esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Offline Optimization Algorithm
### Approximately Equivalent Fuel Consumption Minimum Strategy
\begin{array}{c}U_{\min}=\max(T_{r}-T_{e,\max},T_{m,\min})\\ U_{\max}=\min(T_{r}-T_{e,\min},T_{m,\max})\end{array} \right.. \tag{15}\]  
Figure 2: Fitting curve of the equivalent fuel consumption, (**a**) Engine; (**b**) Battery. Therefore, the constraint in the ECMS mathematical model (Equation (12)) is transformed into Equation (16), and the A-ECMS is then formed. \[\left\{ \begin{array}{l}T_{m}(t)\in\left[0,T_{r},T_{r}-T_{opt},U_{\max},U_{\max }\right]\\ i_{oct,\min}<i_{oct}(t)<i_{oct,\max}\\ SOC_{\min}<soc(t)<SOC_{\max}\end{array} \right. \tag{16}\]  
At this point, in the A-ECMS calculation process, the optimal solution can only be determined by comparing the equivalent fuel function values of these five points. This reduces the search area of the optimal solution from the allowable reachable set of the entire control variable to five search points, significantly reducing the calculation time and calculation storage space. This process also leads to a discontinuous relationship between the optimal energy allocation and the equivalent factor. Figure 3 shows the change in the optimal distribution of the power of the engine and motor corresponding to different equivalent factors when the vehicle speed is 60 km/h and the required torque is 30 Nm. It can be seen that the optimal power distribution curve is stepped; in other words, an interval of equivalent factors corresponds to an optimal power distribution. Therefore, in the A-ECMS solution process, no matter how the equivalent factor changes, the power of the engine and motor corresponding to the optimal equivalent fuel consumption is often concentrated at a few specific values.
# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 3 Proposed Method  
Even though deep neural networks produce a superior quality of object segmentation to classical and machine learning methods in skin lesion segmentation, there are some problems regarding the inhomogeneous appearance that the recent methods still suffer from. In this paper, we tackle the two problems of skin lesion segmentation, which are the variation in sizes and the fuzzy boundary of the target object. For the first problem, we propose the application of the SK Network [18] in the skip connections of the U-Net model [15]. The lightweight module captures and delivers the features from a larger receptive field and adaptively selects relevant features from different sizes of the lesions. When analyzing the contextual structure of the human skin, we notice that the lesions, in most cases, converge into a unified mass and in one dermoscopic image, there is only one lesion. Hence, provided that we comprehend the exterior constraint of the lesion, the desired object should be laid inside the surround mark. Based on this observation, we attach an additional decoder for both distance map regression and contour generation. The multitask learning approach feeds the model on the awareness of the boundary of the lesion and the gradient shift in skin pigments from impaired to healthy tissues. Details of our model are demonstrated in the following sections.
# A Hardware-Oriented Algorithm for Real-Time Music Key Signature Recognition
## 2 Materials and Methods
### Theoretical Foundations
The direction of a given axis is shown by the arrow \"\(\rightarrow\)\". There are 12 directed axes that can be inscribed in the circle of fifths, namely: F\(\sharp\rightarrow\) C; D\(\flat\rightarrow\) G, A\(\flat\rightarrow\) D, E\(\flat\rightarrow\) A, B\(\flat\rightarrow\) E, F \(\rightarrow\) B, C \(\rightarrow\) F\(\sharp\), G \(\rightarrow\) D\(\flat\), D \(\rightarrow\) A\(\flat\), A \(\rightarrow\) E\(\flat\), E \(\rightarrow\) B\(\flat\), and B \(\rightarrow\) F. Each axis divides the space of the circle into two sets of vectors. Assuming that \(K_{L}\) is the sum of the lengths of the vectors located on the left side of the considered axis \(Y\to Z\), given the observer is looking in the direction pointed by the arrow \"\(\rightarrow\)\", and \(K_{R}\) is the sum of lengths of the vectors found on the right side of that axis, for each such axis, the following characteristic value [\(Y\to Z\)] can be defined:  
\[[Y\to Z]=K_{R}-K_{L} \tag{4}\]  
**Example 2. **_--Calculation of the characteristic value [F \(\sharp\to C\)]._  
\[K_{R} =k_{B}+k_{E}+k_{A}+k_{D}+k_{G}\] \[K_{L} =k_{F}+k_{B\flat}\ +k_{E\flat}\ +k_{A\flat}+k_{D\flat}\] \[=(k_{B}+k_{E}\ +k_{A}\ +k_{D}\ +k_{G}\ )\ -(k_{F}+k_{B\flat}\ +k_{E \flat}\ +k_{A\flat}\ +k_{D\flat}\ )\]  
