esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Virtual Sensor Network  
In traditional sensor network, all the nodes in the network perform more or less as equal partners to achieve the goal of deploying sensor nodes [1,2]. Virtual Sensor Network consists of collaborative wireless sensor network. It is formed by a subset of sensor nodes of a wireless sensor network, with the subset being dedicated to a certain task or an application at a given time [19,21]. In contrast, the subset of nodes belonging to the virtual sensor network collaborates to carry out a given application at a specific time. A virtual sensor network can be formed by providing logical connectivity among collaborative sensor nodes. Nodes can be grouped into different virtual sensor networks based on the phenomenon they track or the task they perform. The virtual sensor network protocol should provide the functionality for network formation, usage, adaptation, and maintenance of subset of sensors collaborating on a specific task. Even the nodes that do not sense the particular event could be part of it as long as they are allowing sensing nodes to communicate through them [22].
## Abbreviations  
The following notations are used in this manuscript:  
Objective function and constraints:  
\(F_{com}\) Objective function for the conventional problem  
\(F_{inc}\) Objective function for the proposed incentive method  
\(N_{node}\) Node number of the distribution system  
\(P_{f}\) Active power flow at the interconnection point  
\(p_{f}^{min}\)\(p_{f}^{max}\) Lower and upper limit of active power flow at the interconnection point  
\(P_{Loss,i}\) Active power loss of node \(i\)  
\(Q_{f}\) Reactive power flow at the interconnection point  
\(Q_{f}^{min}\)\(Q_{f}^{max}\) Lower and upper limit of reactive power flow at the interconnection point  
\(t\) Time step at optimization  
\(T_{k}\) Tap positions of LRT and SVR  
\(T_{k}^{unit}\)\(,T_{k}^{max}\) Lower and upper tap limit of LRT and SVRs  
\(V_{min}\)\(,V_{max}\) Minimum and maximum voltage constraints  
\(V_{m}\) Node voltage at node \(m\) :  
BESS :  
\(\eta\) Charging and discharging efficiency of large BESS  
\(\zeta_{LB}\) State of charge of large BESS  
\(C_{LB}\) Capacity of large BESS  
\(P_{L,Binary}\) Active power output of BESS from DisCo  
\(P_{L,Binary}\) Active power output of large BESS  
\(p_{L,Binary}^{min}\)\(,p_{L,Binary}^{max}\) Lower and upper limit of active power of a large BESS inverter  
\(Q_{LB,Binary}\) Reactive power output of large BESS  
\(Q_{LB,Binary}^{min}\)\(,Q_{LB,Binary}^{max}\) Lower and upper limit of reactive power output of a large BESS inverter  
\(S_{LB,Binary}\) Inverter capacity of large BESS  
Photovoltaic system:  
\(P_{PVinv}\) Active power output from the PV generator system  
\(P_{PV}\) Active power output from the PV panel  
\(Q_{PVinv}\) Reactive power output of inverters interfaced with PV  
\(Q_{PVinv}^{c}\) Order value of reactive power  
\(Q_{PVinv}^{min}\)\(,Q_{PVinv}^{max}\) Lower and upper limit of the PV inverter regarding reactive power output  
\(S_{PVinv}\) Inverter capacity of the PV inverter  
Reactive power incentive  
\(\kappa\) Contribution factor of reactive power  
\(\kappa_{lin}\) Linear contribution function  
\(\kappa_{sig}\) Sigmoid contribution function  
\(A_{lin}\) Slope of the linear contribution function  
\(A_{sig}\) Coefficient value for the sigmoid contribution function  
\(C_{Q}\) Total cost regarding voltage regulation devices  
\(C_{SVC}\) Introduction cost of SVC  
\(C_{SVR}\) Introduction cost of SVR  
\begin{tabular}{l l} \(N_{SVR}\) & Total number of SVR \\ \(P_{base}\) & Rated power in the distribution system \\ \(Q_{PV}^{T}\) & Contribution of the customer for DisCo with reactive power output \\ \(v\) & Customer profit of the reactive power incentive \\ \(v_{q}\) & Reactive power incentive unit price \\ \(Y_{D,SVC}\) & Depreciation term of SVC \\ \(\pi\) & Price elasticity of demand \\ \(\pi(i,j)\) & Price elasticity at time \(i\) corresponding to \(j\) \\ \(D\) & Load demand \\ \(p_{l}\) & Price of goods \\ \end{tabular}
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 3 Algorithm Analysis and Optimization
### Simd Vectorization  
SIMD (Single Instruction Multiple Data) is a computing technology that uses a single instruction to process multiple streams of data simultaneously. It can be understood as changing the original calculation of one value at a time to calculate multiple values in parallel. The Phytium 2000+ chip is based on the ARM V8 architecture and supports the NEON SIMD instruction set, which can process multiple data elements in parallel using 64-bit and 128-bit vector registers. This makes it suitable for the LLL_FP algorithm.  
