esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Structure of One-Stage Detector Based on Background Prediction and Group Normalization
### The One-Stage Detector Training Based on Group Normalization  
Batch Normalization (BN) is commonly used in target detection, but it has certain limitations. BN makes normalization in the dimension of the batch; however, this dimension is variable. In the process of training datasets, sliding was used to calculate the mean and variance of data and directly call the training dataset, however when the training dataset and testing dataset distribution is different, this can lead to errors. Because the input image data is large in target detection, batch size can only be set as a small value in order to save memory, and a small batch size will lead to an inaccurate mean value and variance, which could degrade BN performance. In this paper, group normalization (GN) instead of BN normalize the channel dimension, and the formula is:  
\[S_{i}=\left\{k|k_{N}=i_{N},\left[\frac{k_{C}}{C/G}\right]=\left[\frac{i_{C}}{C /G}\right]\right\} \tag{1}\]  
where \(G\) represents the number of groups and is a predefined super parameter; \(C/G\) is the number of channels in each group; \(\left[\frac{k_{C}}{C/G}\right]=\left[\frac{i_{C}}{C/G}\right]\) represents indexes \(i\) and \(k\) in the same set of channels, each set of channels is stored sequentially along the C axis, GN along the (H, W) and along a set C/G channel to calculate the mean and variance; H and W are the height and width axes of space.  
GN is the mean and variance of each group in the direction of the channel, and has nothing to do with batch size, so it is not constrained by batch size. With the reduction of batch size, GN's performance is stable, while BN's performance gets worse and worse.
## 4 Evaluation Metrics  
The most frequently used evaluation metrics to compute the performance of segmentation models are Mean Intersection over Union (mIoU) and F1 score, as represented in Equations (5) and (6), respectively. Intersection over Union (IoU), also known as Jaccard Index, is a very effective and straightforward metric for segmentation. The IoU value is obtained by dividing the overlap area by the union area between the predicted segmentation and the ground truth [25]. The IoU value for \"\(j\)\" different class labels in this work is obtained by Equation (4), and the definitions for TP, FP, and FN are given in Table 3.  
\[J(A,\ B)\ or\ IoU_{j}=\frac{TP_{j}}{TP_{j}+FP_{j}+FN_{j}} \tag{4}\]  
\[Mean\ IoU=\frac{1}{j}\ \sum_{j=1}^{5}IoU_{j} \tag{5}\]  
F1 score is defined as the \"harmonic mean of precision and recall metrics\". A model with a higher F1 score represents the correct classification of each observation to its respective class, described in Equation (6).  
\[F1\ Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{6}\]  
where,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline Ground Truth Positive & True Positive (TP) & True Negative (TN) \\ Ground Truth Negative & False Positive (FP) & False Negative (FN) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for the predicted and actual label in performance measurement.  
\[Precision=\frac{TP}{TP+FP}\] \[Recall=\frac{TP}{TP+FN}\]
## 4 Prospective
### Application of Inverse Design in Optical Neural Networks
#### 4.2.1 Layered ONNs  
Layered ONNs mimic traditional layered feed-forward neural networks, which consist of convolutional layers or fully-connected layers. Taking fully-connected layers for example, the weights matrix \(\mathbf{W}\) can be decomposed by singular value decomposition (SVD) as \(\mathbf{W}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\mathbf{T}}\), where \(\mathbf{U}\) and \(\mathbf{V}^{\mathbf{T}}\) are the unitary matrices and \(\mathbf{\Sigma}\) is the diagonal matrix. It has been proved that the unitary matrix could be realized via all-optical devices [124; 125] and the diagonal matrix could be achieved by attenuators. [PERSON] et al. cascaded programmable Mach-Zehnder interferometers (MZI) for different unitary matrix designs [126], while [PERSON] et al. utilized topology optimization algorithm to design the unitary matrix, which is much more compact than cascaded MZI [80]. In both of the above-mentioned ONN architectures, the optical weights matrices are connected to a personal computer for realizing nonlinear activation, as optics are linear in silicon platform when the intensity of input light is low. In fact, the computation speed of a trained ONN is several orders faster than the traditional electronic DNNs with comparable prediction accuracy. With optical activation function, the computation speed could be accelerated even more [127]. Inverse design of nonlinear material proposed by [PERSON] et al. can be used for designing such optical activation function [128]. Similarly, [PERSON] et al. utilized phase change material (PCM) inserted in silicon platform to work as an optical switch [129] which can also be inserted into silicon platform for activation function design.
