esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Results and Analysis of Datasets
#### 3.3.1 Results and Analysis of the Massachusetts Road Dataset
Region 6 (Bifurcated Roads): While the right-side road was effectively captured by all models, the shorter left segment saw only MixerNet-SAGA (f) emerging victorious. Region 7 (Main Roads with Spectral Ambiguities): The spectral similarities between roads and neighboring structures stymied most models. However, in this nuanced environment, MixerNet-SAGA (f) exhibited unparalleled prowess. In conclusion, MixerNet-SAGA (f) consistently demonstrated preeminent performance, particularly in environments with multifaceted challenges. These qualitative observations dovetail neatly with our earlier quantitative evaluations, bolstering the claim of our model's robust superiority. Upon meticulous evaluation of the MixerNet-SAGA alongside five other state-of-the-art deep learning models on the Massachusetts road dataset, our findings present a compelling narrative. Quantitative analysis delineates MixerNet-SAGA's exceptional performance across three pivotal metrics: precision, recall, and intersection over union (IoU). Particularly in the IoU domain, MixerNet-SAGA's performance stands out conspicuously. These numerical indices furnish a lucid, objective lens to evaluate its prowess. Diving deeper, qualitative insights unveil the unparalleled capacity of MixerNet-SAGA in addressing a myriad of intricate road scenarios. Its deepness remains manifest, be it in the unobstructed urban arteries, intersections abundant in secondary roads, or under nuanced circumstances such as shadows and spectral ambiguities. Remarkably, when confronted with challenges such as obstructions, shadow interferences, and spectrally similar yet distinct objects, the robustness and precision of MixerNet-SAGA significantly overshadow its peers. Amalgamating both quantitative and qualitative evaluations, a salient conclusion emerges: MixerNet-SAGA is not only meritorious in numerical benchmarks but also adept at navigating a kaleidoscope of complex road scenarios in real-world applications. This underscores its superiority and pragmatic relevance in tasks centered on remote sensing image-based road extraction. Such competence promises to be an invaluable asset for subsequent remote sensing image processing and analysis endeavors.
## 4 Results and Discussion
### Sensitivity Analysis of Basic Parameters
#### 4.1.4 Distribution Patterns of Porosity and Permeability  
Figure 13 shows the distribution nephograms of formation-effective porosity and formation-effective permeability at different times after the drilling fluid invaded the formation. Figure 14 shows the distribution patterns of effective porosity and effective permeability in the near-wellbore zone at different times. The distribution characteristics of effective porosity and effective permeability are very similar to those of hydrate saturation of the formation.  
Similarly, the distribution of effective porosity and permeability can be divided into two regions, from the wellbore to the decomposition front, as a lot of hydrate decomposed, the effective porosity and permeability rapidly increased, and from the decomposition front to the far end of the formation, they basically remained almost undisturbed. Therefore, decomposition is the main factor that affects the porosity and permeability. In the near-wellbore zone, the combination of the fluid-solid coupling effect with the hydrate decomposition effect led to the development of fractures in the reservoir massively, making the effective porosity and permeability there significantly higher than the undisturbed formation. A total of 20 h after the drilling fluid invaded the formation, the ultimate effective porosity of the formation was 0.45, about 1.13 times the absolute porosity of the formation, and the ultimate effective permeability of the formation was 5.64 \(\times\) 10\({}^{-14}\) m\({}^{2}\), about 1.14 times of the absolute permeability of the formation.  
Figure 12: Variations of hydrate saturation at two points around the well with time.  
Figure 13: The distribution nephograms of formation-effective porosity and formation-effective permeability at different times. (**a**) formation-effective porosity (**b**) formation-effective permeability.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.2.3 Convolutional Neural Networks (CNNs)  
Because of their hierarchical structure that includes convolutional layers (through which filters are applied to the input data), pooling layers (for dimensionally reducing), and fully connected layers (by which extracted features are interpreted), CNNs are suitable for various tasks in WWTPs, such as the detection of anomalies in pollutant levels supplied by the plant sensors, in monitoring water quality (in distinguishing between clean and dirty water, utilizing reflectance images as inputs), and in real-time and extensive monitoring applications [5; 51]. In addition, the integration of CNNs with other models, such as multi-kernel radial basis function neural network (MKRBFNN), has improved the feature extraction (features extraction from influent parameters) processes, which become automated (the manual feature selection was eliminated), improved the prediction accuracy (the models efficacy in capturing complex relationships) in WWTPs, and also reduced method overfitting (the efficient handling of noisy data was ensured) [11].  
On the other hand, CNNs can efficiently handle missing or noisy data supplied by WWTP sensors and can automatically extract and interpret process features from complex and very large databases, these being very important skills in wastewater quality prediction and environmental monitoring.  