**Example 3. **_--Finding the main directed axis for the music signature presented in Figure 2._  
Let us consider the music signature corresponding to the fragment of the musical piece analyzed in Example 1. The table below (Table 1) contains the characteristic values [\(Y\to Z\)] associated with each of the directed axes \(Y\to Z\). The maximum value of [\(Y\to Z\)] was obtained for the D\(\flat\rightarrow\) G axis, hence D\(\flat\rightarrow\) G becomes the main directed axis of the considered music signature. **Definition 2. **_The main directed axis of the music signature is the axis \(Y\to Z\), for which the characteristic value [\(Y\to Z\)] reaches a maximum_[28]. \begin{table}
\begin{tabular}{c c c c c c} \hline C \(\rightarrow\) F\(\sharp\) & G \(\rightarrow\) D\(\flat\) & D \(\rightarrow\) A\(\flat\) & A \(\rightarrow\) E\(\flat\) & E \(\rightarrow\) B\(\flat\) & B \(\rightarrow\) F \\ \hline \(-\)2.5 & \(-\)2.7 & \(-\)1.5 & 0.3 & 1.5 & 2 \\ \hline F\(\sharp\rightarrow\) C & D\(\flat\rightarrow\) G & A\(\flat\rightarrow\) D & E\(\flat\rightarrow\) A & B\(\flat\rightarrow\) E & F \(\rightarrow\) B \\ \hline
2.5 & 2.7 & 1.5 & \(-\)0.3 & \(-\)1.5 & \(-\)2 \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristic values [\(Y\to Z\)] corresponding to the directed axes of the music signature presented in Figure 2.  
Figure 2: The music signature obtained for the fragment of the musical piece presented in Figure 1.
# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
## 2 Literature Overview
### Scenario Assignment Methods  
There are a few references that assign scenarios to real and virtual test environments. Since they are relevant for the selection of validation scenarios, we present them in more detail. The paper by [PERSON] et al. [35] presents a first idea of how a use case can be assigned to different X-in-the-Loop (XiL) approaches. The authors use components of the AV as dimensions in a Kiviat diagram and distinguish three steps for each component: simulated, emulated and physically present. In addition, they propose quality functionals to consider and weight certain influencing factors such as the quality of results or costs. They describe both the XiL approaches and the use case of a construction site assistant using the Kiviat diagrams. Since only the Vehicle-in-the-Loop approach meets the requirements of their use case, they choose this approach as their test environment.  
[PERSON] et al. [36] aim to find an optimal split between the number of scenarios in simulation and the real world. They decompose the probability that the system satisfies its requirements into two terms based on the law of total probability. The first term describes the probability that the model satisfies the system requirements. It is conditioned on the second term with the probability that the model is a valid representation. They consider also a statistical uncertainty due to sampling of test scenarios. They propose three notions of model validity: all observed trajectories are possible in simulation and lead to the same decisions, each virtual trajectory has at least one physical counterpart, or the likelihood of the virtual and physical trajectories is identical. They select the first and weakest notion and assume that both probabilities are one, which means that all tests are passed and the model is valid, so that only the statistical uncertainty remains. Then they calculate the optimal number of scenarios in simulation and the real world for different costs of both environments.  
Beyond automated driving, there is generally little literature available regarding the split between test environments. [PERSON] et al. [37], [PERSON] [38] find an optimal split between physical data to be used for model calibration and validation. [PERSON] et al. [39] similarly find the optimal number of calibration and validation tests considering both the costs at each test condition and the prediction uncertainty of the simulation model. They use a probabilistic simulation in a Bayesian framework. Model calibration reduces the uncertainty in the posterior distribution of the model parameters. In contrast, model validation again adds uncertainty to the parameters using an overall reliability metric to account for model inadequacy and sparse data, but it reduces the uncertainty about the model prediction itself. They formulate a joint optimization that selects the number of tests so that the costs and the future prediction uncertainty of the model are minimized.
# Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning
## 4 Implementation of Proposed Approach
### State-Space  
The considered state space is continuous and consists of two main input modalities, the robot information and the target object information:  
* Robot information: the data about the robot and its state is a 36-dimensional vector that contains the joint positions, velocities, gripper position, and the end-effector position, velocity, and orientation. The position of the joints is divided and represented by trigonometric functions, i.e., by sine and cosine representations. The robot has 7 DoFs, which gives a vector of 14 values for the position of each joint represented by a sin and cos function. The gripper is defined by a 2-dimensional vector that specifies the position of the left and right fingers. The joint velocities are represented by a single value for each joint, making a 7-dimensional vector. The end-effector data is represented by a 7-dimensional vector, where the position is represented in cartesian coordinates followed by a quaternion to represent the orientation. The end-effector's state also includes a 6-dimensional vector specifying the linear and angular velocity.
* Object information: the data of the object that needs to be picked up is represented by the position and orientation in the world reference frame. In order to facilitate the reaching of the cube, a relative position vector between the object and the robot's gripper is added. The object state, thus, makes a 10-dimensional vector.  
Figure 4: Franka EMIKA panda robot employed in the target grasping task in the MuJoCo environment.
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 4 IMM-TPMB Filter Driven by Model and Data Collaboration
### Predict  
Initialize the state mean \(\hat{x}_{0}\) and the error covariance \(\hat{P}_{0}\) and decompose the Gaussian terms' covariance in the target trajectory composed by Equation (44) through Cholesky decomposition \(chol(\cdot)\).  
\[S_{0}=chol(P_{0})^{\mathrm{T}} \tag{59}\]  
Use the third-order spherical-radial volume criteria to generate volume points \(\chi_{i,k-1}\), where \(\chi_{i,k-1}=S_{k-1}\hat{x}_{i}+\hat{x}_{k-1},i=1,2,\cdots,2n\). The volume points after propagation are yielded through the nonlinear state equation:  
\[\chi_{i,k|k-1}^{*}=f\Big{(}\chi_{k-1|k-1}^{\xi_{f}}\Big{)} \tag{60}\]  
Calculate the predicted state mean and square root covariance from the volume points after propagation:  
\[\left\{\begin{array}{l}\hat{x}_{k|k-1}=\dfrac{1}{2n}\sum\limits_{i=1}^{2n} \chi_{i,k|k-1}\\ \\ S_{k|k-1}=\text{{Tria}}\Big{\{}X_{k|k-1},\sqrt{Q_{k}}\Big{\}}\\ \\ X_{k|k-1}=\dfrac{1}{\sqrt{2n}}\Big{[}\chi_{1,k|k-1}^{*}-\hat{x}_{k|k-1}, \chi_{2,k|k-1}^{*}-\hat{x}_{k|k-1},\cdots,\chi_{2n,k|k-1}^{*}-\hat{x}_{k|k-1} \Big{]}\end{array}\right. \tag{61}\]  
where \(\text{{Tria}}(\cdot)\) represents the triangulation operation. The volume points after propagation are yielded through the measurement equation:  
\[\chi_{i,k|k-1}^{**}=h\Big{(}\chi_{i,k|k-1}\Big{)} \tag{62}\]  
Calculate the measurement mean and the square root covariance matrix:\[\left\{\begin{array}{ll}{\hat{z}}_{k|k-1}=\dfrac{1}{2n}{\sum\limits_{i=1}^{2n}{ \chi}_{i,k|k-1}^{**}}\\ \\ {S}_{zz,k|k-1}=\text{T}ria\Big{\{}Z_{k|k-1},S_{R}\Big{\}}\\ \\ {Z}_{k|k-1}=\dfrac{1}{\sqrt{2n}}\Big{[}{\chi}_{1,k|k-1}^{**}-{\hat{z}}_{k|k-1}, {\chi}_{2,k|k-1}^{**}-{\hat{z}}_{k|k-1},\cdots,{\chi}_{2n,k|k-1}^{**}-{\hat{z}}_ {k|k-1}\Big{]}\end{array}\right. \tag{63}\]  
Calculate the cross-covariance between the state and measurement:  
\[S_{xz,k|k-1}=X_{k|k-1}Z_{k|k-1}^{\text{T}} \tag{64}\]  
Calculate the Kalman gain:  
\[\text{K}_{k}=\Big{(}S_{xz,k|k-1}\Big{|}S_{zz,k|k-1}^{\text{T}}\Big{)}S_{zz,k|k-1} \tag{65}\]
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 2 Quantum Key Distribution Systems and Networks
### Discrete Variable Systems
E91, and its simplified version BBM92, opened a new branch in the taxonomy of the QKD protocols, being entanglement-based as opposed to the so called prepare-and-measure protocols, such as BB84, where photons are encoded (prepared) by the sender and measured by the receiver (measure). The two approaches have been proved to be theoretically equivalent in terms of security guarantee [7].
# Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario +
## 5 Discussion  
In this work, we have shown the first implementation of an IoT open-source platform applied to the management of optical networks. Network elements are implemented as IoT devices, and the optical network controller is built on a scalable IoT server platform conform with the OneM2M specifications. The high scalability of the OneM2M platform allows coping with the expected evolution of optical networks where the number of optical devices and monitors might increase together with the rate of their provided updates. The results of the two experiments show satisfactory scalability performances for the OneM2M in acquiring data from the optical network. The first scenario proves that the devices can easily share status updates with the OneM2M platform. There are no message losses, and the platform manages to handle all the received information, even when the number of nodes increases. The second scenario shows that the time needed to perform multiple simultaneous configurations increases only marginally with the number of configured elements. The timely arrival of the updates allows the controller to build a real-time snapshot of the status of the network. In this way, the network devices result to be responsive in reconfiguring themselves after learning that they cannot use some communication links anymore.  
In addition, this work also shows the potential of radically different solutions designed for IoT here applied to optical networking. This aspect is rather new in literature, where most of the proposed solutions employ Software Defined Networking without an efficient software controller. The OneM2M platform offers adequate flexibility for building custom controllers that can be configured for carrying out even very complex tasks of control and monitoring. It is just a matter of developing the appropriate applications that have to run on top of the OneM2M server. The OpenROADM paradigm makes it easy to build models for the optical devices so that they can be treated like any other sensor device by the OneM2M platform. At the same time, the porting of the NET-CONF functionalities to the REST approach is simple by combining the OpenROADM paradigm and the OneM2M standard.  
Figure 10: CPU and memory usage in one of the VMs hosting the ROADM agent.  
Figure 9: CPU and memory usage in the VM hosting the OneM2M platform.
# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 3 Proposed Method
### Implementation Details
Afterward, global average pooling is performed on the shared intermediate representation, and modulation parameters \(\alpha_{z}^{2},\beta_{z}^{2},\alpha_{z}^{2},\beta_{z}^{2},\alpha_{x}^{P},\beta _{x}^{P},\alpha_{x}^{4},\beta_{z}^{4}\in\mathbb{R}^{N_{g}}\) are obtained using a single linear projection layer. We test with different feature group number hyperparameters \(N_{g}\in\{32,16,4\}\) to find a balance between efficiency and performance. At the training stage, because starting from affine transformation multipliers \(\alpha\): that are initialized to near 0 can cause instability and inefficient training, we formulate the prediction of \(\alpha\): as in  
\[\alpha\text{: }=1+\tilde{\alpha}\text{:}\text{,} \tag{17}\]  
where the network branches \(h_{z},h_{x}\) predict \(\tilde{x}\): instead of directly predicting \(\alpha\):. This stabilizes the training process, achieves faster convergence, and can encourage the target-aware feature bottleneck module to reuse useful feature representations from the original input feature map. To train the overall framework and perform optimization of the total loss function in Equation (16), we use the Adam [64] optimizer where the size of a single batch is 16 image pairs for each update. We use a learning rate of \(10^{-4}\) and it is kept the same throughout the entire training process, and the \(L_{2}\) weight decay hyperparameter is set to \(\lambda^{R}=10^{-5}\). Other loss balancing terms \(\lambda_{p},\lambda_{Q},\lambda_{T},\lambda_{\alpha}\) are all set to 1, and we use the focal loss hyperparameter \(\gamma=2\). We set \(c^{\prime}=128\) and \(N_{g}=16\) as the default settings for our tracker. We initialize the feature extractor \(\phi(\cdot)\) with the weights of ResNet-18 and freeze the residual blocks with the exception of the last residual module, in which the stride size is changed from 2 to 1 and the output feature map has a larger spatial resolution. To implement our algorithm, we use Python 3.6 with PyTorch [67] v1.10.2 installed on an Ubuntu 20.04 environment. We train and evaluate the run-time measurements of our model on a single NVIDIA RTX 4090 GPU with 48 GB of video memory with an i9-12900K CPU with 128 GB of memory.
