esa-sceva/satcom-chunk-collection huggingface dataset:
# Faster MDNet for Visual Object Tracking
###### Abstract  
With the rapid development of deep learning techniques, new breakthroughs have been made in deep learning-based object tracking methods. Although many approaches have achieved state-of-the-art results, existing methods still cannot fully satisfy practical needs. A robust tracker should perform well in three aspects: tracking accuracy, speed, and resource consumption. Considering this notion, we propose a novel model, Faster MDNet, to strike a better balance among these factors. To improve the tracking accuracy, a channel attention module is introduced to our method. We also design domain adaptation components to obtain more generic features. Simultaneously, we implement an adaptive, spatial pyramid pooling layer for reducing model complexity and accelerating the tracking speed. The experiments illustrate the promising performance of our tracker on OTB100, VOT2018, TrackingNet, UAV123, and NFS.  
visual object tracking; deep learning; domain adaptation +
Footnote †: journal: Applied sciences
## 2 Modelling Methodology
### Meanline Design  
The turbine operating conditions obtained from the cycle model are used in this intermediate step to give an insight on the possible performance range and geometric dimensions of the radial expander. A meanline design tool is developed using MATLAB by coding established turbomachinery equations.  
The programme NIST REFPROP has been cited widely throughout the literature for defining the thermodynamic and transport properties in sCO\({}_{2}\) turbomachinery design tools [14; 15; 16; 17]. It provides thermophysical properties of pure and compound fluids over a broad range of states for liquids, gases and supercritical phases based on validated data and calculation methods.  
The initial design procedure is carried out using a meanline approach, which assumes one-dimensional passage conditions at mean radius of the turbomachine. The meanline aerodynamic design procedure of this work follows the approach introduced by [PERSON] [18] that can encompass the full operating range of a radial turbine. This method is used to determine the outline geometry of the turbine, given specific inputs, and to investigate the dependency of overall performance (size, efficiency, power) on key design parameters and assumptions such as specific speed and operating conditions [19]. Within the MATLAB tool, the estimate of the turbomachinery efficiency is obtained without the integration of loss models as these get incorporated in later stages of design. The main goal of this step is to obtain a range of turbine size parameters for use in the preliminary design tool of AxSTREAM in Section 2.3.
# Structured Summarization of League of Legends Match Data Optimized for Large Language Model Input
## 4 Experiments
### Findings and Contributions  
The findings from this study confirm that the LoL-MDC is highly effective at compacting esports match data while preserving its analytical value. The compactor achieved up to 97% reductions in data size and token counts, enabling efficient analysis within the input constraints of LLMs. It demonstrated compatibility with tokenization schemes such as o200k_base and cl100k_base, making it suitable for diverse LLM architectures. By preserving match narratives, key events, and performance metrics, the LoL-MDC enables meaningful insights that align closely with expert analyses.  
The qualitative evaluation further highlights the advantages of compacted JSON inputs for question-answering tasks. Using compacted JSON files, GPT-4o generated coherent and accurate responses across all question categories, including general match statistics, high-level overviews, and cross-match comparisons. In contrast, raw JSON inputs often led to incomplete or incorrect answers due to the token limitations of LLMs. These findings validate the LoL-MDC's ability to transform complex match data into manageable and insightful formats, enabling LLMs to answer a wide range of analytical questions effectively. Our qualitative evaluation demonstrates that the compacted format provided by the LoL-MDC did more than remove redundant metadata. It improves the accuracy and coherence of LLM-generated responses compared with the baseline JSON files, indicating practical analytical advantages beyond data size reduction.  
Additionally, the modular design of the compactor ensures scalability across patch versions and adaptability to other esports titles, further broadening its application scope. The ability to adjust components such as the timeline view intervals and the number of selected key events enables analysts to balance data granularity with computational efficiency. The experimental results also emphasize the LoL-MDC's computational efficiency, allowing it to process entire tournaments or seasons within feasible timeframes. These contributions position the LoL-MDC as a robust and versatile tool for advancing esports analytics and unlocking new possibilities for AI applications in competitive gaming.