The inner product calculation in the iteration of the Gram-Schmidt coefficient calculation is an operation of significant importance. The Kahan summation is the key step in correcting each calculation error. Therefore, the focus of this paper is on vectorizing the inner product calculation and Kahan summation in each Gram-Schmidt coefficient calculation. In the case of cycle ranges that are powers of two, the inner product calculation and Kahan summation can be directly handled in parallel. However, for cycle ranges outside the power of two, ordinary serial computation is employed.  
The optimization of Gram-Schmidt coefficient calculation is shown in Algorithm 3. SIMD vectorization was applied to the inner product computation in line 7, the Gram-Schmidt coefficient computation in line 20, and the Kahan computation in line 23.  
Figure 2: Proportion of function calls.  
The floating point part of the row transformation is optimised to perform multi-step addition, subtraction, and multiplication operations simultaneously. Load the data in the floating point section into the appropriate place in the floating point registers. Use addition, subtraction, and multiplication instructions to reduce the number of instructions and optimise line conversion operations.
## 1 Introduction
[PERSON] proposed hybrid teaching-learning-based optimization using RTS, called as TLBO-RTS, in which two phases of exploration and exploitation exist: all individuals are trained by TLBO algorithm in the first phase and further exploited by RTS [25]. [PERSON] et al. also proposed an RTS-integrated artificial bee colony (ABC) optimization for QAP (ABC-QAP), a population-based algorithm inspired by the behavior of honeybees [26]. Hybrid genetic algorithm with hierarchical iterated tabu search was proposed to solve for the solution of QAP [27]. In addition, cited heuristic algorithms directly are also applicable to urban transportation networks such as vehicle routing problem and ridesharing user equilibrium problem [28] since they also can be formulated as MILP. Whale optimization algorithm combined with tabu search was proposed for green vehicle routing problem [29]. This study proposes not only an item assignment model incorporating lift, profit, and frequency for the effective assignment of items but also a hybrid genetic algorithm for finding better combinations of item assignments. The proposed model unifies profit and frequency as weighting coefficients of associations among items. Since the scales of profit and frequency may be different depending on the problems, in order to make them dimensionless, relative profit and frequency were used instead of absolute values. To find effective item assignments, we propose a hybrid GA among various heuristic algorithms that incorporate a robust tabu search. We adopt genetic algorithm due to its advantages of fast convergence, easy implementation, and competitive performance when combined with proper local search [30,31]. In the hybrid GA, we propose a special crossover, rectangular partially matched crossover, that is suitable for reflecting a physical layout form. The rest of this paper is organized as follows. Section 2 describes the item assignment model. Section 3 explains the effect of profit and frequency in an environment with a given layout and defines the weighted item assignment model. Section 4 describes the proposed algorithm. Section 5 presents experimental results. Section 6 concludes the paper.
## 5 Conclusions  
This paper studied the multi-UAV-assisted data collection and offloading problem in the maritime IoT. The proposed MAHPPO-based joint optimization algorithm could significantly reduce the total mission completion time. First, a MAHPPO-based algorithm was proposed to obtain the UAV trajectories, transmit power of the buoy, and mission mode of the UAVs. Second, an SMP-based algorithm was designed to solve the association relationship subproblem. Finally, a joint optimization algorithm was proposed in which the SAA algorithm and the UAA algorithm were executed alternately to finally determine the association relationship of the buoy-UAV and UAV-OBS. The simulation results showed that the proposed algorithm can effectively reduce the total mission completion time of the UAVs, and its performance was significantly better than the comparison schemes.  
