esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As the feedback microphone should be placed near the speaker, the residual error signal might be different from the one perceived by the human ear. In fact, if the FRs of the primary and the secondary paths corresponding to the human ear show large differences with respect to the feedback microphone, which is called FR mismatches in the rest of this paper, the actual NR performance would deteriorate even if the controller were designed to be optimal for the feedback microphone [15]. In principle, this problem could be alleviated by virtual sensing methods [16]. [PERSON] [17] and [PERSON] et al. [18] tried to estimate the error signal at the endrum and use it to construct the feedback control, where it is assumed that the primary paths are the same for the feedback microphone and the eardrum. However, just as pointed in [15], the error signal at the eardrum corresponds to a larger delay of the plant so that the NR bandwidth would be reduced, even if all models are accurate enough. [PERSON] et al. [15] proposed a two-stage hybrid method where the feedback controller is designed using the conventional method, but an extra feedforward controller is utilized to compensate the performance degradation caused by FR mismatches of the primary and the secondary paths. In this paper, FR mismatches of both the primary and the secondary paths are addressed on the optimization problem of the feedback controller for ANC headphones. The feedback loop is constructed directly with the feedback microphone and the extra filters of the virtual sensing techniques are avoided. Instead, an optimization problem is established in which the NR performance is evaluated at the human ear. Cascade biquad filters are used as the feedback controller which is in accordance with real applications. As the optimization problem is non-convex, a differential evolution (DE) algorithm [19] is used to find the optimal solution, whose efficiency has been confirmed by the optimization of the feedforward controllers [20]. Finally, experiments are carried out through which the effectiveness of the proposed method is validated.
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 1 Introduction
### Research Hypotheses and Methodology  
Autoformer, despite its good performance in many forecasting tasks, has three main limitations in time-series forecasting: imperfect embedding of temporal feature information; a single-scale convolution-based seasonal decomposition pattern with limited representation for complex multi-scale periodic patterns and difficulty in capturing dynamic trends; and model parameter configuration reliant on human expertise, restricting cross-domain applications.  
To address Autoformer's limitations and verify the hypotheses, this paper makes the following key contributions:  
* A multi-scale feature fusion network suitable for time series is proposed. Based on convolutions at different scales, it can effectively extract multi-scale features of time series.
* Based on the above-mentioned feature fusion network, combined with date-time encoding [25], a time-series prediction method, MD-Autoformer, is proposed. Comparative experiments and ablation experiments are carried out with baseline models on real-world clothing sales datasets and time-series datasets from multiple fields, verifying the effectiveness of the method proposed in this paper.  
* We optimized the MD-Autoformer's hyperparameters using the TPE-based Bayesian algorithm, identifying the best parameters to enhance its prediction performance, making it more reliable and effective across diverse tasks. The key research hypotheses explored in this study include:  
**Research Hypothesis 1 (RH1):**: _Introducing date-time encoding as a covariate can enhance the Autoformer model's ability to perceive time information._
**Research Hypothesis 2 (RH2):**: _Using a multi-scale feature fusion network can strengthen the model's ability to perceive dynamic trends, reduce significant errors, and improve prediction accuracy._
**Research Hypothesis 3 (RH3):**: _The hybrid MD-Autoformer model offers better performance than either adding a date-time coding module alone or a single multi-scale feature fusion module._
**Research Hypothesis 4 (RH4):**: _Applying the Bayesian hyperparameter optimization algorithm can further enhance the model's performance._
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)
### Process Framework
#### 2.4.3 LSTM Module  
The LSTM module architecture, shown in Figure 4, focuses on modeling long-term temporal dependencies critical for landslide prediction.  
Function: Long-term temporal dependency modeling.  
Figure 4: LSTM module architecture diagram.  
Figure 3: SAM module architecture diagram.  
Figure 2: CNN module architecture diagram.  
