esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Stability Analysis  
Stability over time is a crucial metric for assessing the reliability of an optical instrument. To evaluate this, the 1064 nm and EDFA modules were maintained at constant power, while the DFG output was monitored for two periods: a short-term duration of 2 min and a long-term duration of 2 h, with no adjustments made during measurement. DFG power was sampled every 2 s during the 2 min interval and every 5 s over the 2 h interval to ensure manageable data density. The resulting data, shown in Figure 7, were analyzed for stability, which was calculated as the root mean square deviation of the DFG dataset. Nonlinear DPSS-based lasers typically demonstrate stability values below 4% [30]. Our DFG setup exhibited a stability of 0.91% after 2 min and 1.08% after 2 h, aligning well within this range.  
Figure 7: Measured DFG power fluctuations over short-term (2 min) and long-term (2 h) periods to assess stability. Power measurements were taken at intervals of 2 s for the short-term test and 5 s for the long-term test. The values are normalized to 1 for presentation purposes, and the actual DFG power corresponding to the figure is 63 mW, as stated in the manuscript.  
High stability is essential for applications such as GHG detection, where consistent output power is crucial for accurate measurements.  
Overall, our IC-DFG design achieves high efficiency and tunability while exhibiting robust stability, making it a promising candidate for real-world applications in environmental monitoring and satellite communication.
## 3 Results
### The Effect of Compressing or Stretching the Original Mosaic Block Duration (OMBD)
#### 3.2.2 Non-Native Listeners of English  
The intelligibility of English mosaic speech for the Indonesian and the Chinese listeners was highest for the MBDs of 20 ms or 40 ms after preserving, and decreased as the MBD was shorter or longer, similar to the intelligibility data of the native-English listeners. The intelligibility decreased by about 19% for the MBDs after compressing for the Indonesian and the Chinese group. The intelligibility decreased sharply when the OMBDs were stretched into MBDs of 80 ms or longer, except in the Chinese group when the OMBD of 40 ms was stretched by a factor of 2.  
By the same statistical method as described above, here too we found significant differences in intelligibility between mosaic speech types, for the Indonesian participants (\(n=19\), \(k=19\), \(\chi^{2}=153.618\), \(p<0.001\)) and the Chinese participants (\(n=20\), \(k=19\), \(\chi^{2}=129.139\), \(p<0.001\)). In detail, for the Indonesian group, the intelligibility of mosaic speech with the MBD of 20 ms after preserving was significantly higher than that of the same MBD after compressing (\(p=0.007\)), but the same significant difference did not appear for the MBDs of 40 ms after preserving and after compressing (\(p=0.077\)). The intelligibility of the MBDs after preserving was significantly higher than the same MBDs after stretching (80, 160, or 320 ms; \(p<0.01\)). Meanwhile for the Chinese group, for both OMBDs, there were no significant differences in intelligibility between the preserved and the compressed or the stretched \(\times\) 2 stimuli (\(p>0.05\)), nor between the compressed and the stretched \(\times\) 2 or \(\times\) 4 stimuli (\(p>0.05\)). Both the Indonesian and the Chinese group obtained higher intelligibility for the stimuli with the MBD of 40 ms (\(p<0.005\)) or 80 ms (\(p<0.05\)) after stretching compared with the stimuli with the MBD of 160 ms after stretching.  
Although the Indonesian and the Chinese listeners overall had significantly lower intelligibility scores than the native-English listeners, as can be seen in Figure 3, there were similar trends in intelligibility across the three language groups.
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 3 Overlapped Apple Images Segmentation  
Generally, the recognition of target fruit depends on image segmentation, so the quality of image segmentation is directly related to the recognition effect. The K-means algorithm is based on a distance similarity clustering algorithm. By comparing the similarity between samples, the closer the distance between two points, the greater the similarity. Clustering segmentation [21; 22; 23] is a classification tool that the image data are divided into several classes according to an algorithm. The similarity between each category is as small as possible, and the similarity within the category is high.
