esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 1 Introduction
This mechanism accurately classifies misbehaviors and distinguishes between honest, erroneous, and malicious vehicles, ensuring timely and reliable reputation updates. We introduce a new entity, the Reputation Server (RS), which provides the Reputation Value (RV). The RS will be linked to the SCMS. Reputation considers the behaving subject through accrued observations and interactions; therefore, providing a vehicle with an  
Figure 2: Challenges of SCMS in disconnected vehicular network. indication of the likelihood that its peer is well-behaving or malicious through an RV. DRAMBR becomes particularly relevant in contexts where direct authority oversight is limited or when vehicles cannot operate in a fully connected manner. DRAMBR improves the accuracy of vehicle misbehavior reporting using two processes of assessment:  
1. Offline Misbehavior Detection: During offline communication, a vehicle detects misbehavior, collects observations from neighbors, and generates an Misbehavior Report (MR). 2. Online RS Processing: Upon reconnection, the MR is sent to the RS, which performs validation steps, aggregates data, and takes appropriate action on the reporter and target vehicles. DRAMBR processes and evaluates MRs through a multi-stage aggregation process integrating advanced classification techniques such as DBSCAN, Isolation Forest, and Gaussian Mixture Model. We analyze vehicle communications under different conditions in disconnected environments using Simulation of Urban MObility (SUMO). DRAMBR's accuracy performance is then evaluated using Random Forest and XGBoost, suggesting that it provides an accurate reputation management approach under challenging conditions. The remainder of this paper is organized into seven further sections. Basic assumptions and background about the technologies and standards adopted in our system are introduced in Section 2. Related work is discussed in Section 3 by presenting previous work related to misbehaving detection and reputation schemes in vehicular contexts. The system model is introduced in detail in Section 4, leading to Section 5, which explains the technical implementations of the proposed DRAMBR. Section 6 then outlines the experiment description and the simulated setup, and Section 7 discusses the results and shows the evaluation of our scheme. Finally, we draw conclusions in Section 8.
## 4 Experimentation
### Experimental Results
#### 4.5.3 Parameter Sensitivity Analysis  
Hyperparameters \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) are used to control the importance of short-term, medium-term, and long-term historical information, respectively, for the task in the HCAE model. To investigate their impact on the performance of HCAE in knowledge graph completion, this paper conducted parameter sensitivity experiments on the ICEWS14 dataset. The values of these hyperparameters were gradually increased from 0.1 to 0.9 with a step size of 0.2. When exploring one hyperparameter, the values of the other two hyperparameters were fixed at 0.3. Additionally, other parameter settings remained unchanged.  
A larger attention coefficient indicates a higher importance of the corresponding period's historical information for the completion task. From Figure 6, it can be observed that the HCAE model performs the best when the value of \(\lambda_{1}\) is 0.3. Subsequently, the performance gradually decreases as the weight of short-term historical information increases. This is because an excessive reliance on short-term history can lead to neglecting long-term  
Figure 5: Generalization performance on irregular timestamp data.  
patterns, resulting in the model overfitting the current situation. Short-term situations are more susceptible to various random factors and exhibit greater dynamism. Figure 7 shows that the HCAE performs best when the value of \(\lambda_{2}\) is 0.5. The ICEWS14 dataset has one year, and the distribution of event times is relatively uniform. The medium-term history captures the development patterns of international relations over a specific period, and paying appropriate attention to medium-term history helps understand the global context. However, excessive reliance on medium-term history leads to neglect of current changes, making it challenging to explain sudden events and predict future trends. Figure 8 shows that the model achieves the best performance when the value of \(\lambda_{3}\) is 0.7  
In conclusion, historical information from different periods plays unique roles, and neglecting the history from any period can result in a partial understanding of the evolution of events and difficulty in capturing global patterns. Therefore, when dealing with datasets with different patterns, it is advisable to adjust the hyperparameters \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) appropriately to strike a balance between the influences of historical information from different periods, thus achieving optimal performance.
