esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Spatial Density  
With the software tools developed at the Institute of Space Systems (IRAS), a scientific description of the distribution of the objects on orbit is possible. If the number of objects is averaged per altitude shell, a very simple representation is received, with which the distribution of debris over different altitudes can roughly be represented. This is the spatial density. It indicates how many objects per unit volume can be found at a certain altitude. In the following, the spatial density of the last release of the MASTER Model in its version of the year 2009 will be compared with current modeling results. It should be noted that the current data has not yet been finally validated and updated intermediate results are presented. These are advanced analyses of results that have already been published before. Figure 7 shows the spatial densities in low Earth orbits for three different size classes.  
The illustrations refer to 1 May 2009. The analysis is limited to orbits between 200 km and 2000 km altitude. The reason is that these orbits today show the highest spatial density, which is related to the high space activities on low earth orbits. As size classes, objects with \(d>1\) mm (Figure 7a), \(d>1\) cm (Figure 7b) and \(d>10\) cm (Figure 7c) were selected. In all three size classes, it can be observed that the spatial density at about 800 km altitude is the highest. In lower orbits, the air drag is still such that the life of debris objects is relatively low. According to the new modeling results, the number of debris at about 800 km is significantly higher than originally assumed. This is mainly due to the revaluation of the Fengyun-1C event.  
In the context of this study, however, not only individual events were examined. All event lists have been updated as well. This causes smaller changes in the results. In Figure 7a, the distribution of debris in the millimeter range at about 1500 km is shown, which changes a little bit. This has to do with updating the list for the solid-rocket motor firings. In this case, a more precise assignment of the positions where the firings have been carried out has led, as far as we know today, to expect somewhat lower numbers of slag particles in the millimeter range at 1500 km altitude.  
Basically, however, it can be said that the results presented here are qualitatively consistent with those of the past and that they quantitatively meet the expectations that existed before the investigation began. In the case of the Fengyun-1C event, simply the number of observed objects has increased significantly. It is therefore necessary to consider these new findings in the model. The final results may differ slightly from those presented here. An important intermediate result, however, can already be seen clearly. The collision probability, especially on the sun-synchronous orbits, is higher than originally assumed.
# Fabrication of a Conductive Pattern on a Photo-Polymerized Structure Using Direct Laser Sintering
## 3 Results
### Laser Scanning Direction  
In this study, after printing, a high-energy laser was irradiated on the surface where the polymer and metal coexisted so that the polymer was ablated and the remaining metal was instantaneously locally sintered to obtain the conductivity. In the case of lasers, as a focused-point light source is rapidly moved to irradiate high light energy, the cooling rate varies depending on the scanning direction even at the same energy; thus, the sintering result can vary. This phenomenon is observed more clearly as the irradiation area is widened [20]. Therefore, in our experiment, as shown in Figure 3, to form a 20 mm conductive pattern with a line width of 1 mm, the horizontal and vertical scanning directions were irradiated, respectively.  
In general laser processing, ablation and rapid cooling by instantaneous high energy rather than melting help to improve the processing precision and minimize the heat-affected layer. However, in the case of sintering, as in this study, sufficient time and temperatures must be secured for an interconnection by heat between the surrounding metal powders when the polymer binder is removed. Otherwise, the metal powder is removed and blown away from the base structure. Therefore, as shown in Figure 3, when the laser was irradiated in the horizontal scanning direction, irregular over-processing was observed because the polymer binder was ablated, but the remaining aluminum powder was also randomly blown away. On the other hand, in the case of the vertical scanning direction, in which thermal energy was concentrated on a local area, the polymer binder was removed and, at the same time, a proper interconnection occurred by sintering between the surrounding aluminum powders. In the case of the vertical direction, it was confirmed that a sufficient time for interconnection and thermal energy irradiation had been secured.
