esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Computational Complexity Analysis of the MSAMT  
Another essential part of measuring the performance of the algorithm is its computational complexity. The time complexity of the MSAMT is mainly determined by five main parts: the real function evaluation, the training of the dynamic ensemble of surrogates and the RBF models, the sorting of the samples in the database, the process of optimization using the G-MFEA, and other operations. The computational costs of other operations in the process are usually negligible, such as the generation and mutation of the population and the updating of the database.  
Run times of the proposed MSAMT and six competing algorithms on 10-, 20-, 30-, 50-, and 100-dimensional test functions are given in Figure 6. Based on the statistics presented in Figure 6, it was evident that for the 10-dimensional functions, the BiS-SAHA consumed the least time, followed by the FSAPSO. For the 20- and 30-dimensional functions, the FSAPSO had the lowest time cost, followed by the BiS-SAHA. The LSADE took the least time on the 50- and 100-dimensional functions, while the HSAOA and the DSP-SAEA required significantly more time than the other algorithms. This is due to the fact that the FSAPSO, LSADE, and GL-SADE were constructed only with a single surrogate, whereas the BiS-SAHA, MSAMT, DSP-SAEA, and HSAOA all used a surrogate ensemble. The proposed MSAMT consumed the most time on the 10-dimensional test functions, but with the increasing of problem dimensions, its time cost was much lower than those of the HSAOA and the DSP-SAEA.  
Figure 6: _Cont._
## 1 Introduction  
Nowadays, traditional emergency evacuation conducted by incident commanders is being superseded by cyber-physical-human systems (CPHS) [1], due to the mass of information exchange and decision-making involved, as well as the advent of low-cost microelectronic devices. As the core of emergency response systems, related emergency navigation algorithms [2; 3; 4] have motivated considerable research.  
However, previous algorithms usually employed a single metric and did not consider personalised service requirements reflected by the evacuees' own capabilities, such as mobility, resistance to hazards and physical strength. This aggravates \"nonadaptive crowd behaviours\" (such as herding, crushing, knocking, _etc._), which contribute considerably to the fatalities and injuries in densely-populated disasters [5]. In this paper, we present a novel routing algorithm that uses diverse goal functions to choose appropriate routes for the associated class of evacuees. The approach is based on the cognitive packet network (CPN) framework, which uses cognitive packets to search [6; 7; 8; 9] for optimised routes rapidly and adaptively with regard to user-defined quality of service (QoS) goals. Congestion has various negative impacts on an evacuation process. For example, it can temporarily block evacuee flows and cause long-time latency. Evacuees in electric powered wheelchairs may create an extra consumption of energy due to the slowing down and acceleration effects caused by jamming. To minimise the total number of congestion situations encountered by civilians, a congestion-aware algorithm, which combines the real-time congestion level and the average arrival and service rate, is embedded in goal functions to distribute evacuees to unoccupied routes. However, because sensitive metrics [10], such as congestion, are involved in the routing protocol, the \"direction oscillation problem\" [11] may occur, due to delays in the available information. This may confuse evacuees and reduce their confidence in the emergency response system. Hence, we set a path switch probability to ease direction oscillations. Although evacuees with portable devices retrieve a whole path to a suitable exit each time they communicate with the system, the suggested route should be updated periodically, due to the fast-changing path situation. Therefore, \"movement depth\" is introduced to facilitate the evacuees to renew their routes after traversing certain hops.  
