text{Q}_{\text{i}}\): Quantity of constructive work (length, surface area, volume, time, number of machines, etc.)  
\(\text{F}_{\text{Pi}}\): Carbon emission factor (carbon emission per unit of engineering work)
## 2 Material and Methods
### Geophysical and Remote Sensing Data
#### 2.4.2 ASTER Satellite Imagery  
The space remote sensing data used in this study correspond to an ASTER sensor image acquired on 9 September 2003, with a cloud cover of the order of 0%. Its spectral resolution makes it possible to map alteration minerals related to mineralization processes since it contains six spectral bands in the short wave infrared region ranging from 1.60 to 2.430 \(\upmu\)m [13]. Other characteristics and features of the Aster sensor are presented in Table 2.
# Controllable Height Hopping of a Parallel Legged Robot
## 3 Dynamic Model Analysis
### Integrated Dynamic Modeling
Then, the complete dynamic equation is simplified as:  
\[M\ddot{z}+B_{\alpha\dot{q}}\dot{z}+M\dot{g}=f_{u} \tag{8}\]  
where \(B_{\alpha\dot{q}}=\frac{B_{1}+B_{2}}{4L_{1}\cos^{2}\theta_{1}}=\frac{B}{4L_{1 }^{2}-z^{2}}\) and \(f_{u}\) is the ideal output force on the toe in the vertical direction, which is transformed from the motor torque \(\tau_{S}\). Accordingly, in the stance phase, the entire dynamic model is a nonlinear second-order system with configuration-related parameters. When compared with the ideal spring-mass model, this robot system has variable equivalent inertia and a variable damper coefficient, which are determined by the robot's posture. On the other side, in the flight phase, the robot has no contact with the floor. Because the leg was controlled with an extra damping effect in the air, the flight process was a relatively static situation with no leg movement. The flight process is considered only to be affected by gravity and friction on guide rail, which represents a linear process in modeling, while the guide rail friction is regarded as a positive constant \(f_{g}\) in this research. In vertical movement, the friction and gravity effects can be combined as a sum of force. Therefore, we set a new acceleration \(g^{\prime}\) to substitute for \(g\) in the takeoff and upward flight phases, as shown in (9). \[g^{\prime}=\frac{Mg+f_{g}}{M} \tag{9}\]  
In the following modeling calculations, we use \(g^{\prime}\) to represent the combined effects of the vertical friction and gravity. The actual value \(g^{\prime}\) was calculated in the experiment introduced in Section 5. According to the modeling above, the foot's mass is neglected in the dynamic analysis. However, at the critical moment of touchdown and takeoff, the dynamic situation is related to the foot mass. We found that the impact between foot and floor occurred in a very short time period, thus the impact process can be considered as a completely inelastic collision. At takeoff moment, the kinetic energy changes over a very short period. As the result for one hopping period, the energy conservative relationship can be expressed as:  
\[\frac{1}{2}M\dot{z}_{LO}^{2}=(M+m)g^{\prime}h_{smt} \tag{10}\]  
where \(\dot{z}_{LO}\) is the instantaneous velocity immediately before takeoff, \(m\) is the foot mass, and \(h_{smt}\) is the summit height during the jump.
# Incoherent Digital Holography: A Review
## 3 Principle of FINCH  
### Setup of FINCH  
FINCH [33; 34; 35; 36; 37; 38] is a technique to record holograms of spatially incoherent light with a single-path interferometer. Figure 4 illustrates an optical setup of FINCH. A FINCH system can obtain holograms of objects illuminated by incoherent light with a common-path optical setup without an imaging lens. Incoherent light illuminates objects, and a lens placed between objects and an interference filter collects light diffracted from objects. The interference filter limits the wavelength bandwidth of incoherent light, and then a polarizer generates linear polarized object light with the polarization direction of 45\({}^{\circ}\) from the liquid crystal (LC) direction of the SLM. On the SLM, only one of the s- and p-wave components of the incident light is phase-modulated, and the other is not. Therefore, the SLM with a quadratic phase distribution loaded introduces both a uniform phase modulation and quadratic phase shifts to either the s- or p-component of the polarized object light [36; 37]. Alternatively, other polarization-sensitive retarders, such as a birefringent crystal or a wave plate, can also be applied to generate two waves that interfere with each other with a single-path optical setup. After the modulation with a SLM, two waves have different wavefronts with different curvature radii and orthogonal polarizations. These waves form an incoherent hologram on the image sensor to reconstruct a 3D object image after passing a linear polarizer with the polarization axis parallel to that used before the SLM. When an object point is set as an incoherent object, a pattern of the FZP is formed on the image sensor plane. The location of the center of a FZP pattern and its fineness depend on the 3D position of a point object, and therefore a hologram recorded by a self-referenced interferometer can reconstruct a 3D image of the incoherent object point. In the case that an arbitrary 3D object is set, the incoherent summation of FZP patterns that are generated from each point on an object is observed with an image sensor. Light diffracted from each object point does not interfere with light diffracted from other object points, and therefore a FINCH system records speckle-less holograms of incoherent 3D objects. In the image-reconstruction process, most of the FINCH systems require in-line configuration and must remove unwanted image components in a hologram, namely, the zeroth-order diffraction wave and conjugate image. FINCH adopts phase-shifting interferometry [65; 66] frequently. When adopting temporal phase-shifting interferometry, phase shifts are introduced typically with a SLM [43; 46], and phase-shifted incoherent holograms are sequentially recorded. An object wave is extracted from holograms by phase-shifting interferometry, and an incoherent 3D object image is reconstructed by calculating diffraction integrals. As a result, a FINCH system is able to record and reconstruct a speckle-less 3D image of an object illuminated by natural light.