Selection justification: The LSTM module effectively models precursor event sequences where timing is critical. Its state compression mechanism balances memory retention with overfitting prevention by reducing dimensionality while preserving essential temporal patterns.
###### Abstract  
Terrestrial net primary production (NPP) is a key carbon flux that changes with rising atmospheric CO2 and CO2+induced climate change. Earth system models are commonly used to investigate these NPP changes because of their fundamentally trustworthy ability to simulate physical climate systems and terrestrial biogeochemical processes. However, many uncertainties remain in projecting NPP responses, due to their complex processes and divergent model characteristics. This study estimated NPP responses to elevated CO2 and CO2+induced climate change using the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM2), as well as 22 CMIP6 models. Based on CMIP6 pre-industrial and abruptly quadrupled CO2 experiments, the analysis focused on a comparison of the CAS-ESM2 with the multi-model ensemble (MME), and on a detection of underlying causes of their differences. We found that all of the models showed an overall enhancement in NPP, and that CAS-ESM2 projected a slightly weaker NPP enhancement than MME. This weaker NPP enhancement was the net result of much weaker NPP enhancement over the tropics, and a little stronger NPP enhancement over northern high latitudes. We further report that these differences in NPP responses between the CAS-ESM2 and MME resulted from their different behaviors in simulating NPP trends with modeling time, and are attributed to their different projections of CO2-induced climatic anomalies and different climate sensitivities. These results are favorable for understanding and further improving the performance of the CAS-ESM2 in projecting the terrestrial carbon cycle, and point towards a need for greater understanding and improvements for both physical climatic processes and the terrestrial carbon cycle.  
Terrestrial net primary production; quadrupled CO2; CO2 fertilization effects; climatic effects; CAS-ESM2; CMIP6
## 6 Real-Life Case Study
### Result and Discussion  
Because the goal is to study the treatment process, medications with the same effect shared the same behaviour in the treatment process. Hence, we applied the proposed framework to detect redundancy in the event log and generate a more representative event log for further analysis. In this case study, the thresholds were set to 0.2 for the control-flow relation, 0.1 for the attribute values, and 0.1 for the label semantic information. As a result, nine groups of redundant activity labels were detected according to the transitive property of equality. The results were further evaluated by the domain knowledge to show that these are different therapies in the STEMI treatment [50]. Therefore, we adopted therapy names (e.g., _Beta Blockers_, _Anticoagulants_) to replace redundant activity labels in the original event log to generate the preprocessed event log. Compared with the original event log, the number of activity labels in the process was reduced from about 600 to 13. The number of distinct trace variants was reduced from about 1300 to 660, which is around half of the original event log.  
We adopted Disco for process discovery on the event logs [16]. As a result, a spaghetti-like process model was discovered from the original event log due to the redundancy. Figure 10 shows a snapshot of the partial process model because the original process model is too complicated to display here. The original process model contains over 600 activity labels and countless relations between them. Such a process model cannot provide any useful insights toward understanding the treatment process for STEMI patients. On the contrary, Figure 11 shows the process model discovered by using Disco from the preprocessed event log after applying the proposed framework in the paper. The discovered process model in Figure 11 overall seems to be simple and insightful, representing only the important treatment behaviours of STEMI patients. We can observe that most patients were treated with _Aspirin_, _Beta blockers_, and _Statins_ during their hospital stays, which is in line with the current treatment guideline [50]. Moreover, several control-flow relations are also presented in the process model, such as _Clopidogrel \(>_{W}\) Digoxin_ and _Insulin \(>_{W}\) Statins_. Such a process model together with additional patients' records can be further used to study other related topics, such as how the treatment process influences a patient's survival time. This case study further shows that our framework can efficiently detect redundant activity labels in the event log. Moreover, the event log with redundant activity labels has a significant impact on the quality of the discovered process model.
