esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 2 Problem Description and System Model
### Communication Model
We followed the same model used in References [11; 21]:  
\[SNR_{j,i,ave}[k]=E(SNR_{j,i,fading}[k])=\frac{\alpha_{ji}[k]}{d_{ji}^{n_{p}}[k]} \tag{10}\]  
where \(SNR_{j,i,ave}[k]\) is the average of fading and \(SNR_{j,i,fading}[k]\) the SNR in the presence of fading. More information can be found in Reference [21]. In order to make sure that node \(i\) can successfully receive information sent by node \(j\) with a small packet loss probability, SNR at node \(i\) is required to be greater than a threshold value \(\gamma\). Therefore, if the SNR of node \(i\) from node \(j\) is greater than \(\gamma\), node \(j\) is one of node \(i\)'s neighbors. An UAV can communicate with its neighbors. \(c^{(1)}{}_{ji}[k]\) and \(\Sigma_{j,i}^{(1,1)}[k]\) represent the communication noise of \(r_{j}\) and its corresponding covariance matrix from the \(j\)-th UAV to the \(i\)-th UAV. From Equation (6) we can get:  
\[\Sigma_{j,i}^{(1,1)}[k]=\mathrm{E}({|c_{ji}^{(1)}[k]|}^{2}{|h_{j,i}[k])} \tag{11}\]  
\[\Sigma_{j,i}^{(1,1)}[k]=\psi^{(1,1)}(SNR_{j,i}[k]) \tag{12}\]  
where \(\psi(\cdot)\) can be determined as:  
\[\Sigma_{j,i}^{(1,1)}[k]=\psi^{(1,1)}(SNR_{j,i}[k])=\frac{q^{2}}{12}+\frac{4^{ Nb}-1}{3}q^{2}\times\Omega(\sqrt{SNR_{j,i}[k]}) \tag{13}\]  
where  
\[\Omega(\eta)=\frac{1}{\sqrt{2\pi}}{\int_{\eta}^{\infty}e^{-s^{2}/2}dz}. \tag{14}\]  
Similarly, the communication noise variance can be obtained with respect to \(\varphi_{j}\). Readers are referred to Reference [21] for more details. Besides the attenuation of a wireless channel, we should also consider the impact of interference between UAVs. In this paper, we used the signal-to-interference-plus-noise ratio (SINR) [13; 17] to describe it. The SINR expression for a receiver is:  
\[SINR_{j,i}[k]=\frac{Power_{j}\cdot G_{ji}}{N_{i}+\mathrm{I}_{ji}[k]} \tag{15}\]  
where \(\mathrm{I}_{ji}[k]\) is the received interference of UAV \(i\) stemming from UAV \(j\). We can replace SNR with SINR if it is needed in Equation (9) or Equation (12).
## 2 Materials and Methods
### Methodology
#### 2.2.2 Support Vector Machines  
Support vector machine is a recently developed nonlinear classification method, which is based on statistical learning theory. It transforms original input space into a higher-dimensional feature space to find optimal separating hyperplane. The hyperplane has the largest distance to the nearest training data point of any class [24].  
