esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technical Briefing of M*
### Graphic-Centric Description of M*  
This section uses the graphic-centric description introduced by [PERSON] [6] to illustrate M*. M* is a complete and optimal MAPP algorithm. The main idea of M* is to iteratively construct/update a so-called _search graph_\(G^{sch}\) (i.e., to iteratively remove the collision configuration vertexes and expand necessary neighbors) and apply the A* algorithm on the new \(G^{sch}\) until the optimal collision-free path to \(v_{d}\) exists in the \(G^{sch}\) and is found by the A* search. Specifically, \(G^{sch}\) is a sub-graph of \(G\) and consists of three other sub-graphs: the _expanded graph_\(G^{exp}\), _neighbor graph_\(G^{nht}\), and _policy graph_\(G^{\phi}\). The expanded graph \(G^{exp}\) is the sub-graph of \(G\) that has been explored by M*. \(G^{nht}\) contains the _limited neighbors_ of all the joint vertexes in \(G^{exp}\). The definition of limited neighbors is given below. \(G^{\phi}\) consists of the paths induced by the _individually optimal policy_\(\phi\) that connects each joint vertex in \(G^{nht}\cup G^{exp}\) to \(v_{d}\) without the collision-free constraint. Specifically, \(\phi^{j}\) is the individually optimal policy for the agent \(j\) that leads any \(v^{j}\) in \(G^{nht}\cup G^{exp}\) to \(v_{d}^{j}\) without considering collisions. Examples of policy \(\phi\) include the standard _[PERSON]'s_ algorithm [24] and A* [5]. Using the above graphic concepts, we can define the _collision set_\(C_{p}\) as  
\[C_{p}=\left\{\begin{array}{c}\psi\left(v_{p}\right)\bigcup\left\{\cup_{v_{q} \in V_{p}}\psi\left(v_{q}\right)\right\},\ for\ v_{p}\in G^{exp}\\ \emptyset\hskip 56.905512 pt,\ for\ v_{p}\
otin G^{exp}\end{array}\right., \tag{3}\]  
where \(V_{p}=\{v_{q}|\exists\pi(v_{p},v_{q})\subseteq G^{exp}\}\) is the set of the joint vertexes to which there exists a path to from \(v_{p}\) in \(G^{exp}\). Let \(\phi^{j}(v^{j})\) be the immediate _successor vertex_ of \(v^{j}\) in the policy path, then the set of _limited neighbors_\(V_{p}^{nht}\) for the joint vertex \(v_{p}\) in \(G^{nht}\) is defined as  
\[V_{p}^{nht}=\left\{v_{q}\left|\begin{array}{c}e_{pq}^{j}\in E^{j},\ for\ j\in C_{p}\\ v_{q}^{j}=\phi^{j}\left(v_{p}^{j}\right),\ for\ j\
otin C_{p}\end{array}\right.\right\}, \tag{4}\]  
where \(e_{pq}^{j}=edge\left(v_{p}^{j},v_{q}^{j}\right)\). The definition of the limited neighbors implies the sub-dimensional expansion strategy: We only expand the search space at the dimensions where the collision occurs (\(j\in C_{p}\)), otherwise for collision-free dimensions (\(j\
otin C_{p}\)), M* will not expand, limiting the unexpanded search space to the graph that only consists of individually optimal path induced by the policy \(\phi\).
###### Abstract
Second, we provide formal definitions of robustness and reliability and summarize the major challenges that DL-based CMR segmentation methods face when trying to meet these two criteria. Finally, we present a review of the current and ongoing research for reliable and robust CMR segmentation. The remainder of the paper is organized as follows: Section 2 motivates this work by illustrating the improvements brought by DL-based algorithms in CMR segmentation over the last decade. Section 3 formalizes the concepts of reliability and robustness and presents the challenges faced by DL-based methods that hinder the reliability and robustness of the CMR segmentations. Section 4 reviews current methods addressing reliability and robustness and categorizes the proposed solutions into two families, Quality Control (QC) and Model Improvement (MI) techniques. Although sharing the same objective, QC techniques are typically external tools that do not require any modification in model architecture or training procedure, allowing an effortless integration into state-of-the-art segmentation pipelines. MI techniques, instead, are harder to integrate into existing pipelines, as their functioning is related to an inner modification of the models. Finally, discussion and conclusions are presented in Section 6.