The rest of the paper is organized as follows. Section 2 presents the related studies of emergency navigation algorithms and congestion management. In Section 3, we recall the concept of CPN. Then, we introduce the congestion-ease algorithm and the QoS metrics designed for diverse categories of evacuees in Section 4 and discuss the issue of oscillations that arise in adaptive networks and their alleviation in Section 5. The simulation model and assumptions are introduced in Section 6, and the results of experiments are discussed in Section 7. Finally, Section 8 presents the conclusion and future work.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Multi-Scale Feature Adaptive Enhancement (MS-FAE) Module
#### 3.2.3 Precise Feature Mapping and Dynamic Anchor Compensation  
To address the precision loss in feature extraction for small objects, we introduce the following two key techniques: Deformable Convolution Calibration: Deformable convolution is applied before ROI pooling to compensate for coordinate quantization errors, improving feature alignment accuracy. By learning additional offsets, deformable convolution adaptively adjusts the receptive field to key parts of the target region as follows:  
\[F_{aligned}=\text{DeformConv}(F_{in},\Delta\mathbf{p}) \tag{6}\]  
where \(\Delta\mathbf{p}\) is the learned offset, generated by an additional convolutional layer based on the input features. Dynamic Anchor Compensation Strategy: For targets of different sizes, we design an adaptive pooling strategy. For small objects (area < 32\({}^{2}\) pixels), a bidirectional adaptive pooling method is used as follows:  
\[F_{\text{fused}}=\lambda(w,h)\cdot\text{MaxPool}(F)+(1-\lambda(w,h))\cdot \text{AvgPool}(F) \tag{7}\]  
The weight coefficient \(\lambda\) is dynamically calculated based on the target area using a Sigmoid function:  
\[\lambda(w,h)=\frac{1}{1+e^{-k\cdot(w\cdot h-S_{0})}} \tag{8}\]  
Here, \(k=0.1\) controls the transition rate, and \(S_{0}=1024\) is the area threshold. This design ensures that small objects retain the most salient features (MaxPool), while larger objects preserve more of the overall distribution (AvgPool).
## 4 Methods for Mitigating Jamming  
In this section, the study extends to the evaluation of error performance in the presence of a jammer for a general SIMO FSO communication system. SIMO technology can mitigate jamming factors such as signal attenuation and atmospheric turbulence in the transmission link, thereby enhancing the reliability of communication systems. Even if one receiver encounters jamming, other receivers can still receive the optical signal. They can then perform merging and processing in the signal processing unit to recover the original data. Now, we analyze the impact of jamming on the communication performance of SIMO FSO systems.
## 3 Problem Formulation
\left\{\max_{u \in\ U}\left[\mathcal{T}_{u,travel}+\mathcal{T}_{u,download}\right]\right\} \tag{5}\]  
subject to the following constraints:  
* \((\textbf{{s}},\textbf{{p}})\cap T=\varnothing\ \forall\textbf{{s}}\in S_{\textbf{{p}}}\) and \(\textbf{{p}}\in DP\) because the reading of the sensor must be made in line of sight,
* \(DP_{u}\cap DP_{v}=\varnothing\ \forall u\
eq v\in U\), so that two different UAVs will not read sensors at the same download points,
* \(U_{u\in\ U}DP_{u}=DP\) so that all the download points are read by some UAV,
* \(S_{\textbf{{p}}}\cap S_{q}=\varnothing\ \forall\textbf{{p}}\ \
eq\ q\in DP\), so that the sensors read at two different download points are all different,
* \(U_{p}\in DP_{S}p=S\) so that all the sensors are read at some download point,
* \(DP_{u}\subseteq\textbf{{W}}_{u}\ \forall u\in U\), so that all the download points for UAV \(u\) are some of its waypoints, and
* \((\textbf{{W}}_{u,i},\textbf{{W}}_{u,i+1})\cap T=\varnothing\) and \((\textbf{{W}}_{u,i},\textbf{{W}}_{u,i+1})\cap NF=\varnothing\ \forall i=1\ldots(N_{u,u-1})\) and \(\forall u\in U\), so that no trajectory leg traverses the terrain nor a no-fly zone. As defined in Equation (5), the problem consists of finding the location of the download points \(p_{i}\), assigning the sensors to the \(\textbf{{p}}_{i}\), assigning the download points to the UAVs and calculating the trajectory for each UAV in order to minimize the total mission time that is the flying time of all UAVs together with their data transfer time. One can note that minimizing the overall mission time results from minimizing the maximum mission time of all UAVs. Although here, Equation (5) is designed to minimize the overall time of the mission, the proposed framework is configurable and can be used to minimize the distance or energy consumption depending on the mission parameters chosen by the user.