## 5 Test Results and Evaluation
### Evaluation of the Relationship Between KDF and \(r/t\)  
The relationship between KDF and \(r/t\) obtained from past experiments by the authors of [24] and the publicly available paper [23] is shown in Figure 15. The dates are added in the green circles on \(r/t\) = 615 (No. 206 and No. 207 in Table 6, no offset) and \(r/t\) = 893 (No. 191-205 in Table 6, no offset). As the diagram indicates, the KDF decreases as \(r/t\) increases, and an inclination of the lower right is evident. The coefficient of \(r/t\) is \(-2\times 10^{-4}\). It seems small, but the \(p\)-value is 2.03 \(\times\)\(10^{-15}\), which is much smaller than 1% and indicates very high statistical significance. The KDFs scatter across both sides of the line;  
Figure 14: KDF versus offset angle for various cylinders of: (**a**) No. 191; (**b**) No. 192; (**c**) No. 201; (**d**) No. 202; (**e**) No. 203; (**f**) No. 206; and (**g**) No. 207.  
thus, this line cannot be used for prediction. This conclusion is consistent with previous studies. Compared with the results of previous studies [24], the data for \(r/t=670\) have decreased. This is because the test specimens for \(r/t=670\) incorporated large elliptical shape imperfections on purpose. These test specimens were excluded from further analyses because large elliptical shape imperfections may degrade the buckling load.
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 5 Experimental Evaluation and Discussion
### Cycle Life and Cumulative Rewards
We can observe that, under the same cycle setting, there are not so many points violating the constraint in the training process because the constraint range of temperature is more extensive than that of \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Experimental Input** & **Sequential** & **PSO** & **GA** & **Q\(-\)Learning** & **DDQN** & **PCA\(-\)DDQN** \\ \hline
4.4 A, 2.01 V, 1.06 Ah & 898 & 903 & 874 & 840 & 879 & 907 \\ (cycle = 10) 4 C\(-\)2 V & 7.7 A, 3.5 V, 0.19 Ah & 515 & 494 & 526 & 499 & 512 & 506 \\ (cycle = 5) C1\(-\)Q1 & 3.3 A, 3.5 V, 0.86 Ah & 733 & 716 & 682 & 712 & 701 & 719 \\
0 A, 3.3 V, 0.88 Ah & 561 & 502 & 573 & 502 & 549 & 564 \\ (cycle = 7) rest & 1.1 A, 3.43 V, 0.94 Ah & 408 & 459 & 372 & 410 & 438 & 453 \\ (cycle = 9) 1 C\(-\)3.6 V & 0.00016 A, 3.29 V, 0.88 Ah & 631 & 662 & 597 & 581 & 612 & 659 \\ (cycle = 8) start & 631 & 662 & 597 & 581 & 612 & 659 \\ \hline \end{tabular}
\end{table} 
Table 3: Battery cycle life under different scheduling algorithms. \(\Delta IR\). In both images, the method proposed in this paper is less likely to violate the policy constraint than other algorithms, which can mitigate some internal resistance violations and help the agent in the broad search space.
# Frame-Based Phone Classification Using EMG Signals +
## 2 Materials and Methods
### Experiments
#### 2.4.2 Speaker and Session Dependency  
Our study involves three separate rounds of experiments, each varying in terms of speaker and session dependencies. The first round of experiments was both speaker-dependent and also session-dependent, which means that the training and testing data were taken from the same session. Additionally, we performed a second round of experiments in which the data were speaker-dependent but session-independent. This means that the training data came from a different session or different sessions than the testing data, but that all sessions were recorded by the same speaker. This method allows for the evaluation of the effect of increasing the amount of data from the same speaker on the performance of the model as well as the impact of inter-session variability on the accuracy. In the third round, we used speaker-independent data by training the model using data from multiple sessions of one speaker and testing it using data from another speaker. The testing session contains a session-specific corpus that was not included in the sessions used to train the model, making the experiment both speaker-independent and session-independent. The goal is to assess the potential to create a model that can be applied to new speakers without the need for adaptation by training it only on data from the actual database.