###### Abstract
[17] performed a numerical study for the AGF technique applied in a Naples underground project, and they found that the soil thermal conductivity was a key parameter in ground freezing. The frozen zone developed more slowly in areas with a low thermal conductivity. [PERSON] et al. [18] pointed out that the earth heat infiltration in the AGF process showed a remarkable dependence on the thermal properties of rock mass, such as the thermal conductivity and heat capacity. However, it is known that the physical and thermal parameters of soil show inherent and great spatial variations [19; 20; 21; 22; 23], which directly influence the engineering performance, probabilistic design and reliability analysis of geotechnical structures [24; 25; 26; 27; 28]. Therefore, it is necessary to investigate whether and how the spatial variations of soil thermal properties affect the AGF technique. [PERSON] et al. [29] investigated the impact of uncertain thermal parameters on the heat transfer around a single freeze pipe, and the soil thermal conductivity was found to be the main factor affecting thermal stability. [PERSON] et al. [10] explored the effect of uncertain soil thermal conductivity on two freeze pipes and the results indicated that the spatially varied soil thermal conductivity had some influences on the freezing performance. However, few similar researches discussed the effects of uncertain soil thermal conductivity on a specific AGF project subject to seepage flow. No quantitative compensation remedy has been proposed for the spatial variability of soil thermal conductivity. For a general tunnel excavation project using AGF technology, Figure 1 depicts the adverse effects of seepage flow and spatial variability of soil thermal conductivity. Distinct to the soil freezing process in homogeneous soil without seepage, groundwater flow affects the shape of the frozen body and its growth on the upstream surface of the tunnel, leading to potential leakage accidents during excavation. In addition, the spatial variability of soil thermal conductivity brings the uncertainty of freezing efficiency around each freeze pipe, increasing the adverse effects caused by seepage flow. To ensure the anti-seepage and supporting capacity of the frozen wall, more additional freezing time is required. Different to previous work, this study combines the effects of seepage flow and uncertain soil thermal conductivity on the soil freezing evolution of a metro tunnel project. In this work, the finite element software COMSOL Multiphysics(r) (Version 5.3) is employed to develop a couple hydro-thermal model and simulate the temperature evolution of the AGF system. The numerical simulation is based on a full coupling of temperature and seepage field by involving the heat transfer in porous media and the physical interfaces of Darcy's law. The spectral representation method (SRM) is used to generate lognormal random fields of soil thermal conductivity, and groups of Monte-Carlo simulations are carried out for statistical analysis.
## 2 Results
### Superconducting Pairing: Nodal Character and Dimensionality
We showed that the leading eigenvalue in the singlet channel had a \(d_{x^{2}-y^{2}}\) instability (B\({}_{1g}\) symmetry), while the leading eigenvalue in the triplet channel was of an extended nodeless \(s\)-wave \(2\delta_{0}+\cos k_{x}+\cos k_{y}\) gap structure with A\({}_{1g}\) irreducible representation in the \(d_{xz,yz}\) basis. A subsequent Bogoliubov quasiparticle scattering interference visualization of the gap structure at milli-Kelvin temperatures was measured to be of a B\({}_{1g}\)-\(d_{x^{2}-y^{2}}\) nature [5]. We observe that for all strains (and without strain), the eigenvalue corresponding to the singlet instability remains the leading one, and the relative strength of the singlet to triplet eigenvalues (\(\lambda_{s}/\lambda_{t}\)) keeps increasing under strain. The enhancement in \(\lambda_{s}/\lambda_{t}\) under strain becomes more apparent at lower temperatures (see Figure 5a). This is concomitant with the mass becoming heavier in the \(d_{xy}\) channel, while the masses relax on other orbitals. Further, this is a direct consequence of the spin fluctuations getting suppressed at \(\mathbf{q}^{\text{FM}}=(0,0,0)\) and rising steeply at \(\mathbf{q}^{\text{IC}}=(0.3,0.3,0)\) and \(\mathbf{q}=(0.5,0.25,0)\). It is understandable that the system can undergo a spin density wave (SDW) order mediated primarily via the fluctuations at and around these quasi-antiferromagnetic vectors. Once the spin susceptibility diverges, at lower temperatures, under large strains, the system will encounter the density wave phase, and the superconducting channel will be suppressed. To check