## 2 Dynamic Modeling of Space Robot and Analysis of Manipulator End Impedance  
### Three Links Free-Floating Rigid Space Robot Model  
As shown in Figure 1, the free-floating three links rigid space robot consists of a carrier \(B_{0}\), three rigid arm \(B_{1}\), \(B_{2}\) and \(B_{3}\), and the end replaceable instrument \(B_{t}\) as the insertion and extraction parts. The global inertial coordinate system \(XOY\) and basic coordinate system \(X^{\prime}O^{\prime}Y^{\prime}\) are established. Taking the base centroid \(O\) and each joint center \(O_{i}\) as the origin, the split contiguous coordinate system \(X_{i}O_{i}Y_{i}\)\((i=0,1,2,3)\) are established. In the global inertial coordinate system, the position vector of the system total centroid is defined as \(r_{c}\) and the position vector of each split contiguous part centroid is defined as \(r_{i}\)\((i=0,1,2,3)\). The position vector of the point 't' on the end of \(B_{t}\) in \(XOY\) is defined as \(r_{t}\) and in \(X^{\prime}O^{\prime}Y^{\prime}\) is defined as \(r_{t}\)'. The mass of space robot carrier and each arm are defined as \(m_{i}\)\((i=0,1,2,3)\). Their moments of inertia are defined as \(I_{i}\)\((i=0,1,2,3)\), respectively. The length, mass and the moments of inertia of \(B_{t}\) are defined as \(L_{t}\), \(m_{t}\) and \(I_{t}\). \(L_{i}\)\((i=1,2,3)\) are used to represent the length between \(O_{i}\) and \(O_{i+1}\) along the direction of \(X_{i}\)\((i=1,2,3)\). The length between \(O_{0}\) and \(O_{1}\) is defined as \(d_{0}\) and \(d_{i}\)\((i=1,2,3)\) are used to represent the length between the joint center \(O_{i}\) and the centroid of arm \(O_{ci}\). The insertion and extraction hole which the position of the hole center axis coincides with \(X_{0}\) is located at the side of carrier and the depth is h.  
The space robot generalized coordinates \(\mathbf{q}\) is defined as \(\mathbf{q}=[q_{0},q_{1},q_{2},q_{3}]^{\mathrm{T}}\), where \(q_{0}\) is the carrier attitude angle, \(q_{1}\), \(q_{2}\) and \(q_{3}\) are the joint angle, respectively. The attitude angle of end replaceable instrument \(B_{t}\) is defined as \(q_{t}\). Its system dynamics equation is expressed in the following structure (the specific derivation process can be seen in the Appendix A):  
\[\mathbf{D}(\mathbf{q})\ddot{\mathbf{q}}\ +\ \mathbf{H}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\dot{\mathbf{q }}\ =\ \tau\ +\ \tau_{d} \tag{1}\]  
where \(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}}\in\Re^{4\times 1}\) are the position, velocity and acceleration of the carrier and joint, respectively, \(\mathbf{D}(\mathbf{q})\in\Re^{4\times 4}\) is the symmetric positive definite mass matrix, \(\mathbf{H}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\dot{\mathbf{q}}\in\Re^{4\times 1}\) is the column vector of centrifugal and Koch forces, \(\tau\in\Re^{4\times 1}\) is the motor rotor control torque, \(\tau_{d}\in\Re^{4\times 1}\) is the system external bounded disturbance.
# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Concession Mechanism
#### 3.2.1 Bid Windows  
The proposed agent employs non-sliding windows of bids in many of its components. In general, the negotiation history is split into disjoint and successive windows of bids received from the opponent. These windows can contain at most \(k\) bids. The most recent window may contain less than \(k\) bids as it is filled with new offers received from the opponent. Both Figures 2 and 3 contain examples of this concept. On the one hand, Figure 2 is an example of a sequence of received bids by an opponent. The horizontal axis shows the negotiation rounds while the vertical axis denotes the received utility of the opponent's bids at each round. Each bid window is shown by colored rectangles. One may think of windows as consecutive slices of received bids. In this figure, there are four completed windows of \(k=20\) offers and one that is currently being filled with opponent offers (the last one to the right). On the other hand, Figure 3 shows a conceptual example with \(k=K\) offers and three complete bid windows, and one that is currently being filled at \(t_{current}\).  
