esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
###### Abstract  
Semantic segmentation of high-resolution remote sensing urban images is widely used in many fields, such as environmental protection, urban management, and sustainable development. For many years, convolutional neural networks (CNNs) have been a prevalent method in the field, but the convolution operations are deficient in modeling global information due to their local nature. In recent years, the Transformer-based methods have demonstrated their advantages in many domains due to the powerful ability to model global information, such as semantic segmentation, instance segmentation, and object detection. Despite the above advantages, Transformer-based architectures tend to incur significant computational costs, limiting the model's real-time application potential. To address this problem, we propose a U-shaped network with Transformer as the decoder and CNN as the encoder to segment remote sensing urban scene images. For efficient segmentation, we design a window-based, multi-head, focused linear self-attention (WMFSA) mechanism and further propose the global-local information modeling module (GLIM), which can capture both global and local contexts through a dual-branch structure. Experimenting on four challenging datasets, we demonstrate that our model not only achieves a higher segmentation accuracy compared with other methods but also can obtain competitive speeds to enhance the model's real-time application potential. Specifically, the mIoU of our method is 68.2% and 52.8% on the UAVid and LoveDA datasets, respectively, while the speed is 114 FPS, with a 1024 \(\times\) 1024 input on a single 3090 GPU.  
urban scene imagery; semantic segmentation; vision transformer; linear attention +
Footnote †: journal: Medical Image Processing  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
###### Abstract  
We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the Particle Swarm Optimization (PSO) algorithm for function optimization. Four parameters drive LCPSO--the number of agents; the inertia weight; the attraction/repulsion weight; and the inter-agent distance. Using APF (Artificial Potential Field), we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target.  
PSO; OSL; tracking; flocking; swarm Article
## 3 Method
### Feature Construction  
In terms of the features used, this study excluded B1, B9, and B10 bands with coarse spatial resolution and only used B2, B3, B4, B5, B6, B7, B8, B8A, B11, and B12; these represent 10 original bands and 12 vegetation indices (Table 3). These indices are selected based on early crop growth characteristics and are widely used in crop identification. Mainly included three categories: (1) The red edge index: Red-edge Spectral Index (RESI), Normalized Difference Red-edge1 (NDRE1), Normalized Difference Red-edge2 (NDRE2), Normalized Difference Red-edge3 (NDRE3), Red Edge Normalized Vegetation Index (RENDVI). (2) Low vegetation cover index: NDVI, Optimization Soil Adjusted Vegetation Index (OS-AVI), Normalized Difference Tillage Index (NDTI), Simple Ratio Index (SR). (3) High vegetation cover index: Enhanced Vegetation Index (EVI), Green Vegetation Index (Vlgreen).
## 3 Methodology
### BIM Query Dataset
By querying component attributes and relationships, users can gain in-depth insights into the components' functionality, performance, and interactions with other components. Based on the information presented in Table 2, the following examples illustrate Precise Queries and Fuzzy Queries. In BIM query tasks, precise queries and fuzzy queries represent two different user interaction modes, corresponding to structured professional query requirements and more natural, colloquial expressions, respectively. Precise queries typically have well-defined query targets and clear parameter definitions, such as specifying a spatial range, component \begin{table}
\begin{tabular}{c c} \hline \hline
**Precise Queries** & **Fuzzy Queries** \\ \hline Locate and retrieve items in the building & Try using a 5 cubic meter area to see what \\ by setting a space with a width of 5, a height of 5, and a depth of 5 & items can be found \\ Query the corresponding buildings in the building layout using the GUID: 234 & GUID: 234, help me find where the \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of Precise Queries and Fuzzy Queries. type, or GUID for retrieval. Due to their strong directionality, models usually achieve high accuracy in understanding and executing such tasks. In contrast, fuzzy queries are closer to the natural language expressions of non-expert users and may include vague descriptions, colloquial phrasing, or context-dependent query needs. For example, users may not directly provide specific values or GUIDs but instead use vague instructions like \"see what you can find in this area\" or \"help me find that wall section.\" These queries are semantically more flexible and require the model to possess a stronger ability to understand context and reason, in order to correctly translate fuzzy expressions into executable queries. In BIM information retrieval, it is crucial to support both precise and fuzzy queries. Precise queries ensure efficiency and reliability in professional environments, while supporting fuzzy queries enhances the user interaction experience for non-expert users, making BIM data access more intuitive and natural. To ensure the validity and practicality of the constructed dataset, it is essential to verify whether the designed query issues can be correctly executed in a real BIM environment. The verification process serves not only to validate the dataset content but also to assess the practical applicability of the query language and the query framework used. For the 12 query types proposed in the dataset, the verification is carried out by executing the queries within the BIMserver open-source project. The executable IFC files for real-world scenarios can be accessed in the dataset available on GitHub (tested using BIMserver v1.5.187).
