esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization  
The field emission-scanning electron microscopy (FE-SEM) images were obtained while using the JSM-6701F system (JEOL Ltd., Tokyo, Japan). X-ray photoelectron spectroscopy (XPS) data were acquired using a Sigma probe (Thermo, Waltham, MA, USA). The electrical conductivity was measured using a Keithley 2400 system. Raman spectra were obtained while using a LabRam Aramis (Horiba Jobin Yvon, Bensheim, Germany) spectrometer. Ultra-violet visible and near infra-red (UV-vis-NIR) spectra were collected on a UV-3600 Plus spectrophotometer (Shimadzu, Tokyo, Japan).
## 5 Metrics
### Metrics for Cell Event Detection  
For mitosis detection, Ref. [54] represents each detected mitosis as a triple \((x,y,t)\) of spatial and temporal position of the event. The detection is considered a true positive (\(TP\)) if its distance from the corresponding ground truth triple is below preset spatial and temporal thresholds. Otherwise, it is considered a false positive (\(FP\)). Undetected ground truth mitotic events are considered as false negative (\(FN\)). Having so defined \(TP,FP\), and \(FN\), Ref. [54] adopts Precision, Recall, and F-score metrics defined in Equations (2)-(4), respectively, to evaluate the performance of mitosis detection algorithms. The same metrics are also adopted in [60], where they are extended also to apoptotic events.  
In [23], **DeepCell** is also used to perform semantic segmentation, i.e., to both segment individual cells and predict their cell type. For evaluating the obtained results, the authors consider the **Cellular Classification Score** (\(\text{CCS}_{c}\)) for each class \(c\), defined as  
\[\text{CCS}_{c}=\frac{\sum_{i\in Cells}s_{i,c}}{\sum_{j\in Classes}\sum_{i\in Cells }s_{i,j}},\]  
where \(s_{i,j}\) indicates the classification score of pixel \(i\) for class \(j\). The authors showed that the closer the \(\text{CCS}_{c}\) is to 1, the more likely the prediction is correct.
## 2 Related Work
### CSI-Based Signal Attenuation Model for Indoor Ranging
The propagation distance between the transmitter and receiver can be represented by effective CSI as follows:  
\[d=\frac{1}{4\pi}[(\frac{c}{f_{c}\times|CSI_{eff}|})^{2}\sigma]^{\frac{1}{n}} \tag{10}\]  
where d represents the distance between the transmitter and the receiver, \(c\) is the radio velocity, \(f_{c}\) is the central frequency of CSI, \(n\) is the path loss attenuation factor, and \(\sigma\) is the environmental factor. The basic process is to collect the original CSI (CFR form existence) value of the ranging node at each reference node. According to the characteristics of the signal attenuation model, the specific relationship between CSI value and distance is obtained, and the mathematical model of CSI value and distance is established. Since CSI ranging is easily affected by the indoor environment, the indoor attenuation model based on the original CSI can not meet the requirements of the target object for ranging accuracy. At the same time, the mathematical model of CSI value and distance is not a linear equation. The function relationship between CSI and \(d\) can be obtained from Equation (11). \[CSI_{eff}=\frac{c}{f_{c}}\sqrt{\frac{\sigma}{(4\pi d)^{n}}}. \tag{11}\]  
Figure 4: The phase frequency response of CFR under three receiving antennas.