# Post-Disaster Recovery Monitoring with Google Earth Engine
## 2 Materials and Methods
### Methods
#### Change Analysis  
Final damage and recovery maps were generated based on the amount of change in the forest, built-up, and crop land classes. The changes were computed at the municipality level, and the damage map was generated by comparing the T0 and T1 land cover classification results; also, the recovery maps for each post-disaster time step (T2-T4) were computed by comparing the T1 and T2-T4 land cover classification results.
## 3 Preliminaries
### RSA Algorithm  
RSA is named after [PERSON], [PERSON], and [PERSON], the three creators of the RSA calculation [19]. It is an asymmetric cryptographic algorithm that contains two keys: public and private. A public key is used to encrypt the data at the transmitter, and for decryption, both these keys are used at the receiver. The primary rationale behind the RSA algorithm is that it generates these two keys by factorizing a given large integer. Since a public key has two numbers, one of them is a product of two prime numbers.  
RSA keys are commonly 1024 or 2048 bits in length. If the key's size increases, the encryption's strength increases exponentially. Similarly, using the same prime numbers, a private key is also generated. Therefore, the algorithm's robustness lies in foolproofing the large number factorizing. RSA Algorithm is shown in Figure 1 respectively. The generated public and private keys using RSA algorithm are provided in Table 1.  
The RSA algorithm employs the following procedure to generate public and private keys:  
* Pick two big prime numbers, **r**, and **s**.
* Find **t*
* = **r*
* by multiplying these values, where t is referred to as the modulus for encryption and decoding.  
- 1)**, which indicates that the only factor in common between k and **(r  
- 1)** is 1. Select \"k\" so that 1 < k < \(\varphi\) (t), k is prime to \(\varphi\) (t), and **gcd (e,d(t)) = 1**.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline
**X(o) (0–1)** & **U (3.56–4)** & **Prime Numbers** & **Public Key** & **Private Key** & **Encrypted Message** & **Decrypted Message** \\ \hline
0.2 & 3.6 & (3, 5) & (1, 15) & (1, 15) & 31514649 & (0.2, 3.6) \\ \hline
0.4 & 3.7 & (5, 7) & (23, 35) & (27, 55) & 2716334111620 & (0.4, 3.7) \\ \hline
0.5 & 3.6 & (5, 13) & (19, 65) & (43, 65) & 226273451624 & (0.5, 3.6) \\ \hline
0.6 & 3.8 & (5, 11) & (11, 55) & (11, 55) & 3746544451461 & (0.6, 3.8) \\ \hline
0.8 & 3.9 & (7, 13) & (35, 91) & (35, 91) & 55249602528 & (0.8, 3.9) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Generation of public and private keys using RSA.  
Figure 1: RSA algorithm.  
* The public key is <e, t> for **t** = **r**\(\times\)**s**. The public key <e, t> encrypts a plaintext message m. The mathematical methodology is employed to obtain ciphertext C from the original message: **C** = **m\({}^{\textbf{k}}\) mod t**.
* 1) \(\times\) (s
- 1)) = 1**.
* <d, t> is the private key. The private key <d, t> is used to decipher the ciphertext message c. The below formula is used to generate plain text m out from ciphertext c: **m** = **c\({}^{\textbf{d}}\) mod t**.
* Different inputs of x(o), u, prime numbers, and the generated public and private keys with an encrypted message and decrypted messages are shown in Table 1.
## 5 Experiments
### Experimental Settings
A dropout ratio of 0.4 is found to provide an optimal balance between regularization and model complexity;
* _Batch Size:_ The number of samples processed before the internal parameters of the model are updated. A batch size of 256 is selected to leverage computational efficiency while maintaining sufficient gradient approximation;
* _Epochs:_ Both the pre-training and fine-tuning phases are conducted over 100 epochs, ensuring ample opportunity for the model to learn and adapt to the training data;
* _Factor Number:_ Our disentangled network architecture incorporates eight factors, allowing the model to capture a diverse array of latent user and group preferences;
* _Embedded Size:_ The dimensionality of the user and group representations in the latent space is set to 192, with each factor contributing a 24-dimensional sub-representation. This configuration facilitates a comprehensive yet computationally efficient representation scheme;* _Number of Negative Samples (\(N_{neg}\)):_ In the mutual information maximization process (Section 4.3), five negative samples are used for each positive instance, balancing the learning signal derived from both positive and negative interactions;
* _SSL Loss Weight (\(\lambda\))_: The weight assigned to the SSL loss function \(\mathcal{L}_{MI}\) during the fine-tuning phase is set to 1, underscoring its equal importance alongside the supervised loss components in model optimization. These parameter settings are uniformly applied across all datasets and experiments presented in Section 5.2, ensuring consistency and comparability in our evaluation. Additionally, Section 5.4 will explore the impact of varying some of the hyperparameters, providing deeper insights into their influence on the recommendation performance of DAGA.