Assuming samples (\(x_{i}\), \(x_{j}\)) = 1, 2..., \(n\), the following function can solve the optimal separating hyperplane:  
\[\left\{\begin{array}{l}\textbf{Min}\left(\frac{1}{2}\|\overrightarrow{w}\|^ {2}+C\sum\limits_{i=1}^{n}\xi_{i}\right)\\ y_{i}\left(\overrightarrow{w}\cdot\vec{x_{i}}+b\right)-1+\xi_{i}\geq 0\\ \xi_{i}\geq 0,i=1,2\cdots,n\end{array}\right. \tag{5}\]  
where w is the weight vector that determines the orientation of the hyperplane, \(b\) is the bias, \(\xi_{i}\) is the positive slack variables for the data points that allow for penalized constraint violation, and \(C\) is the penalty parameter that controls the trade-off between the complexity of the decision function and the number of training examples misclassified. The function can be converted into an equivalent dual problem based on the [PERSON] duality theory:  
\[\left\{\begin{array}{l}\textbf{Max}\left(\sum\limits_{i}\alpha_{i}-\frac{1 }{2}\sum\limits_{ij}\alpha_{i}\alpha_{j}y_{i}y_{j}\left(\overrightarrow{x_{i }}\cdot\overrightarrow{x_{j}}\right)\right)\\ \sum\limits_{i}\alpha_{i}y_{i}=0,0\leq\alpha_{i}\leq C\end{array}\right. \tag{6}\]  
where \(\alpha_{i}\) are Lagrange multipliers and \(C\) is the penalty. Then, the decision function, which will be used for the classification of new data, can be written:  
\[f(x)=\textbf{sgn}\left(\sum\limits_{i=1}^{n}y_{i}\alpha_{i}\textbf{K}\left(x _{i},x_{j}\right)+b\right) \tag{7}\]  
where \(\textbf{K}(x_{i},x_{j})\) is the kernel function. The radial basis kernel was adopted as kernel function for the SVM model in this study.
## 4 Experiments
### Experimental Setup  
#### 4.1.1 Datasets  
The experiments were first conducted on two public datasets: ETH [45] and UCY [46]. The ETH dataset comprises two scenarios: Eth and Hotel. These scenarios contain a total of 650 tracks over 25 min. The UCY dataset includes three scenarios: Zara1, Zara2, and Univ. The Zara1 and Zara2 scenarios contain a total of 343 trajectories, and the Univ scenario contains 434 trajectories. These scenarios involve complex pedestrian behaviors, including walking in groups, avoiding collisions, and standing still.  
We preprocessed the ETH and UCY datasets by eliminating pedestrian trajectories that did not satisfy the observation and prediction time length requirements, which need a trajectory containing more than 20 time steps. We then labeled the remaining valid trajectories with the pedestrian's ID number, frame number, and two-dimensional coordinates at different time intervals. Following prior works [22; 40], we used leave-one-out cross-validation to evaluate our model, i.e., training on four scenarios and testing on the remaining one.  
Existing datasets such as ETH and UCY provide only a single ground-truth future pedestrian trajectory and do not offer multiple possible trajectories. To address this limitation, we used the CARLA simulator [47] to create the simulated multi-trajectory datasets (SMTD), which consist of two scenarios: Crossroad and Plaza. In the Crossroad scenario, 100 vehicles and 200 pedestrians move autonomously and randomly, following the default behavior of the CARLA simulator. In the Plaza scenario, 50 vehicles and 150 pedestrians are generated. We preprocessed the simulated trajectories using the same method used for the public datasets. Next, we manually labeled the multiple possible trajectories for the last 12 time steps of the pedestrian trajectories. Figure 4 shows an example of multi-trajectory labeling for one pedestrian. The red trajectory refers to the path simulated by the CARLA simulator, whereas the green trajectories represent all possible paths that the pedestrian could follow in the last 12 time steps. The simulated datasets are only used for evaluating multi-trajectory metrics.
## 4 Discussion  
### Uncertainties in Input Data and Parameters in MOD17A2  
Many studies have demonstrated that the MODIS product normally underestimates GPP compared with the EC observations, including savanna and grassland in Africa [14], and forest, grassland and cropland in east Asia [12, 13, 27, 87] and in North America [22, 24, 88]. However, yearly overestimations were also found at some forest sites [8, 13, 89, 90]. In this study, overestimated and underestimated GPP were both observed at the selected sites (Figure 2), especially in the deciduous forests and shrubs (Figure 3b). It raises doubts as to the accuracy of the simulated temporal and spatial distributions of the MODIS GPP product at regional or global scale because of errors introduced by the reanalysis of meteorological data [65], the fraction of photosynthetically active radiation (/PAR in Equation (1)) [8, 14, 71], the land cover data [14, 91] and the model structure [24, 71].  