## 2 Acceleration Based Moving Force Identification Theory
### New Acceleration-Based MFI Algorithm  
Existing MFI approaches typically use displacements as the input. In addition to this, nodal rotations and rotational velocities can be used. It is evident from Equations (3) and (12) that acceleration is not an input within the current system of equations. This section proposes an A-MFI formulation capable of using accelerations as an input. When the calculation time step is small enough, \(\{\dot{X}\}_{j+1}\) can be assumed as:  
\[\{\dot{X}\}_{j+1}=\frac{1}{h}(\{X\}_{j+1}-\{X\}_{j}) \tag{13}\]Substituting Equation (11) into Equation (13) gives:  
\[\{\dot{X}\}_{j+1}=\frac{1}{h}(([M]\{X\}_{j}+[P]\{g\}_{j})-\{X\}_{j}) \tag{14}\]  
Rearranging Equation (14) gives:  
\[\{\dot{X}\}_{j+1}=\frac{([M]-[I])}{h}\{X\}_{j}+\frac{[P]}{h}\{g\}_{j} \tag{15}\]  
Combining Equations (12) and (15) gives:  
\[\left\{ \begin{array}{c}X\\ \dot{X}\\ g\end{array} \right\}_{j+1}=\left[ \begin{array}{ccc}[M]&[0]&[P_{j}]\\ \frac{[M]-[I]}{h}&[0]&\frac{[P_{j}^{\prime}]}{h}\\ [0]&[0]&[I]\end{array} \right]\left\{ \begin{array}{c}X\\ \dot{X}\\ g\end{array} \right\}_{j}+\left[ \begin{array}{c}0\\ 0\\ I\end{array} \right]\{r\}_{j} \tag{16}\]  
Now, defining,  
\[\left[\widetilde{M}\right]=\left[ \begin{array}{ccc}[M]&[0]&[P_{j}]\\ \frac{[M]-[I]}{h}&[0]&\frac{[P_{j}^{\prime}]}{h}\\ 0&[0]&[I]\end{array} \right] \tag{17}\]  
And  
\[T=\left[ \begin{array}{c}[0]\\ [0]\\ [I]\end{array} \right] \tag{18}\]  
the new algorithm can be written as:  
\[\{\widetilde{X}\}_{j+1}=\left[\widetilde{M}\right]\{\widetilde{X}\}_{j}+[T] \{r\}_{j} \tag{19}\]  
where \(\left[\widetilde{M}\right]\) is a \([5n_{dof}\times 5n_{dof}]\) matrix and \(\{\widetilde{X}\}_{j}\) is a \([5n_{dof}\times 1]\) vector,  
\[\{\widetilde{X}\}_{j+1}=\left\{ \begin{array}{c}X\\ \dot{X}\\ g\end{array} \right\}_{j+1}=\left\{ \begin{array}{c}u\\ \dot{u}\\ \ddot{u}\\ \ddot{u}\\ g\end{array} \right\}_{j+1} \tag{20}\]  
Notably, the vector in Equation (20) includes the same velocity term twice. It can be seen from Equation (20) that the acceleration vector is included in the new vector, \(\{\widetilde{X}\}\) and that the solution of Equation (19) has a similar form to Equation (12). The next steps are identical to the previous MFI theory, which uses least-squares minimization to calculate the force vector. The error function, including the Tikhonov regularization term, \(B\), for time step \(j\), can be defined as:  
\[E(\{\widetilde{X}\}_{j},\{r_{j}\})=([Q]\{\widetilde{X}\}_{j}-\{d\}_{j},[W]([Q] \{\widetilde{X}\}_{j}-\{d\}_{j}))+(\{r\}_{j},[B]\{r\}_{j}) \tag{21}\]  
where, \(Q\) is a \((2n_{dof}\times(4n_{dof}+n_{\text{arcs}}))\) matrix, used to locate the required input signals (e.g., only bridge mid-span acceleration is used in this paper). The input signal, denoted by \(d_{j}\), is a vector of size \((2n_{dof}\times 1)\). The weighting matrix, \([W]\) is usually the identity matrix. A regularization term consisting of the product of a scalar, \(\lambda\), and the identity matrix reduces numerical instability in the solution. In this case, [PERSON]'s principle of optimality is used to solve the problem [35].
## 2 Methodology  
### Determination of Properties and Equipment  
The PLA samples to be characterized come from the remains of a laser printing company and were collected during a common production day. These samples are in the form of pellets resulting from the residues obtained after the 3D printing process as uncured resin after cleaning with isopropyl alcohol, as well as the residues obtained after the curing process by means of visible ultraviolet radiation. The properties of this material were obtained as follows. The density of the PLA was determined by volume shift and the heat capacity by the mixing method using an adiabatic calorimeter (Calorimeter PHYWE 04401.00). The main components of the PLA structure were identified by FT-IR spectroscopy (Perkin-Elmer Frontier spectrometer) in the wave number comprised between 4000 and 600 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\). To determine the kinetics of the reaction, thermogravimetric analysis (TGA) was performed using the Mettler Toledo STAR System at three heating rates, 5, 10 and 15 \({}^{\circ}\)C min\({}^{-1}\), with an inert nitrogen atmosphere at a flow of 20 mL min\({}^{-1}\) and a temperature program from 25 to 900 \({}^{\circ}\)C. The loss of mass in relation to the reaction time was studied using TGA curves and their first derivative (DTG).
# A 37-40 GHz 6-Bits Switched-Filter Phase Shifter Using 150 nm GaN HEMT
###### Abstract  
In this paper, we present a 6-bit phase shifter designed and fabricated using the 150 nm GaN HEMT process. The designed phase shifter operates within the n260 (37-40 GHz) band, as specified in the 5G NR standard, and employs the structure of a switched-filter phase shifter. By serially connecting six single-bit phase shifters, ranging from 180\({}^{\circ}\) to 5.625\({}^{\circ}\), the designed phase shifter achieves a phase range of 360\({}^{\circ}\). The fabricated phase shifter exhibits a minimum insertion loss of 5 dB and an RMS phase error of less than 5.36\({}^{\circ}\) within the 37 to 40 GHz. This phase shifter is intended for seamless integration with high-power RF circuits.  