that we extract the leading eigenvalue (\(\lambda_{SDW}\)) in the density wave channel, we diagonalise the susceptibility matrix. We observe that while for \(\epsilon_{x}=0.0\), \(\lambda_{SDW}\) and \(\lambda_{s}\) show a very similar temperature dependence (\(\lambda_{s}\) is slightly more steeper than \(\lambda_{SDW}\)), for finite and large strains (\(\epsilon_{x}>0.6\%\)), \(\lambda_{SDW}\) acquires a steeper temperature dependence than \(\lambda_{s}\) (see Figure 5b). This suggests that although \(\lambda_{s}/\lambda_{t}\) continues to enhance under large strains, the superconducting phase will be suppressed by an SDW phase: the normal Fermi liquid phase will make a transition to the SDW phase before it becomes superconducting [3; 9; 13]. We observe that all our essential conclusions for both spin and superconducting instabilities remain qualitatively invariant once the spin-orbit coupling (SOC) is included in the calculations. We observe that under strain, with SOC, the singlet and triplet eigenvalues get further removed from each other, making the scale separation clearer for all strains. The FM spin fluctuations go down under strain, in the presence of SOC, and the IC becomes steeper, making an SDW instability likely for larger strains.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 1 Introduction  
Small satellites and CubeSats are currently being developed by space agencies, universities and private companies. They allow for rapid prototyping and testing with limited cost with respect to larger spacecraft. Over the course of this last decade, many advances have occurred in the technologies for small satellites. CubeSats are now more power capable than in the past; they can be equipped with propulsion systems to perform maneuvers, and they can achieve fine pointing.  
In the field of telecommunications, many developments have occurred to allow CubeSats and small satellites to relay higher volumes of data, to communicate from farther points in the solar system such as Mars, and to enable radiometric data collection for navigational functionalities. Specifically, the Jet Propulsion Laboratory (JPL) has been involved in the design, implementation and operation of many CubeSat missions either as the prime organization or in partnership with others.  
In this paper, we are reviewing three of these missions (ASTERIA, MarCO and ISARA) focusing on the telecommunication system. The paper is organized as follows: Section 2 is dedicated to ASTERIA, Section 3 to MarCO and Section 4 to ISARA. Section 5 presents a summary. For each of these missions: the telecommunication challenges and requirements are listed; the final system design is presented; the characteristics of the different hardware components are shown; the lessons learned through operations are discussed.
## 4 Experiment and Analysis
### Limitations of the Algorithm  
The improved YOLOv4-tiny algorithm in this paper could achieve the detection and classification of construction machinery and materials under complex working conditions, and its detection accuracy and detection speed basically met the actual work requirements, but there were some limitations, including the following: (1) The target detection method based on deep learning needs a large quantity of labeled data for training, and the generalization of the algorithm is influenced by the data, so obtaining high-quality data is a difficult task. (2) Deep learning algorithms may have some problems in detecting obscured objects, as the obscured object will lead to changes in the original appearance of the target, which is not conducive to an accurate detection of the target by the model. (3) Deep learning models have different detection effects when dealing with targets of different scales because targets of different scales have different feature information, which may make it difficult  
Figure 10: Comparison of detection results of different algorithms with low-contrast scenes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & _IOU_ **Thresh** & _Precision_ & _Recall_ & **F1** & _mAP_ \\ \hline \multirow{3}{*}{SSD} & 0.5 & 93.10\% & 96.92\% & 95.00\% & 97.64\% \\ \cline{2-6}  & 0.75 & 85.00\% & 84.32\% & 83.00\% & 82.67\% \\ \hline \multirow{3}{*}{YOLOv3-tiny} & 0.5 & 95.00\% & 95.00\% & 95.00\% & 97.73\% \\ \cline{2-6}  & 0.75 & 80.00\% & 80.00\% & 80.00\% & 79.00\% \\ \hline \multirow{3}{*}{YOLOv4-tiny} & 0.5 & 92.00\% & 97.00\% & 95.00\% & 98.23\% \\ \cline{2-6}  & 0.75 & 82.00\% & 87.00\% & 85.00\% & 86.00\% \\ \hline \multirow{3}{*}{Our Methods} & 0.5 & 96.00\% & 97.00\% & 96.00\% & 98.85\% \\ \cline{2-6}  & 0.75 & 86.00\% & 87.00\% & 87.00\% & 87.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of indicators of different algorithms.  
