esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Vertical Ground Deformation Shape  
The best fit curve for the vertical displacement component, obtained with the methodology described in Section 3, is shown in Figure 6c. It represents the theoretical radial and symmetric geometry of ongoing ground deformation along the vertical component and can be used to predict the best fit expected value at a given distance from the deformation center for each point within the caldera.  
The radial vertical deformation that occurred in the 2016-2021 period is characterized by a bell shape with a maximum in the center and values approaching zero radially moving away. The uplift values decline rapidly between 0.5 and 4.0 km of distance, then the reduction rate progressively slows up to about 6.0 km of distance; finally, the uplift becomes very low up to 8-9 km from the center. This curve is not linked to one of the volcano-tectonic interpretative models discussed in literature and briefly described in Section 1 but represents the best fit geometric representation of the average spatial trend of the first-order vertical ground deformation.
## 2 MUI Cancellation Algorithm of the Proposed SAC Receiver
The variations of signal waveforms in the processes of MUI estimation/elimination are demonstrated. The system structure for simulation is similar to that in Figure 3. The center wavelength, bandwidth, and power of BLS are set to 193.4 THz, 0.7 THz, and 0 dB, respectively. The length of the bit sequence is 1024 and the bit rate is 1 GB/s. The AWG has seven channels, and each of them has a bandwidth of 0.8 THz. The power spectral density (PSD) of the OCDMA signals at the seven stages is presented in the first row of Figure 7. Based on the results, the interference terms can be regenerated by the AWG encoder. It shows that MUI is canceled at the previous two stages, where the received spectrum \(\mathbf{R_{i}}\) matches the result of the numerical analysis in Table 2. The second row in Figure 7 presents the decoded data bits of the seven users. Although there are some slight variations in the waveforms, the amplitude for the decoded bit \"1s\" is significantly greater than that of the decoded bit \"0s\". The results indicate that the data bits of the interference users and the target user can be successfully recognized by the AWG decoder. From the above two figures, the oscillations in the waveforms and the PSDs come from the intensity fluctuations of BLS. However, in a practical scenario, noise effects also have a great impact on the performance of OCDMA systems. Such issues are further investigated in Section 4. Figure 6: Spectral distributions of M-sequence code \(\mathbf{C_{2}}\) in the process of (**a**) decoding and (**b**) encoding, based on AWG structure. Figure 7: Variations of the time-domain waveforms and the power spectral density (PSD) at the seven stages of interference cancellation.
# GE-YOLO for Weed Detection in Rice Paddy Fields
## 2 Materials and Methods
### Network Structure of GE-YOLO
#### 2.2.2 Inject  
To facilitate the distribution of aggregated features generated by Low-GD, the Information Injection module (Inject module) is introduced to integrate global and local features. To clearly illustrate the working principle of the Inject module, please refer to Figure 6, which presents the structural diagram of the Inject module. Our explanation will focus on the Inject module marked with a red triangle (\(\blacktriangle\)) in Figure 4, which depicts the architecture of GE-YOLO.  
First,the Inject module takes the local feature \(F_{local}\) (denoted here as \(B3\)) and the global feature \(F_{global}\) (denoted here as \(F_{inject\_P3}\)) as inputs. Second, \(F_{local}\) undergoes a  
Figure 5: Schematic diagram of the Low-GD module, which consists of two parts: Low-FAM and Low-IFM, responsible for aggregating and fusing low-level feature maps of varying sizes. \(B2\), \(B3\), \(B4\), and \(B5\) represent low-level features from different scales of the backbone. C represents the concatenation operation.  
\(1\times 1\) convolution operation to generate \(F_{local\_embed}\), while \(F_{global}\) is processed through a branching structure, passing through two separate \(1\times 1\) convolution operations to generate \(F_{global\_embed}\) and \(F_{act}\). Third, due to the difference in spatial dimensions between the global and local features, average pooling or bilinear interpolation is applied to \(F_{global\_embed}\) and \(F_{act}\) to generate \(F_{global\_embed\_p}\) and \(F_{global\_act\_p}\), ensuring smooth integration with \(F_{local\_embed}\) in the subsequent fusion process. Then, the rescaled \(F_{global\_embed\_p}\) and \(F_{global\_act\_p}\) are then fused with \(F_{local\_embed}\). Last, the fused feature \(F_{act\_fuse\_p}\) is further processed through the RepBlock to extract and integrate information, ultimately generating the fused feature \(P_{i}\) (denoted here as \(P3\)).  