5G NR (n260); GaN HEMT; phase shifter; STPS; T-type filter +
Footnote †: journal: Nanomaterials
## 3 OTFS Channel Estimation  
The purpose of channel estimation is to determine the parameters of (4), based on pilot signals. In this section, the pilot model and corresponding observation model are presented, followed by the baseline method and the proposed method.
## 4 Discussion and Conclusions  
The issue of reducing PM emissions by using electrostatic precipitators in small heat sources is currently one promising area of research. The first prototypes of commercially available chimney electrostatic precipitators are slowly beginning to appear on the market. Despite their simple construction and low construction costs, these devices do not achieve sufficient efficiencies. The solution to this problem is to improve the chimney separators with elements from the industrial practice of large heat sources [25; 26; 27; 28].  
This work focused on improving the separation efficiency of a standard chimney separator by increasing the number of pipes. During the work, eight measurements of PM emissions were performed on a conventional chimney separator and eight on its improved four-pipe version. Four measurements with and without electrostatic precipitator were performed for each model. The results of the measurements showed a reduction of PM emissions from an initial value of 1011.81 mg.m\({}^{-3}\) to a final value of 152.83 mg.m\({}^{-3}\), which represents separation efficiency at 85%. We achieved this reduction by improving the effect of electrostatic precipitation processes and by improving the precipitation ability of the chimney precipitator itself. The measurements confirmed that the increase in separation efficiency was achieved by reducing the distance between the electrodes and by increasing the collection surface and cross-section of the chimney. Because of the increase in the number of pipes, the collection area increased 2.4 times and the cross-sectional area increased by 30%, which led to slowing the flue gas flow rate. From the electrostatic point of view, we also managed to achieve 50% energy savings by reducing the high voltage of the electrostatic field.  
The use of electrostatic precipitators to reduce PM emissions is shown to be effective. Improving the standard chimney model by increasing the number of pipes brings significant benefits to the issue; combined with further measurements, reduction of PM emissions can be explored further by adding pipes to create a more compact and efficient model.  
Conceptualization, J.T.; Data curation, J.T.; Formal analysis, J.J. and M.H.; Funding acquisition, J.J. and M.H.; Investigation, J.T. and M.H.; Resources, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Integrated Infrastructure Operational Program for the project: Creation of a Digital Biobank to support the systemic public research infra-structure, ITMS: 313011 AFG4, co-financed by the European Regional Development Fund; VEGA 1/0233/19 \"Structural modification of a burner for the combustion of solid fuels in small heat sources\" and VEGA 1/0479/19 \"Influence of combustion conditions on the production of solid pollutants in small heat sources\".  
Not applicable.  
The authors declare no conflict of interest.
## 3 MAPPO-Based Joint Caching and Computation Offloading Scheme  
To effectively address the joint caching and computation offloading optimization problem, this paper adopts a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm. Traditional optimization methods face significant challenges when dealing with complex Mixed-Integer Nonlinear Programming (MINLP) problems, such as high computational complexity, lack of real-time applicability, and sensitivity to model parameters. Deep reinforcement learning, on the other hand, can significantly reduce online computation through offline training and adapt to dynamic environments with uncertainty [54]. Particularly for objectives that are difficult to model explicitly, such as minimizing system latency, MADRL enables efficient and robust optimization by learning the interactions and cooperation among multiple agents. To better implement the MADRL algorithm, we build it upon a Dec-POMDP framework, which will be described in detail below. Dec-POMDPs are suitable for modeling scenarios with multiple agents and partial observations [55].  
In Multi-Agent Reinforcement Learning (MADRL), various training and execution paradigms exist, as illustrated in Figure 3. These paradigms primarily differ in whether agents share information or have a centralized coordinator during the training phase, and whether agents act independently or are controlled by a central entity during the execution phase. Common paradigms include: Centralized Training Centralized Execution (CTCE), Decentralized Training Decentralized Execution (DTDE), and CTDE, which combines the advantages of both training and execution. CTCE is suitable for scenarios with high agent cooperation and fully observable environments, but centralized execution has poor robustness and is difficult to scale to large-scale systems. DTDE emphasizes agent independence, resulting in simple and robust execution, but agents face a non-stationary environment during training, making it difficult to guarantee convergence and optimality. The CTDE paradigm allows the use of global information or centralized coordination during the training phase to stabilize the learning process, while in the execution phase, each agent makes decisions independently based on its local observations. This approach balances training efficiency with the flexibility and robustness of decentralized execution, making it particularly suitable for dynamic and partially observable distributed environments like vehicular networks.