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
## 4 Results and Experimental Validation of the Developed Model
### Parallel Cell Module (PCM)  
With the single-cell model designed and validated, the model forms a building block for developing the PCM and the SMP for further integration in a complete vehicle model.  
A battery PCM consists of cells that are connected in parallel while the SMP consists of battery modules that are connected in series to make a battery pack. A 14s6p battery pack configuration entails 14 SMP and six PCM. The parallel cell configuration is often useful for increasing the energy capacity of the battery pack, especially for various high energy applications.  
The current through each cell in a module \(i_{j}\) can be computed from Equation (29) if the module voltage \(v\) can be derived. By Kirchhoff's law, the sum of all the individual current that passes through a module is equal to the pack current. Also, the voltages at the terminal of all the cells in the module are equal. The cell voltage has two contributions: the instantaneous voltage that changes instantly with the current and the non-instantaneous state voltages \(v_{f}\).  
\[v(k)=v_{f,j}(k)-i_{j}(k)R_{0,j} \tag{29}\]  
\(R_{0}\) of the joule, the loss is modified to accommodate the cell terminal losses.  
The sum of the current through the individual cells in module \(i\) can be computed from Equation (29).  
\[i(k)=\sum_{j}^{p}\left(\frac{v_{f,j}(k)-v(k)}{R_{0,j}}\right) \tag{30}\]  
\(p\) is the number of parallel branches in the module and \(j\) is the branch index.  
By simultaneous computation of Equations (29) and (30), the current through each cell in the module and the voltage can be derived. The modules are connected in series to develop an SMP. To design a 48 V, 0.9 kWh battery pack, a 14s6p configuration was used.  
The temperature distribution within the battery pack was evaluated on the basis of the variation of the SOC and the capacity of the cells within the battery pack. The battery pack was simulated to show these variations. Figure 15(a) shows the variation of temperature when the initial SOC varies from 0.85 to 0.95, assuming an equal total capacity of 3.0 Ah for the individual cells. Figure 15(b) shows the variation of temperature when the cell capacity varies from 2.7 Ah to 3.0 Ah, assuming equal initial SOC of 0.95 for all cells.  
Based on the result of the simulation shown in Figure 16, the maximum temperature of the battery pack is in the range of 60 \({}^{\circ}\)C to 70 \({}^{\circ}\)C at the end of the tests. The temperature variation is larger for varying SOC than for varying capacity.  
Figure 16: Surface temperature of cells in the 14 series, 6 parallel (14s6p) battery pack for: (**a**) initial SOC variation between 0.85–0.95; and (**b**) surface temperature dynamics of cells in the battery pack for capacity variation between 2.7–3.0 Ah.
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning
### Classification of Federated Learning  
#### 2.2.1 Data Partition  
According to the difference between feature space and sample space [17], federated learning can be categorized into three types, which are horizontal federated learning, vertical federated learning, and federated transfer learning. Horizontal federated learning is suitable for scenarios that have the same feature space but different sample spaces. Vertical federated learning applies to scenarios that have the same sample space but with different feature spaces. Federated transfer learning applies to scenarios where the data comes from different participants that are very different in terms of features and samples.  