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 3 Factors Affecting Dust Generations
### Effect of Housing Systems on PM  
Poutlry housing is the major source of PM emissions. Different housing systems (e.g., floor-raised, aviary, conventional caged, and enriched colony) show different PM emissions and concentrations. Among different housing types, the cage-free (CF) housing (aviary) system resulted in significantly higher PM concentrations and emissions [40]. The daily mean PM\({}_{10}\) level in CF housing was about six to nine times higher than the conventional cage (CC) and the enriched colony housing (EC) systems [3]. Therefore, emission mitigation studies should consider CF housing systems as the priority. In addition, CF shows higher concentrations of airborne bacterial concentrations and emissions rates than CC and EC houses because PM is the primary carrier of airborne bacteria. In addition, house types can be divided into high-raised (HR) or manure belts (MB). According to [PERSON] et al. (2012), the HR houses had higher NH\({}_{3}\) concentrations but lower CO\({}_{2}\), H\({}_{2}\)S, and PM\({}_{10}\) concentrations than the MB house [9]. The detail of PM concentration and emissions in different houses is detailed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Housing** \\ **System** \\ \end{tabular} & **Location** & **Bird Density** & \begin{tabular}{c} **Monitoring** \\ **Device** \\ \end{tabular} & **PM Size** & \begin{tabular}{c} **PM Emission (g** \\ **day\({}^{-1}\)AU\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Concentration** \\ **(mg m\({}^{-3}\))** \\ \end{tabular} & **References** \\ \hline CC & Midwest, US & 200,000 & TEOMs & PM\({}_{2.5}\) & N/A & 0.04 & \\  & & PM\({}_{10}\) & & 0.59 & \\ \hline CC & Midwest, US & 200,000 & TEOMs & PM\({}_{2.5}\) & 0.9 \({}^{*}\) & N/A & [40] \\ \hline CC & France & 45,257 \(\pm\) 18,800 & Stationary capor & PM\({}_{2.5}\) & N/A & 0.11 & [41] \\ \hline CC & Germany & 1350 & Glass fiber filter & TSP & N/A & \begin{tabular}{c} 0.6 \\ 1.25 \\ \end{tabular} & [42] \\ \hline Caged layer & South Korea & 5636 & \begin{tabular}{c} Gravimetric \\ method and air sampling pump \\ \end{tabular} & TSP & N/A & \
\begin{tabular}{c} 3.66 \\ 1.99 \\ \end{tabular} & [43] \\ \hline Caged hen & UK & N/A &
\begin{tabular}{c} TEOMs, \\ Micro Orifice \\ Uniform Deposit \\ Impactors \\ \end{tabular} & PM\({}_{2.5}\) & 6.9 \({}^{*}\) & N/A & [44] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particulate matter emissions and concentration of different PM sizes in various poultry housing systems.  
Figure 2: Factors affecting PM emissions in poultry housing.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
## 4 Network Model
### MTC Traffic Model  
This traffic model of MTC is represented by the case when devices have a finite amount of data to transmit and hence the name finite buffer. When this finite data is transmitted, the session is finished and the devices are then removed from the resource scheduler and enter a waiting time during which they start collecting new data for a new session. Finite buffer includes device transmission or reception of a given rate Rb and for finite time intervals Ta, when this time is finished the device is removed from the scheduling process, collects new data for a finite time interval Ts, after that the device starts a new session [35].  
The simulation time should be long enough to accommodate for the sensor's activity time, in which all transmissions occur and sleeping time in which data collection occurs. In this work, 400 ms is considered for the session time (active), after this the session expires; a data collection session starts which lasts for two minutes. The simulation results obtained when using finite buffer traffic model are tends to be more realistic compared with \"full buffer\" traffic model because in normal cellular network, users are not expected to request an infinite amount of data, which is the case characterised by the full buffer model.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 2 Background and Related Work
### Architectures for Securing ITS Networks
#### 2.4.3 Summary  
Despite the high level of security provided by the C-ITS PKI solution, it has limitations. The main one is the lack of efficiency due to the use of asymmetric cryptography and the large size of the attached certificate. The previous papers [13; 14] present lightweight protocols that attempt to mitigate these constraints. Tables 3-5 summarise some of the results claimed by the protocol's authors in terms of security properties and efficiency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation** & **[PERSON] et al. [13]** & **[PERSON] et al. [14]** \\  & **DLAPP [ms]** & **MFSPV [ms]** \\ \hline Signature & 0.0167 & 0.018 \\ Verification & 0.0167 & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computation latency comparison for message signature and verification.  