# Advances in High-Power, Ultrashort Pulse DPSSL Technologies at HiLASE
## 4 Conclusions  
Development of reliable and compact kW-class thin-disk-based picosecond lasers continues at the HiLASE Center. We demonstrated Perla thi- disk platforms for the delivery of high repetition rates and high energy ultrashort pulses. The Perla C regenerative amplifier routinely generates pulses up to almost 500 W of average power. The application potential of Yb:YAG Perla beamlines is broadened by conversion to harmonic frequencies from second to fifth ones, and by OPG and OPA to mid-IR. Lately, we have also started the development of a cryogenically-cooled thin-disk laser beamline for Yb:YAG or Ho-based pulsed lasers with a goal to generate a 1-kW picosecond beam at 1.03 and 2.1 \(\upmu\)m, respectively. In our development, we employ several unique solutions such as CVBG compressors for high average power pulses or atomic diffusion bonding for the preparation of in-house made Yb:YAG thin disks.  
**Acknowledgments: This work is funded by the European Regional Development Fund and the state budget of the Czech Republic (project HiLASE CoE: Grant No. CZ_02.1.01/0.0/0.0/15_006/0000674) and by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 739573. This work was also supported by the Ministry of Education, Youth, and Sports of the Czech Republic (Programmes NPU I Project No. LO1602, and Large Research Infrastructure Project No. LM2015086), and by the Czech Science Foundation (GACR) under project No. GA16-12960S, and by the Technology Agency of the Czech Republic (TACR) under project No. TF03000055 and TG02010056.**  
**Author Contributions: [PERSON] and [PERSON] designed and constructed Perla C beamline; [PERSON] proposed conceptual scheme of the mid-IR frequency conversion and designed all the subsequent tasks and experiments; [PERSON] and [PERSON] carried out mid-IR frequency conversion experiments; [PERSON] designed and carried out harmonic frequency conversion experiments; [PERSON] proposed optical scheme of the Perla B beamline and built front-end and the first amplifier of the beamline; [PERSON] is building second amplifier of the Perla B beamline and together with [PERSON] and [PERSON] carried out experiments testing the atomic diffusion bonding capabilities; [PERSON] proposed concept of Perla A to Perla C beamlines and proposed target parameters; [PERSON] and [PERSON] designed the Perla D beamline; [PERSON], [PERSON], and [PERSON] designed the Perla A beamline; [PERSON] participated on the Perla A and Perla C experiments; [PERSON] participated on fiber front-end experiments for Perla beamlines; [PERSON] and [PERSON] were directing the research. All authors contributed to the scientific discussions, writing and revision of the manuscript.**  
**Conflicts of Interest: The authors declare no conflict of interest.**
# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 2 Materials and Methods
### Preprocessing  
To prepare the data for modeling, several preprocessing steps were applied. Wind direction was originally recorded as a categorical variable with multiple possible values (NNW, N, NW, NNE, ENE, E, NE, W, SSW, WSW, SE, WNW, SSE, ESE, S, SW). Since most machine learning models work with numerical inputs, we transformed this variable into multiple binary indicators using one-hot encoding. This approach enables the model to distinguish between different wind directions without introducing a false sense of order between categories [15].  
The dataset contained missing values in various columns, which could impact model training and performance in an uncertain way. To address this, we used a k-nearest neighbors imputation method. This technique estimates missing values based on the values of the closest data points (neighbors), allowing us to preserve valuable information without discarding rows or columns unnecessarily. We chose this method because it considers relationships between variables and generally provides more accurate imputations compared to simpler approaches like filling with the mean.