Horizontal federated learning partitions the same feature space across participants' data, brings together multiple data holders, and trains on data with the same features.  
Figure 1: A schematic diagram of federated learning.  
Horizontal federated learning improves the accuracy and generalization ability of the model and is widely used in healthcare and finance.  
As shown in Figure 2, samples with the same features from different healthcare organizations are collected through federated learning, and the shared features are used to train the model. Using horizontal federated learning to train the model not only increases the total number of training samples but also improves the accuracy of the model.  
Vertical federated learning partitions the same sample space in the participant data to align the overlapping sample data between different participants and improve the feature dimension of the sample data, and it is mainly applied between non-competing companies or organizations.  
As shown in Figure 3, financial institutions cooperate with e-commerce platforms to use different feature space data of the same users for model training to improve the feature dimension of the samples, better predict user consumption, and facilitate the rating of their service levels afterward.  
Federated transfer learning derives common representations from different feature spaces and a limited set of common samples. Transfer learning is applied to scenarios where the relevant dataset is small, but more data is needed to optimize the model performance [18; 19; 20].  
As shown in Figure 4, federated transfer learning can be used to transfer treatment and diagnosis information from different healthcare organizations to make a more comprehensive diagnosis of a disease. It can make training more flexible under different data structures, but the current research in federated transfer learning is not mature enough, so there is still much room for development.  
Figure 2: A schematic diagram of horizontal federated learning.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Low-Power Thrusters
#### 4.1.1 Sub-Kilowatt Hall Thrusters
Finally, developing flight-ready but compact cathodes to use with sub-kW thrusters is a continuing issue, as many smaller Hall thrusters presently are paired with hollow cathodes rated to handle currents greatly exceeding the discharge current of these systems [18], needlessly complicating the spacecraft integration of these thrusters.
# Simple Compact UWB Vivaldi Antenna Arrays for Breast Cancer Detection
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Antenna, Area (mm\({}^{2}\))**} & \multicolumn{2}{c}{**S\({}_{\text{T1}}\)(dB),**} & \multicolumn{2}{c}{**Peak Realized Gain (dBi)**} & \multicolumn{2}{c}{**Total Efficiency (\%) Range**} \\  & **Frequency Band (GHz), BW (GHz)** & \multicolumn{2}{c}{**\% Improvement**} & \multicolumn{2}{c}{**Total Efficiency (\%) Range**} \\ \hline  & **Meas.** & **Sim** & **Meas.** & **Sim** & **Meas.** & **Sim.** \\ \hline \multirow{2}{*}{VTSA} & \(\textless{-}11.15\), & \(\textless{-}10.84\), & 2.2–6.51 & 2.1–6.63 & 83.72–91.93 & 84.7–96.1 \\  & 3.14–13.48, 10.34 & 2.95–12.71, 9.76 & & & & \\ \hline \multirow{2}{*}{Array 1, 7560 mm\({}^{2}\)} & \(\textless{-}10.28\), & \(\textless{-}10.11\), & 3.36–8.61 & 3.49–8.94 & \multirow{2}{*}{(VTSA: 25.84\%\(\uparrow\))} & \multirow{2}{*}{68.92–81.39} & \multirow{2}{*}{70.98–93.52} \\  & 2.42–11.52, 9.1 & 2.51–10.41, 7.9 & & & & \\ \hline \multirow{2}{*}{VNASA} & \(\textless{-}10.89\), & \(\textless{-}10.32\), & 1.8–6.91 & 2.16–7.1 & 81.14–91.98 & 87.5–97.3 \\  & 2.9–13.55, 10.65 & 2.34–12.88, 10.54 & & & & \\ \hline \multirow{2}{*}{Array 2, 3628.33 mm\({}^{2}\)} & \(\textless{-}10.23\), & \(\textless{-}10.29\), & 2.51–8.14 (Array1: & 2.83–8.39 & \multirow{2}{*}{73.17–92.11} & \multirow{2}{*}{74.75–94.77} \\  & 9.76 (Array1: & 3.22–12.85, 9.63 & 5.46\%\(\downarrow\), VNASA: & (Array1: 6.26\%\(\downarrow\), & \\  & 6.76\%\(\uparrow\)) & (Array1: 17.96\%\(\uparrow\)) & 