The mathematical formulation corresponding to the above steps is provided as follows:  
\[F_{global\_act\_p}=resize(Sigmoid(Conv_{act}(F_{global}))) \tag{4}\]  
\[F_{global\_embed\_p}=resize(Conv_{global\_embed}(F_{global})) \tag{5}\]  
\[F_{att\_fuse\_p}=Conv_{local\_embed\_p}(F_{local}) \tag{6}\]  
\[*F_{global\_act\_p}+F_{global\_embed\_p}\]  
\[P_{i}=RepBlock(F_{att\_fuse\_p}) \tag{7}\]  
By leveraging the Inject module for the distribution of aggregated features, an efficient fusion of local and global features is achieved. This enhances the network's multi-scale feature representation capability for weed detection, enabling the model to effectively capture fine details in weed images and improve detection performance, particularly for small weed targets.
# SimCDL: A Simple Framework for Contrastive Dictionary Learning
## 4 Algorithms
### Incoherent Dictionary Learning via Triplet Distance Barrier (ITDB)  
Incoherent Dictionary Learning via Triplet Distance Barrier (ITDB) is a variant of the IDB approach, inspired by the triplet loss function [18]. The optimization problem is reformulated as follows:  
\[\begin{array}{rl}\min_{\mathbf{D}_{j},\mathbf{X}_{i}}&\sum_{i=1}^{C}\|\mathbf{Y}_{i}-D_{i }\mathbf{X}_{i}\|_{F}^{2}+\gamma\sum_{i=1}^{C}\sum_{j\
eq i}\tilde{b}(\mathbf{D}_{i}, \mathbf{D}_{j})\\ \text{s.t.}&\left\|\mathbf{x}_{i,\ell}\right\|_{0}\leq s,\ell=1:N\\ &\left\|\mathbf{d}_{i,j}\right\|=1,j=1:n\end{array} \tag{21}\]  
where the triplet-based distance barrier function \(\tilde{b}(\mathbf{D}_{i},\mathbf{D}_{j})\) is defined as  
\[\sum_{(\mathbf{d}^{(a)},\mathbf{d}^{(p)},\mathbf{d}^{(n)})}\Big{[}\max(0,M+\| \mathbf{d}^{(a)}-\mathbf{d}^{(p)}\|^{2}-\|\mathbf{d}^{(a)}-\mathbf{d}^{(n)}\|^{2})\] \[\qquad\qquad+\max(0,M+\|\mathbf{d}^{(a)}-\mathbf{d}^{(p)}\|^{2}-\|\mathbf{d}^ {(a)}+\mathbf{d}^{(n)}\|^{2})\Big{]}. \tag{22}\]  
In this approach, \(\mathbf{d}^{(a)}\), \(\mathbf{d}^{(p)}\), and \(\mathbf{d}^{(n)}\) form triplets of atoms. In this approach, \(\mathbf{d}^{(a)}\), \(\mathbf{d}^{(p)}\), and \(\mathbf{d}^{(n)}\) represent the anchor, positive, and negative elements in a triplet of atoms, respectively; \(\mathbf{d}^{(a)}\) (anchor) is the reference point; \(\mathbf{d}^{(p)}\) (positive pair) is similar to the anchor, and the goal is to minimize the distance between them, represented by \(\|\mathbf{d}^{(a)}-\mathbf{d}^{(p)}\|^{2}\); \(\mathbf{d}^{(n)}\) (negative pair) is dissimilar to the anchor, and the objective is to maximize the distance between the anchor and negative pair, represented by \(\|\mathbf{d}^{(a)}-\mathbf{d}^{(n)}\|^{2}\). The max ensures that these distances are adjusted based on a margin \(M\), promoting better separation between the positive and negative pairs. The method ensures that atoms from the same class are closer to each other than atoms from different classes.  
The triplet-based approach (ITDB) offers a more refined control over the atom distribution but incurs higher computational costs due to the management of triplets instead of pairs. To alleviate this, we propose optimizing only a subset of possible triplets, randomly selected, to reduce computational load while maintaining effective representation.  