## 3 Results and Discussions
### The Self-Propulsion Simulation
The legends NoPropeller-Steering, Propeller-NoSteering, Propeller-Steering represent nonpropelled simulation with steering rudder, self-propulsion simulation without steering rudder and self-propulsion simulation with steering rudder, respectively. rising trend of drift angle, i.e., the drift angle is a direct reflection of the roll angle. By manipulating the rudder, the drift angle is limited, which in turn weakens the roll angel. This shows that by keeping the course of the self-propelled model, the roll stability is significantly improved. Two animations of simulation C showing the real-time changes of attitude curves (see the \"Electronic supplementary material\", Video S3 and S4), which intuitively illustrates the beneficial effect of manipulating rudder on reducing roll amplitudes in self-propulsion simulation. On the other hand, the roll angle of self-propulsion simulation increases by about 11\({}^{\circ}\) compared with that of the nonpropelled simulation, which is mainly attributed to the effect of propeller torque. Correspondingly, the course keeping ability of self-propelled model is slightly deteriorated as illustrated in the Figure 10e. It is also mainly is attributed to the reaction influence of roll motion caused by the propeller torque. In other words, the coupling effect between roll and yaw motion makes the model yaw angle slightly increase around the time of broaching the free surface. However, the final heading is also kept in the initial direction, which verifies the effectiveness of the S surface controller in the self-propulsion simulation. In summary, in the constant 362 mm blown position, for the self-propulsion simulation, when the rudder is not manipulated, the model is prone to excessive roll due to the influence of propeller torque. In the meantime, because of the influence of propeller torque and smaller pitch angle, the course keeping ability of the self-propelled model is slightly deteriorated. However, by manipulating the rudder, it is still possible to limit underwater yaw angle within a certain range, e.g., 1\({}^{\circ}\), and further restrain the growth of drift angle, thereby significantly reducing the roll angle. In other words, it is reasonable to improve the roll stability of the model by enhancing the course keeping ability in the self-propulsion simulation. It is proven once again that under the condition of moderate angle of attack, the yaw instability is the root source of the emergency rising roll instability.
## 1 Introduction
### The Case for Synthetic Aperture Radar to Map Rice
Specifically, the novelty in this paper is the development of a rule-based classification approach and parameter selection approach in which the rules and parameters are derived from agronomic knowledge of the rice crop and its management. Thus, the rules and parameters can be easily understood and fine-tuned by users using site-specific knowledge. The purpose here is not to derive the best possible rice map at each site through intensive calibration or large-scale fieldwork, but to introduce a simple approach that is robust, repeatable and suitable for rapid rice mapping over large extents with cost-effective field work. The overarching goal is to demonstrate that SAR-based operational mapping of rice crops across a diverse range of environments is possible based on the increasing availability of multi-temporal SAR data. The paper is a timely contribution to remote-sensing applications for food security, since it presents a method to derive sufficiently accurate rice area maps under different conditions that are typical of the diversity of rice environments in Asia. Our target audience goes beyond the research community and includes the growing community of stakeholders that have a vested interest in operational remote sensing-based systems for food security applications. We first summarize past research on SAR applications for rice mapping, justify our choice of algorithm and outline the comprehensive geographic scope of this study.
# A QoS-Enabled Medium-Transparent MAC Protocol for Fiber-Wireless 5G RAN Transport Networks
## Appendix A Stochastic Self-Similar Traffic Models  
It has long been established, via statistical analysis of captured live network traces, that Poisson-based traffic models are not always realistic, and in fact, real network traffic exhibits Self-Similar (SS) fractal properties, which necessitates the use of more advanced traffic models. This observation was first made in [32] for Ethernet traffic captured within a Local Area Network (LAN) environment (also containing traffic originating or destined for hosts outside the LAN) and was later confirmed also for Wide Area Network (WAN) settings [33], particularly for Variable Bit Rate video-dominated data [34], as well as for Wireless LANs [35]. Intuitively, the self-similarity property exhibits itself as a statistical trend, which does not diminish when averaged over increasing time scales, as is the case in Poisson processes, thus leading to increased \"burstiness\" compared to non-self-similar traffic. Furthermore, the aggregation of multiple self-similar traffic streams typically strengthens the self-similar property of the overall traffic and increases its burstiness, which leads to higher queueing delays and presents additional challenges to QoS provisioning.  
