esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Architecture For 3D Mapping Urban Landscapes
## 2 Related Works  
We reviewed the current state-of-the-art and found different methods to perform 3D structure inference of an object using a single image. These works also attempt to solve the SfM and SLAM problems [31; 32; 33; 34].  
Recent works combine Deep Learning techniques to perform three-dimensional reconstructions, using data from the stereo cameras, mono-LiDAR, and stereo-LiDAR cameras, merging merging the data from these sensors to obtain better results to obtain better results [35; 36; 37; 38; 39]. However, these proposals are focused on solutions for individual objects and many of them only focus on reconstructing structured environments. On the other hand, only a few works [40; 41; 42; 43; 44] focus on the reconstruction of environments with challenging conditions such as high altitudes, textures, etc. In addition, these works do not contemplate the limitations of working only with monocular and aerial 2D images and only a few of them use deep learning techniques.  
Commercial software such as Pix4 DMapper, Agisoft Photoscan, DroneDeploy, and others are able to perform 3D reconstruction of outdoor environments, but often require specific configurations to guarantee results. For example, a minimum flight height and overlapping percentages between the image sequence.
## 5 Computational Experiments
### Validation of the Re-Offloading Mechanism  
In this subsection, we validate the effectiveness of the proposed re-offloading mechanism. We compare it with the existing \(z\)-stage method [37] in two groups. The z-stage method re-offloads all uncompleted tasks. In contrast, our proposed mechanism only re-offloads tasks in \(T_{r}=T_{x}\cup T_{o}\), and uses a pruning approach to avoid unnecessary insertion attempts, further speeding up the re-offloading process. Similar to Section 5.2, ten different testing instances are generated for each combination, and each algorithm independently runs ten times for each instance. The comparison results are presented as follows.  
Figure 7 shows the results of \(aRV\) and \(aRT\) under different numbers of newly generated tasks, \(q\), using origin instances with size of {B, 20}. The proposed re-offloading mechanism consistently generates assignments with \(aRV\) values similar to those of the \(z\)-stage method. When \(q=5\), although the proposed re-offloading mechanism exhibits slight backwardness  
Figure 6: Plots of all algorithms under different instances.  
in \(aRV\), the running time decreases by 89.11% (\(\approx\)(0.1038 \(-\) 0.0113)/0.1038) compared to the \(z\)-stage method. As \(q\) increases, the difference between the two methods diminishes. This is because more newly generated tasks tend to have a larger \(\mu_{b}\) value, leading to more tasks involved in re-offloading. With the instances of \(q=30\), the differences in \(aRV\) between the two approaches are negligible, yet the proposed re-offloading mechanism still reduces running time by 57.36% ((\(\approx\)(0.7024 \(-\) 0.2995)/0.7024)).  
Figure 8 illustrates the comparison results of \(aRV\) and \(aRT\) under different instances with \(q=10\). The difference in \(aRV\) between two approaches remains consistently small. However, as the instance scales increase, there is a notable contrast in their running time. Especially in instances with size \(\{\text{F},100\}\), the proposed re-offloading mechanism reduces the running time by 91.23% (\(\approx\)(34.0785 \(-\) 2.9881)/34.0785). This reduction is attributed to the proposed mechanism retaining several original subsequences based on influence intervals, thereby minimizing re-offloading tasks. Furthermore, the employed pruning approach significantly reduces insertion attempts. These special designs of our proposed mechanism allow a reduction in running time while ensuring assignment quality. Conversely, the \(z\)-stage method includes all uncompleted tasks in re-offloading, leading to a significant increase in runtime as the instance scale grows.  
Figure 7: Indicator trends under different newly generated tasks using instances with size [B, 20].