## 5 Results and Discussions
In addition, it has become a consensus that white Gaussian noise is stationary and linear, which is identical with the conclusion based on the binary statistical RQA measures index proposed in this paper. The RQA measures median value and statistical RQA measure index of each IMF are shown in Figure 18. The left Y-axis refers to the median value of each RQA measure and the right Y-axis refers to the statistical RQA index. The Y-axis headings have the same meaning as Table 2.  
Figure 18 shows that the vibration signal monitored from the model bridge has nonstationary and nonlinear components. IMF1 and IMF2 are stationary and linear and IMF3-IMF11 are nonstationary and nonlinear. The median value of statistical RQA measures indicate the nonstationary and nonlinear level of IMF3-IMF11. The nonstationary level and nonlinear level of IMF3-IMF4 are weak, and the nonstationary level and nonlinear level of IMF5-IMF11 are strong. With the increase of IMF, the level of non-linearity and non-stationarity increases, which indicates that apparent dynamics of the considered data are mainly controlled by its medium-frequency and low- frequency components. The conclusion happens to be consistent with that of Reference [19]. It is worth noting that the previously mentioned law cannot be obtained only according to the single RQA measure. On the basis of the nonlinear and non-stationary test of the model bridge vibration signal, the time-varying dynamical characteristics of the model bridge are studied further. The identification of the time-varying frequency damping ratio has been conducted by using the stochastic subspace identification method with a sliding window technique [48], and the results are shown in Figure 19.  
Figure 17: The IMFs energy of the given signal. Figure 18: The statistical RQA measures of each IMF. Figure 19 has shown that the first two-order frequency and damping ratio of the model bridge are gradually rising with a zigzag line, which indicates obvious time-varying characteristics, and the model bridge system is nonstationary and nonlinear. It is remarkable that the time-varying dynamic information will be lost if the non-stationarity and nonlinearity of the signal is not considered.
