esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Message Replication in OppNets for Sensed Data Collection
### Impact of High Degrees of Spatial Locality
For instance, less copies may be required to guarantee the delivery of messages destined to nodes that are located nearby, while messages to nodes father away may require more copies--since they are more likely to be dropped. Hence, to maximize throughput with minimal transmission overhead, different messages may require different quota. The lack of flexibility in fixed-quota replication may cause unnecessary transmission overhead or reduced throughput, and either case degrades network performance. As shown in Figure 10, messages traversing more regions have less chances of delivery with SnF. Only 26% of messages generated to destinations 3 regions away are delivered, as compared with 74% for messages whose source and destination nodes are located within the same region. Figure 10: Percentage of successful delivery for messages traversing 0, 1, 2 and 3 regions under high degree of spatial locality with SnF (L = 8). Figure 9: Average hop count and percentage of remaining TTL of delivered messages traversing 0, 1, 2 and 3 regions under high locality with PRoPHET. In order to achieve optimal performance, the replication quota needs to be carefully selected, and this may be challenging and almost impractical without global knowledge of network parameters. So far, there is yet to be a suitable means of varying these parameters according to the requirements of different messages under high degrees of spatial locality. With these replication techniques, the choice that better guarantees the delivery of messages traversing multiple local regions may cause other messages to be replicated in excess. The additional transmission overhead may lead to frequent buffer overflows and eventually reduce achievable throughput.
## 6 Comparing Algorithms with Difference DAAs  
Computing the cost distribution for two algorithms allows us to compare their performance characteristics. One natural question, however, cannot be answered by comparing these two (one-dimensional) distributions: What is the probability that algorithm \(A\) needs more text accesses than algorithm \(B\) to scan the same random text? The answer will depend on the correlation of algorithm performances: Do the same instances lead to long runtimes for both algorithms or are there instances that are easy for one algorithm but difficult for the other? This section answers these questions by constructing a PAA to compute the distribution of _cost differences_ of two algorithms. That means, we calculate the probability that algorithm \(A\) needs \(v\) text accesses _more_ than algorithm \(B\) for all \(v\in\mathbb{Z}\).  
We start by giving a general construction of a DAA that computes the difference of the sum of emission of two given DAAs.  
**Definition 6** (Difference DAA): _Let a finite alphabet \(\Sigma\) and two DAAs \(\mathcal{D}^{1}=\big{(}\mathcal{Q}^{1},q_{0}^{1},\Sigma,\delta^{1}, \mathcal{V}^{1},v_{0}^{1},\mathcal{E}^{1},(\eta_{q}^{1})_{q\in\mathcal{Q}^{1 }},(\theta_{q}^{1})_{q\in\mathcal{Q}^{1}}\big{)}\) and \(\mathcal{D}^{2}=\big{(}\mathcal{Q}^{2},q_{0}^{2},\Sigma,\delta^{2},\mathcal{V} ^{2},v_{0}^{2},\mathcal{E}^{2  
* \(\mathcal{Q}:=\mathcal{Q}^{1}\times\mathcal{Q}^{2}\) _and_ \(q_{0}:=(q_{0}^{1},q_{0}^{2})\)_,_
* \(\mathcal{V}:=\mathbb{Z}\) _and_ \(v_{0}:=0\)_,_
* \(\mathcal{E}:=\mathcal{E}^{1}\times\mathcal{E}^{2}\) _and_ \(\eta_{(q^{1},q^{2})}:=\left(\eta_{q^{1}}^{1},\eta_{q^{2}}^{2}\right)\)_,_
* \(\delta:\left((q^{1},q^{2}),\sigma\right)\mapsto\left(\delta^{1}(q^{1},\sigma), \delta^{2}(q^{2},\sigma)\right)\)_,_
* \(\theta_{q}:\left(v,(e^{1},e^{2})\right)\mapsto v+e^{1}-e^{2}\)_._  
**Lemma 4**: _Let \(\mathcal{D}^{1}\) and \(\mathcal{D}^{2}\) be DAAs meeting the criteria given in Definition 6 and \(\mathcal{D}:=\text{DiffDAA}(\mathcal{D}^{1},\mathcal{D}^{2})\). Then, \(\text{value}_{\mathcal{D}}(s)=\text{value}_{\mathcal{D}^{1}}(s)-\text{value}_ {\mathcal{D}^{2}}(s)\) for all \(s\in\Sigma^{*}\)._  
Proof.: Follows directly from Definition 6.  