## 1 Introduction
### Predictive Analytics and Data Mining in Production Time Estimation
Correlation analysis is a well-known method for identifying relationships. The Self-Organising Map (SOM, Kohonen map) and Principal Component Analysis (PCA) are techniques used for data reduction with minimal information loss. PCA selects appropriate combinations of the original attributes and then proposes these combinations as new attributes [8]. SOM is applied in NNs. The algorithm works by moving neurons in the direction of similar data [45; 59]. EL is used to improve the predictive power of the data. Bagging is the process of dividing a dataset into equally sized subsets using sampling with replacement [49]. It is suitable for unstable data, where a small change in the input data significantly affects the model structure. Bagging is combined with NN for real-time estimation modeling [26; 60]. Boosting is an iterative process where the current model is influenced by the previous model and by the data points that were misclassified [61]. Boosting is used with NN [62] and with the SVM [50] for LT prediction. DL is a group of learning methods within neural networks that involve multiple layers [25]. Convolutional NNs are used for image recognition [63] when analyzing manual operations in videos. Deep Recurrent NNs are networks with feedback connections between neurons. They are used to recognize temporal dependencies in manufacturing to estimate LT based on the current state of production [37]. A Deep Belief Network is a stochastic NN based on Boltzmann machines. It is used to estimate LT while considering dependencies between data points [28]. A Deep Autoencoder attempts to reconstruct the input data by transforming it into hidden layers and using this transformed data as input to the decoder. This technique is used to estimate the remaining time using RFID data [25].
## 4 Game Model
### Forwarding Probability
We assume the strategy combination is \(s=(P(i),\)\(P(-i))\) in one game, where \(P(i)\) means the strategy of participant \(i\) and also the forwarding probability of nodes, and \(P(-i)=P\) (\(I\)), \(\ldots\), \(P(i-I)\), \(P(i+I)\), \(\ldots\), \(P(n)\) means the strategy of other participants. We denote the payoff of \(i\) in this strategy combination by \(u_{i}(P(i),\)\(P(-i))\). Here, the payoff for any participants is the expected benefit with a certain participation probability. It is composed of the benefit \(B_{i}\) (\(P(i)\), \(P(-i)\)) and the cost \(C_{i}(P(i),P(-i))\) of \(i\). Theoretically, only the nodes that participate in forwarding would cost and gain the benefit and for the nodes that do not forward packets, they cannot gain any benefit and the payoff is also zero. We define the payoff function of participant \(i\) by Equation (5):  
\[u_{i}(P(i),P(-i))=\left\{ \begin{array}{ll}\frac{B_{i}(P(i),P(-i))}{C_{i}(P( i),P(-i))},&\mbox{forwarding}\\ 0,&\mbox{otherwise}\end{array} \right. \tag{5}\]  
In the game period, each participant would like to use its optimal strategy to maximize its payoff. Therefore, the benefit and cost of the participant would impact the strategy for all the nodes located on the routing path. Furthermore, it would impact the transmission reliability grade of the routing path. We consider the transmission reliability grade as the benefit of intermediate nodes. We assume for strategy combination \(s\), in case the next hop node is \(j\) for intermediate node \(i\), the benefit of \(i\) is shown by Equation (6):  
\[B_{i}(P(i),P(-i))=R_{up}*R_{ij}*R_{j} \tag{6}\]  
where
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 5 Resistojets  
### Operating Principle  
In a resisistojet, the propellant is passed through a heat exchanger (or heating element) where it is super-heated and ejected through an expansion nozzle [48]. For instance, laboratory experiments have shown exit temperatures of 600-1050 \({}^{\circ}\)C for methanol and 300-1175 \({}^{\circ}\)C for ammonia propellants [49]. The heating process reduces the gas (propellant) flow rate from a given upstream pressure through a given nozzle area, thus leading to the increase in specific impulse that is proportional to the square root of temperature as depicted in Equation (11) [50]. The working principle of a resisistojet  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Company/Institution with Location** & **Engine** & **Thrust (N)** & **I\({}_{\text{P}}\) (s)** & **Propellant** & **Remarks** & **Ref.** \\ \hline Aerospace Corporation, El Segundo, CA, USA & \(I_{\text{P}}\) 30 s & 37 & 187 & - & - & [39] \\ Orbital ATK, Dulles, VA, USA & STAR 4G & 13 & 269.4 & Al and Ammonium perchlorate & - & [46] \\ DSSP, Reno, NV, USA & CAPS-3 & - & 245-260 & HIPEP-50A & flown on SPINSAT (57 kg) & [44; 47] \\ DSSP, Reno, NV, USA & CDM-1 & 76 & 226 & AF/HITB & - & [5] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of Solid Propulsion Systems.  
is similar to that of a CGP system except that the propellant is heated before the expansion process. Because of the propellant's high energy (gained by heating), an exhaust velocity much greater than that of a CGP system is achieved in a resistojet [48]. Exit velocities of micro CGP systems range between approximately 300-700 m/s [51; 52], while those of micro resistojets are about approximately 2.2 km/s [53]. A major drawback of resistojets is that their performance (thrust, \(\mathrm{I}_{sp}\)) is limited by the melting temperature of the heating element used. In addition, power and thermal losses during heating of the element contribute to the inefficiency of resistojets [54]. The schematic of a resistojet propulsion is shown in Figure 4, and the main components include propellant storage, heating element and nozzle.  
