esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 2 Related Works
### Pruning Techniques for DETR Neural Networks  
Addressing the computational complexity and resource consumption challenges associated with practical applications of DETR-like neural networks necessitates the introduction and advancement of efficient compression methods. While existing research has endeavored to enhance DETR models from diverse angles, the pruning processes lack specificity and depth overall. For example, [PERSON] et al. [46] introduced a pruning technique grounded in sparse induction, incorporating a sparse regularization term into the neural network pruning method to reduce parameters to a certain degree. However, this approach did not fully align with the intricate requirements of the DETR architecture. [PERSON] et al. [47] detected substantial redundancy in DETR's Transformer heads and formulated a strategy for head recombination and shuffling, leading to a 2.9-fold increase in neural network efficiency. Although effective, this method failed to exploit the inherent optimization potential within the DETR architecture since it solely pruned Transformer layers. [PERSON] et al. [48] proposed a hierarchical pruning method concentrated on the decoding layer to decrease model parameters. Despite these contributions, a comprehensive analysis and targeted enhancements focusing on the deep features of DETR-like architectures are lacking.  
Hence, future research endeavors should center on devising more innovative and purpose-driven pruning methods, seamlessly integrating the intrinsic structural attributes of DETR and its variations for the holistic enhancement of their accuracy, efficiency, and resource utilization balance. Only through meticulous research and optimization of the DETR architecture can a robust foundation be established for enhancing its practical utility.
## 5 Conclusions  
In summary, this study presents a novel methodology for assessing the localization accuracy in binaural audio systems, with an emphasis on identifying possible improvements by using one's own individual HRTF instead of a generic HRTF or an individual HRTF of another person. Although the results show statistically significant improvements in accuracy for one's own individual HRTF in the vertical plane, the results also suggest the absence of universal statistically significant improvements in localization accuracy in the horizontal plane when using one's own HRTF compared to other HRTFs. The individual HRTF recognition experiment also found an indication of statistical significance for the ability to recognize one's own HRTF, but the methodology warrants further refinement in future work. Future studies will focus on comparing the effectiveness of the individual HRTF measurement method used in this research with other approaches for measuring individual HRTF.  
Conceptualization, V.P., K.J., J.R. and H.P.; methodology, V.P., K.J., J.R. and H.P.; software, V.P., J.R. and H.P.; validation, M.H. and K.J.; formal analysis, V.P. and J.R.; investigation, V.P. and J.R.; resources, K.J., M.H. and H.P.; data curation, V.P. and J.R.; writing--original draft preparation, V.P.; writing--review and editing, J.R., H.P., M.H. and K.J.; visualization, V.P.; supervision, K.J. and H.P.; project administration, K.J. and H.P.; funding acquisition, K.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Croatian Science Foundation (HRZZ IP-2018-01-6308, \"Audio Technologies in Virtual Reality Systems for Auralization Applications (AUTAURA)\") and by the Flemish Agency for Innovation and Entrepreneurship (VLAIO).  
The study was conducted in accordance with the Declaration of Helsinki and the Ethical Code of the University of Zagreb and approved by the Ethics Committee of the Faculty of Electrical Engineering and Computing (protocol code EP-17-18, approved on 17 July 2018).  
Informed Consent Statement: Informed consent was obtained from all participants involved in the study.  
The raw data collected for the purpose of this study are available upon request from the corresponding authors. Even though the data are anonymous, it were not made publicly available to maintain confidentiality and protect the privacy of the participants as a group.  
**Acknowledgments:** The authors would like to thank [PERSON] for his generous help with the statistics calculation and implementation. The authors also appreciate the help from all of the participants that were part of the experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Results
### Mesh Quality Metrics  
Many mesh quality metrics have been proposed from a different angle. In this paper, we use aspect radio, facet angle, and dihedral angle to evaluate the quality of the mesh.  
The aspect ratio of tetrahedral mesh is the ratio of the longest edge length to the shortest height.  
\[A_{ratio}=L_{max}/h_{min} \tag{8}\]  
where \(A_{ratio}\) represents the aspect ratio, \(l_{max}\) is the longest edge length and \(h_{min}\) is the shortest height. The aspect ratio of an ideal tetrahedral element is 0.81. In general, elements with an aspect ratio exceeding 10 should not exceed 10%. Elements with values greater than 40 should be carefully checked to determine their location and whether the stresses generated in these areas are meaningful.  