Replacing the reanalysis meteorological and MODIS vegetation data [5, 65] with the tower data (LUEdef) did not obviously improve GPP simulations (Figure 1b). Large and small \(\sigma_{norm}\) were both observed in LUEdef, and the sites with large errors were not consistent with those in MOD17A2 (Figure 1b). For some sites, such as ES-ES1, CA-Oas, CA-Mer, AT-Neu and IE-Dri, the performances were even worse in LUEdef than in MOD17A2. Combining the tower observations with the default parameters in BPLUT [20] could compensate errors introduced by the biome-dependent parameters for some sites, such as ES-ES1, but would increase errors for others, as CA-NS1, FR-Pue and CA-NS6 (Figure 1b). Adjusting parameters using tower data could effectively improve GPP estimate by the MODIS algorithm, but systematic underestimations were observed (Figure 2), thereby indicating model structural errors.
# Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
## 6 Performances Comparison  
Table 1 shows the performance comparison between the proposed antenna and other wideband designs reported in the open literature. By observing Table 1, Refs. [39; 40] are for metallic frame applications. Even though the works in [41; 42] show much smaller sizes than the proposed one, they require very complicated structures, and the feeding technique applied is coupled-fed type. Regarding the proposed eight-antenna array that utilizes a modified loop antenna design, when compared to other loop antenna structures such as [46], it demonstrates significantly improved bandwidth performance without requiring the loop antenna to be printed on both sides of the substrate. This characteristic facilitates a simplified manufacturing process and reduced production costs for the antenna.
## 3 Beamforming Processing
### Beam Steerin  
Considering an array of N = 4 elements, with equal and unitary excitation amplitude for all elements, \(a_{n}=1\), the respective phase delay to apply to each channel in order to have the array steer its radiation to the position \(\theta\) is given by \(\alpha=-kd\ cos\theta\). Table 1 shows the estimated phase delays for each channel, considering the set of chosen locations (\(\theta\)), using an array of half-wavelength spaced elements.
# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 4 Transmission Performance Analysis of RFI-Aided URLLCs with Massive MIMO
### Reliability Analysis for RFI-Aided URLLCs
In summary, since short-packet transmission is different from traditional Shannon capacity theory, we utilized saddle-point approximation to reduce complexity and obtain a closed-form solution that is introduced in detail in the next section.
## 2 Experiments and Methodology  
### Time Series Function Model  
The GNSS coordinate time series function model is:  
\[\begin{split} y(t_{i})=a+bt_{i}+c\sin\left(2\pi t_{i}\right)+d\cos(2 \pi t_{i})\\ +e\sin\left(4\pi t_{i}\right)+f\cos\left(4\pi t_{i}\right)+\sum \limits_{j=1}^{n_{g}}g_{j}\text{H}(t_{i}-T_{hj})+v_{i}\end{split} \tag{1}\]  
where \(y(t_{i})\) denotes the coordinate and baseline time series, \(a\) denotes the initial position, \(b\) denotes the rate, and \(c\),\(d\),\(e\),\(f\) denote the annual and semi-annual period term coefficients, respectively, with the annual period being \(2\pi\) and the semi-annual period being \(4\pi\). \(\sum_{j=1}^{n_{g}}g_{j}\text{H}(t_{i}-T_{hj})\) is the step correction term. \(g_{j}\) is the step shift in position due to earthquake or antenna replacement occurring at epoch \(T_{hj}\). \(n_{g}\) denotes the number of steps occurring. \(H\) is the Heaviside step function, with \(H\) value being zero before the step and one after the step. \(t_{i}\) denotes the time in years. \(v_{i}\) is the time series residual.  