## 2 Background  
### Power System Model  
Electric power system models for transient stability analysis can be formulated as a set of non-linear Differential Algebraic Equations (DAEs), as follows [45]:  
\[\begin{bmatrix}\mathbf{F}&\mathbf{0}_{n,m}\\ \mathbf{G}&\mathbf{0}_{m,m}\end{bmatrix}\begin{bmatrix}\dot{\mathbf{x}}\\ \dot{\mathbf{y}}\end{bmatrix}=\begin{bmatrix}\mathbf{f}(\mathbf{x},\mathbf{y})\\ \mathbf{g}(\mathbf{x},\mathbf{y})\end{bmatrix}\,, \tag{1}\]  
where \(\mathbf{f}:\mathbb{R}^{n+m}\to\mathbb{R}^{n}\), \(\mathbf{g}:\mathbb{R}^{n+m}\to\mathbb{R}^{m}\); \(\mathbf{x}=\mathbf{x}(t)\), \(\mathbf{x}\in\mathbb{R}^{n}\), are the state variables, \(\mathbf{y}=\mathbf{y}(t)\), \(\mathbf{y}\in\mathbb{R}^{m}\), are the algebraic variables; \(\mathbf{F}\in\mathbb{R}^{n\times n}\), \(\mathbf{G}\in\mathbb{R}^{m\times n}\), are assumed to be constant matrices; and \(t\in[0,\infty)\) is the simulation time. Finally, \(\mathbf{0}_{n,m}\) denotes the zero matrix of dimensions \(n\times m\). For the purpose of analysis and, for sufficiently small disturbances, (1) can be linearized around an equilibrium \((\mathbf{x}_{o},\mathbf{y}_{o})\), as follows:  
\[\begin{bmatrix}\mathbf{F}&\mathbf{0}_{n,m}\\ \mathbf{G}&\mathbf{0}_{m,m}\end{bmatrix}\begin{bmatrix}\Delta\dot{\mathbf{x}}\\ \Delta\dot{\mathbf{y}}\end{bmatrix}=\begin{bmatrix}\mathbf{f}_{\mathbf{x}}\,\Delta\mathbf{x}+ \mathbf{f}_{\mathbf{y}}\,\Delta\mathbf{y}\\ \mathbf{g}_{\mathbf{x}}\,\Delta\mathbf{x}+\mathbf{g}_{\mathbf{y}}\,\Delta\mathbf{y}\end{bmatrix}\,, \tag{2}\]  
where \(\Delta\mathbf{x}=\mathbf{x}-\mathbf{x}_{o}\), \(\Delta\mathbf{y}=\mathbf{y}-\mathbf{y}_{o}\); \(\mathbf{f}_{\mathbf{x}^{\prime}}\)\(\mathbf{f}_{\mathbf{y}^{\prime}}\)\(\mathbf{g}_{\mathbf{x}^{\prime}}\)\(\mathbf{g}_{\mathbf{y}^{\prime}}\) are the Jacobian matrices calculated at \((\mathbf{x}_{o},\mathbf{y}_{o})\). This system can be rewritten in the following form:  
\[\mathbf{E}_{\rm i}\,\dot{\mathbf{z}}=\mathbf{A}_{\rm i}\,\mathbf{z}\,, \tag{3}\]  
where  
\[\mathbf{z}=\begin{bmatrix}\Delta\mathbf{x}\\ \Delta\mathbf{y}\end{bmatrix},\ \mathbf{E}_{\rm I}=\begin{bmatrix}\mathbf{F}& \mathbf{0}_{n,m}\\ \mathbf{G}&\mathbf{0}_{m,m}\end{bmatrix},\ \mathbf{A}_{\rm I}=\begin{bmatrix}\mathbf{f}_{\mathbf{x}}& \mathbf{f}_{\mathbf{y}}\\ \mathbf{g}_{\mathbf{x}}&\mathbf{g}_{\mathbf{y}}\end{bmatrix}\,.\]  
the linear system (2) is often reduced to a system of Ordinary Differential Equations (ODEs), by elimination of algebraic variables. The linearized ODE power system model can be described, as follows:  
\[\Delta\dot{\mathbf{x}}=\mathbf{A}_{\rm S}\Delta\mathbf{x}\,, \tag{4}\]  
where \(\mathbf{A}_{\rm S}=(\mathbf{F}-\mathbf{f}_{\mathbf{y}}\mathbf{g}_{\mathbf{y}}^{-1}\mathbf{G})^ {-1}(\mathbf{f}_{\mathbf{x}}-\mathbf{f}_{\mathbf{y}}\mathbf{g}_{\mathbf{y}}^{-1}\mathbf{g}_{\mathbf{x}})\) is the _state matrix_, and where we have assumed that \(\mathbf{g}_{\mathbf{y}^{\prime}}\)\(\mathbf{F}-\mathbf{f}_{\mathbf{y}}\mathbf{g}_{\mathbf{y}}^{-1}\mathbf{G}\) are non-singular.