According to [PERSON] et al. [42], CNNs can be integrated with Gaussian Process Regression (GPR) in order to improve the prediction accuracy and ensure interval predictions, which are very useful when dealing with data uncertainty. The combination of CNNs with GPR and LOST (Long Short-Term Memory Neural Networks) is a viable solution to improve the robustness of predictions in dynamic and high nonlinear processes and in solving data noise and variability problems [42]. Also, CNNs, through the use of a binomial event discriminator, are capable of identifying critical anomalies (significant patterns) in sensor data fluctuations, which has been successfully applied in water supply systems [82]. CNNs combined with Deep NeuralNetworks (DNNs) are another viable solution for efficiently handling noisy data, using the abilities of DNNs as a powerful feature extraction process and for pattern recognition [58].  
Beyond water quality monitoring, such applications based on CNNs are also used in multiple other applications, such as waste management and waste classification systems, highlighting their versatility in environmental applications [83].  
Therefore, though the integration of CNNs with other models and due to their feature extraction abilities, the domain knowledge (the missing or noisy data supplied by sensors) is efficiently used and managed, ensuring accurate and reliable predictions and environmental monitoring.
# An On-Demand Fault-Tolerant Routing Strategy for Secure Key Distribution Network
###### Abstract  
The point-to-point key distribution technology based on twinning semiconductor superlattice devices can provide high-speed secure symmetric keys, suitable for scenarios with high security requirements such as the one-time pad cipher. However, deploying these devices and scaling them in complex scenarios, such as many-to-many communication, poses challenges. To address this, an effective solution is to build a secure key distribution network for communication by selecting trusted relays and deploying such devices between them. The larger the network, the higher the likelihood of relay node failure or attack, which can impact key distribution efficiency and potentially result in communication key leakage. To deal with the above challenges, this paper proposes an on-demand fault-tolerant routing strategy based on the secure key distribution network to improve the fault tolerance of the network while ensuring scalability and availability. The strategy selects the path with better local key status through a fault-free on-demand path discovery mechanism. To improve the reliability of the communication key, we integrate an acknowledgment-based fault detection mechanism in the communication key distribution process to locate the fault, and then identified the cause of the fault based on the Dempster-Shafer evidence theory. The identified fault is then isolated through subsequent path discovery and the key status is transferred. Simulation results demonstrate that the proposed method outperforms OSPF, the adaptive stochastic routing and the multi-path communication scheme, achieving an average 20% higher packet delivery ratio and lower corrupted key ratio, thus highlighting its reliability. Additionally, the proposed solution exhibits a relatively low local key overhead, indicating its practical value.  
secure key distribution; routing; fault tolerance; DS evidence theory; reliability Article
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 2 Related Studies
### Reduction of Convergence Period in Typical ADR  
A performance assessment of the convergence period under different configurable parameters was conducted in [17]. This study provides in-depth insight into the typical ADR under variable channel conditions by highlighting limitations, such as the convergence period. The simulation results showed that the ADR convergence period and energy consumption are primarily dependent on the link conditions and number of EDs. It was revealed that the ADR suffers from a high convergence period under variable channel conditions and when the EDs change their SF or TP to a higher value (such as SF = 12 and TP = 14 dBm) to recover their connectivity with the GW. Furthermore, the convergence period is more sensitive to \(ADR\_ACK\_DELAY\) than \(ADR\_ACK\_LIMIT\) (i.e., \(ADR\_ACK\_DELAY\) and \(ADR\_ACK\_LIMIT\) are equal to 32 and 64 UL packets in the ED-managed ADR, respectively). Their simulation results also revealed that the typical ADR convergence period introduces higher energy consumption and more significant packet losses.  
It was recently indicated that typical ADRs (both ED- and NS-managed) suffer from convergence issues [18]. An ED-managed ADR is inefficient for lossy links, which results in considerable time to converge to a constant and stable state. Meanwhile, the NS-managed ADR takes \(M\) packets to alter the SF and TP, which makes it too time-consuming to determine a reliable configuration. Therefore, Ref. [18] suggests some changes (such as decreasing \(M\) packets during SF and TP configuration adaptation) in both ED- and NS-managed ADRs for enhancement. NS-managed ADR controls the ED-managed ADR by computing the PSR of an individual ED before sending the DL packet (\(LinkADR\_RQ\)) in response to \(ADRACKReq\). Here, the PSR is compared to a predefined threshold (i.e., PSR \(<\) 80%). If the condition holds, NS sends the \(LinkADRReq\) MAC layer command for the ED containing the SF and TP. Indeed, it changes the SF and TP of the individual ED after five UL packets (in a typical ADR, this UL history is set to \(M\) packets). The performance of both enhanced methods (ED- and NS-managed ADRs) has been compared to typical ADRs, where the results show improved outcomes in terms of the convergence period, energy consumption, and PSR. However, the performance evaluation of the enhanced methods is limited to static EDs and it only a confirmed mode of communication. By contrast, we consider mobility under intra-SF interference and different propagation loss models (such as log-distance and shadowing losses).