The baseline time series throughout the observation period are preprocessed with coarse difference rejection and data interpolation. Equation (1) is transformed into matrix form (2) to derive the residual time series \(\Delta\).  
\[L=BX+\Delta \tag{2}\]  
where \(\text{B}_{\text{i}}=\left[1,t_{i},\sin(2\pi t_{i}),\cos(2\pi t_{i}),\sin(4\pi t _{i}),\cos(4\pi t_{i})\right]\) and \(X=\left[a,b,c,d,e,f\right]^{\text{T}}\).
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 3 Waypoint-Based Formation-Switching Method
### Consensus-Based Waypoints Planning
#### 3.2.2 Flying to Formation Waypoint in Consistent Time
\tag{13}\]  
At this point, all local route points could be obtained. The UAVs could arrive at the formation waypoints at the same time, thus forming the expected formation. In this section, a waypoint-based distributed ad hoc network formation-switching control method, derived from the consistency theory, only needed to obtain the formation waypoint from the position information of the UAVs, and then plan the local waypoint from the current waypoint to the formation waypoint under dynamic constraints. The UAV only needed to reach the online planned waypoint under the control of its flight control system. It did not need to call the UAV's control system to track flight parameters in real time, and only required the design of four local waypoints. This method has a small amount of calculation, is practical and straightforward, and is conducive to implementation in engineering. It can also realize dynamic formation control of UAVs when some communication networks are lost and the topology structure changes.
## 2 Materials and Methods
### Research Strategy  
The SLR process involved a multi-stage pipeline, as illustrated in Figure 1, to ensure transparency and reproducibility. The workflow included the following key steps:Paper Identification: Keyword-based searches (e.g., \"LLM4 Rec\", \"BERT recommender\", \"multimodal recommendation\") were conducted to retrieve the relevant literature from scholarly databases.
* Relevance Filtering and Inclusion: Only papers focusing on LLM-based recommendation tasks, benchmark evaluations, novel architectures, or technical challenges were retained. Duplicates and irrelevant works were excluded.
* Taxonomical Classification: Selected works were categorized by architecture type (generative/discriminative), Transformer backbone (e.g., BERT, GPT, T5), modality (text, multimodal), and domain (e.g., healthcare, e-commerce).
* Comparative Analysis: Each model was evaluated using standard metrics such as NDCG, Recall@K, AUC, BLEU, and latency/accuracy trade-offs. Fine-tuning strategies like prompt tuning, LoRA, and multitask learning were also compared.
* Trend and Gap Identification: We analyzed recurring limitations (e.g., cold-start problems, bias, latency) and emerging solutions (e.g., retrieval-augmented generation, federated learning, multimodal fusion) to inform future directions.  
This process enabled us to construct a structured, domain-agnostic synthesis of the LLM4 Rec landscape, identifying both established practices and underexplored areas that warrant further research.
## 1 Introduction
During the MRgHIFU treatment, MRI equipped with a thermal mapping system can make tumors visible clearly and monitor the thermal ablation effect of target tissue based on the sensitivity to temperature changes in real time [20; 21; 22; 23]. The high spatial resolution of MRgHIFU enables the accuracy of image guidance, which offers abundant anatomic details for tumor detection [24]. However, USgHIFU is a more affordable imaging method and has many successful clinical applications. In addition to its high temporal and spatial resolution, USgHIFU is also insensitive to geometrical distortions, and all these characteristics make ultrasound imaging suitable for guiding HIFU therapy [25]. Considering the advantages of imaging capabilities combining MRI with ultrasound, a new HIFU technology has been proposed, in which ultrasound imaging is used for continuous target tracking and real-time MR-thermometry is performed to guide HIFU heating simultaneously [26]. However, MRgHIFU needs large and expensive systems in comparison with USgHIFU, which is relatively cheap and portable [27]. When USgHIFU systems are equipped with specially designed transducers, it is possible to realize real-time visual temperature monitoring of the target soft tissue [28]. It is hard to realize long-time constant temperature hyperthermia and control the temperature with little fluctuation [29] because of the lack of precisely adjustable heating and temperature measurement techniques [30]. In our study, we aim to simplify the hyperthermia system by using a ring array ultrasound transducer to perform two major functions of HIFU heating and temperature measurement in human tissue.
