esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
## 3 An adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm  
The core idea of an improved differential evolution (DE) algorithm is to generate variation disturbance according to vector difference, ensure the diversity of the population, balance global search and local development ability, avoid precocity of population, accelerate the rate of population convergence and improve the convergence accuracy. So, the adaptive dynamic disturbance strategy for differential evolution (ADDSDE) algorithm mainly improves the algorithm from four aspects of population initialization, parameter adaptation, mutation strategy and disturbance strategy, and comprehensively improves the global optimization search ability and convergence speed of the algorithm. Firstly, the chaos mapping theory is used to initialize the population. The standard DE algorithm is a random initialization population. Although it is beneficial to the diversity of population, it is difficult to guarantee that the quality of the population individual and the efficiency of the algorithm is reduced. Second, an adaptive adjustment mechanism for scaling factor F and crossover probability CR is adopted to improve the convergence speed and stability of the algorithm. Third, the mutation strategies \(DE/rand/1\) and \(DE/best/1\) are dynamically weighted, and balance the global search and local search. Finally, the Gauss perturbation strategy is introduced to accelerate individuals to jump out of the local optimum and improve the global search ability.
## 4 Evaluation  
### Datasets and Experimental Settings  
We evaluated our model on a Wizard-of-Wikipedia dataset [1]. The dataset is used for three tasks: Knowledge prediction (i.e., selecting documents containing proper knowledge from a document collection), response generation (i.e., generating responses using the given knowledge), and an end-to-end task (i.e., both knowledge prediction and response generation). In this study, we focused on response generation. Owing to hardware limitations, we refined the Wizard-of-Wikipedia dataset to use two previous utterances as the dialogue context. Our refined dataset comprises 83,247 utterances for training, 4444 utterances for verification, and 4356 utterances for testing.  
We used Bilingual evaluation understudy (BLEU) [29; 30], perplexity (PPL) [1; 31], and bag-of-words (BOW) embedding [32] as performance measures. BLEU measures a ratio of overlapped words between generated responses and gold responses, as shown in the following equation.  
\[BLEU=\min(1,\frac{\textit{length of a generated sentence}}{\textit{length of a gold sentence}})(\prod_{i=1}^{n}\textit{precision}_{i})^{\frac{1}{k}}, \tag{9}\]  
where \(n\) is the maximum length of n-grams, which is commonly set to 4, and \(\textit{precision}_{i}\) is a word \(i\)-gram precision (i.e., the number of correct word \(i\)-grams divided by the number of word \(i\)-grams in a generated sentence). The precision of BLEU is the average score of BLEUs for 10 generated sentences (for these experiments, the decoder returned 10 candidate sentences per query by a beam search algorithm) per query. The recall of BLEU is the maximum score among BLEUs for 10 generated sentences per query. As many studies used unigram-F1, we also used word an unigram F1, called F1-score [1; 31]. PPL is a measure for evaluating language models, and it is commonly used in traditional generative chatbot models that mainly use an RNN decoder. The BOW embedding metric is the cosine similarity of BOW embedding between generated and gold responses. It consists of three metrics: Greedy [33], average [34], and extrema [35]. In our test, we report the maximum BOW embedding score among the 10 sampled responses.
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 3 Results and Analysis
### Vulnerability Assessment of the Disaster-Bearing Body  
As shown in Figure 4, the spatial distribution trend of the disaster-bearing body's vulnerability level in Liaocheng City shows a gradual weakening from the southwest to the northeast, and it is entirely in a state of high vulnerability. The very-high-vulnerability areas are concentrated in Dongchangfu District, Shenxian County, Guanxian County, and Linqing City, accounting for 57.17% of the total area. These areas are densely populated and have a large number of rural populations and dense roads, as well as weak recovery and self-construction capacity after damage. Very-low-vulnerability and low-vulnerability areas account for 11.69% and 8.48% of the total area, respectively, located in Chiping District and Dong'a County, which have low populations and road densities, high economic densities, and high levels of secondary industry development.  