## 3 Dynamic Modeling and Impact Analysis
The impact effect can be obtained by Equations (7) and (9):  
\[\dot{\mathbf{q}}_{\rm r}(t_{0}+\Delta t)=\mathbf{A}^{-1}[\mathbf{M}_{\rm r}\dot{\mathbf{q}}_{ \rm r}(t_{0})+\mathbf{B}\dot{\mathbf{q}}_{\rm s}(t_{0})] \tag{10}\]where \(\mathbf{A}=\mathbf{M}_{\rm r}+\mathbf{B}\mathbf{I}_{\rm s}^{-1}\mathbf{I}_{\rm r}\), \(\mathbf{B}=\mathbf{I}_{\rm r}^{\rm T}{\left(\mathbf{J}_{\rm s}^{\rm T}\right)}^{-1}\mathbf{M}_{ \rm s}\). The impact force can be obtained by Equations (9) and (10)  
\[\mathbf{F}_{\rm P}=\frac{\left(\mathbf{J}_{\rm r}^{\rm T}\right)^{+}\mathbf{M}_{\rm r}\left[ (\mathbf{A}^{-1}\mathbf{M}_{\rm r}-\mathbf{E}_{5\times 1})\hat{\mathbf{q}}_{\rm r}(t_{0})+\mathbf{B}\hat{ \mathbf{q}}_{\rm s}(t_{0})\right]}{\Delta t} \tag{11}\]  
The hybrid system dynamic model can be obtained by Equations (5), (6) and (8) and \(\mathbf{F}_{\rm P}+\mathbf{F}_{\rm P^{\prime}}=0\):  
\[\left[ \begin{array}{cc}M_{\rm rs11}&M_{\rm rs12}\\ M_{\rm rs21}&M_{\rm rs22}\end{array} \right]\left[ \begin{array}{c}\ddot\mathbf{{x}}\\ \ddot{\mathbf{q}}_{\rm h}\end{array} \right]+\left[ \begin{array}{cc}\mathbf{H}_{\rm rs 11}+\mathbf{D}_{\rm L11}&\mathbf{H}_{\rm rs12}+\mathbf{D}_{\rm L12}\\ \mathbf{H}_{\rm rs21}+\mathbf{D}_{\rm L21}&\mathbf{H}_{\rm rs22}+\mathbf{D}_{\rm L22}\end{array}  \right]\left[ \begin{array}{c}\dot{\mathbf{x}}\\ \dot{\mathbf{q}}_{\rm h}\end{array} \right]=\left[ \begin{array}{c}\mathbf{\tau}_{\rm B }\\ \mathbf{\tau}_{\rm h}\end{array} \right] \tag{12}\]  
where \(\mathbf{M}_{\rm rs}=\mathbf{M}_{\rm r}+\mathbf{B}\mathbf{I}_{\rm s}^{-1}\mathbf{I}_{\rm r}\), \(\mathbf{H}_{\rm rs}=\mathbf{H}_{\rm r}+\mathbf{B}\mathbf{I}_{\rm s}^{-1}{\left(\dot{\mathbf{J}}_{ \rm r}-\dot{\mathbf{J}}_{\rm s}\mathbf{J}_{\rm r}^{-1}\mathbf{I}_{\rm r}\right)}\), \(\mathbf{q}_{\rm h}=\left[\theta_{0},\mathbf{q}_{\rm c}^{\rm T}\right]^{\rm T}\), \(\mathbf{X}=\left[x_{0},y_{0}\right]^{\rm T}\), \(\mathbf{\tau}_{\rm h}=\left[\tau_{0},\tau_{\rm c}^{\rm T}\right]^{\rm T}\). The dynamic model of fully controllable hybrid system can be obtained from Equation (12):  
\[\left\{ \begin{array}{l}\mathbf{M}_{\rm h}\ddot{\mathbf{q}}_{\rm h}+\mathbf{H}_{\rm h} \dot{\mathbf{q}}_{\rm h}+\mathbf{D}_{\rm Lh}\dot{\mathbf{q}}_{\rm h}=\mathbf{\tau}_{\rm h}\\ \mathbf{I}_{\rm m}\ddot{\mathbf{q}}_{\rm m}+\mathbf{D}_{\rm mc}\dot{\mathbf{q}}_{\rm m}+\mathbf{ \tau}_{\rm c}=\mathbf{\tau}_{\rm m}\\ \mathbf{K}_{\rm s}(\mathbf{q}_{\rm m}-\mathbf{q}_{\rm c})+\mathbf{D}_{\rm tc}(\dot{\mathbf{q}}_{\rm m }-\dot{\mathbf{q}}_{\rm c})=\mathbf{\tau}_{\rm c}\end{array} \right. \tag{13}\]  
where \(\mathbf{D}_{\rm Lh}=\mathbf{D}_{\rm L22}\), \(\mathbf{M}_{\rm h}=\mathbf{M}_{\rm rs22}-\mathbf{M}_{\rm rs21}\mathbf{M}_{\rm rs11}^{-1}\mathbf{M }_{\rm rs12}\), \(\mathbf{H}_{\rm h}=\mathbf{H}_{\rm rs22}-\mathbf{M}_{\rm rs21}\mathbf{M}_{\rm rs11}^{-1}\mathbf{H }_{\rm rs12}\).