The cause of the emergence of self-similarity was identified in [32] as the fact that, in real network traffic, the inter-arrival frame/packet (depending on the examined layer) distribution is heavy-tailed, allowing for very large inter-arrival times with significant probability. Although numerous heavy-tailed distributions exist and have been studied, the Pareto distribution parameterized by a parameter \(\alpha\), with \(1<\alpha<2\), is the one most commonly proposed in the networking literature [36; 37]. Specifically, the Pareto parameter \(\alpha\) for the inter-arrival time distribution is related to the Hurst exponent \(H\) of the resulting self-similar stream as \(H=(3-a)/2\).  
The detailed statistical analyses performed on network traces have yielded \(H\) values in the range 0.65-0.8, depending on the time of day (i.e., busy-hour vs. breaks) [32; 38] and even the employed transport layer protocol [34]. Specifically, the congestion control of the Transmission Control Protocol (TCP) creates a highly varying instantaneous rate, leading to higher values of \(H\) compared to the User Datagram Protocol (UDP). For these reasons, it is more appropriate to examine the network performance over the entire range of 0.65-0.8 of \(H\) (which corresponds to \(\alpha\) Pareto parameters in the range of 1.4-1.7), rather than single fixed values.  
Based on the above remarks and since, in our proposed work, the qMT-MAC nodes act as traffic aggregators based on the fact that the nodes provide connectivity to RUs that, in turn, serve a large set of users, we followed the well-known traffic model of aggregated ON/OFF sources with Pareto-distributed inter-frame arrival distributions [39] (as implemented in [40]) for the aggregated UL traffic sent by the MNO's RAUs towards the centralized equipment.
## 2 Proposed Method
### Spatial-Channel Mutual Attention-Guided Transformer Module (SCATM)
#### 2.3.3 Channel-Wise Multi-Head Transformer Block (CMTB)
where \(\mathbf{W}\in\mathbb{R}^{C\times C}\) are learnable parameters and \(f_{p}(\cdot)\) represents the position embedding generator, which aims to encode the position information of different channel dimensions. It contains a \(3\times 3\) depth-wise convolution layer with a stride of 1 followed by a GELU layer [56] and another \(3\times 3\) depth-wise convolution layer with a stride of 1. Finally, the output feature maps \(\mathbf{X}_{out}\in\mathbb{R}^{H\times W\times C}\) can be obtained by reshaping the result of Equation (6). Moreover, we introduce GDFN [57] to encode information from spatially neighboring pixel positions in order to effectively learn local image structures. Given feature maps \(\mathbf{X}_{in}\in\mathbb{R}^{H\times W\times C}\) as the input of the GDFN, the output \(\mathbf{X}_{out}\in\mathbb{R}^{H\times W\times C}\) can be obtained by  
\[\mathbf{\hat{X}}=\mathbf{H}_{dconv}^{3\times 3}(\mathbf{H}_{fc}(\mathbf{X}_{in })),\quad\mathbf{X}_{out}=\mathbf{H}_{fc}(\mathbf{\hat{X}}\cdot\sigma( \mathbf{\hat{X}})), \tag{7}\]  
where \(\mathbf{H}_{dconv}^{3\times 3}(\cdot)\) and \(\mathbf{H}_{fc}(\cdot)\) denote the \(3\times 3\) depth-wise convolution layer and the full connection layer, respectively, while \(\sigma(\cdot)\) represents the GELU nonlinearity. The SCAGB distills and guides the key features from inner feature maps, while the CMTB further aggregates and refines the previous feature information. These two blocks complement each other and gradually enhance the relationship between the inner feature maps. Benefiting from the SCAGB and CMTB, the SCATM can simultaneously promote both local facial details and global facial structures, offering a promising solution to the challenging face image reconstruction task.
## 4 Experiments
### Effectiveness of SSL
#### 4.2.1 Influence of the Cropped Image Size on SSL  
In SSL, the size to which the images are cropped is an important factor affecting the time and computational effort of training. Because of the large dissimilarities between remote sensing images and natural images, to explore the effects of different image resolutions on the characterization ability of the network, we compare the top-1 accuracies achieved under different random crop sizes in Figure 6. From the results in this figure, it is obvious that SSL is not highly sensitive to the crop size for remote sensing images. If the crop size is increased, it will bring more GPU hours and memory usage. Considering training time and resource consumption, we adopted a crop size of 96 in the subsequent SSL experiments.