The thrust (shown in Equation (10)) produced by the propellant at stagnation pressure also depends on stagnation number density of propellant (\(n_{o}\)) in \(\mathrm{m}^{-3}\), stagnation temperature (\(T_{o}\)) and the probability (\(\chi\)) of a molecule exiting the expansion slot area (\(A_{o}\)). Specific impulse (shown in Equation (11)) is a function of the stagnation temperature, and the mass of the propellant (\(m\)) [55].  
\[\tau=A_{o}\Big{(}\frac{n_{o}kT_{o}}{2}\Big{)}\chi \tag{10}\]  
\[I_{sp}=\sqrt{\frac{\pi kT_{o}}{2m}}\frac{1}{g_{o}}, \tag{11}\]  
where \(k\) is the Boltzmann constant and \(g_{o}\) is the acceleration due to gravity at sea level.
## 5 Security
### Security Analysis  
* We use the certificateless signature scheme to guarantee the authenticity of public keys. If the signature published by the user is valid, the authenticity of public key is verified. However, the certificateless signature scheme in [21] has only one public key (\(X\) in our scheme), so we use another equation to guarantee the authenticity of another public key (\(Y\) in our scheme). This equation demonstrates that \(X\) and \(Y\) have the same power \(x\).
* Anyone who wants to fork the delegation path with meaningful decryption (i.e. from \(j\rightarrow(j+1)^{\prime}\)) must first compute \(rk_{ID_{j}}^{j-(j+1)^{\prime}}=(H_{1}(ID_{j-1}/H_{1}(ID_{j})))^{x}\) and then compute \(e(u_{1}^{t},rk_{ID_{j}}^{j-(j+1)^{\prime}})=e(u_{1}^{t},(\frac{H_{1}(ID_{(j+1) ^{\prime}})}{H_{1}(ID_{j})})^{x})\). It is obvious that computing this is the same difficulty as CBDH problem without knowing \(x\). That means no one can fork the delegation path with a meaningful decryption even if the data user is on the delegation path.
# A Novel Channel Coding Scheme for RFID Generation-2 Systems
###### Abstract  
Low complexity and efficient algorithms for Radio-Frequency Identification (RFID) are crucial to foster low-cost and energy-saving solutions. With the aim of lowering the complexity of the radio circuitry, and inspired by the superposition codes concept, we propose a novel encoding algorithm for tag-to-reader communication channels of modern RFID systems. We show that the developed scheme--despite the simplicity of its hardware realization--allows a coding gain comparable to the one obtained by the more complex Viterbi decoding of the Miller 4 modulation scheme used in the EPC Generation 2 Class 1 RFID standard. Simulation results prove the effectiveness of the proposed solution for low-cost tags, under different signal-to-noise ratios (SNRs) and interrogation time durations. Although our numerical evaluation mainly focuses on the EPC Generation-2 standard, we believe that the proposed scheme will also be attractive for future Ultra-wideband RFID systems.  
RFID; wireless coding; line codes Article
## 3 System Model
### Computation Model  
The indicator variables \(x_{i,0}\), \(x_{i,S}\), \(x_{i,\text{B}}\in\{0,1\}\), \(\forall i\in\mathcal{N}\) are set to represent task offloading decisions. When UE \(i\) chooses local computing, \(x_{i,0}=1\), otherwise, \(x_{i,0}=0\). When UE \(i\) chooses LEO satellite edge computing, \(x_{i,S}=1\), otherwise, \(x_{i,S}=0\). When UE \(i\) chooses LEO satellite-assisted terrestrial edge computing, \(x_{i,\text{B}}=1\), otherwise, \(x_{i,\text{B}}=0\).  
When \(x_{i,0}=1\), the delay is the delay that UE \(i\) computes task \(i\).  
\[D_{i,0}=\frac{c_{i}a_{i}}{f_{i,0}}, \tag{3}\]  
\(f_{i,0}\) is the computing capability of UE \(i\) (cycles per second).  
Figure 2: Illustration of \(d_{i,S}\).  