# Macro-Modelling Approach for the In-Plane Cyclic Response of Cold-Formed Steel Partition Walls
## 2 Damage Mechanism of CFS Partition Walls  
### General Construction Details of CFS Partition Walls  
A CFS partition wall generally consists of CFS framing members, and inner and outer wall panels which are made of gypsum boards (GBs). Figure 1 presents the typical Korean building construction practices for CFS partition walls. Top and bottom tracks and vertical studs are used for the CFS framing, and tracks are used to align the vertical studs. Tracks and studs are assembled with powder actuated fasteners (PAFs). GBs used as a sheathing material are attached to the CFS framing with self-drilling metal screws placed at regular intervals. Top and bottom conventional tracks without any vertical slotted holes are anchored to reinforced concrete (RC) slabs by PAFs with a spacing of 600 mm and an edge distance less than 200 mm. Studs are installed between the top and bottom tracks with a spacing of 450 or 600 mm which depends on the dimension of the prefabricated GBs. The length of the studs is determined as 10 mm shorter than the distance between the flanges of top and bottom tracks. After finishing the erection of tracks and studs, 12.5 mm thick GBs are attached to them with metal screws of which the vertical spacing is 300 mm whereas the horizontal spacing of the metal screw depends on that of the studs. A gap of 10 mm between the edge of the GBs and RC slabs provides easy erection and accommodation of their rotational deformation. In order to improve the sound and fire protection capacity of partition walls, the Korean construction practices for CFS walls adopts the installation of double layered GBs consisting of inside and finishing GBs. A finishing GB is nailed after an inside GB has been attached to studs or tracks. The edge lines of the finishing GB are installed to avoid the overlapping with those of the inside GB.
## 5 Discussion
### Adaptations in Feedback Efficiency  
The AI-driven feedback group achieved significant concept knowledge improvement in accordance with research that supports personalized learning systems. Adaptive learning technologies transform education through personalized instruction which leads to better student understanding and performance according to [12]. The use of AI-driven adaptive learning programs has proven effective in improving student learning results due to their ability to provide instantaneous feedback and promote student independence [33].  
The experimental group receiving AI-driven feedback exhibited a 28% improvement rate, surpassing the 14% improvement rate of the control group, thus demonstrating AI's capacity to double the learning gains of traditional educational methods. Ref. [34] confirm that AI enhances education by delivering instant, tailored feedback.  
The notably large 28% improvement in conceptual mastery may seem striking but is attributable to several factors: (1) the AI system's ability to deliver real-time, personalized feedback tailored to individual misconceptions, particularly for struggling learners; (2) the focus on mixed-ability classrooms, where baseline performance gaps were wider, amplifying relative gains; and (3) the 20-week intervention period, allowing sustained engagement to compound learning effects. In contrast, the control group's 14% gain reflects traditional feedback's broader, less targeted approach, aligning with prior studies reporting modest improvements (e.g., Ref. [12]). This disparity underscores the AI's precision in addressing specific conceptual deficits, though ceiling effects or initial low performance may also contribute.
## 9 Conclusions  
A four-port flexible smartphone antenna with a wideband regime is proposed for sub-6 GHz 5G and WLAN communications. The planar antenna configuration includes four conducting radiators connected to the common ground, which is printed on the flexible substrate. The optimized antenna elements radiate in the range spanning from (84.64%) 2.37-5.85 GHz. The antenna achieves self-isolation greater than 17.5 dB, a decent gain of above 4 dBi, an efficiency of 85%, and bi/ommidirectional radiation patterns with co-cross
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 3 Taguchi Method
### Phase Synthesis of the Antenna Array Radiation Pattern
Here, we present the results of beam synthesis using the Taguchi method, crossing at \(-3\) dB and minimizing a lobe to \(-20\) dB, \(-29\) dB, and \(-38\) dB, corresponding to the maximum of adjacent lobes. The goal is to cover a given angle by scanning a beam of high gain instead of a wide beam with low gain. For optimal coverage, it is necessary for the beams to intersect at \(-3\) dB. As a possible example, we mention application to a communication system covering \(-90^{\circ}\) to \(+90^{\circ}\) in steps of \(20^{\circ}\). The simulations developed here were based on secondary lobe level synthesis, phase synthesis, and template synthesis achieved by adjusting the geometric and electrical parameters of the array. The obtained results indicate a remarkable reduction in the level of secondary lobes and almost perfect adherence to the radiation pattern against the imposed functions. It is worth mentioning that the Taguchi method is effective in addressing the problem of antenna array synthesis as well, and yields excellent results, paving the way for targeted synthesis of shaped antenna arrays.