## 5 Experimental Setup
### Classification Benchmark  
In order to investigate the performance of the proposed GBM method and evaluate it against other, well-known classifiers that have been previously applied in sentiment analysis tasks with success, as explained in the Related Work section, the following classifiers have been incorporated: decision trees, naive Bayes, support vector machines and deep neural network learning (using recurrent neural networks). In the case of decision trees, naive Bayes and SVM, we have experimented using two different data representation techniques, namely the original _tf-idf_ vector representation of the document-term matrix and the reduced dataset of the PCA transformation or the feature selection stage.
# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 4 Experimental Results
### Results
#### 4.3.1 Negative-Only Data Set (13K)  
For the 13K data set (negative-only comments), we trained twelve classification models and reported their classification performance in terms of accuracy, precision, recall, and F1 score as shown in Table 18. The results indicate that all models had relatively low performance, in terms of classification accuracy, ranging between 33.25% for the BiLSTM with PX_Vec embeddings and 10-fold cross validation to 60.24% for the AraBERTv02 model.  
In terms of classification precision, the results indicated that BERT-based models excelled over DL models. The results showed that AraBERTv02 outperformed all other models, achieving a classification precision of 81.22%, followed by MARBERT, then Qarib, and finally the PX_BERT model. DL models yielded lower classification performance, in terms of precision, ranging from 40% for the BiGRU with PX_Vec embeddings and 10-fold cross-validation to 12% for the tuned BiGRU with AraVec embeddings.  
In terms of recall, all models achieved low classification performance, ranging between 32.54% for the PX_BERT model and 10% for the tuned-BiGRU with AraVec embeddings.  
Finally, in terms of the F1 score, the classification performance of the BERT-based models was significantly better than that of the DL models, with a difference of roughly 8%. The PX_BERT model outperformed all other models, with a F1 score of 43.06%, while the tuned-BiGRU with AraVec embeddings yielded the lowest performance, with 10.91%.
# GAT-ABiGRU Based Prediction Model for AUV Trajectory
## 2 Related Work
### Dynamical Modeling Based Approach  
The most common dynamics modeling approaches are Gaussian process regression and Kalman filtering. [PERSON] et al. [3] investigated the trajectory changes of underwater robots under the influence of ocean currents and waves and proposed a method to deal with and optimize the control error using a terminal sliding mode controller. This method can effectively improve the system's stability and help it cope better with the challenges of underwater complex dynamic environments. [PERSON] et al. [4] proposed a method based on the extended Kalman filter, which uses the AIS system to obtain information such as the position and velocity of a ship in real-time and predicts the ship's trajectory by building a ship motion model and designing a Kalman filter. [PERSON] [5] proposed a real-time trajectory planning method combining a Gaussian process and incremental optimization, which applies to the path planning of an autonomous vehicle. A Gaussian process regression model is used to predict the vehicle trajectory, and then adjustment and optimization by an incremental optimization algorithm ensures that the constraints and optimization objectives are satisfied. However, given the complexity and variability of the marine environment and its inherent uncertainty, these traditional prediction methods based on dynamic modeling often need help to predict trajectories accurately. Although effective in specific contexts, these methods still have certain limitations in the face of complex marine environments, particularly regarding prediction accuracy and response to sudden changes.
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 2 Preliminaries
### Genetic Algorithm Optimization
#### 3.2.1 Why Genetic Algorithms?  
Standard parameter optimization problems are often tackled by the application of gradient-based methods. A very famous example for this is the parameter optimization required to train neural networks in machine learning [16].  
Considering Equation (5) again, however, reveals that using a gradient-based approach is cumbersome in our context. This equation involves a min operation which causes differentiability issues. A typical way to overcome them would be to use a smooth approximation [31], e.g., the softmin operation instead of the min during optimization, but we note that like this, we would in fact not optimize the deterministic mapping rule that we aim for in Equation (5), but only some non-deterministic approximation. Genetic algorithms, however, can directly optimize the deterministic mapping rule, as will be explained soon.  
Moreover, depending on the distance metric used, more issues can arise. If the Manhattan distance from Equation (3) shall be used, the non-differentiability of the absolute magnitude \(|.|\) involved adds to the min from Equation (5) which makes a gradient approach for the optimization of the parameters \(\theta_{t,n}\) very cumbersome and would require mathematical approximations and workarounds [31]. Genetic algorithms, in contrast, can easily deal with this matter.  
Figure 5: Visualization of the approximate nearest neighbor search in a neighborhood graph. The algorithm traverses through the neighborhood graph greedily. \(\times\) markers correspond to the parameters \(\mathbf{\theta}_{t}\) to be optimized, blue arrows indicate neighborhood relations.  