Figure 5: MFSPV proposed message format.  
The DLAPP [13] and MFSPV [14] protocols have additional features. Nonetheless, the focus of this work centres around security guarantees during message exchanges. For more comprehensive details, please consult the respective papers.
## 1 Introduction
The outcomes of simulations reveal that this algorithm significantly diminishes the blindness and randomness in node searches relative to the initial RRT algorithm. [PERSON] et al. [13] introduced the RRT algorithm, which utilizes adjacency extension for initial path exploration. Subsequently, strategies for identifying paths and eliminating redundant ones are employed to enhance the optimization of the chosen path. [PERSON] et al. [14] swiftly investigated the surroundings to identify a viable route using the greedy heuristic approach for rapidly exploring random trees (EP-RRT*), subsequently expanding it to acquire a heuristic sampling zone. The process involves iterative searching within the heuristic sampling zone, which alters as the path is continuously optimized, ultimately identifying the most efficient or less effective route between the initial and final points. This algorithm enhances the rate at which nodes are used, accelerates the rate of convergence, and secures an improved path within an identical iteration count. Although the above experts and scholars have improved the path-planning problem of the RRT algorithm in complex environments from different angles, there are still some shortcomings. This paper therefore proposes the design of a flexible wheeled inspection robot to carry out safety inspection work [15]. Targeting the problems of the complex environment and narrow roadways in underground mines, a fusion algorithm of FGA-RRT and PRM is proposed. To address the limitations of the traditional RRT algorithm, which is prone to blindness and randomness in its search strategy, a fan-shaped goal orientation strategy is proposed to guide the selection of sampling points. This is followed by an adaptive step size expansion strategy, which increases the expansion speed of the random tree in narrow channels. To prevent the RRT algorithm to generate more complex paths, the PRM algorithm and the improved RRT algorithm have been integrated, with the latter utilizing the PRM algorithm's approach to optimizing redundant nodes. Additionally, considering the kinematics law of the robot, the path is optimized by the third-order Bessel curve, enhancing the safety of the robot in the process of autonomous movement. The experiment is conducted on the MATLAB simulation test platform, and the effectiveness of the algorithm is confirmed through comparisons with other algorithms. Finally, the improved algorithm is verified in the real-world environment.
## 4 Experiments
### Results and Discussion  
The quantitative and qualitative results, presented in Table 1 and Figure 3, demonstrate that CriSALAD significantly outperformed DiNO-SALAD and other baseline and SoTA methods across most tested scenarios. CricaVPR(16 bs) refers to the results of CricaVPR using an inference batch size of 16, which matched our settings for a fair comparison. Notably, in terms of the average Top-1 recall, CriSALAD achieved 94.7% on the Pitts30k [44] dataset and 89.3% on the Nordland [46] dataset, surpassing the closest competing methods, which reached only 93.7% and 76.2%, respectively. This performance demonstrates the superiority of our proposed CriSALAD model in structured urban environments and challenging long-term place recognition scenarios.  