## 2 Materials and Methods
### Experiment Site and Data Collection
#### 2.1.2 UAV Data Collection  
RGB and multispectral images were acquired using DJI Phantom 4 Multispectral (P4M) (SZ DJI Technology Co., Ltd., Shenzhen, China), equipped with one RGB sensor and five monochrome sensors to measure spectral response in the blue (450 nm \(\pm\) 16 nm), green (560 nm \(\pm\) 16 nm), red (650 nm \(\pm\) 16 nm), red-edge (730 nm \(\pm\) 16 nm) and nir (840 nm \(\pm\) 16 nm) bands. Flight operations were conducted between 9:00 AM and 12:00 AM at an altitude of 120 m. The Pix4 dMapper software v4.4.9 was employed to process the raw photos captured by UAV to generate the RGB orthomosaics and reflectance maps with a spatial resolution of 6.5 cm/pixel. The reflectance maps underwent correction using the images of the calibrated reflectance panel (MicaSense, RP06), which were captured during the UAV flight. Georeferencing was performed using ArcGIS software v10.8.2 to ensure pixel alignment in the UAV images. In total, 33 UAV observations were collected (Table 1). To build the daily \(V_{cmax,0}^{25}\) model, the daily UAV images were generated using linear interpolation for each pixel. Then, daily UAV images were clipped using the external rectangle of the flux footprint climatology during the rice growth season and masked using daily flux footprint climatology (see Supplementary Material, Section S3).
## 1 Introduction
[12] compared the PWV time series with the very-long-baseline interferometry (VLBI) results, and proved that the accuracy of BeiDou PWV can still be used for real-time meteorology research. Previous researches mainly focused on obtaining high-precision ZTDs and gradients for PPP processing. However, the accuracy of estimated tropospheric delay from PPP decreases when the atmosphere suffers from short-term dramatic change. Although the additional estimation of horizontal tropospheric gradient parameters can reflect the asymmetry of tropospheric horizontal changes, remaining unmodeled errors can be absorbed by carrier phase residuals. [PERSON] et al. [13] proposed a tropospheric modeling method based on slant tropospheric delay (STD) to shorten PPP convergence. In their results, when the elevation angle was greater than 15 degrees, the accuracy of the STD based on NWM was only 3.5 cm. If considering ground-based GNSS enhancement in STD estimation, the residual error after modeling can obviously be reduced. However, no further research was conducted to investigate the impact of STD on PPP. In this study, precise STD models with consideration of the carrier phase residuals from PPP backward filtering were constructed. The effects of the STD model on PPP were investigated. This paper is organized as follows: after this introduction, Section 2 illustrates the zenith and slant tropospheric reconstruction strategies. Section 3 demonstrates the influence of high-precision STD on PPP performance. Section 4 presents the conclusions and perspectives.
# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 3 Proposed Methods
### Pollution Episode Clustering
The most widely used measures are the silhouette coefficient (SC), the Calinski-Harabaz index (CHI), and the Davies-Bouldin index (DBI), as defined as follows:  
Let the \(N\) data points be grouped by the applied clustering algorithm into \(k\) clusters, each with \(n_{i}\) points, \(i\) = 1, 2, \(\ldots\), \(k\). The \(SC\)[29] is the mean of the silhouette width for each point. The silhouette width \(S(j)\) for point \(j\) is defined as  
\[S(j)=\frac{b(j)-a(j)}{max(b(j),\ a(j))} \tag{6}\]  
where \(a(j)\) is the mean distance from point \(j\) to every other point assigned to the same cluster, and \(b(j)\) is the distance from point \(j\) to its nearest neighbor assigned to any different cluster. The \(SC\) is then given by  
\[SC=\frac{\sum_{j=1}^{N}S(j)}{N} \tag{7}\]  
The value of the silhouette coefficient, \(SC\), ranges from \(-1\) to \(1\). The higher the \(SC\), the more appropriate the clustering result. The \(CHI\)[30] measures cluster validity in terms of between-cluster and within-cluster distances. Let's denote the centroid of cluster \(i\) by \(m_{i}\). and the centroid of all clusters by \(m\). The between-cluster square distance (\(SSB\)) and the within-cluster square distance (\(SSW\)) are defined as follows:  
\[SSB=\sum_{i=1}^{k}n_{i}\parallel m_{i}-m\parallel 2 \tag{8}\]  
\[SSW=\sum_{i=1}^{k}\sum_{j=1}^{n_{i}}\parallel x_{ij}-m_{i}\parallel 2 \tag{9}\]  
where \(x_{ij}\) is the coordinate vector of point \(j\) in cluster \(i\). The \(CHI\) is then given by  
\[CHI=\frac{SSB}{SSW}\times\frac{N-k}{k-1} \tag{10}\]  
The higher the \(CHI\) is, the more valid the partition with clustering. In contrast to \(CHI\), which measures the mean \(SSB\) and \(SSW\), the \(DBI\)[31] measures the ratio between the individual \(SSW\) of a cluster and its \(SSB\) to every other cluster. The \(DBI\) is calculated as follows:  
\[DBI=\frac{1}{k}\sum_{i=1}^{k}max_{i\
eq j}\frac{\sqrt{\sum_{h=1}^{n_{i}} \parallel x_{ih}-m_{i}\parallel 2}+\sqrt{\sum_{h=1}^{n_{i}}\parallel x_{jh}-m_{j} \parallel 2}}{\sqrt{\parallel m_{i}-m_{j}\parallel 2}} \tag{11}\]  
The smaller the \(DBI\), the more preferable the clustering result.