for the model to detect them accurately. Due to the above limitations, deep-learning-based target detection algorithms still have a lot of room for improvement.
###### Abstract
Therefore, remote sensing ET or EF is expected to be used to establish the moisture limiting factor in the LUE-based models for global GPP estimation in mid-to-high resolutions. In addition, the value of model parameters is one of the main issues affecting the accuracy of GPP estimation. Many studies optimize model parameters for specific vegetation types based on ground observations at flux tower sites [34]. However, studies show that some vegetation photosynthetic traits, e.g., the maximum carboxylation rate (V\({}_{\text{cmax}}\)), varies in different ecosystems and environmental conditions [35; 36]. The fixed value of parameters obtained from the look-up-table specified by plant function types (PFT) is regarded as a major source of uncertainty for GPP estimation because spatial heterogeneity (species composition and plant functional types) is not considered in the LUE models [37; 38; 39]. Plant functional type is a classification scheme to reduce the diversity of plant types into the major classes which share similar plant attributes and functions [40]. Some earth system models, such as the global dynamic vegetation model, applies the concept of vegetation functional types [41], but GPP models based on remote sensing rarely consider the plant traits differences across different climate zones. It is important to reduce the uncertainty of model parameters by using ground observations to optimize model parameters for a given plant functional type [42; 43], as well as consider the climate conditions. Therefore, the purpose of this research is to (1) assess the performance of LUE-model for GPP estimation by integrating water availability factor expressed by evaporative fraction (EF) (referred to as the EF-LUE model hereafter) based on site-level ground measurements; (2) optimize the parameters of the EF-LUE model using GPP observations from the eddy covariance flux towers at site-level and extrapolate the optimized parameters spatially according to climate zones for global scale application; and (3) analyze the temporal and spatial characteristics of global cropland GPP estimated by the EF-LUE model and the impact of drought and heat-wave events on cropland GPP.
## 3 Methodology
### The IGWO-MALSTM Model  
To address the high cost and time-consuming nature of manually selecting hyperparameters for LSTM models, as well as their poor long-term forecasting capabilities. Building on the original GWO algorithm, this paper improves its initialization process. We propose the IGWO algorithm to optimize LSTM model hyperparameters, minimizing manual intervention and subjectivity in hyperparameter selection and enhancing its predictive capabilities. Additionally, a multi-head attention mechanism is introduced to adjust the  
Figure 3: The structure of the self-attention mechanism.  
Figure 2: The IGWO flowchart.  
weight allocation of gradients, better controlling their magnitude. Attention weights are incorporated at each time step of the LSTM to measure the correlation between the input and previous hidden states. These attention weights, calculated based on the similarity between the input and hidden states, enable more stable gradient flow during training. By introducing the attention mechanism, the gradient propagation path can be more precisely controlled, mitigating gradient explosion issues and enhancing LSTM's long-term forecasting capabilities.  