Figure 3: Zoning map indicates disaster-prone environmental exposure.
## 4 Results
### Comparison of IMERG Series Products with Rain Gauge Data
#### 4.2.2 Simulation Driven by the IMERG FR Product (Scenario II)  
To explore the hydrological utility of the IMERG products in ungauged basins, the FR product is used as rainfall input, with a calibration period of 2014-2015 and a validation period of 2016-2017, to construct a hydrological model and observe the applicability of the model in the XRRB. Precipitation is usually the main source of uncertainty in hydrology, and different sets of precipitation data lead to different ranges of best estimates of the SWAT model calibration parameters [42]. The optimal model parameter settings are shown in Table 10. Scenario II represents an option for sparsely gauged applications, which can make use of the FR product.  
The streamflow simulation results of the FR product for Scenario II are shown in Figures 11 and 12, and the statistical measurements are summarized in Table 4. What can be seen in Table 4 is that the hydrological simulation of the FR product in Scenario II has improved and shows a slightly better accuracy than that of Scenario I. At the daily scale, the CORR of the validation period is 0.64, BIAS is 0.01, and NSE is 0.43. The simulation results are acceptable but not satisfactory, although they are significantly better than those of the validation period in Scenario I (CORR = 0.61, BIAS = 0.18 and NSE = 0.25). The simulation results at the monthly scale show that simulations driving by the FR product during the validation period are significantly better than those in Scenario I, with a CORR of 0.85, an NSE of 0.84, and a BIAS of \(-0.02\). As seen from Table 5, both \(p\)-factors are greater than 0.6 and R-factors are less than 1 during both the calibration and validation periods. The results indicate that the SWAT model constructed in Scenario II is robust and meets the requirements of hydrological simulation. All the results show that the SWAT model based on the FR product is suitable for the XRRB.  
Figure 10: Scatter plots of monthly simulated streamflow driven by (**a**) STA, (**b**) ER, (**c**) LR, and (**d**) FR against the corresponding observed streamflow (***: \(p<0.001\)).  
Overall, Scenario II shows that the hydrological and water resource study of the XRRB can, to a certain extent, use the IMERG FR product as direct input to hydrological and water resource studies instead of ground-based observations. FR can be a reasonable supplement for hydrological applications in areas where rain gauge data are scarce. In Scenario II, the model recalibration even improves the simulations in the calibration period. However, the hydrologic model parameters should be recalibrated carefully using satellite precipitation products, as this may lead to parameter values that do not correspond to reality.
## 3 Material and Methods
### System Design
#### 3.1.1 Digital Twin Architecture  
The system uses a digital twin architecture to provide secure interaction between users. The digital twin is an abstracted digital object that reflects a physical object based on information from the physical world. It is created based on the user's identity information and location information. The identity information is unique data held by each user. The location information is current location data from a user's mobile device in the physical world. This is updated by receiving location information in real time. The characteristics that digital twin maintains and updates the physical information allow the system to use the cyber-physical system (CPS) property that indicates a close correlation between physical and digital information.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 3 Standard, Network and Channel Models
### Network Models  
In WSN, there are different types of scenarios, and therefore different types of network settings or structures are considered during the previous research works. Broadly, the network is divided into two types: single hop WSN and multi-hop WSN. Figure 7 illustrates a single hop WSN model where source sensor node and base station are separated by distance d. A single hop sensor network is called Single Hop Asymmetric Structure (SHAS) network under some assumptions. In SHAS structure, it is assumed that the central base station has access to power supply and possesses computational power, whereas sensor nodes are energy-constrained and computationally-constrained. The link from base station to sensor node is called downlink communication, whereas a reverse link from sensor node to base station is called uplink communication in the SHAS network.  