Figure 2: The concept of bid window. In the figure, the reader can observe four consecutive windows of \(k\) offers.  
In the new Tit-for-Tat concession mechanism, the agent considers the most recent complete window pairs, \(W_{r-1}\) and \(W_{r}\) (just filled), in the history of bid windows \(\mathcal{W}=\{W_{1},\ldots,W_{r}\}\). If we consider an example with \(k=25\), the concession mechanism would work with two consecutive windows of bids of size \(k=25\). In this example, one can formulate the current window of bids as \(W_{r}=[o_{t-24},\ldots,o_{t}]\), where \(t\) is current time step and \(o_{i}\) denotes the offer sent by the opponent at round \(i\). For instance, the agent would take into account Window 2 and Window 3 at \(t_{current}\) for the given example in Figure 3. Only when the current window is filled with \(k\) bids is the concession mechanism executed again. Therefore, the concession and update of the target utility occur in window-sized mini-batches. After the update, the current window is added to the history of previous bid windows, and it is emptied to start a new bid window.
# Experimental Demonstration of Beam Scanning of Dual-Metasurface Antenna
## 2 Materials and Methods
### Design Method
Each unit cell contained two MAVR-011020-1411 varactors that were approximated as RC series elements for simulation purposes; the values of resistance and voltage-dependent capacitance were taken from the manufacturer's datasheet [28]. Varying the bias voltage changes the varactors' capacitance, which in turn affects the sheet admittance of this layer. Figure 2: Unit cell of the reconfigurable metasurface. The computation of \(Y_{\text{MMTTS}}\) must be carried out for every possible value of the gap width in the constitutive unit cell, while \(Y_{\text{RMTTS}}\) must be determined for every possible biasing state of the varactor diodes. This allowed us to obtain the equivalent impenetrable surface reactance \(X_{\text{s}}\) by solving the TRE for every combination of gap width in the upper metasurface and capacitance value in the reconfigurable plane. Moreover, at any given frequency, both admittances are also dependent on the longitudinal wavenumber \(k_{x}\)[14; 29]: namely, \(Y_{\text{MMTTS}}=Y_{\text{MMTTS}}(g,k_{x})\) and \(Y_{\text{RMTTS}}=Y_{\text{RMTTS}}(C,k_{x})\), where \(g\) and \(C\) are the gap width and the varactors' capacitance, respectively. Therefore, to compute such admittances, the technique described in [14; 30] was adopted, where the dependence on the longitudinal wavenumber was retrieved by performing scattering simulations of the constitutive unit cells for different angles of incidence. In particular, similarly to what was carried out in [30], the rational functions used to express \(Y_{\text{MMTTS}}\) and \(Y_{\text{RMTTS}}\) are:  
\[Y_{\mathrm{UP}}=Y_{0}^{\mathrm{TM}} \tag{4}\]0 \[Y_{\mathrm{UP}}=Y_{0}^{\mathrm{TM}} \tag{4}\]1  
In (12) and (13), subscripts \(z\) and \(p\) indicate zeros and poles of the rational functions. These quantities are expressed as third degree polynomials of \(k_{x}\). In the frequency range of interest ([10.55-10.75] GHz), for the considered geometries, \(Y_{\text{MMTTS}}\) is capacitive and \(Y_{\text{RMTTS}}\) is always inductive. Figure 3 shows how the surface reactance of the whole antenna is affected by a change in the varactors' bias voltage. The curve relating \(X_{\text{s}}\) to the gap width is shifted when the bias voltage goes from 0 V to 8.2 V. This means that, for a fixed spatial modulation in the upper layer, the sinusoidal profile of the surface reactance (and its average value \(X_{\text{s,ave}}\)) seen by the traveling wave is dependent on the varactors' basing state. Since \(X_{\text{s,ave}}\) is linked to the radiation angle through (2) and the period \(p\) is fixed by the upper metasurface, beam steering occurs.