The face angle is the angle between any two sides of a triangular element. The face angle of an ideal tetrahedral element is 60\({}^{\circ}\).  
Figure 6: Procedure of the bubble mesh method for a three-dimensional model.  
Each of the six sides of the tetrahedron is surrounded by two faces. The dihedral angle between two faces of a given edge is the intersection of the two faces and a plane perpendicular to the side angle. The dihedral angle of an ideal tetrahedral element is 70.53\({}^{\circ}\).
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 7 Performance Evaluation
### Communication Cost  
The communication cost consists of transmission delay and propagation delay. According to the 3 GPP specification TS 22.261 [30], in urban macro scenarios, the downlink data rate is generally 50 Mbps, and the uplink data rate is 25 Mbps. We assume a data rate of 50 Mbps between gNBs and 25 Mbps between OBUs. The transmission delay within the core network is ignored.  
For propagation delay, the speed of wave propagation in wireless communication is approximately \(3\times 10^{8}\)m/s. Assuming a radius of 200 m, the signal sent by a vehicle will travel 200 m to reach another vehicle at a speed of \(3\times 10^{8}\) m/s. The propagation delay within the core network is also ignored. The theoretical communication  
Finally, we evaluate the robustness by introducing the concept of unknown attacks. Each system is subjected to unknown attacks. When facing unknown attacks, the authentication process may be forced to stop and restart. To carefully analyze the impact of these attacks, we assume that the probability of an unknown attack occurring is uniformly distributed at each step of the authentication process. The average time for a successful handover is calculated as follows:  
\[T=\frac{T_{success}+T_{failed}}{N_{success}}=\frac{\sum_{i=1}^{n}\frac{1}{n} \times t_{fail}\times p+t_{success}\times(1-p)}{1-p}\]  
where \(T\), \(T_{success}\), and \(T_{failed}\) represent the average time for successful handover authentication, the total time for successful authentication, and the total time for failed authentication, respectively; \(N\) success denotes the number of successful authentications; \(p\) is the percentage of unknown attacks; \(n\) is the number of steps in the protocol; \(t_{fail}\) indicates the amount of time spent on handover authentication before an attack occurs; and \(t_{success}\) represents the time spent on successful handover authentication before an attack. The results are shown in Figure 7.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Fungal Aegerolysins from Ascomycata--Sordarionycetes
#### 2.3.2 Aegerolysins from _Trichoderma atroviride_  
Although some _Trichoderma_ species are known to be closely associated with higher plants (endophytes and plant growth promoters), _Basidiomycetes_ (mushroom green mold disease), invertebrates (nematodes, marine sponges), and mammals (opportunistic pathogens of humans), most taxa have been recovered from dead wood and fruiting bodies of other fungi, suggesting that these are the original ecological niches of the fungus [32]. _Hypocrea atroviridis_ [PERSON] et al. (_Trichoderma atroviride_) (_Hypocreales_) is a filamentous, cosmopolitan fungus commonly found in soil and has been isolated in both tropical and temperate climates. This mycoparasitic fungus is best known for its biocontrol capabilities in agriculture, interacting with phytopathogenic fungi and oomycetes, such as _Rhizoctonia solani_ and _Botrytis cinerea_, which are pests of hundreds of crops [33; 34; 35].  
The recently functionally characterized **Ag11** aegerolysin in this fungus shows a role in conidial and antagonism. Gene expression analysis showed higher expression of **ag11** during conidial and during growth in medium enriched with cell wall material from the plant pathogenic fungus _R. solani_ as the sole carbon source. Expression of **ag11** was suppressed under iron-limiting conditions, whereas the **ag11** transcript was not detected during the interaction of _T. atroviride_ with the prey fungi _B. cinerea_ or _R. solani_. Phenotypic analysis of **ag11** deletion strain showed reduced conidial compared with _T. atroviride_ wild type, suggesting involvement of **Ag11** in conidial. In addition, the **ag11** deletion strain showed reduced antagonism to _B. cinerea_ and _R. solani_ based on a secretion assay, although no difference was observed in direct interactions [128].  