In the future work, we will investigate cooperative multi-UAV communication to further improve the data collection and offloading performance. Moreover, the location uncertainties of the buoys will be considered to enhance the channel estimation accuracy to improve the buoy-UAV association efficiency.  
Conceptualization, Z.L., Y.D. and L.L.; methodology, Z.L., Y.D. and L.L.; software, Z.L. and Y.D.; validation, Z.L.; formal analysis, Z.L. and Y.D.; investigation, Z.L., L.L. and Y.D.; data curation, Z.L. and Y.D.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., Y.D., L.L. and B.L.; supervision, Y.D. and L.L.; project administration, Y.D. and L.L.; funding acquisition, Y.D., L.L. and B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Key Research and Development Program of China under Grant 2019 YFE0111600, in part by the National Natural Science Foundation of China under Grant 62101089, Grant 62002042, Grant 51939001, and Grant 61971083, in part by the China Postdoctoral Science Foundation under Grant 2021M700655 and Grant 2021M690022, in part by the Liaoning Revitalization Talents Program under Grant XLYC2002078, and in part by the Major Key Project of PCL under Grant PCL2021A03-1.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors are grateful to the National Key Research and Development Program of China, the National Natural Science Foundation of China, the Liaoning Revitalization Talents Program, and the Major Key Project of PCL for the financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Total mission completion time with different LoS link factors.
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 2 The KLMS Algorithm Based on Random Fourier Features  
### The KLMS Algorithm  
Before introducing the proposed method, the KLMS is briefly described. The KLMS is the combination of the kernel method and the traditional least mean square algorithm [4]. The kernel method can ensure the existence of a representation \(\Phi:X\to H\) that maps the elements of space \(X\) to the elements of the high-dimensional feature space \(H\). Then, the least mean square algorithm performs linear filtering of the transformed data \(\Phi(\mathbf{x}),\mathbf{x}\in X\). The KLMS algorithm is given as follows. The filter output can be represented as  
\[y(n)=\mathbf{\Omega}(n-1)^{T}\Phi(\mathbf{x}(n)). \tag{1}\]  
The weight update equation in the high dimensional space is  
\[\mathbf{\Omega}(n)=\mathbf{\Omega}(n-1)+\mu e(n)\Phi(\mathbf{x}(n)) \tag{2}\]  
where \(e(n)\) is the prediction error, \(\mu\) is the step size, and \(\Phi(\mathbf{x}(n))\) represents transformation of the sample vector \(\mathbf{x}(n)\) into the feature space \(\mathbf{H}\). The direct computation of the weight update Equation (2) is impractical for the Gaussian kernel because the kernel mapping function \(\Phi(\cdot)\) is unavailable. However, the weight vector at iteration \(n\) can be non-recursively represented by the following equation:  
\[\mathbf{\Omega}(n)=\mu\sum_{i=1}^{n}e(i)\Phi(\mathbf{x}(i)). \tag{3}\]  
According to [PERSON]'s theorem, for any kernel, there exists a mapping \(\Phi(\cdot)\) such that  
\[K(\mathbf{x},\mathbf{x}^{\prime})=\Phi(\mathbf{x})^{T}\Phi(\mathbf{x}^{\prime}) \tag{4}\]  
Therefore, the filter output of KLMS is given by  
\[\begin{split} y(n)&=\mathbf{\Omega}(n-1)^{T}\Phi(\mathbf{x }(n))\\ &=\mu\sum_{i=1}^{n-1}e(i)K(\mathbf{x}(i),\mathbf{x}(n))\end{split} \tag{5}\]  
Thus, to compute the filter output, an appropriate kernel function must be chosen. The most commonly used kernel is the Gaussian kernel given by  
\[K(\mathbf{x},\mathbf{x}^{\prime}))=exp\left(-\frac{||\mathbf{x}-\mathbf{x}^{\prime}||^{2}}{2 \sigma^{2}}\right) \tag{6}\]  
where \(\sigma\) denotes the kernel parameter. The default kernel function used in this paper is the Gaussian kernel. When the Gaussian kernel is chosen, the KLMS algorithm is actually a growing radial basis function (GRBF) network [14]. New input samples are added to the dictionary and are called centers \(\{C(x(i))\}_{i=1}^{n}\). The centers \(C_{i}=\{C_{i-1},x(n)\}\) will increase with the training data, resulting in great computational and storage burdens.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 2 Materials and Methods