A multi-hop WSN is showed in Figure 8, where source node and base station is separated by distance \(d\) and the data transmission from sensor node to base station passes \(n\) hops, and the distance between nodes is represented by \(d_{i}\) where \(i\) varies from 1 to n. The model is termed as a linear sensor array model. When the hop distance is equal, it is called a linear multi-hop model with equal hop distances or a linear packet forwarding model. A slightly modified version of multi-hop WSNmodel is shown in Figure 9, where the distance parameter between nodes is removed and a new parameter is introduced called received SNR at a node that depends on the distance between nodes and other factors including Line of Sight (LOS) / Non Line of Sight (NLOS), communication channel, and operating frequency.
# A Traffic-Based Method to Predict and Map Urban Air Quality
## 3 Results and Discussion  
### Urban PM\({}_{2.5}\) Concentrations Based on the Air Quality Network  
Long-term (2017-2018) average PM\({}_{2.5}\) concentration IDW maps for different periods of the day (6:00-11:59, 12:00-16:59, 17:00-20:59 and 21:00-05:59) are presented in Figure 2. It can be seen that the resolution of the air quality information is relatively low, spatially not varying much. Only during the morning hours, the concentrations increase and show some variation, due to the elevated levels in the south of the city. This zone is known for industrial activities and usually shows the highest PM\({}_{2.5}\) pollution in the city [10]. This method is worthy for representing the general air quality conditions in the city, as the monitoring stations are positioned on the elevated platforms 10-20 m above street level. At the same time, people are exposed to the street-level pollution that is highly variable and often more elevated, which implies more serious consequences to health, and must be understood. Apart from the fact that the background PM\({}_{2.5}\) concentrations measured by the monitoring network are lower, they are also not very representative of the actual concentrations at an urban scale, due to mobile source pollution, which might reach as high as six times more than those reported by the air quality network (Figure A1a-d, Appendix A).
# Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation
## 2 Related Work
### Weakly Supervised Semantic Segmentation Based on Image-Level Classification Labels  
Many of the existing studies in this area first locate target objects using CAM and then generate pseudo-labels. [PERSON] et al. proposed three principles that involve initializing seeds based on weak localization cues to expand objects and constrain the segmentation to align object boundaries to refine CAM [23]. [PERSON] et al. proposed the capture of object information by randomly selecting hidden units of object feature graph [24]. [PERSON] et al. proposed a cross-image association module to obtain additional semantic information from images with the same category [21]. [PERSON] et al. proposed to use local consistency, semantic fidelity and completeness as training guidance to obtain accurate semantic masks in only a single stage [25]. [PERSON] et al. proposed a self-supervised differential detection method to refine pixel level semantic affinity [26]. [PERSON] et al. proposed to capture features from transformed images through pixel-correlation modules and equivariantcross regularization [22]. [PERSON] et al. proposed a pseudo-label updating mechanism and a customized super-pixel-based random walk mechanism to rectify mislabeled regions within objects and refine the boundaries of original pseudo-labels to align with the physical structure of each image more accurately [27]. [PERSON] et al. proposed the utilization of the relationship between feature maps and their corresponding gradients to generate more granular object positioning information to determine the target object [28]. [PERSON] et al. proposed a reliable region mining method to find target objects or background regions with high response on the class activation map [29]. [PERSON] et al. proposed an iterative DCRF method based on graph convolution for semantic propagation by using the position relationship between adjacent pixels [30]. [PERSON] et al. proposed to employ a saliency map generator and a globally consistent super-pixel module to enhance localization accuracy and segmentation edge precision [31]. [PERSON] et al. proposed a method that effectively addresses issues of incomplete background and incomplete object representation by capturing multiscale contextual information formed from adjacent spatial feature grids and encoding fine-grained low-level features into high-level representations [32]. [PERSON] et al. proposed a straightforward yet highly effective method by leveraging the AffinityNet model along with semantic affinities in images to compensate for the absence of object shape information without the need for external data or additional supervision [33].  
The models mentioned above have made significant improvements in weakly supervised semantic segmentation; however, they do not fully consider the correlation between features in the channel and spatial dimensions and may overlook some important features, leading to the underactivation of target objects.