## 4 Experimental Results
\[E_{j}y_{it} \leq x_{it}\leq M_{j}y_{it},\forall j\in J,\forall t\in T_{s},\] \[I_{1,t} = I_{1,t-1}+\sum_{i\in I}\sum_{h\in H}q_{i}\cdot z_{ih,t-1}-x_{1,t-1 },\forall t\in T_{s}\setminus\{0\},\] \[I_{j,t} = I_{j,t-1}+\alpha x_{j-1,t-1}-x_{j,t-1},\forall t\in T_{s} \setminus\{0\},j\in J\setminus\{1\},\] \[I_{it} \leq LC_{j},\forall j\in J,t\in T_{s},\] \[\sum_{h\in H}\sum_{t\in T^{c}}z_{iht} = 1,\forall i\in I,\] \[\sum_{i\in I}q_{i}\cdot z_{iht} \leq C,\forall h\in H,\forall t\in T_{c},\] \[x_{it} \geq 0,\,y_{it}\in\{0,1\},\forall t\in T_{s},\forall j\in J,\] \[z_{iht} \in \{0,1\},\forall i\in I,h\in H,t\in T_{c}.\]  
\[\min Z_{c}=\sum_{i\in I}\sum_{h\in H}\sum_{t\in T_{c}}c_{ii}^{c} \cdot z_{iht}\]0 Results obtained by a competing approach. (**a**) shows the number of bids generated and the number of selected bids; (**b**) shows the number of bids generated and the collection cost; (**c**) shows the number of bids generated and the sorting cost; (**d**) shows the number of bids generated and the total cost. We tested P3 in the same test scenario used before and found that P3 produces an optimal solution value of 542, which is the same solution value as that found by our approach when 20 bids are generated. Repeating the same test considering the variant of P3 where the objective function is replaced by the objective of the collection problem P1 leads to the same results as the one obtained when only one bid is allowed. To complete our experimental campaign, we tested our approach by increasing the instance size considering 30 and 70 customers. The number of bids is 20. The results of these tests are reported in \[\min Z_{c}=\sum_{i\in I}\sum_{h\in H}\sum_{t\in T_{c}}c_{ii}^{c} \cdot z_{iht}\]1 As can be seen in the last column, the gain in terms of the total cost (the sum of collection and sorting costs) of our approach compared to the competing approach [1] increases with an increasing number of customers, passing from 4.9% (30 customers) to 9.4% (70 customers), indicating the effectiveness of our approach.
## 2 Coded Aperture Compressive Temporal Imaging (CACTI)
### Spatiotemporal Super-Resolution Reconstruction of Image Sequences by Belief Propagation
The standard BP [30] form of Equation (33) is represented as:  
\[E(f)=\sum\limits_{p\in}D_{p}(f_{p})+\sum\limits_{(p,q)\in NS}V\big{(}f_{p},f_{ q}\big{)} \tag{37}\]  
where, as shown in Figure 3, \(D_{p}\big{(}f_{p}\big{)}\) is cost of assigning label \(f_{p}\) to pixel \(p\), which is referred to the data cost as the term \(\sum\limits_{p}\left(\|\mathbf{D}\mathbf{K}I-f_{0}\|+\mu(\|\mathrm{TV}(I)\|)\right)\); \(V(f_{p},f_{q})\) is cost of assigning label \(f_{p}\) and \(f_{q}\) to two neighboring pixels, and which is normally referred to as the discontinuity cost as the term \(\sum\limits_{(p,q)\in NS}(\beta_{i}\|\mathbf{D}\mathbf{K}\mathbf{M}_{i}I-f_{i} \|)\). To initialize the belief networks, \(m^{t}_{pq}\) is defined as the message that node \(p\) sends to a neighboring node \(q\) at iteration \(t\), and can be expressed as:  
\[m^{t}_{pq}(f_{q})=\min\limits_{f_{p}}\left(V(f_{p}-f_{q})+D_{p}(f_{p})+\sum \limits_{s\in NS(p)q}m^{t-1}_{sp}\big{(}f_{p}\big{)}\right) \tag{38}\]  
The initial value of \(m^{0}_{pq}\) is 0. \(m^{t}_{pq}[0\ldots k]\) is the vector with the scale \(k\). After \(T\) times of iteration, the belief vector \(b_{q}\) is calculated for each node:  
\[b_{q}\big{(}f_{q}\big{)}=D_{q}(f_{q})+\sum\limits_{p\in NS(q)}m^{T}_{pq}(f_{q}) \tag{39}\]  
Following this, there is the MRF stable solution \(f^{*}_{q}=\min\{b_{q}(f_{q})\}\) at each node. The standard implementation of BP algorithm needs \(O(nk^{2}T)\), where \(n\) is the number of pixels in the image, \(k\) is the number of possible labels for each pixel and \(T\) is the number of iterations. Besides current methods to speed up calculation, the possible label statuses, i.e., message length \(k\), can be limited in order to reduce the calculating burden. In SR reconstruction, the initial value of HR image is obtained by the linear interpolation of current frame. The gray scale ranges within the changing gray-scale values in WNS. In this way, the computation will be considerably simplified.