Lemma 4 can now be applied to the DAAs constructed for the analysis of two algorithms as described in Section 4. Since the above construction builds the product of both state spaces, it is advisable to minimize both DAAs before generating the product. Furthermore, in an implementation, only reachable states of the product automaton need to be constructed. Before being used to build a PAA (by applying Lemma 3), the product DAA should again be minimized.  
As discussed in Section 5.2, at most \(m(n-m+1)\) character accesses can result from scanning a text of length \(n\) for a pattern of length \(m\). Thus, the difference of costs for two algorithms lies in the range \(\{-m(n-m+1),\ldots,m(n-m+1)\}\) and, hence, \(\vartheta_{n}\in O(n\cdot m)\).
# Information Propagation and Bionic Evolution Control of the _Sebar_ Model in a Swarm System
## 6 Experiences
### Regulation of Information Propagation  
In the information propagation control simulation, the NW small-world network with \(N=500\) and \(p_{link}=0.5\) is selected to represent the swarm system. In the repeated prisoner's dilemma, the game intensity is \(b=1\), the number of repetitions is \(L=20\), the proportion of controllable individuals is \(p_{ctrl}=0.05\), and the memory length is \(t_{0}=20\). In addition, the selection of controllable individuals is based on the feature vector centrality, \(t_{\max}=300\). The model parameters refer to Group3 in Table 2, and the simulation results are shown in Figure 6.  
Figure 6a,b demonstrate that, when there is no controllable individual, the proportion of state \(S\) gradually approaches 1, while the proportions of states \(E\), \(B\), \(A\), and \(R\) converge to nearly 0. Under the influence of group benefits, the probability \(p_{5}\) rapidly decreases and converges to 0, while the probability \(p_{8}\) rapidly rises and converges to 1 after initially dropping. Under the influence of the suppression mechanism, the proportion of state \(B\) continuously decreases. The information fails to spread effectively throughout the swarm. Therefore, the information dissemination characteristics are consistent with the curves in Figure 6a,b without controllable individuals.  
As highlighted in the shaded areas in Figure 6c,d, when controllable individuals are introduced into the swarm system, the cooperation frequency and average group benefits continuously increase. The parameters \(p_{5}\) and \(p_{8}\) vary according to the designed association functions, and play an important role in regulating information dissemination. As a result, the proportion of state \(S\) decreases, while the proportion of state \(B\) increases. Thus, under adverse information dissemination conditions, the introduction of controllable individuals can function in indirectly adjusting the transition probabilities and realizing the evolution and regulation of information dissemination under the influence of swarm cooperation.
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 3 Results and Discussion
### Cations  
Honey contains a variety of macro and micro minerals that are minor constituents of honey, presented in the range of 0.02-1.03% [66]. These elements mainly include K, Na, Mg, Ca, P, Mn, Fe, Li, Co, etc. [11; 22]. The ash content of honey is the principal source of trace elements [67].  
The qualitative and quantitative content of ash is an important feature of honey, influenced by both botanical and geographical origin [11; 67]. A high degree of ash content in honey is a confirmation of a high concentration of pollen. [68; 69]. Minerals affect the color of honey and are found in greater quantities in dark honey compared to light honey [58].  
Also, it is possible to detect falsification based on ash content. When bees are fed sugar syrup, the ash content is also low [58].  
In the Jara honey, there were identified microelements such as Li, Na, K, Mg, and Ca (Figure 4), with concentrations ranging from 2613.5 to 5568.4 ppm. The analysis of the results is presented in Table 5. Based on the obtained results, it can be concluded that K is the dominant element, with its content ranging from 2174.86 to 5074.36 ppm. This mineral is the most quantitatively important in Jara honey, accounting for around 89% of the total mineral content. The average potassium ion concentrations in the honey samples from Keda (3546.72 ppm) and Shuakhevi (3501.46 ppm) are similar. However, the concentration of potassium ions in the ridge samples varies significantly. For instance, in the first, second, third, and sixth samples, the concentration ranges from 2239.88 ppm to 2998.04 ppm, while in the fourth, fifth, and eighth honey, it ranges from 4702.58 ppm to 48603 ppm, with a difference of up to 36 ppm. In the Shuakhevi honey samples, there is no significant variation in the indicators (3259.64 ppm to 3856.74 ppm). Similar to the ridge samples, the potassium content in the Khulo honey varies. For instance, JH 16 (2356.68 ppm) and JH 18 (2395.72 ppm) have almost identical concentrations. However, compared to these samples, the potassium content is twice as high in the fourteenth sample of Khulo honey, reaching 5074.36 ppm (Table 5).  