# Perceptual Characteristics of Voice Identification in Noisy Environments
## 4 Discussion  
### Robustness of Auditory Analysis  
Compared with acoustic measurements [24], we found that auditory analysis is more accurate in white noise and pink noise environments as shown in Figure 7, especially for a low SNR environment. Since voice identification based on auditory perception is more robust in noise environments, it is necessary to conduct voice identity recognition through auditory analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & **Quadratic** & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Mean**Square**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**Significance**} \\  & **Sum** & & & & & \\ \hline SNR & 0.833 & 1 & 0.833 & 3.404 & 0.065 \\ \hline Frequency band & 0.902 & 3 & 0.301 & 3.274 & 0.020 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ANOVA analysis of impact factors.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**(I) Frequency Band/Hz**} & **(J) Frequency Band/Hz** & **Mean** & \multirow{2}{*}{**Standard Error**} & \multirow{2}{*}{**Significance**} & \multicolumn{2}{c}{**95\% Confidence Interval**} \\ \cline{3-5}  & 640–1370 & \(-\)0.00625 & & 0.01956 & 0.749 & \(-\)0.0446 & 0.0321 \\ \cline{2-5}
300–640 & 1370–2930 & \(-\)0.01042 & 0.01956 & 0.594 & \(-\)0.0488 & 0.0279 \\ \cline{2-5}  & 2930–6250 & 0.04375 * & 0.01956 & 0.025 & 0.0054 & 0.0821 \\ \hline \multirow{2}{*}{640–1370} & 1370–2930 & \(-\)0.00417 & 0.01956 & 0.831 & \(-\)0.0425 & 0.0342 \\ \cline{2-5}  & 2930–6250 & 0.05000 * & 0.01956 & 0.011 & 0.0116 & 0.0884 \\ \hline
1370–2930 & 2930–6250 & 0.05417 * & 0.01956 & 0.006 & 0.0158 & 0.0925 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The LSD Analysis of Frequency Distribution.
## 1 Introduction  
The topic of traffic behavior improvement in urban areas remains to attract researchers' interests. The problem stays relevant due to the continuous increase of vehicle flows and the enormous breakdowns from traffic jams, increased fuel consumption, negatives from emission pollutions and their consequences to climate changes. In brief, traffic control targets the optimization of the transport flows, resulting in a decrease of vehicle congestions and waiting times, and maximization of the flows crossing the transport network, although this is subject to the construction limitations of the roads.  
Different approaches have been investigated for traffic control for a long time [1; 2; 3]. The control influences, which can be used for the traffic control, are the duration of the green lights per phase, the cycle duration, and the offset between a set of neighbor intersections. Mainly the durations of the traffic lights are applied as control variables in the traffic optimization problems. This causes the control space to be narrow, which restricts the management of the transport system to respect various constraints and/or objectives. If the control space is extended with these three possible influences, the traffic behavior can be improved to respect more requirements for the traffic management. The simultaneous usage of these different control influences in a common optimization problem makes it non-analytically defined and complex for a solution. Due to this complexity, the optimization problems of traffic management apply as control variables only the green lights. Our research targets the modeling of optimal control, which implements both the green lights and cycle durations on the crossroad junctions as control variables and problem solutions simultaneously. The extension of the control set allows more goals for traffic control to be considered for the minimization of queue lengths and maximization of traffic flows. The integration of the two control variables is performed with the usage of a hierarchical definition of a traffic optimization problem. We apply a bi-level formalization, which allows two goal functions to be considered in hierarchical order and to achieve better traffic parameters in comparison with the classical optimization with one goal function. This research hypothesis was tested in a city network in Sofia. The results, presented below, illustrate the positive potential, which the bi-level optimization formalism demonstrates.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 4 Method Implementation and Validation
### Testing Protocol  
Tests were performed under the following conditions:  
* Ten 2-s host signals
* Ten 1-s secret messages* A sampling frequency of 8 kHz for all signals
* \(BH\) is an integer within the range [1 6]
* Each secret message was hidden in each host signal, with different \(BH\) values. The total number of tests was 600 (number of host signals \(\times\) number of secret messages \(\times\) amount of \(BH\) values)
* The secret messages, host signals, stego signals, and recovered signals with correct and incorrect key are published in [18], \"Speech steganography based on scrambling and hiding (Spanish audios)\", Mendeley Data, v1.