# Numerical Simulation of the Hydrodynamic Performance and Self-Propulsion of a UUV near the Seabed
## 2 Studied Configuration  
### UUV Geometry Parameters  
The self-propelled UUV used in this research includes three parts: the main body of the vessel (hull), the fin plates and the propellers, as shown in Figure 1. The hull profile is generated using the theoretical equation proposed in reference [6]. Its total length is 2 m and the maximum diameter is 0.2 m. It can be divided into three parts: nose, parallel section and tail end, with the lengths of 0.3 m, 1.2 m and 0.5 m, respectively. The main parameters of the UUV hull are shown in Table 1. To match the hull, a scaled-down 0.6\(\times\) DTMB4119 model propeller is used for the propulsion section, with a diameter of 0.1829 m. Detailed parameters are shown in Table 2. The hub of the propeller is suitably altered to match the stern of the hull.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 5 Simulation Results
### Symbol Synchronization Performance  
In this work, we assume that constant false alarm ratio detection is employed and set \(P_{fa}=10^{-4}\). The threshold \(V_{th}\) is dynamically calculated using Equation (27). The symbol timing synchronization performance with different quantization methods is illustrated in Figure 13. The numerical result provides a theoretical analysis for the detection probability using Equation (29), which matches well with the floating-point simulation result. For the uniform quantization methods, the detection performance increases with the quantization bit width. The proposed 2-bit non-uniform quantization method performs nearly the same as the theoretical approach in the high-\(SNR\) region (\(SNR\geq-6\) dB). For a given detection probability, the proposed 2-bit non-uniform quantization method is approximately 2 dB better than the 2-bit uniform quantization method. As a result, the effectiveness and superiority of the proposed scheme are demonstrated.
## 3 Novel Asynchronous Cooperative Diversity Scheme
### Forming Transmitted Signals  
Let us consider a cooperative system with one source node, one destination node, and four relay nodes, which communicates in bursts of length a frame over flat fading channels, the configuration of which is depicted in Figure 1. First, the source generates a modulated symbol matrix \(\mathbf{S}=\left[\mathbf{s}_{1},\cdots,\mathbf{s}_{k},\cdots,\mathbf{s}_{4}\right]\) with \(\mathbf{s}_{k}=\left[s_{k_{0}},\cdots,s_{k_{N-1}}\right]^{T}\), where \(N\) is the length of a sequence of modulatedsymbols to transmitted during \(k\)th interval, independent entries of which are carved in manner of half (\(k=1,2\)) of them from Z, the concentric rings of complex integers, and the other half (\(k=3,4\)) of them from \(\mathrm{e}^{\phi}\mathbb{Z}\), which means that Z is rotated with an angle \(\phi\).  