15.11\%\(\uparrow\)) & VNASA: 15.38\%\(\uparrow\)) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulated and measured results of UWB Array 1 and 2 as compared to single elements: UWB VTSA and VNA. The normalized simulated and measured radiation patterns of Array 1 and 2 are displayed in 2D polar plots and illustrated in Figure 15a-f at \(f\) = 5.85 GHz, 8.2 GHz, and 10.45 GHz for E-plane (XY-plane) and H-plane (XZ-plane). Due to higher-order modes and losses associated with fabrication errors and measurement tolerance, side lobe levels (SLLs) are introduced to patterns at high frequencies (8.2-10.45 GHz), which makes the measured pattern shape differ from the simulated one and it is more severe at the H-plane. Finally, Table 6 illustrates other recent related work in the literature on different UWB VTSA arrays and DSVA. As illustrated in Table 5, Array 1 and Array 2 are the smallest, among others (for the 1 \(\times\) N array, only the size of 1 \(\times\) 2 is taken for comparison), and they provide wider BW than [5, 9, 11, 12, 18, 20] and moderate high gain without any enhancement techniques. Although the size (1 \(\times\) 2 case) of the VTSA arrays in [18, 26] is smaller than Array 1 and Array 2, they provide higher gain and wider BW, respectively.
## 3 Model of the Digital Beamforming Architecture in System-Level CAD
### RX Signal Chain
The primary objective of this investigation is to ensure adequate signal amplification prior to the ADC stage, thereby fully utilizing its dynamic range. To achieve this, the receiver chain incorporates a base-band variable gain amplifier (VGA). An analysis of the BER as a function of the input power is performed with the subsystem parameters at their nominal value in order to evaluate the sensitivity. Results in Figure 19, blue curve, show that when the input level is below \(-95\) dBm, the BER degrades and is not acceptable. An analysis of the BER was also carried out with the parameters degraded. Also in this case, we considered a PN of \(-108\) dBc/Hz @ 1 MHz and ENOB = 4. The results are reported for better comparison in Figure 19, red curve. Also in this case, the degraded subsystem features are representative of several factors, like, for instance, aging and operative large temperature variations. Figure 19: BER vs. input power in the RX chain (with nominal front-end parameter settings). Figure 18: Influence of the ENOB on the BER, nominal condition is ENOB = 6.
## 3 Electron Spin-Dependent Processes in Si Nanocrystals  
### Excitons in Si Nanocrystals  
The same optical pumping of the electronic system of semiconductor MPs and NPs, which is used for the nuclear spin orientation (see Section 2), can also initiate the electron-spin-dependent processes, which are highly sensitive to both the size of semiconductor particles and their molecular environment. Besides the quantum confinement effect, which results in an increase of the forbidden gap and optical transition energies in nc-Si of different dimensionally and characteristic sizes from 1 to 10 nm [57; 58], the Coulomb interaction between photoexcited electrons and holes in nc-Si promotes the existence of excitons with relatively large binding energies up to 100-200 meV [57] that ensures stability for the excitons at room temperature. Furthermore, the exchange interaction in an exciton can remove the spin degeneracy for the exciton states [59]. It results in two possible spin configurations for the exciton states in nc-Si, which consist of optically active spin-singlet states and optically inactive spin-triplet ones. The triplet exciton state is lowered in energy in respect to the singlet state because of the electron-hole exchange interaction as it was experimentally demonstrated for small nc-Si in electrochemically prepared porous Si (por-Si) [60]. Despite a small singlet-triplet splitting being in the range from 1 to 30 meV the main part of excitons in nc-Si can be in the threefold degenerated triplet state even at elevated temperatures [61].  