###### Abstract  
The BeiDou Global Navigation Satellite System (BDS-3) can provide PPP-B2b satellite-based Precise Point Positioning (PPP) real-time service to the Asia-Pacific region via PPP-B2b signal transmitted from the three Geostationary Earth Orbit (GEO) satellites. This paper provides a comprehensive evaluation of the accuracies of the satellite's precise real-time orbit and clock products, including BDS-3 PPP-B2b precise products and the precise real-time products provided by four IGS centers (CAS, DLR, GFZ, and WHU). In addition, the influences of these real-time precise satellite products on the PPP positioning accuracy with single-frequency and dual-frequencies are also studied. Furthermore, the accuracies of broadcast ephemeris and IGS ultra-rapid products are studied, as well as their impact on PPP accuracies. Results illustrate that the orbits accuracies of PPP-B2b orbits are 9.42 cm, 21.26 cm, and 28.65 cm in the radial, along-track, and cross-track components, which are slightly lower than those of real-time orbits provided by the four IGS centers. However, the accuracy of PPP-B2b clock biases is 0.18 ns, which is higher than those provided by IGS Real-Time Service (RTS). In the static positioning test, the 3D positioning accuracy of B11+B3I dual-frequencies PPP and B1C single-frequency PPP are centimeter-level while using PPP-B2b service, which is slightly lower in horizontal components compared to those obtained based on IGS RTS products. The results of the dynamic vehicle test indicate that the positioning accuracies of B11+B2b dual-frequency PPP are about 50 cm and 120 cm in horizontal and vertical components, which are close to those of B2b single-frequency PPP using PPP-B2b service. Generally, the PPP-B2b orbit and clock accuracies on real-time PPP present similar performance to that based on IGS RTS.  
PPP-B2b service; BDS-3 new signals; RT-PPP; single-/dual-frequency RT-PPP Article
# Methods Improving Energy Efficiency of Photovoltaic Systems Operating under Partial Shading
## 3 Efficiency Estimation of PV Installation
### Proposed Method of Matching PV Modules with Mixed Commutation under Partial Shading  
To solve the problem of matching parallel-connected arrays of PV modules, the authors have developed a method that automatically regulates the output voltages of the arrays at the points of maximum power. The essence of the method is illustrated in Figure 10 and consists of the fact that to take the maximum power from the arrays of PV modules (1), the arrays are matched by including an additional power element (APE) (2) in each of them (which needs to be matched). The voltage of each additional power element is regulated using a microcontroller device (5), to a value at which the total power of the PV installation will be maximum. Electric energy is supplied to the additional battery from the same PV batteries, providing galvanic isolation, or from any other source of electrical energy. At the same time, the maximum power point of the PV arrays are monitored and optimized using the maximum power take-off device (3). After that, electrical energy is transmitted directly to the load (4), or through a converter, or is accumulated.  
Figure 9: Block diagram of differentiated maximum power take-off systems (1—module; 2—array of modules; 3—voltage stabilizers with MPPT; 4—inverter).  
The use of this method allows for the selection of electrical energy from the mismatched array of photoelectric converters in the MPP. This is due to the fact that the load at the output of such an array corresponds to the MPP. In this case, the voltage at its output is equal to the voltage of other parallel arrays. Thus, the use of additional, automatically regulated power elements allows you to set the mode for the selection of electrical energy from the mismatched array of the ESS in the MPP with an output voltage equal to the voltage of other parallel connected arrays. In this case, the power loss of the battery can be calculated from the system of equations:  
\[\left\{\begin{array}{l}P_{loss}=n_{sh}\;P_{arr}-(n_{sh\;il}P_{il\;arr}+n_{sh \;sh}P_{sh\;arr})=n_{sh\;sh}(P_{il\;arr}-P_{sh\;arr});\\ P_{il\;arr}=I_{MPP1}U_{1};\\ P_{sh\;arr}=I_{MPP1}U_{2}-\frac{\Delta U_{1}U_{1}}{U_{1}}U_{2}(1-\eta_{APE});\\ I_{MPP1}=I_{ph}-I_{0}\Big{[}exp\Big{(}\frac{q(U_{1}+I_{MPP1}R_{s})}{n_{s}(ART)} \Big{)}-1\Big{]};\\ \frac{q(U_{1}+I_{MPP1}R_{s})}{n_{s}(ART)}-ln\frac{q(U_{1}+\frac{q(U_{1}+I_{MPP1 }R_{s})}{n_{s}(ART)})}{l_{0}\Big{(}1+\frac{q(U_{1}+I_{MPP1}R_{s})}{n_{s}(ART)} \Big{)}}=0;\\ \frac{q(U_{2}+I_{MPP1}R_{s}+U_{d})}{(n_{s\;sh}(ART)}-ln\frac{q(U_{2}+I_{MPP1}R _{s}+U_{k})}{l_{0}\Big{(}1+\frac{q(U_{2}+I_{MPP1}R_{s}+U_{k})}{n_{s\;sh}(ART)} \Big{)}}=0,\end{array}\right. \tag{20}\]  
where \(\eta_{APE}\) is the efficiency of an additional power element.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 5 Algorithms for VLC-Based IPS
### Trilateration and Triangulation Algorithms  
To implement trilateration algorithms, at least three visible light sources are required for three-dimensional (3D) localization [99]. These light sources emit light signals, and the receiver measures the signal strength or time-of-flight (ToF) of these signals. A typical scheme is shown in Figure 4a. Once the distances between transmitters and receivers are determined, one can construct circles centered at each light source with radii equal to the distances. The receiver's position is then estimated as the intersection point of these circles. The intersection point represents the receiver's location in the environment.  