Subsequently, it performs \(N\)-point inverse discrete Fourier transform (IDFT) operation on \(\mathbf{s}_{1}\mathbf{s}_{3}\) and \(N\)-point discrete Fourier transform (DFT) operation on \(\mathbf{s}_{2}\mathbf{s}_{4}\), respectively. Each yielding vector \(\mathbf{\widetilde{s}}_{k}=[\widetilde{s}_{k\,0},\cdots,\widetilde{s}_{k\,N -1}]^{T}\) is appended by a cycle prefix (CP), which is composed of the last \(N_{\mathrm{cp}}\) symbols of the vector \(\mathbf{\widetilde{s}}_{k}\), as a protection interval to form one of 4 OFDM frames \(\mathbf{\widetilde{X}}\), expressed as:  
\[\mathbf{\widetilde{X}}=\left[\mathbf{X}_{1},\mathbf{X}_{2}, \mathbf{X}_{3},\mathbf{X}_{4}\right]\] \[=\left[\begin{array}{cccc}\widetilde{s}_{1\,N-N_{\sigma}}& \widetilde{s}_{2\,N-N_{\sigma}}&\widetilde{s}_{3\,N-N_{\sigma}}&\widetilde{s }_{4\,N-N_{\sigma}}\\ \vdots&\vdots&\vdots&\vdots\\ \widetilde{s}_{1\,N-1}&\widetilde{s}_{2\,N-1}&\widetilde{s}_{3\,N-1}& \widetilde{s}_{4\,N-1}\\ \widetilde{s}_{1\,0}&\widetilde{s}_{2\,0}&\widetilde{s}_{3\,0}&\widetilde{s }_{4\,0}\\ \vdots&\vdots&\vdots&\vdots\\ \widetilde{s}_{1\,N-1}&\widetilde{s}_{2\,N-1}&\widetilde{s}_{3\,N-1}& \widetilde{s}_{4\,N-1}\end{array}\right] \tag{2}\] \[=\left[\begin{array}{cccc}IDFT(s_{1\,N-N_{\sigma}})_{N-N_{ \sigma}}&DFT(s_{2\,N-N_{\sigma}})_{N-N_{\sigma}}&IDFT(s_{3\,N-N_{\sigma}})_{N -N_{\sigma}}&DFT(s_{4\,N-N_{\sigma}})_{N-N_{\sigma}}\\ \vdots&\vdots&\vdots&\vdots\\ IDFT(s_{1\,N-1})_{N-1}&DFT(s_{2\,N-1})_{N-1}&IDFT(s_{3\,N-1})_{N-1}&DFT(s_{4\,N -1})_{N-1}\\ IDFT(s_{1\,0})_{0}&DFT(s_{2\,0})_{0}&IDFT(s_{3\,0})_{0}&DFT(s_{4\,0})_{0}\\ \vdots&\vdots&\vdots&\vdots\\ IDFT(s_{1\,N-1})_{N-1}&DFT(s_{2\,N-1})_{N-1}&IDFT(s_{3\,N-1})_{N-1}&IDFT(s_{4\,N -1})_{N-1}\end{array}\right]\]  
Finally, the OFDM frames are broadcasted to the four relays. Note that here, we assume that the length of a CP is \(N_{\mathrm{cp}}\) that is larger than the maximum of the relative time delays of the signals arrived at the destination from the source.
## 5 Conclusions  
In this paper, we have proposed a new range ambiguity suppression method for up and down chirp modulation. The most important feature is the ambiguous target detection based on pulse compression using a contrary modulation rate and CFAR detection method. The method can obtain the ambiguity area image and reduce the ambiguity signal power appropriately. The effectiveness and correctness of the approach are demonstrated by processing archive images acquired by the Gaofen-3 SAR sensor in full-polarization mode. Therefore, the proposed method can be viewed as an alternative way to improve the image quality for images with range ambiguity. At the same time, the method also has some limitations as mentioned in Section 3.4, and these limitations along with the problem of scientifically evaluating the detection results are the direction of the research in the future.  
Conceptualization, X.Q. and C.D.; Methodology, X.Q., X.W. and B.L.; Software, X.W.; Validation, X.Q., X.W. and B.H.; Formal Analysis, X.Q.; Investigation, X.Q. and C.D.; Resources, Q.C. and B.L.; Data Curation, Q.C. and X.W.; Writing-Original Draft Preparation, X.W.; Writing-Review & Editing, X.W. and X.Q.; Visualization, C.D.; Supervision, C.D. and B.L.; Project Administration, X.Q.; Funding Acquisition, C.D. This work was supported by the National Science Foundation of China (grant 61331017) and the GF-3 High-Resolution Earth Observation System under Project (30-Y20A12-9004-15/16, 03-Y20A11-9001-15/16).  
The authors declare no conflict of interest.