## 2 Fundamental Concepts of TDA
K-dimensional features consist of persistence diagrams; 0-dimensional features represent components that are connected, 1-dimensional features represent holes, 2-dimensional features voids, etc. [35]. Concurrently, a PDR \(W_{m}\) is made of \(n\) features, \(W_{m_{i}}=(b_{i},d_{i})\), with \(i=(1,2,\ldots,n)\). Each point corresponds to the lifespan of one topological feature, where \(b_{i}\) and \(d_{i}\) are its birth time and death time, respectively (birth time indicates when the geometrical structure appears, while death time indicates when the geometrical structure disappears). Points are entirely located in the half-plane above the diagonal [36] (Figure 3). When it comes to machine learning and statistics, a Persistence Landscapes Representation (LR) is more straightforward to work with than PDR and can be considered an alternative representation [37]. The approach takes the topological information that was previously encoded on a PDR and presents it as elements of a Hilbert space. Statistical learning methods can then be applied directly. Additionally, Persistence Silhouette representation (SR) [38] are constructed by mapping each point \(z=(d,b)\) of a PDR to a piecewise linear function, namely the 'triangle' function \(T_{z}\), which can be defined as follows:  
\[T_{z}(y)=(y-b+d)l_{[b-d,b]}(y)+(b+d-y)l_{[b,b+d]}(y) \tag{2}\]  
where \(l_{A}(x)\) is the standard indicator function: \(l_{A}(x)=1\) if \(x\in A\) and \(l_{A}(x)=0\), otherwise. A triangle function binds the points of the diagram to the diagonal, with segments parallel to the axes, and later they are rotated by 45 degrees. The triangles \(T_{z}\) can be merged together in various manners, and if we take their \(k_{Amax}\), i.e., the \(k^{th}\) largest value in the set \(T_{z}(y)\), the \(k^{th}\) persistence landscape \(\lambda^{k}=k-max_{z\geq D}T_{z}(y),k\in N^{+}\) results. The Persistence Landscape \(\lambda_{D}\) is the gathering of functions \(\lambda^{k}(y)\). Finally, the Power Weighted Silhouette representation \(\Psi_{p}(t)\) (later named SR) is obtained by taking the weighted average of the functions \(T_{z}(y)\), as the following equation shows. \[SR=\Psi_{p}(t)=\frac{\sum_{z\geq D}\omega^{p}T_{z}(y)}{\sum_{z\geq D}\omega^{ p}Z} \tag{3}\]  
In Figure 4 we can see a representation of the PDR and the LR. Figure 3: Representation of Point Cloud—RPC (**left**) on a Persistence Diagram representation—PDR (**right**). Another means of persistence diagram transformation is Persistence Images (PI) [23]. This allows for representations to be simply vectorized. Persistence images can be informally considered as a type of heatmap coming from a Calculate Gaussian KDE [39], which can be defined as follows:  
\[\hat{f}(x)=\sum\alpha_{i}k(x-x_{i}) \tag{4}\]  
where \(k\) is kernel function centered at the data points \(x_{i}\) with \(i=(0,\ldots,n)\), and \(\alpha_{i}\) are the weighting coefficients.
## 1 Introduction
It is noted that a compromise must be reached, ensuring a sufficiently low-dimensional observation vector while still providing a sufficiently rich observation of the current environment [27]. Furthermore, it is not limited to a single agent that can be set up in a reinforcement learning environment, resulting in problem solutions with different dimensions. Two independent agents have been considered to solve the tracking and obstacle avoidance problems separately, and finally to realize the coordinated control of both [28]. The focus of this paper is to further explore how recent advances in RL can be applied to both the path-following and obstacle avoidance problems of nonholonomic wheeled mobile robots (NWMRs). The main contributions of the proposed method are as follows:  
* The path-following control method is designed and implemented by considering the deep reinforcement learning algorithm DDPG, which reveals excellent performance regarding the efficiency and accuracy of the following control. * A new path-following and obstacle avoidance control strategy for NWMRs is proposed based on the RL algorithm, specifically in the design of a new mechanism for the state and reward for both in the environment, which simplifies the dimensionality of the environment state, ensuring that the mobile robot can achieve the optimal solution between path selection and obstacle avoidance actions. Moreover, the minimum representative value approach for avoiding collisions is proposed to solve for multiple obstacles, along with path-following control. The rest of this paper is organized as follows. In Section 2, a kinematics model for the NWMRs is established and the basics of the path-following problem are briefly introduced. The basics of reinforcement learning are briefly introduced, and DDPG and the path-following and obstacle avoidance control strategy incorporating RL developed in Section 3. In Section 4, the simulation results are presented and the experiments are discussed. The conclusion is presented in Section 5.