## 3 EBI Signal Processing and Algorithm Development for HR Estimation  
This section outlines a method for estimating a person's HR from EBI signals. The method consists of several parts involving preprocessing of the signal, frequency analysis, peak detection, and outlier handling.  
We analyzed two ways of signal preprocessing to remove the noise and the baseline variation, the classical band-pass filter (BPF) and, as recently proposed in [33], the Hankel Matrix-based filtering. As a result, we assessed which preprocessing approach gives more accurate HR estimation.  
This algorithm is implemented in the MATLAB R2023b environment, utilizing certain built-in functions in a novel way to improve the precision of HR (BPM) calculation by performing the following steps (the respective flow diagram is visible in Figure 5):  
1. Preprocessing: 1. BPF [40]; 2. Baseline removing using Hankel Matrix [33]. Differences between a signal filtered with BPF and Savitzky-Golay filters can be seen in Figure 6.
2. Normalization.
3. First HR estimation using Cross-correlation [41].
4. Accurate HR estimation by peak detection.  
Figure 4: EBI-based pulse waveform with illustrated definition of the modulation depth.  
Figure 5: Flow diagram describing the data processing steps for accurate heart rate estimation.  
Figure 6: The raw EBI signal (**top panel**) and the results after applying a band-pass and a Savitzky–Golay filter (**the bottom panel**).
## 4 Enabling Technologies for FSO Communication
### Error Correction Codes and Modulated Signal Optimization  
ECC are crucial in reducing the impact of signal degradation in FSO communication. By detecting and correcting errors in transmitted data, ECC improves the reliability and robustness of FSO links, ensuring consistent data transmission even in challenging atmospheric conditions. However, Forward Error Correction (FEC) alone cannot meet the capacity demands of optical wireless systems. Incorporating high-order modulations, such as PAM and QAM, can enhance channel capacity. Despite this, a gap still exists between the Shannon limit and the capacity of uniform symbols. This section presents advanced schemes designed to enhance communication reliability and efficiency. It also outlines the essential requirements that transceivers must meet to approach channel capacity. We explore ECC, which improves data integrity by detecting and correcting errors. CM techniques are introduced to increase SE by combining coding and modulation processes. Lastly, we delve into capacity-approaching Probabilistic Amplitude Shaping (PAS), a cutting-edge method that optimizes data transmission rates close to the channel capacity limit. Each scheme contributes uniquely to advancing communication technologies, ensuring robust and high-performance data transfer.
## 4 Conclusions and Future Direction  
The mutual coherences in the measurement matrix are dependent on the mutual coherences of the spatial spectrum of the antennas' equivalent radiation sources. In this relationship, the imaging scenario functions equivalently as a low-pass filter in the k-space, which limits the spatial frequency of the measurement modes. Growing imaging range narrows this spectral constraint and thus leads to surges in the mutual coherences in the measurement matrix. The minimization of the spatial spectral coherences gives a possible direction for the antenna optimization in MCI applications.  
It should be noted that the stationary phase approximation applied in our derivation constraints the spectrum of the modes because it only retains components that contains most of the energy but with low spatial frequency. Applying a more accurate approximation which reserves the terms with high-order phase in Equation (2) maybe an interesting option to extend the spectrum. This is a possible future direction for achieving super-resolution under the inevitable low-pass constraint.  
Conceptualization, J.G.; Formal analysis, J.G.; Investigation, J.G.; Methodology, J.G.; Supervision, W.C.; Validation, J.G.; Visualization, J.G.; Writing--original draft, J.G.; Writing--review & editing, W.C.  
The authors thank [PERSON] for his assistance in the Ansoft HFSS simulation and [PERSON] for his advices in the manuscript writing.  
The authors declare no conflict of interest.