Despite the fact that CriSALAD did not achieve the highest performance on Tokyo24/7 [45] and MSLS [47], it remained highly competitive. On the one hand, our method did not outperform other methods on Tokyo24/7 and MSLS due to the lack of generalization in illumination and dynamic issues, which need to be addressed in future work. On the other hand, our method outperformed other methods on Nordland and Pitts30k, demonstrating its strength in handling weather and viewpoint changes. These results highlight the robustness of CriSALAD, demonstrating its strong generalization ability across diverse datasets while maintaining top-tier accuracy in challenging scenarios. Our CriSALAD model can be further optimized to deal with illuminance changes, as in the Tokyo24/7 dataset, and occlusions, as in the MSLS dataset. In terms of inference speed, CricaVPR, using the multi-resolution convolutional adapter, achieved 148.96 FPS on a single NVIDIA GeForce RTX 4090D GPU, whereas our method achieved 427.36 FPS on the same hardware.  
Figure 3: Examples of Top-1 retrieval from the datasets. Check and fork marks stand for correct and incorrect retrievals.
## 3 Experimental Results  
An experimental system was implemented on a 2.2-inch 16 \(\times\) 16 point LED display and on a demodulator APP for an Android smartphone, and a series of experiments were conducted to verify the performance of the proposed long-distance LED-DCC system, including data transmission distance, data transmission rate, and data decoding latency, which are critical for real-time data reception.  
Figure 3: The demodulation procedure of lightweight, back-forth, fast image processing algorithm.
# MMNET: A Multi-Modal Network Architecture for Underwater Networking
## 3 Intelligent Network Management Based on Application Requirements  
### MMNET Personalized Network Resource Allocation  
In UWSNs, the application scenarios and types are complex, and each application needs different network resources and functions, so it is necessary to provide personalized network support for applications [22; 28; 29; 30].  
In order to provide personalized networks for different applications, considering the specific situation of MMNET, we propose a personalized network support scheme based on K-nearest neighbor (KNN). The main idea of this method are as follows: Firstly, according to the type of task, the network requirements are extracted. Secondly, according to the extracted requirements, KNN is used to recommend the application scheme of network resources based on historical data.
# Data Access Based on a Guide Map of the Underwater Wireless Sensor Network
## 5 Performance Analysis of DAGM
### Energy Consumption Analysis  
In UWSN, energy consumption mainly includes the consumption on data processing and consumption on data transmission, as usually the processing energy consumption is much less than the transmission energy consumption. Transmission energy consumption includes data-sending energy consumption (\(E_{Send}\)) and data-receiving energy consumption (\(E_{Receive}\)). In general, the data-receiving energy consumption is a constant, and data-sending energy consumption has a connection with communication distance, and transmission channel characteristics (bandwidth, error rate, etc.). Thus, the energy consumption is shown in Equation (8):  
\[E_{Tn}=E_{Send}+E_{Receive}=(pE_{dec}+(pE_{dec}+pf(d)))\times m \tag{8}\]  
\(E_{dec}\) represents the energy consumed by the circuit. \(f(d)\) is a function of the energy consumption on the transmission data in a specific transmission channel, mainly related to the distance. \(m\) indicates the time of retransmission.  
Processing energy consumption is mainly the energy consumption of the node _processing_ data storage and query sentence, and it is proportional to the query times and amount of data \(d\) stored on the node, so we can obtain: \(E_{proc}=n_{q}\times e_{q}\times d\).  
\(n_{q}\) represents _the_ number of storage or query within a unit time, and \(e_{q}\) represents the energy consumption of single query or storage.  
The total energy _consumption_ of the network \(E_{Total}\) includes data storage energy consumption \(E_{DStro}\), metadata diffusion energy consumption \(E_{MDSyn}\), data query energy consumption (\(E_{DQue}\)) and specific application data transmission energy consumption (\(E_{DTna}\)). Therefore, we can obtain Equation (9):  
\[E_{Total}=\sum\left(E_{DStro}+E_{MDSyn}\right)+\sum\left(E_{DQue}+E_{DTna}\right) \tag{9}\]  
That is, the energy consumption of the network is the total energy consumption of all data storage, metadata diffusion and data query in network.