In summary, the IDB and ITDB methods extend the standard IDL approach by introducing advanced barriers that ensure greater separation between class dictionaries, thereby enhancing their effectiveness in classification tasks.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 2 Related Work and Motivation
### Related Work
Therefore, this article proposes a fine-grained traffic routing optimization method for latency-sensitive applications based on the flexible control capability of SDN.
# A Flexible and Simple Lossless DWT Filter Bank Using a MAXFLAT FIR Half-Band Filter
## 3 Design of Lossless s-Filter Banks
### Design Examples of MAXFLAT Half-Band FIR Filters  
The design examples were demonstrated here using \(K=1,\ 2,\ 3,\ 4,\ 5\) for the filter order of \(2N=4K-2\). In addition, the computations of MAXFLAT half-band filter coefficients \(h_{2n}\)'s for the lossless s-filter bank were performed on the Equation (15) derived in closed form. This is due to the fact that two s-filter banks depicted in Figure 2 can produce equivalent results since the designed filter \(Q_{0}(z)\) has a zero-phase half-band frequency response with respect to \(\omega=\pi/2\).  
Table 1 shows the integer filter coefficients computed by solving Equation (15) for given \(K=1,\ 2,\ 3,\ 4,\ 5\). For example, in the case of \(K=3\) (i.e., the order of filter is such that \(4K-2=10\)), a closed form polynomial of order 10 can  
Note that the odd number indexed coefficients of the half-band filter given by Equation (10) are zero--i.e., \(h_{2n-1}=0\) (\(n=1,\,2\)). It is also shown that all the rest coefficients can be obtained as integer values. From Equation (13), the closed-form frequency response is given by  
\[Q_{0}(\omega)=\left(\cos\frac{\omega}{2}\right)^{4}\left\{\sum_{\ell=0}^{2} \binom{2+\ell}{\ell}\left(\sin\frac{\omega}{2}\right)^{2\ell}\right\} \tag{21}\]  
Figure 3 depicts the frequency responses of MAXFLAT half-band filters indicated in Table 1. The example shows that the impulse-response formula Equation (15), available for directly obtaining MAXFLAT half-band filters with integer coefficients, is effective and practically useful in designing highly accurate filters with a magnitude response passing exactly through the half-band cut-off frequency \(\omega=\pi/2\). Further, the MAXFLAT half-band filters can be found to have a trade-off between transition bandwidth and filter length. In another way, using a steepness parameter into Equation (10) can allow the design flexibility to obtain half-band FIR filters with a sharp transition band [29].
## 2 Problem Description and Methods
### Prediction and Optimization Methods
#### 2.2.2. Whale Optimization Algorithm
The spiral swimming pattern can be expressed by Equations (13) and (14), where \(\overrightarrow{D}^{\prime}\) is the distance vector from the whale's current position to the prey, \(b\) is a constant that determines the degree of spiraling, and \(l\) is a random number that takes values in the range \([-1,1]\): \[\overrightarrow{X}(t+1)=\overrightarrow{D}^{\prime}\cdot e^{bl}\cdot cos(2\pi l )+\overrightarrow{X}^{*}(t),\] (13) \[\overrightarrow{D}^{\prime}=\left|\overrightarrow{X}^{*}(t)-\overrightarrow {X}(t)\right|,\] (14)  
In this stage, the encircling contraction and spiral swimming cannot occur simultaneously, so it is assumed that the two behaviors are executed with equal probability, and the integrated position updating process of both is described as Equation (15), where \(p\) is a random variable in the interval \([0,1]\), which is used to ensure that the two behaviors are carried out with equal probability. \[\overrightarrow{X}(t+1)=\left\{ \begin{matrix}\overrightarrow{X}^{*}(t)- \overrightarrow{A}\cdot\overrightarrow{D}&p<0.5\\ \overrightarrow{D}^{\prime}\cdot e^{bl}\cdot cos(2\pi l)+\overrightarrow{X }^{*}(t)&p\geq 0.5\end{matrix} ^{\prime},\right. \tag{15}\]
3. Search for prey: In addition to the encircling contraction of whales according to the optimal whale position in the population, there is also a random swimming process of individuals and the whales in this phase move towards the position of random individuals in the population. This stage is the global search process of the algorithm. The random vector \(\overrightarrow{A}\) determines the swimming direction of the whales: if \(|\overrightarrow{A}|<1\), the whales search randomly and swim towards the position of the random whale in the population; if \(|\overrightarrow{A}|\geq 1\), the whales encircling and contract and swim towards the position of the optimal whale in the population. This behavior makes the convergence of the algorithm more global and reduces the probability of falling into a local optimum. The behavior of this stage can be expressed by Equations (16) and (17), where \(\overrightarrow{X}_{\text{rand}}\) denotes the current position of some random whale. \[\overrightarrow{D}^{\prime\prime}=\left|\overrightarrow{C}\cdot\overrightarrow {X}_{\text{rand}}\ (t)-\overrightarrow{X}(t)\right|, \tag{16}\]  
\[\overrightarrow{X}(t+1)=\left|\overrightarrow{X}_{rand}(t)-\overrightarrow {A}\cdot\overrightarrow{D}^{\prime\prime}\right|. \tag{17}\]  
According to the principle of the above three stages, the flowchart of WOA is shown in Figure 5.