There is no bicomponent partner protein gene encoded alongside the **ag11** gene; there is only one amino acid variation between strains of _T. atroviride_ IMI 206040, B10, F7, and P1 (ATCC 74058).
## 4 Random Analysis
### Effect of Skew Angle a  
Figure 18 presents the changes in the mean values of the normalized maximum tunnel responses with increasing \(a\) under different \(\theta_{E}\) (\(\theta_{E}=1D\), \(3D\), \(10D\)). It is seen that as \(a\) increases from \(30^{\circ}\) to \(90^{\circ}\), the mean values of \(w_{\rm m}\) decrease, and the mean values of \(M_{\rm m}\) and \(Q_{\rm m}\) increase at a decreasing rate. The effect of \(a\) on the COVs of normalized maximum tunnel responses under different \(\theta_{E}\) (\(\theta_{E}=1D\), \(3D\), \(10D\)) is presented in Figure 19. The decrease in \(a\) leads to an apparent negative influence on the COVs of \(w_{\rm m}\), \(M_{\rm m}\), and \(Q_{\rm m}\). For example, the COV of \(M_{\rm m}\) decreases by 47% with the reduction in \(a\) from \(90^{\circ}\) to \(30^{\circ}\). The decreasing \(a\) induces wider greenfield settlement profiles as seen in Equation (3). Therefore, it can be deduced that the effect of spatial variability of the soil elastic modulus becomes less remarkable when the profile of the greenfield soil settlement widens. Furthermore, the effect of variation in \(a\) on the variability of the normalized maximum tunnel responses becomes more prominent with increasing \(\theta_{E}\) because the larger slope can be observed under larger \(\theta_{E}\).  
Figure 18: Effect of \(a\) on mean values of normalized maximum tunnel responses under different \(\theta_{E}\) values: (**a**) Normalized maximum tunnel settlement, (**b**) Normalized maximum bending moment; (**c**) Normalized maximum shear force.
### UHI Effect and Energy Consumption and Pollution  
The rising surface and near-surface temperatures within urban areas bring about changes in the resource and energy balance at the regional level, altering the local climate within urban ecosystems, and leading to modifications in their structure and function. Cities play an indirect role in affecting climate change due to their significant energy consumption, material demands, waste production, and pollution [13].  
Heat islands have a significant effect on the energy expenditure of buildings. The overall energy dynamics of urban structures shift from cooling-dominant to heating-dominant, leading to a reduction in the effectiveness of natural and nocturnal cooling ventilation methods. Long-term analyses of station data regarding the spatial distribution of building cooling loads in urban areas indicate that cooling demand at the city center is approximately twice as much as in surrounding areas [14]. The heightened cooling demand placed on buildings elevates the peak electricity requirements for cooling while simultaneously reducing the efficiency of air conditioners. The UHI effect in Athens, Greece, led to a significant increase in the cooling requirements for buildings [15]. This increase was nearly twice the usual cooling demand and resulted in nearly three times the peak electricity consumption primarily due to a 25% reduction in the efficiency of the air-conditioning cooling systems. Reports on the UHI effect in major cities of the world show that the peak urban electric demand rises by 0.45-4.6% for each 1-degree rise in daily maximum temperature above a threshold of 15-20 \({}^{\circ}\)C [16; 17].  
Increased urbanization, and increased energy consumption due to UHI, have worsened urban pollution and the urban pollution pattern. Higher urban temperatures increase smog production and tropospheric ozone in cities as the photochemical reactions of pollutants intensify at higher temperatures [18; 19]. The impact of UHI on photochemical pollutants during a summer anticyclonic event was reported in Paris [20]. There is also an increasing trend in the occurrence and intensity of heat wave periods, which are days with abnormally high temperatures, in urban cities.