The size of computing result data is usually much smaller than that of input data. Therefore, the delay of sending result data from the MEC server at the LEO satellite or the BS back to UEs is ignored. However, the propagation delay from the LEO satellite to UEs and from the gateway to the LEO satellite cannot be ignored [29].  
When \(x_{i,S}=1\), the delay includes the delay that UE \(i\) offloads task \(i\) to the LEO satellite, the propagation delay between UE \(i\) and the LEO satellite, and the delay that the LEO satellite's MEC server computes task \(i\).  
\[D_{i,S}=\frac{a_{i}}{r_{i,S}}+\frac{2d_{i,S}}{c}+\frac{c_{i}a_{i}}{f_{i,S}}, \tag{4}\]  
\(f_{i,S}\) are computing resources allocated to task \(i\) by the LEO satellite's MEC server (cycles per second).  
When \(x_{i,\text{B}}=1\), the delay includes the delay that UE \(i\) offloads task \(i\) to the LEO satellite, the propagation delay between UE \(i\) and the LEO satellite, the delay that the LEO satellite migrates task \(i\) to the gateway, the propagation delay between the LEO satellite and the gateway, and the delay that the BS's MEC server computes task \(i\). Because there is a wired connection between the gateway and the BS, the corresponding delay between them is very low, so it is ignored here.  
\[D_{i,\text{B}}=\frac{a_{i}}{r_{i,S}}+\frac{2d_{i,\text{S}}}{c}+\frac{a_{i}}{r _{\text{S,G}}}+\frac{2d_{\text{S,G}}}{c}+\frac{c_{i}a_{i}}{f_{i,\text{B}}}, \tag{5}\]  
\(f_{i,\text{B}}\) are computing resources allocated to task \(i\) by the BS's MEC server (cycles per second).
## 5 Snake Robot Design Considerations for SAR Response Phase  
In [13], an \"robot-assisted SAR\" was taken as an example to find different values (i.e., autonomy, accountability, privacy, etc.) and value tensions of robots or stakeholders. Value tensions describe conflicts between values, which are used in the design process to determine which value should be prioritised. In [42], a urine testing \"wee-bot\" robot is designed by using a \"care-centred value sensitive design\" (CCVSD) approach, which demonstrated that ethical principles could be incorporated into technical design requirements. In the CCVSD framework for care robots, certain values (e.g., attentiveness, responsibilities, and responsiveness) are chosen as per the care practice requirements, and used robot's characteristics/components such as the context of use, care practice, actors involved, and type of robot (e.g., capabilities and appearance etc.). These considered components and values vary based on the robot's application. Based on this, the selected values and ethical issues are converted into the design requirements.  
As discussed in the previous sections, several ethical issues need to be considered to using snake robots for SAR missions. However, there are certain use scenarios of snake robots in SAR missions covered in [5; 86; 95; 96]. In the case of an earthquake in Mexico, a 16-module U-snake teleoperated with a lengthy extension cord is used to reach places that conventional robots or sensing equipment cannot [5]. A number of drawbacks are cited from this practical implementation. Rescue workers assumed that the robot has many capabilities such as carrying food and water, and is equipped with a microphone and speaker. From a design standpoint, including these additional features to the U-snake may reduce its mobility. Moreover, issues such as tether management, deployment speed, multi-environment use, visibility (for situational awareness), human-to-robot ratio, path planning, and mid-level control are pointed out for better snake robot interventions in the SAR response phase. These mentioned design issues lead to ethical issues. For example, rescue workers' expectations exceed the U-snake robot's sensor capabilities (i.e., only the camera is mounted but expected microphone and speaker features). Furthermore, tether management is difficult, it might entangle and harm the victim. Hence, ethical issues can be mapped to design requirements and VSD can be used for developing snake robot technology for SAR.  
Consequently, in this section, we discuss several design requirements for snake robots based on ethical issues in SAR situations. We classified design requirements into three categories: mechanical/hardware, control and software. These categories are discussed as follows.
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 4 Observer Design
### Observer Design  
The dynamic model of the vehicle is described as (8). Define the observed object as (35):  
\[\left\{\begin{array}{l}\dot{x}=f(x,u)+w\\ y=h(x,u)+v\end{array}\right. \tag{35}\]  
where the state vector is \(x=[v_{x}\;v_{y}\;\dot{\phi}]^{T}\), the measurement vector is \(y=[a_{x}a_{y}\dot{f}]^{T}\), the controlling input is \(u=[\omega_{l}\;\omega_{r}]^{T}\), \(w=[q_{1}\;q_{2}\;q_{3}]^{T}\) is the process noise vector, and \(v=[r_{1}\;r_{2}\;r_{3}]^{T}\) is the measurement noise vector.  