### Dataset Acquisition  
This experiment uses two different devices to capture data. First, a Realsense d435i depth camera is used to capture depth images and RGB images, obtaining depth information with a pixel size of 1280 \(\times\) 720. Then, an iPhone 12 is used to capture RGB images with a pixel size of 4032 \(\times\) 3024, with a shooting distance of 30-80 cm from the main trunk and a shooting height of 30-40 cm, which is basically the same as the first load-bearing steel wire. Capturing images at different angles of light on sunny days, 500 images were obtained with front light, 314 images with back light and 313 images were obtained with shaded light, totaling 1127 samples. Using methods such as brightness adjustment, image translation and image flipping for data augmentation, 2254 training samples were processed. Random selection was conducted based on a ratio of 6:2:2 for the training set, validation set and test set, resulting in 1352 training samples, 451 validation samples and 451 test samples. The test data are also divided into 200, 126 and 125 sheets based on front light, back light and shaded light. This experiment requires the design of two datasets. The first one is used to segment the main trunk and branches. The original image is annotated using LabelMe [28], labeled as Main Cordon and Cane, as shown in Figure 5a. A VOC format dataset [29] is created. The second method is used to detect the position of buds. The RGB values of the colors in the mask generated after annotation of the first dataset are made transparent and then made to overlapped with the original image. The RGB values of the colors in the overlapped image are made transparent, resulting in a new image. The new image is annotated with Labeling and labeled as node, as shown in Figure 5b, to create a VOC format dataset. The specific information of the two datasets is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Tool**} & \multirow{2}{*}{**Object**} & \multirow{2}{*}{**Train**} & \multirow{2}{*}{**Val**} & \multicolumn{3}{c}{**Test**} & \multirow{2}{*}{**Format**} \\ \cline{5-6} \cline{7-8}  & & & & & **Front** & & **Back** & **Shaded** \\ \hline Cane Cordon & LabelMe & Original & & & & & \\ node & Labelimg & processed & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of images in the dataset.  
Figure 4: (a) Location of buds. (b) Pruning position.
## 4 Research on Critical Technologies
### All-Weather Technology  
The unfavorable factors faced by airborne platforms include high vibration frequencies, the effects of electromagnetic interference, complex geographic environments, and unstable power supply environments. In addition, harsh external space environments also pose challenges to the design of airborne laser communication systems. These environmental factors include extreme temperature changes, barometric pressure changes, high winds, heavy rain, sand, dense fog, and the atmospheric boundary layer, which may coexist and have compound effects, adding many difficulties to the design of airborne laser communication systems. An unmanned airborne laser's communication payload must maintain good high-speed communication performance under the compound influence of various unfavorable factors. This requires a highly environmentally adaptable system that is robust and capable of stable operation under different extreme conditions.  
From the error perspective, in a study by [PERSON] from the Changchun University of Science and Technology, the conversion relationship between the false symbol rate and the BER in atmospheric optical communication systems is first derived. Then, by analyzing the Shannon limit communication rate of visual communication in an atmospheric channel, the atmospheric channel bandwidth is emphasized as a critical technical parameter affecting laser communication. Then, the pulse state interval modulation coding method is proposed, and its structural characteristics are analyzed. Finally, the feasibility of PSIM (Polarized Spectral Intensity Modulation (PSIM) is a new technique for obtaining polarization spectral information, and measurement data processing is one of the key modules of a PSIM polarization spectrometer) and the importance of atmospheric channel bandwidth are verified through communication experiments in harsh environments. The experimental results show that real-time feedback of channel characteristics is a crucial prerequisite for realizing all-weather laser communication [27].  