# The Role of Low Temperature Waste Heat Recovery in Achieving 2050 Goals: A Policy Positioning Paper
## 3 Results
### Creating Demand for Green Energy  
A question often asked is whether consumer preference can be changed by appeal to public good, in this case represented by low carbon solutions. Put simply, are consumers willing to accept higher prices for types of energy with a lower carbon footprint? Classical economic theory base would say \"no\", because this would be a distortion of the market. A \"yes\" would imply that there are issues other than price which affect decisions. In fact, there is some recent evidence that public good arguments can change behaviour such as the successful recent campaigns to curtail the use of plastic bags. Such campaigns, however, may not be enough to solve the problems as it may be hard to lay the moral issues at the consumers' door.  
In general, consumers tend to value causes in which they can'see' the impacts. The success of plastic bag campaigns has been the emotional reaction to stories of plastic pollution in the oceans, for example. In the context of waste heat recovery, it may be beneficial to emphasise the public good in a local context. The idea that locally sourced heat that would otherwise 'go to waste' can be recovered and used instead of non-renewables may be an appealing one. In some areas, this may create a demand for more expensive, but greener, heat.  
Creating demand for green energy is a primary aim for the Nice demonstrator on the ReUseHeat project. The demonstrator involves the construction of an online dashboard for customers connected to a district heating network used in conjunction with heat recovery from a waste water treatment plant. The dashboard aims to make customers aware of the origin of their heat and the effect on carbon emissions. It is hoped that this will increase awareness of the issue and increase demand for green energy. Information about heat alternatives empowers the customers to make active choices (become energy citizens) and select green alternatives over others.
# Robust Adaptive Control for an Aircraft Landing
## 3 Problem Definition  
### Landing Gear System Concept  
Figure 2 shows the concept of a landing gear system equipped with an MR damper. For reducing the weight and the cost of equipment, the system has only two sensors, which are a position sensor and an accelerometer. After receiving the input signal from sensors, the control algorithm computes the required electrical current, which is then applied to the MR damper for generating the damping force. The key part of this system is the development of the control strategy to improve the performance of the landing gear.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Quantity** & **Value** & **Unit** \\ \hline \(A_{p}\) & cross area of head piston & \(2.5\times 10^{-3}\) & m\({}^{2}\) \\ \(b\) & tire force index & 1.13 & \\ \(C\) & viscous damping coefficient & 5.9–7.2 & Ns/m \\ \(D_{b}\) & moment arm of tire force from the centerline of the piston & 0.3 & m \\ \(D_{v}\) & minimum distance between the piston head and the lower seal & 0.3 & m \\ \(g\) & gravitational acceleration & 9.81 & m/s\({}^{2}\) \\ \(m_{1}\) & sprung mass (aircraft mass) & 560–680 & kg \\ \(m_{2}\) & un-sprung mass & 18 & kg \\ \(n\) & polytropic process index & 1.3 & \\ \(p_{0}\) & initial air chamber charging pressure & 636–964 & kPa \\ \(p_{ATM}\) & atmospheric pressure & 101.3 & kPa \\ \(k_{T}\) & tire force constant & 412 & kN/m \\ \(v\) & initial sink speed of aircraft at touchdown & 1.5–3.5 & m/s \\ \(V_{0}\) & initial air chamber volume & \(6.37\times 10^{-4}\) & m\({}^{3}\) \\ \(\mu\) & friction coefficient for bearing & 0.1 & \\ \(u\) & control input (MR force) & 0–10 & kN \\ \(T\) & environment temperature & \(-\)40–40 & \({}^{\circ}\)C \\ \hline \hline \end{tabular} All parameters are referenced in [18].  
\end{table}
Table 1: Landing gear parameters.  
Figure 2: Concept of a landing gear system.