## 5 Results
### Validation by Comparison with Experiments
#### 5.2.1 Results for Experiments Involving Fog  
Figure 13 shows the comparison of the real experiment and the simulation. This shows that the simulation behaves very similar to the results from the physical experiments reported by [12]. The results for the Livox and Aeye lidar types have almost identical performance in the simulation, and therefore, the traces are indistinguishable. The results for the Valeo lidar also show good correlation between simulation and physical experiments, with the exception of the experiment with low visibility of (25 \(\pm\) 5) m. Here, the experiment already detects some points, which make no practical difference, as the amount of returned points is very low.  
Across the experiments, the results show that if visibility is higher than 40 m, then the number of detected points does not increase, and the graph is more or less linear. This finding is also very similar to the analysis of [11]. Overall, the simulation of foggy weather with the empirical models proposed in this paper shows results that are consistent and comparable to the real experiments, providing good validation.  
Figure 12: Simulated view of a front camera on the target car at a distance of 23 m.  
Figure 13: Comparison of real and simulated lidar during fog. The vertical and horizontal spread and shadows stand for the standard deviation of the number of detected points and was extracted from the references.
## 2 A Repeater and the Spoofing Rebroadcasted by It  
In most cases, the counterfeit signals generated by a satellite signal simulator are not overlapped effectively with the authentic signals present in the space in which the receiver is located. If the counterfeit signal and the authentic signal correspond to the same satellite, and the code phase difference between them is less than \(1+d\) chips, then the overlapping of the two signals is considered to be effective in this paper, where \(d\) is the spacing between early and prompt correlators (or the spacing between prompt and late correlators) of the code loop. The autocorrelation and cross-correlation properties of C/A codes mean that the counterfeit signal is equivalent to the noise for the receiver channel which locks the authentic signal, i.e., it is difficult for the simplistic spoofing to affect the positioning results of the tracking receiver. By contrast, the spoofing executed by the repeater can affect the positioning results of the target receiver without reacquisition. As shown in Figure 1, the repeater consists of two modules, namely the receiver module and the spoofer module. The receiver module is the same with an ordinary GPS receiver. The spoofer module properly delays and amplifies the received authentic signals and rebroadcasts the resulting counterfeit signals toward the target receiver. In order to ensure that counterfeit signals are effectively overlapped with the authentic signals, the repeater needs to know the approximate location of the target receiver to determine the appropriate time delay. Since the size of the repeater is usually small, it can be inconspicuously deployed near the target receiver, which facilitates the execution of spoofing [1]. Since it is easy for the simplistic spoofing to be handled, the spoofing mentioned subsequently in this paper is always executed by the repeater and has the form shown in Figure 2. The correlation peaks of the counterfeit signal and the authentic signal have successively gone through three stages: approaching, effective overlapping, and moving away. The amplitude of the counterfeit signal is higher than that of the authentic signal throughout the process. In fact, as long as \(\lambda\) is greater than 1.1, the target receiver will gradually lose its lock on the authentic signal [16]. \(\lambda\) is the power ratio of the counterfeit signal to the authentic signal.