In summary, integrating the multi-head attention mechanism into LSTM and optimizing hyperparameters with IGWO addresses the gradient explosion issue, enhancing the model's stability and predictive performance. This approach offers a novel method for improving LSTM networks' ability to handle complex sequential data, with broad application potential.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Proof That Our Method Uses the Minimum Number of Drones
#### 3.6.3. Wastage of Coverage in _TwoNeighbours_  
Considering two circles with centers \(C_{1}\) and \(C_{2}\) and radii \(r_{1}\) and \(r_{2}\), we want to find the center \(C\) and radius \(r\) of the third circle such that the wastage is distributed among the two circles proportionally to their areas. To this end, our approach is to find a point \(C_{3}\) which is \(r_{1}+r\) distant from \(C_{1}\) and \(r_{2}+r\) distant from \(C_{2}\), as in Figure 16a.  
Let \(A\) be the intersecting point of circles with centers \(C_{1}\) and \(C_{2}\) near to point \(C_{3}\), and let \(r_{1}=r_{2}=r\). The locus of all points equidistant at an arbitrary distance from points \(C_{1}\) and \(C_{2}\) is then a straight line passing through the point of the line joining \(C_{1}\) and \(C_{2}\). Thus, \(\overline{CAB}\) is a straight line. Here, \(AC_{2}BC_{1}\), \(CDC_{1}A\), and \(CEC_{2}A\) are each in the shape of a rhombus, while \(\triangle CAC_{1}\) and \(\triangle CAC_{2}\) are congruent.  
\[\angle C_{1}CA =\angle C_{2}AC\] (CPCT) \[\angle ACD =2\angle C_{1}CA=2\angle C_{2}CA\] \[=\angle ACE=\theta\] \[Area(Segment\,AD) =r^{2}(\frac{\pi\theta}{360}-\frac{sin\theta}{2})\] \[=(Area(Segment\,AE))\] \[2(Area(Segment\,AD)) =2(Area(Segment\,AE))\]  
Hence, the wastage is equally distributed among the two neighbours.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 4 Adaptive Kalman Filter
### Basic Equations of KF
\(\sigma^{2}\) is estimated by,  
\[\sigma^{2}=(r_{N}-\hat{r}_{N})(r_{N}-\hat{r}_{N})^{H}/2N \tag{28}\]  
As explained above, \(A^{2}/2\sigma^{2}\) is the SNR of integration result signal. Therefore, \(R_{k}\) is adjusted adaptively by SNR estimation in each update. Meanwhile, \(C/N_{0}\) of the received signal can be estimated by (29). \[C/N_{0}=10\log_{10}\left(A^{2}/2\sigma^{2}\right)-10\log_{10}(f_{\rm s}T)+10 \log_{10}(BW) \tag{29}\]
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 3 Materials and Methods
### Implementation of the PAOC Technique  
Figure 6 shows the implementation architecture of the PAOC technique in the DBT loop, which consists of four main parts: correlator channels, a PAOC-PLL for the carrier, a PAOC-SPLL for the subcarrier, and a DLL for the code. The S-OC discriminator, S-P discriminator, and PAOC complementary filter are implemented in the PAOC-SPLL. The C-OC discriminator, C-P discriminator, and PAOC complementary filter are implemented in the PAOC-PLL.  
The correlator channels are divided into correlator channels for the USB signals and correlator channels for the LSB signals. Every correlator channel has four complex correlators, denoted as the offset correlator (OC), early (E), prompt (P), and late (L), respectively. The received BOC signal is correlated with local replicas in correlator channels to obtain correlation values.  
The outputs of the OC and P correlators are fed into the PAOC-PLL to estimate the carrier frequency and carrier phase of the received BOC signal. Moreover, the outputs of the OC and P correlators are also fed into the PAOC-SPLL to estimate the subcarrier frequency and subcarrier phase of the received BOC signal. The outputs of the E and L correlators are fed into the DLL to estimate the code rate and code delay of the received BOC signal. It is worth noting that the spacing between the E and L correlators is usually smaller than 0.1 chips, making the code multipath error less than half of the subcarrier wavelength. According to the parameter estimation results, the frequency words of carrier NICOs and code NICOs are adjusted in real-time to generate local replicas, thus forming a closed tracking loop.  
Figure 5: Schematic diagram of the PAOC technique for carrier multipath mitigation.