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
###### Abstract  
Lossless image compression plays a vital role in improving data storage and transmission efficiency without compromising data integrity. However, the throughput of current lossless compression and decompression systems remains limited and is unable to meet the growing demands of high-speed data transfer. To address this challenge, a previously proposed hybrid lossless compression and decompression algorithm has been implemented on an FPGA platform. This implementation significantly improves processing speed and efficiency. A multi-core system architecture is introduced, utilizing the processing system (PS) and programmable logic (PL) of a Xilinx Zynq-706 evaluation board. The PS handles coordination. The PL performs compression and decompression using multiple cores. Each core can process up to eight image tiles at the same time. The compression process is designed with a four-stage pipeline, and decompression is managed by a dynamic state machine to ensure optimized control. The parallel architecture and innovative algorithm design enable high-throughput operation, achieving compression and decompression rates of 480 Msubpixels/s and 372 Msubpixels/s, respectively. Through this work, a practical and high-performance solution for real-time lossless image compression is demonstrated.  
Keywords:field programmable gate array (FPGA); compression; decompression; lossless; parallel process +
Footnote †: journal: Applied
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
## 3 Modified Nonlinear Fatigue Damage Accumulation Model Considering the Vibration Load Effect
### Vibration Cycle Counting  
To effectively determine the cycles \(n_{i}\) of bending stress vibration, the Rainflow tool is used. This tool is validated by [31] Section 5.4.4. According to [10], this cycle counting method is as well represented as a variable amplitude cyclic loading as it is for random vibration. In Figure 3, it is shown how the rainflow cycle counting is performed. Doing this, (1) the sample time history of stress vs. time is obtained and (2) the time history diagram is rotated \(90^{\circ}\) clockwise and the counting of cycles at specific stress range is completed. The cycles' counting results are shown in Table 1.  
Once the applied cycles \(n_{i}\) are known by using the Rainflow method, they will be used to determine the fatigue damage caused by the random vibration's effect.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Rainflow Cycles** & \\ \hline Path & Cycles & Stress Span \\ A–B & 0.5 & 2 \\ B–C & 0.5 & 3 \\ C–D & 0.5 & 5 \\ E–F & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rainflow cycling counting.  
Figure 3: Rainflow diagram.  
Figure 2: Response to a base acceleration.  
Now, it is required to estimate the total cycles \(N_{i}\) at the given bending stress level \(\sigma i_{vb}\) that the material's component can withstand.
## 3 MSVPC Design and Implementation
### Adaptive Video Streaming
The network paves its continuity path, and all users joining the group will be added to the multicast member list. This allows short interparticle distance nodes to act as forwarders and stream video as shown in Figure 5.  
The physical 5G communication is configured in the physical medium using the 256 QAM Modulation and Beamforming. The software-defined network SDN is used to virtualize the network function by utilizing the virtual network. Multicast communication between the users and the server is generated by forming the multicast tree in the network layer as IP multicast, as shown in Figure 6. The multicast users can join and leave in the multicast group and the corresponding tree is updated dynamically. Once the IP multicast is created, the data center is used to encode the video to be streamed to all multicast users as in Figure 6.  
Descriptions are distributed across the multicast tree, during the video transmission. IP multicast minimizes the unnecessary transmission of replicated packets in the network. Data centre-based video multicast servers are represented as description providers which exhibit the packet in multicast IP containing information about its description which is forwarded to the controller. The transmission is completed using the SDN which also monitors and optimizes the video transmission. The video optimizer service is deployed on the edge of the 5G network with video traffic filtering. The video traffic is forwarded from a load balancer to the optimizer virtual network which handles the management plane as an efficient assignment of IP addresses to active users of the infrastructure. Also, the cognitive framework continuously quantifies the congestion degree of the network at a fine-grain level using the congestion index. When the congestion index surpasses the threshold, then it is processed by the cognitive layer leading to the application of the filtering rule in the associated virtual network. Figure 5: Streaming flow. Figure 6: Multicast tree.