## 2 New Extended Semi-Weight Function Method for Calculating SIFs under Hydro-Mechanical Coupling
### Calculation of Hydro-Mechanical Coupling Stress-Intensity Factors
\tag{24}\]  
For the right side of Equation (22) (i.e., integral path \(\Gamma_{2}\)), substitute two groups of \(\sigma^{\prime}_{r}\), \(\sigma^{\prime}_{\theta}\), \(\tau^{\prime}_{r\theta}\), \(u^{\prime}_{r}\), and \(u^{\prime}_{\theta}\), represented by the semi-weight function (determined using Equations (18) and (24)), and \(\sigma_{r}\), \(\tau_{r\theta}\), \(u_{r}\), and \(u_{\theta}\), determined by finite element calculation, and calculate in combination with Equation (23) to solve hydro-mechanical coupling SIFs (\(K_{1}\) and \(K_{2}\)):  
\[\left\{ \begin{array}{l}K_{1}+\delta_{3}K_{1}P_{h}\tau_{2}^{1.5}+\frac{\delta _{4}}{\delta_{1}}P_{h}\tau_{2}^{0.5}=\int\limits_{\Gamma_{2}}\big{(}\sigma^{ \prime}_{r,1}u_{r}+\tau^{\prime}_{r\theta,1}u_{\theta}-\sigma_{r}u^{\prime}_{r,1}-\tau_{r\theta}u^{\prime}_{\theta,1}\big{)}\mathrm{d}s\\ K_{2}+\delta_{3}K_{1}P_{h}\tau_{2}^{1.5}=\int\limits_{\Gamma_{2}}\big{(}\sigma^{ \prime}_{r,2}u_{r}+\tau^{\prime}_{r\theta,2}u_{\theta}-\sigma_{r}u^{\prime}_{r,2}-\tau_{r\theta}u^{\prime}_{\theta,2}\big{)}\mathrm{d}s\end{array} \right. \tag{25}\]  
where the subscripts \" \({}_{1}\) \" and \" \({}_{2}\) \" correspond to two different groups of \(Q_{1}\) and \(Q_{2}\) (Equation (24)), respectively. In summary, the detailed procedure used to solve the hydro-mechanical coupling SIFs by adopting the extended semi-weight function method is as follows:  
(1) Substitute the material parameters (\(G\), \(\kappa\)) into Equation (15) to obtain the stress field singularity (\(\lambda\));  
(2) Substitute \(\lambda\) into Equations (9), (12), (18) and (19) to solve Equation (22);  
(3) Calculate \(\sigma^{\prime}_{r}\), \(\sigma^{\prime}_{\theta}\), \(\tau^{\prime}_{r\theta}\), \(u^{\prime}_{r}\), and \(u^{\prime}_{\theta}\) in terms of the semi-weight functions by combining Equations (18) and (24), and the \(\sigma_{r}\), \(\tau_{r\theta}\), \(u_{r}\), and \(u_{\theta}\) obtained using finite element software for the right side of Equation (22);  
(4) Substitute the stress and displacement fields determined by the semi-weight function and finite element results into Equation (25) to calculate the hydro-mechanical coupling SIFs.
## 1 Introduction
### Smart Skins Within Smart Radio Environments  
The smart skin concept arises as an important new paradigm that refers to an advanced, multifunctional surface layer integrated into a structure or object, capable of dynamically responding to environmental stimuli. Smart skins in building envelopes, facilitated by FSSs, represent a groundbreaking advancement in architectural design and urban infrastructure. These smart skins act as dynamic, responsive layers on the outer surfaces of buildings, capable of interacting with electromagnetic waves in a selective manner. This ability to fine-tune the building's interaction with its electromagnetic environment enhances both the security and connectivity of modern smart buildings, while simultaneously improving the energy efficiency by managing electromagnetic radiation.  
The evolution of wireless communication systems is pushing the boundaries of the design of radio frequency (RF) environments. Traditionally, communication systems have been designed to adapt to the physical environment, treating the propagation medium as a passive element. However, the concept of Smart Radio Environments (SREs) is emerging, wherein the propagation environment itself can be dynamically controlled and optimized. In an SRE, surfaces and objects in the environment can be made intelligent, actively participating in the communication process to improve signal quality, enhance coverage, and support advanced applications like 6G and the Internet of Things (IoT). The idea of the SRE is illustrated in Figure 1.  
Figure 1: Smart radio environment: (**a**) outdoor; (**b**) indoor. Images generated by Flux Fast 1.1 in coordination with Gamma AI (beta version).  
One key enabler of SREs is the integration of smart skins and FSSs. These technologies can manipulate EM waves, facilitating the environment to be reconfigured to optimize wireless communication.