It is worth noting that the accuracy of the trilateration algorithm depends on the accuracy of distance measurements and the geometry of the visible light sources. Factors such as signal attenuation, interference, and multipath effects can affect the accuracy of the localization. In [100], the coordinates or orientation of a receiver, which can have either multiple or single photodiodes, are determined using the information about spherical coordinates that represent the orientation of LEDs.  
Figure 3: The number of papers for different algorithms.  
The trilateral measurement algorithm is the basis for the VLC-based indoor positioning system and provides a reliable method for determining a user's exact position. However, the effectiveness of the algorithm would much rely on the accurate distance measurements and the ability to mitigate errors caused by environmental factors.
# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 3 Methods
### Unsupervised Learning Algorithms  
This study employed five unsupervised learning algorithms to classify musical modes: Agglomerative Clustering, K-Means, DBSCAN, Mean Shift, and Self-Organizing Maps (SOM). These algorithms represent a range of classical clustering techniques [46; 47; 48]. Each algorithm was configured with specific parameters to improve its performance and adapt in to the characteristics of the data set.  
* **K-Means**[49]. For K-Means, it is necessary to specify the number of clusters to be identified. This value was set to 4, corresponding to the four diatonic modes in the dataset: Ionian, Aeolian, Mixolydian, and Dorian. The scikit-learn implementation of this algorithm was used in this study [41].
* **Agglomerative Clustering**[50]. Agglomerative Clustering also requires the user to specify the number of clusters to be identified. As with the K-Means algorithm, the number of clusters was set to 4. The scikit-learn implementation of this algorithm was used [41].  
* **DBSCAN (Density-Based Spatial Clustering of Applications with Noise)**[51]. DBSCAN relies on two key parameters: the minimum number of samples required to form a cluster (min_samples) and the maximum distance between points within a cluster (eps). In this study, min_samples was fixed at 5, the default value, as it offers a balance between identifying meaningful clusters and mitigating noise. The eps parameter was determined dynamically based on the characteristics of the input data. Specifically, a function was used to estimate the optimal eps by identifying the point at which the data distribution shifted significantly, ensuring well-separated clusters while minimizing noise. For instance, eps was set to 0.3 for PCP representations reduced with UMAP and 1.0 for Binary PCP representations. The scikit-learn implementation of this algorithm was used in this study [41].
* **Mean Shift**[52]. Mean Shift clustering requires the bandwidth parameter, which controls the search space size to identify clusters. In this study, the algorithm implementation automatically determined the optimal bandwidth based on the data distribution, eliminating the need for manual tuning. The scikit-learn implementation of this algorithm was used in this work [41].
* **Self-Organizing Maps (SOM)**[53]. The key parameter in SOM is the number of centroids, which defines the number of clusters to be identified. As with Agglomerative Clustering and K-Means, the number of centroids was set to 4, reflecting the four modes in the dataset. The MiniSom implementation of this algorithm was used [54].  
These algorithms were chosen to explore a diverse range of clustering strategies, from centroid-based methods like K-Means to density-based techniques such as DBSCAN and Mean Shift, as well as hierarchical (Agglomerative Clustering) and neural network-based approaches (SOM).