# Crouch Gait Analysis and Visualization Based on Gait Forward and Inverse Kinematics
## 4 Discussion  
Gait analysis is commonly developed in the anatomical space, and the works presented in the workspace have generally been developed in the sagittal plane[14; 15; 34; 35; 36; 37; 38; 39]. However, the information available in the frontal and transverse planes, mainly as regards pelvic movements, is very useful. Our findings allow for the determination that assessments in the Cartesian frontal and transverse planes are significant in determining gait abnormalities related to pelvic movements. In this sense, it is possible to develop an assessment for each of the lower limbs or for both together. In addition, the instrumentation advantages of acquiring and analyzing gait data in the gait space can reduce the need for invasive approaches and improve non-invasive ones.  
The gait inverse kinematic method for gait analysis and visualization to solve the inverse kinematics of the eight-DoF lower limb model is useful for obtaining the joint angle references from the Cartesian coordinates. This approach could be used for vision-based system perception. The reduction in the calculation, computational complexity, and instrumentation required for data acquisition in the workspace is another advantage. The comparison between mild and severe crouch gaits using the normal gait as a reference is carried out in the anatomical space, achieving closer results than in workspace analysis. Gait analysis can be improved using metrics such as the arithmetic mean, standard deviation, RMS value, and shape factor to quantify and assess joint performance during walking. However, the manual data analysis developed in this work is commonly performed by a specialist, and represents high temporal and economic costs. This approach leads to bias and assessment errors, making necessary the application of artificial intelligence tools.  
Visualization for abnormal crouched gaits using the normal gait pattern in the three anatomical planes as a reference provides a powerful bio-feedback tool. In an exergaming approach, gait visualization based on animation tools increases the success of the rehabilitation process for both the patient and the specialist. The versatility and performance of virtual environment modeling related to exergaming can be used to represent the user through an avatar and virtually modify the terrain context. The impact on a patient's emotions through kinesthetic perception during rehabilitation represents a useful stimulus during rehabilitation tasks. In addition, the advantages of developing a visualization framework in Autodesk Maya make it possible to visualize in real-time any patient that is not already in the database, automatically adjusting to the subject's anthropometry.
## 3 Description of the Solution
In addition to that, there are other components that have been specifically designed and implemented to abstract the features presented in the acoustic and wireless networks used to collect data from the underwater and overwater environments (namely, the CDT and VDT that were referred to in the STANAG proposal. Their implementation, though, has been tailored from the beginning for the semantic middleware architecture described here). 4. _Development of a Publish/Subscribe infrastructure for underwater vehicles_. To the best of the authors' knowledge, it has not been used for information transfer at the data level of a layered architecture in middleware solutions for underwater or maritime vehicles. The implementation works that have been done in the semantic middleware architecture make use of CoreDX, a DDS implementation provided by Twin Oaks, for a Publish/Subscribe Manager installed as part of a Command and Control Station (CCS) used to send commands and receive information resulting from the execution of those commands in the vehicles. However, the Publish/Subscribe Manager is but a component of the architecture, which makes use of a plethora of them that, while dependent on the DDS infrastructure to receive data, do not use the latter technology and have been developed from scratch. As it will be described, there are many other components that use different resources. Considering these aspects and the open issues that were found during the study of related works, Table 1 summarizes the contributions that are made by the middleware architecture described in this manuscript: \begin{table}
\begin{tabular}{l l} \hline \hline
**Technical Issues Addressed** & **Actions Taken in the Middleware to Solve the Issue** \\ \hline Interoperability among & Hardware abstraction, information model, simple interfaces to \\ autonomous vehicles & access middleware services from higher levels \\ \hline Lack of specific set of services & Definition of specific set of services for middleware architectures \\  & applied to underwater environments \\ \hline Publish/Subscribe infrastructure & Development of a middleware with Publish/Subscribe paradigm for \\ for underwater vehicles & decoupled reception and delivery of messages (better for \\  & underwater environments with unreliable transmission medium) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarized contributions of the middleware. Its characteristics and how it has been used in the environment represented in this paper are described in this section.