# A Survey of Machine Learning Methods for Time Series Prediction
## 6 M5 and M6 Forecasting Competitions
### M5 Forecasting Competition
#### 6.1.1 M5 Accuracy Competition  
The M5 Accuracy Competition [90] tasked participants with providing the most accurate point forecasts, evaluated using a Weighted Root Mean Squared Scaled Error (WRMSSE) metric. A total of 5507 teams from 101 countries participated, with LightGBM emerging as the dominant model among the top 50 best performing teams. Brief insights from the top five models include the following:  
1. First Place: combined recursive and non-recursive LightGBM models to create 220 models, where the average of 6 models was used to forecast the series, each exploiting a different learning approach and training set;
2. Second Place: created 50 LightGBM models, 5 for each of the 10 stores, utilizing a DL neural network to adjust multipliers based on historical sales data for each store;
3. Third Place: employed 43 recursive neural networks (LSTMs) incorporating over 100 features;
4. Fourth Place: created 40 non-recursive LightGBM models;
5. Fifth Place: utilized seven recursive LightGBM models.  
Nearly all of the top 50 submissions applied the last four 28-day windows of data for cross-validation to fine-tune the hyperparameters. Many top-performing teams, including 1 st and 3 rd place, used exogenous features like special days and zero-sales periods in their models. The researchers of this challenge concluded that the competition reinforced the value of model combination, cross-learning, and cross-validation. New findings included the superior performance of the LightGBM model compared to all others, as well as the importance of exogenous/explanatory variables used for forecasting.
# An Improved Model-Free Adaptive Nonlinear Control and Its Automatic Application
## 3 Model-Free Adaptive Control Design with Output Error Rate (MFAC-OER)
### Improved Control Strategy  
To reduce the tracking time, we refer to the idea of [22] and propose the following calculation criteria based on Formula (5):  
\[J(u(t))=\theta|e(t+1)|^{2}+(1-\theta)|\frac{|e(t+1)|-|e(t)|}{T}|^{2}+\lambda|u (t)-u(t-1)|^{2}, \tag{11}\]  
where \(e(t+1)=y_{d}(t+1)-y(t+1)\) is the output error; \(0<\theta<1\) is a weight factor, which balances the weight between the output error and the output error rate.  
Equation (11) limits the influence of a large error on control input by introducing \([\frac{|e(t+1)|-|e(t)|}{T}]^{2}\), and the control law can be optimized by being adjusted. Moreover, the addition of the output error rate enables the system to reach steady state faster [22].  
By solving the first-order partial differential of \(u(t)\) in Formula (11) and making it 0, the following control law expression can be obtained:  
\[\Delta u(t)=\frac{\hat{\phi}_{2}(t)(\theta+\frac{1-\theta}{T^{2}})(y_{d}(t+1)-y(t ))-\hat{\phi}_{2}(t)\hat{\phi}_{1}(t)(\theta+\frac{1-\theta}{T^{2}})\Delta y(t )-\hat{\phi}_{2}(t)\frac{1-\theta}{T^{2}}e(k)}{\lambda+(\theta+\frac{1-\theta} {T^{2}})|\phi_{2}(t)|^{2}}. \tag{12}\]  
To sum up, the control scheme of this paper is organized as follows:  
\[\left\{\begin{array}{l}\Delta u(t)=\frac{\hat{\phi}_{2}(t)(\theta+\frac{1- \theta}{T^{2}})(y_{d}(t+1)-y(t))-\hat{\phi}_{2}(t)\hat{\phi}_{1}(t)(\theta+ \frac{1-\theta}{T^{2}})\Delta y(t)-\hat{\phi}_{2}(t)\frac{1-\theta}{T^{2}}e( k)}{\lambda+(\theta+\frac{1-\theta}{T^{2}})|\phi_{2}(t)|^{2}}\\ \hat{\[PERSON]}(t)=\hat{\[PERSON]}(t-1)+\frac{\eta\Delta H(t-1)\left(\Delta y(t)-\hat{ \[PERSON]}^{2}(t-1)\Delta H(t-1)\right)}{\mu+\left\|\Delta H(t-1)\right\|^{2}}\\ \hat{\[PERSON]}(t)=\hat{\Phi}(1)\;\;\mbox{if}\;\;\left\|\hat{\Phi}(t)\right\|\leq \;\mbox{or}\;\;\left\|\Delta H(t-1)\right\|^{2}\leq\varepsilon\\ \mbox{or}\;\;sign\Big{(}\hat{\phi}_{t_{y+1}}(t)\Big{)}\
eq sign\Big{(}\hat{ \phi}_{t_{y+1}}(1)\Big{)}\end{array}\right. \tag{13}\]  
The control block diagram is shown in Figure 1. It can be seen that the estimated value \(\hat{\Phi}(t)\) of PG is one of the components of the control law, thus obtaining the control \(u(t)\). Then, the control signal is fed back to the controlled system to complete the control of the whole system.