## 5 Conclusions  
This research has focused on evaluating the accuracy of using a camera equipped with an electronic rolling shutter applied to UAV-DAP on large flat areas, by analysing four scenarios. Combinations of forward strips, cross strips at different flight heights, and convergent perimeter strips were used to assess the accuracy. Scenario B combining forward strips and convergent perimeter strips was the only one that achieved an acceptable RMSE\({}_{\text{XYZ}}\) result for the CPs. Other scenarios cannot be recommended for UAV-DAP with a rolling shutter camera.  
Furthermore, the scenarios combining forward strips and convergent perimeter strips showed the lowest difference between focal length, principal point coordinates, radial, and tangential distortion coefficients during the geometric camera calibration process. Thus, we can deduce that combining nadir and oblique images substantially improves the results in the calibration of inner camera parameters, which avoids additional adjustments to improve them.  
Analysing the adequate number and the best location of the GCPs in the block, the best RMSE CPs value was obtained using one GCP for each flight block corner, considering a ratio of 0.1 GCP/ha as a reference. Finally, locating GCPs in the corners and distributing them along the edges of the block improves the model's accuracy (Configuration 4) by obtaining the minimum mean value for RMSE CPs (3x the GSD). However, the errors could be equal to or higher than 11x the GSD for configurations 1, 2 and 3, which make them unsuited for UAV-DAP.  
Future analyses will consider the effect of including individual multi-shot oblique images in conventional non-ideal frontal overlapping swaths and advanced camera calibration models in a sloping terrain.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conceptualized the paper; [PERSON], [PERSON], [PERSON] and [PERSON] performed the data collection; [PERSON] developed the methodology; [PERSON] and [PERSON] wrote the first version of the paper, and all authors revised the manuscript. [PERSON] and [PERSON] acquired the funding. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work was supported by the Spanish Agencia Estatal de Investigacion (10.13039/501100011033) grant PID2021-122142 OB-I00 (G2 HOTSPOTS). This work represents a contribution to the CSIC Thematic Interdisciplinary Platform PTI TELEDETECT.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request from the authors.  
**Acknowledgments:** We would like to express our gratitude to the reviewers, whose comments and suggestions have been essential in strengthening and enriching the content of the manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
###### Abstract  
Underwater Acoustic Sensor Networks (UASNs) have become one of the promising technologies for exploring underwater natural resources and collecting scientific data from the aquatic environment. As obstacles hinder the communications among sensor nodes in UASNs, designing an effective bypass routing protocol to avoid obstacles is an urgent need. Moreover, the sensor nodes are typically powered by batteries, which are difficult to replace, restricting the network lifetime of UASNs. In this paper, an Energy-efficient and Obstacle-Avoiding Routing protocol (EOAR) is proposed not only to address the issue of marine animals acting as obstacles that interfere with communications, but also to balance the network energy according to the residual energy. In the EOAR protocol, when the current node perceives the existence of marine animals, the interference area of the animal-nodes is first calculated using the underwater acoustic channel model, and then the candidate forwarding relay set of the current node is obtained according to the constraint conditions. The optimal candidate forwarding relay is determined by a fuzzy logic-based forwarding relay selection scheme based on considering the three parameters of the candidate forwarding relay, which includes the propagation delay, the included angle between two neighbor nodes, and the residual energy. Furthermore, in order to solve the problem of energy waste caused by packet collision, we use a priority-based forwarding method to schedule the packet transmission from the candidate forwarding relay to the destination node. The proposed EOAR protocol is simulated on the Aqua-sim platform and the simulation results show that proposed protocol can increase the packet delivery ratio by 28.4% and 11.8% and can reduce the energy consumption by 53.4% and 32.7% and, respectively, comparing with the hop-by-hop vector-based forwarding routing protocol (HHVBF) and void handling using geo-opportunistic routing protocol (VHGOR).  