Finally and most importantly, in Sections 3.1.2 and 3.1.3, we have also studied approximate solutions to the nearest neighbor problem. These have drastically reduced complexity in terms of the number of distance calculations required. If such heuristic algorithms are applied, one can imagine the min operation from Equation (5) to be replaced with an approximate min. This operation is extremely hard, if not impossible, to describe analytically. Considering the greedy processing of the approximate nearest neighbor search algorithms from Sections 3.1.2 and 3.1.3, it is intuitively clear that for both, there is no mathematical expression to adequately describe the mapping of \(\mathbf{y}\) onto \(t\), even though it is deterministic. The mapping rules are rather given by subsequent processing steps in greedy algorithms.  
As a result, the parameter optimization to maximize \(\mathrm{I}(\mathsf{X};\mathsf{T})\) with standard gradient methods is not possible in these cases. Genetic algorithms, however, can be applied easily as discussed in the following.
## 3 Results
### Diurnal and Monthly Investigation  
As of 2018, the majority of newly installed wind turbines in Germany featured a hub height of above 140 m [3], which will increase in the future. Therefore, in the following the measurement height bin of 152.5 m was explicitly looked at.  
In the diurnal cycle at 152.5 m measurement height bin (see Figure 5), the availability in the cloud-free case in general is very high with only slight variations. For the cloudy case, availability ranges from 63% (at 09:00 UTC) to 77% (at 19:00 UTC). Availability is the lowest during the morning hours.  
In order to investigate seasonal effects, the availability was averaged on a monthly basis for the measurement height bin of 152.5 m. It can be seen (Figure 6) that, compared to the cloud free case, the cloudy situations show substantially lower data availability during winter season. During winter, the situations with cloud base altitudes below 100 m are often characterized by extremely low stratus or even ground fog, and therefore most of the lidar signal is lost at very low heights. During the summer months, this is not the case. In addition, the summer overall has far less situations with cloud base heights below 100 m, and the uncertainty of the estimated CBA from the HYFOG algorithm is higher (see [23]) than during winter. This higher uncertainty of the CBA of the HYFOG algorithm may cause the miss of low cloud events (CBA \(\leq\) 100 m) or the other way around cloud-free/CBA \(\geq\) 100 m events may be selected as alleged low cloud events.  
Due to the non-associative property of the mean function the total mean can differ from the mean of the groups (month) if the groups have different numbers of items. Therefore, it seems that the numbers in Figures 4 and 5 do not match those of Figure 6 because the months from March to September show lower case counts than the months from October to February. The total mean (Figure 4) is therefore dominated by October to February.  
Figure 5: Diurnal mean availability for the measurement height bin of 152.5 m.
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 4 Simulation Results
In general, both filters can track the targets over time and can output the trajectory of each target accurately and stably. From the OSPA miss distances in Table 3, the estimated performances of two filters are similar, but the execution time of the S-LMB-GOM filter is 1/11 of the G-LMB-GOM filter. Compared with the G-LMB-GOM filter, our proposed filter can greatly improve the computational efficiency. Figure 12: The OSPA miss distance at different SNRs for S-LMB-GOM filter. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Localization** & **Cardinality** & **Total** \\  & **Component (m)** & **Component (m)** & **OSPA (m)** \\ \hline S-LMB & 4.1 & 2.95 & 7.05 \\ G-LMB & 4.48 & 2.81 & 7.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average OSPA distances for the S-LMB-GOM filter and the G-LMB-GOM filter. Finally, we note that, the trajectories of the targets we studied turn out to be smooth curves (cf. Figures 3, 4 and 7), for which the trajectory fitting approach [31; 32] may be applied for better results, either in the manner working by themselves independently or jointly with our proposed filter. To avoid distracting the attention of the reader from our key contribution, we refer the reader to the mentioned works.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 4 Simulation Results and Discussion  
### Simulation Background and Parameters  
Assume that WSNs composed of \(m\) sensor nodes were randomly distributed in the monitoring area \(S_{M}\). When multiple nodes monitor the moving target, some network nodes in the vicinity of target are selected to perform monitoring tasks. These working nodes need to consume energy for tasks such as information collection, data processing, and communication transmission. Once the remaining energy of one working node is less than zero, it is considered dead and loses itself monitoring ability. Network nodes that are inactive can be considered dormant, and they do not consume their own energy.  
In this section, performances of proposed method are verified by comparative experiments on MATLAB simulation platform. The main parameters of simulation are shown in Table 1. Besides, the proposed algorithm LEACH-MTC is compared to three other LEACH-based algorithms: LEACH [24], LEACH-DBCH [13], and LEACH-RARE [14], and conducts performance analysis from the aspects of the working nodes selection, nodes survival number, network residual energy and network monitoring accuracy, and so on.