## 2 Related Work
### Deep Learning Approaches  
The use of DL models as feature extractors in the context of lung cancer classification has been a growing trend within the medical domain [25]. One of the advantages of using DL models lies in their higher degree of automation, as they are capable of capturing relevant and intricate patterns within the images, and thereby extracting high-level features without the necessity of labour-intensive manual feature engineering [26]. The DL models employed can receive images as input and perform directly the feature extraction and classification. The models can receive as input a 2D single slice [12; 13; 14; 17; 18; 19; 21; 27; 28]; or receive as input a 3D volume with multiple CT slices, allowing the network to consider the spatial context of the whole volume [15; 16; 29]. Deep features can be extracted from either the nodule patches cropped from the CT images [13; 14; 15; 16; 21; 28; 29] or the complete CT slice [12; 17; 18; 19; 20; 27], removing the necessity of having annotated or segmented CT datasets. A common pre-processing step involves the implementation of data augmentation techniques, to balance the class distribution within the training set or increase the quantity of training samples available [8; 12; 13; 16; 17; 18; 19; 20; 21; 29]. The utilisation of data augmentation techniques addresses one of the major drawbacks associated with DL models, which is the necessity of substantial amounts of training data to achieve optimal performances, due to the inherent complexity of these types of models.  
In terms of Convolutional Neural Networks (CNN), a wide range of architectures have been explored, particularly including ResNet [8; 13; 16; 19; 29], VGG [12; 13; 14; 19; 27], Inception [8; 12; 19], and AlexNet [8; 13; 28]. Considering classifiers, an end-to-end deep learning approach can be adopted [8; 13; 14; 15; 16; 20; 27; 29], or employing typical ML classifiers, such as KNN and SVM [12; 13; 19; 27; 28].  
The utilisation of transformer-based models for computer vision tasks has been gaining popularity with the development of ViT architectures [30]. ViTs divide the input images into a sequence of patches that are converted into embeddings and processed by a self-attention mechanism, and then passed through a feed-forward network, allowing the model to capture long-range relationships within the input image [31]. ViTs have demonstrated their potential across several application domains, including medical image classification, where imaging exams such as CT and Magnetic Resonance Imaging have been considered and promising results were achieved [17; 21; 30]. Furthermore, Hybrid ViTs combine the advantages of both CNNs and ViTs. This approach enables models to benefit from the ability of CNNs to identify local features, while also leveraging the capability of ViTs to capture the global context of the input for improved performances [18; 32].
## 3 Model and Algorithm Flow
### Algorithm Flow  
According to the train movement status and the observation environment of the GNSS signal, three filter models were established. The algorithm flow is shown in Figure 6.  
Figure 5: Physical map of train odometer.  
When GNSS signal and the motion constraints are all available, they are used to provide measurements for the 17-dimensional EKF1 to estimate state quantities. The installation angles can be found through feedback correction. When there is a GNSS signal outage in a tunnel and the motion constraints are available, the observations of the 15-dimensional EKF2 are provided by the odometer and motion constraints with the installation angles estimated by EKF1. When the GNSS signal is available but the motion constraints are unavailable, observations of the 15-dimensional EKF3 are provided by GNSS. The errors of the inertial devices estimated by the filter are fed back to the inertial navigation algorithm.  
If the navigation mode changes, the public states and their error covariance matrix are transferred from the old algorithm to the new algorithm, and other quantities are not updated [25]. If the state vector is switched from \(\mathbf{X}_{2}\) to \(\mathbf{X}_{1}\) at time \(k\), \(\mathbf{X}_{a}\) in \(\mathbf{X}_{1}\) is directly removed and saved, the elements in the row and column corresponding to \(\mathbf{X}_{a}\) in \(\mathbf{P}_{k}\) are directly removed and saved, and the remaining quantities remain unchanged. If the state vector at time \(k\) switches from \(\mathbf{X}_{2}\) to \(\mathbf{X}_{1}\), the previously saved estimate of \(\hat{\mathbf{X}}_{a}\) is expended on \(\mathbf{X}_{2}\). In the absence of GNSS, the accuracy of state estimation will be reduced; the original saved covariance matrix elements corresponding to \(\mathbf{X}_{a}\) are then enlarged, \(\mathbf{P}_{k}\) is expanded, and the remaining elements of \(\mathbf{P}_{k}\) are multiplied by an appropriate factor. The above configuration of \(\mathbf{X}\) and \(\mathbf{P}\) is necessary to maintain the consistency of EKF.