## 3 Methodology
### Proposed Pipeline
#### 3.2.2 Architectures  
We selected the given architectures due their common use in similar problems that use transfer learning, and computer vision techniques [18; 39]. Additionally, specific architectures, such as MobilenetV1, were chosen due to the real-time-based nature of the given task, which focuses on mobile, lightweight deployment.  
VGG [26] is an architecture focused on spatial exploitation, which attempts to consider various filter sizes to analyze both low- and high-level details [15]. It is known for its vast number of parameters, which complicates deployment and increases computational costs, but achieves good results in image classification and object localization tasks.  
ResNet [27] introduced the concept of residual learning in CNNs by creating connections between layers that speed up network convergence. It comprises 152 layers, eight times more than the original VGG, while being less complicated. Due to these connection types and its focus on depth, it is classified as both depth- and multi-path-based. The network variations using 50 and 101 layers, respectively, have shown remarkable results in  
Figure 2: Transfer learning and fine-tuning pipeline. Transfer learning and fine-tuning were used to train classifiers and output the final labels.  
tasks of image classification [15]. ResNet152v2's [27] main difference when compared to the original is that it uses batch normalization before each of the weight layers.  
Inception-V3 [29] is a depth- and width-based CNN focused on reducing the computational cost of these types of networks. However, it has a sophisticated design and lacks homogeneity. Inception-ResNet-V2 [36] was introduced as a higher-cost variant of the Inception network, with a higher object recognition performance.  
Xception [35] attempts to regulate computational complexity, making learning more efficient and improving the architecture's performance, but has the disadvantage of a high computational cost compared to other architectures.  
NasNetLarge [19] designs a search space to reduce architecture complexity and make it independent of image size or network depth, finding the best possible network on a smaller dataset before scaling it to bigger datasets. The resulting architecture can be further modified to fit a larger scale, achieving state-of-the-art results.  
Lastly, MobileNet-V1 [31] is an architecture oriented towards mobile device applications, and is designed to have a light architecture for ease of deployment. MobileNet-V2 [32] improves the original proposal by introducing linear bottlenecks between the layers and shortcut connections between them, reducing operation complexity and the parameters needed.
## 3 Inland Waters
### Rivers  
1.1 Comparative Analysis of Water Level Retrieval Using Ku and Ka-Band Altimeters Over Brahmaputra River  
Over inland water bodies, presence of sand bars, islands and vegetation contaminate radar return echo. Hence, with narrow footprint in Ka-band, altimeters enhance the possibilities of better data acquisition over the inland water bodies with multiple features. The decrease in time decorrelation between successive echoes has increased the pulse repetition frequency to 4000 Hz in Ka-band as compared to 2000 Hz with Ku-band altimeters. The limitation with higher frequency is that it has higher rainfall attenuation with increasing rainfall intensity, although total data loss due to this is less than 5%. In this study, river water levels were retrieved using Jason-2 and SARAL/AltiKa waveform datasets over Brahmaputra river for 2013. Three passes of Jason-2 (track number 242, 53 and 166) over the Brahmaputra river and nearby tracks of SARAL/AltiKa (track number 537, 810 and 438) were selected (see Figure 15). Time of pass of SARAL/AltiKa tracks and nearby pass data of Jason-2 were chosen. Beta parameter algorithm has been applied using the 20-Hz (Jason-2) and 40-Hz (AltiKa) waveform datasets to retrieve the river water levels with respect to mean sea level. A comparative analysis was carried out to assess the Ku-band vs. Ka-band altimetry performance for the river water  
Figure 14: RMSE of SARAL/Jason-2 SWH with respect to buoy observed wave in mid-latitude (30–60\({}^{\circ}\)N) and Tropics (0-30\({}^{\circ}\)N).  
level retrievals. Individual tracks were compared; it was found that SARAL/AltiKa performs slightly better for the upstream tracks, i.e., 537 vs. 242 and 810 vs. 53. For the downstream track where braiding pattern is high (track number 166, Jason-2 and 438, SARAL/AltiKa), Jason-2 performance decreased sharply (RMSE 1.19 m, Jason-2 vs. 0.41, SARAL/AltiKa) as compared to SARAL/AltiKa. This may be attributed due to higher band width (500 MHz) in Ka-band altimetry which provides shorter pulse duration; hence increase the vertical range resolution (0.3 m) as well as spatial resolution. Figure 16a,b presents the overall performance of the SARAL/AltiKa and Jason-2, respectively, taking into account all three tracks. The improved accuracy over the braided rivers highlight the high frequency Ka-band altimeter performance.