# Neighbor Session Solutions for Integrated Routing Protocols
## 2 Dual-Stack IP Routing Protocols  
The IPv6 network protocol came later than IPv4, and for most routing protocols at the time, their designers did not count on support for multiple network protocols. Therefore, some protocols, such as RIPv2 or OSPF, do not support the IPv6 network protocol at all, and a new version has been created, supporting only the IPv6 protocol. Other protocols have been extended to support IPv6, which is the case, for example, with the IS-IS protocol. That is why currently, there are the following integrated routing protocols, which support dual-stack: MP-BGP, OSPFv3, EIGRP, and IS-IS [35, 36, 37].
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 5 Network Model and Discussion
### Network Overhead  
In Figure 11, the comparison of ESR protocol is illustrated against the existing solution under varying nodes density topologies. The numerical results demonstrated that ESR protocol had attained an average of 28% reduction in network overhead than the existing solutions because ESR protocol provided a light-weight secret sharing scheme between cluster heads and BS, which was not only computationally secure to handle the intrusions against malicious nodes but also extensible in terms of varying network topologies. In addition, trustworthy routing paths led to a fewer number of route re-construction messages in the sensor field and thus reduced network overhead. Furthermore, the wireless channels were less congested, as ESR protocol minimized re-election packets because of bounded clustering.
###### Abstract  
Heterogeneous networks (HetNets), consisting of macro-cells and overlaying pico-cells, have been recognized as a promising paradigm to support the exponential growth of data traffic demands and high network energy efficiency (EE). However, for two-tier heterogeneous architecture deployment of HetNets, the inter-tier interference will be challenging. Time domain further-enhanced inter-cell interference coordination (FeICIC) proposed in 3 GPP Release-11 becomes necessary to mitigate the inter-tier interference by applying low power almost blank subframe (ABS) scheme. Therefore, for HetNets deployment in reality, the pico-cell range expansion (CRE) bias, the power of ABS and the density of pico base stations (PBSs) are three important factors for the network EE improvement. Aiming to improve the network EE, the above three factors are jointly considered in this paper. In particular, we first derive the closed-form expression of the network EE as a function of pico CRE bias, power reduction factor of low power ABS and PBS density based on stochastic geometry model. Then, the approximate relationship between pico CRE bias and power reduction factor is deduced, followed by a linear search algorithm to get the near-optimal pico CRE bias and power reduction factor together at a given PBS density. Next, a linear search algorithm is further proposed to optimize PBS density based on fixed pico CRE bias and power reduction factor. Due to the fact that the above pico CRE bias and power reduction factor optimization and PBS density optimization are optimized separately, a heuristic algorithm is further proposed to optimize pico CRE bias, power reduction factor and PBS density jointly to achieve global network EE maximization. Numerical simulation results show that our proposed heuristic algorithm can significantly enhance the network EE while incurring low computational complexity.  
HetNets; interference coordination; energy efficiency; stochastic geometry 1