The relatively long radiative lifetime of the excitons in nc-Si is due to both a contribution of the indirect optical transitions [61] and the spin-conservation rule [59; 60; 61]. In particular, the triplet exciton states are more populated and the corresponding radiative times of the order of several milliseconds and it is detected as the long-lived exciton PL of nc-Si at cryogenic temperatures [60; 61]. At room temperature the radiative lifetimes of excitons in nc-Si are partially controlled by the singlet exciton states, which exhibit the radiative lifetimes varied from 10 to 100 \(\upmu\)s, depending on nc-Si dimensions [61]. Because small nc-Si possess a huge accessible internal surface area up to \(\sim\)1000 m\({}^{2}\)/cm\({}^{3}\), the electronic surface states and adsorbed molecules can influence the excitons and therefore the photoluminescence properties of nc-Si [62].
# From Iris Image to Embedded Code: System of Methods
## 8 Error Correction Cascade
### Reed-Solomon Message Coding  
The unit of encoding for [PERSON]'s algorithm is the entire message, which is divided into codewords of fixed size, \(s\) bits each. A stream of \(L\) bits is cut into \(k=\lceil L/s\rceil\) words. Then additional words can be added up to the total count of \(n\) by the coding algorithms. It turns out that if no more than \(t=\lfloor(n-k)/2\rfloor\) codewords are altered then it is possible to recover the message. So, Reed-Solomon code corrects no more than \(t\) of errors, where \(t\) is half the number of redundant words. Denoting \(p=t/n\), we get  
\[p\leqslant\frac{n-k}{2n}\;. \tag{49}\]  
This number is an estimate of the tolerated error probability of a codeword. The Reed-Solomon method also imposes a limitation to a codeword count in the whole message:  
\[n\leqslant 2^{s}-1\;. \tag{50}\]  
The error probability in (49) is determined by the previous step: \(p=p_{H}\). Hence, possible Reed-Solomon codes here are determined by codeword length \(s\) and message length \(L\).
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 3 Components of Co-Simulation Tool
### Simulator System--Software
#### 3.1.2 Microsimulation Tool--PTV Vissim  
PTV Vissim is a leading microscopic traffic simulation software widely used for modeling road transportation systems [1]. It is commonly applied to evaluate junction geometries, transportation planning strategies, capacity analysis, traffic control systems, signal operations, and public transit studies. The software's reputation is built on its ability to model traffic flow with high precision, using advanced car-following and lane-changing models.  
Traffic modeling in PTV Vissim is based on [PERSON]'s car-following model, which categorizes driving behavior into four distinct states: free driving, approaching, following, and braking [36]. The transition between these states is determined by speed differences and distance to the leading vehicle, taking driver behavior characteristics into account. By adjusting the model parameters, PTV Vissim can represent different driving styles, including aggressive, neutral, and cautious behaviors. The software also includes a lane-changing model, which is rule based and can be calibrated to reflect varying levels of courtesy, aggressiveness, and lane-switching frequency among drivers.  
PTV Vissim supports multimodal transportation modeling, allowing the simulation of bicycles using the same car-following and lane-changing models as vehicles. It enables the designation of shared lanes or dedicated bicycle lanes, ensuring that bicycle interactions with motorized traffic are accurately represented. For pedestrian modeling, PTV Vissim incorporates a microscopic pedestrian simulation module that enables the representation of pedestrian behavior in various traffic environments. Pedestrians in PTV Vissim can be modeled as individual agents with distinct characteristics, such as walking speed, waiting behavior, and reaction time. Their movement is based on a social force model, which accounts for interactions with both other pedestrians and surrounding traffic elements. This model ensures that pedestrian movements are naturalistic, as they adapt their speed and trajectory based on proximity to other pedestrians, obstacles, and approaching vehicles.  
Additionally, the software includes a range of traffic control mechanisms, from simple priority rules at intersections to real-world traffic signal controllers such as Econolite ASC/3, SCATS, SCOOT, and Siemens VA. These features make PTV Vissim particularly well suited for co-simulation applications where detailed traffic dynamics and adaptive control strategies need to be incorporated.