The second most abundant mineral in all samples was Ca, ranging from 141.28-795.06 ppm. The average content of Mg and Na varied significantly among samples. The concentrations  
Figure 4: Chromatograms of the cations in the Jara honey samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Lithium ppm**} & \multirow{2}{*}{**Sodium ppm**} & \multirow{2}{*}{**Potassium ppm**} & \multirow{2}{*}{**Magnesium ppm**} & \multirow{2}{*}{**Calcium ppm**} & \multirow{2}{*}{**Total Ions ppm**} \\  & & & & ppm & & ppm \\ \hline JH-1 & 10.54 \(\pm\) 0.26 & 17.58 \(\pm\) 0.44 & 2998.04 \(\pm\) 89.9 & 80.22 \(\pm\) 2.  
found in the samples ranged from 41.6 to 262.34 ppm for Mg and from 2.4 to 33.58 ppm for Na. The concentration of Li was the lowest (8.22-19.44 ppm) (Table 5).
# Pseudorange Measurement Method Based on AIS Signals
## 3 Detective Feature of Timestamp
### Amplitude Characteristics of Timestamp
#### 3.2.3 Timestamp Decision Threshold  
According to the previous analysis, the amplitude midpoint of the Gaussian baseband waveform is the bit-0-phase of the digital message sequence. Therefore, the decision threshold depends on the magnitude of the baseband signal, which is related to signal strength. In general, this means that the decision threshold is not a fixed value and increases the complexity of the detection as well as introducing additional detection errors.  
However, in the AIS system, the digital message sequence uses bipolar signal coding, the corresponding unit step signal is expressed as  
\[i(t)=2U(t)-1 \tag{13}\]  
where \(U(t)\) is standard unit step signal.  
Since the Gaussian filter is a linear system which has homogeneity and superposition, the output result corresponding to the input signal \(i(t)\) is  
\[\begin{array}{ll}O(t)&=2a_{h}(t)-1\\ &=2\times\frac{1}{2}[1+erf(kt)]-1=erf(kt)\end{array} \tag{14}\]  
Ideally, the amplitude decision threshold of the detected timestamp is a fixed value of zero. In this paper, the timestamp detection method based on the amplitude characteristics of the Gaussian baseband waveform is called zero-crossing detection.
# An Omnidirectional Near-Field Comprehensive Damage Detection Method for Composite Structures
## 1 Introduction
However, the research on the application of the ultrasonic phased-array technique in structural health monitoring of composite materials is still not mature, and there are still many difficulties and problems that need to be studied further. For instance, the piezoelectric element beam directivity in an ultrasonic phased-array transducer can be thought of as parallel in the near-field, but there is no obvious directivity; also, the signals of the piezoelectric elements cause interference and influence each other, and it is difficult to get an accurate damage identification due to the formation of the near-field blind area. Moreover, the anisotropy and structure of the composite materials have a complicated form, which also introduces a lot of difficulties to the application of ultrasonic phased-array technology. In this paper, a sampling of the phased-array technique for near-field damage monitoring is proposed. The rest of the paper is organized as follows: in Section 2, the omnidirectional near-field comprehensive damage detection method is presented; in Section 3, the proposed method is validated by the experiments on a complex composite fuel tank; in Section 4, an analysis of the experimental results is provided; and finally, the conclusion is given in Section 5.
## 1 Introduction
However, other authors have demonstrated that the reliability of the obtained results also depends on the point sampling density and distribution [34] while current scientific literature does not support the suitability for an interpolation method to be applied in every region of the Earth [24]. Therefore, a general consensus does not exist in the literature on the best method for interpolating monthly rainfall, and several research articles comparing different spatial interpolation methods have been published in recent years [10; 14; 15; 35]. The present paper aimed to compare different methods for interpolating spatial patterns of monthly rainfall in New Zealand. Considering that, in New Zealand, agriculture represents one of the most important economic sectors, including with reference to overseas exports, the knowledge of a reliable spatial distribution of rainfall in the different areas of this country is crucial. Moreover, these kinds of research findings are helpful in implementing sustainable landscape management programmes that require precise knowledge of diverse spatial phenomena and whose outcomes are frequently related to models and geostatistical parameters adopted. In the above-depicted scope, this research provides a comparative analysis with significant novelties. Among these, we proposed spatial interpolation approaches of monthly precipitation based on multivariate geostatistics integrating auxiliary variables such as elevation and geographic coordinates.