To maintain the stability of the time axis of an airborne optical communication system undergoing strong vibrations, the design of a precise and environmentally applicable mechanical structure is essential, and the use of a gyroscopic stabilization system and a highly efficient vibration-damping system should also be considered. Using a hermetically sealed structure ensures the system resists high and low air pressure variations. The overall structural design should effectively reduce the wind disturbance moment caused by high wind speeds. In terms of installation, the shape and structure of the fairing should be considered when designing it to minimize the impact of airflow on the UAV by reasonably selecting and evaluating the installation location. By optimizing the fairing's design, the influence of the atmospheric advective layer effect on the system's performance can be reduced.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 6 Discussion
### General Guidelines  
This section presents research and management issues that impact the performance of USV deployment in real disaster scenarios.  
_Training:_ There are many complex aspects associated with USVs, including data fusion & interpretation, vehicle control, and maintenance, which should be considered while using USVs. Ideally, they should be easy to use and deploy. However, a major problem for response teams is that disaster events are sporadic. Thus, there may be a long period between training disaster response teams to the USV use. Therefore, training exercises and competitions are fundamental [170; 245; 246] to test, prepare, and maintain emergency response personnel readiness, mitigating the long periods where response teams and technology are idle.  
_Transparency:_ Many disaster-related problems can raise concerns from the population regarding effectiveness to respond to disasters. USVs (and unmanned systems in general), combined with social media, can be valuable assets for real-time disclosure of risk management information. For instance, unmanned systems strategically positioned along the coast can be used to forecast and automatically warn affected populations about extreme weather or HAB beforehand, using social networks.
## 1 Introduction
### Our Proposed Solution--BEE(M) and Smart-BEEM  
In this paper, we propose a Balanced Energy-Efficient clustering algorithm, named as BEE. It can elect CHs according to both energy consumption and sensor distributions. It not only extends the networks longevity, but also maintains the network coverage. We also provide another version of BEE, called Multihop BEE (BEEM), which can support multihop inter-cluster communication. BEEM can further improve the network coverage comparing with BEE through reducing high energy consumption and long distance inter-cluster communication. Smart-BEEM is an extended work based on BEEM. Firstly, it can elect CHs to extend the coverage sensitive longevity. Secondly, it can select communication interface based on the user scenarios, including the available interfaces, CHs in range and user applications. By considering those factors, Smart-BEEM can further extend the coverage sensitive longevity without scarifying the user utility.  
The rest of this paper is organised as follows: The related work is introduced in Section 2. The motivation for BEE(M) and Smart-BEEM is delivered in Section 3. The detailed design for BEE(M) is provided in Section 4. Based on BEEM, algorithm Smart-BEEM is proposed and analysed in Section 5. The construction of the experimental environment for evaluation is given in Section 6 and the experimental results are illustrated in Section 7. Future work is discussed in Section 8. Finally the conclusions of this paper are drawn in Section 9.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 2 Materials and Methods
### Experimental Setup  
The applied experimental setup depicted in Figure 2 reflects a typical client-server data transmission scenario, in which the client device fetches content from a server situated in a remote data center.  
The server is accessible via a public IP address and the streaming content is transferred via public networks. The server device is equipped with high processing capabilities and sufficient storage to handle the streaming without extra delay.  
Figure 2: Experimental setup.  
The client device features dual communication interfaces:  
* Ethernet: Connected to the first LTE router via an Ethernet cable;
* Wi-Fi: Linked to the second LTE router through Wi-Fi 802.11 bgn.  
Two distinct LTE networks from different operators were used for the network path diversity. The signal quality was continuously monitored to maintain appropriate test conditions. The packets sent through the Ethernet interface traverse 10 network hops, while those sent via the Wi-Fi interface go through 12 hops.  
Both the client and server devices have Linux operating system, version 4.19, installed. It was patched to support MPTCP version 0.95. A specialized software program was used to generate the streaming data.  
The test scenario lasted 10 s and was repeated 30 times to collect a representative data sample. Statistical methods were used to calculate the extreme and average values for each metric. The following metrics were applied to gauge the algorithm performance:  
* HoL Blocking Degree;
* protocol delay;
* Path Delay;
* mean drop rate;
* throughput.  
as explained in Section 2.1.