## 4 Comparative Discussion
### Discussion  
From the explanation above, we can see that all the routing protocols presented here have the same method to estimate a node's temperature. They calculate the temperature by observing the node'sactivities: how many packets are transmitted and received by the nodes. They also use a distributed algorithm in the sense that each node has the capability to decide the route; it does not depend on one node to decide the route.  
However, after observing the advantages and disadvantages of each routing protocol, we can find some room for improvement in routing protocols for WBSNs in the future. Table 1 shows that all the protocols use a proactive or table-driven routing approach, which means that each node maintains a routing table to be used for the routing decision. However, the reactive or on-demand approach is also worth considering to be implemented in the routing protocol in WBSNs, especially for applications that transmit data periodically, e.g., every 30 min or every hour. This way, each node does not have to maintain a routing table when there is no data traffic, thus, reducing the routing overhead. For a resource-limited environment, such as WBSNs, this approach needs to be considered.  
Another issue to deal with is in the link model assumption. All the routing protocols proposed so far assume the link between nodes to be bi-directional, that is, each pair of nodes is able to transmit data to each other. However, in the real environment, due to node heterogeneity, the link is most likely to be uni- directional. There is no guarantee that if node A can transmit data to node B, node B can also transmit data to node A. This situation also needs to be coped with by routing protocols in WBSNs. The mechanism to transmit the data using an optimal power level of transmission is also an interesting issue to be explored.
## 3 Feature Engineering  
### Feature Standardization  
The essence of feature standardization is being able to conduct dimensionless operations on numerical-valued features, thus eliminating the influence of dimension on data features. For the data set used in this article, the attribute \"Volume\" had an overwhelmingly large number of values between 0 and 2, while the attribute \"Perimeter\" was able to reach values higher than 2000. To analyze the impact of these two characteristics on the final classification results, the analysis results will inevitably favor features with larger numerical values. Therefore, in order to obtain more accurate results, it was important to normalize the features to ensure similar orders of magnitude and facilitate subsequent analysis.  
Standardizing the features of a numerical type can bring all the features into a roughly identical numerical interval. In subsequent experiments, we adopted the min-max standardization method. Linear transformation was carried out to scale the original data by mapping the values to an interval:  
\[x_{norm}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{1}\]  
where \(x\) represents the original data value, and \(x_{\text{max}},x_{\text{min}}\) are the maximum and minimum values of similar features, respectively.
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
###### Abstract  
Even though audio replay detection has improved in recent years, its performance is known to severely deteriorate with the existence of strong background noises. Given the fact that different frames of an utterance have different impacts on the performance of spoofing detection, this paper introduces attention-based long short-term memory (LSTM) to extract representative frames for spoofing detection in noisy environments. With this attention mechanism, the specific and representative frame-level features will be automatically selected by adjusting their weights in the framework of attention-based LSTM. The experiments, conducted using the ASVspoof 2017 dataset version 2.0, show that the equal error rate (EER) of the proposed approach was about 13% lower than the constant Q cepstral coefficients-Gaussian mixture model (CQCC-GMM) baseline in noisy environments with four different signal-to-noise ratios (SNR). Meanwhile, the proposed algorithm also improved the performance of traditional LSTM on audio replay detection systems in noisy environments. Experiments using bagging with different frame lengths were also conducted to further improve the proposed approach.  
audio replay attack; noise robustness; attention mechanism; long short-term memory +
Footnote †: journal: Accepted: 10 April 2019; Published: 13 April 2019