Discretize dynamic model (8):  
\[\left\{\begin{array}{l}x_{1}(k+1)=\left(\left(\begin{array}{cc}\sum\\ \begin{array}{cc}j=l,r\\ i=f,m,r\end{array}\end{array}\right)/m+x_{2}(k)x_{3}(k)\right)\Delta T+x_{1}( k)\\ x_{2}(k+1)=\left(\left(\begin{array}{cc}\sum\\ \begin{array}{cc}j=l,r\\ i=f,m,r\end{array}\end{array}\right)/m-x_{1}(k)x_{3}(k)\right)\Delta T+x_{2}( k)\\ x_{3}(k+1)=\left(\begin{array}{cc}\frac{\beta}{2}\sum\limits_{i=f,m,r}(F_{x, iI}-F_{x,il})\\ +\sum\limits_{j=l,r}\left(aF_{y,j,i}+bF_{y,mj}+cF_{y,rj}\right)\end{array} \right)\Delta T/I_{z}+x_{3}(k)\end{array}\right. \tag{36}\]  
where \(F_{x,ij}\) and \(F_{y,ij}\) is the function of \(x\) and \(u\), respectively, which is calculated by (9), \(F_{y,ij}(k)=F_{y,ij}(x(k),u(k))\) and \(F_{x,ij}(k)=F_{x,ij}(x(k),u(k))\); \(\Delta T\) is the time interval.  
The discretization for measurement function is the same as (37):  
\[\left\{\begin{array}{l}y_{1}(k+1)=\frac{\sum\limits_{i=f,m,r}\sum\limits_{i =f}F_{x,ij}(k+1)}{m}\\ y_{2}(k+1)=\frac{\sum\limits_{i=f,m,r}\sum\limits_{i=f}F_{y,ij}(k+1)}{m}\\ y_{3}(k+1)=x_{3}(k+1)\end{array}\right. \tag{37}\]  
The discrete system model is:  
\[\left\{\begin{array}{l}x(k+1)=f(x(k),u(k))+w(k)\\ y(k+1)=h(x(k+1),u(k))+v(k)\end{array}\right. \tag{38}\]  
The road friction factor and suspension stiffness are identified in a traditional algorithm and are not described in this study. The observer structure is shown in Figure 5.
## 3 Proposed Scheme: FRBP  
To achieve two goals, real time and energy efficiency, the proposed scheme constructs a real-time broadcast tree to support the application-specific (\(m\), \(k\))-firm model. However, since states of links and nodes could be changed anytime and anywhere due to the dynamic characteristics of wireless sensor networks, the proposed scheme includes a recovery mechanism based on DBP and local monitoring to deal with state changes. The FRBP includes a procedure to construct and maintain the broadcast tree in a local and global way according to the current DBP value.
# A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board
## 4 Model Evaluation
### Experimental Results
The above results indicate that the modified BN combing CMI, LSP, and expert experience maintains the DAG requirement of BN and produces a more nuanced network that captures the main dependency relationships among evidence nodes (variables) while deleting some weak dependency relationships without allowing arbitrary graphical structures that would make it harder to interpret and extract relations to enhance the prediction model. At the same time, the conditional expected loss can benefit the final classification and it can exhibit better performance especially when compared to TAN. In addition, the modified BN has the clearest common sense interpretation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multicolumn{3}{c}{**Training Samples**} & \multicolumn{3}{c}{**Test Samples**} \\ \cline{2-7}  & _MSE_ & _MAE_ & _MAPE_ & _MSE_ & _MAE_ & _MAPE_ \\ \hline Modified BN & 689.98 & 0.2306 & 10.6965 & 291.74 & 0.2239 & 11.0818 \\ TAN & 666.8964 & 0.2421 & 11.5641 & 302.2279 & 0.2509 & 11.9724 \\ AdaBoost & 738.0686 & 0.2407 & 11.2314 & 308.0735 & 0.2358 & 11.0472 \\ ANN & 692.7819 & 0.2349 & 11.3129 & 293.1946 & 0.2346 & 11.1991 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of classifiers according to different indicators. Figure 9: Confusion matrix of different approaches. (**a**) Confusion matrix of different methods for training samples. (**b**) Confusion matrix of different methods for test samples.