## 3 Results and Discussion
### Green Strength Test  
The load and displacement results of mixtures MIX-L, MIX-M, and MIX-H are seen in Figure 8a-c. The changes in the mechanical performance of each mix design at a very early age (t = 30, 45, 60, 90, 150 and 240 min) were quite evident. For each mix design, the older samples (t = 90, 150 and 240 min) had a higher peak force than the samples with younger ages (t = 30, 45 and 60 min). As shown in Figure 8a-c, for each specimen, the load increased near linearly with the vertical displacement in the initial path. After that stage, the increasing rate of the load was reduced as deformation grew. The specimens with the younger ages, such as t = 30, 45, and 60 min, had a rising load as the deformation increased until reaching a certain height. Whereas the older specimens (t = 90, 150 and 240 min) showed a load decrease after the peak force. [PERSON] et al. (2018) and [PERSON] et al. (2019) explained this phenomenon. They mentioned that the difference between older and young ages samples was the occurrence of lateral deformations and failure mode. The specimens with the age of t = 30, 45, and 60 min had relatively small stiffness which led to an expansion in the horizontal direction with significant deformation.  
Figure 7: **(a)** Extruded filaments of mixture MIX-L; **(b)** Extruded filaments of mixture MIX-M; **(c)** Extruded filaments of mixture MIX-H.  
As shown in Figure 9, the younger specimens fail by barreling instead to generate a distinct crack pattern. The area of cross-section grows under the increases of vertical displacement. The squeezing process resulted in force increases. However, the older specimens showed a typical failure pattern. After the peak force, the load decreased causes the formation of fractures. The older had higher brittleness and lower ductility than the younger specimens.  
A high-resolution camera recorded the lateral displacement of each sample. Through using ImageJ (see Figure 8d) and MATLAB, the change of cross section area was determined. For each sample at the very early ages (t = 30, 45, 60 and 90 min), true stresses were calculated using the updated areas of the cross-section. Thus, the load-displacement curves in Figure 8a-c were transferred into the stress and strain curves with the average results demonstrated in Figure 10a-c. All curves were cut-off after the peak stress which is regarded as the ultimate green strength. The self-weight of the sample was excluded in the calculation, since the failure plane of each sample may happen at a different height [30]. In this study, the green strength of mixtures MIX-L, MIX-M, and MIX-H at the different ages are illustrated in Figure 10d. Increasing MK content in calcined clay could significantly increase the green strength. Mixture MIX-H showed the highest green strength and strength development rate from 30 min to 240 min. The test results in this section indicated that increasing MK content in calcined clay could benefit the very early-age strength development which may be used to improve the buildability of the fresh mixtures in 3 DCP process.  
Figure 8: \(Cont.\)  
Figure 8: _Cont._
## 5 Result and Discussion  
In partial admission situation, the double entry turbine works in windmill mode. The empty part of the rotor uses the useful power provided in the flowing part. The dissipated work generates entropy, and so causes a reduction in the turbine efficiency. Figure 7 illustrates the turbine efficiency in four unequal admission cases. Efficiency decreased with increasing the flow inequality of the two volute limbs. The turbine efficiency when the interspace region between the stator and rotor was isolated in two tongue regions was showed in four different (1-MFR) number which shows the degree of inequality of each case (Equation (5)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Surface Pressure** & **Equal Admission Case** & **SPR** & **Partial Admission Case** & **SPR** \\ \hline S.s \textless{} P.s & 5–6 & 1.02 & 3–4 & 1.25 \\ S.s \textless{} P.s & 10–11 & 1 & 4–5 & 1 \\ S.s \textgreater{} P.s & 6–7 & 0.973 & 9–10 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The strategy of passages selection and their surface pressure ratio (SPR) number.  
Figure 6: Selected passages and sections on the turbine blades (**a**) in equal admission; (**b**) in three-dimensional (3D) view; (**c**) in two-dimensional (2D) view; (**d**) in partial admission.  