## 4 Encryption Process  
The encryption process can be described according to three aspects: the chunking disorder process, the data combination process, and the ciphertext-splitting process.
# Q-Learning Approach Applied to Network Security
## 4 Results
### Evaluating Q-Learning Policy on Test Episodes  
After deriving the optimal policy, we tested the trained model on 300 episodes to evaluate its performance when deployed. Figure 5 visualizes the cumulative rewards on 300 test episodes when applying the Q-learning policy. The blue line represents the reward obtained by following the Q-learning policy, while the red line represents the policy's mean performance across the 300 test episodes, with a mean of 337.23.  
Q-learning demonstrated exemplary performance in both training (330) and testing (337.23), indicating that an optimal policy will be achieved for unseen data and that the model has learned a sound decision-making strategy. This performance provides additional evidence of why Q-learning policy is well-suited for intrusion detection in network security. While the majority of IR mechanisms nowadays rely on familiar patterns and experience to respond to the influential cases in the environment, the current method is not only robust but also fast and adaptive, capable of handling unseen traffic and providing an adequate response.  
Figure 4: Discrepancy means for different alpha values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**State** & **Optimal Policy** \\ \hline State 0 (Normal) & Allow \\ State 1 (DoS) & Block \\ State 2 (Probe) & Block \\ State 3 (R2L) & Block \\ State 4 (U2R) & Block \\ Cumulative Reward & 330 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal policy derived from Q-learning.
## 2 System Model
### Communication Model
#### 2.3.2 Channel Estimation Using MMSE  
Accurate channel estimation is essential for beamforming and adaptive power allocation. We employ the Minimum Mean Square Error (MMSE) method, which estimates the channel matrix \(\hat{H}\) as follows:  
\[\hat{H}=R_{HH}X^{H}\Big{(}XR_{HH}X^{H}+\sigma^{2}I\Big{)}^{-1}Y, \tag{15}\]  
where \(R_{HH}\) is the channel correlation matrix, \(X\) is the transmitted signal matrix, \(\sigma^{2}\) is the noise variance, \(I\) is the identity matrix, \(Y\) is the received signal matrix.  
This method reduces channel estimation error by considering the statistical properties of the channel and noise, leading to improved performance in dynamic environments.
## 4 Results and Discussion
### Impact of Phase Wind-Up
#### 4.2.2 Validation and Discussion  
Figure 7 shows the PWU variations for GRCC and CS01 in DOY 273, 2018. It can be clearly seen that the average PWU error for GRCC is smaller than CS01 and the overall fluctuation trend of GRCC is more stable. For GRCC, the average RMS of PWU is 3.84 cm, while the value is 4.77 cm for CS01. This means that the performance of attitude control in GRCC is superior to that of CS01.  
Figure 8 shows the overlap difference in RMS between solutions with and without PWU corrections for GRCC and CS01, and Table 3 lists the corresponding statistical results. Without PWU correction, the 3D RMS of overlap for GRCC and CS01 is 2.15 cm, 1.93 cm, respectively. Adding the PWU correction leads to improvements, with 3D RMS values for GRCC and CS01 of 2.11 cm, 1.88 cm, respectively. The RMS of the phase residual for GRCC and CS01 can be improved by 0.12 mm and 0.06 mm, respectively. The improvement reflected in the orbit comparison with JPL orbit differences for GRCC is more obvious, with 3D RMS values decreasing from 3.17 cm to 2.98 cm. Meanwhile, the clock offset difference in RMS (seen in Figure 9) for GRCC and CS01 is 0.047 ns, 0.052 ns, respectively. The combined orbit and clock SISRE for GRCC and CS01 caused by PWU corrections can reach up to 1.42 cm and 1.57 cm, respectively. This means that more PWU errors have a significant impact on the service capability of an LEO-based navigation enhancement system. Therefore, the PWU corrections must be taken into consideration in the precise orbit and clock offset determination.  
Figure 7: The PWU variations for GRCC and CS01 in DOY 273, 2018.