## 5 Results and Discussion
### BER Performance  
The BER performances of the C-DMT (clipping-only), Golay complementary sequences (GCSs) [12], and PTS are carefully investigated and discussed in this section. To provide a fair comparison, the transmitting signals have the same modulation parameters as those depicted in Table 1, and the number of sub-carriers \(N=512\) is used. The Chow algorithm with \(P_{e}=1\ \times\ 10^{-3}\) and a desired transmission rate of 1.0 Gbps is also adopted. In addition, the CR is chosen to be \(\gamma\ =10\) dB and the input electrical signal power is varied from \(-2\)-\(6\) decibel-milliwatts.  
As shown in the Figure 10, the GCS offers the lowest BER because of its excellent error-correction ability. Clipping in the C-DMT is the simplest way to reduce the PAPR, whereas the BER is limited by \(\gamma\). The proposed scheme with \(L=4\) provides a better BER performance than the C-DMT, and the required signal power is reduced at least by four decibel-milliwatts for the given BER \(=1\ \times\ 10^{-3}\). Compared to the C-DMT, smaller clipping distortion is generated in the proposed scheme because most of the large signal amplitudes are compressed. The PTS also achieves a better BER performance. However, the side information of PTS must be known by the receiver. If this information fails to be detected, the demodulator cannot recover the original signal correctly and the BER performance degrades accordingly.
## 4 Future Research Challenges
### Cognitive Radio  
CR for spectrum utilization offers the opportunity for flexible spectrum access in the current wireless systems [245]. Spectrum sensing involves the classification of a part of the spectrum or a frequency band as either \"occupied\" or \"unoccupied\" [246]. Several types of CR-based schemes are presented recently (e.g., matched filter, energy, and cyclostationary feature detection) [247]. When more accurate information about the primary user is needed, then the best-matched filter is required to perform optimal detection [248]. The cyclostationary approach can also be utilized by using cyclostationary elements of the available spectrum [249]. Moreover, the implementation of cooperative sensing must be performed in a distributed manner; that is, SUs receive information from the neighbors and make a choice on an individual basis [250]. Another way to use the free spectrum efficiently is the utilization of the dynamic genetic algorithm for PUs and SUs [251]. A new promising approach is to utilize ML techniques with CR to improve the spectral and energy efficiency of the network [252]. The handover between the PUs and SUs during resource sharing is a critical task that needs some dynamic handover schemes to achieve high QoS [253]. Moreover, various AI-based approaches are required for effective resource management in CR networks [254]. Although this requires different optimization parameters for different environments, real-time processing can be achieved by combining CR with AI into the Multi-Agent System (MAS), and real-time processing can be achieved [255].
# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 4 Results and Discussion
However, to be able to make a thorough conclusion, more study cases should be investigated in future work. On the basis of our current findings, only a general assumption can be drawn that if the standard deviation, skewness, and kurtosis values are small then SSA-LSSVR may perform better as seen in Table 3; otherwise, SSA-RF should be preferred. Thus, the current study preliminarily investigated the characteristics of the monthly rainfall series for the two reservoir watersheds. In Table 3, the rainfall data for both watersheds can be observed with higher positive skewness and very high kurtosis values. This illustrates a strong right-tailed distribution with a very sharp peak and a fat tail. Furthermore, with the standard deviation values of monthly rainfall (Table 3), it could be concluded that rainfall data for the Shihmen Reservoir watershed is slightly more scattered than that of Deji Reservoir watershed. Moreover, the difference in standard deviation values for the rainfall can justify the difference in the behavior of the reservoir watersheds. In the current study, it seems that the SSA-RF performs better than the SSA-LSSVR for the watershed with more scattered rainfall data. Nevertheless, linking the rainfall characteristics of watersheds to the forecasting model performances should be further investigated for more study sites in future work.
# Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
## 3 Methodology  
### Residual-Attention UNet++  
Here, we proposed the Residual-Attention UNet++, an integrated neural network that combines strengths of UNet++, residual unit, and attention mechanism for medical image segmentation, Figure 4 shows its overall structure. As we can see, the proposed model uses UNet++ as the basic network framework, which adopts redesigned skip pathways to connect the encoder network and the decoder network. The feature map of the encoder network was mapped to the decoder network through dense convolution blocks. In the above way, the feature graph semantic level in the encoder is close to the feature graph semantic level in the decoder.  
The skip pathway was formulated in the following form: \(x^{i,j}\) represents the output of node \(X^{i,j}\), \(i\) indexes the down-sampling layer according to the encoder sub-network, and \(j\) indexes the convolution layer of the dense block along the skip pathway. \(x^{i,j}\) can be calculated by the following mathematical formula:  
\[x^{i,j}=\begin{cases}\mathcal{H}\big{\{}x^{i-1,j}\big{\}},&j=0\\ \mathcal{H}\Big{\{}\Big{[}\int_{k=0}^{j-1}AG\Big{(}x^{i,k}\Big{)},T\big{(}x^{i+ 1,j-1}\big{)}\Big{]}\Big{\}},&j>0\end{cases} \tag{2}\]  
where \(\mathcal{H}\{\cdot\}\) represents a convolution operation followed by an ReLU activation, \(AG(\cdot)\) and \(T(\cdot)\) are used for attention gate and upsampling operations respectively, and [] denotes the concatenation layer. Figure 5 further explains the first skip pathway in Residual-Attention UNet++.  
Figure 4: Architecture of Residual-Attention UNet++.  
This combination brings three benefits: (1) UNet++ improves the semantic gap between the feature maps of encoder and decoder subnetworks; (2) the residual unit eases training of the network and addresses the degradation problem; and (3) the attention mechanism can increase the weight of the target area and suppress the background area irrelevant to the segmentation task, so the accuracy of Residual-Attention UNet++ is improved.
## 4 Discussion
### Limitations  
The first and the most serious limitation lies in the exclusion of fluid dynamics. Because the current ablation model is a highly non-linear time-dependent system, therefore, the model did not implement fluid dynamics to avoid increasing the complexity of the computation. Besides, the blood flow in the cardiac chamber around the CMC is more complicated than that of a single tip catheter due to its relatively larger scale. Each electrode may experience different direction or velocity of the blood flow. The current construction of the blood chamber did not satisfy an accurate allocation of the inlet and outlet of the blood. The absence of fluid dynamics will lead to an overestimation of the lesion surface width and the maximum blood temperature. The use of the convection coefficients is difficult for predicting realistic ablation activity as blood flow could shift the temperature distribution both in the myocardium and blood [27]. Although blood temperature was not the main quest here, the temperature of blood around the electrode tip is essential for predicting thrombus formation [37]. Therefore, the results from the current study, especially the surface lesion area, are still insufficient for a realistic quantitative conclusion. There is a potential uncertainty regarding the lesion contiguity on tissue surface which is a crucial aspect to assess ablation outcome. The best possible way to accurately describe lesion dimension is to induce fluid dynamics in an anatomy-based atria model, the blood flow rate of which needs to be classified into high and low conditions in order to simulate diastole and systole [27]. Second, the current construction of the ablation model did not fully satisfy the real scenario. We simplified the construction of the torso and ignored some other organs or regions, such as the esophagus, bone, and skin. The degree of bending of the catheter body was assumed to be perfectly circular during the simulation. However, the real catheter body is soft and deformable when it attaches the cardiac tissue. The body portion of the CMC is not consistently flat but spiral because of the anatomy of the myocardium, which is also not considered in this study. A contacting problem may be encountered when using a circular catheter because it is challenging for the operator to firmly make all electrodes contact [38].
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 2 Materials and Methods
### Reference Data  
#### 2.3.1 Field Data  
Field datasets were collected during the autumn of 2019 (October-November). Field surveys were conducted in 100 quadrants located in subtropical forests. Each quadrant measured 100 m\({}^{2}\) (10 m \(\times\) 10 m), equivalent to one pixel of Sentinel-2 satellite imagery. To minimize positioning errors, we used a high-precision Global Navigation Satellite System to record the coordinates of the center and one corner of each plot. Subsequently, we identified the tree species present in each quadrant and documented all individual trees with a diameter at breast height >8 cm. This included trees from the canopy layer, as well as some larger intermediate and bottom layer individuals. The tree species data recorded for each quadrant were then used to calculate the biodiversity index.