The performance change in relation to the isolating in a different inequality of incoming flow from entries is shown in Table 5. The mean for category 1 (original) is approximately 0.697, and the mean for category 2 (isolated) is approximately 0.627, which is 0.9 times the power output without the isolating. So, isolating leads to an efficiency drop.  
Isolating prevented the flow from the previous limb from rotating and entering with proper incidence. Also, the flow with high momentum flow was stroked to the isolator, which leads to it missing its energy. The section just upstream of the volute tongue has the lowest isentropic availability, and therefore operates at the highest efficiency [24]. Through isolating in this way, the effective area of the stator passage that is related to these two passages is decreased, which may lead to the blockage of these two stator passages. All of these effects enhance the interspace and stator losses. When the high-velocity fluid mixes with the lower velocity fluid, high entropy is generated in the interspace region. If isolating is applied, it helps to decrease the waste of energy in this mixing loss. A higher MFR leads to a higher efficiency gain. Cases 6, 7, and 8, respectively, have higher MFR, so the efficiency gain is decreased from cases 6 to 8. Interspace losses in the non-flowing sector contribute a significant portion to the overall losses in the turbine, but in the full admission case, the entropy generation in the Interspace regions is minimal throughout the whole operating range of the turbine [25]. So, in the case 9 which is almost an equal admission (MFR = 0.9) isolating in tongue region has no significant impact. In this case, all the passages are full of high and equal momentum flow and efficiency is not affected by isolating.
## 3 Case Study--Goldeneye CCS Site
### Model for TSC: Bayesian Convolutional Neural Networks  
We use a traditional 1D Convolutional Neural Network model to classify time series. The model has 4 convolutional layers with ReLu activation functions [58], each followed by a MC dropout and MaxPooling layer, where MaxPooling is a down-sampling technique in convolutional neural networks. The first and last convolutional layers have 128 filters, while the middle layers both have filter size of 256. The kernel size of the convolutional layers is \(\{7,5,3,2\}\) from first to last and we apply a stride of 1. We use the same dropout rate of 50% for all MC dropout layers and all MaxPooling layers have a pool size of 2. The dropout rate of 50% gives the largest variance over the model weights. Interpreting dropout as an ensemble of networks, a dropout rate of 50% produces most possible combinations, and thus the largest variability over weights and posterior predictive distribution. According to [PERSON] et al. [59], the 50% rate results in the strongest regularization effect, but has the disadvantage of potentially worse convergence than other dropout rates. There may exist dropout rates that gives better accuracy. However, to maximize the variance of the predictive posterior distribution, 50% rate is favorable. The last part of the model is a flattening layer followed by a dense layer with two nodes and SoftMax activation function, resulting in two SoftMax probabilities, one for each class. An illustration of the model is presented in Figure 2.  
Mathematically we can express the model in terms of the weights, bias and activation functions  
\[\widehat{\mathbf{y}}=\sigma_{S}(\sigma_{R}(\sigma_{R}(\sigma_{R}(\sigma_{R}( \mathbf{x}\widehat{\mathbf{W}}_{1}+\mathbf{b})\widehat{\mathbf{W}}_{2}) \widehat{\mathbf{W}}_{3})\widehat{\mathbf{W}}_{4})\mathbf{W}_{5}.\]  
Here the subscript \(S\) and \(R\) refer to a SoftMax and ReLu activation functions, respectively. The \(\widehat{\mathbf{W}}_{i}\) notation, indicates that nodes are dropped out with a Bernoulli distribution, and that there are in total four layers \(l=\{1,2,3,4,5\}\). It is only the first four layers that include MC dropout regularization. The model has 435842 trainable parameters and for optimization of the we use the well-known ADAM [60] algorithm. The goal is to find the parameters \(\phi=\{\mathbf{W}_{1},\mathbf{W}_{2},\mathbf{W}_{3},\mathbf{W}_{4},\mathbf{ W}_{5},\mathbf{b}\}\) such that the \(\widehat{\mathcal{L}}_{dropout}(\phi)\) is minimized; see Equation (3). A early stopping regime is also introduced to avoid overfitting and the validation loss is monitored and the optimization stopped after 50 epochs if no improvement is observed. We used a batch size of 256 and monitored both the accuracy and the loss. An illustration of the convergence of the training and validation of the model is showed in Figure 3.  
Figure 2: Illustration of the Bayesian Convolutional Neural Network Model used.