underwater acoustic sensor networks; routing protocol; energy-efficient; obstacle-avoiding; fuzzy logic-based forwarding relay selection
# Throughput and Packet Loss Probability Analysis of Long Range Wide Area Network
## 3 Markov Chain Model
Then, we can express \(b_{l,k}\) as  
\[b_{l,k}=(1-p)^{S-k}b_{l,S}\text{ for }0\leq l\leq N_{ij}-2. \tag{14}\]  
In addition, \(b_{N_{ij}-1,k}\) for \(0\leq k\leq S-1\) can be expressed by  
\[b_{N_{ij}-1,k}=b_{N_{ij}-1,S}. \tag{15}\]  
Notice that starting in state \(b_{N_{ij}-1,S}\) will be in state \(b_{N_{ij}-1,k}\) after S steps because the length of uplink packet transmission interval is extremely larger than the length of \(ToA\). From the chain regularity, we have  
\[b_{0,S}=\sum_{l=0}^{N_{ij}-1}b_{l,0},\ \ \ b_{l,S}=\sum_{k=1}^{S}pb_{l-1,k}. \tag{16}\]  
From (14)-(16), we have  
Figure 1:0  
where \(q=\sum_{k=1}^{S}p(1-p)^{S-k}\). Thus, \(b_{0,0}\) is expressed as a function of the probability of an ED to try to transmit uplink packet at a time slot \(p\), which is given by  
Figure 1:1  
\(b_{0,0}\times S\) is the probability of the uplink channel state going into success state under the condition that there are only the success and collision states except idle state. Let \(P_{T}\) and \(P_{S}\) be the probability of an ED being in an uplink packet transmission and the probability of an ED being in sleep and RX windows, respectively. Then, \(P_{T}\) and \(P_{S}\) are expressed by  
Figure 1:2  
Let \(P_{Idle}(SF_{i},CF_{j})\) and \(P_{Busy}(SF_{i},CF_{j})\) be the probabilities of the \(j\)-th carrier frequency channel being in idle and busy states in view of EDs with given \(SF_{i}\), respectively. Then, we have  
Figure 1:3We call \(T(SF_{i},CF_{j})\) as the normalized throughput of the channel using the \(j\)-th carrier frequency and spreading factor \(i\), which is defined as the fraction of time during when the channel is used to transmit packets without collisions. Then, it is calculated by  
Figure 1:4  
The packet loss probability \(PLP(SF_{i},CF_{j})\) is defined as the percentage of packets lost with respect to packets sent, and then it is calculated by  
Figure 1:5
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 3 Results and Analysis
### Results of Individual Tree Segmentation  
#### 3.1.1 Overall Segmentation Result  
Figure 5 shows the individual tree detection results and accuracy of six plots. The total recall, precision, and F1-score of our method in the six plots are 90.84%, 95.38% and 0.93, respectively. The number of trees in Plot 1, Plot 2, Plot 3, and Plot BR04 is similar, while Plot 4 and Plot KA09 have fewer trees. The TP and FN values are added to the number of trees in the actual plot. The recall of individual tree segmentation of the six plots ranges from 87.18% to 95.74%, with a total value of 90.84%. The value of precision varies from 91.89% to 97.83%, with a total value of 95.38%. The value of F1-score varies from 0.89 to 0.97, with a total value of 0.93. Compared with Plot 2 and Plot 4, Plot 1 and Plot 3 have better recall, precision, and F1-score. Compared with the complex forest plot, the recall, precision, and F1-score of the simple forest plot are better. The number of FP in the six plots is lower than that of FN. The number of FN in complex Plot BR04 is seven which is the most. The number of FP in complex Plot BR04 is four, which is the most.  
Figure 4: Results of different segmentation algorithm. The experimental plots include (**a**) Plot 1, (**b**) Plot 2, (**c**) Plot 3, (**d**) Plot 4, (**e**) Plot BR04, (**f**) Plot KA09.